From 6365447a91d6e0612ea5aaa03d6b7b60c878888f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 14:03:18 +0200
Subject: [PATCH 01/65] Rename pipeline_config.yaml into
 pipeline_config_default.yaml

I want to make sure I'm not using it.
---
 .../{pipeline_config.yaml => pipeline_config_default.yaml}        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename LHCb_Pipeline/{pipeline_config.yaml => pipeline_config_default.yaml} (100%)

diff --git a/LHCb_Pipeline/pipeline_config.yaml b/LHCb_Pipeline/pipeline_config_default.yaml
similarity index 100%
rename from LHCb_Pipeline/pipeline_config.yaml
rename to LHCb_Pipeline/pipeline_config_default.yaml
-- 
GitLab


From 0a600a44482e9bd17555d004bfe97842cf0b3c6a Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 14:03:35 +0200
Subject: [PATCH 02/65] Empty notebook and define `run_training` boolean

---
 LHCb_Pipeline/full_pipeline.ipynb | 2547 +----------------------------
 1 file changed, 83 insertions(+), 2464 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index 2340cc88..c6ac6ab7 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -18,30 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"bk-root\">\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"1001\">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(\"1001\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"1001\")).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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -79,7 +58,9 @@
     "    )\n",
     ")\n",
     "\n",
-    "CONFIG = 'pipeline_config.yaml'\n"
+    "CONFIG = 'pipeline_config.yaml'\n",
+    "\n",
+    "run_training: bool = False\n"
    ]
   },
   {
@@ -103,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,18 +151,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Output directory /scratch/acorreia/data/__test__/velo-sim10b-nospillover exists and is not empty. Thus, the preprocessing was not run. Please use `reproduce=True` if you need to run the preprocessing again.\n",
-      "INFO:Output directory /scratch/acorreia/data/__test__/velo-sim10b-nospillover-only-long-electrons exists and is not empty. Thus, the preprocessing was not run. Please use `reproduce=True` if you need to run the preprocessing again.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from Preprocessing.run_preprocessing import run_preprocessing_test_dataset\n",
     "\n",
@@ -204,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,17 +186,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Output directory /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/preprocessed exists and is not empty. Thus, the preprocessing was not run. Please use `reproduce=True` if you need to run the preprocessing again.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "run_preprocessing(CONFIG, reproduce=False)"
    ]
@@ -239,116 +203,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Input directory: /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/preprocessed\n",
-      "INFO:Writing outputs to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/train\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "79a4354694d242649bb242d246b86eaa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Writing outputs to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/val\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e49f2a9d35104ea28b4ceeb1582b8b17",
-       "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:Splitting was saved in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/splitting.json.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "run_processing_from_config(CONFIG, reproduce=False)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Input directory: /scratch/acorreia/data/__test__/velo-sim10b-nospillover\n",
-      "WARNING:Output directory is not empty: /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover\n",
-      "INFO:Writing outputs to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d24f6696e5fa490aa8fbebd77448b4b1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Input directory: /scratch/acorreia/data/__test__/velo-sim10b-nospillover-only-long-electrons\n",
-      "WARNING:Output directory is not empty: /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover-only-long-electrons\n",
-      "INFO:Writing outputs to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover-only-long-electrons\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c14ebb56bf2249a39d8b6a965b4efc30",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "for required_test_dataset_name in get_required_test_dataset_names(CONFIG):\n",
     "    run_processing_from_config(\n",
@@ -386,141 +252,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.111529</td>\n",
-       "      <td>-0.667453</td>\n",
-       "      <td>-0.065705</td>\n",
-       "      <td>0.269231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.128399</td>\n",
-       "      <td>0.804811</td>\n",
-       "      <td>-0.059295</td>\n",
-       "      <td>0.269231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.188874</td>\n",
-       "      <td>0.026247</td>\n",
-       "      <td>0.003205</td>\n",
-       "      <td>0.269231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.181054</td>\n",
-       "      <td>0.414890</td>\n",
-       "      <td>-0.003205</td>\n",
-       "      <td>0.269231</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.160351</td>\n",
-       "      <td>-0.668314</td>\n",
-       "      <td>0.059295</td>\n",
-       "      <td>0.307692</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>1309</th>\n",
-       "      <td>0.687105</td>\n",
-       "      <td>0.048836</td>\n",
-       "      <td>3.746795</td>\n",
-       "      <td>0.961538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1310</th>\n",
-       "      <td>0.652916</td>\n",
-       "      <td>-0.226920</td>\n",
-       "      <td>3.746795</td>\n",
-       "      <td>0.961538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1311</th>\n",
-       "      <td>0.413747</td>\n",
-       "      <td>-0.241537</td>\n",
-       "      <td>3.746795</td>\n",
-       "      <td>0.961538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1312</th>\n",
-       "      <td>0.488235</td>\n",
-       "      <td>0.473346</td>\n",
-       "      <td>3.746795</td>\n",
-       "      <td>0.961538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1313</th>\n",
-       "      <td>0.326944</td>\n",
-       "      <td>0.237681</td>\n",
-       "      <td>3.746795</td>\n",
-       "      <td>0.961538</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1314 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             0         1         2         3\n",
-       "0     0.111529 -0.667453 -0.065705  0.269231\n",
-       "1     0.128399  0.804811 -0.059295  0.269231\n",
-       "2     0.188874  0.026247  0.003205  0.269231\n",
-       "3     0.181054  0.414890 -0.003205  0.269231\n",
-       "4     0.160351 -0.668314  0.059295  0.307692\n",
-       "...        ...       ...       ...       ...\n",
-       "1309  0.687105  0.048836  3.746795  0.961538\n",
-       "1310  0.652916 -0.226920  3.746795  0.961538\n",
-       "1311  0.413747 -0.241537  3.746795  0.961538\n",
-       "1312  0.488235  0.473346  3.746795  0.961538\n",
-       "1313  0.326944  0.237681  3.746795  0.961538\n",
-       "\n",
-       "[1314 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from Embedding.embedding_base import get_example_data\n",
     "example_data_df, example_data_pyg = get_example_data(CONFIG)\n",
@@ -529,27 +263,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:Unable to obtain driver using Selenium Manager: /scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/selenium/webdriver/common/linux/selenium-manager is missing.  Please open an issue on https://github.com/SeleniumHQ/selenium/issues\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "graphplot.plot_true_graph(example_data_pyg, CONFIG, num_tracks=50)"
    ]
@@ -566,142 +282,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nvcc: NVIDIA (R) Cuda compiler driver\n",
-      "Copyright (c) 2005-2023 NVIDIA Corporation\n",
-      "Built on Mon_Apr__3_17:16:06_PDT_2023\n",
-      "Cuda compilation tools, release 12.1, V12.1.105\n",
-      "Build cuda_12.1.r12.1/compiler.32688072_0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "! nvcc --version"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fri May 19 23:59:17 2023       \n",
-      "+---------------------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 530.30.02              Driver Version: 530.30.02    CUDA Version: 12.1     |\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  Tesla V100-PCIE-16GB            On | 00000000:D8:00.0 Off |                  Off |\n",
-      "| N/A   28C    P0               36W / 250W|   8722MiB / 16384MiB |      3%      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      6995      C   ...ge/envs/etx4velo_updated/bin/python     2158MiB |\n",
-      "|    0   N/A  N/A     12003      C   ...ge/envs/etx4velo_updated/bin/python      610MiB |\n",
-      "|    0   N/A  N/A     23754      C   ...mambaforge/envs/etx4velo/bin/python     1248MiB |\n",
-      "|    0   N/A  N/A     25505      C   ...ge/envs/etx4velo_updated/bin/python      608MiB |\n",
-      "|    0   N/A  N/A     27423      C   ...ge/envs/etx4velo_updated/bin/python      614MiB |\n",
-      "|    0   N/A  N/A     27613      C   ...mambaforge/envs/etx4velo/bin/python     1788MiB |\n",
-      "+---------------------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "! nvidia-smi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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",
-      "INFO:/home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi-nospillover/version_2\n",
-      "INFO:Save hyperparameters, metrics and artifacts in /home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi-nospillover/version_2\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",
-      "0.807     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5b3eb7fc5854b7aa256fd91214d24c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "932a4983813448dcaf9de742d179b72c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-------------------------------- c) Saving model --------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "send_telegram_message('Started metric learning training.', chat_id, api_key)\n",
+   "outputs": [],
+   "source": [
+    "if run_training:\n",
+    "    send_telegram_message('Started metric learning training.', chat_id, api_key)\n",
     "\n",
-    "metric_learning_trainer, metric_learning_model = train_metric_learning(CONFIG)\n",
+    "    metric_learning_trainer, metric_learning_model = train_metric_learning(CONFIG)\n",
     "\n",
-    "send_telegram_message('Finished metric learning training.', chat_id, api_key)"
+    "    send_telegram_message('Finished metric learning training.', chat_id, api_key)"
    ]
   },
   {
@@ -715,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -757,18 +369,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "embedding_metric_path='artifacts/metric_learning/velo-minbias-sim10b-xdigi-nospillover/version_0/metrics.csv'\n",
-      "embedding_artifact_path='artifacts/metric_learning/velo-minbias-sim10b-xdigi-nospillover/version_0/checkpoints/epoch=39-step=8000.ckpt'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from Embedding.layerless_embedding import LayerlessEmbedding\n",
     "\n",
@@ -791,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -831,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -856,442 +459,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "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>0.007284</td>\n",
-       "      <td>0.007694</td>\n",
-       "      <td>0.985433</td>\n",
-       "      <td>0.176610</td>\n",
-       "      <td>0.000125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.006824</td>\n",
-       "      <td>0.006385</td>\n",
-       "      <td>0.986507</td>\n",
-       "      <td>0.319255</td>\n",
-       "      <td>0.000250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0.006755</td>\n",
-       "      <td>0.006146</td>\n",
-       "      <td>0.986714</td>\n",
-       "      <td>0.344710</td>\n",
-       "      <td>0.000375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0.006737</td>\n",
-       "      <td>0.005861</td>\n",
-       "      <td>0.985466</td>\n",
-       "      <td>0.376399</td>\n",
-       "      <td>0.000350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.006706</td>\n",
-       "      <td>0.006016</td>\n",
-       "      <td>0.986673</td>\n",
-       "      <td>0.359266</td>\n",
-       "      <td>0.000625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.006710</td>\n",
-       "      <td>0.006275</td>\n",
-       "      <td>0.987965</td>\n",
-       "      <td>0.330205</td>\n",
-       "      <td>0.000750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0.006666</td>\n",
-       "      <td>0.006394</td>\n",
-       "      <td>0.988266</td>\n",
-       "      <td>0.317921</td>\n",
-       "      <td>0.000875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>0.006578</td>\n",
-       "      <td>0.006120</td>\n",
-       "      <td>0.987130</td>\n",
-       "      <td>0.348125</td>\n",
-       "      <td>0.000700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>0.006569</td>\n",
-       "      <td>0.006526</td>\n",
-       "      <td>0.987505</td>\n",
-       "      <td>0.304211</td>\n",
-       "      <td>0.000700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0.006553</td>\n",
-       "      <td>0.006388</td>\n",
-       "      <td>0.987014</td>\n",
-       "      <td>0.319074</td>\n",
-       "      <td>0.000700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10</td>\n",
-       "      <td>0.006566</td>\n",
-       "      <td>0.006586</td>\n",
-       "      <td>0.987718</td>\n",
-       "      <td>0.297488</td>\n",
-       "      <td>0.000700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>11</td>\n",
-       "      <td>0.006512</td>\n",
-       "      <td>0.006777</td>\n",
-       "      <td>0.987616</td>\n",
-       "      <td>0.276253</td>\n",
-       "      <td>0.000490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>12</td>\n",
-       "      <td>0.006504</td>\n",
-       "      <td>0.006687</td>\n",
-       "      <td>0.986587</td>\n",
-       "      <td>0.285839</td>\n",
-       "      <td>0.000490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>13</td>\n",
-       "      <td>0.006500</td>\n",
-       "      <td>0.006713</td>\n",
-       "      <td>0.986830</td>\n",
-       "      <td>0.283272</td>\n",
-       "      <td>0.000490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>14</td>\n",
-       "      <td>0.006494</td>\n",
-       "      <td>0.006630</td>\n",
-       "      <td>0.986150</td>\n",
-       "      <td>0.292134</td>\n",
-       "      <td>0.000490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>15</td>\n",
-       "      <td>0.006471</td>\n",
-       "      <td>0.006381</td>\n",
-       "      <td>0.985302</td>\n",
-       "      <td>0.319926</td>\n",
-       "      <td>0.000343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>16</td>\n",
-       "      <td>0.006476</td>\n",
-       "      <td>0.006387</td>\n",
-       "      <td>0.984533</td>\n",
-       "      <td>0.318936</td>\n",
-       "      <td>0.000343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17</td>\n",
-       "      <td>0.006467</td>\n",
-       "      <td>0.006365</td>\n",
-       "      <td>0.984371</td>\n",
-       "      <td>0.321488</td>\n",
-       "      <td>0.000343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.006464</td>\n",
-       "      <td>0.006352</td>\n",
-       "      <td>0.984213</td>\n",
-       "      <td>0.322925</td>\n",
-       "      <td>0.000343</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>19</td>\n",
-       "      <td>0.006446</td>\n",
-       "      <td>0.006410</td>\n",
-       "      <td>0.984618</td>\n",
-       "      <td>0.316373</td>\n",
-       "      <td>0.000240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>20</td>\n",
-       "      <td>0.006455</td>\n",
-       "      <td>0.006383</td>\n",
-       "      <td>0.984384</td>\n",
-       "      <td>0.319312</td>\n",
-       "      <td>0.000240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>21</td>\n",
-       "      <td>0.006449</td>\n",
-       "      <td>0.006324</td>\n",
-       "      <td>0.983958</td>\n",
-       "      <td>0.326010</td>\n",
-       "      <td>0.000240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>22</td>\n",
-       "      <td>0.006446</td>\n",
-       "      <td>0.006302</td>\n",
-       "      <td>0.983899</td>\n",
-       "      <td>0.328593</td>\n",
-       "      <td>0.000240</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>23</td>\n",
-       "      <td>0.006433</td>\n",
-       "      <td>0.006329</td>\n",
-       "      <td>0.984010</td>\n",
-       "      <td>0.325450</td>\n",
-       "      <td>0.000168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>24</td>\n",
-       "      <td>0.006429</td>\n",
-       "      <td>0.006345</td>\n",
-       "      <td>0.984506</td>\n",
-       "      <td>0.323699</td>\n",
-       "      <td>0.000168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>25</td>\n",
-       "      <td>0.006430</td>\n",
-       "      <td>0.006325</td>\n",
-       "      <td>0.984534</td>\n",
-       "      <td>0.326009</td>\n",
-       "      <td>0.000168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>26</td>\n",
-       "      <td>0.006431</td>\n",
-       "      <td>0.006309</td>\n",
-       "      <td>0.984501</td>\n",
-       "      <td>0.327886</td>\n",
-       "      <td>0.000168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>27</td>\n",
-       "      <td>0.006422</td>\n",
-       "      <td>0.006324</td>\n",
-       "      <td>0.984577</td>\n",
-       "      <td>0.326187</td>\n",
-       "      <td>0.000118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>28</td>\n",
-       "      <td>0.006421</td>\n",
-       "      <td>0.006285</td>\n",
-       "      <td>0.984886</td>\n",
-       "      <td>0.330849</td>\n",
-       "      <td>0.000118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>29</td>\n",
-       "      <td>0.006423</td>\n",
-       "      <td>0.006285</td>\n",
-       "      <td>0.984907</td>\n",
-       "      <td>0.330838</td>\n",
-       "      <td>0.000118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>30</td>\n",
-       "      <td>0.006423</td>\n",
-       "      <td>0.006293</td>\n",
-       "      <td>0.984882</td>\n",
-       "      <td>0.329897</td>\n",
-       "      <td>0.000118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>31</td>\n",
-       "      <td>0.006410</td>\n",
-       "      <td>0.006301</td>\n",
-       "      <td>0.985032</td>\n",
-       "      <td>0.329003</td>\n",
-       "      <td>0.000082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>32</td>\n",
-       "      <td>0.006414</td>\n",
-       "      <td>0.006312</td>\n",
-       "      <td>0.985583</td>\n",
-       "      <td>0.327749</td>\n",
-       "      <td>0.000082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>33</td>\n",
-       "      <td>0.006413</td>\n",
-       "      <td>0.006316</td>\n",
-       "      <td>0.985677</td>\n",
-       "      <td>0.327323</td>\n",
-       "      <td>0.000082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>34</td>\n",
-       "      <td>0.006414</td>\n",
-       "      <td>0.006327</td>\n",
-       "      <td>0.985670</td>\n",
-       "      <td>0.325925</td>\n",
-       "      <td>0.000082</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>35</td>\n",
-       "      <td>0.006406</td>\n",
-       "      <td>0.006312</td>\n",
-       "      <td>0.985665</td>\n",
-       "      <td>0.327777</td>\n",
-       "      <td>0.000058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>36</td>\n",
-       "      <td>0.006406</td>\n",
-       "      <td>0.006317</td>\n",
-       "      <td>0.986060</td>\n",
-       "      <td>0.327092</td>\n",
-       "      <td>0.000058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>37</td>\n",
-       "      <td>0.006406</td>\n",
-       "      <td>0.006312</td>\n",
-       "      <td>0.986091</td>\n",
-       "      <td>0.327765</td>\n",
-       "      <td>0.000058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>38</td>\n",
-       "      <td>0.006408</td>\n",
-       "      <td>0.006311</td>\n",
-       "      <td>0.986045</td>\n",
-       "      <td>0.327846</td>\n",
-       "      <td>0.000058</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    epoch  train_loss  val_loss       eff       pur  current_lr\n",
-       "0       0    0.007284  0.007694  0.985433  0.176610    0.000125\n",
-       "1       1    0.006824  0.006385  0.986507  0.319255    0.000250\n",
-       "2       2    0.006755  0.006146  0.986714  0.344710    0.000375\n",
-       "3       3    0.006737  0.005861  0.985466  0.376399    0.000350\n",
-       "4       4    0.006706  0.006016  0.986673  0.359266    0.000625\n",
-       "5       5    0.006710  0.006275  0.987965  0.330205    0.000750\n",
-       "6       6    0.006666  0.006394  0.988266  0.317921    0.000875\n",
-       "7       7    0.006578  0.006120  0.987130  0.348125    0.000700\n",
-       "8       8    0.006569  0.006526  0.987505  0.304211    0.000700\n",
-       "9       9    0.006553  0.006388  0.987014  0.319074    0.000700\n",
-       "10     10    0.006566  0.006586  0.987718  0.297488    0.000700\n",
-       "11     11    0.006512  0.006777  0.987616  0.276253    0.000490\n",
-       "12     12    0.006504  0.006687  0.986587  0.285839    0.000490\n",
-       "13     13    0.006500  0.006713  0.986830  0.283272    0.000490\n",
-       "14     14    0.006494  0.006630  0.986150  0.292134    0.000490\n",
-       "15     15    0.006471  0.006381  0.985302  0.319926    0.000343\n",
-       "16     16    0.006476  0.006387  0.984533  0.318936    0.000343\n",
-       "17     17    0.006467  0.006365  0.984371  0.321488    0.000343\n",
-       "18     18    0.006464  0.006352  0.984213  0.322925    0.000343\n",
-       "19     19    0.006446  0.006410  0.984618  0.316373    0.000240\n",
-       "20     20    0.006455  0.006383  0.984384  0.319312    0.000240\n",
-       "21     21    0.006449  0.006324  0.983958  0.326010    0.000240\n",
-       "22     22    0.006446  0.006302  0.983899  0.328593    0.000240\n",
-       "23     23    0.006433  0.006329  0.984010  0.325450    0.000168\n",
-       "24     24    0.006429  0.006345  0.984506  0.323699    0.000168\n",
-       "25     25    0.006430  0.006325  0.984534  0.326009    0.000168\n",
-       "26     26    0.006431  0.006309  0.984501  0.327886    0.000168\n",
-       "27     27    0.006422  0.006324  0.984577  0.326187    0.000118\n",
-       "28     28    0.006421  0.006285  0.984886  0.330849    0.000118\n",
-       "29     29    0.006423  0.006285  0.984907  0.330838    0.000118\n",
-       "30     30    0.006423  0.006293  0.984882  0.329897    0.000118\n",
-       "31     31    0.006410  0.006301  0.985032  0.329003    0.000082\n",
-       "32     32    0.006414  0.006312  0.985583  0.327749    0.000082\n",
-       "33     33    0.006413  0.006316  0.985677  0.327323    0.000082\n",
-       "34     34    0.006414  0.006327  0.985670  0.325925    0.000082\n",
-       "35     35    0.006406  0.006312  0.985665  0.327777    0.000058\n",
-       "36     36    0.006406  0.006317  0.986060  0.327092    0.000058\n",
-       "37     37    0.006406  0.006312  0.986091  0.327765    0.000058\n",
-       "38     38    0.006408  0.006311  0.986045  0.327846    0.000058"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# embedding_metrics = checkpoint_utils.get_training_metrics(metric_learning_trainer) \n",
     "\n",
@@ -1302,22 +472,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perfplot.plot_training_metrics(embedding_metrics, CONFIG, \"metric_learning\")"
    ]
@@ -1334,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1343,22 +502,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perfplot.plot_neighbor_performance(metric_learning_model, CONFIG)"
    ]
@@ -1373,22 +521,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "graphplot.plot_predicted_graph(metric_learning_model, CONFIG)\n"
    ]
@@ -1403,54 +540,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perfplot.plot_track_lengths(metric_learning_model, CONFIG)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a20014a72b914b83af57cf84ef0b02c8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "metric_learning_model.setup(stage=\"fit\")\n",
     "perfplot.plot_graph_sizes(metric_learning_model)"
@@ -1468,102 +569,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "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:Load model from artifacts/metric_learning/velo-minbias-sim10b-xdigi-nospillover/version_0/checkpoints/epoch=39-step=8000.ckpt.\n",
-      "INFO:----------------------------- b) Running inferencing -----------------------------\n",
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/train`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/train to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/train\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bf6a90d8ae504e3bbe59c3660251f0cf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/val`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/val to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/val\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "31bf0f7a0cc84172aab7ce080fd68e5f",
-       "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:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c558b3143cc34fa5b8f74d7369218e60",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover-only-long-electrons`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/test/velo-sim10b-nospillover-only-long-electrons to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover-only-long-electrons\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "060d475ddec0464e961df31d37890424",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "graph_builder = run_metric_learning_inference(CONFIG, checkpoint=embedding_artifact_path)\n"
    ]
@@ -1586,99 +594,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import warnings\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "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",
-      "INFO:Save hyperparameters, metrics and artifacts in artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover\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",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover/version_0/checkpoints exists and is not empty.\n",
-      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name                   | Type       | Params\n",
-      "------------------------------------------------------\n",
-      "0 | node_encoder           | Sequential | 34.2 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",
-      "1.398     Total estimated model params size (MB)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/lightning_fabric/loggers/csv_logs.py:188: UserWarning: Experiment logs directory artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
-      "  rank_zero_warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8075ff7b1d204476a26ae71041469bf6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6cee9f343bb14d89b283896645066bd2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception ignored in: <function _releaseLock at 0x7f1c282d2290>\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/logging/__init__.py\", line 228, in _releaseLock\n",
-      "    def _releaseLock():\n",
-      "KeyboardInterrupt: \n"
-     ]
-    }
-   ],
-   "source": [
-    "send_telegram_message('Started GNN training.', chat_id, api_key)\n",
-    "with warnings.catch_warnings():\n",
-    "    warnings.filterwarnings(\n",
-    "        \"ignore\", message=\"None of the inputs have requires_grad=True.\"\n",
-    "    )\n",
-    "    gnn_trainer, gnn_model = train_gnn(CONFIG)\n",
+    "if run_training:\n",
+    "    send_telegram_message('Started GNN training.', chat_id, api_key)\n",
+    "    with warnings.catch_warnings():\n",
+    "        warnings.filterwarnings(\n",
+    "            \"ignore\", message=\"None of the inputs have requires_grad=True.\"\n",
+    "        )\n",
+    "        gnn_trainer, gnn_model = train_gnn(CONFIG)\n",
     "\n",
-    "send_telegram_message('Finished GNN training.', chat_id, api_key)"
+    "    send_telegram_message('Finished GNN training.', chat_id, api_key)"
    ]
   },
   {
@@ -1691,18 +619,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gnn_metric_path='artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover/version_0/metrics.csv'\n",
-      "gnn_artifact_path='artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover/version_0/checkpoints/epoch=59-step=12000.ckpt'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from utils.modelutils.checkpoint_utils import (\n",
     "    get_last_version_dir_from_config,\n",
@@ -1719,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1759,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1776,642 +695,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "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>0.659408</td>\n",
-       "      <td>0.788474</td>\n",
-       "      <td>0.596087</td>\n",
-       "      <td>0.573045</td>\n",
-       "      <td>0.000200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.483006</td>\n",
-       "      <td>0.542319</td>\n",
-       "      <td>0.800532</td>\n",
-       "      <td>0.709555</td>\n",
-       "      <td>0.000400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0.340198</td>\n",
-       "      <td>0.426015</td>\n",
-       "      <td>0.870512</td>\n",
-       "      <td>0.736958</td>\n",
-       "      <td>0.000600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0.235443</td>\n",
-       "      <td>0.232383</td>\n",
-       "      <td>0.940909</td>\n",
-       "      <td>0.860927</td>\n",
-       "      <td>0.000800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0.211653</td>\n",
-       "      <td>0.242536</td>\n",
-       "      <td>0.918787</td>\n",
-       "      <td>0.875938</td>\n",
-       "      <td>0.001000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0.160190</td>\n",
-       "      <td>0.154017</td>\n",
-       "      <td>0.955661</td>\n",
-       "      <td>0.912495</td>\n",
-       "      <td>0.001200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0.109014</td>\n",
-       "      <td>0.101347</td>\n",
-       "      <td>0.969122</td>\n",
-       "      <td>0.950928</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>0.105684</td>\n",
-       "      <td>0.080654</td>\n",
-       "      <td>0.980279</td>\n",
-       "      <td>0.951296</td>\n",
-       "      <td>0.001120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>8</td>\n",
-       "      <td>0.139840</td>\n",
-       "      <td>0.264559</td>\n",
-       "      <td>0.949467</td>\n",
-       "      <td>0.814598</td>\n",
-       "      <td>0.001800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0.081387</td>\n",
-       "      <td>0.100758</td>\n",
-       "      <td>0.979138</td>\n",
-       "      <td>0.932923</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>10</td>\n",
-       "      <td>0.064681</td>\n",
-       "      <td>0.060861</td>\n",
-       "      <td>0.980511</td>\n",
-       "      <td>0.973254</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>11</td>\n",
-       "      <td>0.072462</td>\n",
-       "      <td>0.194240</td>\n",
-       "      <td>0.938012</td>\n",
-       "      <td>0.908976</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>12</td>\n",
-       "      <td>0.047993</td>\n",
-       "      <td>0.046970</td>\n",
-       "      <td>0.991336</td>\n",
-       "      <td>0.966166</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>13</td>\n",
-       "      <td>0.045154</td>\n",
-       "      <td>0.044763</td>\n",
-       "      <td>0.988489</td>\n",
-       "      <td>0.974312</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>14</td>\n",
-       "      <td>0.041053</td>\n",
-       "      <td>0.041982</td>\n",
-       "      <td>0.988421</td>\n",
-       "      <td>0.978552</td>\n",
-       "      <td>0.002000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>15</td>\n",
-       "      <td>0.036799</td>\n",
-       "      <td>0.038051</td>\n",
-       "      <td>0.991238</td>\n",
-       "      <td>0.975638</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>16</td>\n",
-       "      <td>0.032609</td>\n",
-       "      <td>0.048267</td>\n",
-       "      <td>0.990333</td>\n",
-       "      <td>0.967551</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17</td>\n",
-       "      <td>0.030960</td>\n",
-       "      <td>0.032651</td>\n",
-       "      <td>0.992576</td>\n",
-       "      <td>0.979370</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>18</td>\n",
-       "      <td>0.028722</td>\n",
-       "      <td>0.030055</td>\n",
-       "      <td>0.994639</td>\n",
-       "      <td>0.978173</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>19</td>\n",
-       "      <td>0.026472</td>\n",
-       "      <td>0.030481</td>\n",
-       "      <td>0.991737</td>\n",
-       "      <td>0.983738</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>20</td>\n",
-       "      <td>0.026223</td>\n",
-       "      <td>0.035555</td>\n",
-       "      <td>0.990734</td>\n",
-       "      <td>0.979898</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>21</td>\n",
-       "      <td>0.029589</td>\n",
-       "      <td>0.030425</td>\n",
-       "      <td>0.995231</td>\n",
-       "      <td>0.975879</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>22</td>\n",
-       "      <td>0.026972</td>\n",
-       "      <td>0.032319</td>\n",
-       "      <td>0.992113</td>\n",
-       "      <td>0.979491</td>\n",
-       "      <td>0.001400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>23</td>\n",
-       "      <td>0.020816</td>\n",
-       "      <td>0.027095</td>\n",
-       "      <td>0.994568</td>\n",
-       "      <td>0.983053</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>24</td>\n",
-       "      <td>0.019336</td>\n",
-       "      <td>0.020879</td>\n",
-       "      <td>0.995267</td>\n",
-       "      <td>0.987057</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>25</td>\n",
-       "      <td>0.018778</td>\n",
-       "      <td>0.021654</td>\n",
-       "      <td>0.994982</td>\n",
-       "      <td>0.987067</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>26</td>\n",
-       "      <td>0.018736</td>\n",
-       "      <td>0.022782</td>\n",
-       "      <td>0.994512</td>\n",
-       "      <td>0.987327</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>27</td>\n",
-       "      <td>0.018350</td>\n",
-       "      <td>0.022171</td>\n",
-       "      <td>0.993946</td>\n",
-       "      <td>0.988492</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>28</td>\n",
-       "      <td>0.017058</td>\n",
-       "      <td>0.021817</td>\n",
-       "      <td>0.994746</td>\n",
-       "      <td>0.987509</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>29</td>\n",
-       "      <td>0.018438</td>\n",
-       "      <td>0.020757</td>\n",
-       "      <td>0.994487</td>\n",
-       "      <td>0.988596</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>30</td>\n",
-       "      <td>0.018608</td>\n",
-       "      <td>0.022976</td>\n",
-       "      <td>0.994566</td>\n",
-       "      <td>0.985951</td>\n",
-       "      <td>0.000980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>31</td>\n",
-       "      <td>0.014621</td>\n",
-       "      <td>0.025216</td>\n",
-       "      <td>0.993656</td>\n",
-       "      <td>0.985654</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>32</td>\n",
-       "      <td>0.014509</td>\n",
-       "      <td>0.020265</td>\n",
-       "      <td>0.994693</td>\n",
-       "      <td>0.989267</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>33</td>\n",
-       "      <td>0.013394</td>\n",
-       "      <td>0.019021</td>\n",
-       "      <td>0.995410</td>\n",
-       "      <td>0.988526</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>34</td>\n",
-       "      <td>0.011331</td>\n",
-       "      <td>0.020077</td>\n",
-       "      <td>0.994831</td>\n",
-       "      <td>0.990296</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>35</td>\n",
-       "      <td>0.011922</td>\n",
-       "      <td>0.018384</td>\n",
-       "      <td>0.995052</td>\n",
-       "      <td>0.990089</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>36</td>\n",
-       "      <td>0.012270</td>\n",
-       "      <td>0.019601</td>\n",
-       "      <td>0.995129</td>\n",
-       "      <td>0.989779</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>37</td>\n",
-       "      <td>0.015554</td>\n",
-       "      <td>0.018940</td>\n",
-       "      <td>0.995477</td>\n",
-       "      <td>0.989308</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>38</td>\n",
-       "      <td>0.014366</td>\n",
-       "      <td>0.020681</td>\n",
-       "      <td>0.994269</td>\n",
-       "      <td>0.988788</td>\n",
-       "      <td>0.000686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>39</td>\n",
-       "      <td>0.010620</td>\n",
-       "      <td>0.018546</td>\n",
-       "      <td>0.995698</td>\n",
-       "      <td>0.989377</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>40</td>\n",
-       "      <td>0.009366</td>\n",
-       "      <td>0.018560</td>\n",
-       "      <td>0.995683</td>\n",
-       "      <td>0.989514</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>41</td>\n",
-       "      <td>0.008911</td>\n",
-       "      <td>0.019390</td>\n",
-       "      <td>0.995132</td>\n",
-       "      <td>0.991078</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>42</td>\n",
-       "      <td>0.008429</td>\n",
-       "      <td>0.018899</td>\n",
-       "      <td>0.995001</td>\n",
-       "      <td>0.991310</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>43</td>\n",
-       "      <td>0.008286</td>\n",
-       "      <td>0.019599</td>\n",
-       "      <td>0.995348</td>\n",
-       "      <td>0.990892</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>44</td>\n",
-       "      <td>0.008304</td>\n",
-       "      <td>0.019385</td>\n",
-       "      <td>0.995407</td>\n",
-       "      <td>0.990927</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>45</td>\n",
-       "      <td>0.008251</td>\n",
-       "      <td>0.020172</td>\n",
-       "      <td>0.994721</td>\n",
-       "      <td>0.991485</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>46</td>\n",
-       "      <td>0.007368</td>\n",
-       "      <td>0.021831</td>\n",
-       "      <td>0.994789</td>\n",
-       "      <td>0.991465</td>\n",
-       "      <td>0.000480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>47</td>\n",
-       "      <td>0.006908</td>\n",
-       "      <td>0.020716</td>\n",
-       "      <td>0.995447</td>\n",
-       "      <td>0.990374</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>48</td>\n",
-       "      <td>0.006331</td>\n",
-       "      <td>0.020699</td>\n",
-       "      <td>0.994999</td>\n",
-       "      <td>0.991760</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>49</td>\n",
-       "      <td>0.005596</td>\n",
-       "      <td>0.021027</td>\n",
-       "      <td>0.995137</td>\n",
-       "      <td>0.991521</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>50</td>\n",
-       "      <td>0.006214</td>\n",
-       "      <td>0.021821</td>\n",
-       "      <td>0.994982</td>\n",
-       "      <td>0.991860</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>51</td>\n",
-       "      <td>0.005888</td>\n",
-       "      <td>0.021646</td>\n",
-       "      <td>0.995046</td>\n",
-       "      <td>0.991365</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>52</td>\n",
-       "      <td>0.005638</td>\n",
-       "      <td>0.021685</td>\n",
-       "      <td>0.994775</td>\n",
-       "      <td>0.991146</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>53</td>\n",
-       "      <td>0.005379</td>\n",
-       "      <td>0.021299</td>\n",
-       "      <td>0.995093</td>\n",
-       "      <td>0.991552</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>54</td>\n",
-       "      <td>0.005713</td>\n",
-       "      <td>0.021159</td>\n",
-       "      <td>0.995055</td>\n",
-       "      <td>0.991150</td>\n",
-       "      <td>0.000336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>55</td>\n",
-       "      <td>0.004986</td>\n",
-       "      <td>0.022516</td>\n",
-       "      <td>0.995101</td>\n",
-       "      <td>0.991471</td>\n",
-       "      <td>0.000235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>56</td>\n",
-       "      <td>0.004542</td>\n",
-       "      <td>0.022060</td>\n",
-       "      <td>0.994969</td>\n",
-       "      <td>0.992146</td>\n",
-       "      <td>0.000235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>57</td>\n",
-       "      <td>0.004487</td>\n",
-       "      <td>0.024956</td>\n",
-       "      <td>0.994621</td>\n",
-       "      <td>0.992040</td>\n",
-       "      <td>0.000235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>58</td>\n",
-       "      <td>0.004333</td>\n",
-       "      <td>0.024619</td>\n",
-       "      <td>0.994677</td>\n",
-       "      <td>0.992355</td>\n",
-       "      <td>0.000235</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    epoch  train_loss  val_loss       eff       pur  current_lr\n",
-       "0       0    0.659408  0.788474  0.596087  0.573045    0.000200\n",
-       "1       1    0.483006  0.542319  0.800532  0.709555    0.000400\n",
-       "2       2    0.340198  0.426015  0.870512  0.736958    0.000600\n",
-       "3       3    0.235443  0.232383  0.940909  0.860927    0.000800\n",
-       "4       4    0.211653  0.242536  0.918787  0.875938    0.001000\n",
-       "5       5    0.160190  0.154017  0.955661  0.912495    0.001200\n",
-       "6       6    0.109014  0.101347  0.969122  0.950928    0.001400\n",
-       "7       7    0.105684  0.080654  0.980279  0.951296    0.001120\n",
-       "8       8    0.139840  0.264559  0.949467  0.814598    0.001800\n",
-       "9       9    0.081387  0.100758  0.979138  0.932923    0.002000\n",
-       "10     10    0.064681  0.060861  0.980511  0.973254    0.002000\n",
-       "11     11    0.072462  0.194240  0.938012  0.908976    0.002000\n",
-       "12     12    0.047993  0.046970  0.991336  0.966166    0.002000\n",
-       "13     13    0.045154  0.044763  0.988489  0.974312    0.002000\n",
-       "14     14    0.041053  0.041982  0.988421  0.978552    0.002000\n",
-       "15     15    0.036799  0.038051  0.991238  0.975638    0.001400\n",
-       "16     16    0.032609  0.048267  0.990333  0.967551    0.001400\n",
-       "17     17    0.030960  0.032651  0.992576  0.979370    0.001400\n",
-       "18     18    0.028722  0.030055  0.994639  0.978173    0.001400\n",
-       "19     19    0.026472  0.030481  0.991737  0.983738    0.001400\n",
-       "20     20    0.026223  0.035555  0.990734  0.979898    0.001400\n",
-       "21     21    0.029589  0.030425  0.995231  0.975879    0.001400\n",
-       "22     22    0.026972  0.032319  0.992113  0.979491    0.001400\n",
-       "23     23    0.020816  0.027095  0.994568  0.983053    0.000980\n",
-       "24     24    0.019336  0.020879  0.995267  0.987057    0.000980\n",
-       "25     25    0.018778  0.021654  0.994982  0.987067    0.000980\n",
-       "26     26    0.018736  0.022782  0.994512  0.987327    0.000980\n",
-       "27     27    0.018350  0.022171  0.993946  0.988492    0.000980\n",
-       "28     28    0.017058  0.021817  0.994746  0.987509    0.000980\n",
-       "29     29    0.018438  0.020757  0.994487  0.988596    0.000980\n",
-       "30     30    0.018608  0.022976  0.994566  0.985951    0.000980\n",
-       "31     31    0.014621  0.025216  0.993656  0.985654    0.000686\n",
-       "32     32    0.014509  0.020265  0.994693  0.989267    0.000686\n",
-       "33     33    0.013394  0.019021  0.995410  0.988526    0.000686\n",
-       "34     34    0.011331  0.020077  0.994831  0.990296    0.000686\n",
-       "35     35    0.011922  0.018384  0.995052  0.990089    0.000686\n",
-       "36     36    0.012270  0.019601  0.995129  0.989779    0.000686\n",
-       "37     37    0.015554  0.018940  0.995477  0.989308    0.000686\n",
-       "38     38    0.014366  0.020681  0.994269  0.988788    0.000686\n",
-       "39     39    0.010620  0.018546  0.995698  0.989377    0.000480\n",
-       "40     40    0.009366  0.018560  0.995683  0.989514    0.000480\n",
-       "41     41    0.008911  0.019390  0.995132  0.991078    0.000480\n",
-       "42     42    0.008429  0.018899  0.995001  0.991310    0.000480\n",
-       "43     43    0.008286  0.019599  0.995348  0.990892    0.000480\n",
-       "44     44    0.008304  0.019385  0.995407  0.990927    0.000480\n",
-       "45     45    0.008251  0.020172  0.994721  0.991485    0.000480\n",
-       "46     46    0.007368  0.021831  0.994789  0.991465    0.000480\n",
-       "47     47    0.006908  0.020716  0.995447  0.990374    0.000336\n",
-       "48     48    0.006331  0.020699  0.994999  0.991760    0.000336\n",
-       "49     49    0.005596  0.021027  0.995137  0.991521    0.000336\n",
-       "50     50    0.006214  0.021821  0.994982  0.991860    0.000336\n",
-       "51     51    0.005888  0.021646  0.995046  0.991365    0.000336\n",
-       "52     52    0.005638  0.021685  0.994775  0.991146    0.000336\n",
-       "53     53    0.005379  0.021299  0.995093  0.991552    0.000336\n",
-       "54     54    0.005713  0.021159  0.995055  0.991150    0.000336\n",
-       "55     55    0.004986  0.022516  0.995101  0.991471    0.000235\n",
-       "56     56    0.004542  0.022060  0.994969  0.992146    0.000235\n",
-       "57     57    0.004487  0.024956  0.994621  0.992040    0.000235\n",
-       "58     58    0.004333  0.024619  0.994677  0.992355    0.000235"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# gnn_metrics = checkpoint_utils.get_training_metrics(gnn_trainer)\n",
     "\n",
@@ -2422,20 +708,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "perfplot.plot_training_metrics(gnn_metrics, CONFIG, \"gnn\")"
    ]
@@ -2452,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2461,22 +736,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plot_edge_performance(gnn_model, CONFIG)"
+    "perfplot.plot_edge_performance(gnn_model, CONFIG)"
    ]
   },
   {
@@ -2489,102 +753,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:--------------------- Step 4: Scoring graph edges using GNN  ---------------------\n",
-      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
-      "INFO:Load model from artifacts/gnn/velo-minbias-sim10b-xdigi-nospillover/version_0/checkpoints/epoch=59-step=12000.ckpt.\n",
-      "INFO:----------------------------- b) Running inferencing -----------------------------\n",
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/train`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/train to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/train\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "161f43bc4d7e467cab2f45540a17d120",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/val`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/val to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/val\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f1e72a8c56e4403a21e3891b9974109",
-       "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:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e7f94a0f6cde42cda9d8bd025f1bf1c3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover-only-long-electrons`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/metric_learning_processed/test/velo-sim10b-nospillover-only-long-electrons to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover-only-long-electrons\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0e86300cdbd444c0a1cb1f68056336df",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "run_gnn_inference(CONFIG, checkpoint=gnn_artifact_path)"
    ]
@@ -2599,99 +770,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/train`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/train to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/train\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "743c8224221a4ae69e6d37b18a1dceef",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/val`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/val to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/val\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1dcc332c1ab425e930748957f129d13",
-       "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:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/test/velo-sim10b-nospillover`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/test/velo-sim10b-nospillover\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b661449fd21450cba7110fae6d8c8e6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/test/velo-sim10b-nospillover-only-long-electrons`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/gnn_processed/test/velo-sim10b-nospillover-only-long-electrons to /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/test/velo-sim10b-nospillover-only-long-electrons\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f41e54da2d174fe8aa77e1668fa60da3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "build_track_candidates(CONFIG)"
    ]
@@ -2706,179 +787,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:------------------------------ Evaluation for train ------------------------------\n",
-      "INFO:1) Load dataframe of tracks, hits-particles and particles\n",
-      "INFO:Load tracks in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/train.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "973e04c642a540fb8d3ec41f448746d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Load truncated paths for train in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/splitting.json\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f57b43a316640cdb30de1bbe2f3b425",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8d35272c66b4b6883ed61d5458951cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/200 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:2) Matching\n",
-      "INFO:3) Evaluation\n",
-      "INFO:Report was saved in output/report-2023.05.20-18.23.29-train.txt\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TrackChecker output                               :       345/    25916   1.33% ghosts\n",
-      "01_velo                                           :     23690/    24517  96.63% ( 96.86%),         8 (  0.03%) clones, pur  99.93%, hit eff  99.73%\n",
-      "02_long                                           :     23690/    24517  96.63% ( 96.86%),         8 (  0.03%) clones, pur  99.93%, hit eff  99.73%\n",
-      "03_long_P>5GeV                                    :     15576/    16063  96.97% ( 97.23%),         2 (  0.01%) clones, pur  99.95%, hit eff  99.90%\n",
-      "04_long_strange                                   :      1166/     1267  92.03% ( 92.62%),         2 (  0.17%) clones, pur  99.60%, hit eff  98.70%\n",
-      "05_long_strange_P>5GeV                            :       581/      629  92.37% ( 93.27%),         0 (  0.00%) clones, pur  99.68%, hit eff  99.65%\n",
-      "06_long_fromB                                     :        48/       51  94.12% ( 88.03%),         0 (  0.00%) clones, pur 100.00%, hit eff 100.00%\n",
-      "07_long_fromB_P>5GeV                              :        37/       38  97.37% ( 98.57%),         0 (  0.00%) clones, pur 100.00%, hit eff 100.00%\n",
-      "08_long_electrons                                 :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan%\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",
-      "*** Benchmark score:  96.58\n",
-      "\n",
-      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
-      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
-      "| Velo                 | 96.63%       | 96.86%               | 0.03%      | 99.93%               | 99.73%                   |\n",
-      "| Long                 | 96.63%       | 96.86%               | 0.03%      | 99.93%               | 99.73%                   |\n",
-      "| Velo, no electrons   | 96.63%       | 96.86%               | 0.03%      | 99.93%               | 99.73%                   |\n",
-      "| Velo, only electrons | nan%         | nan%                 | nan%       | nan%                 | nan%                     |\n",
-      "| Long, only electrons | nan%         | nan%                 | nan%       | nan%                 | nan%                     |\n",
-      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
-      "|:-------------|-----------:|:-----------|:-----------|\n",
-      "| Everything   |        345 | 25,916     | 1.33%      |\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Plot for category velo_reconstructible_acceptance saved in output/hist1d_velo_reconstructible_acceptance-train.pdf\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "INFO:Plot for category long_reconstructible_acceptance_only_electrons saved in output/hist1d_long_reconstructible_acceptance_only_electrons-train.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<montetracko.evaluation.trackevaluator.TrackEvaluator at 0x7f8419740fd0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "evaluate_candidates_montetracko(\n",
     "    CONFIG,\n",
@@ -2890,175 +803,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:---------------------- Step 6: Evaluation using MonteTracko ----------------------\n",
-      "INFO:1) Load dataframe of tracks, hits-particles and particles\n",
-      "INFO:------------------------------ Evaluation for val. ------------------------------\n",
-      "INFO:Load tracks in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/val.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f037ad177c64bdcae8c97bd21e49f4c",
-       "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:Load truncated paths for val in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/processed/splitting.json\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c704e6fcc6b6426082033eeea9c322e2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "158da533990a4820ad84d1bca91662a8",
-       "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) Evaluation\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TrackChecker output                               :       233/    12754   1.83% ghosts\n",
-      "01_velo                                           :     11593/    12140  95.49% ( 95.64%),         1 (  0.01%) clones, pur  99.76%, hit eff  99.62%\n",
-      "02_long                                           :     11593/    12140  95.49% ( 95.64%),         1 (  0.01%) clones, pur  99.76%, hit eff  99.62%\n",
-      "03_long_P>5GeV                                    :      7619/     7941  95.95% ( 96.07%),         1 (  0.01%) clones, pur  99.75%, hit eff  99.73%\n",
-      "04_long_strange                                   :       545/      628  86.78% ( 87.32%),         1 (  0.18%) clones, pur  99.43%, hit eff  98.33%\n",
-      "05_long_strange_P>5GeV                            :       269/      301  89.37% ( 90.51%),         1 (  0.37%) clones, pur  99.40%, hit eff  98.24%\n",
-      "06_long_fromB                                     :        32/       33  96.97% ( 98.81%),         0 (  0.00%) clones, pur  99.80%, hit eff 100.00%\n",
-      "07_long_fromB_P>5GeV                              :        22/       23  95.65% ( 98.15%),         0 (  0.00%) clones, pur  99.72%, hit eff 100.00%\n",
-      "08_long_electrons                                 :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan%\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",
-      "*** Benchmark score:  95.38\n",
-      "\n",
-      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
-      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
-      "| Velo                 | 95.49%       | 95.64%               | 0.01%      | 99.76%               | 99.62%                   |\n",
-      "| Long                 | 95.49%       | 95.64%               | 0.01%      | 99.76%               | 99.62%                   |\n",
-      "| Velo, no electrons   | 95.49%       | 95.64%               | 0.01%      | 99.76%               | 99.62%                   |\n",
-      "| Velo, only electrons | nan%         | nan%                 | nan%       | nan%                 | nan%                     |\n",
-      "| Long, only electrons | nan%         | nan%                 | nan%       | nan%                 | nan%                     |\n",
-      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
-      "|:-------------|-----------:|:-----------|:-----------|\n",
-      "| Everything   |        233 | 12,754     | 1.83%      |\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/matplotlib/axes/_base.py:2503: UserWarning: Warning: converting a masked element to nan.\n",
-      "  xys = np.asarray(xys)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<montetracko.evaluation.trackevaluator.TrackEvaluator at 0x7f7d027a0670>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "evaluate_candidates_montetracko(\n",
     "    CONFIG,\n",
@@ -3078,137 +825,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:---------------------- Step 6: Evaluation using MonteTracko ----------------------\n",
-      "INFO:1) Load dataframe of tracks, hits-particles and particles\n",
-      "INFO:-------------------- Evaluation for velo-sim10b-nospillover. --------------------\n",
-      "INFO:Load tracks in /scratch/acorreia/data/velo-minbias-sim10b-xdigi-nospillover/track_building_processed/test/velo-sim10b-nospillover.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "397d9b34a2d744e79e59df55b298f146",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Load pre-processed test datasets in /scratch/acorreia/data/__test__/velo-sim10b-nospillover.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "462e4dd737544057b82cf5bd9d713849",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89dc7518d3b6419288fc60a3b4d7f4a1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:2) Matching\n",
-      "INFO:3) Evaluation\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TrackChecker output                               :    108888/   340744  31.96% ghosts\n",
-      "01_velo                                           :    174031/   210946  82.50% ( 86.24%),        91 (  0.05%) clones, pur  98.59%, hit eff  99.04%\n",
-      "02_long                                           :    104569/   120326  86.90% ( 90.04%),        33 (  0.03%) clones, pur  98.92%, hit eff  99.33%\n",
-      "03_long_P>5GeV                                    :     68752/    77253  89.00% ( 91.75%),        10 (  0.01%) clones, pur  99.11%, hit eff  99.50%\n",
-      "04_long_strange                                   :      4247/     6247  67.98% ( 71.36%),         7 (  0.16%) clones, pur  97.30%, hit eff  97.87%\n",
-      "05_long_strange_P>5GeV                            :      2129/     3029  70.29% ( 73.03%),         1 (  0.05%) clones, pur  97.75%, hit eff  98.93%\n",
-      "06_long_fromB                                     :       204/      241  84.65% ( 85.50%),         0 (  0.00%) clones, pur  98.79%, hit eff  99.23%\n",
-      "07_long_fromB_P>5GeV                              :       139/      158  87.97% ( 85.91%),         0 (  0.00%) clones, pur  99.50%, hit eff  99.49%\n",
-      "08_long_electrons                                 :      3787/     8825  42.91% ( 46.33%),         1 (  0.03%) clones, pur  93.14%, hit eff  97.69%\n",
-      "09_long_fromB_electrons                           :        16/       28  57.14% ( 61.90%),         0 (  0.00%) clones, pur  96.96%, hit eff  98.12%\n",
-      "10_long_fromB_electrons_P>5GeV                    :        10/       17  58.82% ( 64.29%),         0 (  0.00%) clones, pur  97.14%, hit eff 100.00%\n",
-      "\n",
-      "*** Benchmark score:  80.68\n",
-      "\n",
-      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
-      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
-      "| Velo                 | 74.66%       | 78.55%               | 0.08%      | 98.22%               | 98.56%                   |\n",
-      "| Long                 | 83.90%       | 87.13%               | 0.03%      | 98.72%               | 99.27%                   |\n",
-      "| Velo, no electrons   | 82.50%       | 86.24%               | 0.05%      | 98.59%               | 99.04%                   |\n",
-      "| Velo, only electrons | 34.72%       | 38.83%               | 0.47%      | 93.83%               | 92.75%                   |\n",
-      "| Long, only electrons | 42.91%       | 46.33%               | 0.03%      | 93.14%               | 97.69%                   |\n",
-      "| Categories   | # ghosts   | # tracks   | % ghosts   |\n",
-      "|:-------------|:-----------|:-----------|:-----------|\n",
-      "| Everything   | 108,888    | 340,744    | 31.96%     |\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<montetracko.evaluation.trackevaluator.TrackEvaluator at 0x7f7cff7418d0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Lue6662zWX3/9dbVr106StGXLFi1atEgDBgzItt22bdt02223mXUYNWqUXn75ZZs8CQkJ6tmzZ67HIklHjx7V7t27zfWWLVs6tV1BDRw4UIsWLcr2+y+++EIjRozQlClT9Morr+jixYs26a1bt9ZXX31lBnsbhqEJEyZo6tSp2WYsadGihb766is1bty42NYBAFB8EGABALmw/rIgPDxcnTt31unTpyVJpUqVUuPGjRUYGKgdO3YoLi5OkpSWlqZ7771XTZs2deqCqySoXLmyRowYoY8//liStGLFCm3atEnXXnutw+127typH374QU2aNHH6ISR7Dh06pPHjx2vhwoU5Tt0YFBSke++9V88//3y2i+ErDRs2zGYaxaioKKcexMp6y+3QoUPl5+fnVL0XL15sLuf2Nzh27JheeOEFzZ07V8nJyTnmqV69uvr376/7779fLVq0cKoOAAAAKBhPuS5gXH85n8S4HgAAAMgr62ujiIgIvfTSS7luExISYi6HhoYWRrUAAADghbZu3arevXubD7zXq1dPb775pgYNGmTe9zUMQ/Pnz9f999+v+Ph4HTt2TA8//LC+/fZbm7LKli2rqlWr6vjx45KkPXv25Hpf/6+//tLChQslZd7Xtg5seOWVVyRJP/30k/r06SNJatiwoZYuXerUsV177bUaMGCA+cD/pEmT1L9/f/n4+Jh5oqOj1adPH/P4e/furU8++SRbWT179nQqwOLAgQPq2rWrud69e3cNGTLEqfoW1PXXX2++eGnlypXm9y9SZlD31KlT1bhxYzVu3FhxcXH6448/lJiYqC1btqh79+5av369qlWrpgcffFAzZ85Us2bNVL9+fZ09e1YbNmxQamqqtm/frq5du2r37t0qV65csawDAKD48HV3BQCguLN+U+3GjRt1+vRphYWF6YMPPtDJkye1adMm/f777zp9+rReeOEFM296eromTpzojioXmnHjxtk8fPT666/nus3rr78uwzD07LPP2lzo5dWKFSvUvHlzffXVV/Lx8dGDDz6oRYsWac2aNZozZ4769++vpKQkffDBB2rTpk2uUxz2799fZcuWNdfnzZtnM615TtLS0jRv3jxJcvotxIZhmA98tWzZUjVq1LCbd82aNbr66qv1+eefKyUlRUOGDNHXX3+t3377TYsXL9b48eNVoUIFHT16VB9++KFatmzp9MU3AAAACsaTrgsY1zOuBwAAAPLLOsBixIgRCg4OznWbkydPmst169YtjGoBAADAy8TFxemWW24xgwuuu+46bdq0SbfddpvN/W8fHx/dddddmjlzpvm777//Xvv3789WZrNmzczlPXv25FqHZ599VoZhSJIef/xxVa9ePVse6/FzXmeDePXVV+Xr62uW83//939mWmxsrG6++WYzIKRt27ZasGCBLJb8vW87K7ji6NGjkjKDKxYvXuzUeN8Vxo4dq6+++kpfffWVmjRpYv5+xYoV+vjjj7VkyRLt3btX33//vVasWKF///1XzZs3l5QZaPLaa6/pu+++09dff63ly5dr586d+v777/Xbb79p586dqlatmqTMa5O33nqr2NYBAFB8EGABALm4crrriIgIbdq0SQ8//LDNW5cCAgL00ksv6YEHHjB/t3z5cpuI5pKuTp06uvPOO831xYsX20wNeKUDBw5owYIFql27doGi2jdv3qybb75ZFy9eVGhoqFatWqWPPvpI/fv3V+fOnTV06FAtWrRIn332maTMt8X26tXLZgrIKwUGBuqOO+4w18+ePasff/zRYT2WLVummJgYtWrVymZ6SEc2bdpkfnnk6C23W7duVa9evXT+/HlZLBYtWrRI8+bN0+23367rr79e/fr10+uvv65du3bZXMglJSU5VQ8AAAAUjCddFzCuZ1wPAAAA5Nf27dvN5ZtuuinX/EeOHFF8fLwkyWKxqH79+oVWNwAAAHiPV199VYcOHZIkVatWTd99953DGQHuvPNOMwDCMAwtW7YsWx7r+8W5BVgsX75cK1askJQ5+8Wzzz6bY76CBFg0a9bM5p78pEmTZBiGUlJSNGjQIPO+fr169fTDDz/ke7Y4dwdXODJnzhx9+OGH6tu3r83vq1Spovfff98m31NPPaWZM2eqe/fuNnkbNWqkV1991VyfP39+iasDAKDoEWABAA5cunRJBw4cMNf9/Pz0f//3f7rqqqvsbjNu3Dhz2TAM/fXXX4Vax6Jm/cZawzD0xhtv2M375ptvKj09XU8//XS+o+RTU1N15513mm+hfeWVV3TdddflmHfUqFHmw1UnTpywewGb5cq31UZGRjrMHxUVleN2jixevNhctvcgVmpqqu644w7zoapx48apX79+OeatVKmSZs+e7fT+AQAAUHCeeF3AuJ5xPQAAAJBXhw4dUmxsrKTMNwFfe+21uW5jPRvgVVddpaCgoMKqHgAAALzEqVOn9O6775rrH3zwgSpWrOhwG19fX7Vv395c/+eff7LlcXYGC8MwNH78eHN9/PjxNjMtWytIgIUkvfTSS/L395ck7dixQwsXLtTIkSO1evVqSZkvg1q6dGmux29PcQ6ukKSaNWvq7rvvzjGtU6dOCg8PlyRdvHhRhmHo9ttvzzGvdXBEdHR0nl6KVRzqAAAoegRYAIAD27dvN6fzk6T77rtPHTt2dLhN3bp1zWndpMy3rnqSZs2a2Qz6v/rqK/3333/Z8h09elRffvmlKlasqFGjRuV7f/PmzTMfZgsPD7d5E3BOHnzwQXN5zpw5Dt922759ezVq1Mhc//nnn+1ewMTGxmrJkiWyWCy66667nK5/1oNY1apVU5s2bXLMM2/ePHP6SYvForFjxzoss02bNnbLAgAAgOt54nUB43rG9QAAAEBeWT8cVrduXfNBIkesAyxatWpVGNUCAACAl5kzZ475Mp8WLVpowIABTm1nHYRw4cKFbOnWM1gcPHjQ7szD33zzjTZv3ixJqlGjhh577LEc88XHx+vff/811/MTYFGvXj2be/PDhw/XvHnzJEkhISH64Ycf8j1L3JXBFTfeeGOxCq6QMmfNy3pZ1JV8fX1Vp04dc71Hjx52y6lQoYLN9UtevrMpDnUAABQ9AiwAwAHrG/++vr6aOHGiU9tVqVLFXE5ISHB5vdzNOhI/PT1dkydPzpZnypQpSklJ0f/+978CvZHK+u2zXbt2VUhIiMP87dq1k5+fnyQpKSlJv/76q8P8w4YNM5dTU1PNC9Erff3110pKStLNN9+siIgIp+p+6NAh7dq1S5LsvrlWsj3Gjh07Opy2Mss999yjG264QRUqVHCqLgAAAMg/T70uYFzPuB4AAADIC+sAC2eDJQiwAAAAgKt988035vLQoUOd3i4rKEOSSpUqlS29SZMm8vXNfJwyIyMjx1ku0tLS9Nxzz5nrL730kt1759u3b1dGRoakzO8L8jvLxAsvvGDuIzExUVLmTNvffPONU7PK5SSn4IolS5YUq+AKSTYvV8qJdcBCw4YNnc578eLFElUHAEDRI8ACAByw/rKge/fuql69ulPbpaWlmcvWD9UMHjxYPj4++fr57rvvXHZcBdWhQwfdcMMN5npUVJSOHz9urp8+fVqffvqpSpcurYcffjjf+0lJSdGGDRvMdWei+YODg1WmTBlzfePGjQ7z33PPPeYFsmT7UJS1qKgoSZlvA3DWokWLzOX+/fvnmOfKY2zevLlTZY8ZM0arV69Wp06dnK4PAAAA8sfV1wXFBeN65zCuBwAAADIRYAEAAAB3S0hI0JYtW8z1m266yeltY2NjzeXy5ctnSw8ODlbdunXN9T179mTLM2vWLHO25mbNmjm812w9fs7P7BVZqlatqg4dOtj87r333lOfPn3yVV5JCa6QZPM9QU6svxfILW/WS52kzJdOlaQ6AACKnsXdFQCA4sz6xn/v3r2d3u7UqVPmcqVKlczlo0eP2qxniY2NVXJysgICAlS2bNkcy2zTpo3T+y8KEyZM0Jo1ayRJycnJevvtt/X2229LkqZPn66EhARNmDDBqSnC7Tl8+LDNlIsLFiyweWjJnvj4eHP5yJEjDvPWqFFDXbt21YoVKyRlXuDu2LHD5oGoAwcO6I8//lC5cuXUt29fp+u/ePFiSVJYWJi6deuWY54rj7FWrVpOlw8AAICi4errguKEcX3uGNcDAAAAmfIaYHHu3DlFR0eb6wV5qAwAAACQMsekqampkqSAgAA1bdrU6W337dtnLjdr1izHPFdffbUZQHFlgEVCQoJefvllc/2NN96webg+p7pmadGihdP1vNLrr7+uVatW2fzuzJkz+SqrJAVXSJLF4vzjrXnJW9LqAAAoepzRAcCOtLQ07d6921xv3769U9udOnXK5q2v9erVM5f/+OOPHLdp06aNtmzZomHDhmnWrFn5rHHRuummm9S6dWvzzQCffPKJJkyYIIvFog8++EDBwcEaM2ZMgfZx7tw5m/U9e/bk+IYAR6zfQGDP8OHDzQexpMy33b7zzjvmetZbbocMGaKAgACn9nvhwgX9/vvvkjL/VoGBgTnmu/IYQ0NDnSofAAAARaMwrguKE8b1jjGuBwAAADLFxsbq8OHD5rozARbWwep169ZV6dKlC6VuAAAA8B4nT540lyMiIpx+oD0hIcGpe/3NmjXT999/Lyl7gMW0adPM/V9//fW5vsjHFTNYzJ49WxMnTsz2+7fffluPPvponmbPLmnBFQAAuJP9EEoA8HJ79+5VcnKyuV6nTh2ntst6+6uU+YWB9fSBObF+YKukvb1p/Pjx5nJ8fLzeffddffDBB7pw4YLuvfdeVaxY0aX7++ijj2QYRp5+li9fnmu5t9xyi8LCwsz1uXPnKi0tTZJkGIa+/PJLSdKwYcOcrutPP/1kvjWhf//+Tm/n4+PjdF4AAAAUvqK6LnAnxvX2Ma4HAAAAMlk/HFa5cmVVrlw5122sAyycCcgAAAAAcnP+/HlzuVSpUk5v9/333ys9PV2S1KhRI9WsWTPHfFdffbW5bB1gcfbsWU2ZMsVcf+uttxzuLz09Xbt27TLX8/M80PLlyzV69Ghzffz48apRo4YkKS4uTm+++abTZRFcAQBA3hBgAQB2WN/4l+R01Pe8efPM5Z49e+aaf8+ePeYDWyXtC4ZbbrlFjRo1MtdnzJih6dOny2Kx6Kmnnipw+eXLl7dZv3TpUoHLzEloaKhuvfVWc/3UqVP6+eefJWU+GHf48GE1btxY1157rdNlLl68WJLk5+enPn362M1XVMcIAACA/Cmq6wJ3YlxvH+N6AAAAIJN1gIWz32VYX0+1bt3a1VUCAACAFwoJCTGX4+Pjnd7u448/Npfvvvtuu/maNWtmLh84cMB8Ac/rr7+uCxcuSJIGDRqU62zX+/fvV2JiolnnBg0aOF1XKXMsfeutt5r7HzlypF5//XVNmDDBzDNjxgydOHEi17KuDK7o1q0bwRUAAOSCAAsAsMP6ywJJSklJyXWbo0eP6scffzTX7733Xqf34+Pjo+bNm+epju7m6+urZ555xlw/f/68Tp8+raFDh6pWrVoFLr9mzZo2F3TR0dEFLtOe4cOH26xHRUXZ/HtluiNpaWlaunSpJKlDhw6qUKGC3bxFeYwAAADIu6K6LnAnxvU5Y1wPAAAAXFbQAIuS9oIpAAAAFE/Vq1c3l48dO+bUy26+/vprrV27VpIUHByskSNH2s3bsGFDBQYGSsq8R7xv3z4dOXJEH3zwgSTJYrHojTfeyHWfBw4cMJfr1asnX1/nH9M8fPiw+vTpo4sXL0rKfInTzJkzJWV+35B13z4xMVGvvPJKrvW4Mrjihx9+ILgCAIBcEGABAHZc+aba/fv357rNhAkTzOjxm2++WW3atHF6P/Xr11dYWFg+auped999tzkFoZQZKGL9cFZBBAQE2ET9X9km9sTHx+uee+7R3XffrRUrVji1TZcuXWweHluyZImOHj2qb7/9Vr6+vg7fYHClNWvWKDY2VpLUv39/h3mvPMYdO3Y4tY/ly5fr448/1tdff+10vQAAAJB3RXVd4G6M67NjXA8AAABcltcAi4SEBO3bty9P2wAAAAC5ueaaaxQQECBJysjI0KJFixzm37Nnjx566CFz/amnnlK1atXs5vfz81Pjxo1ttn/hhReUnJwsSRo1apTNjND2WAd+ZGRk5Jo/y7lz59SrVy9zZopWrVpp4cKFslgskiR/f38999xzZv5PP/1UBw8ezLEsgisAAMg/AiwAwI7t27fbrH/33XcO8y9YsEBz5syRlBmxnluUeJasLyVatmyZ5zoWB/7+/ho7dqy5PnDgQF111VUuK3/EiBHm8h9//KGYmJhct1m8eLHmzJmjuXPn2jwk5oiPj4/uuececz0lJUVDhgzRxYsXdeONN9q8BcGZ/WfJ7UEsyfYY169fr/PnzzvMbxiGHnjgAT300EM2b0YGAACA6xXVdYG7Ma7Pef9ZGNcDAADA2zVt2lS33nqrbr31VrVr1y7X/Dt27FB6erokqXLlyqpcuXJhVxEAAABeIDg4WLfddpu5PmHCBB0/fjzHvKtWrVL37t3Ne7Vt27bVxIkTc91Hs2bNzOWFCxfqyy+/lCSFhIRo0qRJTtWzUqVK5vLevXu1bNmyXLdJSkpS//799ffff0uSatWqpR9//DHby1pHjBihunXrSpJSU1P14osvZiuL4AoAAAqGAAsAyMGhQ4eyPQzzzjvvZHu4KsuXX36p4cOHyzAMSdIbb7yh1q1bO7WvrACLkvz2pvvvv18///yzfv75Z73//vsuLXvIkCFq2LChJCk9PV2vv/66w/wpKSlmnt69e5vbOmPYsGE261lTRA4fPjwvVdaSJUskZU4d6cybC6yPMTU1VdOnT3eYf/HixeYbCO6777481Q0AAADOK8rrguKAcb0txvUAAADAZXPnztXChQu1cOFCpwKgrWeuK8nffwAAAKD4eeGFFxQSEiJJio6OVqtWrfTKK6/ol19+0cqVK/XZZ5/p5ptvVvfu3c2ZIBo2bKjFixcrMDAw1/Kvvvpqc/mbb74xA4efeOIJValSxak6tm/fXuXKlZOUOYNFr1691LRpU/Xo0UO9evVSr1699N5775n5MzIyNHToUK1bt06SVLZsWf3888857s9isdjMYjF37lzt2bPHJs9LL71kBldIUlpamgYNGmTu29HPhAkTnDpGAAA8mcXdFQCA4sh6qutatWqpevXqWrdunTp16qRHHnlEXbt2lcVi0YEDBzR37lz9/vvvZv5BgwbZvPnVkUOHDik2NlZSyZ3BQsp8Q0CvXr0KpWx/f3999dVX6tixo5KSkvT++++rRYsWGjVqVLa8ycnJGjVqlHbt2qXQ0FBNmzYtT/tq0KCBOnbsqPXr15u/K1WqlAYNGuR0GTt37jQfknLmLbdS9mN844031K5dO918883Z8v7999968MEHJWW+Vfj66693um4AAADIm6K6LiguGNdfxrgeAAAAKBgCLAAAAFBYGjVqpMjISA0dOlSpqak6deqUXnjhBbv5u3Tpom+++UYRERFOlW89g0WW8uXLa9y4cU7XMTg4WDNmzNA999xjBmjs2bPHJhCid+/e5vKYMWPMGbQDAwO1aNEih7NMDxs2TK+//roOHDigjIwMPffcczYzcFuPxyXpt99+c7ru9evXdzqvq8yZM8ecHXzHjh3m799++2199dVXkqSoqChVqlTJqbxLly41f7d8+XJJsplZe9y4cWYATFbe4lAHAEDxQYAFAOTA+kKjdevWmjhxom644QZdunRJkydP1uTJk3PcbujQofriiy/k4+OT5/0UlwAL6weqsgb21oP6u+++W3fffXeeytyxY4fdC805c+Zow4YNkqQePXrk+BBaq1attHTpUg0cOFCxsbG699579c033+jOO+9U7dq1lZaWpq1bt2rWrFnav3+/goODtXDhQqfeMnul4cOH2zyINXjwYPPNB85YvHixuezsg1hS9mPs27ev7rrrLg0YMECVK1fW+fPntXLlSn366aeKj49X69at9fnnnztdPgAAAPKuqK4LCgPjesb1AAAAgDsRYAEAAIDCNHjwYFWrVk1jxozRX3/9lWOeRo0aady4cRo5cmSe7tdbz2CRZeLEiSpdunSe6jhkyBA1atRI7777rtauXavjx48rKSnJTM+aAfutt97SjBkzJEk+Pj6aPXt2ri/l8fPz0wsvvGDO6Pz999/rzz//VNu2bZWUlKR//vknT3W11rx583xvm18HDhzQsmXLsv1+165d2rVrlyQpMTHR6bxZdu7cmWPeP//8s1jWAQBQfPgYhmG4uxIAUNwMGDDAfKDmlVde0XPPPaeNGzdqxIgR+vvvv7Plb9iwoV577TXddtttedrPiy++qJdfflkVK1a0iVJ2JeuLxFWrVqlLly5O58/Jiy++qEmTJuWpDqtXr1bXrl1zzTd8+HBFRkbaTT969KheeOEFzZ07VykpKdnS/fz81KdPH02ZMkUNGzbMUx2zxMbGqkqVKuZF7erVq3XDDTc4vX27du20adMmlS9fXjExMfLz88vT/nM7xrCwMD300EOaNGmS0w+IdenSRWvWrJGUv/YDAADwVkV1XeAMxvV544njeomxPQAAAEqGtLQ0lSpVyhyPHzhwQPXq1XNzrQAAAOCp9uzZoz/++EOnTp2Sr6+vqlatqmuuucbhDBAofHn9XgP28d0AABQ9ZrAAgBxs27bNXG7Tpo2kzAds9u7dq3Xr1mnPnj06c+aMKlasqDZt2uR79omsNzgVl9krJKkw4u66dOniknKrV6+uzz//XO+//75Wr16tQ4cOKTY2ViEhIapbt646dOigihUrFmgfZcqUMSPO8+rkyZNmhHmfPn3y/BCWZP8Yw8LC1LhxY1133XUKCwvLV/0AAACQN0V1XVAYGNczrgcAAADcZe/evWZwRXh4uOrWrevmGgEAAMCTNWnSRE2aNHF3NQAAgAchwAIArnDu3DlFR0eb61lT8mW57rrrdN1117lkX1kPbDE9dt6EhoaqT58+7q5GNkuWLDEfOOvfv3+ByiquxwgAAOAtivK6wFsV1zEv43oAAACgYLJeLiVlvmAqtxn2AAAAAAAAgOLE190VAIDixvottVWrVlWlSpUKZT/nzp3TkSNHJBWvGSyQf4sXL5YkBQYGqmfPnm6uDQAAAAqiqK4LUPwwrgcAAAAKxjrAghdMAQAAAOjatat8fHzMn9q1a7u7SsVWZGSkzd/Kx8dHa9ascXe1AMDrMIMFAFzB+sb/lW+pLaz9FFWARdeuXW3Wa9WqpUOHDhXJvr3B9ddfrzZt2qhatWoKCwtzSx0iIyM1cuRIt+wbAADAkxTVdUF+MK4vXMVhXC8xtgcAAEDJNW3aNE2bNs3d1QAAAADgRo5eYMRLreyrVq2aw79d/fr1i7A2AOC9CLAAgCtYv6m2KAIsQkJC1LBhw0LbDxcsRWfcuHHurgIXWgAAAC5SVNcFzmJcX3SKw7heYmwPAAAAAAAAACi5li5d6u4qlEg9evRQjx493F0NAPB6BFgAwBWK6k21WQ9sXX311fL19S20/XDB4l240AIAAHCN4jaDBeN678PYHgAAAAAAAAAAAACKno9hGIa7KwEAxUVSUpJKlSqltP9v346JAIhhGAhqwiP8eRlHimfxLrSLQK2Key9JMjO59y6vAgAA/uQXAAAAAAAAAEAngQUAAAAAAAAAAAAAAFDvbA8AAAAAAAAAAAAAAADYJrAAAAAAAAAAAAAAAADqCSwAAAAAAAAAAAAAAIB6AgsAAAAAAAAAAAAAAKCewAIAAAAAAAAAAAAAAKgnsAAAAAAAAAAAAAAAAOoJLAAAAAAAAAAAAAAAgHoCCwAAAAAAAAAAAAAAoJ7AAgAAAAAAAAAAAAAAqCewAAAAAAAAAAAAAAAA6n3wUugNG6PqmwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "evaluate_candidates_montetracko(\n",
     "    CONFIG,\n",
-- 
GitLab


From dfc18578ad62858bdcbb7680f9edeeaa0514916f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 14:03:41 +0200
Subject: [PATCH 03/65] ignore artifact_old

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 26504ef2..6f6e521e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,7 @@ __pycache__
 # Data
 data/
 artifacts/
+artifacts_old/
 lightning_logs/
 LHCb_Pipeline/output/
 LHCb_Pipeline/analysis/
-- 
GitLab


From 144aa6f32dc0592cfae2188ba830ce8b9b254cec Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 18:59:55 +0200
Subject: [PATCH 04/65] Rename particle line metrics functions

---
 LHCb_Pipeline/Preprocessing/particle_line_fitting.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/particle_line_fitting.py b/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
index f0969ab9..4a82abf8 100644
--- a/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
+++ b/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
@@ -132,7 +132,7 @@ def compute_particle_metric(
 
 
 @nb.jit(nopython=True, cache=True)
-def compute_particle_distances_to_lines_events_impl(
+def compute_particle_line_metrics_events_impl(
     array_metric_values: np.ndarray,
     coords_events_particles: np.ndarray,
     event_ids: np.ndarray,
@@ -178,7 +178,7 @@ def compute_particle_distances_to_lines_events_impl(
         event_idx += n_particles
 
 
-def compute_particle_distances_to_lines_dataframe(
+def compute_particle_line_metrics_dataframe(
     hits: pd.DataFrame,
     metric_names: typing.List[str],
 ) -> pd.DataFrame:
@@ -208,7 +208,7 @@ def compute_particle_distances_to_lines_dataframe(
     events_particles_group = hits.groupby(["event", "particle_id"], sort=False).size()
     n_particles = events_particles_group.shape[0]
     array_metric_values = np.zeros(shape=(n_particles, len(metric_names)))
-    compute_particle_distances_to_lines_events_impl(
+    compute_particle_line_metrics_events_impl(
         event_ids=hits["event"].to_numpy(),
         particle_ids=hits["particle_id"].to_numpy(),
         coords_events_particles=hits[["x", "y", "z"]].to_numpy(),
-- 
GitLab


From bb1007d9d9c7192ce017c4b84ab94bd979377f95 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 19:58:12 +0200
Subject: [PATCH 05/65] When applying particle mask, use `particle_id` instead
 of `mcid`

---
 LHCb_Pipeline/Preprocessing/particle_line_metrics.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/LHCb_Pipeline/Preprocessing/particle_line_metrics.py b/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
index dca906a9..9fd535ff 100644
--- a/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
+++ b/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
@@ -1,6 +1,13 @@
+import warnings
 import numpy as np
 import numba as nb
 
+warnings.filterwarnings(
+    "ignore", message=(
+        "Encountered the use of a type that is scheduled for deprecation: "
+        "type 'reflected list' found for argument"
+    )
+)
 
 @nb.jit(nopython=True, cache=True)
 def compute_distance_to_line(
-- 
GitLab


From 127ce2d7cde3ce21557612040bc9db41f8eaf0bc Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Mon, 22 May 2023 20:18:40 +0200
Subject: [PATCH 06/65] Correct "apply_mask": keep same index

---
 LHCb_Pipeline/Preprocessing/selecting.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index 87143928..f31dcb07 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -1,5 +1,8 @@
 import typing
+import logging
+import numpy as np
 import pandas as pd
+from .particle_line_fitting import compute_particle_line_metrics_dataframe
 
 
 class SelectionFunction(typing.Protocol):
@@ -29,12 +32,15 @@ def apply_mask(
     """
     # About 3 seconds in a dataframe with 5000 events
     hits_particles_mask = (
-        hits_particles[["event", "mcid"]]
+        hits_particles[["event", "particle_id"]]
+        .reset_index()
         .merge(
-            right=particles[["event", "mcid"]].assign(mask_=particles_mask),
-            on=["event", "mcid"],
+            right=particles[["event", "particle_id"]].assign(mask_=particles_mask),
+            on=["event", "particle_id"],
             how="left",
-        )["mask_"]
+            sort=False,
+        )
+        .set_index("index")["mask_"]
         .fillna(True)
     )  # fillna to keep fake hits
 
-- 
GitLab


From 0109c92773ccc998c596a8b157d26630141e75d2 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 00:04:38 +0200
Subject: [PATCH 07/65] Reduce size of test samples

---
 LHCb_Pipeline/test_samples.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/LHCb_Pipeline/test_samples.yaml b/LHCb_Pipeline/test_samples.yaml
index 4463948a..8a8fb603 100644
--- a/LHCb_Pipeline/test_samples.yaml
+++ b/LHCb_Pipeline/test_samples.yaml
@@ -1,11 +1,11 @@
 velo-sim10b-nospillover:
   input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/198
   selection: null
-  n_events: 2000
+  n_events: 1000
   num_true_hits_threshold: 0
 
 velo-sim10b-nospillover-only-long-electrons:
   input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/198
   selection: only_long_electrons
-  n_events: 2000
+  n_events: 1000
   num_true_hits_threshold: 0
-- 
GitLab


From ea40609048c6448020fd048124ed51c660d8ef2e Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:12:03 +0200
Subject: [PATCH 08/65] Small formatting change

---
 LHCb_Pipeline/Preprocessing/run_preprocessing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Preprocessing/run_preprocessing.py b/LHCb_Pipeline/Preprocessing/run_preprocessing.py
index fcbecaa5..1a1db4a8 100644
--- a/LHCb_Pipeline/Preprocessing/run_preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/run_preprocessing.py
@@ -18,7 +18,7 @@ def run_preprocessing(path_or_config: str | dict, reproduce: bool = True):
     """
     config = load_config(path_or_config)
     output_dir = config["preprocessing"]["output_dir"]
-    
+
     if config["preprocessing"]["n_events"] is None:
         config["preprocessing"]["n_events"] = (
             config["processing"]["n_train_events"]
-- 
GitLab


From 25bd88b2f00f5c15368cb9ba5a3bb94a05ef6a92 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:12:19 +0200
Subject: [PATCH 09/65] Be able to load smog2 sample

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 111 ++++++++++++++++---
 1 file changed, 96 insertions(+), 15 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 6241961c..892b4bad 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -31,10 +31,78 @@ def cast_boolean_columns(particles: pd.DataFrame):
     particles[boolean_columns] = particles[boolean_columns].astype(dtype="bool")
 
 
+def _load_particles(
+    indir: str | typing.List[str],
+    particles_columns: typing.List[str] | None = None,
+    **kwargs,
+) -> pd.DataFrame:
+    indirs = [indir] if isinstance(indir, str) else indir
+
+    dataframes = []
+    for indir in indirs:
+        particles = pd.read_parquet(
+            f"{indir}/mc_particles.parquet.lz4",
+            columns=None
+            if particles_columns is None
+            else ["event", "mcid"] + particles_columns,
+            **kwargs,
+        )
+    return pd.concat(dataframes)
+
+
+def _load_hits_particles(
+    indir: str | typing.List[str],
+    hits_particles_columns: typing.List[str] | None = None,
+    **kwargs,
+) -> pd.DataFrame:
+    indirs = [indir] if isinstance(indir, str) else indir
+
+    dataframes = []
+    for indir in indirs:
+        particles = pd.read_parquet(
+            f"{indir}/mc_particles.parquet.lz4",
+            columns=None
+            if hits_particles_columns is None
+            else ["event", "mcid"] + hits_particles_columns,
+            **kwargs,
+        )
+    return pd.concat(dataframes)
+
+
+def load_dataframes_in_different_directories(
+    indirs: str | typing.List[str],
+    filename: str,
+    new_event_id_per_file: bool = False,
+    **kwargs,
+) -> pd.DataFrame:
+    """Load concatenated dataframes stored in different folders.
+
+    Args:
+        indirs: input directory or list of input directories
+        filename: name of the file in all the directories
+
+    Returns:
+        Concatenated dataframes located in the different input directories under
+        the name ``filename``.
+    """
+    indirs = [indirs] if isinstance(indirs, str) else indirs
+    dataframes = []
+    for prod_idx, indir_ in enumerate(indirs):
+        dataframe = pd.read_parquet(path=os.path.join(indir_, filename), **kwargs)
+        if new_event_id_per_file:
+            dataframe["event"] = dataframe["event"] + prod_idx * 10 ** (
+                9 - len(str(len(indirs) - 1))
+            )
+
+        dataframes.append(dataframe)
+    return pd.concat(dataframes)
+
+
 def load_dataframes(
-    indir: str,
+    indir: str | typing.List[str],
     particles_columns: typing.List[str] | None = None,
     hits_particles_columns: typing.List[str] | None = None,
+    new_event_id_per_file: bool = False,
     **kwargs,
 ) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
     """Load the dataframes of hits_particles and particles that are stored in a folder.
@@ -54,20 +122,23 @@ def load_dataframes(
         The function also defines the column ``particle_id = mcid + 1``
         in both dataframes.
     """
-
-    particles = pd.read_parquet(
-        f"{indir}/mc_particles.parquet.lz4",
+    particles = load_dataframes_in_different_directories(
+        indirs=indir,
+        filename="mc_particles.parquet.lz4",
         columns=None
         if particles_columns is None
         else ["event", "mcid"] + particles_columns,
+        new_event_id_per_file=new_event_id_per_file,
         **kwargs,
     )
     cast_boolean_columns(particles)
-    hits_particles = pd.read_parquet(
-        f"{indir}/hits_velo.parquet.lz4",
+    hits_particles = load_dataframes_in_different_directories(
+        indirs=indir,
+        filename="hits_velo.parquet.lz4",
         columns=None
         if hits_particles_columns is None
         else ["event", "mcid", "lhcbid"] + hits_particles_columns,
+        new_event_id_per_file=new_event_id_per_file,
         **kwargs,
     )
 
@@ -134,11 +205,12 @@ def enough_true_hits(
 
 
 def preprocess(
-    input_dir: str,
+    input_dir: str | typing.List[str],
     output_dir: str,
-    n_events: int,
+    n_events: int = -1,
     selection: str | None = None,
     num_true_hits_threshold: int | None = None,
+    new_event_id_per_file: bool = False,
 ):
     """Preprocess the first `n_events` events in the input files,
     into the form of the TrackML dataset.
@@ -181,6 +253,7 @@ def preprocess(
         indir=input_dir,
         # particles_columns=particles_columns,
         hits_particles_columns=hits_particles_columns,
+        new_event_id_per_file=new_event_id_per_file,
     )
 
     # Add truth particle information to the dataframe of hits
@@ -192,10 +265,13 @@ def preprocess(
             particles=particles,
         )
 
-    event_list = particles["event"].unique()  # The order is not mixed
+    event_list = hits_particles["event"].unique()  # The order is not mixed
     n_output_saved = 0  # Count the number of events outputted
     event_idx = 0
 
+    if n_events == -1:
+        n_events = len(event_list)
+
     with tqdm(total=n_events) as pbar:
         while n_output_saved < n_events and event_idx < len(event_list):
             current_event_id = event_list[event_idx]
@@ -207,12 +283,17 @@ def preprocess(
             #: String representation of the event ID
             event_id_str = str(current_event_id).zfill(9)
 
-            if (num_true_hits_threshold is None) or enough_true_hits(
-                event_hits_particles=event_hits_particles,
-                num_true_hits_threshold=num_true_hits_threshold,
-                event_id_str=event_id_str,
-                num_events=n_output_saved,
-                required_num_events=n_events,
+            no_hits = event_hits_particles.shape[0] == 0
+
+            if not no_hits and (
+                (num_true_hits_threshold is None)
+                or enough_true_hits(
+                    event_hits_particles=event_hits_particles,
+                    num_true_hits_threshold=num_true_hits_threshold,
+                    event_id_str=event_id_str,
+                    num_events=n_output_saved,
+                    required_num_events=n_events,
+                )
             ):
                 # Save subset of columns
                 if hits_particles_columns is None:
-- 
GitLab


From f015d26b80e74fb3b50ba2533872b3f372ad7df0 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:12:57 +0200
Subject: [PATCH 10/65] Don't reproduce if reproduce = False in processing

---
 LHCb_Pipeline/Processing/run_processing.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/LHCb_Pipeline/Processing/run_processing.py b/LHCb_Pipeline/Processing/run_processing.py
index 0ad2e97e..de7975ba 100644
--- a/LHCb_Pipeline/Processing/run_processing.py
+++ b/LHCb_Pipeline/Processing/run_processing.py
@@ -36,16 +36,19 @@ def run_processing_in_parallel(
     """
     if reproduce:
         delete_directory(output_dir)
-    elif is_directory_not_empty(output_dir):
-        logging.warn(f"Output directory is not empty: {output_dir}")
     os.makedirs(os.path.join(output_dir), exist_ok=True)
-    logging.info("Writing outputs to " + output_dir)
 
-    # Process input files with a worker pool and progress bar
-    process_func = partial(
-        prepare_event, output_dir=output_dir, **processing_config
-    )
-    process_map(process_func, truncated_paths, max_workers=max_workers, chunksize=1)
+    if is_directory_not_empty(output_dir):
+        logging.info(
+            f"Output folder is not empty so processing was not run: {output_dir}"
+        )
+    else:
+        logging.info("Writing outputs to " + output_dir)
+        # Process input files with a worker pool and progress bar
+        process_func = partial(
+            prepare_event, output_dir=output_dir, **processing_config
+        )
+        process_map(process_func, truncated_paths, max_workers=max_workers, chunksize=1)
 
 
 def run_processing_test_dataset(
-- 
GitLab


From cc2ee163a7402e77ba46d1ce60dfe9f69ce50443 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:15:44 +0200
Subject: [PATCH 11/65] Choose true edges in processing

---
 LHCb_Pipeline/Embedding/build_embedding.py    |  3 -
 LHCb_Pipeline/Embedding/embedding_base.py     | 16 ----
 LHCb_Pipeline/Processing/processing.py        | 16 ++--
 .../velo-sim10b-nospillover-lot.yaml          | 88 +++++++++++++++++++
 4 files changed, 98 insertions(+), 25 deletions(-)
 create mode 100644 LHCb_Pipeline/pipeline_configs/velo-sim10b-nospillover-lot.yaml

diff --git a/LHCb_Pipeline/Embedding/build_embedding.py b/LHCb_Pipeline/Embedding/build_embedding.py
index d1bc0793..41cd719b 100644
--- a/LHCb_Pipeline/Embedding/build_embedding.py
+++ b/LHCb_Pipeline/Embedding/build_embedding.py
@@ -73,6 +73,3 @@ class EmbeddingInferenceBuilder(ModelBuilderBase):
 
         return results["truth"], results["preds"], results["truth_graph"]
 
-    def select_data(self, event: Data) -> Data:
-        event.signal_true_edges = event.modulewise_true_edges
-        return event
diff --git a/LHCb_Pipeline/Embedding/embedding_base.py b/LHCb_Pipeline/Embedding/embedding_base.py
index 89bcfb46..bc3a531b 100644
--- a/LHCb_Pipeline/Embedding/embedding_base.py
+++ b/LHCb_Pipeline/Embedding/embedding_base.py
@@ -36,22 +36,6 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 class EmbeddingBase(ModelBase):
     """A class that implements the metric learning model."""
 
-    def load_dataset(self, input_path: str) -> Data:
-        """Load and process one PyTorch DataSet.
-
-        Args:
-            input_path: path to the PyTorch dataset
-
-        Returns:
-            PyTorch DataSet
-        """
-        loaded_event = super(EmbeddingBase, self).load_dataset(input_path=input_path)
-        # Define which column corresponds to the true edges
-        loaded_event["signal_true_edges"] = loaded_event[
-            self.hparams["true_edges_column"]
-        ]
-        return loaded_event
-
     def get_query_points(self, batch, spatial):
         if "query_all_points" in self.hparams["regime"]:
             query_indices = torch.arange(len(spatial)).to(spatial.device)
diff --git a/LHCb_Pipeline/Processing/processing.py b/LHCb_Pipeline/Processing/processing.py
index 84b82c36..af45093a 100644
--- a/LHCb_Pipeline/Processing/processing.py
+++ b/LHCb_Pipeline/Processing/processing.py
@@ -12,6 +12,7 @@ import torch
 from torch_geometric.data import Data
 
 from .modulewise_edges import get_modulewise_edges
+from .planewise_edges import get_planewise_edges
 from .compute import compute_columns
 
 
@@ -55,6 +56,7 @@ def build_event(
     feature_scales: typing.List[float],
     kept_hits_columns: typing.List[str],
     kept_particles_columns: typing.List[str],
+    true_edges_column: str,
 ) -> Data:
     """Load the event, compute the necessary columns.
 
@@ -98,12 +100,14 @@ def build_event(
     )
 
     # Find the true edges
-    true_edges = get_modulewise_edges(hits_particles)
-    logging.debug(
-        "Modulewise truth graph built for {} with size {}".format(
-            truncated_path, true_edges.shape
+    if true_edges_column == "modulewise":
+        true_edges = get_modulewise_edges(hits_particles)
+    elif true_edges_column == "planewise":
+        true_edges = get_planewise_edges(hits_particles)
+    else:
+        raise ValueError(
+            f"`true_edges_column` is `{true_edges_column}`, which is not recognised."
         )
-    )
 
     normalised_features = get_normalised_features(
         hits_particles,
@@ -131,7 +135,7 @@ def build_event(
             column: torch.from_numpy(hits_particles[column].to_numpy())
             for column in kept_columns
         },
-        modulewise_true_edges=torch.from_numpy(true_edges),
+        signal_true_edges=torch.from_numpy(true_edges),
     )
 
     return torch_data
diff --git a/LHCb_Pipeline/pipeline_configs/velo-sim10b-nospillover-lot.yaml b/LHCb_Pipeline/pipeline_configs/velo-sim10b-nospillover-lot.yaml
new file mode 100644
index 00000000..d9cd78b4
--- /dev/null
+++ b/LHCb_Pipeline/pipeline_configs/velo-sim10b-nospillover-lot.yaml
@@ -0,0 +1,88 @@
+common:
+  experiment_name: velo-sim10b-nospillover-lot
+  data_directory: /scratch/acorreia/data
+  artifact_directory: artifacts
+  performance_directory: output # plots and reports
+  gpus: 1
+  test_dataset_names:
+  - velo-sim10b-nospillover
+  - velo-sim10b-nospillover-only-long-electrons
+  - bu2kstee-sim10aU1-xdigi
+  - smog2-xdigi
+
+preprocessing:
+  input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/0
+  output_subdirectory: "preprocessed"
+  selection: default_old_training_for_rta_presentation
+  n_events: null # if `null`, default to `n_train_events + n_test_events`
+  num_true_hits_threshold: 0
+
+processing:
+  input_subdirectory: "preprocessed"
+  output_subdirectory: "processed"
+  n_workers: 32
+  features: ["r", "phi", "z"]
+  feature_means: [18., 0., 281.]
+  feature_scales: [9.75, 1.82, 287]
+  kept_hits_columns: ["plane"]
+  kept_particles_columns: []
+  n_train_events: 1000
+  n_val_events: 1000
+  split_seed: 0
+  true_edges_column: planewise
+
+metric_learning:
+  # Dataset parameters
+  input_subdirectory: "processed"
+  output_subdirectory: "metric_learning_processed"
+
+  # Model parameters
+  feature_indices: 3
+  emb_hidden: 256
+  nb_layer: 4
+  emb_dim: 4
+  activation: Tanh
+  weight: 2
+  randomisation: 2
+  points_per_batch: 100000
+  r: 0.015
+  r_inference: 0.015
+  knn: 50
+  warmup: 8
+  margin: 0.1
+  lr: 0.001
+  factor: 0.7
+  patience: 10
+  regime: [rp, hnm, norm]
+  max_epochs: 40
+
+gnn:
+  # Dataset parameters
+  input_subdirectory: "metric_learning_processed"
+  output_subdirectory: "gnn_processed"
+  edge_cut: 0.5
+  noise: True
+
+  # Model parameters
+  feature_indices: 3
+  hidden: 256
+  n_graph_iters: 12
+  nb_node_layer: 4
+  nb_edge_layer: 4
+  layernorm: True
+  aggregation: sum_max
+  hidden_activation: SiLU
+  weight: 2
+  warmup: 10
+  lr: 0.002
+  factor: 0.7
+  patience: 8
+  truth_key: pid_signal
+  regime: [pid]
+  mask_background: True
+  max_epochs: 200
+
+track_building:
+  score_cut: 0.9
+  input_subdirectory: "gnn_processed"
+  output_subdirectory: "track_building_processed"
-- 
GitLab


From 5567a1c02d90c57ef74ae25d12b7bbce6ae729c0 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:16:09 +0200
Subject: [PATCH 12/65] Small formatting

---
 LHCb_Pipeline/utils/modelutils/checkpoint_utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/LHCb_Pipeline/utils/modelutils/checkpoint_utils.py b/LHCb_Pipeline/utils/modelutils/checkpoint_utils.py
index b8b442a7..9c5290cc 100644
--- a/LHCb_Pipeline/utils/modelutils/checkpoint_utils.py
+++ b/LHCb_Pipeline/utils/modelutils/checkpoint_utils.py
@@ -114,7 +114,6 @@ def get_last_version_dir_from_config(
     return get_last_version_dir(experiment_dir=experiment_dir)
 
 
-
 def get_training_metrics(trainer: Trainer | str) -> pd.DataFrame:
     """Get the dataframe of the training metrics.
 
-- 
GitLab


From ec29a38b07104c618a8ee0d4e8ff6cf36a910a5f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:19:26 +0200
Subject: [PATCH 13/65] Move true_edges_column to processing in default
 pipeline

---
 LHCb_Pipeline/pipeline_config_default.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/pipeline_config_default.yaml b/LHCb_Pipeline/pipeline_config_default.yaml
index 277465aa..5c0f8b5d 100644
--- a/LHCb_Pipeline/pipeline_config_default.yaml
+++ b/LHCb_Pipeline/pipeline_config_default.yaml
@@ -28,12 +28,12 @@ processing:
   n_train_events: 200
   n_val_events: 100
   split_seed: 0
+  true_edges_column: modulewise_true_edges
 
 metric_learning:
   # Dataset parameters
   input_subdirectory: "processed"
   output_subdirectory: "metric_learning_processed"
-  true_edges_column: modulewise_true_edges
 
   # Model parameters
   feature_indices: 4
-- 
GitLab


From d9e887ce2a42138517e37a09bd8ac25260f8c9ea Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:19:36 +0200
Subject: [PATCH 14/65] Define new test samples

---
 LHCb_Pipeline/test_samples.yaml | 30 ++++++++++++++++++++++++++----
 1 file changed, 26 insertions(+), 4 deletions(-)

diff --git a/LHCb_Pipeline/test_samples.yaml b/LHCb_Pipeline/test_samples.yaml
index 8a8fb603..b76aeb12 100644
--- a/LHCb_Pipeline/test_samples.yaml
+++ b/LHCb_Pipeline/test_samples.yaml
@@ -1,11 +1,33 @@
 velo-sim10b-nospillover:
-  input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/198
+  input_dir: /scratch/acorreia/data_validation/minbias-sim10b-xdigi-nospillover/500
   selection: null
   n_events: 1000
-  num_true_hits_threshold: 0
+  num_true_hits_threshold: null
 
 velo-sim10b-nospillover-only-long-electrons:
-  input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/198
+  input_dir: /scratch/acorreia/data_validation/minbias-sim10b-xdigi-nospillover/500
   selection: only_long_electrons
   n_events: 1000
-  num_true_hits_threshold: 0
+  num_true_hits_threshold: null
+
+bu2kstee-sim10aU1-xdigi:
+  input_dir: /scratch/acorreia/data_validation/bu2kstee-sim10aU1-xdigi/500
+  selection: null
+  n_events: 1000
+  num_true_hits_threshold: null
+
+smog2-xdigi:
+  input_dir:
+  - /scratch/acorreia/data_validation/smog2-digi/432
+  - /scratch/acorreia/data_validation/smog2-digi/433
+  - /scratch/acorreia/data_validation/smog2-digi/434
+  - /scratch/acorreia/data_validation/smog2-digi/435
+  - /scratch/acorreia/data_validation/smog2-digi/436
+  - /scratch/acorreia/data_validation/smog2-digi/437
+  - /scratch/acorreia/data_validation/smog2-digi/438
+  - /scratch/acorreia/data_validation/smog2-digi/439
+  - /scratch/acorreia/data_validation/smog2-digi/440
+  - /scratch/acorreia/data_validation/smog2-digi/441
+  n_events: -1
+  num_true_hits_threshold: null
+  new_event_id_per_file: True
-- 
GitLab


From d5be38d0c3de18c7321d86618187c365e42489fc Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:19:53 +0200
Subject: [PATCH 15/65] Fix bug: remove select_data in build_embedding

---
 LHCb_Pipeline/Embedding/build_embedding.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/LHCb_Pipeline/Embedding/build_embedding.py b/LHCb_Pipeline/Embedding/build_embedding.py
index 41cd719b..d0721251 100644
--- a/LHCb_Pipeline/Embedding/build_embedding.py
+++ b/LHCb_Pipeline/Embedding/build_embedding.py
@@ -41,8 +41,6 @@ class EmbeddingInferenceBuilder(ModelBuilderBase):
         self.radius = radius
 
     def construct_downstream(self, batch: Data):
-        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
         )
-- 
GitLab


From bf93efa9cf39bbfa4eda5a8b0be99800a48a1794 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:20:05 +0200
Subject: [PATCH 16/65] Define default selection used in previous training

---
 LHCb_Pipeline/Preprocessing/selecting.py | 38 ++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index f31dcb07..da06f24f 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -95,3 +95,41 @@ def everything_but_long_electrons(
         mask_particles_to_keep, particles, hits_particles
     )
     return hits_particles, particles
+
+
+
+def default_old_training_for_rta_presentation(
+    hits_particles: pd.DataFrame, particles: pd.DataFrame
+) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
+    """Selection that was used in the training presented in the RTA meeting."""
+
+    # Drop duplicates hits
+    hits_particles = hits_particles.drop_duplicates(
+        subset=["event", "particle_id", "plane"], keep="first"
+    )
+    # Remove fake hits (there shouldn't be any already)
+    hits_particles = hits_particles[hits_particles["particle_id"] != 0]
+
+    # Compute distance to line and add it to the dataframe of particles
+    logging.info("Compute distance to line (that might take some time)")
+    new_distances = compute_particle_line_metrics_dataframe(
+        hits=hits_particles,
+        metric_names=["distance_to_line"],
+    )
+    particles = particles.merge(new_distances, how="left", on=["event", "particle_id"])
+
+    # Only keep reconstructible particles that are straight enough
+    logging.info("Apply particle selection mask")
+    mask_particles_to_keep = (
+        (particles["has_velo"] == 1)
+        & (particles["nhits_velo"] >= 3)
+        & (particles["distance_to_line"] < np.sqrt(0.6))
+    )
+    particles, hits_particles = apply_mask(
+        mask_particles_to_keep, particles, hits_particles
+    )
+    # assert that there is not any nan values at this point
+    assert not particles.isna().any().any()
+    assert not hits_particles.isna().any().any()
+
+    return hits_particles, particles
-- 
GitLab


From 954f9bd14b87b12429304a3bf22c07143da8cc7e Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:26:45 +0200
Subject: [PATCH 17/65] Be able to load a model from various ways

---
 LHCb_Pipeline/utils/modelutils/basemodel.py | 46 +++++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/LHCb_Pipeline/utils/modelutils/basemodel.py b/LHCb_Pipeline/utils/modelutils/basemodel.py
index ed777027..29592bea 100644
--- a/LHCb_Pipeline/utils/modelutils/basemodel.py
+++ b/LHCb_Pipeline/utils/modelutils/basemodel.py
@@ -1,6 +1,8 @@
 """Define a base model for GNN and Embedding, to avoid copy of functions.
 """
+from __future__ import annotations
 import typing
+import logging
 import os
 import os.path as op
 
@@ -107,3 +109,47 @@ class ModelBase(LightningModule):
             }
         ]
         return optimizer, scheduler
+
+
+    @classmethod
+    def get_model_from_checkpoint(
+        cls,
+        checkpoint: LightningModule | str | None,
+        default_checkpoint: str | None = None,
+        **kwargs,
+    ):
+        """Helper function to get a model at inference step.
+        
+        Args:
+            checkpoint: the model already loaded, or path to it
+            Mode: Model class
+            default_checkpoint: path to fall back to if ``checkpoint`` is None.
+            **kwargs: other parameters passed to :py:func:`Model.load_from_checkpoint`
+        
+        Return:
+            Loaded model
+        """
+        if isinstance(checkpoint, cls):
+            model = checkpoint
+        elif checkpoint is None:  # Default loading mode from last artifact
+            assert default_checkpoint is not None, (
+                "Both `checkpoint` and `default_checkpoint` are None."
+            )
+            checkpoint = default_checkpoint
+            model = cls.load_from_checkpoint(
+                default_checkpoint, **kwargs,
+            )
+            logging.info(f"Load model from {checkpoint}.")
+        elif isinstance(checkpoint, str):
+            model = cls.load_from_checkpoint(
+                checkpoint_path=checkpoint,
+                **kwargs,
+            )
+            logging.info(f"Load model from {checkpoint}.")
+        else:
+            raise TypeError(
+                f"Type of checkpoint is {type(checkpoint).__name__} "
+                "which is not recognised"
+            )
+        return model
+        
\ No newline at end of file
-- 
GitLab


From cf0b9318a85465c7cbb2fe4db813595a68f5b345 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:27:09 +0200
Subject: [PATCH 18/65] Proper reproduce and inference from model

---
 .../Scripts/Step_2_Run_Metric_Learning.py     | 55 +++++++++++++------
 LHCb_Pipeline/Scripts/Step_4_Run_GNN.py       | 30 +++++-----
 2 files changed, 53 insertions(+), 32 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py b/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
index b9bfb26e..e8ab337a 100644
--- a/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
+++ b/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
@@ -21,8 +21,32 @@ configure_logger()
 def train(
     path_or_config: str | dict,
     partitions: typing.List[str] = ["train", "val", "test"],
-    checkpoint: str | None = None,
+    checkpoint: LayerlessEmbedding | str | None = None,
+    reproduce: bool = True,
+    override_hparams: bool = False,
+    **kwargs,
 ):
+    """Run the inference of the metric learning stage.
+
+    Args:
+        path_or_config: configuration dictionary, or path to the YAML file that contains
+            the configuration
+        partitions: Partitions to run the inference on:
+
+            * ``train``: train dataset
+            * ``val``: validation dataset
+            * ``test``: all the test datasets
+            * A specific test dataset name
+
+        checkpoint: Model already loaded, or path to its checkpoint. If ``None``,
+            try to find it automatically in the artifact folder given
+            the configuration.
+        reproduce: whether to delete an existing folder
+        override_hparams: whether to override the hyparameters of the model
+            that is loaded, with the ones in the YAML configuration
+        **kwargs: Other keyword arguments passed to the
+            :py:func:`PyTorch.LightingModel.load_from_checkpoint` class method
+    """
     all_configs = load_config(path_or_config)
     logging.info(headline("Step 2: Constructing graphs from metric learning model"))
 
@@ -32,24 +56,20 @@ def train(
     logging.info(headline("a) Loading trained model"))
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if checkpoint is None:  # Default loading mode from last artifact
-        checkpoint = os.path.join(
+    
+    if override_hparams:
+        kwargs = {**kwargs, **metric_learning_configs}
+
+    model = LayerlessEmbedding.get_model_from_checkpoint(
+        checkpoint=checkpoint,
+        default_checkpoint=os.path.join(
             common_configs["artifact_directory"],
             "metric_learning",
             common_configs["experiment_name"] + ".ckpt",
-        )
-        model = LayerlessEmbedding.load_from_checkpoint(
-            checkpoint, map_location=device
-        )
-    else:
-        model = LayerlessEmbedding.load_from_checkpoint(
-            checkpoint_path=checkpoint,
-            map_location=device,
-            **metric_learning_configs,  # Override the hyperparameters
-        )
-
-    # Load checkpoint from specified path
-    logging.info(f"Load model from {checkpoint}.")
+        ),
+        map_location=device,
+        **kwargs,
+    )
 
     logging.info(headline("b) Running inferencing"))
 
@@ -70,10 +90,9 @@ def train(
         output_dir=metric_learning_configs["output_dir"],
         partitions=partitions,
         test_dataset_names=get_required_test_dataset_names(all_configs),
-        reproduce=True,
+        reproduce=reproduce,
     )
 
-
     return graph_builder
 
 
diff --git a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
index b3b6b1dd..3896cc08 100644
--- a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
+++ b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
@@ -22,7 +22,10 @@ configure_logger()
 def train(
     path_or_config: str | dict,
     partitions: typing.List[str] = ["train", "val", "test"],
-    checkpoint: str | None = None,
+    checkpoint: InteractionGNN| str | None = None,
+    reproduce: bool = True,
+    override_hparams: bool = False,
+    **kwargs,
 ):
     all_configs = load_config(path_or_config)
 
@@ -32,22 +35,21 @@ def train(
     gnn_configs = all_configs["gnn"]
 
     logging.info(headline("a) Loading trained model"))
+    
+    if override_hparams:
+        kwargs = {**kwargs, **gnn_configs}
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if checkpoint is None:  # Default loading mode from last artifact
-        checkpoint = os.path.join(
+    model = InteractionGNN.get_model_from_checkpoint(
+        checkpoint=checkpoint,
+        default_checkpoint=os.path.join(
             common_configs["artifact_directory"],
-            "gnn",
+            "metric_learning",
             common_configs["experiment_name"] + ".ckpt",
-        )
-
-        model = InteractionGNN.load_from_checkpoint(checkpoint, map_location=device)
-    else:
-        model = InteractionGNN.load_from_checkpoint(
-            checkpoint_path=checkpoint,
-            map_location=device,
-            **gnn_configs,  # Override the hyperparameters
-        )
+        ),
+        map_location=device,
+        **kwargs,
+    )
 
     logging.info(f"Load model from {checkpoint}.")
 
@@ -59,7 +61,7 @@ def train(
         output_dir=gnn_configs["output_dir"],
         partitions=partitions,
         test_dataset_names=get_required_test_dataset_names(all_configs),
-        reproduce=True,
+        reproduce=reproduce,
     )
 
 
-- 
GitLab


From 11c86e2ca1d912eb0a37460ea831e767cbf9f801 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:27:34 +0200
Subject: [PATCH 19/65] Set up reproduce correctly in inference of GNN and
 metric learning

---
 LHCb_Pipeline/utils/modelutils/build.py | 29 +++++++++++++++----------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/LHCb_Pipeline/utils/modelutils/build.py b/LHCb_Pipeline/utils/modelutils/build.py
index 710ea928..579a152c 100644
--- a/LHCb_Pipeline/utils/modelutils/build.py
+++ b/LHCb_Pipeline/utils/modelutils/build.py
@@ -10,7 +10,7 @@ import torch
 from pytorch_lightning import LightningModule
 from torch_geometric.data import Data
 
-from utils.tools.tfiles import delete_directory
+from utils.tools.tfiles import delete_directory, is_directory_not_empty
 
 
 class BuilderBase(abc.ABC):
@@ -28,18 +28,23 @@ class BuilderBase(abc.ABC):
         if reproduce:
             delete_directory(output_dir)
         os.makedirs(output_dir, exist_ok=True)
+        
+        if is_directory_not_empty(output_dir):
+            logging.info(
+                f"Output folder is not empty the inference was not run: {output_dir}"
+            )
+        else:
+            logging.info(f"Inference from {input_dir} to {output_dir}")
 
-        logging.info(f"Inference from {input_dir} to {output_dir}")
-
-        with torch.no_grad():
-            for file_name in tqdm(file_names):
-                input_path = os.path.join(input_dir, file_name)
-                if not os.path.exists(os.path.join(output_dir, file_name)):
-                    batch = self.load_batch(input_path)
-                    self.construct_downstream(batch)
-                    self.save_downstream(
-                        batch, os.path.join(output_dir, batch.event_str)
-                    )
+            with torch.no_grad():
+                for file_name in tqdm(file_names):
+                    input_path = os.path.join(input_dir, file_name)
+                    if not os.path.exists(os.path.join(output_dir, file_name)):
+                        batch = self.load_batch(input_path)
+                        self.construct_downstream(batch)
+                        self.save_downstream(
+                            batch, os.path.join(output_dir, batch.event_str)
+                        )
 
     def load_batch(self, input_path: str) -> Data:
         """Load a PyTorch Data object from its path.
-- 
GitLab


From 7232d4fe0c3f827aac6237b1c324a1278955ba82 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 02:27:51 +0200
Subject: [PATCH 20/65] Move true_edges_column to processing in testing
 pipeline

---
 Monitoring/gitlab-ci/testing_config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Monitoring/gitlab-ci/testing_config.yaml b/Monitoring/gitlab-ci/testing_config.yaml
index 11dd476f..dd32072f 100644
--- a/Monitoring/gitlab-ci/testing_config.yaml
+++ b/Monitoring/gitlab-ci/testing_config.yaml
@@ -25,12 +25,12 @@ processing:
   n_train_events: 10
   n_val_events: 10
   split_seed: 0
+  true_edges_column: modulewise
 
 metric_learning:
   # Dataset parameters
   input_subdirectory: processed
   output_subdirectory: metric_learning_processed
-  true_edges_column: modulewise_true_edges
 
   # Model parameters
   feature_indices: 3 # indices in `batch.x`. If `null`, everything is taken.
-- 
GitLab


From 59fa0dca8bb763fe27b4796ac602c63e7750f565 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 03:42:29 +0200
Subject: [PATCH 21/65] Correct output directory where evaluation is saved

---
 .../Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
index b6feb716..050fbc2c 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
@@ -20,6 +20,7 @@ from Preprocessing.preprocessing_paths import get_truncated_paths_for_partition
 from utils.plotutils import plotconfig
 from utils.commonutils.config import load_config
 from utils.commonutils.ctests import get_required_test_dataset_names
+from utils.commonutils.cpaths import get_performance_directory
 from utils.scriptutils import configure_logger, headline
 
 configure_logger()
@@ -337,7 +338,7 @@ def evaluate(
         allen_report=allen_report,
         table_report=table_report,
         plot_categories=plot_categories,
-        output_dir=all_configs["common"]["performance_directory"],
+        output_dir=get_performance_directory(all_configs),
         suffix=f"-{partition}"
     )
 
-- 
GitLab


From 7587e7c6fdcd9dba72c83086e55a4b2f264880c8 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 03:42:49 +0200
Subject: [PATCH 22/65] Be able to change the score_cut in arg

---
 LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
index a593d52c..117d5878 100644
--- a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
+++ b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
@@ -1,6 +1,7 @@
 """
 This script runs step 5 of the TrackML Quickstart example: Labelling spacepoints based on the scored graph.
 """
+from __future__ import annotations
 import typing
 import logging
 
@@ -40,12 +41,17 @@ class TrackBuilder(BuilderBase):
 def train(
     path_or_config: str | dict,
     partitions: typing.List[str] = ["train", "val", "test"],
+    score_cut: float | None = None,
 ):
     all_configs = load_config(path_or_config)
     track_building_configs = all_configs["track_building"]
     logging.info(headline(" Step 5: Building track candidates from the scored graph "))
 
-    trackBuilder = TrackBuilder(score_cut=track_building_configs["score_cut"])
+    trackBuilder = TrackBuilder(
+        score_cut=(
+            track_building_configs["score_cut"] if score_cut is None else score_cut
+        )
+    )
     run_for_different_partitions(
         trackBuilder.infer,
         input_dir=track_building_configs["input_dir"],
-- 
GitLab


From d849134eaa4cf2690dfb25dcbbcc589e0e716146 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 18:37:22 +0200
Subject: [PATCH 23/65] Save fig for report

---
 LHCb_Pipeline/analysis.ipynb | 42 ++++++++++++++++++++++++++++++------
 1 file changed, 36 insertions(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/analysis.ipynb b/LHCb_Pipeline/analysis.ipynb
index cc0fe9e7..5adeae21 100644
--- a/LHCb_Pipeline/analysis.ipynb
+++ b/LHCb_Pipeline/analysis.ipynb
@@ -40,6 +40,32 @@
     ")\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.03448266360631707"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    (df_particles[\"has_velo\"]) & (df_particles[\"has_scifi\"]) & (df_particles[\"eta\"] > 2)\n",
+    "    & (df_particles[\"eta\"] < 5.0)  & (df_particles[\"pid\"].abs() == 11) \n",
+    ").sum() / (\n",
+    "    (df_particles[\"has_velo\"]) & (df_particles[\"eta\"] > 2)\n",
+    "    & (df_particles[\"eta\"] < 5.0)\n",
+    ").sum()\n"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -50,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +91,7 @@
     "        **kwargs,\n",
     "    )\n",
     "    ax.set_xlabel(\"# hits in the velo\")\n",
-    "    ax.set_ylabel(\"Origin vertex coordinate z-position\")\n",
+    "    ax.set_ylabel(\"$v_z$\")\n",
     "    fig.colorbar(im, ax=ax)\n",
     "\n",
     "    return fig, ax\n"
@@ -73,12 +99,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -88,6 +114,9 @@
     }
    ],
    "source": [
+    "from utils.plotutils.plotconfig import configure_matplotlib\n",
+    "configure_matplotlib()\n",
+    "\n",
     "fig, _ = compare_nhits_vz(\n",
     "    df_particles=df_particles[\n",
     "        (df_particles[\"has_velo\"] == 1)\n",
@@ -95,8 +124,8 @@
     "        & (df_particles[\"pid\"].abs() == 11)\n",
     "    ]\n",
     ")\n",
-    "\n",
-    "fig.savefig(\"nhits_velo_vs_vz_long_electrons.pdf\")\n"
+    "fig.tight_layout()\n",
+    "fig.savefig(\"nhits_velo_vs_vz_long_electrons.png\", dpi=300, transparent=True)\n"
    ]
   },
   {
@@ -161,6 +190,7 @@
     }
    ],
    "source": [
+    "\n",
     "fig, _ = compare_nhits_vz(\n",
     "    df_particles=df_particles[\n",
     "        (df_particles[\"has_velo\"] == 1)\n",
-- 
GitLab


From 5578055f14c86dc05acf2f70ef0886ffe63d536b Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 18:38:12 +0200
Subject: [PATCH 24/65] Add name of the event ID column in
 compute_particle_line_metrics_dataframe

---
 LHCb_Pipeline/Preprocessing/particle_line_fitting.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/particle_line_fitting.py b/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
index 4a82abf8..24b34f72 100644
--- a/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
+++ b/LHCb_Pipeline/Preprocessing/particle_line_fitting.py
@@ -181,6 +181,7 @@ def compute_particle_line_metrics_events_impl(
 def compute_particle_line_metrics_dataframe(
     hits: pd.DataFrame,
     metric_names: typing.List[str],
+    event_id_column: str = "event",
 ) -> pd.DataFrame:
     """Compute the pandas Series of the distance from particle hits to straight lines
     fitted to these lines. The "distance" actually corresponds to the square-root
@@ -197,6 +198,7 @@ def compute_particle_line_metrics_dataframe(
             * ``xz_angle``
             * ``yz_angle``
 
+        event_id_column: name of the event ID column
 
     Returns:
         A pandas Series with index ``event`` and ``particle_id``, and for every
@@ -204,12 +206,15 @@ def compute_particle_line_metrics_dataframe(
         fitted to the points. The distance is the square-root of the average of the
         squared distances from the hits to the straight line.
     """
-    hits = hits.sort_values(by=["event", "particle_id"])
-    events_particles_group = hits.groupby(["event", "particle_id"], sort=False).size()
+
+    hits = hits.sort_values(by=[event_id_column, "particle_id"])
+    events_particles_group = hits.groupby(
+        [event_id_column, "particle_id"], sort=False
+    ).size()
     n_particles = events_particles_group.shape[0]
     array_metric_values = np.zeros(shape=(n_particles, len(metric_names)))
     compute_particle_line_metrics_events_impl(
-        event_ids=hits["event"].to_numpy(),
+        event_ids=hits[event_id_column].to_numpy(),
         particle_ids=hits["particle_id"].to_numpy(),
         coords_events_particles=hits[["x", "y", "z"]].to_numpy(),
         array_metric_values=array_metric_values,
-- 
GitLab


From f46ceac2f6927fc5913314d94cb99d4fdaa720c5 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 18:41:05 +0200
Subject: [PATCH 25/65] Properly filter deprecating warnings in numba

---
 LHCb_Pipeline/Preprocessing/particle_line_metrics.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/particle_line_metrics.py b/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
index 9fd535ff..b29f3f90 100644
--- a/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
+++ b/LHCb_Pipeline/Preprocessing/particle_line_metrics.py
@@ -3,12 +3,10 @@ import numpy as np
 import numba as nb
 
 warnings.filterwarnings(
-    "ignore", message=(
-        "Encountered the use of a type that is scheduled for deprecation: "
-        "type 'reflected list' found for argument"
-    )
+    "ignore", ".*type 'reflected list'.*", category=DeprecationWarning
 )
 
+
 @nb.jit(nopython=True, cache=True)
 def compute_distance_to_line(
     coords: np.ndarray,
-- 
GitLab


From e63577fbd660b6b71140a45b09642acf475e1b51 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 23:01:11 +0200
Subject: [PATCH 26/65] Fix typo: metric_learing instead of gnn

---
 LHCb_Pipeline/Scripts/Step_4_Run_GNN.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
index 3896cc08..218192f9 100644
--- a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
+++ b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
@@ -44,7 +44,7 @@ def train(
         checkpoint=checkpoint,
         default_checkpoint=os.path.join(
             common_configs["artifact_directory"],
-            "metric_learning",
+            "gnn",
             common_configs["experiment_name"] + ".ckpt",
         ),
         map_location=device,
-- 
GitLab


From de02649d16341bc2a3ed4d61a08b6accea42a2fe Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 23:24:18 +0200
Subject: [PATCH 27/65] Define new column ranges, labels and bins

---
 LHCb_Pipeline/utils/plotutils/plotconfig.py | 23 +++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/LHCb_Pipeline/utils/plotutils/plotconfig.py b/LHCb_Pipeline/utils/plotutils/plotconfig.py
index 39438b4b..7886959b 100644
--- a/LHCb_Pipeline/utils/plotutils/plotconfig.py
+++ b/LHCb_Pipeline/utils/plotutils/plotconfig.py
@@ -1,6 +1,6 @@
 """A module that defines common configurations for the plots.
 """
-
+import numpy as np
 import matplotlib as mpl
 
 #: Associates a column name with its label for the plots
@@ -9,14 +9,33 @@ column_labels = {
     "p": "$p$ [MeV/c]",
     "eta": r"$\eta$",
     "vz": r"$ovtxz$ [mm]",
+    "nhits_velo": "# hits",
+    "n_repeated_planes": "# skipped plane",
+    "n_skipped_planes": "# hits in same plane",
+    "r_squared": "$R^2$",
+    "distance_to_line": "Distance to line [mm]",
+    "distance_to_z_axis": "Distance to the $z$-axis [mm]",
+    "xz_angle": "Angle to $x$-$z$ plane [Degree]",
+    "yz_angle": "Angle to $y$-$z$ plane [Degree]",
 }
 
 #: Associates a column name with its range for the plots
 column_ranges = {
     "pt": (0, 2000),
     "p": (0, 50000),
-    "eta": None,
+    "eta": (-2.0, 5.0),
     "vz": (-200, 700),
+    "r_squared": (0.0, 0.02),
+    "distance_to_line": (0.0, 0.05),
+    "distance_to_z_axis": (0.0, 0.05),
+    "xz_angle": (0.0, 1.5),
+    "yz_angle": (0.0, 1.5),
+}
+
+column_bins = {
+    "n_repeated_planes": np.arange(4) - 0.5,
+    "n_skipped_planes": np.arange(4) - 0.5,
+    "nhits_velo": np.arange(3, 19) - 0.5,
 }
 
 
-- 
GitLab


From 8b662e98fa230d52486ea33d7abc6aff4fb3ea80 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 23:49:50 +0200
Subject: [PATCH 28/65] Remove overwrite from pipeline configuration

---
 LHCb_Pipeline/pipeline_config_default.yaml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/LHCb_Pipeline/pipeline_config_default.yaml b/LHCb_Pipeline/pipeline_config_default.yaml
index 5c0f8b5d..7190be32 100644
--- a/LHCb_Pipeline/pipeline_config_default.yaml
+++ b/LHCb_Pipeline/pipeline_config_default.yaml
@@ -6,7 +6,6 @@ common:
   gpus: 1
   test_dataset_names:
   - velo-sim10b-nospillover
-  - velo-sim10b-nospillover-only-long-electrons
 
 preprocessing:
   input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover/0
@@ -18,7 +17,6 @@ preprocessing:
 processing:
   input_subdirectory: "preprocessed"
   output_subdirectory: "processed"
-  overwrite: True
   n_workers: 32
   features: ["r", "phi", "z", "plane"]
   feature_means: [0., 0., 0., 0.]
-- 
GitLab


From 545ffa6028e006adecf1895e45c15a8296709025 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Tue, 23 May 2023 23:50:24 +0200
Subject: [PATCH 29/65] modulewise_edge_columns became modulewise in
 configuration

---
 LHCb_Pipeline/pipeline_config_default.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/pipeline_config_default.yaml b/LHCb_Pipeline/pipeline_config_default.yaml
index 7190be32..bcb3af03 100644
--- a/LHCb_Pipeline/pipeline_config_default.yaml
+++ b/LHCb_Pipeline/pipeline_config_default.yaml
@@ -26,7 +26,7 @@ processing:
   n_train_events: 200
   n_val_events: 100
   split_seed: 0
-  true_edges_column: modulewise_true_edges
+  true_edges_column: modulewise
 
 metric_learning:
   # Dataset parameters
-- 
GitLab


From 27bc6123226183c91c72e7bd103466fcb54cd99f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Wed, 24 May 2023 00:10:51 +0200
Subject: [PATCH 30/65] Fix bug onversion r, phi to x, y

---
 LHCb_Pipeline/utils/plotutils/graph.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/LHCb_Pipeline/utils/plotutils/graph.py b/LHCb_Pipeline/utils/plotutils/graph.py
index e89a035c..64e1353b 100644
--- a/LHCb_Pipeline/utils/plotutils/graph.py
+++ b/LHCb_Pipeline/utils/plotutils/graph.py
@@ -36,8 +36,9 @@ def plot_true_graph(sample_data, path_or_config: str | dict, num_tracks=100):
         path_or_config=path_or_config,
         feature_names=["r", "phi"],
     )
+    x = r * np.cos(phi)
+    y = r * np.sin(phi)
 
-    x, y = r * np.cos(phi * np.pi), r * np.sin(phi * np.pi)
     cmap = viridis(num_tracks)
     source = ColumnDataSource(dict(x=x.numpy(), y=y.numpy()))
     p.circle(x="x", y="y", source=source, color=cmap[0], size=1, alpha=0.1)
@@ -93,7 +94,8 @@ def plot_predicted_graph(model, path_or_config: str | dict):
         path_or_config=path_or_config,
         feature_names=["r", "phi"],
     )
-    x, y = r * np.cos(phi * np.pi), r * np.sin(phi * np.pi)
+    x = r * np.cos(phi)
+    y = r * np.sin(phi)
     cmap = viridis(11)
     source = ColumnDataSource(dict(x=x.numpy(), y=y.numpy()))
     p.circle(x="x", y="y", source=source, color=cmap[0], size=1, alpha=0.1)
-- 
GitLab


From bacec6eb0e338358175f143761fd7c34c39f446a Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Wed, 24 May 2023 00:12:54 +0200
Subject: [PATCH 31/65] Put r and r_inference back to default

---
 LHCb_Pipeline/pipeline_config_default.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/LHCb_Pipeline/pipeline_config_default.yaml b/LHCb_Pipeline/pipeline_config_default.yaml
index bcb3af03..266fd2b4 100644
--- a/LHCb_Pipeline/pipeline_config_default.yaml
+++ b/LHCb_Pipeline/pipeline_config_default.yaml
@@ -42,8 +42,8 @@ metric_learning:
   weight: 2
   randomisation: 2
   points_per_batch: 100000
-  r: 0.035
-  r_inference: 0.08
+  r: 0.015
+  r_inference: 0.015
   knn: 50
   warmup: 8
   margin: 0.1
-- 
GitLab


From 640ea73ae49af3deaa18dc9ffb663b33e7b274c2 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 13:24:40 +0200
Subject: [PATCH 32/65] Use batch = in builder to support more complicated
 construction

---
 LHCb_Pipeline/Embedding/build_embedding.py    |  1 +
 LHCb_Pipeline/GNN/build_gnn.py                |  1 +
 .../Scripts/Step_5_Build_Track_Candidates.py  | 32 ++++++++++++-------
 LHCb_Pipeline/utils/modelutils/build.py       |  2 +-
 4 files changed, 23 insertions(+), 13 deletions(-)

diff --git a/LHCb_Pipeline/Embedding/build_embedding.py b/LHCb_Pipeline/Embedding/build_embedding.py
index d0721251..7a1d465f 100644
--- a/LHCb_Pipeline/Embedding/build_embedding.py
+++ b/LHCb_Pipeline/Embedding/build_embedding.py
@@ -64,6 +64,7 @@ class EmbeddingInferenceBuilder(ModelBuilderBase):
 
         batch.edge_index = e_spatial
         batch.y = y_cluster
+        return batch
 
     def get_performance(self, batch: Data, r_max: float, k_max: int):
         with torch.no_grad():
diff --git a/LHCb_Pipeline/GNN/build_gnn.py b/LHCb_Pipeline/GNN/build_gnn.py
index d2c40418..e44d8e0c 100644
--- a/LHCb_Pipeline/GNN/build_gnn.py
+++ b/LHCb_Pipeline/GNN/build_gnn.py
@@ -20,3 +20,4 @@ class GNNInferenceBuilder(ModelBuilderBase):
             )
         output = self.model.shared_evaluation(batch, 0, log=False)
         batch.scores = output["score"][: int(len(output["score"]) / 2)]
+        return batch
diff --git a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
index 117d5878..0d5ae6c0 100644
--- a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
+++ b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
@@ -18,24 +18,32 @@ from utils.scriptutils import parse_args, configure_logger, headline
 configure_logger()
 
 
+def get_track_ids(edge_indices: torch.Tensor, n_hits: int) -> np.ndarray:
+    row, col = edge_indices
+    edge_attr = np.ones(row.size(0))
+
+    sparse_edges = sps.coo_matrix(
+        (edge_attr, (row.numpy(), col.numpy())), (n_hits, n_hits)
+    )
+
+    _, candidate_labels = sps.csgraph.connected_components(
+        sparse_edges, directed=False, return_labels=True
+    )
+    return candidate_labels
+
+
 class TrackBuilder(BuilderBase):
     def __init__(self, score_cut: float) -> None:
         super(TrackBuilder, self).__init__()
         self.score_cut = float(score_cut)
 
     def construct_downstream(self, batch: Data):
-        edge_mask = batch.scores > self.score_cut
-
-        row, col = batch.edge_index[:, edge_mask]
-        edge_attr = np.ones(row.size(0))
-
-        N = batch.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
-        )
-        batch.labels = torch.from_numpy(candidate_labels).long()
+        batch.labels = torch.from_numpy(
+            get_track_ids(
+                batch.edge_index[:, batch.scores > 0.99], n_hits=batch.x.shape[0]
+            )
+        ).long()
+        return batch
 
 
 def train(
diff --git a/LHCb_Pipeline/utils/modelutils/build.py b/LHCb_Pipeline/utils/modelutils/build.py
index 579a152c..61b646b0 100644
--- a/LHCb_Pipeline/utils/modelutils/build.py
+++ b/LHCb_Pipeline/utils/modelutils/build.py
@@ -41,7 +41,7 @@ class BuilderBase(abc.ABC):
                     input_path = os.path.join(input_dir, file_name)
                     if not os.path.exists(os.path.join(output_dir, file_name)):
                         batch = self.load_batch(input_path)
-                        self.construct_downstream(batch)
+                        batch = self.construct_downstream(batch)
                         self.save_downstream(
                             batch, os.path.join(output_dir, batch.event_str)
                         )
-- 
GitLab


From 26746a501d0023dcdbeff056b85ce5fbb4bd0bfb Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 18:43:04 +0200
Subject: [PATCH 33/65] Update montetracko to v0.1

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

diff --git a/montetracko b/montetracko
index 15ca8781..6bde2215 160000
--- a/montetracko
+++ b/montetracko
@@ -1 +1 @@
-Subproject commit 15ca87819f350195715152e9f6e4c73ce8654f5d
+Subproject commit 6bde221571d630ae6198a02878b13c4abb1df46a
-- 
GitLab


From e477c1408abbf71d337930ca35970687b2e5a05a Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 19:47:08 +0200
Subject: [PATCH 34/65] Fix bug in montetracko for missing bins

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

diff --git a/montetracko b/montetracko
index 6bde2215..04f60297 160000
--- a/montetracko
+++ b/montetracko
@@ -1 +1 @@
-Subproject commit 6bde221571d630ae6198a02878b13c4abb1df46a
+Subproject commit 04f6029771f355400b7369ef450f1a00fa13b173
-- 
GitLab


From 545b99e52a0e9418895ec68a2ea9bcd7bfb6dfc9 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 19:47:21 +0200
Subject: [PATCH 35/65] Set up my own debug configuration

---
 .vscode/launch.json | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index f206ccc6..b3da7cf2 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -12,6 +12,14 @@
             "console": "integratedTerminal",
             "justMyCode": true,
             "cwd": "/home/fgias/etx4velo/LHCb_Pipeline/"
-        }
-    ]
+        },
+        {
+            "name": "anthonyc",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "cwd": "${fileDirname}"
+        },
+    ],
 }
\ No newline at end of file
-- 
GitLab


From 2cc2a95a9d9f0fdf49b422aaa8cd70b83d3bbe79 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 19:56:55 +0200
Subject: [PATCH 36/65] Update full_pipeline

---
 LHCb_Pipeline/full_pipeline.ipynb | 692 ++++++++++++++++++++++++++++--
 1 file changed, 647 insertions(+), 45 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index c6ac6ab7..2b817855 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -18,23 +18,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"1002\">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(\"1002\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"1002\")).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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"1003\">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(\"1003\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"1003\")).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 __future__ import annotations\n",
     "import typing\n",
+    "import logging\n",
     "import os\n",
     "import sys\n",
     "import warnings\n",
     "\n",
     "sys.path.append('../montetracko')\n",
     "\n",
+    "import yaml\n",
     "import torch\n",
     "\n",
+    "from Preprocessing.run_preprocessing import run_preprocessing_test_dataset\n",
+    "from Preprocessing.run_preprocessing import run_preprocessing\n",
+    "from Processing.run_processing import run_processing_from_config\n",
+    "\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",
@@ -49,6 +96,7 @@
     "from utils.commonutils.ctests import get_required_test_dataset_names\n",
     "from utils.commonutils.config import load_config\n",
     "from utils.modelutils import checkpoint_utils\n",
+    "from utils.scriptutils.loghandler import headline\n",
     "\n",
     "warnings.filterwarnings(\n",
     "    \"ignore\", message=(\n",
@@ -58,7 +106,7 @@
     "    )\n",
     ")\n",
     "\n",
-    "CONFIG = 'pipeline_config.yaml'\n",
+    "CONFIG = 'pipeline_configs/velo-sim10b-nospillover-lot.yaml'\n",
     "\n",
     "run_training: bool = False\n"
    ]
@@ -109,6 +157,7 @@
    "source": [
     "import requests\n",
     "import json\n",
+    "\n",
     "# from datetime import datetime\n",
     "\n",
     "# def send_telegram_message(message: str,\n",
@@ -155,8 +204,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from Preprocessing.run_preprocessing import run_preprocessing_test_dataset\n",
-    "\n",
     "for required_test_dataset_name in get_required_test_dataset_names(CONFIG):\n",
     "    run_preprocessing_test_dataset(\n",
     "        test_dataset_name=required_test_dataset_name,\n",
@@ -176,21 +223,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from Preprocessing.run_preprocessing import run_preprocessing\n",
-    "from Processing.run_processing import run_processing_from_config\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_preprocessing(CONFIG, reproduce=False)"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Remove directory `/scratch/acorreia/data/velo-sim10b-nospillover-lot-validation/preprocessed`.\n",
+      "INFO:Preprocessing: output will be written in /scratch/acorreia/data/velo-sim10b-nospillover-lot-validation/preprocessed\n",
+      "INFO:Load dataframe\n",
+      "INFO:Apply selection\n",
+      "INFO:Compute distance to line (that might take some time)\n",
+      "/home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/Preprocessing/particle_line_fitting.py:174: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\n",
+      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_metric'.\n",
+      "\n",
+      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
+      "\u001b[1m\n",
+      "File \"Preprocessing/particle_line_fitting.py\", line 89:\u001b[0m\n",
+      "\u001b[1m@nb.jit(nopython=True, cache=True)\n",
+      "\u001b[1mdef compute_particle_metric(\n",
+      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\n",
+      "  compute_particle_metric(\n",
+      "/home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/Preprocessing/particle_line_fitting.py:174: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\u001b[1m\n",
+      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_line_metrics'.\n",
+      "\n",
+      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
+      "\u001b[1m\n",
+      "File \"Preprocessing/particle_line_fitting.py\", line 55:\u001b[0m\n",
+      "\u001b[1m@nb.jit(nopython=True, cache=True)\n",
+      "\u001b[1mdef compute_particle_line_metrics(\n",
+      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0m\n",
+      "  compute_particle_metric(\n",
+      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/numba/core/ir_utils.py:2147: NumbaPendingDeprecationWarning: \u001b[1m\n",
+      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_line_metrics_events_impl'.\n",
+      "\n",
+      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
+      "\u001b[1m\n",
+      "File \"Preprocessing/particle_line_fitting.py\", line 135:\u001b[0m\n",
+      "\u001b[1m@nb.jit(nopython=True, cache=True, parallel=True)\n",
+      "\u001b[1mdef compute_particle_line_metrics_events_impl(\n",
+      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
+      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/numba/core/ir_utils.py:2147: NumbaPendingDeprecationWarning: \u001b[1m\n",
+      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric__names' of function '__numba_parfor_gufunc_0x7f5c50c9a590'.\n",
+      "\n",
+      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
+      "\u001b[1m\n",
+      "File \"<string>\", line 1:\u001b[0m\n",
+      "\u001b[1m<source missing, REPL/exec in use?>\u001b[0m\n",
+      "\u001b[0m\n",
+      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tbb\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Apply particle selection mask\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b9166c16321d499a97fa3e6e74ed5a6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_preprocessing(CONFIG, reproduce=True)"
    ]
   },
   {
@@ -375,13 +494,15 @@
    "source": [
     "from Embedding.layerless_embedding import LayerlessEmbedding\n",
     "\n",
-    "version_dir = checkpoint_utils.get_last_version_dir_from_config(\n",
+    "embedding_version_dir = checkpoint_utils.get_last_version_dir_from_config(\n",
     "    step=\"metric_learning\", path_or_config=CONFIG\n",
     ")\n",
-    "embedding_metric_path = os.path.join(version_dir, \"metrics.csv\")\n",
-    "embedding_artifact_path = checkpoint_utils.get_last_artifact(version_dir=version_dir)\n",
+    "embedding_metric_path = os.path.join(embedding_version_dir, \"metrics.csv\")\n",
+    "embedding_artifact_path = checkpoint_utils.get_last_artifact(\n",
+    "    version_dir=embedding_version_dir\n",
+    ")\n",
     "print(f\"{embedding_metric_path=}\")\n",
-    "print(f\"{embedding_artifact_path=}\")"
+    "print(f\"{embedding_artifact_path=}\")\n"
    ]
   },
   {
@@ -438,7 +559,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_learning_model = LayerlessEmbedding.load_from_checkpoint(embedding_artifact_path)\n"
+    "os.path.join(embedding_version_dir, \"hparams.yaml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric_learning_model = LayerlessEmbedding.load_from_checkpoint(\n",
+    "    embedding_artifact_path,\n",
+    "    # If importing model from another experiment\n",
+    "    # **load_config(CONFIG)[\"metric_learning\"],\n",
+    ")\n"
    ]
   },
   {
@@ -553,7 +687,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_learning_model.setup(stage=\"fit\")\n",
+    "metric_learning_model.setup(stage=\"fit\")  # load train and val datasets\n",
     "perfplot.plot_graph_sizes(metric_learning_model)"
    ]
   },
@@ -573,7 +707,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "graph_builder = run_metric_learning_inference(CONFIG, checkpoint=embedding_artifact_path)\n"
+    "graph_builder = run_metric_learning_inference(\n",
+    "    CONFIG,\n",
+    "    # checkpoint=embedding_artifact_path,\n",
+    "    checkpoint=metric_learning_model,  # here directly use the model\n",
+    "    reproduce=False,\n",
+    ")\n"
    ]
   },
   {
@@ -619,9 +758,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gnn_metric_path='artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/metrics.csv'\n",
+      "gnn_artifact_path='artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/checkpoints/epoch=199-step=2000000.ckpt'\n"
+     ]
+    }
+   ],
    "source": [
     "from utils.modelutils.checkpoint_utils import (\n",
     "    get_last_version_dir_from_config,\n",
@@ -630,15 +778,15 @@
     "from GNN.interaction_gnn import InteractionGNN\n",
     "\n",
     "gnn_version_dir = get_last_version_dir_from_config(step=\"gnn\", path_or_config=CONFIG)\n",
-    "gnn_metric_path = os.path.join(version_dir, \"metrics.csv\")\n",
-    "gnn_artifact_path = get_last_artifact(version_dir=version_dir)\n",
+    "gnn_metric_path = os.path.join(gnn_version_dir, \"metrics.csv\")\n",
+    "gnn_artifact_path = get_last_artifact(version_dir=gnn_version_dir)\n",
     "print(f\"{gnn_metric_path=}\")\n",
     "print(f\"{gnn_artifact_path=}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,11 +826,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "gnn_model = InteractionGNN.load_from_checkpoint(gnn_artifact_path)\n"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Lightning automatically upgraded your loaded checkpoint from v1.9.2 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/checkpoints/epoch=199-step=2000000.ckpt`\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(os.path.join(gnn_version_dir, \"hparams.yaml\"), 'r') as hparams_file:\n",
+    "    embedding_hparams =yaml.load(hparams_file, Loader=yaml.FullLoader)\n",
+    "\n",
+    "gnn_model = InteractionGNN.load_from_checkpoint(gnn_artifact_path, **embedding_hparams)\n"
    ]
   },
   {
@@ -695,9 +854,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "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>110</td>\n",
+       "      <td>0.057258</td>\n",
+       "      <td>0.063774</td>\n",
+       "      <td>0.998611</td>\n",
+       "      <td>0.945246</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>111</td>\n",
+       "      <td>0.057216</td>\n",
+       "      <td>0.064113</td>\n",
+       "      <td>0.998529</td>\n",
+       "      <td>0.945324</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>112</td>\n",
+       "      <td>0.057159</td>\n",
+       "      <td>0.063693</td>\n",
+       "      <td>0.998549</td>\n",
+       "      <td>0.945324</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>113</td>\n",
+       "      <td>0.057116</td>\n",
+       "      <td>0.064088</td>\n",
+       "      <td>0.998611</td>\n",
+       "      <td>0.945250</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>114</td>\n",
+       "      <td>0.057342</td>\n",
+       "      <td>0.063899</td>\n",
+       "      <td>0.998518</td>\n",
+       "      <td>0.945387</td>\n",
+       "      <td>0.002</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>194</td>\n",
+       "      <td>0.056589</td>\n",
+       "      <td>0.064133</td>\n",
+       "      <td>0.998660</td>\n",
+       "      <td>0.945044</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>195</td>\n",
+       "      <td>0.056639</td>\n",
+       "      <td>0.064363</td>\n",
+       "      <td>0.998576</td>\n",
+       "      <td>0.944959</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>196</td>\n",
+       "      <td>0.056593</td>\n",
+       "      <td>0.064429</td>\n",
+       "      <td>0.998432</td>\n",
+       "      <td>0.945342</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>197</td>\n",
+       "      <td>0.056801</td>\n",
+       "      <td>0.064165</td>\n",
+       "      <td>0.998465</td>\n",
+       "      <td>0.945365</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>198</td>\n",
+       "      <td>0.056630</td>\n",
+       "      <td>0.064177</td>\n",
+       "      <td>0.998609</td>\n",
+       "      <td>0.945090</td>\n",
+       "      <td>0.002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>89 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    epoch  train_loss  val_loss       eff       pur  current_lr\n",
+       "0     110    0.057258  0.063774  0.998611  0.945246       0.002\n",
+       "1     111    0.057216  0.064113  0.998529  0.945324       0.002\n",
+       "2     112    0.057159  0.063693  0.998549  0.945324       0.002\n",
+       "3     113    0.057116  0.064088  0.998611  0.945250       0.002\n",
+       "4     114    0.057342  0.063899  0.998518  0.945387       0.002\n",
+       "..    ...         ...       ...       ...       ...         ...\n",
+       "84    194    0.056589  0.064133  0.998660  0.945044       0.002\n",
+       "85    195    0.056639  0.064363  0.998576  0.944959       0.002\n",
+       "86    196    0.056593  0.064429  0.998432  0.945342       0.002\n",
+       "87    197    0.056801  0.064165  0.998465  0.945365       0.002\n",
+       "88    198    0.056630  0.064177  0.998609  0.945090       0.002\n",
+       "\n",
+       "[89 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# gnn_metrics = checkpoint_utils.get_training_metrics(gnn_trainer)\n",
     "\n",
@@ -708,9 +1023,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:Unable to obtain driver using Selenium Manager: /scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/selenium/webdriver/common/linux/selenium-manager is missing.  Please open an issue on https://github.com/SeleniumHQ/selenium/issues\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "perfplot.plot_training_metrics(gnn_metrics, CONFIG, \"gnn\")"
    ]
@@ -727,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,9 +1069,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"3955\">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(\"3955\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"3955\")).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"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
+      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            eff         pur  score cut\n",
+      "170   0.9159544  0.98619634      0.851\n",
+      "171   0.9145299   0.9861751      0.856\n",
+      "172   0.9145299   0.9861751      0.861\n",
+      "173  0.91381764   0.9861645      0.866\n",
+      "174   0.9131054  0.98615384      0.871\n",
+      "175   0.9131054  0.98615384      0.876\n",
+      "176  0.91096866  0.98612183      0.881\n",
+      "177   0.9102564   0.9861111      0.886\n",
+      "178  0.90954417   0.9868624      0.891\n",
+      "179   0.9088319   0.9876161      0.896\n",
+      "180   0.9059829   0.9891135      0.901\n",
+      "181   0.9059829   0.9891135      0.906\n",
+      "182   0.9059829  0.98988324      0.911\n",
+      "183   0.9059829  0.98988324      0.916\n",
+      "184   0.9031339  0.98985165      0.921\n",
+      "185   0.9017094   0.9906103      0.926\n",
+      "186   0.8995727   0.9929245      0.931\n",
+      "187   0.8981481  0.99291337      0.936\n",
+      "188   0.8967236   0.9929022      0.941\n",
+      "189  0.89529914    0.992891      0.946\n",
+      "190  0.89387465   0.9936659      0.951\n",
+      "191  0.89316237  0.99366087      0.956\n",
+      "192   0.8903134   0.9944312      0.961\n",
+      "193   0.8888889   0.9944223      0.966\n",
+      "194   0.8881766   0.9952115      0.971\n",
+      "195  0.88532764  0.99519616      0.976\n",
+      "196   0.8810541  0.99597424      0.981\n",
+      "197   0.8767806   0.9959547      0.986\n",
+      "198   0.8710826    0.997553      0.991\n",
+      "199   0.8618234  0.99834985      0.996\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "perfplot.plot_edge_performance(gnn_model, CONFIG)"
    ]
@@ -757,7 +1166,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "run_gnn_inference(CONFIG, checkpoint=gnn_artifact_path)"
+    "run_gnn_inference(CONFIG, checkpoint=gnn_model)"
    ]
   },
   {
@@ -812,6 +1221,7 @@
     "    partition=\"val\",\n",
     "    allen_report=True,\n",
     "    table_report=True,\n",
+    "    plot_categories=[],\n",
     ")\n"
    ]
   },
@@ -829,12 +1239,204 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluate_candidates_montetracko(\n",
+    "for test_dataset_name in get_required_test_dataset_names(CONFIG):\n",
+    "    logging.info(headline(test_dataset_name))\n",
+    "    evaluate_candidates_montetracko(\n",
+    "        CONFIG,\n",
+    "        partition=test_dataset_name,\n",
+    "        allen_report=True,\n",
+    "        table_report=True,\n",
+    "        plot_categories=[],\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
+      "INFO:Remove directory `/scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover`.\n",
+      "INFO:Inference from /scratch/acorreia/data/velo-sim10b-nospillover-lot/gnn_processed/test/velo-sim10b-nospillover to /scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f3965c3d891415fb29a028090616592",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:--------------------- Evaluation for velo-sim10b-nospillover ---------------------\n",
+      "INFO:1) Load dataframe of tracks, hits-particles and particles\n",
+      "INFO:Load tracks in /scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bad00e4b1cd4830854bc59a2e0ce446",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Load pre-processed test datasets in /scratch/acorreia/data/__test__/velo-sim10b-nospillover.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0eeb0b61c9af4ce6aceceb8632dfc948",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6034826e8084ff7b91b0ccc5e9ba721",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Compute plat stats\n",
+      "INFO:Compute particle line metrics\n",
+      "INFO:2) Matching\n",
+      "INFO:3) Evaluation\n",
+      "INFO:Report was saved in output/velo-sim10b-nospillover-lot/report-2023.05.25-01.24.10-velo-sim10b-nospillover.txt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TrackChecker output                               :      1299/   238776   0.54% ghosts\n",
+      "01_velo                                           :    100380/   103884  96.63% ( 96.83%),      3167 (  3.06%) clones, pur  99.91%, hit eff  93.03%\n",
+      "02_long                                           :     58396/    59302  98.47% ( 98.57%),      2015 (  3.34%) clones, pur  99.95%, hit eff  93.35%\n",
+      "03_long_P>5GeV                                    :     37943/    38166  99.42% ( 99.50%),      1338 (  3.41%) clones, pur  99.95%, hit eff  93.68%\n",
+      "04_long_strange                                   :      2910/     3101  93.84% ( 94.43%),       100 (  3.32%) clones, pur  99.74%, hit eff  89.16%\n",
+      "05_long_strange_P>5GeV                            :      1448/     1509  95.96% ( 96.21%),        34 (  2.29%) clones, pur  99.71%, hit eff  92.90%\n",
+      "06_long_fromB                                     :        95/       95 100.00% (100.00%),         4 (  4.04%) clones, pur 100.00%, hit eff  93.43%\n",
+      "07_long_fromB_P>5GeV                              :        70/       70 100.00% (100.00%),         4 (  5.41%) clones, pur 100.00%, hit eff  91.88%\n",
+      "08_long_electrons                                 :      3178/     4280  74.25% ( 75.87%),        76 (  2.34%) clones, pur  99.89%, hit eff  77.28%\n",
+      "09_long_fromB_electrons                           :         8/        8 100.00% (100.00%),         0 (  0.00%) clones, pur 100.00%, hit eff  93.75%\n",
+      "10_long_fromB_electrons_P>5GeV                    :         5/        5 100.00% (100.00%),         0 (  0.00%) clones, pur 100.00%, hit eff 100.00%\n",
+      "\n",
+      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
+      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
+      "| Velo                 | 92.69%       | 93.06%               | 3.14%      | 99.88%               | 91.18%                   |\n",
+      "| Long                 | 96.84%       | 96.99%               | 3.28%      | 99.94%               | 92.53%                   |\n",
+      "| Velo, no electrons   | 96.63%       | 96.83%               | 3.06%      | 99.91%               | 93.03%                   |\n",
+      "| Velo, only electrons | 72.81%       | 72.58%               | 3.71%      | 99.69%               | 78.86%                   |\n",
+      "| Long, only electrons | 74.25%       | 75.87%               | 2.34%      | 99.89%               | 77.28%                   |\n",
+      "| Categories   | # ghosts   | # tracks   | % ghosts   |\n",
+      "|:-------------|:-----------|:-----------|:-----------|\n",
+      "| Everything   | 1,299      | 238,776    | 0.54%      |\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Plot basic for category velo_reconstructible_acceptance saved in output/velo-sim10b-nospillover-lot/hist1d_basic_velo_reconstructible_acceptance-velo-sim10b-nospillover.pdf\n",
+      "INFO:Plot challenging for category velo_reconstructible_acceptance saved in output/velo-sim10b-nospillover-lot/hist1d_challenging_velo_reconstructible_acceptance-velo-sim10b-nospillover.pdf\n",
+      "INFO:Plot basic for category long_reconstructible_acceptance_only_electrons saved in output/velo-sim10b-nospillover-lot/hist1d_basic_long_reconstructible_acceptance_only_electrons-velo-sim10b-nospillover.pdf\n",
+      "INFO:Plot challenging for category long_reconstructible_acceptance_only_electrons saved in output/velo-sim10b-nospillover-lot/hist1d_challenging_long_reconstructible_acceptance_only_electrons-velo-sim10b-nospillover.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3200x1800 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3200x1800 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "build_track_candidates(\n",
+    "    CONFIG, partitions=[\"velo-sim10b-nospillover\"]\n",
+    ")\n",
+    "\n",
+    "trackEvaluator = evaluate_candidates_montetracko(\n",
     "    CONFIG,\n",
     "    partition=\"velo-sim10b-nospillover\",\n",
     "    allen_report=True,\n",
     "    table_report=True,\n",
-    ")\n"
+    ")"
    ]
   }
  ],
-- 
GitLab


From 7c260db8081ec6ebb6f2f2ac201ee5d1f13a1ade Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:02:10 +0200
Subject: [PATCH 37/65] Use a different variable name for allen_report and
 table_report

---
 ...p_6_Evaluate_Reconstruction_MonteTracko.py | 125 ++++++++++++++----
 1 file changed, 100 insertions(+), 25 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
index 050fbc2c..cbc21448 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
@@ -16,6 +16,7 @@ import montetracko as mt
 import montetracko.lhcb as mtb
 
 from Preprocessing.preprocessing_paths import get_truncated_paths_for_partition
+from Preprocessing.particle_line_fitting import compute_particle_line_metrics_dataframe
 
 from utils.plotutils import plotconfig
 from utils.commonutils.config import load_config
@@ -23,6 +24,7 @@ from utils.commonutils.ctests import get_required_test_dataset_names
 from utils.commonutils.cpaths import get_performance_directory
 from utils.scriptutils import configure_logger, headline
 
+
 configure_logger()
 
 
@@ -145,7 +147,7 @@ def load_dataframes_given_partition(
     df_hits_particles = load_parquet_files(
         truncated_paths=truncated_paths,
         ending="-hits_particles",
-        columns=["particle_id", "hit_id"],
+        columns=["particle_id", "hit_id", "plane", "x", "y", "z"],
     )
     df_particles = load_parquet_files(
         truncated_paths=truncated_paths, ending="-particles"
@@ -195,15 +197,15 @@ def perform_evaluation(
     if allen_report or table_report:
         list_reports = []
         if allen_report:
-            allen_report = trackEvaluator.report(
+            allen_report_str = trackEvaluator.report(
                 reporter=mt.AllenReporter(),
                 categories=mtb.category.allen_categories,
             )
 
-            list_reports.append(allen_report)
+            list_reports.append(allen_report_str)
 
         if table_report:
-            table_report_categories = trackEvaluator.report(
+            table_report_str = trackEvaluator.report(
                 reporter=mt.TabReporter(
                     [
                         "efficiency",
@@ -217,7 +219,7 @@ def perform_evaluation(
                 ),
                 categories=mtb.category.velo_categories,
             )
-            list_reports.append(table_report_categories)
+            list_reports.append(table_report_str)
             table_report_global = trackEvaluator.report(
                 reporter=mt.TabReporter(
                     metric_names=["n_ghosts", "n_tracks", "ghost_rate"],
@@ -264,26 +266,79 @@ def plot(
             of the track matching.
     """
     plotconfig.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=plotconfig.column_labels,
-        column_ranges=plotconfig.column_ranges,
-        category=category,
-        bins=20,
+
+    to_plot = {
+        "basic": dict(
+            columns=["pt", "p", "eta", "vz"],
+            metric_names=[
+                "efficiency",
+                "clone_rate",
+                # "hit_purity_per_candidate",
+                "hit_efficiency_per_candidate",
+            ],
+        ),
+        "challenging": dict(
+            columns=["vz", "nhits_velo"],
+            metric_names=["efficiency"],
+        ),
+        # "geometry": dict(
+        #     columns=["distance_to_line", "distance_to_z_axis", "xz_angle", "yz_angle"],
+        #     metric_names=["efficiency"],
+        # ),
+    }
+
+    for subname, to_plot_config in to_plot.items():
+        fig, _, _ = trackEvaluator.plot_histograms(
+            **to_plot_config,
+            column_labels=plotconfig.column_labels,
+            column_ranges=plotconfig.column_ranges,
+            category=category,
+            bins=plotconfig.column_bins,
+        )
+        if output_dir is not None:
+            os.makedirs(output_dir, exist_ok=True)
+            if suffix is None:
+                suffix = ""
+            plot_path = op.join(
+                output_dir, f"hist1d_{subname}_{category.name}{suffix}.pdf"
+            )
+            fig.savefig(plot_path, dpi=200, bbox_inches="tight")
+            logging.info(
+                f"Plot {subname} for category {category.name} saved in {plot_path}"
+            )
+
+
+def compute_plane_stats(
+    df_hits_particles: pd.DataFrame, df_particles: pd.DataFrame
+) -> pd.DataFrame:
+    min_planes = (
+        df_hits_particles.groupby(["event_id", "particle_id"])["plane"]
+        .min()
+        .rename("min_plane")
+    )
+    max_planes = (
+        df_hits_particles.groupby(["event_id", "particle_id"])["plane"]
+        .max()
+        .rename("max_plane")
+    )
+    n_unique_planes = (
+        df_hits_particles.groupby(["event_id", "particle_id"])["plane"]
+        .nunique()
+        .rename("n_planes")
+    )
+    n_hits = (
+        df_hits_particles.groupby(["event_id", "particle_id"]).size().rename("n_hits")
+    )
+    n_repeated_planes = (n_hits - n_unique_planes).rename("n_repeated_planes")
+    n_skipped_planes = (max_planes - min_planes + 1 - n_unique_planes).rename(
+        "n_skipped_planes"
+    )
+
+    return df_particles.merge(
+        pd.concat((n_repeated_planes, n_skipped_planes), axis=1).reset_index(),
+        how="left",
+        on=["event_id", "particle_id"],
     )
-    if output_dir is not None:
-        os.makedirs(output_dir, exist_ok=True)
-        if suffix is None:
-            suffix = ""
-        plot_path = op.join(output_dir, f"hist1d_{category.name}{suffix}.pdf")
-        fig.savefig(plot_path, dpi=200, bbox_inches="tight")
-        logging.info(f"Plot for category {category.name} saved in {plot_path}")
 
 
 def evaluate(
@@ -317,6 +372,26 @@ def evaluate(
     df_tracks, df_hits_particles, df_particles = load_dataframes_given_partition(
         path_or_config=all_configs, partition=partition
     )
+    logging.info("Compute plat stats")
+    df_particles = compute_plane_stats(
+        df_hits_particles=df_hits_particles,
+        df_particles=df_particles,
+    )
+    logging.info("Compute particle line metrics")
+    # new_distances = compute_particle_line_metrics_dataframe(
+    #     hits=df_hits_particles,
+    #     metric_names=[
+    #         "distance_to_line",
+    #         "distance_to_z_axis",
+    #         "xz_angle",
+    #         "yz_angle",
+    #     ],
+    #     event_id_column="event_id",
+    # )
+
+    # df_particles = df_particles.merge(
+    #     new_distances, how="left", on=["event_id", "particle_id"]
+    # )
 
     logging.info("2) Matching")
     trackEvaluator = perform_matching(
@@ -339,7 +414,7 @@ def evaluate(
         table_report=table_report,
         plot_categories=plot_categories,
         output_dir=get_performance_directory(all_configs),
-        suffix=f"-{partition}"
+        suffix=f"-{partition}",
     )
 
     return trackEvaluator
-- 
GitLab


From 59af2a35a08886d528cd054324c30983fd057d11 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:09:48 +0200
Subject: [PATCH 38/65] Fix typehints errors in montetracko

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

diff --git a/montetracko b/montetracko
index 04f60297..ab74a054 160000
--- a/montetracko
+++ b/montetracko
@@ -1 +1 @@
-Subproject commit 04f6029771f355400b7369ef450f1a00fa13b173
+Subproject commit ab74a05497f187932247e54a3f642a4f4d27c5f5
-- 
GitLab


From b0f6ac3cdd251ba31fe371b7d4c00cb2299d066f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:22:47 +0200
Subject: [PATCH 39/65] Define various plot of groups to plot or not

---
 ...p_6_Evaluate_Reconstruction_MonteTracko.py | 95 ++++++++++++++-----
 1 file changed, 69 insertions(+), 26 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
index cbc21448..8d283d21 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
@@ -175,6 +175,7 @@ def perform_evaluation(
     allen_report: bool = True,
     table_report: bool = True,
     plot_categories: typing.Iterable[mt.requirement.Category] | None = None,
+    plotted_groups: typing.List[str] | None = ["basic"],
     output_dir: str | None = None,
     suffix: str | None = None,
 ):
@@ -189,7 +190,13 @@ def perform_evaluation(
             histograms are plotted for the reconstructible tracks in the velo,
             and the long electrons.
             In order not to plot, you may set this variable to an empty list.
+        plotted_groups: Pre-configured metrics and columns to plot.
+            Each group corresponds to one plot that shows the the distributions of
+            various metrics as a function of various truth variables,
+            as hard-coded in :py:func:`plot`.
+            There are 3 groups: ``basic``, ``geometry`` and ``challenging``.
         output_dir: Output directory where to save the report and the plots
+        suffix: string to append to the file name of the reports and figures produced.
     """
     timestr = time.strftime("%Y.%m.%d-%H.%M.%S")
 
@@ -242,12 +249,13 @@ def perform_evaluation(
                 report_file.write(total_report)
             logging.info(f"Report was saved in {output_path}")
 
-    if plot_categories is not None:
+    if plot_categories is not None and plotted_groups is not None and plotted_groups:
         for plot_category in plot_categories:
             plot(
                 trackEvaluator=trackEvaluator,
                 category=plot_category,
                 output_dir=output_dir,
+                plotted_groups=plotted_groups,
                 suffix=suffix,
             )
 
@@ -255,6 +263,7 @@ def perform_evaluation(
 def plot(
     trackEvaluator: mt.TrackEvaluator,
     category: mt.requirement.Category,
+    plotted_groups: typing.List[str] = ["basic"],
     output_dir: str | None = None,
     suffix: str | None = None,
 ):
@@ -263,11 +272,17 @@ def plot(
 
     Args:
         trackEvaluator: A ``TrackEvaluator`` instance containing the results
-            of the track matching.
+            of the track matching
+        category: Truth category for the plot
+        plotted_groups: Pre-configured metrics and columns to plot.
+            Each group corresponds to one plot that shows the the distributions of
+            various metrics as a function of various truth variables,
+            as hard-coded in this function.
+            There are 3 groups: ``basic``, ``geometry`` and ``challenging``.
     """
     plotconfig.configure_matplotlib()
 
-    to_plot = {
+    group_configurations = {
         "basic": dict(
             columns=["pt", "p", "eta", "vz"],
             metric_names=[
@@ -281,15 +296,23 @@ def plot(
             columns=["vz", "nhits_velo"],
             metric_names=["efficiency"],
         ),
-        # "geometry": dict(
-        #     columns=["distance_to_line", "distance_to_z_axis", "xz_angle", "yz_angle"],
-        #     metric_names=["efficiency"],
-        # ),
+        "geometry": dict(
+            columns=["distance_to_line", "distance_to_z_axis", "xz_angle", "yz_angle"],
+            metric_names=["efficiency"],
+        ),
     }
 
-    for subname, to_plot_config in to_plot.items():
+    for group_name in plotted_groups:
+        if group_name not in group_configurations:
+            raise ValueError(
+                f"Group `{group_name}` is unknown. "
+                "Valid groups are: " + ", ".join(group_configurations.keys())
+            )
+
+        group_config = group_configurations[group_name]
+
         fig, _, _ = trackEvaluator.plot_histograms(
-            **to_plot_config,
+            **group_config,
             column_labels=plotconfig.column_labels,
             column_ranges=plotconfig.column_ranges,
             category=category,
@@ -300,17 +323,28 @@ def plot(
             if suffix is None:
                 suffix = ""
             plot_path = op.join(
-                output_dir, f"hist1d_{subname}_{category.name}{suffix}.pdf"
+                output_dir, f"hist1d_{group_name}_{category.name}{suffix}.pdf"
             )
             fig.savefig(plot_path, dpi=200, bbox_inches="tight")
             logging.info(
-                f"Plot {subname} for category {category.name} saved in {plot_path}"
+                f"Plot {group_name} for category {category.name} saved in {plot_path}"
             )
 
 
 def compute_plane_stats(
     df_hits_particles: pd.DataFrame, df_particles: pd.DataFrame
 ) -> pd.DataFrame:
+    """Compute variables related to the numbers of hits w.r.t. the planes.
+    
+    Args:
+        df_hits_particles: Dataframe of hits-particles association. Must have
+            the columns ``event_id``, ``particle_id`` and ``plane``.
+        df_particles: Dataframe of particles. Must have the columns ``event_id``
+            and ``particle_id``.
+    
+    Returns:
+        Dataframe of particles with the new columns.
+    """
     min_planes = (
         df_hits_particles.groupby(["event_id", "particle_id"])["plane"]
         .min()
@@ -347,6 +381,7 @@ def evaluate(
     allen_report: bool = True,
     table_report: bool = True,
     plot_categories: typing.Iterable[mt.requirement.Category] | None = None,
+    plotted_groups: typing.List[str] | None = ["basic"],
     min_track_length: int = 3,
 ) -> mt.TrackEvaluator:
     """Runs truth-based tracking evaluation.
@@ -360,6 +395,11 @@ def evaluate(
             histograms are plotted for the reconstructible tracks in the velo,
             and the long electrons.
             In order not to plot, you may set this variable to an empty list.
+        plotted_groups: Pre-configured metrics and columns to plot.
+            Each group corresponds to one plot that shows the the distributions of
+            various metrics as a function of various truth variables,
+            as hard-coded in :py:func:`plot`.
+            There are 3 groups: ``basic``, ``geometry`` and ``challenging``.
 
     Returns:
         object containing the evaluation.
@@ -377,21 +417,23 @@ def evaluate(
         df_hits_particles=df_hits_particles,
         df_particles=df_particles,
     )
-    logging.info("Compute particle line metrics")
-    # new_distances = compute_particle_line_metrics_dataframe(
-    #     hits=df_hits_particles,
-    #     metric_names=[
-    #         "distance_to_line",
-    #         "distance_to_z_axis",
-    #         "xz_angle",
-    #         "yz_angle",
-    #     ],
-    #     event_id_column="event_id",
-    # )
-
-    # df_particles = df_particles.merge(
-    #     new_distances, how="left", on=["event_id", "particle_id"]
-    # )
+    
+    if plotted_groups is not None and "geometry" in plotted_groups:
+        logging.info("Compute particle line metrics")
+        new_distances = compute_particle_line_metrics_dataframe(
+            hits=df_hits_particles,
+            metric_names=[
+                "distance_to_line",
+                "distance_to_z_axis",
+                "xz_angle",
+                "yz_angle",
+            ],
+            event_id_column="event_id",
+        )
+
+        df_particles = df_particles.merge(
+            new_distances, how="left", on=["event_id", "particle_id"]
+        )
 
     logging.info("2) Matching")
     trackEvaluator = perform_matching(
@@ -413,6 +455,7 @@ def evaluate(
         allen_report=allen_report,
         table_report=table_report,
         plot_categories=plot_categories,
+        plotted_groups=plotted_groups,
         output_dir=get_performance_directory(all_configs),
         suffix=f"-{partition}",
     )
-- 
GitLab


From 26af22aad115d3c2dc2d3de727ee229b16091502 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:23:55 +0200
Subject: [PATCH 40/65] Push some notebooks for "exploration"

---
 exploration/check_tracks.ipynb | 2088 ++++++++++++++++++++++++++++++++
 exploration/test.ipynb         |  333 +++++
 2 files changed, 2421 insertions(+)
 create mode 100644 exploration/check_tracks.ipynb
 create mode 100644 exploration/test.ipynb

diff --git a/exploration/check_tracks.ipynb b/exploration/check_tracks.ipynb
new file mode 100644
index 00000000..879f7ace
--- /dev/null
+++ b/exploration/check_tracks.ipynb
@@ -0,0 +1,2088 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from tqdm.auto import tqdm\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "from utils.commonutils.config import load_config\n",
+    "\n",
+    "#: Path to the configuration file\n",
+    "config_path = \"pipeline_configs/velo-sim10b-nospillover-lot.yaml\"\n",
+    "\n",
+    "config = load_config(config_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c5ae4ce3390435bb39c16ff2f76df63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_dir = os.path.join(\n",
+    "    config[\"track_building\"][\"input_dir\"], \"test\", \"velo-sim10b-nospillover\"\n",
+    ")\n",
+    "input_paths = [entry.path for entry in os.scandir(input_dir) if entry.is_file]\n",
+    "\n",
+    "batches = []\n",
+    "\n",
+    "for input_path in tqdm(input_paths):\n",
+    "    batches.append(\n",
+    "        torch.load(input_path, map_location=torch.device(\"cpu\"))\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = batches[0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hits_particles = pd.read_parquet(batch.truncated_path + \"-hits_particles.parquet\")\n",
+    "particles = pd.read_parquet(batch.truncated_path + \"-particles.parquet\")\n",
+    "\n",
+    "hits_particles = hits_particles.merge(\n",
+    "    particles[[\"particle_id\", \"nhits_velo\"]],\n",
+    "    on=[\"particle_id\"],\n",
+    "    how=\"left\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "edge_indices = batch.edge_index\n",
+    "\n",
+    "particle_id_edge_indices = batch.particle_id[edge_indices]\n",
+    "\n",
+    "true_edge_mask = particle_id_edge_indices[0] == particle_id_edge_indices[1]\n",
+    "\n",
+    "true_edge_indices = edge_indices[:, true_edge_mask]\n",
+    "true_edge_scores = batch.scores[true_edge_mask]\n",
+    "true_particle_ids = particle_id_edge_indices[:, true_edge_mask][0]\n",
+    "fake_edge_indices = edge_indices[:, ~true_edge_mask]\n",
+    "true_plane_edge_indices = batch.plane[edge_indices][:, true_edge_mask]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_true_edges = pd.DataFrame(\n",
+    "    {\n",
+    "        \"edge_idx_left\": true_edge_indices.numpy().min(axis=0),\n",
+    "        \"edge_idx_right\": true_edge_indices.numpy().max(axis=0),\n",
+    "        \"score\": true_edge_scores.numpy(),\n",
+    "        \"particle_id\": true_particle_ids,\n",
+    "        \"plane_left\": true_plane_edge_indices[0],\n",
+    "        \"plane_right\": true_plane_edge_indices[1],\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_true_edges = df_true_edges.merge(\n",
+    "    particles[[\"particle_id\", \"nhits_velo\"]],\n",
+    "    on=[\"particle_id\"],\n",
+    "    how=\"left\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>edge_idx_left</th>\n",
+       "      <th>edge_idx_right</th>\n",
+       "      <th>score</th>\n",
+       "      <th>particle_id</th>\n",
+       "      <th>plane_left</th>\n",
+       "      <th>plane_right</th>\n",
+       "      <th>nhits_velo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>385</th>\n",
+       "      <td>279</td>\n",
+       "      <td>298</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>14</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>279</td>\n",
+       "      <td>319</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>15</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>298</td>\n",
+       "      <td>319</td>\n",
+       "      <td>0.999992</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>298</td>\n",
+       "      <td>339</td>\n",
+       "      <td>0.999886</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>14</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>319</td>\n",
+       "      <td>339</td>\n",
+       "      <td>0.999770</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>457</th>\n",
+       "      <td>339</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0.999197</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>17</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>477</th>\n",
+       "      <td>339</td>\n",
+       "      <td>372</td>\n",
+       "      <td>0.999621</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>18</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478</th>\n",
+       "      <td>360</td>\n",
+       "      <td>372</td>\n",
+       "      <td>0.999989</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>18</td>\n",
+       "      <td>17</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>506</th>\n",
+       "      <td>360</td>\n",
+       "      <td>390</td>\n",
+       "      <td>0.999974</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>19</td>\n",
+       "      <td>17</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>507</th>\n",
+       "      <td>372</td>\n",
+       "      <td>390</td>\n",
+       "      <td>0.999976</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>390</td>\n",
+       "      <td>428</td>\n",
+       "      <td>0.999986</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>19</td>\n",
+       "      <td>20</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>428</td>\n",
+       "      <td>442</td>\n",
+       "      <td>0.999991</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>21</td>\n",
+       "      <td>20</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>442</td>\n",
+       "      <td>463</td>\n",
+       "      <td>0.999987</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>463</td>\n",
+       "      <td>487</td>\n",
+       "      <td>0.999978</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655</th>\n",
+       "      <td>487</td>\n",
+       "      <td>553</td>\n",
+       "      <td>0.999557</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>23</td>\n",
+       "      <td>25</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     edge_idx_left  edge_idx_right     score  particle_id  plane_left  \\\n",
+       "385            279             298  0.999993         1529          14   \n",
+       "410            279             319  0.999993         1529          15   \n",
+       "411            298             319  0.999992         1529          15   \n",
+       "434            298             339  0.999886         1529          14   \n",
+       "435            319             339  0.999770         1529          15   \n",
+       "457            339             360  0.999197         1529          17   \n",
+       "477            339             372  0.999621         1529          18   \n",
+       "478            360             372  0.999989         1529          18   \n",
+       "506            360             390  0.999974         1529          19   \n",
+       "507            372             390  0.999976         1529          18   \n",
+       "552            390             428  0.999986         1529          19   \n",
+       "553            428             442  0.999991         1529          21   \n",
+       "571            442             463  0.999987         1529          21   \n",
+       "609            463             487  0.999978         1529          22   \n",
+       "655            487             553  0.999557         1529          23   \n",
+       "\n",
+       "     plane_right  nhits_velo  \n",
+       "385           13          14  \n",
+       "410           13          14  \n",
+       "411           14          14  \n",
+       "434           16          14  \n",
+       "435           16          14  \n",
+       "457           16          14  \n",
+       "477           16          14  \n",
+       "478           17          14  \n",
+       "506           17          14  \n",
+       "507           19          14  \n",
+       "552           20          14  \n",
+       "553           20          14  \n",
+       "571           22          14  \n",
+       "609           23          14  \n",
+       "655           25          14  "
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_true_edges.query(\"particle_id == 1529 and score > 0.999\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_reco_hits(df_edges: pd.DataFrame) -> pd.DataFrame:\n",
+    "    return pd.DataFrame(\n",
+    "        {\n",
+    "            \"hit_idx\": np.concatenate(\n",
+    "                (df_true_edges[\"edge_idx_left\"], df_true_edges[\"edge_idx_right\"])\n",
+    "            ),\n",
+    "            \"particle_id\": np.concatenate(\n",
+    "                (df_true_edges[\"particle_id\"], df_true_edges[\"particle_id\"])\n",
+    "            ),\n",
+    "        }\n",
+    "    ).drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nhits_per_particle = df_true_edges.groupby([\"particle_id\"]).size().rename(\"n_edges\")\n",
+    "average_score_per_particle = (\n",
+    "    df_true_edges.groupby([\"particle_id\"])[\"score\"].mean().rename(\"avg_score\")\n",
+    ")\n",
+    "\n",
+    "score_cut = 0.9\n",
+    "\n",
+    "df_true_edges[\"accepted\"] = df_true_edges[\"score\"] >= score_cut\n",
+    "n_accepted_edges = (\n",
+    "    df_true_edges.groupby([\"particle_id\"])[\"accepted\"].sum().rename(\"n_accepted\")\n",
+    ")\n",
+    "\n",
+    "n_reco_hits = (\n",
+    "    get_reco_hits(df_true_edges)\n",
+    "    .groupby([\"particle_id\"])[\"hit_idx\"]\n",
+    "    .nunique()\n",
+    "    .rename(\"n_reco_hits\")\n",
+    ")\n",
+    "n_reco_accepted_hits = (\n",
+    "    get_reco_hits(df_true_edges.query(f\"score >= {score_cut}\"))\n",
+    "    .groupby([\"particle_id\"])[\"hit_idx\"]\n",
+    "    .nunique()\n",
+    "    .rename(\"n_reco_accepted_hits\")\n",
+    ")\n",
+    "\n",
+    "df_particle_stats = pd.concat(\n",
+    "    (nhits_per_particle, average_score_per_particle, n_accepted_edges, n_reco_hits, n_reco_accepted_hits),\n",
+    "    axis=1,\n",
+    ").reset_index()\n",
+    "\n",
+    "df_particle_stats = df_particle_stats.merge(\n",
+    "    particles[[\"particle_id\", \"nhits_velo\"]],\n",
+    "    how=\"left\",\n",
+    "    on=[\"particle_id\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_particle_stats[\"score_efficiency\"] = df_particle_stats[\"n_reco_accepted_hits\"] / df_particle_stats[\"n_reco_hits\"]\n",
+    "df_particle_stats[\"hit_efficiency\"] = df_particle_stats[\"n_reco_accepted_hits\"] / df_particle_stats[\"nhits_velo\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nhits_velo\n",
+       "2     1.000000\n",
+       "3     0.958333\n",
+       "4     0.944444\n",
+       "5     1.000000\n",
+       "6     0.958333\n",
+       "7     1.000000\n",
+       "8     1.000000\n",
+       "9     1.000000\n",
+       "10    1.000000\n",
+       "11    1.000000\n",
+       "12    1.000000\n",
+       "13    1.000000\n",
+       "14    1.000000\n",
+       "15    1.000000\n",
+       "17    1.000000\n",
+       "19    1.000000\n",
+       "20    0.650000\n",
+       "Name: hit_efficiency, dtype: float64"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_particle_stats.groupby(\"nhits_velo\")[\"hit_efficiency\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(8, 6 * 2))\n",
+    "axes[0].bar(\n",
+    "    x=df_particle_stats[\"nhits_velo\"].unique(),\n",
+    "    height=df_particle_stats.groupby(),\n",
+    "    width=1.0,\n",
+    "    fill=False,\n",
+    "    edgecolor=\"blue\",\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>edge_idx_left</th>\n",
+       "      <th>edge_idx_right</th>\n",
+       "      <th>score</th>\n",
+       "      <th>particle_id</th>\n",
+       "      <th>plane_left</th>\n",
+       "      <th>plane_right</th>\n",
+       "      <th>nhits_velo</th>\n",
+       "      <th>n_edges</th>\n",
+       "      <th>avg_score</th>\n",
+       "      <th>accepted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.968121</td>\n",
+       "      <td>805</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.955308</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>368</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.999983</td>\n",
+       "      <td>240</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.806738</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>1059</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.999775</td>\n",
+       "      <td>245</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.544625</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>527</td>\n",
+       "      <td>554</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>866</td>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>528</td>\n",
+       "      <td>555</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>868</td>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.999991</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>531</td>\n",
+       "      <td>556</td>\n",
+       "      <td>0.999969</td>\n",
+       "      <td>642</td>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>20</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.999379</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>534</td>\n",
+       "      <td>544</td>\n",
+       "      <td>0.979185</td>\n",
+       "      <td>558</td>\n",
+       "      <td>25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.979185</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>535</td>\n",
+       "      <td>559</td>\n",
+       "      <td>0.999988</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.999989</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>702 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     edge_idx_left  edge_idx_right     score  particle_id  plane_left  \\\n",
+       "0                8              48  0.968121          805           0   \n",
+       "1               11              78  0.999993          368           0   \n",
+       "2                2              20  0.999983          240           0   \n",
+       "3                3              21  0.999993         1059           0   \n",
+       "4                5              22  0.999775          245           1   \n",
+       "..             ...             ...       ...          ...         ...   \n",
+       "697            527             554  0.999993          866          24   \n",
+       "698            528             555  0.999993          868          24   \n",
+       "699            531             556  0.999969          642          24   \n",
+       "700            534             544  0.979185          558          25   \n",
+       "701            535             559  0.999988         1286          25   \n",
+       "\n",
+       "     plane_right  nhits_velo  n_edges  avg_score  accepted  \n",
+       "0              2           3        3   0.955308      True  \n",
+       "1              3           4        6   0.999993      True  \n",
+       "2              1           6       14   0.806738      True  \n",
+       "3              1           4        6   0.999993      True  \n",
+       "4              0           4        6   0.544625      True  \n",
+       "..           ...         ...      ...        ...       ...  \n",
+       "697           25           3        3   0.999993      True  \n",
+       "698           25           6        9   0.999991      True  \n",
+       "699           25          20       17   0.999379      True  \n",
+       "700           24           3        1   0.979185      True  \n",
+       "701           24           8       14   0.999989      True  \n",
+       "\n",
+       "[702 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_particle_stats.groupby([\"nhits_velo\"])[\"accepted\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build tracks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import typing\n",
+    "import scipy.sparse as sps\n",
+    "from torch_geometric.data import Data\n",
+    "\n",
+    "\n",
+    "def get_track_ids(edge_indices: torch.Tensor, n_hits: int) -> np.ndarray:\n",
+    "    row, col = edge_indices\n",
+    "    edge_attr = np.ones(row.size(0))\n",
+    "\n",
+    "    sparse_edges = sps.coo_matrix(\n",
+    "        (edge_attr, (row.numpy(), col.numpy())), (n_hits, n_hits)\n",
+    "    )\n",
+    "\n",
+    "    _, candidate_labels = sps.csgraph.connected_components(\n",
+    "        sparse_edges, directed=False, return_labels=True\n",
+    "    )\n",
+    "    return candidate_labels\n",
+    "\n",
+    "\n",
+    "def update_next_labels(\n",
+    "    labels: np.ndarray,\n",
+    "    edge_indices: np.ndarray,\n",
+    "    scores: np.ndarray,\n",
+    "    score_cut: float,\n",
+    "):\n",
+    "    unique_labels, n_labels = np.unique(labels, return_counts=True)\n",
+    "    consistent_unique_labels = unique_labels[n_labels >= 3]\n",
+    "\n",
+    "    hit_mask = ~np.isin(labels, consistent_unique_labels)\n",
+    "\n",
+    "    n_hits = hit_mask.sum()\n",
+    "    # Only keep edges between TWO remaining hits\n",
+    "    edges_mask = torch.from_numpy(hit_mask[edge_indices].min(axis=0))\n",
+    "    new_indices = np.full(fill_value=-1, shape=labels.shape[0], dtype=int)\n",
+    "    new_indices[hit_mask] = np.arange(hit_mask.sum())\n",
+    "\n",
+    "    reduced_edge_indices = new_indices[edge_indices][:, edges_mask]\n",
+    "    reduced_scores = scores[edges_mask]\n",
+    "\n",
+    "    # Build again\n",
+    "    reduced_labels = get_track_ids(\n",
+    "        torch.from_numpy(\n",
+    "            reduced_edge_indices[:, reduced_scores > score_cut],\n",
+    "        ),\n",
+    "        n_hits=n_hits\n",
+    "    )\n",
+    "    reduced_labels += labels.max() + 1\n",
+    "    labels[hit_mask] = reduced_labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = get_track_ids(\n",
+    "    batch.edge_index[:, batch.scores > 0.99], n_hits=batch.x.shape[0]\n",
+    ")\n",
+    "edge_indices = batch.edge_index.numpy()\n",
+    "scores = batch.scores.numpy()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "update_next_labels(labels, edge_indices, scores, score_cut=0.98)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([560])"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch.hit_id.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172,)"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_labels.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  2,   2,   5,   0,   6,   8,   8,  11,  12,   9,   9,  11,   9,\n",
+       "          3,   3,  13,   5,  13,   1,  14,  14,   1,   5,  18,  13,  14,\n",
+       "          1,   5,  19,  19,  14,  35,  36,  37,  22,  22,  43,  23,  22,\n",
+       "         50,  53,  62,  53,  54,  62,  54,  57,  57,  66,  65,  51,  62,\n",
+       "         56,  56,  63,  80,  57,  69,  74,  75,  75,  76, 102,  91, 104,\n",
+       "         91,  91, 109, 109, 113, 141, 131, 139, 116, 139, 131, 140, 141,\n",
+       "        155, 162, 170, 162, 160, 169],\n",
+       "       [ 11,  12,   1,   6,   1,   2,   3,   8,   8,   2,   3,   9,  12,\n",
+       "         11,  12,   1,  13,   6,  14,   5,   6,  18,  18,   6,  18,  18,\n",
+       "         19,  19,   6,  13,  19,  22,  22,  22,  38,  39,  22,  44,  46,\n",
+       "         27,  59,  53,  65,  59,  54,  65,  25,  64,  58,  59,  60,  65,\n",
+       "         63,  67,  67,  68,  69,  64,  70,  64,  69,  71,  91, 103,  91,\n",
+       "        105, 106, 107, 111, 141, 114, 115, 115, 131, 116, 139, 155, 145,\n",
+       "        144, 150, 150, 151, 164, 167]])"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 84])"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_edge_indices.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(560,)"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduced_labels.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "NumPy boolean array indexing assignment cannot assign 560 input values to the 172 output values where the mask is true",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[214], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Assign the labels to the remaining hits\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m labels[hit_mask] \u001b[39m=\u001b[39m reduced_labels\n",
+      "\u001b[0;31mValueError\u001b[0m: NumPy boolean array indexing assignment cannot assign 560 input values to the 172 output values where the mask is true"
+     ]
+    }
+   ],
+   "source": [
+    "# Assign the labels to the remaining hits\n",
+    "labels[hit_mask] = reduced_labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True, False, ..., False, False,  True],\n",
+       "       [ True,  True, False, ..., False, False, False]])"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hit_mask[batch.edge_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([  0,   1,   2,  ..., 473, 474, 475])"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_track_id(batches[9], 0.999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ True, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False,  True, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False,  True, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "         True, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False, False])"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch.labels"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merge tracks into doublets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch_geometric.data import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([False,  True, False,  ..., False,  True, False])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch.y_pid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_cut = 0.3\n",
+    "\n",
+    "# 1. Filter edges and hits with the given score_cut (needs to be conservative)\n",
+    "# Filter edges \n",
+    "edge_mask = batch.scores > score_cut\n",
+    "edge_indices = batch.edge_index[:, edge_mask]\n",
+    "y_pid = batch.y_pid[edge_mask]\n",
+    "\n",
+    "# Filter hits not connected to any edge\n",
+    "unique_edge_indices = torch.unique(edge_indices, sorted=True)\n",
+    "features = batch.x[unique_edge_indices]\n",
+    "\n",
+    "# Reindex edges given the filtering\n",
+    "n_hits = unique_edge_indices.shape[0]\n",
+    "mapping_new_indices = torch.full((edge_indices.max() + 1,), -1, dtype=torch.long)\n",
+    "mapping_new_indices[unique_edge_indices] = torch.arange(n_hits)\n",
+    "reindexed_edge_indices = mapping_new_indices[edge_indices]\n",
+    "\n",
+    "# 2. Compute doublet features\n",
+    "new_features = batch.x[reindexed_edge_indices]\n",
+    "doublet_features = torch.cat((new_features[0], new_features[1]), dim=-1)\n",
+    "\n",
+    "# 3. Form edges between doublets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+       "        -1, -1])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.full((edge_indices.max() + 1,), -1, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_features = batch.x\n",
+    "edge_indices = batch.edge_index\n",
+    "edge_mask = batch.scores > 0.2\n",
+    "unique_edge_indices = torch.unique(edge_indices[:, edge_mask], sorted=True)\n",
+    "filtered_node_features = node_features[unique_edge_indices]\n",
+    "\n",
+    "# Reindex the node indices in `filtered_edge_indices`\n",
+    "n_filtered_nodes = unique_edge_indices.shape[0]\n",
+    "mapping_new_indices = torch.full(\n",
+    "    (edge_indices.max() + 1,), -1, dtype=torch.long  # type: ignore\n",
+    ")\n",
+    "mapping_new_indices[unique_edge_indices] = torch.arange(n_filtered_nodes)\n",
+    "reindexed_edge_indices = mapping_new_indices[edge_indices]\n",
+    "filtered_reindexed_edge_indices = reindexed_edge_indices[:, edge_mask]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['plane',\n",
+       " 'signal_true_edges',\n",
+       " 'truncated_path',\n",
+       " 'scores',\n",
+       " 'x',\n",
+       " 'y',\n",
+       " 'particle_id',\n",
+       " 'edge_index',\n",
+       " 'hit_id',\n",
+       " 'event_str',\n",
+       " 'y_pid']"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch.keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(False)"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(filtered_reindexed_edge_indices == -1).any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triplet_index = torch.from_numpy(\n",
+    "    edge_to_triplet_scipy(reindexed_edge_indices.numpy())\n",
+    ").long()\n",
+    "y_triplet = y_pid[triplet_index].min(dim=0).values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "         True,  True,  True,  True, False,  True,  True,  True,  True,  True,\n",
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+       "         True,  True,  True,  True, False, False, False, False, False, False,\n",
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+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
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+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True, False, False,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True, False,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "         True,  True,  True,  True,  True,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ True,  True,  True,  ...,  True,  True,  True],\n",
+       "        [False,  True,  True,  ...,  True,  True,  True]])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pid[triplet_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True, ...,  True,  True,  True],\n",
+       "       [False,  True,  True, ...,  True,  True,  True]])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pid.numpy()[triplet_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 13,  13,  46, ..., 661, 639, 680],\n",
+       "       [  0,   1,   2, ..., 702, 704, 707]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edge_to_triplet_scipy(reindexed_edge_indices.numpy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[44],\n",
+       "        [28]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices[:, reindexed_edge_indices[0, :] == 44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort edges by plane\n",
+    "edge_planes = planes[reindexed_edge_indices]\n",
+    "\n",
+    "flip_mask = edge_planes[0] > edge_planes[1]\n",
+    "ordered_edge_indices = reindexed_edge_indices.clone()\n",
+    "ordered_edge_indices[:, flip_mask] = reindexed_edge_indices[:, flip_mask].flip(dims=[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[  8,   8,   8,  ...,   8,   8,   8],\n",
+       "         [  8,   8,   8,  ...,   8,   8,   8],\n",
+       "         [ 11,  11,  11,  ...,  11,  11,  11],\n",
+       "         ...,\n",
+       "         [445, 445, 445,  ..., 445, 445, 445],\n",
+       "         [446, 446, 446,  ..., 446, 446, 446],\n",
+       "         [447, 447, 447,  ..., 447, 447, 447]],\n",
+       "\n",
+       "        [[ 44,  44,  44,  ...,  44,  44,  44],\n",
+       "         [ 45,  45,  45,  ...,  45,  45,  45],\n",
+       "         [ 74,  74,  74,  ...,  74,  74,  74],\n",
+       "         ...,\n",
+       "         [466, 466, 466,  ..., 466, 466, 466],\n",
+       "         [455, 455, 455,  ..., 455, 455, 455],\n",
+       "         [468, 468, 468,  ..., 468, 468, 468]]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered_edge_indices.unsqueeze(-1).expand(-1, -1, ordered_edge_indices.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 10,  31,  43,  13,  17,  18,  11,  36,  19,  36,  12,  16,   6,  25,\n",
+       "         10,  31,   0,  23,  42,   1,  26,   7,   2,  16,  43,   3,  20,  22,\n",
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+       "        281, 279, 280, 246, 266, 247, 247, 295, 266, 295, 265, 270, 283, 265,\n",
+       "        283, 262, 287, 302, 251, 290, 270, 284, 270, 271, 286, 252, 271, 255,\n",
+       "        285, 255, 285, 272, 272, 287, 302, 261, 277, 288, 303, 288, 303, 277,\n",
+       "        264, 280, 266, 281, 295, 295, 270, 262, 265, 290, 284, 271, 286, 285,\n",
+       "        272, 302, 302, 303, 303, 276, 282, 277])"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered_edge_indices[0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447]])"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0,  0,  0,  ..., 24, 24, 24],\n",
+       "        [ 2,  2,  3,  ..., 25, 25, 25]])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "planes[ordered_edge_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,   2,   3,   5,   4,   0,  23,   7,   6,   1,  15,   8,\n",
+       "          28,  28,   9,  14,  10,  13,  17,  18,  11,  36,  36,  12,  16,   6,\n",
+       "          25,   0,  23,  42,   1,  26,   7,  24,   2,  19,   3,  20,  22,  22,\n",
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+       "          43,  67,  86,   2,  19,  47,  64,  14,  30,  51,   2,  19,  64,  12,\n",
+       "          38,  59,  16,  39,  57,  82,  83,  94,  94,   1,  26,  43,  67,  85,\n",
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+       "         251, 270, 252, 271, 255, 272, 287, 287, 288, 288, 264, 277, 278, 261,\n",
+       "         280, 266, 281, 295, 295, 262, 265, 282, 270, 284, 271, 285, 272, 286,\n",
+       "         302, 302, 303, 303, 276, 290, 277, 291, 278, 292, 282, 298, 308, 309,\n",
+       "         296, 284, 299, 285, 300, 314, 314, 286, 301, 315, 316, 317, 317, 304,\n",
+       "         290, 305, 291, 306, 307, 280, 293, 322, 294, 323, 323, 298, 308, 324,\n",
+       "         324, 299, 311, 312, 301, 315, 329, 329, 330, 330, 313, 334, 334, 335,\n",
+       "         335, 318, 336, 319, 337, 321, 338, 293, 322, 339, 339, 340, 340, 341,\n",
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+       "         332, 378, 378, 379, 379, 380, 381, 381, 381, 382, 382, 382, 383, 383,\n",
+       "         383, 383, 384, 384, 385, 385, 385, 386, 386, 386, 387, 387, 387, 388,\n",
+       "         388, 388, 389, 389, 390, 390, 390, 391, 391, 391, 392, 392, 392, 392,\n",
+       "         393, 393, 394, 394, 394, 395, 395, 395, 396, 396, 396, 397, 399, 401,\n",
+       "         401, 402, 402, 403, 403, 404, 405, 405, 406, 406, 407, 407, 408, 408,\n",
+       "         409, 409, 410, 410, 411, 411, 412, 412, 413, 414, 414, 415, 415, 416,\n",
+       "         416, 417, 417, 418, 418, 419, 419, 420, 420, 421, 421, 422, 422, 423,\n",
+       "         425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438,\n",
+       "         439, 440, 441, 442, 443, 445, 446, 447]])"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices.gather(\n",
+    "    0,\n",
+    "    torch.min(edge_planes, dim=0).indices.unsqueeze(0),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 708])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([708])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.min(edge_planes, dim=0).indices.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        ...,\n",
+       "        [  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447]])"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices[edge_planes.argmin(axis=0), :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
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+       "        1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,\n",
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+       "        0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
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+       "        1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
+       "        1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered_reindex_edge_indices = torch.cat(\n",
+    "    (\n",
+    "        \n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
+       "         1,  1,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
+       "         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,\n",
+       "         3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,\n",
+       "         3,  3,  3,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,\n",
+       "         4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,\n",
+       "         5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,\n",
+       "         6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,\n",
+       "         6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,\n",
+       "         7,  7,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,\n",
+       "         8,  8,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,\n",
+       "         9,  9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+       "        10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
+       "        11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
+       "        12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
+       "        13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
+       "        14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
+       "        15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
+       "        16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
+       "        17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
+       "        18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
+       "        19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
+       "        20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21,\n",
+       "        21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,\n",
+       "        21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,\n",
+       "        22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
+       "        23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
+       "        23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,\n",
+       "        24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25,\n",
+       "        25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,\n",
+       "        25, 25])"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch.plane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "reindexed_edge_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_batch = Data(\n",
+    "    x=features,\n",
+    "    edge_index=reindexed_edge_indices,\n",
+    "    particle_id=batch.particle_id[unique_edge_indices],\n",
+    "    signal_true_edges=mapping_new_indices[batch.signal_true_edges],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_28393/2550153991.py:3: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
+      "  from IPython.core.display import Image, display\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"bk-root\">\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
+       "        <span id=\"2677\">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(\"2677\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"2677\")).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": [
+    "import os.path as op\n",
+    "\n",
+    "from IPython.core.display import Image, display\n",
+    "import numpy as np\n",
+    "from bokeh.plotting import figure, show\n",
+    "from bokeh.models import ColumnDataSource\n",
+    "from bokeh.palettes import viridis\n",
+    "from bokeh.io import export_png, output_notebook\n",
+    "\n",
+    "output_notebook()\n",
+    "\n",
+    "def plot_graph(batch):\n",
+    "    p = figure(\n",
+    "        title=\"Truth graphs\", x_axis_label=\"x\", y_axis_label=\"y\", height=500, width=500\n",
+    "    )\n",
+    "    q = figure(\n",
+    "        title=\"Predicted graphs\",\n",
+    "        x_axis_label=\"x\",\n",
+    "        y_axis_label=\"y\",\n",
+    "        height=500,\n",
+    "        width=500,\n",
+    "    )\n",
+    "\n",
+    "    true_edges = batch.signal_true_edges\n",
+    "    true_unique, true_lengths = batch.particle_id[true_edges[0]].unique(\n",
+    "        return_counts=True\n",
+    "    )\n",
+    "    pred_edges = batch.edge_index\n",
+    "    particle_ids = batch.particle_id\n",
+    "\n",
+    "    nr = batch.x[:, 0]\n",
+    "    nphi = batch.x[:, 1]\n",
+    "    r = nr * 9.75 + 18\n",
+    "    phi = nphi * 1.82\n",
+    "\n",
+    "    x = r * np.cos(phi)\n",
+    "    y = r * np.sin(phi)\n",
+    "    cmap = viridis(11)\n",
+    "    source = ColumnDataSource(dict(x=x.numpy(), y=y.numpy()))\n",
+    "    p.circle(x=\"x\", y=\"y\", source=source, color=cmap[0], size=1, alpha=0.1)\n",
+    "    q.circle(x=\"x\", y=\"y\", source=source, color=cmap[0], size=1, alpha=0.1)\n",
+    "\n",
+    "    for i, track in enumerate(true_unique[true_lengths >= 10][:10]):\n",
+    "        # Get true track plot\n",
+    "        track_true_edges = true_edges[:, particle_ids[true_edges[0]] == track].cpu()\n",
+    "        X_edges, Y_edges = x[track_true_edges].numpy(), y[track_true_edges].numpy()\n",
+    "        X = np.concatenate(X_edges)\n",
+    "        Y = np.concatenate(Y_edges)\n",
+    "\n",
+    "        p.circle(X, Y, color=cmap[i], size=5)\n",
+    "        p.multi_line(X_edges.T.tolist(), Y_edges.T.tolist(), color=cmap[i])\n",
+    "\n",
+    "        track_pred_edges = (\n",
+    "            pred_edges[:, (particle_ids[pred_edges] == track).any(0)]\n",
+    "        ).cpu()\n",
+    "\n",
+    "        X_edges, Y_edges = x[track_pred_edges].numpy(), y[track_pred_edges].numpy()\n",
+    "        X = np.concatenate(X_edges)\n",
+    "        Y = np.concatenate(Y_edges)\n",
+    "\n",
+    "        q.circle(X, Y, color=cmap[i], size=5)\n",
+    "        q.multi_line(X_edges.T.tolist(), Y_edges.T.tolist(), color=cmap[i])\n",
+    "\n",
+    "    show(p)\n",
+    "    show(q)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 445, 455, 468],\n",
+       "        [ 44,  45,  74,  ..., 466, 446, 447]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reindexed_edge_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  8,   8,  11,  ..., 531, 544, 559],\n",
+       "        [ 47,  48,  78,  ..., 556, 534, 535]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "edge_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "etx4velo_updated",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exploration/test.ipynb b/exploration/test.ipynb
new file mode 100644
index 00000000..2b7d7427
--- /dev/null
+++ b/exploration/test.ipynb
@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-05-14 04:30:22,422 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-v1exanh9', purging\n",
+      "2023-05-14 04:30:22,422 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-_3n17svj', purging\n",
+      "2023-05-14 04:30:22,423 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-pecvj_nz', purging\n",
+      "2023-05-14 04:30:22,423 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-8mxforuv', purging\n",
+      "2023-05-14 04:30:22,423 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-cfdom5ui', purging\n",
+      "2023-05-14 04:30:22,423 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-fijvmhzu', purging\n",
+      "2023-05-14 04:30:22,424 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-dwrafs7q', purging\n",
+      "2023-05-14 04:30:22,424 - distributed.diskutils - INFO - Found stale lock file and directory '/tmp/dask-worker-space/worker-8avxlnms', purging\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import dask.dataframe as dd\n",
+    "from dask.distributed import Client, progress\n",
+    "\n",
+    "client = Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hits_particles = pd.read_parquet(\n",
+    "    \"/scratch/acorreia/minbias-sim10b-xdigi-nospillover/0/hits_velo.parquet.lz4\"\n",
+    ")\n",
+    "particles = pd.read_parquet(\n",
+    "    \"/scratch/acorreia/minbias-sim10b-xdigi-nospillover/0/mc_particles.parquet.lz4\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"../montetracko/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Preprocessing.particle_line_fitting import (\n",
+    "    compute_particle_distances_to_lines_dataframe,\n",
+    ")\n",
+    "\n",
+    "hits_particles[\"particle_id\"] = hits_particles[\"mcid\"] + 1\n",
+    "particles[\"particle_id\"] = particles[\"mcid\"] + 1\n",
+    "\n",
+    "hits_particles = hits_particles.merge(\n",
+    "    particles[[\"event\", \"particle_id\", \"has_velo\"]],\n",
+    "    how=\"left\",\n",
+    "    on=[\"event\", \"particle_id\"],\n",
+    ")\n",
+    "hits_particles = hits_particles[hits_particles[\"has_velo\"] == 1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_distances = compute_particle_distances_to_lines_dataframe(\n",
+    "    hits=hits_particles,\n",
+    "    metric_names=[\"distance_to_line\", \"distance_to_z_axis\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hits_particles = hits_particles.merge(\n",
+    "    new_distances, how=\"left\", on=[\"event\", \"particle_id\"]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particles = particles.merge(\n",
+    "    new_distances, how=\"left\", on=[\"event\", \"particle_id\"]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0          0.000002\n",
+       "1          0.010684\n",
+       "2          0.155721\n",
+       "3          0.032444\n",
+       "4          0.034152\n",
+       "             ...   \n",
+       "5345106    0.025152\n",
+       "5345107    0.005537\n",
+       "5345108    0.855370\n",
+       "5345109         NaN\n",
+       "5345110    5.439347\n",
+       "Name: distance_to_line, Length: 5345111, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "particles[\"distance_to_line\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Abundance')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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7WooCAAAAKsPtMOvv76/8/PxS7Xv37lXjxo3dLmDRokWKjIyUv7+/oqKilJycXGH/1atXq3Pnzqpfv77CwsI0atQoHT9+3O3rAgAAwH5uh9k77rhDs2fP1rlz5yRJDodDmZmZmjp1qu688063zrVmzRpNmjRJ06dPV1pamvr06aPY2FhlZmaW2X/btm0aPny4Ro8era+++kpr167VZ599pvvuu8/d2wAAAMBlwO0wO2fOHP3www9q0qSJfvzxR/Xt21dt2rTRVVddpaeeesqtc82bN0+jR4/Wfffdp/bt22v+/PkKDw/X4sWLy+z/73//W61atdKECRMUGRmpX/3qVxozZoxSUlLcvQ0AAABcBtxezSAoKEjbtm3TBx98oF27dqm4uFjdunVT//793TrP2bNnlZqaqqlTp7q0x8TEaMeOHWUe06tXL02fPl2JiYmKjY1VTk6O1q1bp0GDBpV7ncLCQhUWFjr3y3pEAgAAAHZyO8yWuPnmm3XzzTdX+cK5ubkqKipSaGioS3toaGi5L5L16tVLq1evVlxcnP773//q/Pnzuv322/XSSy+Ve52EhATNmjWrynUCAACg7nL7MYMJEyboxRdfLNW+YMECTZo0ye0CHA6Hy74xplRbiT179mjChAl6/PHHlZqaqs2bN+vQoUMaO3ZsueefNm2a8vLynNuRI0fcrhEAAAB1k9thdv369erdu3ep9l69emndunWVPs8111wjb2/vUrOwOTk5pWZrSyQkJKh3796aMmWKOnXqpFtvvVWLFi3S8uXLlZWVVeYxfn5+CgoKctkAAABweXA7zB4/flzBwcGl2oOCgpSbm1vp8/j6+ioqKkpJSUku7UlJSerVq1eZx5w5c0ZeXq4le3t7S7owowsAAIAri9thtk2bNtq8eXOp9nfeeUetW7d261zx8fFaunSpli9froyMDE2ePFmZmZnOxwamTZum4cOHO/sPHjxYGzZs0OLFi3Xw4EFt375dEyZMUI8ePdSsWTN3bwUAAACWc/sFsPj4eD300EP64YcfnC+Avf/++5o7d67mz5/v1rni4uJ0/PhxzZ49W1lZWerYsaMSExMVEREhScrKynJZc3bkyJEqKCjQggUL9PDDD6thw4a6+eab9eyzz7p7GwAAALgMOEwV/v/5xYsX66mnntJ3330nSWrVqpVmzpzpMotaV+Xn5ys4OFh5eXm1+vxsq6lv19q1POHwM+UvjwYAAOAOd/JalZbmeuCBB/TAAw/ohx9+UEBAgAIDA6tUKAAAAHApqrzOrCQ1bty4uuoAAAAA3Ob2C2Dff/+97rnnHjVr1kw+Pj7y9vZ22QAAAIDa4vbM7MiRI5WZmak///nPCgsLK/cHDgAAAICa5naY3bZtm5KTk9WlS5caKAcAAACoPLcfMwgPD+cHCgAAAFAnuB1m58+fr6lTp+rw4cM1UA4AAABQeW4/ZhAXF6czZ87o2muvVf369VWvXj2Xz0+cOFFtxQEAAAAVcTvMuvsrXwAAAEBNcTvMjhgxoibqAAAAANx2ST+a8OOPP+rcuXMubbX5E7EAAAC4srn9Atjp06f10EMPqUmTJgoMDNTVV1/tsgEAAAC1xe0w++ijj+qDDz7QokWL5Ofnp6VLl2rWrFlq1qyZVq1aVRM1AgAAAGVy+zGDt956S6tWrdJNN92ke++9V3369FGbNm0UERGh1atX6+67766JOgEAAIBS3J6ZPXHihCIjIyVdeD62ZCmuX/3qV9q6dWv1VgcAAABUwO0w27p1a+cPJnTo0EH/+Mc/JF2YsW3YsGF11gYAAABUyO0wO2rUKH3++eeSpGnTpjmfnZ08ebKmTJlS7QUCAAAA5XH7mdnJkyc7/3O/fv309ddfKyUlRddee606d+5crcUBAAAAFbmkdWYlqWXLlmrZsmV11AIAAAC4pVJh9sUXX6z0CSdMmFDlYgAAAAB3VCrMPv/88y77P/zwg86cOeN84evkyZOqX7++mjRpQpgFAABAranUC2CHDh1ybk899ZS6dOmijIwMnThxQidOnFBGRoa6deumJ554oqbrBQAAAJzcXs3gz3/+s1566SW1a9fO2dauXTs9//zzeuyxx6q1OAAAAKAibofZrKwsnTt3rlR7UVGRvv/++2opCgAAAKgMt8PsLbfcovvvv18pKSkyxkiSUlJSNGbMGPXv37/aCwQAAADK43aYXb58uZo3b64ePXrI399ffn5+6tmzp8LCwrR06dKaqBEAAAAok9vrzDZu3FiJiYnat2+fvv76axlj1L59e7Vt27Ym6gMAAADKVeUfTWjbti0BFgAAAB7ldpgtKirSypUr9f777ysnJ0fFxcUun3/wwQfVVhwAAABQEbfD7MSJE7Vy5UoNGjRIHTt2lMPhqIm6AAAAgItyO8y+8cYb+sc//qGBAwfWRD0AAABApbm9moGvr6/atGlTE7UAAAAAbnE7zD788MN64YUXnGvMAgAAAJ7i9mMG27Zt04cffqh33nlH119/verVq+fy+YYNG6qtOAAAAKAibofZhg0b6je/+U1N1AIAAAC4xe0wu2LFipqoAwAAAHCb28/MAgAAAHWF2zOzkZGRFa4te/DgwUsqCAAAAKgst8PspEmTXPbPnTuntLQ0bd68WVOmTKmuugAAAICLqtIvgJVl4cKFSklJueSCAAAAgMqqtmdmY2NjtX79+uo6HQAAAHBR1RZm161bp5CQkOo6HQAAAHBRbj9m0LVrV5cXwIwxys7O1g8//KBFixZVa3EAAABARdwOs0OGDHHZ9/LyUuPGjXXTTTfpuuuuq666AAAAgItyO8zOmDGjJuoAAAAA3OZ2mJWkoqIibdy4URkZGXI4HGrfvr3uuOMO+fhU6XQAAABAlbidPr/88kvdcccdys7OVrt27SRJ+/btU+PGjbVp0ybdcMMN1V4kAAAAUBa3VzO47777dP311+vo0aPatWuXdu3apSNHjqhTp076wx/+UBM1AgAAAGVye2b2888/V0pKiq6++mpn29VXX62nnnpKv/zlL6u1OAAAAKAibs/MtmvXTt9//32p9pycHLVp06ZaigIAAAAqo1JhNj8/37k9/fTTmjBhgtatW6ejR4/q6NGjWrdunSZNmqRnn322pusFAAAAnCr1mEHDhg1L/VDC0KFDnW3GGEnS4MGDVVRUVANlAgAAAKVVKsx++OGHlTpZWlraJRUDAAAAuKNSYbZv377lfpaXl6fVq1dr6dKl+vzzzzVp0qTqqg0AAACokNsvgJX44IMP9Pvf/15hYWF66aWXNHDgQKWkpLh9nkWLFikyMlL+/v6KiopScnJyhf0LCws1ffp0RUREyM/PT9dee62WL19e1dsAAACAxdxamuvo0aNauXKlli9frtOnT2vo0KE6d+6c1q9frw4dOrh98TVr1mjSpElatGiRevfurVdeeUWxsbHas2ePWrZsWeYxQ4cO1ffff69ly5apTZs2ysnJ0fnz592+NgAAAOznMCVvb13EwIEDtW3bNv3617/W3Xffrdtuu03e3t6qV6+ePv/88yqF2Z49e6pbt25avHixs619+/YaMmSIEhISSvXfvHmz/vd//1cHDx5USEiI29eTLqzMEBwcrLy8PAUFBVXpHFXRaurbtXYtTzj8zCBPlwAAAC4T7uS1Sj9msGXLFt13332aNWuWBg0aJG9v70sq8uzZs0pNTVVMTIxLe0xMjHbs2FHmMZs2bVL37t31l7/8Rc2bN1fbtm31yCOP6Mcffyz3OoWFhS5Li+Xn519S3QAAAKg7Kh1mk5OTVVBQoO7du6tnz55asGCBfvjhhypfODc3V0VFRQoNDXVpDw0NVXZ2dpnHHDx4UNu2bdOXX36pjRs3av78+Vq3bp3GjRtX7nUSEhIUHBzs3MLDw6tcMwAAAOqWSofZ6Ohovfrqq8rKytKYMWP0xhtvqHnz5iouLlZSUpIKCgqqVMBP16+VLqxZ+/O2EsXFxXI4HFq9erV69OihgQMHat68eVq5cmW5s7PTpk1TXl6eczty5EiV6gQAAEDd4/ZqBvXr19e9996rbdu2affu3Xr44Yf1zDPPqEmTJrr99tsrfZ5rrrlG3t7epWZhc3JySs3WlggLC1Pz5s0VHBzsbGvfvr2MMTp69GiZx/j5+SkoKMhlAwAAwOWhyktzSVK7du30l7/8RUePHtXrr7/u1rG+vr6KiopSUlKSS3tSUpJ69epV5jG9e/fWd999p1OnTjnb9u3bJy8vL7Vo0cL9GwAAAIDVLinMlvD29taQIUO0adMmt46Lj4/X0qVLtXz5cmVkZGjy5MnKzMzU2LFjJV14RGD48OHO/sOGDVOjRo00atQo7dmzR1u3btWUKVN07733KiAgoDpuBQAAABZxa53Z6hYXF6fjx49r9uzZysrKUseOHZWYmKiIiAhJUlZWljIzM539AwMDlZSUpPHjx6t79+5q1KiRhg4dqieffNJTtwAAAAAPqvQ6s5cL1pmtGawzCwAAqkuNrDMLAAAA1DWEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLU8HmYXLVqkyMhI+fv7KyoqSsnJyZU6bvv27fLx8VGXLl1qtkAAAADUWR4Ns2vWrNGkSZM0ffp0paWlqU+fPoqNjVVmZmaFx+Xl5Wn48OG65ZZbaqlSAAAA1EUeDbPz5s3T6NGjdd9996l9+/aaP3++wsPDtXjx4gqPGzNmjIYNG6bo6OhaqhQAAAB1kcfC7NmzZ5WamqqYmBiX9piYGO3YsaPc41asWKEDBw5oxowZlbpOYWGh8vPzXTYAAABcHjwWZnNzc1VUVKTQ0FCX9tDQUGVnZ5d5zP79+zV16lStXr1aPj4+lbpOQkKCgoODnVt4ePgl1w4AAIC6weMvgDkcDpd9Y0ypNkkqKirSsGHDNGvWLLVt27bS5582bZry8vKc25EjRy65ZgAAANQNlZverAHXXHONvL29S83C5uTklJqtlaSCggKlpKQoLS1NDz30kCSpuLhYxhj5+Phoy5Ytuvnmm0sd5+fnJz8/v5q5CQAAAHiUx2ZmfX19FRUVpaSkJJf2pKQk9erVq1T/oKAg7d69W+np6c5t7NixateundLT09WzZ8/aKh0AAAB1hMdmZiUpPj5e99xzj7p3767o6GgtWbJEmZmZGjt2rKQLjwgcO3ZMq1atkpeXlzp27OhyfJMmTeTv71+qHQAAAFcGj4bZuLg4HT9+XLNnz1ZWVpY6duyoxMRERURESJKysrIuuuYsAAAArlwOY4zxdBG1KT8/X8HBwcrLy1NQUFCtXbfV1Ldr7VqecPiZQZ4uAQAAXCbcyWseX80AAAAAqCrCLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFoeD7OLFi1SZGSk/P39FRUVpeTk5HL7btiwQQMGDFDjxo0VFBSk6Ohovfvuu7VYLQAAAOoSj4bZNWvWaNKkSZo+fbrS0tLUp08fxcbGKjMzs8z+W7du1YABA5SYmKjU1FT169dPgwcPVlpaWi1XDgAAgLrAYYwxnrp4z5491a1bNy1evNjZ1r59ew0ZMkQJCQmVOsf111+vuLg4Pf7445Xqn5+fr+DgYOXl5SkoKKhKdVdFq6lv19q1POHwM4M8XQIAALhMuJPXPDYze/bsWaWmpiomJsalPSYmRjt27KjUOYqLi1VQUKCQkJBy+xQWFio/P99lAwAAwOXBY2E2NzdXRUVFCg0NdWkPDQ1VdnZ2pc4xd+5cnT59WkOHDi23T0JCgoKDg51beHj4JdUNAACAusPjL4A5HA6XfWNMqbayvP7665o5c6bWrFmjJk2alNtv2rRpysvLc25Hjhy55JoBAABQN/h46sLXXHONvL29S83C5uTklJqt/bk1a9Zo9OjRWrt2rfr3719hXz8/P/n5+V1yvQAAAKh7PDYz6+vrq6ioKCUlJbm0JyUlqVevXuUe9/rrr2vkyJF67bXXNGgQLx0BAABcyTw2MytJ8fHxuueee9S9e3dFR0dryZIlyszM1NixYyVdeETg2LFjWrVqlaQLQXb48OF64YUXdOONNzpndQMCAhQcHOyx+wAAAIBneDTMxsXF6fjx45o9e7aysrLUsWNHJSYmKiIiQpKUlZXlsubsK6+8ovPnz2vcuHEaN26cs33EiBFauXJlbZcPAAAAD/PoOrOewDqzNYN1ZgEAQHWxYp1ZAAAA4FIRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFqEWQAAAFiLMAsAAABrEWYBAABgLcIsAAAArEWYBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1CLMAAACwFmEWAAAA1iLMAgAAwFo+ni4Al4dWU9/2dAk16vAzgzxdAgAAKAMzswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwiwAAACsRZgFAACAtQizAAAAsBZhFgAAANYizAIAAMBaPp4uALBBq6lve7qEGnf4mUGeLgEAALcxMwsAAABrEWYBAABgLY8/ZrBo0SI999xzysrK0vXXX6/58+erT58+5fb/+OOPFR8fr6+++krNmjXTo48+qrFjx9ZixcDl6XJ/lILHKADg8uTRMLtmzRpNmjRJixYtUu/evfXKK68oNjZWe/bsUcuWLUv1P3TokAYOHKj7779ff//737V9+3Y9+OCDaty4se68804P3AEAWxDWAeDy5DDGGE9dvGfPnurWrZsWL17sbGvfvr2GDBmihISEUv3/+Mc/atOmTcrIyHC2jR07Vp9//rl27txZqWvm5+crODhYeXl5CgoKuvSbqKTL/X9IAaCmEdiBK4c7ec1jM7Nnz55Vamqqpk6d6tIeExOjHTt2lHnMzp07FRMT49J26623atmyZTp37pzq1atX6pjCwkIVFhY69/Py8iRd+JJqU3HhmVq9HgBcblpOXuvpEoAr2pezbq21a5XktMrMuXoszObm5qqoqEihoaEu7aGhocrOzi7zmOzs7DL7nz9/Xrm5uQoLCyt1TEJCgmbNmlWqPTw8/BKqBwAAuLIEz6/9axYUFCg4OLjCPh5/AczhcLjsG2NKtV2sf1ntJaZNm6b4+HjnfnFxsU6cOKFGjRpVeJ3qlJ+fr/DwcB05cqRWH21A9WD87McY2o8xtBvjZ7/aHkNjjAoKCtSsWbOL9vVYmL3mmmvk7e1dahY2Jyen1OxriaZNm5bZ38fHR40aNSrzGD8/P/n5+bm0NWzYsOqFX4KgoCD+EluM8bMfY2g/xtBujJ/9anMMLzYjW8Jj68z6+voqKipKSUlJLu1JSUnq1atXmcdER0eX6r9lyxZ17969zOdlAQAAcHnz6I8mxMfHa+nSpVq+fLkyMjI0efJkZWZmOteNnTZtmoYPH+7sP3bsWH377beKj49XRkaGli9frmXLlumRRx7x1C0AAADAgzz6zGxcXJyOHz+u2bNnKysrSx07dlRiYqIiIiIkSVlZWcrMzHT2j4yMVGJioiZPnqyFCxeqWbNmevHFF+v8GrN+fn6aMWNGqccdYAfGz36Mof0YQ7sxfvary2Po0XVmAQAAgEvh0ccMAAAAgEtBmAUAAIC1CLMAAACwFmEWAAAA1iLMVoNFixYpMjJS/v7+ioqKUnJycoX9P/74Y0VFRcnf31+tW7fWyy+/XEuVojzujOGGDRs0YMAANW7cWEFBQYqOjta7775bi9WiLO7+PSyxfft2+fj4qEuXLjVbIC7K3TEsLCzU9OnTFRERIT8/P1177bVavnx5LVWLn3N3/FavXq3OnTurfv36CgsL06hRo3T8+PFaqhY/t3XrVg0ePFjNmjWTw+HQm2++edFj6kyeMbgkb7zxhqlXr5559dVXzZ49e8zEiRNNgwYNzLfffltm/4MHD5r69eubiRMnmj179phXX33V1KtXz6xbt66WK0cJd8dw4sSJ5tlnnzWffvqp2bdvn5k2bZqpV6+e2bVrVy1XjhLujmGJkydPmtatW5uYmBjTuXPn2ikWZarKGN5+++2mZ8+eJikpyRw6dMh88sknZvv27bVYNUq4O37JycnGy8vLvPDCC+bgwYMmOTnZXH/99WbIkCG1XDlKJCYmmunTp5v169cbSWbjxo0V9q9LeYYwe4l69Ohhxo4d69J23XXXmalTp5bZ/9FHHzXXXXedS9uYMWPMjTfeWGM1omLujmFZOnToYGbNmlXdpaGSqjqGcXFx5rHHHjMzZswgzHqYu2P4zjvvmODgYHP8+PHaKA8X4e74Pffcc6Z169YubS+++KJp0aJFjdWIyqtMmK1LeYbHDC7B2bNnlZqaqpiYGJf2mJgY7dixo8xjdu7cWar/rbfeqpSUFJ07d67GakXZqjKGP1dcXKyCggKFhITURIm4iKqO4YoVK3TgwAHNmDGjpkvERVRlDDdt2qTu3bvrL3/5i5o3b662bdvqkUce0Y8//lgbJeMnqjJ+vXr10tGjR5WYmChjjL7//nutW7dOgwYNqo2SUQ3qUp7x6C+A2S43N1dFRUUKDQ11aQ8NDVV2dnaZx2RnZ5fZ//z588rNzVVYWFiN1YvSqjKGPzd37lydPn1aQ4cOrYkScRFVGcP9+/dr6tSpSk5Olo8P/zXoaVUZw4MHD2rbtm3y9/fXxo0blZubqwcffFAnTpzgudlaVpXx69Wrl1avXq24uDj997//1fnz53X77bfrpZdeqo2SUQ3qUp5hZrYaOBwOl31jTKm2i/Uvqx21x90xLPH6669r5syZWrNmjZo0aVJT5aESKjuGRUVFGjZsmGbNmqW2bdvWVnmoBHf+HhYXF8vhcGj16tXq0aOHBg4cqHnz5mnlypXMznqIO+O3Z88eTZgwQY8//rhSU1O1efNmHTp0SGPHjq2NUlFN6kqeYUriElxzzTXy9vYu9S/PnJycUv9aKdG0adMy+/v4+KhRo0Y1VivKVpUxLLFmzRqNHj1aa9euVf/+/WuyTFTA3TEsKChQSkqK0tLS9NBDD0m6EIyMMfLx8dGWLVt0880310rtuKAqfw/DwsLUvHlzBQcHO9vat28vY4yOHj2qX/ziFzVaM/5PVcYvISFBvXv31pQpUyRJnTp1UoMGDdSnTx89+eST/L+UFqhLeYaZ2Uvg6+urqKgoJSUlubQnJSWpV69eZR4THR1dqv+WLVvUvXt31atXr8ZqRdmqMobShRnZkSNH6rXXXuMZLw9zdwyDgoK0e/dupaenO7exY8eqXbt2Sk9PV8+ePWurdPz/qvL3sHfv3vruu+906tQpZ9u+ffvk5eWlFi1a1Gi9cFWV8Ttz5oy8vFwjiLe3t6T/m91D3Van8kytv3J2mSlZjmTZsmVmz549ZtKkSaZBgwbm8OHDxhhjpk6dau655x5n/5KlLCZPnmz27Nljli1bxtJcHubuGL722mvGx8fHLFy40GRlZTm3kydPeuoWrnjujuHPsZqB57k7hgUFBaZFixbmrrvuMl999ZX5+OOPzS9+8Qtz3333eeoWrmjujt+KFSuMj4+PWbRokTlw4IDZtm2b6d69u+nRo4enbuGKV1BQYNLS0kxaWpqRZObNm2fS0tKcy6vV5TxDmK0GCxcuNBEREcbX19d069bNfPzxx87PRowYYfr27evS/6OPPjJdu3Y1vr6+plWrVmbx4sW1XDF+zp0x7Nu3r5FUahsxYkTtFw4nd/8e/hRhtm5wdwwzMjJM//79TUBAgGnRooWJj483Z86cqeWqUcLd8XvxxRdNhw4dTEBAgAkLCzN33323OXr0aC1XjRIffvhhhf/bVpfzjMMY5vMBAABgJ56ZBQAAgLUIswAAALAWYRYAAADWIswCAADAWoRZAAAAWIswCwAAAGsRZgEAAGAtwixwhXM4HHrzzTc9XQaq6KOPPpLD4dDJkycrfczMmTPVpUuXarl+q1atNH/+fOd+bf15WrJkicLDw+Xl5aX58+dX6z1VxuHDh+VwOJSenl7j16rtewNsQ5gFLkMjR46Uw+GQw+FQvXr1FBoaqgEDBmj58uUqLi526ZuVlaXY2NhKndem4Dty5EgNGTLE02WUUt0hqFevXsrKylJwcHC1nK/ETTfdpEmTJrl9nDt/nqoqPz9fDz30kP74xz/q2LFj+sMf/qBHHnlE77//vrNPWeNfmwFUKh30q6oy9wZcyQizwGXqtttuU1ZWlg4fPqx33nlH/fr108SJE/XrX/9a58+fd/Zr2rSp/Pz8PFgpynL27NlK9fP19VXTpk3lcDhquKLKqY0/T5mZmTp37pwGDRqksLAw1a9fX4GBgWrUqFGNXtdTLud7A6qFR35EF0CNGjFihLnjjjtKtb///vtGknn11VedbZLMxo0bjTHGFBYWmnHjxpmmTZsaPz8/ExERYZ5++mljjDEREREuv9cdERFhjDHmm2++Mbfffrtp0qSJadCggenevbtJSkpyuW5ERIR56qmnzKhRo0xgYKAJDw83r7zyikufI0eOmLi4OHP11Veb+vXrm6ioKPPvf//b+fmmTZtMt27djJ+fn4mMjDQzZ840586dK/P+Z8yYUer3xT/88ENjjDFffPGF6devn/H39zchISHm/vvvNwUFBeV+l+fPnzf33nuvadWqlfH39zdt27Y18+fPL7e/McacOHHCDBs2zFxzzTXG39/ftGnTxixfvtz5ff90K/mt85Ixe/rpp01YWJjz+/3b3/5moqKiTGBgoAkNDTW/+93vzPfff++8Vsnvqf/nP/9xti1ZssS0aNHCBAQEmCFDhpi5c+ea4OBgl++nc+fOZtWqVSYiIsIEBQWZuLg4k5+f76zl53UeOnSozHuNiIgwzz//vHP/p3+eDh06ZCSZ9evXm5tuuskEBASYTp06mR07dricY/v27aZPnz7G39/ftGjRwowfP96cOnWqzOutWLGizNpK7qnk/soa//K+e2OMWb58ubnuuuuMn5+fadeunVm4cKHLdT/55BPTpUsX4+fnZ6KiosyGDRuMJJOWllZmnX379i11vRLr1q0zHTp0ML6+viYiIsLMmTOnzHOUqMy9AVcywixwGSovzBpjTOfOnU1sbKxz/6fh47nnnjPh4eFm69at5vDhwyY5Odm89tprxhhjcnJyjCSzYsUKk5WVZXJycowxxqSnp5uXX37ZfPHFF2bfvn1m+vTpxt/f33z77bfOa0RERJiQkBCzcOFCs3//fpOQkGC8vLxMRkaGMcaYgoIC07p1a9OnTx+TnJxs9u/fb9asWeMMPZs3bzZBQUFm5cqV5sCBA2bLli2mVatWZubMmWXeY0FBgRk6dKi57bbbTFZWlsnKyjKFhYXm9OnTplmzZua3v/2t2b17t3n//fdNZGSkGTFiRLnf5dmzZ83jjz9uPv30U3Pw4EHz97//3dSvX9+sWbOm3GPGjRtnunTpYj777DNz6NAhk5SUZDZt2mSMMebTTz81ksx7771nsrKyzPHjx51jFhgYaO655x7z5Zdfmt27dxtjjFm2bJlJTEw0Bw4cMDt37jQ33nijy/j9PMxu27bNeHl5meeee87s3bvXLFy40ISEhJQKs4GBgc7vYevWraZp06bmT3/6kzHGmJMnT5ro6Ghz//33O7+/8+fPl3mvlQmz1113nfnXv/5l9u7da+666y4TERHh/IfIF198YQIDA83zzz9v9u3bZ7Zv3266du1qRo4cWeb1zpw5Y9577z0jyXz66afO2n4a+Mob//K++yVLlpiwsDCzfv16c/DgQbN+/XoTEhJiVq5caYwx5tSpU6Zx48YmLi7OfPnll+att94yrVu3rjDMHj9+3LRo0cLMnj3bWYMxxqSkpBgvLy8ze/Zss3fvXrNixQoTEBBgVqxYUeZ5SsbrYvcGXMkIs8BlqKIwGxcXZ9q3b+/c/2n4GD9+vLn55ptNcXFxmcf+tG9FOnToYF566SXnfkREhPn973/v3C8uLjZNmjQxixcvNsYY88orr5irrrrKGS5+rk+fPs4Z4hJ/+9vfTFhYWLk1lPUdLFmyxFx99dUus35vv/228fLyMtnZ2Re9rxIPPvigufPOO8v9fPDgwWbUqFFlflYS8H4egkaMGGFCQ0MvGkxKAlnJbPLPw2xcXJwZNGiQyzF33313qTBbv35950ysMcZMmTLF9OzZ07nft29fM3HixAprMaZyYXbp0qXOz7/66isjyfkPmXvuucf84Q9/cDlncnKy8fLyMj/++GOZ10xLSys1W/zTwGdM2eNf3ncfHh7u/EdbiSeeeMJER0cbYy78+QwJCTGnT592fr548eIKw6wxpb8bY4wZNmyYGTBggEvblClTTIcOHco9T2XuDbiS8cwscIUxxpT7fOXIkSOVnp6udu3aacKECdqyZctFz3f69Gk9+uij6tChgxo2bKjAwEB9/fXXyszMdOnXqVMn5392OBxq2rSpcnJyJEnp6enq2rWrQkJCyrxGamqqZs+ercDAQOd2//33KysrS2fOnKnsrSsjI0OdO3dWgwYNnG29e/dWcXGx9u7dW+5xL7/8srp3767GjRsrMDBQr776aqn7+6kHHnhAb7zxhrp06aJHH31UO3bsqFR9N9xwg3x9fV3a0tLSdMcddygiIkJXXXWVbrrpJkkq9/p79+5Vjx49XNp+vi9deDnpqquucu6HhYU5x6O6/XTsw8LCJMl5rdTUVK1cudJlbG+99VYVFxfr0KFDNVLPT/3www86cuSIRo8e7VLDk08+qQMHDkj6vz839evXdx4XHR1dpetlZGSod+/eLm29e/fW/v37VVRUVPUbAa5gPp4uAEDtysjIUGRkZJmfdevWTYcOHdI777yj9957T0OHDlX//v21bt26cs83ZcoUvfvuu5ozZ47atGmjgIAA3XXXXaVeYKpXr57LvsPhcK6sEBAQUGHNxcXFmjVrln7729+W+szf37/CY3+qoiBfXvs//vEPTZ48WXPnzlV0dLSuuuoqPffcc/rkk0/KvU5sbKy+/fZbvf3223rvvfd0yy23aNy4cZozZ06F9f00ZEsX/qEQExOjmJgY/f3vf1fjxo2VmZmpW2+9tdwXxMq6R2NMqX4VjUd1++m1SmoruVZxcbHGjBmjCRMmlDquZcuWNVLPT5XU8eqrr6pnz54un3l7e0sq+/urqsqOD4DKI8wCV5APPvhAu3fv1uTJk8vtExQUpLi4OMXFxemuu+7SbbfdphMnTigkJET16tUrNXuUnJyskSNH6je/+Y0k6dSpUzp8+LBbdXXq1ElLly51XufnunXrpr1796pNmzaVPqevr2+pWjt06KC//vWvOn36tDM4bt++XV5eXmrbtm2Z50lOTlavXr304IMPOttKZuwq0rhxY40cOVIjR45Unz59NGXKFM2ZM8c581qZWbivv/5aubm5euaZZxQeHi5JSklJqfCY6667Tp9++qlL28WOKUtZ319N6Natm7766iu3xrYyyqq/rO8+NDRUzZs318GDB3X33XeXea4OHTrob3/7m3788UfnP7z+/e9/V6mGDh06aNu2bS5tO3bsUNu2bZ3huSrnBa5kPGYAXKYKCwuVnZ2tY8eOadeuXXr66ad1xx136Ne//rWGDx9e5jHPP/+83njjDX399dfat2+f1q5dq6ZNm6phw4aSLvxf0++//76ys7P1n//8R5LUpk0bbdiwQenp6fr88881bNgwt2f4fve736lp06YaMmSItm/froMHD2r9+vXauXOnJOnxxx/XqlWrNHPmTH311VfKyMjQmjVr9Nhjj5V7zlatWumLL77Q3r17lZubq3Pnzunuu++Wv7+/RowYoS+//FIffvihxo8fr3vuuUehoaFlnqdNmzZKSUnRu+++q3379unPf/6zPvvsswrv5/HHH9c///lPffPNN/rqq6/0r3/9S+3bt5ckNWnSRAEBAdq8ebO+//575eXllXueli1bytfXVy+99JIOHjyoTZs26Yknnqjw2uPHj1diYqLmzZun/fv365VXXtE777zj9tJdrVq10ieffKLDhw8rNze3xmZt//jHP2rnzp0aN26c0tPTtX//fm3atEnjx4+/pPOWNf7lffczZ85UQkKCXnjhBe3bt0+7d+/WihUrNG/ePEnSsGHD5OXlpdGjR2vPnj1KTEy86Cx7SQ1bt27VsWPHlJubK0l6+OGH9f777+uJJ57Qvn379Ne//lULFizQI488ckn3BlzRPPi8LoAa8tOllXx8fEzjxo1N//79zfLly01RUZFLX/3khZ0lS5aYLl26mAYNGpigoCBzyy23mF27djn7btq0ybRp08b4+Pg4l446dOiQ6devnwkICDDh4eFmwYIFpV4eKutFmM6dO5sZM2Y49w8fPmzuvPNOExQUZOrXr2+6d+9uPvnkE+fnmzdvNr169TIBAQEmKCjI9OjRwyxZsqTc7yAnJ8cMGDDABAYGXtLSXP/973/NyJEjTXBwsGnYsKF54IEHzNSpU11eyPm5J554wrRv394EBASYkJAQc8cdd5iDBw86P3/11VdNeHi48fLyKrU018+99tprplWrVsbPz89ER0ebTZs2ubx4VN7SXM2bN3cuzfXkk0+apk2bOj//+QtFxhjz/PPPO8fUGGP27t1rbrzxRhMQEHDJS3P99CWp//znP6WWk/r000+dY9WgQQPTqVMn89RTT5V5PWMq9wJYeeNf1ndvjDGrV682Xbp0Mb6+vubqq682/+///T+zYcMG5+c7d+40nTt3Nr6+vqZLly5m/fr1F30BbOfOnaZTp07Gz8+vzKW56tWrZ1q2bGmee+65cs/hzr0BVyqHMTysAwCXs/vvv19ff/21kpOTPV0KAFQ7npkFgMvMnDlzNGDAADVo0EDvvPOO/vrXv2rRokWeLgsAagQzswBwmRk6dKg++ugjFRQUqHXr1ho/frzGjh3r6bIAoEYQZgEAAGAtVjMAAACAtQizAAAAsBZhFgAAANYizAIAAMBahFkAAABYizALAAAAaxFmAQAAYC3CLAAAAKxFmAUAAIC1/j9eTJGCdud4qQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "ax.hist(particles[\"distance_to_line\"], range=(0., 1.))\n",
+    "ax.set_xlabel(\"Distance to a straight line fitted to it\")\n",
+    "ax.set_ylabel(\"Abundance\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Abundance')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "ax.hist(particles[\"distance_to_z_axis\"], range=(0., 1.))\n",
+    "ax.set_xlabel(\"Distance to the z-axis\")\n",
+    "ax.set_ylabel(\"Abundance\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def plot_xy_graph(\n",
+    "    df_hits_particles: pd.DataFrame,\n",
+    "    seed: int | None = None,\n",
+    "):\n",
+    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
+    "\n",
+    "    ax.axhline(y=0.0, color=\"k\", linewidth=0.5)\n",
+    "    ax.axvline(x=0.0, color=\"k\", linewidth=0.5)\n",
+    "\n",
+    "    n_lines = 20\n",
+    "    event_ids = df_hits_particles[\"event\"].unique()\n",
+    "\n",
+    "    rng = np.random.default_rng(seed=seed)\n",
+    "    rng.shuffle(event_ids)\n",
+    "\n",
+    "    for idx, (_, hits_particle) in enumerate(\n",
+    "        df_hits_particles[\n",
+    "            df_hits_particles[\"event\"].isin(event_ids[:10])\n",
+    "            & (\n",
+    "                (df_hits_particles[\"distance_to_z_axis\"] < 0.5)\n",
+    "                & (df_hits_particles[\"distance_to_z_axis\"] > 0.2)\n",
+    "            )\n",
+    "        ][[\"event\", \"particle_id\", \"x\", \"y\", \"z\", \"plane\"]].groupby(\n",
+    "            by=[\"event\", \"particle_id\"]\n",
+    "        )\n",
+    "    ):\n",
+    "        hit_coordinates = hits_particle.sort_values(by=\"plane\")[[\"x\", \"y\"]]\n",
+    "        ax.plot(\n",
+    "            hit_coordinates[\"x\"],\n",
+    "            hit_coordinates[\"y\"],\n",
+    "            linestyle=\"-\",\n",
+    "            markersize=5.0,\n",
+    "            marker=\"o\",\n",
+    "        )\n",
+    "        if idx > n_lines:\n",
+    "            break\n",
+    "\n",
+    "    ax.set_xlim(-50.0, 50.0)\n",
+    "    ax.set_ylim(-50.0, 50.0)\n",
+    "    ax.grid(color=\"grey\", alpha=0.5)\n",
+    "\n",
+    "    return fig, ax\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 1200x1200 with 1 Axes>, <Axes: >)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_xy_graph(hits_particles)"
+   ]
+  },
+  {
+   "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab


From a82bcbde61af22f7196d06e89ea8e813939bce38 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:37:20 +0200
Subject: [PATCH 41/65] Correct `everything_but_long_electrons` cut

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

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index dbadc5df..a421cce8 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -87,7 +87,9 @@ def everything_but_long_electrons(
     """
     # 1. Create a mask of the particles to keep:
     mask_particles_to_keep = (
-        particles["has_velo"] & particles["has_scifi"] & (particles["pid"].abs() != 11)
+        particles["has_velo"] & ~(
+            particles["has_scifi"] & (particles["pid"].abs() == 11)
+        )
     )
 
     # 2. Propagate the mask to the dataframe of `particles` and `hits_particles`
-- 
GitLab


From fefeed4655fc33037f74b1e2eab0f2fbc5176442 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:38:11 +0200
Subject: [PATCH 42/65] Clear notebook outputs

---
 LHCb_Pipeline/full_pipeline.ipynb | 613 ++----------------------------
 1 file changed, 26 insertions(+), 587 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index 2b817855..c3cb71d2 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -18,50 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"bk-root\">\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"1002\">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(\"1002\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"1002\")).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"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"bk-root\">\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"1003\">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(\"1003\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"1003\")).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"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -223,91 +182,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Remove directory `/scratch/acorreia/data/velo-sim10b-nospillover-lot-validation/preprocessed`.\n",
-      "INFO:Preprocessing: output will be written in /scratch/acorreia/data/velo-sim10b-nospillover-lot-validation/preprocessed\n",
-      "INFO:Load dataframe\n",
-      "INFO:Apply selection\n",
-      "INFO:Compute distance to line (that might take some time)\n",
-      "/home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/Preprocessing/particle_line_fitting.py:174: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\n",
-      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_metric'.\n",
-      "\n",
-      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
-      "\u001b[1m\n",
-      "File \"Preprocessing/particle_line_fitting.py\", line 89:\u001b[0m\n",
-      "\u001b[1m@nb.jit(nopython=True, cache=True)\n",
-      "\u001b[1mdef compute_particle_metric(\n",
-      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0m\n",
-      "  compute_particle_metric(\n",
-      "/home/acorreia/Documents/tracking/etx4velo/LHCb_Pipeline/Preprocessing/particle_line_fitting.py:174: NumbaPendingDeprecationWarning: \u001b[1m\u001b[1m\u001b[1m\n",
-      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_line_metrics'.\n",
-      "\n",
-      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
-      "\u001b[1m\n",
-      "File \"Preprocessing/particle_line_fitting.py\", line 55:\u001b[0m\n",
-      "\u001b[1m@nb.jit(nopython=True, cache=True)\n",
-      "\u001b[1mdef compute_particle_line_metrics(\n",
-      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0m\u001b[0m\n",
-      "  compute_particle_metric(\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/numba/core/ir_utils.py:2147: NumbaPendingDeprecationWarning: \u001b[1m\n",
-      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric_names' of function 'compute_particle_line_metrics_events_impl'.\n",
-      "\n",
-      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
-      "\u001b[1m\n",
-      "File \"Preprocessing/particle_line_fitting.py\", line 135:\u001b[0m\n",
-      "\u001b[1m@nb.jit(nopython=True, cache=True, parallel=True)\n",
-      "\u001b[1mdef compute_particle_line_metrics_events_impl(\n",
-      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/numba/core/ir_utils.py:2147: NumbaPendingDeprecationWarning: \u001b[1m\n",
-      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'metric__names' of function '__numba_parfor_gufunc_0x7f5c50c9a590'.\n",
-      "\n",
-      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
-      "\u001b[1m\n",
-      "File \"<string>\", line 1:\u001b[0m\n",
-      "\u001b[1m<source missing, REPL/exec in use?>\u001b[0m\n",
-      "\u001b[0m\n",
-      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tbb\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Apply particle selection mask\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b9166c16321d499a97fa3e6e74ed5a6c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/2000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "run_preprocessing(CONFIG, reproduce=True)"
    ]
@@ -758,18 +635,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gnn_metric_path='artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/metrics.csv'\n",
-      "gnn_artifact_path='artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/checkpoints/epoch=199-step=2000000.ckpt'\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from utils.modelutils.checkpoint_utils import (\n",
     "    get_last_version_dir_from_config,\n",
@@ -786,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -826,17 +694,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Lightning automatically upgraded your loaded checkpoint from v1.9.2 to v2.0.2. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file artifacts_training/gnn/velo-sim10b-nospillover-lot/version_10/checkpoints/epoch=199-step=2000000.ckpt`\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "with open(os.path.join(gnn_version_dir, \"hparams.yaml\"), 'r') as hparams_file:\n",
     "    embedding_hparams =yaml.load(hparams_file, Loader=yaml.FullLoader)\n",
@@ -854,165 +714,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "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>110</td>\n",
-       "      <td>0.057258</td>\n",
-       "      <td>0.063774</td>\n",
-       "      <td>0.998611</td>\n",
-       "      <td>0.945246</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>111</td>\n",
-       "      <td>0.057216</td>\n",
-       "      <td>0.064113</td>\n",
-       "      <td>0.998529</td>\n",
-       "      <td>0.945324</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>112</td>\n",
-       "      <td>0.057159</td>\n",
-       "      <td>0.063693</td>\n",
-       "      <td>0.998549</td>\n",
-       "      <td>0.945324</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>113</td>\n",
-       "      <td>0.057116</td>\n",
-       "      <td>0.064088</td>\n",
-       "      <td>0.998611</td>\n",
-       "      <td>0.945250</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>114</td>\n",
-       "      <td>0.057342</td>\n",
-       "      <td>0.063899</td>\n",
-       "      <td>0.998518</td>\n",
-       "      <td>0.945387</td>\n",
-       "      <td>0.002</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>194</td>\n",
-       "      <td>0.056589</td>\n",
-       "      <td>0.064133</td>\n",
-       "      <td>0.998660</td>\n",
-       "      <td>0.945044</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>195</td>\n",
-       "      <td>0.056639</td>\n",
-       "      <td>0.064363</td>\n",
-       "      <td>0.998576</td>\n",
-       "      <td>0.944959</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>196</td>\n",
-       "      <td>0.056593</td>\n",
-       "      <td>0.064429</td>\n",
-       "      <td>0.998432</td>\n",
-       "      <td>0.945342</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>197</td>\n",
-       "      <td>0.056801</td>\n",
-       "      <td>0.064165</td>\n",
-       "      <td>0.998465</td>\n",
-       "      <td>0.945365</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>198</td>\n",
-       "      <td>0.056630</td>\n",
-       "      <td>0.064177</td>\n",
-       "      <td>0.998609</td>\n",
-       "      <td>0.945090</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>89 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    epoch  train_loss  val_loss       eff       pur  current_lr\n",
-       "0     110    0.057258  0.063774  0.998611  0.945246       0.002\n",
-       "1     111    0.057216  0.064113  0.998529  0.945324       0.002\n",
-       "2     112    0.057159  0.063693  0.998549  0.945324       0.002\n",
-       "3     113    0.057116  0.064088  0.998611  0.945250       0.002\n",
-       "4     114    0.057342  0.063899  0.998518  0.945387       0.002\n",
-       "..    ...         ...       ...       ...       ...         ...\n",
-       "84    194    0.056589  0.064133  0.998660  0.945044       0.002\n",
-       "85    195    0.056639  0.064363  0.998576  0.944959       0.002\n",
-       "86    196    0.056593  0.064429  0.998432  0.945342       0.002\n",
-       "87    197    0.056801  0.064165  0.998465  0.945365       0.002\n",
-       "88    198    0.056630  0.064177  0.998609  0.945090       0.002\n",
-       "\n",
-       "[89 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# gnn_metrics = checkpoint_utils.get_training_metrics(gnn_trainer)\n",
     "\n",
@@ -1023,27 +727,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:Unable to obtain driver using Selenium Manager: /scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/selenium/webdriver/common/linux/selenium-manager is missing.  Please open an issue on https://github.com/SeleniumHQ/selenium/issues\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "perfplot.plot_training_metrics(gnn_metrics, CONFIG, \"gnn\")"
    ]
@@ -1060,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1069,85 +755,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class=\"bk-root\">\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"3955\">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(\"3955\");\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-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.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(\"3955\")).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"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/scratch/acorreia/mambaforge/envs/etx4velo_updated/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
-      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            eff         pur  score cut\n",
-      "170   0.9159544  0.98619634      0.851\n",
-      "171   0.9145299   0.9861751      0.856\n",
-      "172   0.9145299   0.9861751      0.861\n",
-      "173  0.91381764   0.9861645      0.866\n",
-      "174   0.9131054  0.98615384      0.871\n",
-      "175   0.9131054  0.98615384      0.876\n",
-      "176  0.91096866  0.98612183      0.881\n",
-      "177   0.9102564   0.9861111      0.886\n",
-      "178  0.90954417   0.9868624      0.891\n",
-      "179   0.9088319   0.9876161      0.896\n",
-      "180   0.9059829   0.9891135      0.901\n",
-      "181   0.9059829   0.9891135      0.906\n",
-      "182   0.9059829  0.98988324      0.911\n",
-      "183   0.9059829  0.98988324      0.916\n",
-      "184   0.9031339  0.98985165      0.921\n",
-      "185   0.9017094   0.9906103      0.926\n",
-      "186   0.8995727   0.9929245      0.931\n",
-      "187   0.8981481  0.99291337      0.936\n",
-      "188   0.8967236   0.9929022      0.941\n",
-      "189  0.89529914    0.992891      0.946\n",
-      "190  0.89387465   0.9936659      0.951\n",
-      "191  0.89316237  0.99366087      0.956\n",
-      "192   0.8903134   0.9944312      0.961\n",
-      "193   0.8888889   0.9944223      0.966\n",
-      "194   0.8881766   0.9952115      0.971\n",
-      "195  0.88532764  0.99519616      0.976\n",
-      "196   0.8810541  0.99597424      0.981\n",
-      "197   0.8767806   0.9959547      0.986\n",
-      "198   0.8710826    0.997553      0.991\n",
-      "199   0.8618234  0.99834985      0.996\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "perfplot.plot_edge_performance(gnn_model, CONFIG)"
    ]
@@ -1252,180 +862,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
-      "INFO:Remove directory `/scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover`.\n",
-      "INFO:Inference from /scratch/acorreia/data/velo-sim10b-nospillover-lot/gnn_processed/test/velo-sim10b-nospillover to /scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f3965c3d891415fb29a028090616592",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:--------------------- Evaluation for velo-sim10b-nospillover ---------------------\n",
-      "INFO:1) Load dataframe of tracks, hits-particles and particles\n",
-      "INFO:Load tracks in /scratch/acorreia/data/velo-sim10b-nospillover-lot/track_building_processed/test/velo-sim10b-nospillover.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bad00e4b1cd4830854bc59a2e0ce446",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Load pre-processed test datasets in /scratch/acorreia/data/__test__/velo-sim10b-nospillover.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0eeb0b61c9af4ce6aceceb8632dfc948",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6034826e8084ff7b91b0ccc5e9ba721",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Compute plat stats\n",
-      "INFO:Compute particle line metrics\n",
-      "INFO:2) Matching\n",
-      "INFO:3) Evaluation\n",
-      "INFO:Report was saved in output/velo-sim10b-nospillover-lot/report-2023.05.25-01.24.10-velo-sim10b-nospillover.txt\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TrackChecker output                               :      1299/   238776   0.54% ghosts\n",
-      "01_velo                                           :    100380/   103884  96.63% ( 96.83%),      3167 (  3.06%) clones, pur  99.91%, hit eff  93.03%\n",
-      "02_long                                           :     58396/    59302  98.47% ( 98.57%),      2015 (  3.34%) clones, pur  99.95%, hit eff  93.35%\n",
-      "03_long_P>5GeV                                    :     37943/    38166  99.42% ( 99.50%),      1338 (  3.41%) clones, pur  99.95%, hit eff  93.68%\n",
-      "04_long_strange                                   :      2910/     3101  93.84% ( 94.43%),       100 (  3.32%) clones, pur  99.74%, hit eff  89.16%\n",
-      "05_long_strange_P>5GeV                            :      1448/     1509  95.96% ( 96.21%),        34 (  2.29%) clones, pur  99.71%, hit eff  92.90%\n",
-      "06_long_fromB                                     :        95/       95 100.00% (100.00%),         4 (  4.04%) clones, pur 100.00%, hit eff  93.43%\n",
-      "07_long_fromB_P>5GeV                              :        70/       70 100.00% (100.00%),         4 (  5.41%) clones, pur 100.00%, hit eff  91.88%\n",
-      "08_long_electrons                                 :      3178/     4280  74.25% ( 75.87%),        76 (  2.34%) clones, pur  99.89%, hit eff  77.28%\n",
-      "09_long_fromB_electrons                           :         8/        8 100.00% (100.00%),         0 (  0.00%) clones, pur 100.00%, hit eff  93.75%\n",
-      "10_long_fromB_electrons_P>5GeV                    :         5/        5 100.00% (100.00%),         0 (  0.00%) clones, pur 100.00%, hit eff 100.00%\n",
-      "\n",
-      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
-      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
-      "| Velo                 | 92.69%       | 93.06%               | 3.14%      | 99.88%               | 91.18%                   |\n",
-      "| Long                 | 96.84%       | 96.99%               | 3.28%      | 99.94%               | 92.53%                   |\n",
-      "| Velo, no electrons   | 96.63%       | 96.83%               | 3.06%      | 99.91%               | 93.03%                   |\n",
-      "| Velo, only electrons | 72.81%       | 72.58%               | 3.71%      | 99.69%               | 78.86%                   |\n",
-      "| Long, only electrons | 74.25%       | 75.87%               | 2.34%      | 99.89%               | 77.28%                   |\n",
-      "| Categories   | # ghosts   | # tracks   | % ghosts   |\n",
-      "|:-------------|:-----------|:-----------|:-----------|\n",
-      "| Everything   | 1,299      | 238,776    | 0.54%      |\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Plot basic for category velo_reconstructible_acceptance saved in output/velo-sim10b-nospillover-lot/hist1d_basic_velo_reconstructible_acceptance-velo-sim10b-nospillover.pdf\n",
-      "INFO:Plot challenging for category velo_reconstructible_acceptance saved in output/velo-sim10b-nospillover-lot/hist1d_challenging_velo_reconstructible_acceptance-velo-sim10b-nospillover.pdf\n",
-      "INFO:Plot basic for category long_reconstructible_acceptance_only_electrons saved in output/velo-sim10b-nospillover-lot/hist1d_basic_long_reconstructible_acceptance_only_electrons-velo-sim10b-nospillover.pdf\n",
-      "INFO:Plot challenging for category long_reconstructible_acceptance_only_electrons saved in output/velo-sim10b-nospillover-lot/hist1d_challenging_long_reconstructible_acceptance_only_electrons-velo-sim10b-nospillover.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ryQCLli1bOpyzHzNmjIwxOnfunG644QbrvH+jRo30888/F3vmuNIeXOHKN998o48//lj/r717D466uv8//soFaEIAFQh30IAJlwCFqNwHoqBAKIhIKxIIl9pKL0M70nQITABpQWQQEJhaoZZLQiGmUoIWLFWgimi1ViINCGkNgaJGC+ELCSG3/f2R2fPbTXY3n81tN8nzMcN4zu75fM77AzPOOfs573OmTJni9HmXLl20ZcsWp3ZLlizRyy+/rPHjxzu1jYqK0q9+9StT/8Mf/tDoYgAA+BYJFgCavJs3byo7O9vUg4KC9Kc//Un9+vVze01iYqIp22w2ffTRR/UaY0Nz3KHWZrNp7dq1bts+99xzKisr0y9+8YsaZ72XlJToiSeeMLvOrl69WqNGjXLZdsGCBWYx1RdffOF2QmpXeXfanTt3emy/a9cul9d5kpGRYcruFl6VlJToe9/7nllElZiYqO985zsu23bq1Em7d++23D8AAADqXlOcJzDOZ5wPAAAA1IWcnBzl5+dLqtj994EHHqj2GscTAvv166dvfetb9RUeAAAAmqG8vDxt3rzZ1Ldt26bw8HCP1wQGBmr48OGm/tlnn1VpY/UEC5vNpqVLl5r60qVLnU5idlSbBAtJWrVqlVq0aCFJyszMVHp6uubPn6/jx49Lqtgs6siRI9U+vzv+nFwhST179lR8fLzL70aPHq22bdtKkm7cuCGbzabvfve7Lts6Jkfk5uZ6tWmWP8QAAPAtEiwANHmnT582x/NJ0lNPPaWRI0d6vCYiIsIc0yZV7LLalERHRzsN4vft26f//Oc/VdpdvnxZe/bsUXh4uBYsWFDj/vbu3WsWr7Vt29Zp519Xnn76aVNOSUnxuLvt8OHDFRUVZeqHDx92OyHJz8/XoUOHFBwcrCeffNJy/PaFV926dVNMTIzLNnv37jXHSQYHB+uZZ57xeM+YmBi39wIAAED9a4rzBMb5jPMBAACAuuC4ICwiIsIsHvLEMcFiyJAh9REWAAAAmrGUlBSz2c/gwYM1bdo0S9c5JiFcv369yveOJ1h8/vnnbk8mTktL0z/+8Q9JUo8ePfTTn/7UZbuCggL9+9//NvWaJFj07t3b6bf7hIQE7d27V5IUGhqq119/vcYnxlVOrnjooYf8KrlCqjhBz76ZVGWBgYG65557TH3ChAlu79OhQwenuYw373T8IQYAgG+RYAGgyXP8UT8wMFDLli2zdF2XLl1MubCwsM7j8jXHzPqysjKtW7euSpv169eruLhYP//5z2u125TjbrOxsbEKDQ312H7YsGEKCgqSJBUVFemvf/2rx/Zz58415ZKSEjOxrGz//v0qKirS5MmT1bFjR0ux5+Tk6MyZM5LkdqdayfkZR44c6fEYSrs5c+Zo7Nix6tChg6VYAAAAUHea6jyBcT7jfAAAAKC2HBMsrCZLkGABAACA+pSWlmbKs2fPtnydPSlDktq0aVPl+/79+yswsGIJZXl5uctTLkpLS7V8+XJTX7Vqldvf1k+fPq3y8nJJFe8TanrKRHJysunj1q1bkipO4k5LS7N0wpwrrpIrDh065FfJFZKcNl9yxTFhITIy0nLbGzduNKoYAAC+RYIFgCbP8UXA+PHj1b17d0vXlZaWmrLjIpqZM2cqICCgRn9ee+21Onuu2hoxYoTGjh1r6rt27dKVK1dM/euvv9aOHTvUrl07/ehHP6pxP8XFxXr//fdN3Up2fkhIiO644w5T/+CDDzy2nzNnjpnwSs6LoBzt2rVLUkV2v1UHDx405alTp7psU/kZBw0aZOneixcv1vHjxzV69GjL8QAAAKBu1PU8wV8wzreGcT4AAADgHgkWAAAA8CeFhYX6+OOPTf3hhx+2fG1+fr4pt2/fvsr3ISEhioiIMPWsrKwqbbZv325Oc46Ojvb4W7TjWLomp1fYde3aVSNGjHD67MUXX1RcXFyN7tdYkiskOb1HcMXxvUF1be2bPkkVm1I1phgAAL4V7OsAAKC+Of6oP2nSJMvX5eXlmXKnTp1M+fLly051u/z8fN2+fVstW7bUnXfe6fKeMTExlvtvCElJSTpx4oQk6fbt29qwYYM2bNggSdq0aZMKCwuVlJRk6fhvdy5evOh0hOKrr77qtEjJnYKCAlO+dOmSx7Y9evRQbGys3nrrLUkVE9bMzEynBVDZ2dk6deqU7rrrLk2ZMsVy/BkZGZKksLAwPfjggy7bVH7GXr16Wb4/AAAAfKOu5wn+hHF+9RjnAwAAAO55m2Bx9epV5ebmmnptFpIBAAAAlX3yyScqKSmRJLVs2VIDBgywfO358+dNOTo62mWbgQMHmgSKygkWhYWFevbZZ0197dq1TovrXcVqN3jwYMtxVrZmzRodO3bM6bNvvvmmRvdqTMkVkhQcbH1JqzdtG1sMAADf4v/uAJq00tJS/etf/zL14cOHW7ouLy/PaZfX3r17m/KpU6dcXhMTE6OPP/5Yc+fO1fbt22sYccN6+OGHNXToUJPp/9vf/lZJSUkKDg7Wtm3bFBISosWLF9eqj6tXrzrVs7KyXGb8e+K4o4A7CQkJZuGVVLG77QsvvGDq9l1tZ82apZYtW1rq9/r163rnnXckVfxdtWrVymW7ys/YunVrS/cHAACAb9THPMGfMM73jHE+AAAA4F5+fr4uXrxo6lYSLBwT2CMiItSuXbt6iQ0AAADN05dffmnKHTt2tLygvbCw0NK7gOjoaB04cEBS1QSLjRs3mv7HjBlT7UY/dXGCxe7du7Vs2bIqn2/YsEE/+clPvDpdu7ElVwAA4C/cp1MCQBNw9uxZ3b5929TvueceS9fZd3uVKl4GOB4H6IrjAq3GtjPT0qVLTbmgoECbN2/Wtm3bdP36dS1cuFDh4eF12t9vfvMb2Ww2r/4cPXq02vs+9thjCgsLM/XU1FSVlpZKkmw2m/bs2SNJmjt3ruVY//znP5tdEKZOnWr5uoCAAMttAQAA0PAaap7gS4zz3WOcDwAAALjnuCCsc+fO6ty5c7XXOCZYWEnIAAAAALxx7do1U27Tpo3l6w4cOKCysjJJUlRUlHr27Omy3cCBA03ZMcHif//7n9avX2/qzz//vMf+ysrKdObMGVOvyfqho0eP6vvf/76pL126VD169JAk/d///Z+ee+45y/ciuQIAgJojwQJAk+b4o74ky1nce/fuNeVHHnmk2vZZWVlmgVZje3nw2GOPKSoqytS3bt2qTZs2KTg4WEuWLKn1/du3b+9Uv3nzZq3v6Urr1q01Y8YMU8/Ly9Phw4clVSyEu3jxovr27asHHnjA8j0zMjIkSUFBQYqLi3PbrqGeEQAAAHWjoeYJvsQ43z3G+QAAAIB7jgkWVt93OM6xhg4dWtchAQAAoJkLDQ015YKCAsvXvfTSS6YcHx/vtl10dLQpZ2dnmw161qxZo+vXr0uSpk+fXu1p2BcuXNCtW7dMzPfee6/lWKWKcfWMGTNM//Pnz9eaNWuUlJRk2mzdulVffPFFtfeqnFzx4IMPklwBAIAXSLAA0KQ5vgiQpOLi4mqvuXz5st544w1TX7hwoeV+AgICNGjQIK9i9LXAwED98pe/NPVr167p66+/1uzZs9WrV69a379nz55OE7Tc3Nxa39OdhIQEp/quXbuc/lv5e09KS0t15MgRSdKIESPUoUMHt20b8hkBAABQew01T/AlxvmuMc4HAAAAPKttgkVj24QKAAAA/q979+6m/N///tfSZjj79+/Xu+++K0kKCQnR/Pnz3baNjIxUq1atJFX8hnz+/HldunRJ27ZtkyQFBwdr7dq11faZnZ1tyr1791ZgoPWlmRcvXlRcXJxu3LghqWKTp5dffllSxfsI++/6t27d0urVq6uNo3Jyxeuvv05yBQAAXiDBAkCTVnln2gsXLlR7TVJSkskGnzx5smJiYiz306dPH4WFhdUgUt+Kj483RwpKFYkijouxaqNly5ZOWfyV/03cKSgo0Jw5cxQfH6+33nrL0jXjxo1zWix26NAhXb58WX/84x8VGBjocUeCyk6cOKH8/HxJ0tSpUz22rfyMmZmZlvo4evSoXnrpJe3fv99yXAAAAKi9hpon+Brj/KoY5wMAAACeeZtgUVhYqPPnz3t1DQAAAOCN++67Ty1btpQklZeX6+DBgx7bZ2VladGiRaa+ZMkSdevWzW37oKAg9e3b1+n65ORk3b59W5K0YMECpxOj3XFM/CgvL6+2vd3Vq1c1ceJEczLFkCFDlJ6eruDgYElSixYttHz5ctN+x44d+vzzz13ei+QKAADqBgkWAJq006dPO9Vfe+01j+1fffVVpaSkSKrIQK8u69vO/sLh29/+ttcx+oMWLVromWeeMfVHH31U/fr1q7P7z5s3z5RPnTqlr776qtprMjIylJKSotTUVKdFYZ4EBARozpw5pl5cXKxZs2bpxo0beuihh5x2NbDSv111C68k52d87733dO3aNY/tbTabfvjDH2rRokVOOyEDAACg/jXUPMHXGOe77t+OcT4AAABQ1YABAzRjxgzNmDFDw4YNq7Z9ZmamysrKJEmdO3dW586d6ztEAAAANDMhISF6/PHHTT0pKUlXrlxx2fbYsWMaP368+S33/vvv17Jly6rtIzo62pTT09O1Z88eSVJoaKhWrlxpKc5OnTqZ8tmzZ/Xmm29We01RUZGmTp2qc+fOSZJ69eqlN954o8rmrvPmzVNERIQkqaSkRCtWrKhyL5IrAACoOyRYAGiycnJyqix+eeGFF6osprLbs2ePEhISZLPZJElr167V0KFDLfVlT7BozDsz/eAHP9Dhw4d1+PBhbdmypU7vPWvWLEVGRkqSysrKtGbNGo/ti4uLTZtJkyaZa62YO3euU91+5GNCQoI3IevQoUOSKo6CtLITgeMzlpSUaNOmTR7bZ2RkmB0FnnrqKa9iAwAAQM015DzBHzDOd8Y4HwAAAPAsNTVV6enpSk9Pt5QU7XiaXWN+RwIAAAD/lpycrNDQUEl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-      "text/plain": [
-       "<Figure size 3200x1800 with 24 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3200x1800 with 24 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "build_track_candidates(\n",
     "    CONFIG, partitions=[\"velo-sim10b-nospillover\"]\n",
-- 
GitLab


From ba26858defc7dcf41238e753d37dfdd95d236a4f Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 20:44:07 +0200
Subject: [PATCH 43/65] Set pipeline config to default

---
 LHCb_Pipeline/full_pipeline.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index c3cb71d2..30878466 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -65,7 +65,7 @@
     "    )\n",
     ")\n",
     "\n",
-    "CONFIG = 'pipeline_configs/velo-sim10b-nospillover-lot.yaml'\n",
+    "CONFIG = 'pipeline_config_default.yaml'\n",
     "\n",
     "run_training: bool = False\n"
    ]
-- 
GitLab


From 3cf8a9fd67527bb4a428a9d9f5b7eaf671ef613d Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:12:47 +0200
Subject: [PATCH 44/65] Remove unused _load_hits_particles and _load_particles

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 38 --------------------
 1 file changed, 38 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 892b4bad..938cfef2 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -31,44 +31,6 @@ def cast_boolean_columns(particles: pd.DataFrame):
     particles[boolean_columns] = particles[boolean_columns].astype(dtype="bool")
 
 
-def _load_particles(
-    indir: str | typing.List[str],
-    particles_columns: typing.List[str] | None = None,
-    **kwargs,
-) -> pd.DataFrame:
-    indirs = [indir] if isinstance(indir, str) else indir
-
-    dataframes = []
-    for indir in indirs:
-        particles = pd.read_parquet(
-            f"{indir}/mc_particles.parquet.lz4",
-            columns=None
-            if particles_columns is None
-            else ["event", "mcid"] + particles_columns,
-            **kwargs,
-        )
-    return pd.concat(dataframes)
-
-
-def _load_hits_particles(
-    indir: str | typing.List[str],
-    hits_particles_columns: typing.List[str] | None = None,
-    **kwargs,
-) -> pd.DataFrame:
-    indirs = [indir] if isinstance(indir, str) else indir
-
-    dataframes = []
-    for indir in indirs:
-        particles = pd.read_parquet(
-            f"{indir}/mc_particles.parquet.lz4",
-            columns=None
-            if hits_particles_columns is None
-            else ["event", "mcid"] + hits_particles_columns,
-            **kwargs,
-        )
-    return pd.concat(dataframes)
-
-
 def load_dataframes_in_different_directories(
     indirs: str | typing.List[str],
     filename: str,
-- 
GitLab


From 6b4a15040138225cd16bc6b79739f60263cf6fa0 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:16:00 +0200
Subject: [PATCH 45/65] Drop mcid: only use particle_id

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 938cfef2..3b472ef9 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -107,6 +107,9 @@ def load_dataframes(
     # Define `particle_id = mcid + 1` directly in the original dataframes
     particles["particle_id"] = particles["mcid"] + 1
     hits_particles["particle_id"] = hits_particles["mcid"] + 1
+    
+    particles.drop("mcid", axis=1, inplace=True)
+    hits_particles.drop("mcid", axis=1, inplace=True)
 
     # Rename `lhcbid` to `hit_id`
     hits_particles.rename(columns={"lhcbid": "hit_id"}, inplace=True)
-- 
GitLab


From a316cb8de38a62013602c0ab9933fa7c11d19e3a Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:37:17 +0200
Subject: [PATCH 46/65] Create `analyses` folder

---
 LHCb_Pipeline/analyses/README.md | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 LHCb_Pipeline/analyses/README.md

diff --git a/LHCb_Pipeline/analyses/README.md b/LHCb_Pipeline/analyses/README.md
new file mode 100644
index 00000000..ebe04f5e
--- /dev/null
+++ b/LHCb_Pipeline/analyses/README.md
@@ -0,0 +1,8 @@
+# Analyses
+
+This folder contains a collection of scripts and notebooks to answer
+typical questions about the data distribution.
+
+How many hits are there in average / event? In average, how many repeated planes
+do a track have? This folder contains scripts and notebooks to answer these kind of
+questions in a reproducible way.
-- 
GitLab


From c2e2c94f198c685b7644ee3e46a1af4a748bc492 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:57:23 +0200
Subject: [PATCH 47/65] Plot distance to line distributions

---
 LHCb_Pipeline/analyses/distance_to_line.ipynb | 539 ++++++++++++++++++
 1 file changed, 539 insertions(+)
 create mode 100644 LHCb_Pipeline/analyses/distance_to_line.ipynb

diff --git a/LHCb_Pipeline/analyses/distance_to_line.ipynb b/LHCb_Pipeline/analyses/distance_to_line.ipynb
new file mode 100644
index 00000000..8246ae7e
--- /dev/null
+++ b/LHCb_Pipeline/analyses/distance_to_line.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os.path as op\n",
+    "\n",
+    "import anaconfig\n",
+    "import anatools\n",
+    "\n",
+    "import montetracko.lhcb as mtb\n",
+    "import montetracko.lhcb.category as mtbc\n",
+    "\n",
+    "from Preprocessing.preprocessing import load_dataframes\n",
+    "from Preprocessing.particle_line_fitting import compute_particle_line_metrics_dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils.plotutils.plotconfig import configure_matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "configure_matplotlib()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "particles, hits_particles = load_dataframes(\n",
+    "    indir=\"/scratch/acorreia/minbias-sim10b-xdigi-nospillover/92\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute `distance_to_line`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_distances = compute_particle_line_metrics_dataframe(\n",
+    "        hits=hits_particles,\n",
+    "        metric_names=[\"distance_to_line\"],\n",
+    "    )\n",
+    "particles = particles.merge(new_distances, how=\"left\", on=[\"event\", \"particle_id\"])\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>run</th>\n",
+       "      <th>event</th>\n",
+       "      <th>pid</th>\n",
+       "      <th>p</th>\n",
+       "      <th>pt</th>\n",
+       "      <th>eta</th>\n",
+       "      <th>phi</th>\n",
+       "      <th>charge</th>\n",
+       "      <th>mother_mcid</th>\n",
+       "      <th>origin_mother_mcid</th>\n",
+       "      <th>...</th>\n",
+       "      <th>from_cdecay</th>\n",
+       "      <th>from_sdecay</th>\n",
+       "      <th>nhits_velo</th>\n",
+       "      <th>nhits_ut</th>\n",
+       "      <th>nhits_scifi</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "      <th>vz</th>\n",
+       "      <th>particle_id</th>\n",
+       "      <th>distance_to_line</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>17208522</td>\n",
+       "      <td>11231197</td>\n",
+       "      <td>211</td>\n",
+       "      <td>9511.86000</td>\n",
+       "      <td>139.244000</td>\n",
+       "      <td>4.91716</td>\n",
+       "      <td>0.961659</td>\n",
+       "      <td>1</td>\n",
+       "      <td>480</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-9.4447</td>\n",
+       "      <td>0.9730</td>\n",
+       "      <td>4649.640</td>\n",
+       "      <td>483</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>17208522</td>\n",
+       "      <td>11231197</td>\n",
+       "      <td>-211</td>\n",
+       "      <td>4480.96000</td>\n",
+       "      <td>607.332000</td>\n",
+       "      <td>2.68704</td>\n",
+       "      <td>2.790250</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>355</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0062</td>\n",
+       "      <td>106.728</td>\n",
+       "      <td>673</td>\n",
+       "      <td>0.026845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>17208522</td>\n",
+       "      <td>11231197</td>\n",
+       "      <td>-321</td>\n",
+       "      <td>6227.42000</td>\n",
+       "      <td>193.901000</td>\n",
+       "      <td>4.16227</td>\n",
+       "      <td>2.854050</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>352</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0062</td>\n",
+       "      <td>106.728</td>\n",
+       "      <td>684</td>\n",
+       "      <td>0.013621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>17208522</td>\n",
+       "      <td>11231197</td>\n",
+       "      <td>211</td>\n",
+       "      <td>4549.16000</td>\n",
+       "      <td>126.413000</td>\n",
+       "      <td>4.27610</td>\n",
+       "      <td>-0.474969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>338</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0008</td>\n",
+       "      <td>0.0062</td>\n",
+       "      <td>106.728</td>\n",
+       "      <td>694</td>\n",
+       "      <td>0.034668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>17208522</td>\n",
+       "      <td>11231197</td>\n",
+       "      <td>211</td>\n",
+       "      <td>4443.72000</td>\n",
+       "      <td>121.978000</td>\n",
+       "      <td>4.28837</td>\n",
+       "      <td>2.605250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>693</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>32.9574</td>\n",
+       "      <td>-18.0215</td>\n",
+       "      <td>1486.230</td>\n",
+       "      <td>695</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5371249</th>\n",
+       "      <td>17204880</td>\n",
+       "      <td>8041596</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2888.13000</td>\n",
+       "      <td>345.225000</td>\n",
+       "      <td>2.81372</td>\n",
+       "      <td>1.483150</td>\n",
+       "      <td>1</td>\n",
+       "      <td>66</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>-0.0038</td>\n",
+       "      <td>-74.378</td>\n",
+       "      <td>8208</td>\n",
+       "      <td>0.048801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5371250</th>\n",
+       "      <td>17204880</td>\n",
+       "      <td>8041596</td>\n",
+       "      <td>-211</td>\n",
+       "      <td>12977.80000</td>\n",
+       "      <td>361.493000</td>\n",
+       "      <td>4.27371</td>\n",
+       "      <td>-1.990970</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>-0.0038</td>\n",
+       "      <td>-74.378</td>\n",
+       "      <td>8211</td>\n",
+       "      <td>0.018632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5371251</th>\n",
+       "      <td>17204880</td>\n",
+       "      <td>8041596</td>\n",
+       "      <td>-211</td>\n",
+       "      <td>1975.39000</td>\n",
+       "      <td>97.846900</td>\n",
+       "      <td>3.69765</td>\n",
+       "      <td>-0.943525</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>8211</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>-12.0457</td>\n",
+       "      <td>-26.2259</td>\n",
+       "      <td>968.991</td>\n",
+       "      <td>8216</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5371253</th>\n",
+       "      <td>17204880</td>\n",
+       "      <td>8041596</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2.87014</td>\n",
+       "      <td>0.630635</td>\n",
+       "      <td>2.19624</td>\n",
+       "      <td>2.048630</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>8215</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-2736.2000</td>\n",
+       "      <td>-336.5390</td>\n",
+       "      <td>9401.810</td>\n",
+       "      <td>8218</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5371254</th>\n",
+       "      <td>17204880</td>\n",
+       "      <td>8041596</td>\n",
+       "      <td>211</td>\n",
+       "      <td>2479.65000</td>\n",
+       "      <td>171.929000</td>\n",
+       "      <td>3.36073</td>\n",
+       "      <td>0.881167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8211</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11</td>\n",
+       "      <td>-12.0457</td>\n",
+       "      <td>-26.2259</td>\n",
+       "      <td>968.991</td>\n",
+       "      <td>8227</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2149728 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              run     event  pid            p          pt      eta       phi  \\\n",
+       "6        17208522  11231197  211   9511.86000  139.244000  4.91716  0.961659   \n",
+       "42       17208522  11231197 -211   4480.96000  607.332000  2.68704  2.790250   \n",
+       "44       17208522  11231197 -321   6227.42000  193.901000  4.16227  2.854050   \n",
+       "45       17208522  11231197  211   4549.16000  126.413000  4.27610 -0.474969   \n",
+       "46       17208522  11231197  211   4443.72000  121.978000  4.28837  2.605250   \n",
+       "...           ...       ...  ...          ...         ...      ...       ...   \n",
+       "5371249  17204880   8041596  211   2888.13000  345.225000  2.81372  1.483150   \n",
+       "5371250  17204880   8041596 -211  12977.80000  361.493000  4.27371 -1.990970   \n",
+       "5371251  17204880   8041596 -211   1975.39000   97.846900  3.69765 -0.943525   \n",
+       "5371253  17204880   8041596   11      2.87014    0.630635  2.19624  2.048630   \n",
+       "5371254  17204880   8041596  211   2479.65000  171.929000  3.36073  0.881167   \n",
+       "\n",
+       "         charge  mother_mcid  origin_mother_mcid  ...  from_cdecay  \\\n",
+       "6             1          480                  -1  ...        False   \n",
+       "42           -1          355                  -1  ...        False   \n",
+       "44           -1          352                  -1  ...        False   \n",
+       "45            1          338                  -1  ...        False   \n",
+       "46            1          693                  -1  ...        False   \n",
+       "...         ...          ...                 ...  ...          ...   \n",
+       "5371249       1           66                  -1  ...        False   \n",
+       "5371250      -1           -1                  -1  ...        False   \n",
+       "5371251      -1         8211                  -1  ...        False   \n",
+       "5371253      -1         8215                  -1  ...        False   \n",
+       "5371254       1         8211                  -1  ...        False   \n",
+       "\n",
+       "         from_sdecay  nhits_velo  nhits_ut  nhits_scifi         vx        vy  \\\n",
+       "6               True           0         0           12    -9.4447    0.9730   \n",
+       "42             False           6         4           12     0.0008    0.0062   \n",
+       "44             False           7         4           11     0.0008    0.0062   \n",
+       "45             False           7         0            0     0.0008    0.0062   \n",
+       "46             False           0         0            6    32.9574  -18.0215   \n",
+       "...              ...         ...       ...          ...        ...       ...   \n",
+       "5371249        False          12         4           13     0.0020   -0.0038   \n",
+       "5371250        False          13         0            0     0.0020   -0.0038   \n",
+       "5371251        False           0         4           12   -12.0457  -26.2259   \n",
+       "5371253        False           0         0            1 -2736.2000 -336.5390   \n",
+       "5371254        False           0         4           11   -12.0457  -26.2259   \n",
+       "\n",
+       "               vz  particle_id  distance_to_line  \n",
+       "6        4649.640          483               NaN  \n",
+       "42        106.728          673          0.026845  \n",
+       "44        106.728          684          0.013621  \n",
+       "45        106.728          694          0.034668  \n",
+       "46       1486.230          695               NaN  \n",
+       "...           ...          ...               ...  \n",
+       "5371249   -74.378         8208          0.048801  \n",
+       "5371250   -74.378         8211          0.018632  \n",
+       "5371251   968.991         8216               NaN  \n",
+       "5371253  9401.810         8218               NaN  \n",
+       "5371254   968.991         8227               NaN  \n",
+       "\n",
+       "[2149728 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mtbc.in_acceptance.filter(dataframe=particles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Figure was saved in output/analyses/distance_to_line.pdf\n",
+      "Figure was saved in output/analyses/distance_to_line.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "_, edges, _ = ax.hist(\n",
+    "    mtbc.category_velo.filter(particles)[\"distance_to_line\"],\n",
+    "    color=\"blue\",\n",
+    "    label=mtbc.category_velo.label,\n",
+    "    range=(0.0, 0.4),\n",
+    "    bins=20,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "_, edges, _ = ax.hist(\n",
+    "    mtbc.category_long_only_electrons.filter(particles)[\"distance_to_line\"],\n",
+    "    color=\"green\",\n",
+    "    label=mtbc.category_long_only_electrons.label,\n",
+    "    bins=edges,\n",
+    "    density=True,\n",
+    "    alpha=0.5,\n",
+    ")\n",
+    "ax.set_xlabel(\"Distance to line\")\n",
+    "ax.legend()\n",
+    "\n",
+    "anatools.save_fig(\n",
+    "    fig, path=op.join(anaconfig.PLOTDIR, \"distance_to_line_velo_vs_long_electrons\")\n",
+    ")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "etx4velo_updated",
+   "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": "3.10.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
GitLab


From 70642192ea0fd365ab3095a793737047b79c4043 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:57:47 +0200
Subject: [PATCH 48/65] Empty notebook outputs

---
 LHCb_Pipeline/analyses/distance_to_line.ipynb | 410 +-----------------
 1 file changed, 8 insertions(+), 402 deletions(-)

diff --git a/LHCb_Pipeline/analyses/distance_to_line.ipynb b/LHCb_Pipeline/analyses/distance_to_line.ipynb
index 8246ae7e..c3e61dc8 100644
--- a/LHCb_Pipeline/analyses/distance_to_line.ipynb
+++ b/LHCb_Pipeline/analyses/distance_to_line.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,412 +80,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "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>run</th>\n",
-       "      <th>event</th>\n",
-       "      <th>pid</th>\n",
-       "      <th>p</th>\n",
-       "      <th>pt</th>\n",
-       "      <th>eta</th>\n",
-       "      <th>phi</th>\n",
-       "      <th>charge</th>\n",
-       "      <th>mother_mcid</th>\n",
-       "      <th>origin_mother_mcid</th>\n",
-       "      <th>...</th>\n",
-       "      <th>from_cdecay</th>\n",
-       "      <th>from_sdecay</th>\n",
-       "      <th>nhits_velo</th>\n",
-       "      <th>nhits_ut</th>\n",
-       "      <th>nhits_scifi</th>\n",
-       "      <th>vx</th>\n",
-       "      <th>vy</th>\n",
-       "      <th>vz</th>\n",
-       "      <th>particle_id</th>\n",
-       "      <th>distance_to_line</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>17208522</td>\n",
-       "      <td>11231197</td>\n",
-       "      <td>211</td>\n",
-       "      <td>9511.86000</td>\n",
-       "      <td>139.244000</td>\n",
-       "      <td>4.91716</td>\n",
-       "      <td>0.961659</td>\n",
-       "      <td>1</td>\n",
-       "      <td>480</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>-9.4447</td>\n",
-       "      <td>0.9730</td>\n",
-       "      <td>4649.640</td>\n",
-       "      <td>483</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>17208522</td>\n",
-       "      <td>11231197</td>\n",
-       "      <td>-211</td>\n",
-       "      <td>4480.96000</td>\n",
-       "      <td>607.332000</td>\n",
-       "      <td>2.68704</td>\n",
-       "      <td>2.790250</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>355</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.0008</td>\n",
-       "      <td>0.0062</td>\n",
-       "      <td>106.728</td>\n",
-       "      <td>673</td>\n",
-       "      <td>0.026845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>17208522</td>\n",
-       "      <td>11231197</td>\n",
-       "      <td>-321</td>\n",
-       "      <td>6227.42000</td>\n",
-       "      <td>193.901000</td>\n",
-       "      <td>4.16227</td>\n",
-       "      <td>2.854050</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>352</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.0008</td>\n",
-       "      <td>0.0062</td>\n",
-       "      <td>106.728</td>\n",
-       "      <td>684</td>\n",
-       "      <td>0.013621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>17208522</td>\n",
-       "      <td>11231197</td>\n",
-       "      <td>211</td>\n",
-       "      <td>4549.16000</td>\n",
-       "      <td>126.413000</td>\n",
-       "      <td>4.27610</td>\n",
-       "      <td>-0.474969</td>\n",
-       "      <td>1</td>\n",
-       "      <td>338</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0008</td>\n",
-       "      <td>0.0062</td>\n",
-       "      <td>106.728</td>\n",
-       "      <td>694</td>\n",
-       "      <td>0.034668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>17208522</td>\n",
-       "      <td>11231197</td>\n",
-       "      <td>211</td>\n",
-       "      <td>4443.72000</td>\n",
-       "      <td>121.978000</td>\n",
-       "      <td>4.28837</td>\n",
-       "      <td>2.605250</td>\n",
-       "      <td>1</td>\n",
-       "      <td>693</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>32.9574</td>\n",
-       "      <td>-18.0215</td>\n",
-       "      <td>1486.230</td>\n",
-       "      <td>695</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5371249</th>\n",
-       "      <td>17204880</td>\n",
-       "      <td>8041596</td>\n",
-       "      <td>211</td>\n",
-       "      <td>2888.13000</td>\n",
-       "      <td>345.225000</td>\n",
-       "      <td>2.81372</td>\n",
-       "      <td>1.483150</td>\n",
-       "      <td>1</td>\n",
-       "      <td>66</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>12</td>\n",
-       "      <td>4</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0.0020</td>\n",
-       "      <td>-0.0038</td>\n",
-       "      <td>-74.378</td>\n",
-       "      <td>8208</td>\n",
-       "      <td>0.048801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5371250</th>\n",
-       "      <td>17204880</td>\n",
-       "      <td>8041596</td>\n",
-       "      <td>-211</td>\n",
-       "      <td>12977.80000</td>\n",
-       "      <td>361.493000</td>\n",
-       "      <td>4.27371</td>\n",
-       "      <td>-1.990970</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0020</td>\n",
-       "      <td>-0.0038</td>\n",
-       "      <td>-74.378</td>\n",
-       "      <td>8211</td>\n",
-       "      <td>0.018632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5371251</th>\n",
-       "      <td>17204880</td>\n",
-       "      <td>8041596</td>\n",
-       "      <td>-211</td>\n",
-       "      <td>1975.39000</td>\n",
-       "      <td>97.846900</td>\n",
-       "      <td>3.69765</td>\n",
-       "      <td>-0.943525</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>8211</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>12</td>\n",
-       "      <td>-12.0457</td>\n",
-       "      <td>-26.2259</td>\n",
-       "      <td>968.991</td>\n",
-       "      <td>8216</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5371253</th>\n",
-       "      <td>17204880</td>\n",
-       "      <td>8041596</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2.87014</td>\n",
-       "      <td>0.630635</td>\n",
-       "      <td>2.19624</td>\n",
-       "      <td>2.048630</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>8215</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-2736.2000</td>\n",
-       "      <td>-336.5390</td>\n",
-       "      <td>9401.810</td>\n",
-       "      <td>8218</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5371254</th>\n",
-       "      <td>17204880</td>\n",
-       "      <td>8041596</td>\n",
-       "      <td>211</td>\n",
-       "      <td>2479.65000</td>\n",
-       "      <td>171.929000</td>\n",
-       "      <td>3.36073</td>\n",
-       "      <td>0.881167</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8211</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>11</td>\n",
-       "      <td>-12.0457</td>\n",
-       "      <td>-26.2259</td>\n",
-       "      <td>968.991</td>\n",
-       "      <td>8227</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2149728 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              run     event  pid            p          pt      eta       phi  \\\n",
-       "6        17208522  11231197  211   9511.86000  139.244000  4.91716  0.961659   \n",
-       "42       17208522  11231197 -211   4480.96000  607.332000  2.68704  2.790250   \n",
-       "44       17208522  11231197 -321   6227.42000  193.901000  4.16227  2.854050   \n",
-       "45       17208522  11231197  211   4549.16000  126.413000  4.27610 -0.474969   \n",
-       "46       17208522  11231197  211   4443.72000  121.978000  4.28837  2.605250   \n",
-       "...           ...       ...  ...          ...         ...      ...       ...   \n",
-       "5371249  17204880   8041596  211   2888.13000  345.225000  2.81372  1.483150   \n",
-       "5371250  17204880   8041596 -211  12977.80000  361.493000  4.27371 -1.990970   \n",
-       "5371251  17204880   8041596 -211   1975.39000   97.846900  3.69765 -0.943525   \n",
-       "5371253  17204880   8041596   11      2.87014    0.630635  2.19624  2.048630   \n",
-       "5371254  17204880   8041596  211   2479.65000  171.929000  3.36073  0.881167   \n",
-       "\n",
-       "         charge  mother_mcid  origin_mother_mcid  ...  from_cdecay  \\\n",
-       "6             1          480                  -1  ...        False   \n",
-       "42           -1          355                  -1  ...        False   \n",
-       "44           -1          352                  -1  ...        False   \n",
-       "45            1          338                  -1  ...        False   \n",
-       "46            1          693                  -1  ...        False   \n",
-       "...         ...          ...                 ...  ...          ...   \n",
-       "5371249       1           66                  -1  ...        False   \n",
-       "5371250      -1           -1                  -1  ...        False   \n",
-       "5371251      -1         8211                  -1  ...        False   \n",
-       "5371253      -1         8215                  -1  ...        False   \n",
-       "5371254       1         8211                  -1  ...        False   \n",
-       "\n",
-       "         from_sdecay  nhits_velo  nhits_ut  nhits_scifi         vx        vy  \\\n",
-       "6               True           0         0           12    -9.4447    0.9730   \n",
-       "42             False           6         4           12     0.0008    0.0062   \n",
-       "44             False           7         4           11     0.0008    0.0062   \n",
-       "45             False           7         0            0     0.0008    0.0062   \n",
-       "46             False           0         0            6    32.9574  -18.0215   \n",
-       "...              ...         ...       ...          ...        ...       ...   \n",
-       "5371249        False          12         4           13     0.0020   -0.0038   \n",
-       "5371250        False          13         0            0     0.0020   -0.0038   \n",
-       "5371251        False           0         4           12   -12.0457  -26.2259   \n",
-       "5371253        False           0         0            1 -2736.2000 -336.5390   \n",
-       "5371254        False           0         4           11   -12.0457  -26.2259   \n",
-       "\n",
-       "               vz  particle_id  distance_to_line  \n",
-       "6        4649.640          483               NaN  \n",
-       "42        106.728          673          0.026845  \n",
-       "44        106.728          684          0.013621  \n",
-       "45        106.728          694          0.034668  \n",
-       "46       1486.230          695               NaN  \n",
-       "...           ...          ...               ...  \n",
-       "5371249   -74.378         8208          0.048801  \n",
-       "5371250   -74.378         8211          0.018632  \n",
-       "5371251   968.991         8216               NaN  \n",
-       "5371253  9401.810         8218               NaN  \n",
-       "5371254   968.991         8227               NaN  \n",
-       "\n",
-       "[2149728 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mtbc.in_acceptance.filter(dataframe=particles)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Figure was saved in output/analyses/distance_to_line.pdf\n",
-      "Figure was saved in output/analyses/distance_to_line.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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0zspnx6RJk9SxY0fNmDFDS5Ys0Zw5c1S2bFnde++9Gj58OHfrAwAAALIgywG/ZMmSWZ7XNyMtW7Z0eIEuAAAAgKzJ8TSZAAAAAAoOAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAywkNDZXNZkv355NPPnF1E5ELIiIi0ry37dq1c3WzsuyTTz5JcxyhoaGubhYKMQ9XNwAA4FhoWKirm5CnQtuF5tm+u3fvrpo1a0qSnnnmGZ0/f15ly5bVe++9J0lq2bJlntWN/BMYGKjPPvtMkvT666/r0KFDLm5R9rRt29Y8jr59+7q4Nc7t3btX3377rSRp9OjRKlmypEvbA8cI+AAAy2nQoIEaNGggSZowYYLOnz+vEiVK6PHHH3dxy5Cb7N/T+fPnF9qAX6NGDdWoUUNS4Qj4L7/8siRpwIABBPwCiiE6AAAAgIUQ8AEAAAALIeADAAAAFkLABwAgk1atWqWePXuqUqVK8vT0VOnSpdWiRQtNnjxZly9fdrhNpUqV0syQIkl//PGH+vbtq+DgYHl6eqpq1aoaPny4zp07l2E79u/frz59+pjbli9fXp07d9aPP/4oSapWrVqq+vJ6nPTWrVvVv39/Va9eXd7e3goICFDjxo310ksv6ezZsw63ad26dZrXJSIiQpGRkXrqqadUtWpVeXp6Kjg4WP369VN4eHiW2+Volp30Zqm5+TXLyUw2mzZtUp8+fVSlShV5enqqZMmSatSokZ577jmdOnUqW/u8WUREhEaNGqW6deuqRIkSKlGihGrWrKkBAwZo+/btGW6fkJCgefPmqWPHjipXrpyKFSumkiVL6o477tDw4cP13//+V8nJyWb5lJmpBg4caD5XvXr1VK9XtWrVJElr1qxJ81oOGDBAkrR06VKzTjc3t1S/E/ZiY2M1depUtWrVSmXKlJGnp6cqVqyohx9+WN99953DY3JW7+eff65WrVopICBAJUqU0B133KEPP/zQ6WuUmJioDz74QO3bt1fZsmVVrFgxlSpVSs2bN9fYsWO1cePGDF9nV7EZhmG4uhE5FRMTo4CAAF26dEn+/v6ubo5LuHq2jbycDQOwiri4OIWHh6t69ery8vLKsLyrf6/zWn793ahWrZpOnDihqlWrKiIiIlv7uHbtmvr166clS5bI399fTzzxhOrXr69//vlHX3zxhfbs2aOgoCB9++23at68eaptly9frtjYWC1btkzLly+XJIWFhalPnz7q06ePbr31VkVGRmr27NmKiopS7dq1tXv3bpUoUcJhWxYuXKhBgwYpMTFRzZo1U8+ePeXv7689e/ZowYIFeuaZZ7R48WKdOHFCbdq00ZNPPqnixYurZ8+e2Tp2Z5KSkvTMM89o5syZ8vT0VP/+/XXHHXfo6tWrWr58udavXy8/Pz8tXrxYXbp0SbXtzz//rLNnz2rjxo1m0Prpp580dOhQdenSRY0bN9b58+c1b948HTt2TOXKldPu3btVsWJFh21p166d1q9fr5CQEIWFhUm6ERKXL1+uLVu2aM6cOZKkkSNH6s4770x1IbYkffvtt7py5YrGjBmjkiVLauLEiWnKZOb1GDVqlN5//335+fnpiSee0O23367Lly/rxx9/1Jo1a+Tl5aUFCxbosccec7iPlLA7adKkdD9gLFy4UE8++aQSEhLUo0cPtWvXTjabTVu3btXnn3+uhIQEPfPMM3rnnXcchueIiAh16dJFBw4cULVq1dS/f39VqlRJp0+f1ooVK7Rnzx5JUr169XTgwAFJNz5U7t+/P9X79d5776ls2bLmfn19fdW1a1f9/fffWrNmjaT/zWLVv39/lS1bVsuXL9eAAQNUvnx5rV+/XosXL5Yk2cfRPXv26MEHH1RkZKQaNmyoXr16qWzZsvr999/18ccf6+LFi+rcubO++OIL+fr6mtulV6+fn5+2bNmiPn36yN/fX5s2bdLChQtlGIZefPFFvf7662leo9jYWHXq1EmbN29WvXr19Oijjyo4OFhnz57VTz/9pE2bNkmShg8frlmzZjl8n9KT1f8FKbKSdwn4FuHqIEDABzJGwE+tMAX87t27a/ny5QoMDNTWrVt1yy23mOuSkpI0YMAALVq0SL6+vtq1a5dq166dZh+hoaHm7CN169bVsmXLVKdOHXP9H3/8odtvv11JSUl68803NX78+DT72Lp1q1q3bq3k5GQNHDhQ8+fPl5vb/76M37dvn9q0aaOrV68qKSlJ/fv3z9M5/5955hlNmzZN3t7eWrdunZo1a5Zq/YsvvqjJkyfL3d1d69atU5s2bdLs45NPPjHPCletWlWfffZZqnLnzp1TrVq1dOnSJQ0dOtQM6jdzFPBTREdHKzg4WHFxcerVq5c+//xzh/vYt2+fGjVqpPfee0+jR4/OwitxQ8rrUbZsWW3dutWcqjXFm2++qRdeeEHu7u4KCwtT69at0+wjo4C/YsUKde3aVTabTUuXLlW3bt1Srd+wYYM6dOighIQETZ48Wc8//3yq9RcvXlSTJk0UHh6uli1b6ueff071YdIwDI0YMUKzZ89WQECALl68mGp7+/crPDzcPGufnpTfv0qVKqlixYr65ZdfUtXXsWNHrVmzxgz4ERERatKkiaKjo9WzZ08tXrxYHh7/m/QxIiJCLVu21F9//aVOnTpp5cqVDj/EpNRbsWJFNWzYUN99953c3d3N9S+99JLeeOMNubu7m+Xs/ec//9Grr76qxo0ba9u2bSpevHiq9ePHj9eUKVOy9TuWHwGfIToWERbm2h8AsKqvv/7aPPP+xhtvpAr3kuTu7q73339fpUqV0pUrVzRkyJAM9/nwww+nCvfSjdCfMj//999/73C7kSNHKjk5WSVLltT06dNThXtJatiwocaNG6ekpKRMH1927dixQ9OmTZMkPfvss2nCvSS9+uqrql27tvkhKKN2tWnTJs2HgMDAQD344IOS0n9dMlKqVCk9/PDDkqRly5bpn3/+cVhu3rx58vT0zNZUlfavx6uvvpom3Es3QmHdunWVlJSk5557Lst1XL161exfPXv2TBPupRtz6j/xxBNmO24+1hdeeEHh4eFyc3PTxx9/nOabIpvNpnfffVeBgYFZbp8zkZGRmjNnTpr6hgwZov79+5uPhw0bpujoaPn5+WnOnDmpwr10I7hPmTJFkvTf//43w3B9+vRpTZ8+PVW4l6RBgwZJuvEBfeXKlWm2++GHHyRJd999d5pwL934wH7zPgsSAj4AAE5Mnz5dklSsWDE9+uijDsv4+/ubYWvDhg3au3ev033ePFwlxW233Sbpxtn8m+3du1c7d+6UJHXu3Fl+fn4O99GrVy+ndeeWlNdFSn/udnd3d3Oe+uPHj2cY0DN6XU6fPp3utQ4ZSQnG8fHx5k2l7F27dk2LFy9W9+7dVaZMmSzvf8aMGZIkNze3dPuJzWbTfffdJ0natm2bjh07lqU6Pv/8c/Majd69e6db7l//+pckmUOlUsTExJiB+K677tKtt97qcHtPT0899NBDCggIyFL7nKlZs6YaN26c5vmePXuabTpy5IgZth944AGVLl3a4b4eeeQR+fj4SErdDx2pW7euww9bNWrUMM+eO/p9S7n+YPPmzamuRUjh7e2tNWvWaMyYMU7rdxUCPgAA6bh8+bJ5wWKdOnXSDdWSdOedd5rLKeOA05NesCpVqpQk6dKlS2nW2V/Q17Rp03T3fcstt6Q7fj83pRyjn59fuscj5e7rIjl+bTIjJCTEHDo1b968NOuXLFmiixcvZuobGEdSjq1KlSpKSkrS+fPnHf7YX/CcmYth7a1evdpcrlatWrp12L//9nVs2rRJcXFxklK/L47MmzdPJ06cyFL7nKlXr16GZez7h7P2eXl56fbbb5d0Y1iVswvTnfXNlPfCUZ8KCQmRdOODWLt27bRy5UolJiamKtOuXTuzHQUNd7IFACAd4eHh5rCS9C7uTFGpUiVz+ejRo07LpvdBwdPTU5IcDmU5fvy4uRwcHOx0/yVLllRsbKzTMjkRExNjzo6Tn6+LpDQhKysGDx6scePG6eDBg9q6datatGhhrps3b55q1qypdu3aZXm/ly9fVlRUlKQbY8QzO7zl77//zlI99q9fw4YNs1yH/fb270t+yMw1kkeOHDGXM9OvUj68HDt2LN3X3NmH8pR+5ahPhYaGKiwszLyweOPGjSpTpow6d+6srl276v7770/VLwsaAj4AAOmwHw7i7e3ttKz9+ozOMt88dj6rbcnowrybxy3ntoL0umTFgAEDNGHCBF2/fl3z5883A/7hw4e1ceNGvfnmmw4v2MxITEyMuXzLLbdo7ty5mdru5us5slLPt99+m6lvauyHuWSlD+W2zLy3edGvstunSpUqpR07duiDDz7QvHnzdODAAV24cEGffvqpPv30U5UpU0bjxo3T2LFj87zfZgcBHwCAdNif/UsZ2pCea9eumcu5OXY5O23J64tsC9LrkhWBgYF66KGH9M033+irr77StGnT5Ofnp/nz58vDw8OcMz2r7F8Pd3d3dejQIZdanH49d911lypUqJDt7TN631yhoPUrT09PjRw5UiNHjtShQ4e0bNkyffnll/rtt9904cIFjR8/XsePH8/0B7r8VPA+cgAA4ELbtm0zL5KtXr26OVNGZGSk0+3s1zu6qC+natSoYS6fPn3aadno6Ohcr9+ev7+/ypUrl6m25PXrklUpY+xjY2PNOeM//fRTPfjggwoKCsrWPu1fj1OnTjm8KDM31KpVy1zOzvh4+9c/o/7sCllpn/36rH4Tkh116tTRiy++qP379+uXX35R5cqVJUkffPCB/vzzzzyvP6sI+AAA2HnsscfMOdD9/PzM6R8PHz6sK1eupLvdr7/+ai7nxRlc++kjd+3alW658PDwPB1/nyLlGGNiYpwGnLx+XbKqQ4cOql69uiRp/vz5WrFihc6dO5fti2tTdOzYUdKNM8sZzaJUu3ZtFStWLMNrEtKrQ5K2bNnitOzgwYPl4eGhjz/+2HyudevW5tCclBmZ0vPWW2/pscceS9PXsjOEKbPs+4ez9sXFxZk34GrYsGGuT+kpSe+8846+/PJLh+vuvvtuvf/+++bj/fv353r9OcUQnVxi9RvSAEBRNWrUKG3ZskXXr1/XV199Zc6fbe/y5cv69ttvJd2Yh7xRo0a53o5GjRrpjjvu0M6dO/Xjjz8qJibG4YWLX331Va7X7cjIkSPNG0Z99tlnevXVV9OUSU5ONu9UWqNGjXSnwcxPNptNgwYN0oQJE7Rz5069+OKLqlKliu69994c7XfUqFHmsc6fP1+zZ892WG716tU6cuSImjdvnuVvNHr37q0XX3xR586d04IFCzR69GiH478vXLigr7/+Wl5eXuratav5vL+/vwYOHKg5c+Zo+/btOnLkSKpvBVLExMTolVdekWEYaW4sljI9pSQlJCSYy4sXL9akSZP03HPP6d///neWjitF7dq1df/992vlypX68ccfFR0dnWoGpRRLly41P8SOGjUqW3VlZObMmSpTpky6dxy2vwi4IN5klTP4AAA4YX9DoRdffDHVbDbSjRD79NNP659//pGvr68+/PDDPGvLjBkz5ObmposXL2r06NFphoIcOHBAM2bMkK+vb4b7atu2rWw2W7Y/jDRr1sz8puPdd9/Vjh070pSZNGmSDh8+LHd3dy1YsKDA3Bho4MCBZluOHDmiQYMG5fhCyTvvvNOcE/3DDz80P/DZi4iIMOt+++23s1yHj4+P5s+fL5vNpgMHDmj8+PHmHWBTXL16Vb169dLly5c1YcKENHPJv/HGG6pevbqSk5M1aNAgXb16NdX6xMREPfHEE7p69aqeffbZNAHb/i7N9sOENm7cqGPHjqWaBjQ7Zs+erVKlSikmJkZPPfVUmutJTp48qXHjxkmS7r333mxfN5EZu3fv1pIlSxyuS/lmpEKFCmrbtm2etSG7OIMPALCcLVu2mEMYUmYeiYmJ0dSpUzPc1n6mkhSLFy9W3759tXTpUjVu3FiDBg1SvXr1FB0drS+//FK7du1SUFCQvv322zTzbq9evVpRUVGpvsZftGiRJKlbt24qUaKEtm7dqmPHjjks07FjR3NseIsWLbRgwQINGjRICxYs0O+//65HH31Ufn5+2rdvnxYuXKj3339fEyZMcDqcSJLOnz8vSeZY4uyYOnWqEhMTNWvWLLVt21b9+/fXHXfcoWvXrunbb7/VunXr5Ovrq8WLF6cJQSnHvHXrVvO5b7/9VmXLljWPef/+/dq/f7/DMi1btlRQUJB5I6eUaSqjoqLSvL43Cw4O1gMPPKDvvvtObm5u5p1fc2rKlClKSkrStGnT1L17d3Xt2lV333233Nzc9Ntvv+nTTz9VcnKyPv74Y7Vu3drc7vjx42mG3Ozfv1+LFi2Sr69vqrPwDz74oBYtWqTBgwdr6tSpWr9+vR555BGVLl1ax44d02effabTp09r2LBhZhC2V7JkSa1du1adO3fWxo0bdfvtt6t///6qWLGiTp06pS+++EJ//vmnHnnkEU2aNCnN9g0bNlTDhg21b98+Pfvss3rqqad04sQJffzxxwoODlbnzp0VGxtrvi8pZ9qPHz9uvi8NGjRQgwYNHL6G1apV0y+//KIuXbroq6++0qFDh9S7d2+VKVNGhw4d0kcffaTo6Gg98MAD+vLLL1MNGXJWb1BQkDnEKaUdN5exf61Tzsr37NlTnTt3VqtWrRQYGKizZ8/qxx9/1KZNm1SyZEnzm5KCxmbc/NGvEIqJiVFAQIAuXbrksq9JXD1EJyzMpdUrLDTUtQ0ACoG4uDiFh4erevXqmfqH4Oq/K3kttF1o3u07NFQvv/xytrcPCQlRmIM/rCtXrtRHH32krVu36vz58/Lx8VHt2rX10EMPacSIEQ7/B7Vr107r1693WE94eLiqVaumAQMG6NNPP3VYZt26dWnmZt+/f7/efPNNrVu3Tv/884/KlCmjVq1a6dlnn1WLFi1UpUoVnTp1SoMHD3Z4U6e4uDiVKVNGV69e1csvv6z//Oc/Gb8oTmzdulWzZ8/Whg0bFBUVpeLFi6tGjRr617/+pVGjRjm8eDUzx+zsfVywYIHatWtnjqd3JOX1deSHH35Qly5d9MADD+iHH37I+CCzYMeOHZozZ47CwsL0119/yc3NTVWrVtU999yjUaNGpRkW88knn2jgwIEO91W1alVFRESkeT4yMlLTp0/XqlWrFBERoevXrysoKEgtWrTQ0KFD1b59e6dtTEhI0IIFC/T1119r3759unjxogICAtS0aVMNGjRIPXv2THfbyMhIjR8/XmvWrFF0dLQqVKigNm3a6PXXXzfb6+x9mTRpkkIzyA2xsbGaPXu2li9frkOHDunKlSsqW7asmjVrpgEDBuihhx5Ks42zeu1/p9O7jsD+tY6Pj9f333+vH3/8UTt37tSJEycUGxsrX19f1apVS/fff7+efvpp8+LqrMjq/4IUWcm7BPxc4up/xAR8oODL7h91IKtKlSqlixcvaty4cXrrrbfSrF+yZIl69Oghd3d3RURE5PtNjwqCVatW6f7779e3337rMCwCeSU/Aj5j8AEAKCRiY2O1c+fOVBc33uzs2bO6ePGiJDkcBhEbG6vx48dLunGhbFEM95L00UcfqUKFCnrggQdc3RQg1xHwAQAoJH777TfdeeedWrVqVbplUsYg+/j46L777ku1LjY2Vvfee6+OHz+ubt26acqUKXna3oIgISFBR48eVWJiovnc2bNn9d1332nAgAF5ftdfwBUI+AAAFDKhoaFpZj+RbswIkzKe/vnnn1eZMmVSrS9RooSaNm2qCRMmaMmSJUUi3B47dky1atVKdR3EO++8o+TkZA0dOtSFLQPyjvV/swEAsIiUiwN3796tW2+9VY8//rg55eGuXbv0+eef6+rVq3rqqaf00ksvOdzHjBkz8rPJBcbTTz+tkSNHKjw8XO+8845GjhypKlWquLpZQJ4g4AMAUEg0a9ZMmzZt0vLly7V9+3Z9/fXXioqKUlJSksqXL69u3brpySefLJDzcrtKQECA2rZtqwMHDmjkyJEKDg7W888/n+EsLkBhRsAHAKAQadWqlVq1auXqZhQaFSpUSHeaUsCqGIMPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+ACQzwzDcHUTAAAukh//A5hFJ5eEhbm6BQAKOje3G+dUkpOTXdwSAICrpPwPSPmfkBc4gw8A+aRYsWJyd3dXbGysq5sCAHCRuLg4ubm55emdpAn4AJBPbDab/Pz8FBMTwzAdACiirly5Ih8fH87gA4BVBAQEKCEhQWfOnCHkA0AREx0dratXr8rf3z9P62EMPgDkIx8fH1WqVEmRkZG6du2a/P395ePjI3d3d9lsNlc3DwCQywzDUGJioi5duqTLly+rVKlSCggIyNM6CfgAkM/8/PxUtWpVXbp0SRcvXtSFCxdc3SQAQB7z9PRUUFCQSpUqled1EfABwAV8fHzk4+Oj8uXLKyEhgZl1AMDC3N3d5eHhkW/f1BLwAcCFbDabihcv7upmAAAshItsAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAvJccA3DEOzZs2Sr6+vbDabwsLCMtwmIiJCNpstUz8jRozIaRMBAACAIsMjJxsfO3ZMTzzxhDZs2JBb7QEAAACQA9kK+IZh6P3339fzzz8vd3d3NW/eXNu2bcvyft544w1169bNaZlSpUplp4kAAABAkZStgP/yyy/r5ZdfVqdOnTRv3jx99NFH2Qr4FSpUUJ06dbLTBAAAAAAOZPsM/vz58zVo0KDcbg8AAACAHMhWwA8NDZXNZsvttgAAAADIoWzNopMX4T4xMVGxsbG5vl8AAACgKHHpPPgHDx5U7969VaVKFXl5ecnX11clSpTQPffcowULFigxMdGVzQMAAAAKnRxNk5lTU6dOVe3atTVixAjVrVtX8fHx2rBhg+bNm6e1a9fqww8/1LJly1ShQgVXNhMAAAAoNFwa8B9++GEtXrxYnp6e5nOPPPKIBg4cqHbt2mnbtm3q0qWLNm/enKpMemJiYlI99vT0zNR2AAAAgFW4ZIhOpUqVFB4eri+++MJhAG/cuLH+85//SJJ27dqluXPnZmq/lStXVkBAgPkzefLkXG03AAAAUNC5JOB7eHioWrVqKlasWLplBg4caF7M+/HHH2dqv6dOndKlS5fMnxdeeCFX2gsAAAAUFi69yNaZ0qVLq0aNGpKk3377TVeuXMlwG39//1Q/DM8BAABAUVNgA74klStXTtKNG2tFRUW5uDUAAABAwVegA35ycrK57O7u7sKWAAAAAIWDSwL+a6+9phUrVmRY7u+//5Ykubm5mWfzAQAAAKTPJQF/4sSJmjNnjtMyf/31l06cOCFJatKkiXx8fPKjaQAAAECh5rIhOlu2bEkzb709+6kxn3zyyfxoEgAAAFDouSzgX758WU8++aSuX7+eZt3PP/+st956S5LUtm1bDRw4ML+bBwAAABRK2b6T7aJFi8zl/fv3m8urV69WZGSkJCkoKEgdO3ZMs23Dhg21b98+ffXVV9q1a5cee+wx1ahRQ7GxsQoLC9OyZctkGIbuv/9+LV68WB4eLr3hLgAAAFBo2AzDMLK14f/fhMqZkJAQhYWFOVy3fft2ffvtt9q8ebMOHz6s6OhoFStWTOXLl1eLFi3Ut29fderUKVNtiYmJUUBAgC5duiR/f/+sHEauaRca6pJ6C4qwIn78AAAAeSkreTfbp8az+bnA1KxZMzVr1ixH+wAAAACQWoGeBx8AAABA1hDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALCTHAd8wDM2aNUu+vr6y2WwKCwvL0vZbtmxR7969VaVKFXl5ealy5crq2bNnlvcDAAAAIIcB/9ixY2rXrp2efvppxcbGZnn7V155RW3atNGKFSvUrVs3zZw5Uz169NDKlSvVvn17jRs3LifNAwAAAIocj+xsZBiG3n//fT3//PNyd3dX8+bNtW3btiztY+7cuZo0aZK8vLy0du1aNWvWzFzXt29ftWnTRm+//bbKlSunsWPHZqeZAAAAQJGTrTP4L7/8sp5++mm1bt1aBw4cUKdOnbK0/blz5/Tcc89JkkaNGpUq3EtS48aNzVA/ceJERUZGZqeZAAAAQJGTrYBvGIbmz5+vVatWqXLlylnefvr06bpy5YokafDgwQ7LDB48WDabTXFxcXrnnXey00wAAACgyMlWwA8NDdWgQYOyXemSJUskSdWqVVPNmjUdlqlUqZLq1KljljcMI9v1AQAAAEVFtgK+zWbLdoWnT5/W4cOHJUmNGjVyWrZx48aSpMjISB05ciTbdQIAAABFRb7Pg3/gwAFzOaPhPfbrDx48mGdtAgAAAKwi3wN+RESEuRwUFOS0rP16++0AAAAAOJataTJz4vLly+ayl5eX07Le3t4Ot0tPTExMqseenp7y9PTMYgsBAACAwivfz+Bfu3bNXC5evLjTsvbrr169muG+K1eurICAAPNn8uTJ2W8oAAAAUAjl+xl8+7Py169fd1rWfr2Pj0+G+z516pT8/f3Nx5y9BwAAQFGT7wHfz8/PXI6Li3Na1v5sv/126fH3908V8AEAAICiJt+H6FSrVs1cjoqKclrWfr39dgAAAAAcy/eAX79+fXP51KlTTstGRkaay/Xq1cuzNgEAAABWke8Bv2LFiqpdu7Ykae/evU7L7t69W9KNu9rWqlUrr5sGAAAAFHr5HvAlqUePHpJuzG1//Phxh2VOnz6tQ4cOSZIeeeSRHN09FwAAACgqXBLwR44cKV9fX0nS/PnzHZb56KOPZBiGvLy8NGbMmPxsHgAAAFBouSTglytXTm+//bYk6b333tOvv/6aav2+ffs0ZcoUSdKrr76qSpUq5XsbAQAAgMIo29NkLlq0yFzev3+/ubx69Wrz4tigoCB17NjR4fZDhw5VVFSUXnnlFbVv316DBw9WvXr1dOjQIc2bN0+xsbEaO3asxo4dm90mAgAAAEWOzTAMI1sbZmJMfEhIiMLCwpyW2bJli2bMmKFNmzbp3LlzKlu2rFq0aKHhw4erffv2mWpLTEyMAgICdOnSJZfNg98uNNQl9RYUYUX8+AEAAPJSVvJuts/gZ/NzQRotW7ZUy5Ytc2VfAAAAQFHnkjH4AAAAAPIGAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFiIh6sbAGsIDQt1Xd3tXFc3AABAQcMZfAAAAMBCCPgAAACAhRDwAQAAAAsh4AMAAAAWQsAHAAAALISADwAAAFgIAR8AAACwEAI+AAAAYCEEfAAAAMBCCPgAAACAhbg04Ldr1042my3DH19fX1c2EwAAACg0OIMPAAAAWIiHqxtw5513auHChU7LuLnxOQQAAADIDJcHfB8fH9WpU8fVzQAAAAAsweUBH9YQFubCytu5sG4AAIAChrEvAAAAgIUUuIB/+fJlJScnu7oZAAAAQKHk8oB/9epVvfLKK2rQoIFKlCghf39/FStWTLfddpuee+45nTlzxtVNBAAAAAoNlwf8X3/9VZMnT1abNm20YMEC/fDDD5oyZYqSkpI0depU1alTR999952rmwkAAAAUCi6/yLZcuXJat26dbrvtNvO5Bx54QCNGjFCXLl20evVq9ejRQ+vWrVPLli2d7ismJibVY09PT3l6euZJuwEAAICCyKVn8L/88ksdOnQoVbhP4enpqU8++USenp66fv26RowYkeH+KleurICAAPNn8uTJedFsAAAAoMBy6Rn88uXLO10fHBys++67TytWrNCePXu0Z88eNW7cON3yp06dkr+/v/mYs/cAAAAoalw+Bj8jd9xxh7m8ZcsWp2X9/f1T/RDwAQAAUNQU+IBfrlw5c/nvv/92YUsAAACAgq/AB3z7OfHd3d1d2BIAAACg4HNZwP/+++/12muvyTAMp+Xsz9pXqFAhr5sFAAAAFGouC/hLly7VxIkTFRUV5bTctm3bzOXWrVvndbMAAACAQs3lQ3RWrlyZ7rojR45ozZo1kqSWLVuqXr16+dUsAAAAoFByecB/4YUX9Oeff6Z5/p9//lGvXr2UlJQkHx8fzZo1ywWtAwAAAAoXl82Df9ttt6lYsWKKiopSo0aN9Nhjj+nOO+9U8eLF9ccff2jhwoU6d+6cgoKC9NVXXzmd/x4AAADADS4L+OPGjVO/fv20bNkyrV69Whs2bNBXX32lhIQElSpVSg0aNFDnzp01aNAg+fn5uaqZAAAAQKHi8jvZDhs2TMOGDXNlMwAAAADLcPkYfAAAAAC5h4APAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAgBHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAAC/FwdQOAnAoNLdr1AwAA2OMMPgAAAGAhBHwAAADAQhiig0IvTKEuboGr6wcAAPgfzuADAAAAFkLABwAAACyEgA8AAABYCAEfAAAAsBACPgAAAGAhBHwAAADAQgj4AAAAgIUQ8AEAAAALIeADAAAAFkLABwAAACyEgA8AAABYCAEfAAAAsBACPgAAAGAhBHwAAADAQgj4AAAAgIUQ8AEAAAALIeADAAAAFkLABwAAACyEgA8AAABYiIerGwAUdqGhRbt+AABQsHAGHwAAALAQAj4AAABgIQR8AAAAwEII+AAAAICFEPABAAAACyHgAwAAABZCwAcAAAAshIAPAAAAWAg3ugJyKEyhLm6Bq+sHAAAFCWfwAQAAAAvhDD5QyIWGFu36AQBAapzBBwAAACyEgA8AAABYCAEfAAAAsBACPgAAAGAhBHwAAADAQphFB0COuHoWHVfXDwBAQcMZfAAAAMBCCPgAAACAhRDwAQAAAAthDD6AQs2VY/AZ/w8AKIgKTMA/f/68pk+fruXLlysiIkLFixfXrbfeql69emno0KEqXry4q5sIFEhhCnVp/e1cXD8AAEitQAzR2bFjhxo0aKDXXntNwcHBeuutt/Tiiy/qypUrGjVqlJo3b64zZ864upkAAABAgefyM/gnT55U586dde7cOY0ePVrvvfeeue7pp5/W/fffr3Xr1unBBx/U5s2b5enp6cLWAsD/uHqIjqvrBwAUTC4P+M8995zOnTunKlWq6M0330y1ztPTU/PmzdOtt96qXbt2adasWRozZoyLWgoABYurA76r6wcAOObSgH/kyBF98803kqR+/fo5PDt/yy23qH379lqzZo3eeustjRo1Sh4eLv9cAuD/cQ0AAAAFi0uT8pIlS2QYhiSpQ4cO6Zbr2LGj1qxZo3PnziksLMxpWQBA/nD1GXxX1w8ABZVLA/7atWvN5UaNGqVbrnHjxqm2KWgBPz4+XhFhYarSurXc+HYB+Sg5MVEnN22i7yHfJSbGa9OmyWrd+gV5eLjm2qiiHPCL8rHHx8dr8uTJeuGFF7guD/mqMPU9lyaCAwcOSJL8/PwUEBCQbrnKlSubywcPHszzdmVVfHy8Tqxfr0otWhCykK+Sk5Loe3CJpKR4rV//slq0eNZlAb8oK+oB/803X9azzz5b4EMWrCU+Pl4vv1w4+p7LEkF8fLz+/vtvSVJQUJDTsvbrIyIi8rJZAAoZV14DwPh/wHUmT5YKeMayrKL8AbOwcFnAv3z5srns5eXltKy3t7fD7QDAlVx9gbErJSre1U0A4CJFNeDHF6I/ey4L+NeuXTOXM7pLrf36q1evplmfcqHu6dOnFRMTYz7v6emZL1+hpNSZWJjeeVhCSp+j7yG/pfS5dfGh8hCnUZF/6HtFWxu94LK64+Nv5D37rJmfUupNyb3O2IzMlMoD58+fV2BgoCSpfv36+u2339ItGxcXZ57Fr169uo4fP55qfWRkZKpx+gAAAIAVnTp1SpUqVXJaxmVn8P38/MzluLg4p2Xtz/bbb5ciODhYx44dU7FixWSz2czn8+sMPgAAAJCXDMPQ5cuXFRwcnGFZlwV8T09PlS9fXn///beioqKclrVfX61atTTr3dzcVKNGjdxuIgAAAFBgOJt10p5bHrfDqfr160u6ceHspUuX0i0XGRlpLterVy/P2wUAAAAUVi4N+Hfffbe5vHfv3nTL7d692+E2uen8+fOaOHGi6tevL19fX5UuXVotWrTQjBkzdP369Vyr56efftKDDz6oihUrysvLS9WrV9fAgQNTHSOKlvzqe5J07NgxhYSEyGazqV27drm6bxQ+edn3DMPQxo0bNWrUKN15550qVaqUihUrpjJlyqhFixaaNGmSzpw5k0tHgsImr//u7dy5U6+++qo6deqkypUry8vLS97e3qpSpYq6deumb775JlMXKsJ68vN/boq//vpLJUuWlM1mSzWUPE8ZLvTnn38aNpvNkGRMnDgx3XIdO3Y0JBlly5Y1EhIScr0d27dvNypUqGBIMjp27GjMmjXLePvtt4369esbkozGjRsbp0+fzlEdSUlJxpAhQwxJRunSpY3nn3/e+OCDD4whQ4YYxYsXNzw8PIzp06fn0hGhsMiPvmcYhpGcnGzMnDnTKFGihCHJkGSEhITk/ABQaOVl39u1a5fRoEEDs6/dd999xquvvmrMnz/fmDhxolG1alVDkuHr62ssWrQol48MBV1e/93r3Lmz2fcqV65sPP/888bcuXONyZMnG//617/MdXfffbcRExOTi0eGgi6//uferFu3bma/y6/o7dKAbxiG0bNnT0OSUa1aNSM+Pj7N+mPHjhnu7u6GJOPtt9/O9fpPnDhhBAYGGpKM0aNHp1oXFxdntG/f3pBkNG3a1IiLi8t2PePHjzc/pBw5ciTVupUrVxpubm6GzWYzvv7662zXgcIlv/re0aNHjbZt2xqSjE6dOhHwked9b86cOYYkw2azGcuWLUuzPjY21qzDzc3N+Omnn7J9LChc8uPvXrNmzQxJxr333mtcu3YtzfqPPvrI/Dv4+OOPZ6sOFD759T/3ZkuWLEkV7otMwLd/wceMGZNqXVxcnHHPPfeYL7ijX9ScSvmAUaVKFYdv6NGjR80PGFOnTs1WHQcOHDDc3NwMScacOXMclunfv78hyQgKCjKuXLmSrXpQuORH37ty5YpRokQJw9/f35g3b55hGAYBH3ne91IC/pAhQ9Itc/LkSaNYsWKGJOPWW2/Nch0onPLj715KwD958mS6ZZo3b25IMjw8PIwLFy5kqx4ULvnR924WHR1tVKhQwahUqZLRpEmTohXwDcMwtm3bZn5l0qlTJ+P99983pk6datx+++2GJKNRo0Z58pWJ/RChCRMmpFuuQ4cOhiQjMDAwW0OE+vTpY0gyvL290/06cNOmTeYbP23atCzXgcIlv/pedHS00alTJ+PUqVPmcwT8oi0/+l5KwP/hhx+clmvZsqXZH//8888s1YHCJ7/+7g0aNMh4+OGHnZYZPny42fe2b9+e5TpQuORX37vZ4MGDDUnGihUrjJCQkHwN+C69yDZFs2bNtH//fr300ks6deqUxo0bp9dee03e3t6aNm2atm/fnqk5P7NqyZIl5kU2HTp0SLdcx44dJUnnzp1TWFhYluqIj4/X999/L+nGcTqax1+SWrRoIV9fX0nSN998k6U6UPjkR9+TbkyntWrVqgxviIGiIz/63gMPPKCVK1eqffv2TstVrVrVXD558mSW6kDhk19/9+bPn68lS5Y4LePj42Muc78c68uvvmdv/fr1+uijj/TII4/owQcfzNG+sqNABHxJKlu2rF577TUdPHhQV65cUXR0tLZv365Ro0apePHieVLn2rVrzeVGjRqlW65x48YOt8mMnTt3mrcWdlaHm5ubGjRoIEnaunVrqpt7wXryo+9Jyr+r9VFo5Effq1y5su67775UIcqRixcvmsslSpTIUh0ofPLr715m/Prrr5KkcuXKMf12EZDffS8uLk5DhgxRQECAZs6cme395ESBCfiucODAAUk37o7r7MYBlStXNpcPHjyYrTpu3o+zepKTk3Xo0KEs1YPCJT/6HuBIQep74eHhZluc/dOFNRSUvvfdd9+ZZ2enTp0qDw+X3fMT+SS/+97LL7+sI0eOaMqUKSpfvny295MTRTbgx8fH6++//5YkBQUFOS1rvz4iIiJL9diXz8t6UHjkV98DblaQ+t7hw4fNExkDBw6Ul5dXrteBgsOVfS86OlqnTp3SunXrNGzYMHXv3l2BgYH67rvv1Ldv3xzvHwVbfve9ffv2aerUqWrbtq0GDx6crX3khiL7sfXy5cvmckb/WLy9vR1uV5DqQeFBn4CrFKS+98EHH0iSSpcurQkTJuT6/lGwuLLvNW7cWCdOnJB0Y9hinz599Oabb6pixYo53jcKvvzse0lJSRo0aJDc3d314YcfunSYbJE9g28/xj2jMf72669evVog60HhQZ+AqxSUvvfHH39o9uzZkqS5c+cqMDAwV/ePgseVfW/x4sVatWqVPvvsMw0ePFjLli1T9erVNWTIEPMaOVhXfva99957T7t27dKECRN06623Znn73FRkz+Dbf0rL6NbE9uszumjMVfWg8KBPwFUKQt+7evWqevXqpfj4eI0bN049evTItX2j4HJl32vVqpW5/Pjjj2vs2LFq37695s+fr19//VWbNm0yZ7GD9eRX3wsPD9ekSZNUr149jR8/PmuNzANF9gy+/XSVcXFxTsvaf/pLb5pLV9eDwoM+AVdxdd9LTExUr169tG/fPvXt21dvvvlmruwXBZ+r+5692rVra86cOZJujJeeOHFirteBgiO/+t6TTz6puLg4zZs3T8WKFctaI/NAkQ34np6e5pXNUVFRTsvar69WrVqW6rEvn5f1oPDIr74H3MyVfS85OVkDBgzQd999p969e2vBggVM41qEFLS/e507dzZnU/nkk0+UnJycJ/XA9fKj7y1cuFBr1qxRnz59VKtWLZ0/fz7NT0JCglne/vlLly5l7YAyqcgGfEmqX7++pBsXUjh7gSMjI83lrM6Xm1KHJJ06dcpp2ZR63NzcVKdOnSzVg8IlP/oe4Igr+l5ycrIGDhyoxYsX67HHHtPChQvl7u6eo32i8ClIf/fc3NxUq1YtSTfux2BfJ6wnr/teypz5n332mQIDAx3+bNmyxSxv//xDDz2U1cPJlCId8O+++25zee/evemW2717t8NtMuOOO+4wv+ZxVkdycrL27dsn6cZdbe3HjMF68qPvAY7kd98zDENDhgzRwoUL1aNHDy1atIhwX0TlR987ceKElixZonPnzmVY1r4fJiYmZqkeFC553ffGjRun1atXO/1JuZmppFTPv/POO1k7mMwyirA///zTsNlshiRj4sSJ6Zbr2LGjIckoW7askZCQkOV6+vTpY0gyvL29jcuXLzsss3nzZkOSIcmYNm1alutA4ZJffc+RlH4WEhKSK/tD4ZKffS85OdkYMmSIIcl4+OGHHe7nzJkzRtOmTY0PPvggW3Wg8MiPvrdgwQJDkvHNN984LZecnGyULVvWkGQUK1bMuHr1apbqQeHiyv+5KUJCQsz/v/mhSJ/Br1WrljmDw2effebw6urjx4+bX72MHz8+zR3vDhw4oNq1a6tSpUrasGGDw3qef/55ubm56dq1a/r8888dlpk/f76kGzdZcOWNEZA/8qvvATfLz743fPhwzZs3T127dtUXX3zh8I6h8fHx2rVrl86cOZOTw0IhkJ9976effnLalh9//FHnz5+XJHXo0IFvzS2uSP7PzZePEQXYiRMnjMDAQEOSMWbMmFTr4uLijHvuuceQZDRt2tS4du1amu0fe+wx8xNZixYt0q1n/PjxhiQjMDDQOHbsWKp1//3vfw03NzdDkvH111/nzoGhwMuvvnczcQa/yMuPvjdixAhDklGzZk3j559/NtatW+fw54svvjAkGZMmTcqLQ0UBk9d9L+UMvru7u/Hpp586bMOBAweM8uXLm9+s7927N3cODgWaq/7npsjvM/hFdh78FFWqVNH333+vbt266Z133tGBAwf04IMP6tq1a/r000/122+/qVGjRvruu+8c3gHN/sp7wzDSreeNN97QhQsXNH/+fN11113697//rWrVqmnXrl1asGCB3Nzc9O677zIndBGSX31v//792r9/f5rno6KitGjRIvNxx44dM7yNN6whr/vetGnTNGvWLEnS0aNHde+99+bdwaBQyeu+V6VKFZUsWVIXL15U//79NXPmTHXs2FHVqlVTQkKCtm7dqm+++UbXr19XcHCwFi1apIYNG+bpMaNgyK//ufbs/8faz9CTL/978+VjRCFw7tw546WXXjJuu+02o0SJEkbJkiWNu+66y5g2bZoRHx+f7nb79u0zatasaVSsWNFYv359hvX8+OOPRufOnY3y5csbnp6eRtWqVY1+/foZu3btys3DQSGS131v0qRJ5lkDZz/r1q3Lg6NDQZZXfW/UqFGZ6nP2P5zBL1ry8u9ebGys8eWXXxoDBgwwGjVqZJQqVcrw8PAwvL29jcqVKxudO3c25syZY8TExOTV4aEAy6+8ZxiGS//32v6/AQAAAAAsoEhfZAsAAABYDQEfAAAAsBACPgAAAGAhBHwAAADAQgj4AAAAgIUQ8AEAAAALIeADAAAAFkLABwAAACyEgA8AAABYCAEfAAAAsBACPgAAAGAhBHwAAADAQgj4AADkkYiICNlsNoc/AJBXCPgAHHIWTG7+8fHxUaVKlXT77berW7dueuedd7Rjx45s1Xv8+HE1adJEZcqU0aeffprLRwXkL29vb3Xq1EmdOnVS27ZtM73dhQsX1L59e5UsWVJvvPFGHrYQgBXZDMMwXN0IAAVPVFSU+vfvbz5ev3694uLiJEnBwcG6/fbbzXWXLl3S+fPnderUKcXHx5vP16tXT2PGjFGfPn1UvHjxTNXbt29fLVq0SJLk6emp6OhoeXt758YhAS4VERGh6tWrm4+d/fudOHGiXnvtNfPx0aNHdcstt+Rp+wBYB2fwATgUFBSkVatWmT9BQUHmuo4dO6Zat3XrVh05ckTR0dH6+eef1b9/f7m7u+vgwYN64okn1Lx5cx09ejRT9SYlJZnLycnJSk5OzvVju1loaKj5bcSAAQPyvD7kTFhYmPl+VatWzdXNyRP2vweOHgOAMwR8ALnG29tbHTt21CeffKIDBw6oQYMGkqQ9e/aoadOm+uWXXzLcR2hoqBo0aKBSpUpp5syZKlGiRF43GyhwRo8erTZt2sjPz08TJ05U7dq1Xd0kAIWIh6sbAMCa6tSpo02bNqlLly5av369YmJi9PDDD2vz5s2qV69eutvVrl1b+/bty8eWAgVPuXLltGHDBlc3A0AhxRl8AHnGz89PX331lcqVKyfpxlj9hx56SNevX3dxywAAsC4CPoA8FRQUpClTppiPjx07pnnz5rmwRQAAWBtDdADkuV69emn8+PGKioqSJL3++usaOnSo3N3dc2X/ly9f1rZt23T48GHFxMTIy8tLFStWVOPGjfNl7PL169e1Z88eHTx4UOfOnZNhGCpdurTq16+vpk2bytPTM1fq2bVrl/bu3auzZ8+qTJkyatGiRarZjLIiNjZWGzZsUEREhP755x/5+voqODhYDRs2zNZrtmfPHrNtxYsXV7ly5dSiRQvVqFEjW+3LD1euXNH69et14sQJXbp0SaVKldItt9yiNm3ayMvLy9XNyxUxMTFas2aNjh8/Ljc3N91yyy1q3769/P39s7W/wvg+A0WSAQCZULVqVUOSIcno379/lrcfNmyYub0kY9u2benu3/5n3bp16e4zOjraGD58uOHj4+NwW0lG5cqVjREjRhi7d+9Ote26devS3cbRz6RJk1Jtn5SUZHz77bdG165djRIlSqS7XalSpYwJEyYYcXFx6R7Hnj17HG5btWpVwzAM4+jRo0arVq0clgkJCTEiIyMz/T5ERkYaTzzxhOHp6ZlumytVqmQMGzbM2Lt3r9N9JSUlGQsWLEj3vZNk3H777cby5csz3T5nsvJ+hYSEpLufEydOGH379jWKFy/ucFsfHx9jxIgRxtmzZ3Ol3SnCw8NT1eNISEiIwzYtWLAgTdmHHnrIadm33nrL8Pf3T7Pe39/feP/99zPd7vx+nwHkHGfwAeSL5s2ba/bs2ebj1atXq1mzZubjkJAQ8wy//Zz76fnnn3/Utm1bHTx4UB4eHurZs6e6dOmi4OBgxcTE6Pfff9fMmTN16tQpzZo1S7NmzUo173jp0qXVqVMnSTfmGD927JiktHP8p6hZs2aqx2fPnlXXrl3Nxx07dtRjjz2mqlWrymaz6ciRI/r666+1du1avfbaa1q1apXWrl0rPz+/NPsuVaqUHn30UXO/69atM9f9+eefatOmjS5duqSQkBCVLl1a4eHh2rt3r/la3X333dq3b1+GZ53Xr1+vbt26KTo6WjabTb169VLXrl1VoUIFXbx4UVu3btW8efMUGRmp2bNna/bs2Vq5cqXuu+++NPuKi4tTnz59tGzZMklS/fr19dRTT6lu3bq6fv26duzYoffff1+//fabunXrppEjR+q9996Tm1v2R4amvF///POPfv31V0mSl5eXQkJC0pRNmcHpZmvXrlX37t116dIlubm5qW/fvnrwwQcVGBioM2fO6JtvvtHy5cs1a9YsLV26VD/99JMaNWqU7TZn1V133WW+jzt27FB0dHS6Ze2/aVi7dq3OnTtnrhs9erSmT5+uGjVqqF27drp+/bq2bdumixcvKiYmRsOHD5eXl5eeeOIJp+1xxfsMIBe4+hMGgMIhp2fwf/vtt1Rn/Lp3756putI7g//kk0+aZebPn++wzJUrV4y7777b6RlTwzCMSZMmZfnY/vrrL3ObmTNnpltu9uzZZrnHHnssw/3af7NQpUoVo02bNkanTp3SnE1es2ZNqm8upk2b5nS/u3fvNry8vAxJhoeHh/Hdd985LPf3338bt912m7nf9M7KPvzww2aZHj16GAkJCWnKXLhwwWjUqJFZ7o033sjw+DPD/jVK+ZYjM3bu3Gl+c+Hh4WH88MMPDst99NFH5v5LlixphIeH50q7M3MG35792XxHZ/DTK9unTx/D29vb+Oqrr1KVuXLlitG9e3ezXGBgoHH9+nWn+3Xl+wwg+wj4ADIlpwHfPhBLMtq2bZupuhwF/MTERMPPz8+QZHh7exuJiYnp7uvPP//M04DftGnTDMt26tTJkGTYbDbjwIEDTsvePHTolltuMa5eveqw7JgxY8xyLVu2THef169fN2rVqmWWffHFF522YefOnU4D/scff2yuDw4ONi5fvpzuvg4ePGjYbDZDklGsWDHj+PHjTuvOjOwE/OvXrxs1a9Y0t3vppZeclu/Xr59ZtlWrVjlus2HkX8CXZHz44YcOy50/fz7V8Kyff/453X26+n0GkH18hwYgX5QqVSrVY/vhBFl17tw5Xb58WZLk5uYmm82WbtlatWqpXr16qe7EmxtKly6tlStXatGiRRmWTRniYhiGfvjhhyzVM3bsWHl7eztc98ADD5jLe/fuTfeuv59//rmOHDkiSfLw8NCYMWOc1tm0aVM1bdrU4brk5GS9/vrr5uOnnnpKvr6+6e7rtttuU9u2bSVJCQkJmjNnjtO688rixYvNuyl7eHjo2WefdVp+3Lhx5vLmzZszdZO2giI4OFiDBg1yuK5MmTKphsbt3r3bYbnC+j4DuIGADyBf3Bw+nYXyjNgH3tjYWM2dO9dp+QMHDujvv//Odn2OFC9eXPfdd5/q1KmTYdmSJUuay1m9ide//vWvdNfZz3Zz9epVXbp0yWG5Tz75xFxu2bKlSpcunWG9ffv2VUhIiMqWLZvq+Y0bN5rXK0hS586dM9xXy5YtzeXvv/8+w/J54dNPPzWXM/Ma1KtXT9WrVzcf27+GBV2nTp2cjoG37zdnzpxxWKawvs8AbuAiWwD54uaLBQMDA7O9r4CAADVv3lzbtm2TJI0YMUJbt27V6NGj0z3znJf+/PNP/fzzz/rtt9904cIFxcbGprqg9/Tp0+byhQsXMr1fHx8fValSJd31N4fUK1eupPmmJOXiyhTpXXx6s1GjRmnUqFFpnl+/fr257OHhofr162e4r+DgYHP58OHDunTpkgICAjLVjtyQ3degQYMGCg8Pl6RCdVfZunXrOl1v32+uXLnisExhfJ8B/A8BH0C+uHlITk4CviTNmjVL7du31+XLl2UYhhYtWqRFixapdu3a6tq1q7p06aKWLVvm6WweR48e1YgRI/Tf//4309vEx8dnuqz9mX9Hbp41JykpKU2ZEydOpJqRqGrVqpmu35HDhw+by25ubpk6s2v/AccwDEVGRuZr8Dt58mSq18DZhyZ79uVOnjyp+Pj4XLunQV7KSr9x1Gekwvk+A/gfAj6AfHHzWN8mTZrkaH9NmzbV9u3bNXz48DTTSk6ZMkVTpkxRhQoV1K9fP40ePVrly5fPUX03O3DggNq1a2eekW/atKlefPFFtWrVSoGBgak+WHzyyScaOHBglusoVqyY0/WZGeb0zz//pHpcokSJLLcjvf1dv349Sx9uUly8eDFHbciqm781cTaW3Fm5CxcupDpLXVDldr8pLO8zgP9hDD6AfLF169ZUjzt27JjjfdatW1dr167Vrl279Mwzz6hy5cqp1v/111966623dOutt+rLL7/McX0pDMNQv379zOB41113afPmzerevbuCgoIK9BzgObn24WZBQUEybszGlqWfVq1a5VobkPd4n4HCp+D+FwJgGdeuXdPSpUvNx5UqVcrxGXx7TZo00bvvvqsTJ05o69atevrpp1MNU4iJiVGfPn20du3aXKlv+/bt2rNnj/l40qRJBXboRpkyZVI9Tm/MdXb2l9N95ZfsvgY3l7t5P1ZWGN9nAP9DwAeQ5z777LNUX/lPmDAhT85y22w2NW/eXDNmzFBkZKQmTJhgrktOTtZrr72WK/Xs2rUr1eMWLVrkyn7zQpUqVVLNOnTy5Mk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-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "_, edges, _ = ax.hist(\n",
-- 
GitLab


From 549a141d56de0cace5026c125488a4584fd7a0ff Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:58:05 +0200
Subject: [PATCH 49/65] Define ana tools and config

---
 LHCb_Pipeline/analyses/anaconfig.py |  9 +++++++++
 LHCb_Pipeline/analyses/anatools.py  | 23 +++++++++++++++++++++++
 2 files changed, 32 insertions(+)
 create mode 100644 LHCb_Pipeline/analyses/anaconfig.py
 create mode 100644 LHCb_Pipeline/analyses/anatools.py

diff --git a/LHCb_Pipeline/analyses/anaconfig.py b/LHCb_Pipeline/analyses/anaconfig.py
new file mode 100644
index 00000000..53a3e21c
--- /dev/null
+++ b/LHCb_Pipeline/analyses/anaconfig.py
@@ -0,0 +1,9 @@
+"""Common configurations for the folder analyses.
+"""
+import sys
+import os.path as op
+
+sys.path.append(op.abspath(op.join(op.dirname(__file__), "../../montetracko")))
+sys.path.append(op.abspath(op.join(op.dirname(__file__), "..")))
+
+PLOTDIR = op.join("output", "analyses")
diff --git a/LHCb_Pipeline/analyses/anatools.py b/LHCb_Pipeline/analyses/anatools.py
new file mode 100644
index 00000000..713c5de6
--- /dev/null
+++ b/LHCb_Pipeline/analyses/anatools.py
@@ -0,0 +1,23 @@
+"""Tools for the `analysis` folder.
+"""
+import typing
+import os
+from matplotlib.figure import Figure
+
+def save_fig(fig: Figure, path: str, exts: typing.List[str] = [".pdf", ".png"]):
+    """Save a figure.
+
+    Args:
+        fig: Matplotlib figure to save
+        path: path where to save the figure
+
+    """
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    fig.tight_layout()
+
+    path_without_ext, _ = os.path.splitext(path)
+
+    for ext in exts:
+        overall_path = path_without_ext + ext
+        fig.savefig(path_without_ext + ext)
+        print("Figure was saved in", overall_path)
-- 
GitLab


From 61abb121cd4329beb62d895cad3461547f4a3a14 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 22:58:34 +0200
Subject: [PATCH 50/65] Save distance to line plots in repo

---
 .../distance_to_line_velo_vs_long_electrons.pdf | Bin 0 -> 11685 bytes
 .../distance_to_line_velo_vs_long_electrons.png | Bin 0 -> 24931 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.pdf
 create mode 100644 LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.png

diff --git a/LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.pdf b/LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f2a113a6689635b981f5fb33dd04c3461608dd2a
GIT binary patch
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diff --git a/LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.png b/LHCb_Pipeline/analyses/output/analyses/distance_to_line_velo_vs_long_electrons.png
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zn}}s7udAbCAA$&(j|6Khe55?^QQ;qyh`??_`RCN)pAJUkbQ>~`4tJ-4L06DJ%VPt5
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yKjpK<jcW;Y_@8P*{(mf^{om5X{hz!wbZJ%KTvT6JZL~RA0_stfBPoZ^UHfmDdUxjl

literal 0
HcmV?d00001

-- 
GitLab


From 56b9e885c1babe3e0741fdd52957f7bd980617aa Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sat, 27 May 2023 23:49:02 +0200
Subject: [PATCH 51/65] Use Black formatter available in vscode

---
 .vscode/settings.json | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 2364b7bb..a56d0d3c 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -4,5 +4,8 @@
         "./montetracko"
     ],
     "python.linting.flake8Enabled": true,
-    "python.linting.enabled": true
+    "python.linting.enabled": true,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
 }
\ No newline at end of file
-- 
GitLab


From 165dd85e7b55743ab887419521b3335ccb384073 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:39:04 +0200
Subject: [PATCH 52/65] Refactor preprocessing and allow for more than one
 dataframe

- Remove ugly event and prod merging
- Add subdirs property
- Loop over dataframes
- More verbosity on input files
- Use groupby to iterate over events (faster if we loop over the all dataframe)
---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 370 ++++++++++++-------
 1 file changed, 227 insertions(+), 143 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 3b472ef9..23c7721a 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -2,7 +2,9 @@ from __future__ import annotations
 import typing
 import os
 import logging
+from functools import partial
 from tqdm.auto import tqdm
+import numpy as np
 import pandas as pd
 
 from . import selecting
@@ -31,40 +33,10 @@ def cast_boolean_columns(particles: pd.DataFrame):
     particles[boolean_columns] = particles[boolean_columns].astype(dtype="bool")
 
 
-def load_dataframes_in_different_directories(
-    indirs: str | typing.List[str],
-    filename: str,
-    new_event_id_per_file: bool = False,
-    **kwargs,
-) -> pd.DataFrame:
-    """Load concatenated dataframes stored in different folders.
-
-    Args:
-        indirs: input directory or list of input directories
-        filename: name of the file in all the directories
-
-    Returns:
-        Concatenated dataframes located in the different input directories under
-        the name ``filename``.
-    """
-    indirs = [indirs] if isinstance(indirs, str) else indirs
-    dataframes = []
-    for prod_idx, indir_ in enumerate(indirs):
-        dataframe = pd.read_parquet(path=os.path.join(indir_, filename), **kwargs)
-        if new_event_id_per_file:
-            dataframe["event"] = dataframe["event"] + prod_idx * 10 ** (
-                9 - len(str(len(indirs) - 1))
-            )
-
-        dataframes.append(dataframe)
-    return pd.concat(dataframes)
-
-
 def load_dataframes(
-    indir: str | typing.List[str],
+    indir: str,
     particles_columns: typing.List[str] | None = None,
     hits_particles_columns: typing.List[str] | None = None,
-    new_event_id_per_file: bool = False,
     **kwargs,
 ) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
     """Load the dataframes of hits_particles and particles that are stored in a folder.
@@ -78,43 +50,43 @@ def load_dataframes(
         **kwargs: other keyword arguments passed to the function that load the files
 
     Returns:
-        A 2-tuple containing the dataframe of hits and the dataframes of particles
+        A 2-tuple containing the dataframe of hits-particles and the dataframes
+        of particles
 
     Notes:
         The function also defines the column ``particle_id = mcid + 1``
         in both dataframes.
     """
-    particles = load_dataframes_in_different_directories(
-        indirs=indir,
-        filename="mc_particles.parquet.lz4",
-        columns=None
-        if particles_columns is None
-        else ["event", "mcid"] + particles_columns,
-        new_event_id_per_file=new_event_id_per_file,
+    particles = pd.read_parquet(
+        path=os.path.join(indir, "mc_particles.parquet.lz4"),
+        columns=(
+            None if particles_columns is None else ["event", "mcid"] + particles_columns
+        ),
         **kwargs,
     )
-    cast_boolean_columns(particles)
-    hits_particles = load_dataframes_in_different_directories(
-        indirs=indir,
-        filename="hits_velo.parquet.lz4",
-        columns=None
-        if hits_particles_columns is None
-        else ["event", "mcid", "lhcbid"] + hits_particles_columns,
-        new_event_id_per_file=new_event_id_per_file,
+    hits_particles = pd.read_parquet(
+        path=os.path.join(indir, "hits_velo.parquet.lz4"),
+        columns=(
+            None
+            if hits_particles_columns is None
+            else ["event", "mcid", "lhcbid"] + hits_particles_columns
+        ),
         **kwargs,
     )
 
+    cast_boolean_columns(particles)
+
     # Define `particle_id = mcid + 1` directly in the original dataframes
     particles["particle_id"] = particles["mcid"] + 1
     hits_particles["particle_id"] = hits_particles["mcid"] + 1
-    
+
     particles.drop("mcid", axis=1, inplace=True)
     hits_particles.drop("mcid", axis=1, inplace=True)
 
     # Rename `lhcbid` to `hit_id`
     hits_particles.rename(columns={"lhcbid": "hit_id"}, inplace=True)
 
-    return particles, hits_particles
+    return hits_particles, particles
 
 
 def enough_true_hits(
@@ -169,13 +141,140 @@ def enough_true_hits(
         return True
 
 
+def load_and_filter_dataframes(
+    indir: str,
+    particles_columns: typing.List[str] | None = None,
+    hits_particles_columns: typing.List[str] | None = None,
+    selection: str | None = None,
+    **kwargs,
+) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
+    """Load and filter the dataframes of hits-particles and particles.
+
+    Args:
+        indir: directory where the dataframes are saved
+        particles_columns: columns to load for the dataframe of particles
+        hits_particles_columns: columns to load for the dataframe of hits
+            and the hits-particles association information
+        selection: function to use to filter the candidates.
+            The latter is defined in the :py:mod:`.selecting` module.
+        **kwargs: other keyword arguments passed to the function that load the files
+
+    Returns:
+        Filtered dataframes of hits-particles and of particles
+    """
+
+    # Load dataframes
+    logging.info(f"Load dataframes in {indir}")
+    hits_particles, particles = load_dataframes(
+        indir=indir,
+        particles_columns=particles_columns,
+        hits_particles_columns=hits_particles_columns,
+        **kwargs,
+    )
+
+    # Add truth particle information to the dataframe of hits
+    if selection:
+        logging.info(f"Apply selection `{selection}`")
+        selection_function: selecting.SelectionFunction = getattr(selecting, selection)
+        hits_particles, particles = selection_function(
+            hits_particles=hits_particles,
+            particles=particles,
+        )
+
+    return hits_particles, particles
+
+
+def get_indirs(
+    input_dir: str | None = None,
+    subdirs: int | str | typing.List[str] | typing.Dict[str, int] | None = None,
+):
+    """Get the input directories that can be used as input of the preprocessing.
+
+    Args:
+        input_dir: A single input directory if ``subdirs`` is ``None``,
+            or the main directory where sub-directories are
+        subdirs:
+        
+        * If ``subdirs`` is None, there is a single input directory, ``input_dir``
+        * If ``subdirs`` is a string or a list of strings, they specify \
+        the sub-directories with respect to ``input_dir``. If ``input_dir`` \
+        is ``None``, then they are the (list of) input directories directly, which \
+        can be useful if the input directories are not at the same location \
+        (even though it is discouraged)
+        * If ``subdirs`` is an integer, it corresponds to the the name of the last \
+        sub-directory to consider (i.e., from 0 to ``subdirs``). If ``subdirs`` \
+        is ``-1``, all the sub-directories are considered as input.
+        * If ``subdirs`` is a dictionary, the keys ``start`` and ``stop`` specify \
+        the first and last sub-directories to consider as input.
+    
+    Returns:
+        List of input directories that can be considered.
+    """
+    if input_dir is None:
+        if isinstance(subdirs, str):
+            return [subdirs]
+        elif isinstance(subdirs, list):
+            return [str(subdir) for subdir in subdirs]
+        else:
+            raise TypeError(
+                "`input_dir` is `None` but `subdirs` is neither a string nor "
+                "a list of strings, so the input directories of the preprocessing "
+                "cannot be determined."
+            )
+    else:
+        # Get the list of all the sub-directories inside ``input_dir``
+
+        # Filter this list according to ``subdirs``
+        if subdirs is None:
+            return [input_dir]
+        elif isinstance(subdirs, (int, dict)):
+            available_subdirs = sorted(
+                [
+                    int(file_or_dir.name)
+                    for file_or_dir in os.scandir(input_dir)
+                    if file_or_dir.is_dir()
+                ]
+            )
+            if subdirs == -1:
+                final_subdirs = available_subdirs
+            else:
+                if isinstance(subdirs, int):
+                    start = 0
+                    stop = subdirs
+                else:  # dict
+                    start = subdirs.get("start", 0)
+                    stop = subdirs["stop"]
+
+                assert (
+                    stop >= start
+                ), f"`start` ({start}) is strictly higher than `stop ({stop})"
+                final_subdirs = [
+                    subdir
+                    for subdir in available_subdirs
+                    if subdir >= start and subdir <= stop
+                ]
+        elif isinstance(subdirs, str):
+            final_subdirs = [subdirs]
+        elif isinstance(subdirs, list):
+            final_subdirs = subdirs
+        else:
+            raise ValueError(
+                f"`input_dir` is not `None` and `subdirs` is `{subdirs}`, which are "
+                "not valid inputs."
+            )
+
+        return [os.path.join(input_dir, str(subdir)) for subdir in final_subdirs]
+
+
 def preprocess(
-    input_dir: str | typing.List[str],
+    input_dir: str,
     output_dir: str,
+    subdirs: int | str | typing.List[str] | None = None,
     n_events: int = -1,
     selection: str | None = None,
     num_true_hits_threshold: int | None = None,
-    new_event_id_per_file: bool = False,
+    hits_particles_columns: typing.List[str] | None = None,
+    particles_columns: typing.List[str] | None = None,
 ):
     """Preprocess the first `n_events` events in the input files,
     into the form of the TrackML dataset.
@@ -185,107 +284,92 @@ def preprocess(
     os.makedirs(output_dir, exist_ok=True)
     logging.info(f"Preprocessing: output will be written in {output_dir}")
 
-    #: Columns to load from the `hits_particles` dataframe
-    hits_particles_columns = [
-        # Features
-        "x",
-        "y",
-        "z",
-        # Plane-wise edges
-        "plane",
-    ]
-
-    #: Columns to load from the `particles` dataframe
-    # particles_columns = [
-    #     # Module-wise true edges
-    #     "vx",
-    #     "vy",
-    #     "vz",
-    #     # For evaluation and selection
-    #     "has_velo",
-    #     "has_scifi",
-    #     "charge",
-    #     "pid",
-    #     "nhits_velo",
-    #     "pt",
-    #     "p",
-    # ]
-    particles_columns = None
+    logging.info("Get input directories")
+    indirs = get_indirs(input_dir=input_dir, subdirs=subdirs)
+    if len(indirs) == 0:
+        raise ValueError("No input directories.")
+    logging.info("Input directories:")
+    for indir in indirs:
+        logging.info(f"- {indir}")
 
-    # Load dataframes
-    logging.info("Load dataframe")
-    particles, hits_particles = load_dataframes(
-        indir=input_dir,
-        # particles_columns=particles_columns,
+    load_and_filter_dataframes_partial = partial(
+        load_and_filter_dataframes,
         hits_particles_columns=hits_particles_columns,
-        new_event_id_per_file=new_event_id_per_file,
+        particles_columns=particles_columns,
+        selection=selection,
     )
 
-    # Add truth particle information to the dataframe of hits
-    if selection:
-        logging.info("Apply selection")
-        selection_function: selecting.SelectionFunction = getattr(selecting, selection)
-        hits_particles, particles = selection_function(
-            hits_particles=hits_particles,
-            particles=particles,
-        )
-
-    event_list = hits_particles["event"].unique()  # The order is not mixed
     n_output_saved = 0  # Count the number of events outputted
     event_idx = 0
 
-    if n_events == -1:
-        n_events = len(event_list)
-
-    with tqdm(total=n_events) as pbar:
-        while n_output_saved < n_events and event_idx < len(event_list):
-            current_event_id = event_list[event_idx]
-            event_hits_particles = hits_particles[
-                hits_particles["event"] == current_event_id
-            ]
-            event_particles = particles[particles["event"] == current_event_id]
-
-            #: String representation of the event ID
-            event_id_str = str(current_event_id).zfill(9)
-
-            no_hits = event_hits_particles.shape[0] == 0
-
-            if not no_hits and (
-                (num_true_hits_threshold is None)
-                or enough_true_hits(
-                    event_hits_particles=event_hits_particles,
-                    num_true_hits_threshold=num_true_hits_threshold,
-                    event_id_str=event_id_str,
-                    num_events=n_output_saved,
-                    required_num_events=n_events,
-                )
-            ):
-                # Save subset of columns
-                if hits_particles_columns is None:
-                    hits_particles_csv = event_hits_particles
-                else:
-                    hits_particles_csv = event_hits_particles[
-                        ["particle_id", "hit_id"] + hits_particles_columns
-                    ]
-                if particles_columns is None:
-                    particles_csv = event_particles
-                else:
-                    particles_csv = event_particles[["particle_id"] + particles_columns]
-
-                # Save
-                hits_particles_csv.to_parquet(
-                    f"{output_dir}/event{event_id_str}-hits_particles.parquet",
-                )
-                particles_csv.to_parquet(
-                    f"{output_dir}/event{event_id_str}-particles.parquet",
-                )
-                n_output_saved += 1
-                pbar.update()
-            event_idx += 1
-        pbar.close()
+    n_required_events = np.inf if n_events == -1 else n_events
+    left_indirs = indirs
+    logging.info(f"Number of events to produce: {n_required_events}")
+    with tqdm(total=n_required_events) as pbar:
+        while n_output_saved < n_required_events and left_indirs:
+            hits_particles, particles = load_and_filter_dataframes_partial(
+                indir=indirs[0]
+            )
+            left_indirs = left_indirs[1:]
+
+            event_ids_in_df_hits_particles = hits_particles["event"].unique()
+            event_ids_df_particles = particles["event"].unique()
+            event_ids = np.intersect1d(
+                event_ids_in_df_hits_particles, event_ids_df_particles
+            )
+
+            # Loop over the events in the dataframe of hits-particles
+            grouped_df_hits_particles = hits_particles.groupby("event")
+            grouped_df_particles = particles.groupby("event")
+            for event_id in event_ids:
+                if n_output_saved >= n_required_events:
+                    break
+                event_hits_particles = grouped_df_hits_particles.get_group(event_id)
+                event_particles = grouped_df_particles.get_group(event_id)
+
+                #: String representation of the event ID
+                event_id_str = str(event_id).zfill(9)
+
+                no_hits = event_hits_particles.shape[0] == 0
+
+                if not no_hits and (
+                    (num_true_hits_threshold is None)
+                    or enough_true_hits(
+                        event_hits_particles=event_hits_particles,
+                        num_true_hits_threshold=num_true_hits_threshold,
+                        event_id_str=event_id_str,
+                        num_events=n_output_saved,
+                        required_num_events=n_events,
+                    )
+                ):
+                    # Save subset of columns
+                    if hits_particles_columns is None:
+                        hits_particles_csv = event_hits_particles
+                    else:
+                        hits_particles_csv = event_hits_particles[
+                            ["particle_id", "hit_id"] + hits_particles_columns
+                        ]
+                    if particles_columns is None:
+                        particles_csv = event_particles
+                    else:
+                        particles_csv = event_particles[
+                            ["particle_id"] + particles_columns
+                        ]
+
+                    # Save
+                    hits_particles_csv.to_parquet(
+                        f"{output_dir}/event{event_id_str}-hits_particles.parquet",
+                    )
+                    particles_csv.to_parquet(
+                        f"{output_dir}/event{event_id_str}-particles.parquet",
+                    )
+                    n_output_saved += 1
+                    pbar.update()
+                event_idx += 1
+            pbar.close()
 
     pd.set_option("chained_assignment", "warn")  # re-enable chained-assignment warning
-    if n_output_saved < n_events:
+    if n_output_saved < n_required_events:
         raise Exception(
             "Not enough events found with more than "
             f"{num_true_hits_threshold} true hits"
-- 
GitLab


From febee8199380b38ab9876216250c778658fce09d Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:39:29 +0200
Subject: [PATCH 53/65] Correct everything_but_long_electrons and add
 `track_weighting_selection`

---
 LHCb_Pipeline/Preprocessing/selecting.py | 44 +++++++++++++++++++++---
 1 file changed, 40 insertions(+), 4 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index a421cce8..7b70d4de 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -86,10 +86,8 @@ def everything_but_long_electrons(
         long electrons are left.
     """
     # 1. Create a mask of the particles to keep:
-    mask_particles_to_keep = (
-        particles["has_velo"] & ~(
-            particles["has_scifi"] & (particles["pid"].abs() == 11)
-        )
+    mask_particles_to_keep = particles["has_velo"] & ~(
+        particles["has_scifi"] & (particles["pid"].abs() == 11)
     )
 
     # 2. Propagate the mask to the dataframe of `particles` and `hits_particles`
@@ -157,3 +155,41 @@ def everything_but_electrons(
         mask_particles_to_keep, particles, hits_particles
     )
     return hits_particles, particles
+
+
+def track_weighting_selection(
+    hits_particles: pd.DataFrame, particles: pd.DataFrame
+) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
+    """The selection performed in the ``track-weighting`` experiment."""
+
+    # Drop particles that are not on at least on 3 planes
+    # but keep the clusters
+    logging.info("Only keep particles with at least 3 planes")
+    n_unique_planes = (
+        hits_particles.groupby(["event", "particle_id"])["plane"]
+        .nunique()
+        .rename("n_unique_planes")
+    )
+    particles = particles.merge(
+        n_unique_planes, how="left", on=["event", "particle_id"]
+    ).fillna(0)
+    particles = particles[particles["n_unique_planes"] >= 3]
+
+    # Only keep reconstructible particles that are straight enough
+    # Also remove the hits to avoid splitted tracks
+    # (we will counter-balance by requiring enough clusters / event)
+    logging.info("Compute distance to line (that might take some time)")
+    new_distances = compute_particle_line_metrics_dataframe(
+        hits=hits_particles, metric_names=["distance_to_line"]
+    )
+    particles = particles.merge(new_distances, how="left", on=["event", "particle_id"])
+    mask_particles_to_keep = particles["distance_to_line"] < 0.8
+    particles, hits_particles = apply_mask(
+        mask_particles_to_keep, particles, hits_particles
+    )
+
+    # assert that there is not any nan values at this point
+    assert not particles.isna().any().any()
+    assert not hits_particles.isna().any().any()
+
+    return hits_particles, particles
-- 
GitLab


From b702349cbf1d0a021ea39863c0c5947bb6676698 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:39:39 +0200
Subject: [PATCH 54/65] Define track-weighting pipeline

---
 .../pipeline_configs/track-weighting.yaml     | 90 +++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 LHCb_Pipeline/pipeline_configs/track-weighting.yaml

diff --git a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
new file mode 100644
index 00000000..6e83d365
--- /dev/null
+++ b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
@@ -0,0 +1,90 @@
+common:
+  experiment_name: track-weighting
+  data_directory: /scratch/acorreia/data
+  artifact_directory: artifacts
+  performance_directory: output # plots and reports
+  gpus: 1
+  test_dataset_names:
+  - velo-sim10b-nospillover
+  - velo-sim10b-nospillover-only-long-electrons
+  - bu2kstee-sim10aU1-xdigi
+
+preprocessing:
+  input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover
+  subdirs: 10
+  output_subdirectory: "preprocessed"
+  selection: track_weighting_selection
+  n_events: 11000 # if `null`, default to `n_train_events + n_test_events`
+  num_true_hits_threshold: 0
+  hits_particles_columns: ["x", "y", "z", "plane"]
+  particles_columns: null
+
+processing:
+  input_subdirectory: "preprocessed"
+  output_subdirectory: "processed"
+  n_workers: 32
+  features: ["r", "phi", "z"]
+  feature_means: [18., 0., 281.]
+  feature_scales: [9.75, 1.82, 287]
+  kept_hits_columns: ["plane"]
+  kept_particles_columns: []
+  n_train_events: 1000
+  n_val_events: 500
+  split_seed: 0
+  true_edges_column: planewise
+
+metric_learning:
+  # Dataset parameters
+  input_subdirectory: "processed"
+  output_subdirectory: "metric_learning_processed"
+
+  # Model parameters
+  feature_indices: 3
+  emb_hidden: 256
+  nb_layer: 4
+  emb_dim: 4
+  activation: Tanh
+  weight: 2
+  randomisation: 2
+  points_per_batch: 100000
+  r: 0.015
+  r_inference: 0.015
+  knn: 50
+  warmup: 8
+  margin: 0.1
+  lr: 0.001
+  factor: 0.7
+  patience: 10
+  regime: [rp, hnm, norm]
+  max_epochs: 40
+
+gnn:
+  # Dataset parameters
+  input_subdirectory: "metric_learning_processed"
+  output_subdirectory: "gnn_processed"
+  edge_cut: 0.5
+  noise: True
+
+  # Model parameters
+  feature_indices: 3
+  hidden: 256
+  n_graph_iters: 12
+  nb_node_layer: 4
+  nb_edge_layer: 4
+  layernorm: True
+  aggregation: sum_max
+  hidden_activation: SiLU
+  weight: 2
+  warmup: 10
+  lr: 0.002
+  factor: 0.7
+  patience: 8
+  truth_key: pid_signal
+  regime: [pid]
+  mask_background: True
+  max_epochs: 200
+
+track_building:
+  score_cut: 0.9
+  input_subdirectory: "gnn_processed"
+  output_subdirectory: "track_building_processed"
-- 
GitLab


From b2a704ad020520aa1fdc582768a6befc11c23b06 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:41:03 +0200
Subject: [PATCH 55/65] Instantiate pbar at the right place

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 23c7721a..29d363a8 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -366,7 +366,7 @@ def preprocess(
                     n_output_saved += 1
                     pbar.update()
                 event_idx += 1
-            pbar.close()
+        pbar.close()
 
     pd.set_option("chained_assignment", "warn")  # re-enable chained-assignment warning
     if n_output_saved < n_required_events:
-- 
GitLab


From fee917640e4f5050d17f2ff5b3f2553779343b30 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:51:58 +0200
Subject: [PATCH 56/65] Don't use functools.partial anymore

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 29d363a8..709ed7cc 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -2,7 +2,6 @@ from __future__ import annotations
 import typing
 import os
 import logging
-from functools import partial
 from tqdm.auto import tqdm
 import numpy as np
 import pandas as pd
@@ -292,13 +291,6 @@ def preprocess(
     for indir in indirs:
         logging.info(f"- {indir}")
 
-    load_and_filter_dataframes_partial = partial(
-        load_and_filter_dataframes,
-        hits_particles_columns=hits_particles_columns,
-        particles_columns=particles_columns,
-        selection=selection,
-    )
-
     n_output_saved = 0  # Count the number of events outputted
     event_idx = 0
 
@@ -307,8 +299,11 @@ def preprocess(
     logging.info(f"Number of events to produce: {n_required_events}")
     with tqdm(total=n_required_events) as pbar:
         while n_output_saved < n_required_events and left_indirs:
-            hits_particles, particles = load_and_filter_dataframes_partial(
-                indir=indirs[0]
+            hits_particles, particles = load_and_filter_dataframes(
+                indir=left_indirs[0],
+                hits_particles_columns=hits_particles_columns,
+                particles_columns=particles_columns,
+                selection=selection,
             )
             left_indirs = left_indirs[1:]
 
-- 
GitLab


From 838825af75877f20a958b14ce36b7adbf552c7c7 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:53:08 +0200
Subject: [PATCH 57/65] Remove "get input directories" message

---
 LHCb_Pipeline/Preprocessing/preprocessing.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/LHCb_Pipeline/Preprocessing/preprocessing.py b/LHCb_Pipeline/Preprocessing/preprocessing.py
index 709ed7cc..6512500c 100644
--- a/LHCb_Pipeline/Preprocessing/preprocessing.py
+++ b/LHCb_Pipeline/Preprocessing/preprocessing.py
@@ -283,7 +283,6 @@ def preprocess(
     os.makedirs(output_dir, exist_ok=True)
     logging.info(f"Preprocessing: output will be written in {output_dir}")
 
-    logging.info("Get input directories")
     indirs = get_indirs(input_dir=input_dir, subdirs=subdirs)
     if len(indirs) == 0:
         raise ValueError("No input directories.")
-- 
GitLab


From 176df60168dc3d449af570d0b0ee4798012348bd Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 03:53:28 +0200
Subject: [PATCH 58/65] Add plane and n_unique_planes in processing

---
 LHCb_Pipeline/pipeline_configs/track-weighting.yaml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
index 6e83d365..267adb68 100644
--- a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
+++ b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
@@ -23,12 +23,12 @@ processing:
   input_subdirectory: "preprocessed"
   output_subdirectory: "processed"
   n_workers: 32
-  features: ["r", "phi", "z"]
-  feature_means: [18., 0., 281.]
-  feature_scales: [9.75, 1.82, 287]
+  features: ["r", "phi", "z", "plane"]
+  feature_means: [18., 0.0, 281.0, 7.5]
+  feature_scales: [9.75, 1.82, 287.0, 12.5]
   kept_hits_columns: ["plane"]
-  kept_particles_columns: []
-  n_train_events: 1000
+  kept_particles_columns: ["n_unique_planes"]
+  n_train_events: 10000
   n_val_events: 500
   split_seed: 0
   true_edges_column: planewise
-- 
GitLab


From 8651ba5968a53545de303cb73652a8b3ce296c82 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 04:05:12 +0200
Subject: [PATCH 59/65] Define sorted-wise edges

---
 LHCb_Pipeline/Processing/processing.py       |   3 +
 LHCb_Pipeline/Processing/sortedwise_edges.py | 101 +++++++++++++++++++
 2 files changed, 104 insertions(+)
 create mode 100644 LHCb_Pipeline/Processing/sortedwise_edges.py

diff --git a/LHCb_Pipeline/Processing/processing.py b/LHCb_Pipeline/Processing/processing.py
index af45093a..a3bb11bc 100644
--- a/LHCb_Pipeline/Processing/processing.py
+++ b/LHCb_Pipeline/Processing/processing.py
@@ -13,6 +13,7 @@ from torch_geometric.data import Data
 
 from .modulewise_edges import get_modulewise_edges
 from .planewise_edges import get_planewise_edges
+from .sortedwise_edges import get_sortedwise_edges
 from .compute import compute_columns
 
 
@@ -104,6 +105,8 @@ def build_event(
         true_edges = get_modulewise_edges(hits_particles)
     elif true_edges_column == "planewise":
         true_edges = get_planewise_edges(hits_particles)
+    elif true_edges_column == "sortedwise":
+        true_edges = get_sortedwise_edges(hits_particles)
     else:
         raise ValueError(
             f"`true_edges_column` is `{true_edges_column}`, which is not recognised."
diff --git a/LHCb_Pipeline/Processing/sortedwise_edges.py b/LHCb_Pipeline/Processing/sortedwise_edges.py
new file mode 100644
index 00000000..53969f3a
--- /dev/null
+++ b/LHCb_Pipeline/Processing/sortedwise_edges.py
@@ -0,0 +1,101 @@
+"""A module that defines a way of defines the edges by sorting the hits by z-abscissa
+(instead of by distance from the origin vertex).
+
+This way, we define the edge orientation using a left to right convention.
+"""
+import numpy as np
+import pandas as pd
+import numba as nb
+from montetracko.array_utils.groupby import group_lengths
+from utils.tools.tgroupby import get_group_indices_from_group_lengths
+
+
+@nb.jit(nopython=True, cache=True)
+def get_edges_from_sorted_impl(
+    edges: np.ndarray,
+    hit_ids: np.ndarray,
+    particle_group_indices: np.ndarray,
+) -> None:
+    """Fill the array of sorted-wise edges by grouping by hits belonging to the
+    same particle, already sorted by z, and forming edge by linking "adjacent" hit IDs.
+
+    Args:
+        edges: Pre-allocated empty array of edges to fill
+        hit_ids: List of hit IDs, sorted by particle IDs and z-coordinates.
+        particle_group_indices: Start and end indices in ``hit_ids``
+            that delimits hits that have same particle ID.
+    """
+    edge_idx = 0
+    for start_idx, end_idx in zip(
+        particle_group_indices[:-1], particle_group_indices[1:]
+    ):
+        n_edges = end_idx - start_idx - 1
+        next_edge_idx = edge_idx + n_edges
+        edges[0, edge_idx:next_edge_idx] = hit_ids[start_idx : end_idx - 1]
+        edges[1, edge_idx:next_edge_idx] = hit_ids[start_idx + 1 : end_idx]
+        edge_idx = next_edge_idx
+
+    # Sanity check
+    assert edge_idx == edges.shape[1]
+
+
+def get_sortedwise_edges_impl(
+    hit_ids: np.ndarray,
+    particle_ids: np.ndarray,
+) -> np.ndarray:
+    """Get the sorted-wise edges
+    
+    Args:
+        hit_ids: array of hit IDs, sorted by particle IDs
+        particle_ids: z-sorted array of particle IDs for every hit
+    
+    Returns:
+        Two-dimensional array where every column represent an edge. In this array,
+        for every edge, a hit is referred to by its index in the dataframe of hits.
+    """
+    n_hits_per_particles = group_lengths(particle_ids)[0]
+    particle_group_indices = get_group_indices_from_group_lengths(n_hits_per_particles)
+
+    # Create, fill and return array of edges
+    n_edges = (n_hits_per_particles - 1).sum()
+    edges = np.zeros(shape=(2, n_edges), dtype=int)
+    get_edges_from_sorted_impl(
+        edges=edges,
+        hit_ids=hit_ids,
+        particle_group_indices=particle_group_indices,
+    )
+    return edges
+
+
+def get_sortedwise_edges(
+    hits: pd.DataFrame, drop_duplicates: bool = False
+) -> np.ndarray:
+    """Get edges by sorting the hits by ``z`` for every particle in the event,
+    and linking the adjacent hits by edges.
+
+    Args:
+        hits: dataframe of hits, with columns ``particle_id`` and ``z``
+        drop_duplicates: whether to drop hits of a particle that belong to the same
+            z
+
+    Returns:
+        Two-dimensional array where every column represent an edge. In this array,
+        for every edge, a hit is referred to by its index in the dataframe of hits.
+    """
+    # Exclude noise
+    signal_hits = hits[hits.particle_id != 0]
+
+    # Remove hits on the same z belonging to the same particle
+    if drop_duplicates:
+        signal_hits = signal_hits.drop_duplicates(subset=["particle_id", "z"])
+
+    # Sort by particle ID and z in order to group by particle ID and z in Numba
+    signal_hits = signal_hits.sort_values(["particle_id", "z"]).reset_index(
+        drop=False
+    )  # produce `index`, the indices before sorting
+
+    # Get edges
+    return get_sortedwise_edges_impl(
+        hit_ids=signal_hits["index"].to_numpy(),
+        particle_ids=signal_hits["particle_id"].to_numpy(),
+    )
-- 
GitLab


From f4d5cdb6204fdb26f361c082dbc45ebc92c076a3 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 04:56:12 +0200
Subject: [PATCH 60/65] Set short tracks as fake hits

---
 LHCb_Pipeline/Preprocessing/selecting.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index 7b70d4de..afb48f5f 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -173,7 +173,10 @@ def track_weighting_selection(
     particles = particles.merge(
         n_unique_planes, how="left", on=["event", "particle_id"]
     ).fillna(0)
-    particles = particles[particles["n_unique_planes"] >= 3]
+    enough_planes_mask = particles["n_unique_planes"] >= 3
+    particles = particles[enough_planes_mask]
+    # The hits are set as fake hits
+    hits_particles.loc[enough_planes_mask, "particle_id"] = 0
 
     # Only keep reconstructible particles that are straight enough
     # Also remove the hits to avoid splitted tracks
-- 
GitLab


From 41b061119c61f1bbe6420e892c27167918074314 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 05:00:14 +0200
Subject: [PATCH 61/65] Finally don't remove hits of small tracks

---
 LHCb_Pipeline/Preprocessing/selecting.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/Preprocessing/selecting.py b/LHCb_Pipeline/Preprocessing/selecting.py
index afb48f5f..87c65ce7 100644
--- a/LHCb_Pipeline/Preprocessing/selecting.py
+++ b/LHCb_Pipeline/Preprocessing/selecting.py
@@ -162,8 +162,9 @@ def track_weighting_selection(
 ) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
     """The selection performed in the ``track-weighting`` experiment."""
 
-    # Drop particles that are not on at least on 3 planes
-    # but keep the clusters
+    # Define `n_unique_planes`
+    # We'll train with all the hits for the training
+    # And cut before the GNN
     logging.info("Only keep particles with at least 3 planes")
     n_unique_planes = (
         hits_particles.groupby(["event", "particle_id"])["plane"]
@@ -173,10 +174,6 @@ def track_weighting_selection(
     particles = particles.merge(
         n_unique_planes, how="left", on=["event", "particle_id"]
     ).fillna(0)
-    enough_planes_mask = particles["n_unique_planes"] >= 3
-    particles = particles[enough_planes_mask]
-    # The hits are set as fake hits
-    hits_particles.loc[enough_planes_mask, "particle_id"] = 0
 
     # Only keep reconstructible particles that are straight enough
     # Also remove the hits to avoid splitted tracks
-- 
GitLab


From 558d3085494efc8dbec9a30748534cd2337e1814 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 05:00:33 +0200
Subject: [PATCH 62/65] Set # hits threshold to 1000

---
 LHCb_Pipeline/pipeline_configs/track-weighting.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
index 267adb68..2daa9244 100644
--- a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
+++ b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
@@ -15,7 +15,7 @@ preprocessing:
   output_subdirectory: "preprocessed"
   selection: track_weighting_selection
   n_events: 11000 # if `null`, default to `n_train_events + n_test_events`
-  num_true_hits_threshold: 0
+  num_true_hits_threshold: 1000
   hits_particles_columns: ["x", "y", "z", "plane"]
   particles_columns: null
 
@@ -28,8 +28,8 @@ processing:
   feature_scales: [9.75, 1.82, 287.0, 12.5]
   kept_hits_columns: ["plane"]
   kept_particles_columns: ["n_unique_planes"]
-  n_train_events: 10000
-  n_val_events: 500
+  n_train_events: 50
+  n_val_events: 50
   split_seed: 0
   true_edges_column: planewise
 
-- 
GitLab


From 6c83a85d923af7839d625ad62871da90666c1674 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 05:01:10 +0200
Subject: [PATCH 63/65] Bump `num_true_hits_threshold` to 1500

---
 LHCb_Pipeline/pipeline_configs/track-weighting.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
index 2daa9244..090daf7b 100644
--- a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
+++ b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
@@ -15,7 +15,7 @@ preprocessing:
   output_subdirectory: "preprocessed"
   selection: track_weighting_selection
   n_events: 11000 # if `null`, default to `n_train_events + n_test_events`
-  num_true_hits_threshold: 1000
+  num_true_hits_threshold: 1500
   hits_particles_columns: ["x", "y", "z", "plane"]
   particles_columns: null
 
-- 
GitLab


From bdd8c35ef6cad858f3f83e73a1899823a41b8916 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 05:01:48 +0200
Subject: [PATCH 64/65] Update defininition of planewise edges

Planewise edges = link all the hits belonging to adjacent planes
---
 LHCb_Pipeline/Processing/planewise_edges.py | 73 ++++++++++++++-------
 1 file changed, 49 insertions(+), 24 deletions(-)

diff --git a/LHCb_Pipeline/Processing/planewise_edges.py b/LHCb_Pipeline/Processing/planewise_edges.py
index a0757960..dcf4107e 100644
--- a/LHCb_Pipeline/Processing/planewise_edges.py
+++ b/LHCb_Pipeline/Processing/planewise_edges.py
@@ -4,6 +4,7 @@
 This way, we define the edge orientation using a left to right convention. However,
 if a plane a multiple hits for the same particle, the edges can be not well defined.
 """
+import typing
 import numpy as np
 import pandas as pd
 import numba as nb
@@ -13,12 +14,12 @@ from utils.tools.tgroupby import get_group_indices_from_group_lengths
 
 @nb.jit(nopython=True, cache=True)
 def get_edges_from_sorted_impl(
-    edges: np.ndarray,
     hit_ids: np.ndarray,
-    particle_group_indices: np.ndarray,
-) -> None:
+    particle_ids: np.ndarray,
+    plane_ids: np.ndarray,
+) -> typing.List[np.ndarray]:
     """Fill the array of plane-wise edges by grouping by particle ID already sorted
-    by plane, and forming edge by linking "adjacent" hit IDs.
+    by plane, and forming edge by linking "adjacent" planes.
 
     Args:
         edges: Pre-allocated empty array of edges to fill
@@ -26,46 +27,69 @@ def get_edges_from_sorted_impl(
         particle_group_indices: Start and end indices in ``hit_ids``
             that delimits hits that have same particle ID.
     """
-    edge_idx = 0
-    for start_idx, end_idx in zip(
+    n_hits_per_particles = group_lengths(particle_ids)[0]
+    particle_group_indices = get_group_indices_from_group_lengths(n_hits_per_particles)
+
+    list_edges = [np.zeros(dtype=hit_ids.dtype, shape=(2, 1)) for _ in range(0)]
+
+    for particle_start_idx, particle_end_idx in zip(
         particle_group_indices[:-1], particle_group_indices[1:]
     ):
-        n_edges = end_idx - start_idx - 1
-        next_edge_idx = edge_idx + n_edges
-        edges[0, edge_idx:next_edge_idx] = hit_ids[start_idx : end_idx - 1]
-        edges[1, edge_idx:next_edge_idx] = hit_ids[start_idx + 1 : end_idx]
-        edge_idx = next_edge_idx
+        particle_hit_ids = hit_ids[particle_start_idx: particle_end_idx]
+        n_planes_per_hits = group_lengths(
+            plane_ids[particle_start_idx:particle_end_idx]
+        )[0]
+        plane_group_indices = get_group_indices_from_group_lengths(n_planes_per_hits)
+
+        n_edges = np.sum(np.multiply(n_planes_per_hits[:-1], n_planes_per_hits[1:]))
+        edges = np.full(shape=(2, n_edges), dtype=hit_ids.dtype, fill_value=-1)
+        edge_idx = 0
+        for plane_group in range(
+            len(plane_group_indices) - 2
+        ):  # up second to last plane
+            plane_start_idx = plane_group_indices[plane_group]
+            plane_end_idx = plane_group_indices[plane_group + 1]
+            next_plane_end_idx = plane_group_indices[plane_group + 2]
+            for hit_idx in range(plane_start_idx, plane_end_idx):
+                n_edges_to_add = next_plane_end_idx - plane_end_idx
+                edges[0, edge_idx : edge_idx + n_edges_to_add] = particle_hit_ids[
+                    hit_idx
+                ]
+                edges[1, edge_idx : edge_idx + n_edges_to_add] = particle_hit_ids[
+                    plane_end_idx:next_plane_end_idx
+                ]
+                edge_idx += n_edges_to_add
 
-    # Sanity check
-    assert edge_idx == edges.shape[1]
+        # Sanity check
+        assert edge_idx == n_edges
+        list_edges.append(edges)
+
+    return list_edges
 
 
 def get_planewise_edges_impl(
     hit_ids: np.ndarray,
     particle_ids: np.ndarray,
+    plane_ids: np.ndarray,
 ) -> np.ndarray:
     """Get the plane-wise edges
-    
+
     Args:
         hit_ids: array of hit IDs, sorted by particle IDs
         particle_ids: Sorted array of particle IDs for every hit
-    
+        plane_ids: Sorted array of plane IDs for every hit
+
     Returns:
         Two-dimensional array where every column represent an edge. In this array,
         for every edge, a hit is referred to by its index in the dataframe of hits.
     """
-    n_hits_per_particles = group_lengths(particle_ids)[0]
-    particle_group_indices = get_group_indices_from_group_lengths(n_hits_per_particles)
-
     # Create, fill and return array of edges
-    n_edges = (n_hits_per_particles - 1).sum()
-    edges = np.zeros(shape=(2, n_edges), dtype=int)
-    get_edges_from_sorted_impl(
-        edges=edges,
+    list_edges = get_edges_from_sorted_impl(
         hit_ids=hit_ids,
-        particle_group_indices=particle_group_indices,
+        particle_ids=particle_ids,
+        plane_ids=plane_ids,
     )
-    return edges
+    return np.hstack(list_edges)
 
 
 def get_planewise_edges(
@@ -99,4 +123,5 @@ def get_planewise_edges(
     return get_planewise_edges_impl(
         hit_ids=signal_hits["index"].to_numpy(),
         particle_ids=signal_hits["particle_id"].to_numpy(),
+        plane_ids=signal_hits["plane"].to_numpy(),
     )
-- 
GitLab


From ce7893c1896b27d0d47034cb9bd5cb42c47cb493 Mon Sep 17 00:00:00 2001
From: Anthony Correia <anthony.correia@cern.ch>
Date: Sun, 28 May 2023 05:07:07 +0200
Subject: [PATCH 65/65] Update track-weighting

---
 LHCb_Pipeline/pipeline_configs/track-weighting.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
index 090daf7b..d2afbcce 100644
--- a/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
+++ b/LHCb_Pipeline/pipeline_configs/track-weighting.yaml
@@ -6,8 +6,8 @@ common:
   gpus: 1
   test_dataset_names:
   - velo-sim10b-nospillover
-  - velo-sim10b-nospillover-only-long-electrons
-  - bu2kstee-sim10aU1-xdigi
+  # - velo-sim10b-nospillover-only-long-electrons
+  # - bu2kstee-sim10aU1-xdigi
 
 preprocessing:
   input_dir: /scratch/acorreia/minbias-sim10b-xdigi-nospillover
@@ -28,8 +28,8 @@ processing:
   feature_scales: [9.75, 1.82, 287.0, 12.5]
   kept_hits_columns: ["plane"]
   kept_particles_columns: ["n_unique_planes"]
-  n_train_events: 50
-  n_val_events: 50
+  n_train_events: 10000
+  n_val_events: 500
   split_seed: 0
   true_edges_column: planewise
 
-- 
GitLab