notebooks/per-experiment/03-fund_inclusion.ipynb

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    "<span style=\"color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;\">An Exception was encountered at '<a href=\"#papermill-error-cell\">In [5]</a>'.</span>"
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    "# Escrow Inclusion Sweep Run Experiment\n",
    "\n",
    "*Danilo Lessa Bernardineli, Shawn Anderson November 2023*\n",
    "\n",
    "In this notebook, we run an escrow inclusion sweep run that compares two parameter sets. "
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    "## Part 1. Dependences & Set-up"
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       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1002'>\n",
       "  <div id=\"baf37af0-3aaf-42be-8046-7d8d7293db76\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"a98613f4-6ccd-4601-8a59-a758742f1de6\":{\"version\":\"3.3.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"a9cc6e388b50456c9e4fe71844f52db6\",\"client_comm_id\":\"d3acaa43c9f24c02b95e348ffb6f10d4\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"a98613f4-6ccd-4601-8a59-a758742f1de6\",\"roots\":{\"p1002\":\"baf37af0-3aaf-42be-8046-7d8d7293db76\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../../')\n",
    "\n",
    "\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "import matplotlib.ticker as mtick\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "import hvplot.pandas\n",
    "import holoviews as hv\n",
    "\n",
    "pio.renderers.default = \"png\" # For GitHub rendering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f2cedf-a24d-484e-8e7b-5929ca2f5f39",
   "metadata": {
    "papermill": {
     "duration": 0.001729,
     "end_time": "2024-01-31T00:57:46.354923",
     "exception": false,
     "start_time": "2024-01-31T00:57:46.353194",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Part 2. Load Simulation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a4fc625-9859-4aa2-8ebe-4a96b02210ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-31T00:57:46.359361Z",
     "iopub.status.busy": "2024-01-31T00:57:46.359119Z",
     "iopub.status.idle": "2024-01-31T00:57:46.364018Z",
     "shell.execute_reply": "2024-01-31T00:57:46.363725Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sim_df = pd.read_pickle(\n",
    "    \"../data/simulations/fund_inclusion-2024-01-02_10-17-17.pkl.gz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f89dd22-45f8-40c7-b2c0-0532a454621e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-31T00:57:46.370716Z",
     "iopub.status.busy": "2024-01-31T00:57:46.370574Z",
     "iopub.status.idle": "2024-01-31T00:57:46.380647Z",
     "shell.execute_reply": "2024-01-31T00:57:46.380417Z"
    },
    "papermill": {
     "duration": 0.01308,
     "end_time": "2024-01-31T00:57:46.381229",
     "exception": false,
     "start_time": "2024-01-31T00:57:46.368149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_passed</th>\n",
       "      <th>blocks_passed</th>\n",
       "      <th>circulating_supply</th>\n",
       "      <th>user_supply</th>\n",
       "      <th>issued_supply</th>\n",
       "      <th>total_supply</th>\n",
       "      <th>sum_of_stocks</th>\n",
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       "      <th>dsf_relative_disbursal_per_day</th>\n",
       "      <th>reward_issuance_balance</th>\n",
       "      <th>...</th>\n",
       "      <th>run</th>\n",
       "      <th>timestep</th>\n",
       "      <th>delta_days</th>\n",
       "      <th>delta_blocks</th>\n",
       "      <th>average_compute_weight_per_budle</th>\n",
       "      <th>label</th>\n",
       "      <th>environmental_label</th>\n",
       "      <th>timestep_in_days</th>\n",
       "      <th>block_time_in_seconds</th>\n",
       "      <th>max_credit_supply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.320000e+09</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3000000000</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>13.698630</td>\n",
       "      <td>13.698630</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>3.000000e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.320000e+09</td>\n",
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       "      <td>3000000000</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
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       "      <td>28800.0</td>\n",
       "      <td>27.363014</td>\n",
       "      <td>27.363014</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>3.000000e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.320000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>default-issuance-function</td>\n",
       "      <td>standard</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>43200.0</td>\n",
       "      <td>41.319709</td>\n",
       "      <td>41.319777</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>3.000000e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.320000e+09</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>default-issuance-function</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>57600.0</td>\n",
       "      <td>55.718919</td>\n",
       "      <td>55.719222</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>1.680000e+09</td>\n",
       "      <td>3.000000e+09</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.320000e+09</td>\n",
       "      <td>...</td>\n",
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       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14400.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    days_passed  blocks_passed  circulating_supply  user_supply  \\\n",
       "0             0            0.0            0.000000     0.000000   \n",
       "14            1        14400.0           13.698630    13.698630   \n",
       "28            2        28800.0           27.363014    27.363014   \n",
       "42            3        43200.0           41.319709    41.319777   \n",
       "56            4        57600.0           55.718919    55.719222   \n",
       "\n",
       "    issued_supply  total_supply  sum_of_stocks  block_utilization  \\\n",
       "0    0.000000e+00  0.000000e+00   0.000000e+00           0.000000   \n",
       "14   1.680000e+09  1.680000e+09   3.000000e+09           0.000002   \n",
       "28   1.680000e+09  1.680000e+09   3.000000e+09           0.000002   \n",
       "42   1.680000e+09  1.680000e+09   3.000000e+09           0.000002   \n",
       "56   1.680000e+09  1.680000e+09   3.000000e+09           0.000002   \n",
       "\n",
       "    dsf_relative_disbursal_per_day  reward_issuance_balance  ...  run  \\\n",
       "0                              0.0             1.320000e+09  ...    1   \n",
       "14                             0.0             1.320000e+09  ...    1   \n",
       "28                             0.0             1.320000e+09  ...    1   \n",
       "42                             0.0             1.320000e+09  ...    1   \n",
       "56                             0.0             1.320000e+09  ...    1   \n",
       "\n",
       "    timestep  delta_days  delta_blocks  average_compute_weight_per_budle  \\\n",
       "0          0         NaN           NaN                               NaN   \n",
       "14         1         1.0       14400.0                      1.000000e+10   \n",
       "28         2         1.0       14400.0                      1.000000e+10   \n",
       "42         3         1.0       14400.0                      1.000000e+10   \n",
       "56         4         1.0       14400.0                      1.000000e+10   \n",
       "\n",
       "                        label  environmental_label  timestep_in_days  \\\n",
       "0   default-issuance-function             standard                 1   \n",
       "14  default-issuance-function             standard                 1   \n",
       "28  default-issuance-function             standard                 1   \n",
       "42  default-issuance-function             standard                 1   \n",
       "56  default-issuance-function             standard                 1   \n",
       "\n",
       "    block_time_in_seconds  max_credit_supply  \n",
       "0                       6         3000000000  \n",
       "14                      6         3000000000  \n",
       "28                      6         3000000000  \n",
       "42                      6         3000000000  \n",
       "56                      6         3000000000  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_df.head()"
   ]
  },
  {
   "cell_type": "code",
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       "<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>label</th>\n",
       "      <th>environmental_label</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>default-issuance-function</td>\n",
       "      <td>standard</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>no-fund</td>\n",
       "      <td>standard</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   run                      label environmental_label  Counts\n",
       "0    1  default-issuance-function            standard    1097\n",
       "1    2                    no-fund            standard    1097"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_df.groupby(['run', 'label', 'environmental_label']).size().reset_index(name='Counts')"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## KPIs"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {
    "tags": [
     "papermill-error-cell-tag"
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    "<span id=\"papermill-error-cell\" style=\"color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;\">Execution using papermill encountered an exception here and stopped:</span>"
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     "exception": true,
     "start_time": "2024-01-31T00:57:46.396883",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "2024-01-30 16:57:46 - subspace-digital-twin - INFO\n",
      "------------subspace-digital-twin------------\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'profit1_trajectory' from 'subspace_model.experiments.metrics' (/home/ygg/Workspace/BlockScience/subspace/notebooks/../subspace_model/experiments/metrics.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msubspace_model\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperiments\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m profit1_trajectory, profit1_mean, total_supply_max, total_supply_mean\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'profit1_trajectory' from 'subspace_model.experiments.metrics' (/home/ygg/Workspace/BlockScience/subspace/notebooks/../subspace_model/experiments/metrics.py)"
     ]
    }
   ],
   "source": [
    "from subspace_model.experiments.metrics import profit1_trajectory, profit1_mean, total_supply_max, total_supply_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ee0f196-df52-4185-93c4-0482d2920541",
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     "duration": null,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "profit1_trajectory_df = profit1_trajectory(sim_df)\n",
    "profit1_trajectory_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc5ab609-9693-4c6d-a6d6-b93e91f2b647",
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   "outputs": [],
   "source": [
    "curve_dict = {column: profit1_trajectory_df.reset_index().groupby(['label', 'environmental_label', 'days_passed']).mean().reset_index().hvplot.line(y=column, by=['label', 'environmental_label'], x='days_passed').opts(title=column) for column in profit1_trajectory_df.columns}\n",
    "holomap = hv.HoloMap(curve_dict)\n",
    "holomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79202f8b-cdb1-42e4-ae65-c1a49711f827",
   "metadata": {
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     "duration": null,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_layout = hv.NdLayout(hv.GridSpace(holomap)).cols(5)\n",
    "grid_layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529e2a47-658b-4873-ad35-f6879f6ccb4a",
   "metadata": {
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     "duration": null,
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    },
    "tags": []
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   "outputs": [],
   "source": [
    "def plot_line(df, column):\n",
    "    plot = df.reset_index().groupby(['label', 'environmental_label', 'days_passed']).mean().reset_index().hvplot.line(y=column, by=['label', 'environmental_label'], x='days_passed').opts(title=column)\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c54f623-e5d6-4f0e-8a49-d9ed923371b5",
   "metadata": {
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   "outputs": [],
   "source": [
    "def plot_quantile(df, column):\n",
    "    plot = df.reset_index().groupby(['label', 'environmental_label', 'days_passed']).mean().reset_index().hvplot.box(y=column, by=['label', 'environmental_label']).opts(title=column)\n",
    "    return plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "603c7948-0b63-42ee-9569-04cbf2261483",
   "metadata": {
    "papermill": {
     "duration": null,
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    "tags": []
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   "outputs": [],
   "source": [
    "curve_dict = {column: plot_quantile(profit1_trajectory_df, column) for column in profit1_trajectory_df.columns}\n",
    "holomap = hv.HoloMap(curve_dict)\n",
    "holomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c220d1d-c53c-4289-83d1-ef56b23f7307",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "grid_layout = hv.NdLayout(hv.GridSpace(holomap)).cols(5)\n",
    "grid_layout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5db587-330f-4736-8f0d-01cdc20fe396",
   "metadata": {
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     "duration": null,
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    "tags": []
   },
   "source": [
    "## Part 3. Visualizations\n",
    "\n",
    "On this section, we'll visualize some base metrics on the simulations results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6628f4e",
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   "outputs": [],
   "source": [
    "sns.lineplot(sim_df, x='days_passed', y='circulating_supply', hue='label').set(title='AB Test Circulating Supply')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a4a28ca",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.lineplot(sim_df, x='days_passed', y='operator_pool_shares', hue='label').set(title='AB Test Operator Pool Shares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91fdd0f5",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.lineplot(sim_df, x='days_passed', y='nominator_pool_shares', hue='label').set(title='AB Test Nominator Pool Shares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d82c49c4",
   "metadata": {
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     "duration": null,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.lineplot(sim_df, x='days_passed', y='block_utilization', hue='label').set(title='AB Test Block Utilization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee2496ca",
   "metadata": {
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     "duration": null,
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from subspace_model.experiments.metrics import *\n",
    "\n",
    "\n",
    "lst = []\n",
    "for i, g_df in sim_df.set_index(['label', 'run', 'days_passed']).groupby('run'):\n",
    "    s = window_volatility(g_df.circulating_supply.diff()).reset_index()\n",
    "    lst.append(s)\n",
    "\n",
    "df = pd.concat(lst).dropna()\n",
    "sns.lineplot(df, x='days_passed', y='circulating_supply', hue='label').set(title='AB Test Windowed Volatility of Circulating Supply')"
   ]
  }
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