FinOps_MCMC_Budget_Forecast.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3890af1d-573e-43be-a641-a78d5e60fc08",
   "metadata": {},
   "source": [
    "# Cloud Spend Forecasting with Monte Carlo Simulaton"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b920c01-18f6-4bfa-9484-9b70d55de529",
   "metadata": {},
   "source": [
    "#### Using FinOps FOCUS Data and Python’s PyMC to Create Probabilistic Budget Forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e581cce9-059e-4f74-8cdd-82ada00d479a",
   "metadata": {},
   "source": [
    "## Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aed1a85-80cf-4f5f-b186-596b6ec1a1b0",
   "metadata": {},
   "source": [
    "This project demonstrates a simple approach to generate probabilistic budget forcasts using Python's PyMC package. By simulating a range of possible outcomes, Monte Carlo techniques enable Finance teams to consider uncertainty and variability in financial forecasts, providing more robust insights than the fixed-point estimates used in traditional budgeting.\n",
    "\n",
    "Additionally, the project leverages the FOCUS dataset, a new standardized format for FinOps cloud spend data. Tracking and managing cloud computing costs has become critical as rapid growth in cloud services threaten to cause unallocated spending and budget overruns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93e901fe-6640-4230-a0f2-f6ab129e4b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "054d72ce-17f9-47be-8a44-3bf13e086375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5987fe88-9970-439b-bac7-ebb5a0e43c82",
   "metadata": {},
   "source": [
    "## Data Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bee90f8-a2ee-405f-9726-54ecbfaaa95e",
   "metadata": {},
   "source": [
    "### 1. FinOps FOCUS Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1521901-0897-4007-9d99-a79bf750105d",
   "metadata": {},
   "source": [
    "For actual year-to-date cloud spend data, we will use a sample FOCUS report provided my Microsoft's FinOps toolkit here: \n",
    "\n",
    "https://microsoft.github.io/finops-toolkit/data#%EF%B8%8F-dataset-examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f2b005-655a-4e08-9948-3c8e73616d19",
   "metadata": {},
   "source": [
    "The FOCUS report is a unified template for analyzing cloud spend data from any service provider. You can learn more about the FOCUS report here:\n",
    "\n",
    "https://focus.finops.org/what-is-focus/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43e60ab5-b514-4fa8-8746-1f9510713099",
   "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>BilledCost</th>\n",
       "      <th>BillingAccountId</th>\n",
       "      <th>BillingAccountName</th>\n",
       "      <th>BillingAccountType</th>\n",
       "      <th>BillingCurrency</th>\n",
       "      <th>BillingPeriodEnd</th>\n",
       "      <th>BillingPeriodStart</th>\n",
       "      <th>ChargeCategory</th>\n",
       "      <th>ChargeClass</th>\n",
       "      <th>ChargeDescription</th>\n",
       "      <th>...</th>\n",
       "      <th>x_SkuMeterName</th>\n",
       "      <th>x_SkuMeterSubcategory</th>\n",
       "      <th>x_SkuOfferId</th>\n",
       "      <th>x_SkuOrderId</th>\n",
       "      <th>x_SkuOrderName</th>\n",
       "      <th>x_SkuPartNumber</th>\n",
       "      <th>x_SkuRegion</th>\n",
       "      <th>x_SkuServiceFamily</th>\n",
       "      <th>x_SkuTerm</th>\n",
       "      <th>x_SkuTier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.760000e-05</td>\n",
       "      <td>/providers/Microsoft.Billing/billingAccounts/8...</td>\n",
       "      <td>Trey Research Demo (DO NOT USE)</td>\n",
       "      <td>Billing Account</td>\n",
       "      <td>USD</td>\n",
       "      <td>2024-07-01T00:00Z</td>\n",
       "      <td>2024-06-01T00:00Z</td>\n",
       "      <td>Usage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Azure DNS - Public Queries</td>\n",
       "      <td>...</td>\n",
       "      <td>Public Queries</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MS-AZR-0017P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T6Z-00026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.106411e-08</td>\n",
       "      <td>/providers/Microsoft.Billing/billingAccounts/8...</td>\n",
       "      <td>Trey Research Demo (DO NOT USE)</td>\n",
       "      <td>Billing Account</td>\n",
       "      <td>USD</td>\n",
       "      <td>2024-07-01T00:00Z</td>\n",
       "      <td>2024-06-01T00:00Z</td>\n",
       "      <td>Usage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bandwidth Inter-Region - Intra Continent Data ...</td>\n",
       "      <td>...</td>\n",
       "      <td>Intra Continent Data Transfer Out</td>\n",
       "      <td>Inter-Region</td>\n",
       "      <td>MS-AZR-0148P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAD-84507</td>\n",
       "      <td>North America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>/providers/Microsoft.Billing/billingAccounts/8...</td>\n",
       "      <td>Trey Research Demo (DO NOT USE)</td>\n",
       "      <td>Billing Account</td>\n",
       "      <td>USD</td>\n",
       "      <td>2024-07-01T00:00Z</td>\n",
       "      <td>2024-06-01T00:00Z</td>\n",
       "      <td>Usage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Microsoft Defender for Storage - Free Transact...</td>\n",
       "      <td>...</td>\n",
       "      <td>Free Transactions</td>\n",
       "      <td>Storage</td>\n",
       "      <td>MS-AZR-0017P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAN-32388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.605405e-01</td>\n",
       "      <td>/providers/Microsoft.Billing/billingAccounts/8...</td>\n",
       "      <td>Trey Research Demo (DO NOT USE)</td>\n",
       "      <td>Billing Account</td>\n",
       "      <td>USD</td>\n",
       "      <td>2024-07-01T00:00Z</td>\n",
       "      <td>2024-06-01T00:00Z</td>\n",
       "      <td>Usage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tables - LRS Data Stored</td>\n",
       "      <td>...</td>\n",
       "      <td>LRS Data Stored</td>\n",
       "      <td>Tables</td>\n",
       "      <td>MS-AZR-0148P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N9H-00637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.500000e-06</td>\n",
       "      <td>/providers/Microsoft.Billing/billingAccounts/8...</td>\n",
       "      <td>Trey Research Demo (DO NOT USE)</td>\n",
       "      <td>Billing Account</td>\n",
       "      <td>USD</td>\n",
       "      <td>2024-07-01T00:00Z</td>\n",
       "      <td>2024-06-01T00:00Z</td>\n",
       "      <td>Usage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Files - List Operations</td>\n",
       "      <td>...</td>\n",
       "      <td>List Operations</td>\n",
       "      <td>Files</td>\n",
       "      <td>MS-AZR-0148P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N9H-00817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BilledCost                                   BillingAccountId  \\\n",
       "0  5.760000e-05  /providers/Microsoft.Billing/billingAccounts/8...   \n",
       "1  1.106411e-08  /providers/Microsoft.Billing/billingAccounts/8...   \n",
       "2  0.000000e+00  /providers/Microsoft.Billing/billingAccounts/8...   \n",
       "3  5.605405e-01  /providers/Microsoft.Billing/billingAccounts/8...   \n",
       "4 -1.500000e-06  /providers/Microsoft.Billing/billingAccounts/8...   \n",
       "\n",
       "                BillingAccountName BillingAccountType BillingCurrency  \\\n",
       "0  Trey Research Demo (DO NOT USE)    Billing Account             USD   \n",
       "1  Trey Research Demo (DO NOT USE)    Billing Account             USD   \n",
       "2  Trey Research Demo (DO NOT USE)    Billing Account             USD   \n",
       "3  Trey Research Demo (DO NOT USE)    Billing Account             USD   \n",
       "4  Trey Research Demo (DO NOT USE)    Billing Account             USD   \n",
       "\n",
       "    BillingPeriodEnd BillingPeriodStart ChargeCategory  ChargeClass  \\\n",
       "0  2024-07-01T00:00Z  2024-06-01T00:00Z          Usage          NaN   \n",
       "1  2024-07-01T00:00Z  2024-06-01T00:00Z          Usage          NaN   \n",
       "2  2024-07-01T00:00Z  2024-06-01T00:00Z          Usage          NaN   \n",
       "3  2024-07-01T00:00Z  2024-06-01T00:00Z          Usage          NaN   \n",
       "4  2024-07-01T00:00Z  2024-06-01T00:00Z          Usage          NaN   \n",
       "\n",
       "                                   ChargeDescription  ...  \\\n",
       "0                         Azure DNS - Public Queries  ...   \n",
       "1  Bandwidth Inter-Region - Intra Continent Data ...  ...   \n",
       "2  Microsoft Defender for Storage - Free Transact...  ...   \n",
       "3                           Tables - LRS Data Stored  ...   \n",
       "4                            Files - List Operations  ...   \n",
       "\n",
       "                      x_SkuMeterName x_SkuMeterSubcategory  x_SkuOfferId  \\\n",
       "0                     Public Queries                   NaN  MS-AZR-0017P   \n",
       "1  Intra Continent Data Transfer Out          Inter-Region  MS-AZR-0148P   \n",
       "2                  Free Transactions               Storage  MS-AZR-0017P   \n",
       "3                    LRS Data Stored                Tables  MS-AZR-0148P   \n",
       "4                    List Operations                 Files  MS-AZR-0148P   \n",
       "\n",
       "  x_SkuOrderId x_SkuOrderName x_SkuPartNumber    x_SkuRegion  \\\n",
       "0          NaN            NaN       T6Z-00026            NaN   \n",
       "1          NaN            NaN       AAD-84507  North America   \n",
       "2          NaN            NaN       AAN-32388            NaN   \n",
       "3          NaN            NaN       N9H-00637            NaN   \n",
       "4          NaN            NaN       N9H-00817            NaN   \n",
       "\n",
       "  x_SkuServiceFamily  x_SkuTerm x_SkuTier  \n",
       "0                NaN        NaN       NaN  \n",
       "1                NaN        NaN       NaN  \n",
       "2                NaN        NaN       NaN  \n",
       "3                NaN        NaN       NaN  \n",
       "4                NaN        NaN       NaN  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focus_df = pd.read_csv('data/dataset-examples/EA-Cost-FOCUS_1.0.csv')#, encoding='ISO-8859-1')\n",
    "focus_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39afe8aa-c554-455c-a404-8474dd021adc",
   "metadata": {},
   "source": [
    "### 2. Historical Budget Variances"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e05d32d1-5e37-49b5-a056-9d3be6e539ee",
   "metadata": {},
   "source": [
    "Since this is a toy problem, I will use made-up data for historical budget variances.\r\n",
    "\r\n",
    "The simulated dataset below contains 6 years of historical budget variances for 2 cost drivers (API Management Services and Apache Spark pool). This historical data will be used to parameterize the prior distributions, which we will later sample from in the Monte Carlo simulation.By design,that the first cost drv(er, API Management Ser)vice, has historically accurate budgets, with all budget variances less than 1%. Conversely, the Apache cost driver had very inaccurate btudges with as much as a 50% unfavorable budget variance.\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16f2aed9-b04f-42e9-a35c-57c7824de973",
   "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>Cost Driver</th>\n",
       "      <th>Year</th>\n",
       "      <th>Projected</th>\n",
       "      <th>Actual</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2018</td>\n",
       "      <td>150</td>\n",
       "      <td>149.850</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2019</td>\n",
       "      <td>152</td>\n",
       "      <td>152.912</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2020</td>\n",
       "      <td>180</td>\n",
       "      <td>179.100</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2021</td>\n",
       "      <td>200</td>\n",
       "      <td>200.400</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2022</td>\n",
       "      <td>202</td>\n",
       "      <td>201.192</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>API Management service</td>\n",
       "      <td>2023</td>\n",
       "      <td>210</td>\n",
       "      <td>209.790</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apache Spark pool</td>\n",
       "      <td>2018</td>\n",
       "      <td>250</td>\n",
       "      <td>252.750</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apache Spark pool</td>\n",
       "      <td>2019</td>\n",
       "      <td>130</td>\n",
       "      <td>156.000</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Apache Spark pool</td>\n",
       "      <td>2020</td>\n",
       "      <td>300</td>\n",
       "      <td>204.000</td>\n",
       "      <td>-0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Apache Spark pool</td>\n",
       "      <td>2021</td>\n",
       "      <td>304</td>\n",
       "      <td>456.000</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Cost Driver  Year  Projected   Actual  Variance\n",
       "0  API Management service  2018        150  149.850    -0.001\n",
       "1  API Management service  2019        152  152.912     0.006\n",
       "2  API Management service  2020        180  179.100    -0.005\n",
       "3  API Management service  2021        200  200.400     0.002\n",
       "4  API Management service  2022        202  201.192    -0.004\n",
       "5  API Management service  2023        210  209.790    -0.001\n",
       "6       Apache Spark pool  2018        250  252.750     0.011\n",
       "7       Apache Spark pool  2019        130  156.000     0.200\n",
       "8       Apache Spark pool  2020        300  204.000    -0.320\n",
       "9       Apache Spark pool  2021        304  456.000     0.500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budget_var_df = pd.read_excel('data/budget_variances.xlsx')#, encoding='ISO-8859-1')\n",
    "budget_var_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be8e259e-c28f-4daf-bffb-0b0ef5477da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary where the key is the cost driver name and the value is a list of historical budget variances\n",
    "budget_names = budget_var_df['Cost Driver'].unique()\n",
    "\n",
    "cost_variances = {}\n",
    "for driver in budget_names:\n",
    "    cost_variances[driver] = np.array([i+1 for i in budget_var_df[budget_var_df['Cost Driver']==driver]['Variance']]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "056878f2-3b6a-4cac-b776-42c44164bc28",
   "metadata": {},
   "source": [
    "## Generating the Monte Carlo Simulation with PyMC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8245084b-5329-4d82-a04e-251517650673",
   "metadata": {},
   "source": [
    "The Monte Carlo simulation below works in 3 steps:\n",
    "\n",
    "<br>**Step 1**:<br>\n",
    "For each cost driver, we must specify a prior distribution. The priors represent our best estimate of how likely we are to see different values of the driver. This could be based on expert opinion or past experience in similar cloud projects. \n",
    "\n",
    "For the first 2 cost drivers (API Management and Apache Spark), I parameterize the prior distribution using the actual historical budget variances. I use a normal distribution centered at the mean budget variance and a standard deviation consistent with the historical deviation. These are different for the 2 drivers.\n",
    "\n",
    "The rest of the cost drivers will come from a normal distribution with a mean equal to the actual cost and a standard deviation of 1. We can expect these to be fairly inaccurate.\n",
    "\n",
    "Note: In the real world, a cost driver could be any variable that drives costs (e.g. resource hours, estimated monthly users, etc). But for simplicity in this example, I will assume that each unique FOCUS ResourceType is a cost driver. \n",
    "\n",
    "\n",
    "<br>**Step 2**: <br>\n",
    "With the prior distributions in hand, we then draw a large number of random samples for each cost driver. \n",
    "\n",
    "\n",
    "<br>**Step 3**: <br>\n",
    "Finally, we can compute the target costs of interest as a function of the cost drivers. For our basic example here, I will only compute “total costs” by doing a simple sum of all cost drivers. However, more complex cost functions can easily be modeled with the same setup.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7dd7fb97-039c-4cae-94c2-745bf3bf86d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup/ Data Prep\n",
    "\n",
    "# Assume each unique ResourceType is a cost driver\n",
    "cost_driver = 'ResourceType'\n",
    "grouped = focus_df.groupby([cost_driver])[['BilledCost']].sum()\n",
    "\n",
    "# Exclude resources that billed less than $10 (avoids computation errors)\n",
    "grouped = grouped[grouped['BilledCost']>10]\n",
    "\n",
    "driver_names = grouped.index\n",
    "YTD_billed_costs = [i for i in grouped['BilledCost']]\n",
    "priors = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c80ed53-9de7-4090-b8d1-3fcab17bf381",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [API Management service, Apache Spark pool, App Service Environment, App Service plan, Application gateway, Azure Database for MySQL flexible server, Azure Machine Learning workspace, Container instances, Data Explorer pool, Data factory (V2), Defender for Cloud, Disk, ExpressRoute circuit, Fabric Capacity, Kubernetes service, Load balancer, Log Analytics workspace, Microsoft.Compute/capacityReservationGroups/capacityReservations, Private endpoint, Public IP address, Reservation order, SQL database, SQL elastic pool, SQL managed instance, SQL server, Search service, Storage account, Storage account (classic), Virtual machine, Virtual machine scale set, Virtual network gateway]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 40 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Generate Priors and Run the Monte Carlo Simulation\n",
    "\n",
    "with pm.Model() as m:\n",
    "    \n",
    "    # Step 1: Generate priors for each Cost Driver\n",
    "    for name, cost in zip(driver_names, YTD_billed_costs):\n",
    "        # For the drivers with historical budget variance data, use the historical data to paramaterize the prior\n",
    "        if name in budget_names:\n",
    "            mu = (cost_variances[name]*cost).mean()\n",
    "            sigma = (cost_variances[name]*cost).std()\n",
    "            priors[name] = pm.Normal(name, mu=mu, sigma=sigma)\n",
    "        # For the remaining drivers, use a generic parameterization\n",
    "        else:\n",
    "            sigma = 1\n",
    "            mu = cost\n",
    "            priors[name] = pm.Normal(name, mu=mu, sigma=sigma)\n",
    "\n",
    "    # Step 3:  Calculate \"Total Cost\" as a function of the cost drivers\n",
    "    total_cost = pm.Deterministic(\"Total Cost\", np.sum([i for i in priors.values()]))\n",
    "    \n",
    "    \n",
    "    # Step 2: Draw a large number of random samples\n",
    "    trace = pm.sample(1000)\n",
    "\n",
    "    # used to store samples\n",
    "    ppc = pm.sample_posterior_predictive(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee52814-1160-4ec0-bc13-da174da5e36e",
   "metadata": {},
   "source": [
    "## Analyzing and Using the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec95ab2-7918-480d-94af-ab0511f27185",
   "metadata": {},
   "source": [
    "Python’s ArviZ package contains convenient functionality to visualize the sampled distributions. In the visualizations below, we can see sampled budget projections for each cost driver, as well as the calculated “Total Cost” (at the end).\n",
    "\n",
    "As expected, the API Management Service cost has a very narrow range of budget projections because it has learned from historical budget variances that projections for the API service tend to be very accurate and reliable. Conversely, the Apache Spark driver has a much wider range of budget projections because it learned not to trust the expected cost.\n",
    "\n",
    "The rest of the cost drivers are unreliable, since we put very little thought into their priors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be16a974-4be0-4e1c-8106-3f1b63ee26c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2944x4416 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_posterior(trace, kind='hist', show=True, hdi_prob=.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aeb6573-3aa4-4442-8a30-532616f32432",
   "metadata": {},
   "source": [
    "The final Monte Carlo samples can be extracted from the “trace” variable (an arviz.InferenceData object) to be used for any downstream application or budget reporting. \n",
    "\n",
    "Depending on the needs of your organization, these can be reported in any desired format, such as a web dashboard with the histograms above, and Excel report, or any other FinOps application.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0ff802-b5d7-42c8-8e10-30be036b2eb2",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f51ad13-783d-41c5-bd3e-e96a7cf8eee0",
   "metadata": {},
   "source": [
    "Working with Inference Data:\n",
    "https://python.arviz.org/en/latest/getting_started/WorkingWithInferenceData.html#working-with-inferencedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f690b59-4732-411f-a946-4e53943ea862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posteriorc5459aff-7821-4632-9452-92791ea5daa7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posteriorc5459aff-7821-4632-9452-92791ea5daa7\" class = \"xr-section-summary\">posterior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                           (\n",
       "                                                                       chain: 4,\n",
       "                                                                       draw: 1000)\n",
       "Coordinates:\n",
       "  * chain                                                             (chain) int64 ...\n",
       "  * draw                                                              (draw) int64 ...\n",
       "Data variables: (12/32)\n",
       "    API Management service                                            (chain, draw) float64 ...\n",
       "    Apache Spark pool                                                 (chain, draw) float64 ...\n",
       "    App Service Environment                                           (chain, draw) float64 ...\n",
       "    App Service plan                                                  (chain, draw) float64 ...\n",
       "    Application gateway                                               (chain, draw) float64 ...\n",
       "    Azure Database for MySQL flexible server                          (chain, draw) float64 ...\n",
       "    ...                                                                ...\n",
       "    Storage account                                                   (chain, draw) float64 ...\n",
       "    Storage account (classic)                                         (chain, draw) float64 ...\n",
       "    Virtual machine                                                   (chain, draw) float64 ...\n",
       "    Virtual machine scale set                                         (chain, draw) float64 ...\n",
       "    Virtual network gateway                                           (chain, draw) float64 ...\n",
       "    Total Cost                                                        (chain, draw) float64 ...\n",
       "Attributes:\n",
       "    created_at:                 2024-10-14T16:36:57.156808\n",
       "    arviz_version:              0.17.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.16.1\n",
       "    sampling_time:              40.21694207191467\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-39f341b7-7405-45d7-8f78-6687e3192a6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-39f341b7-7405-45d7-8f78-6687e3192a6f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d37e609c-0248-4ee1-8fd4-7b44fb9e5267' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d37e609c-0248-4ee1-8fd4-7b44fb9e5267' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-988e0221-5cea-41d1-879f-99752d5c961e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-988e0221-5cea-41d1-879f-99752d5c961e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dd6eef9-bc8d-4edf-85d9-665b0780b204' class='xr-var-data-in' type='checkbox'><label for='data-0dd6eef9-bc8d-4edf-85d9-665b0780b204' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-c25b6b12-5f7d-4fc2-b7b9-cfd0f04be493' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c25b6b12-5f7d-4fc2-b7b9-cfd0f04be493' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c37ec87-755b-4fdb-9bbf-b760078783eb' class='xr-var-data-in' type='checkbox'><label for='data-5c37ec87-755b-4fdb-9bbf-b760078783eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01b14424-780f-4e7a-a824-2b1ac859afaa' class='xr-section-summary-in' type='checkbox'  ><label for='section-01b14424-780f-4e7a-a824-2b1ac859afaa' class='xr-section-summary' >Data variables: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>API Management service</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>29.38 29.43 29.23 ... 29.39 29.58</div><input id='attrs-79abd0fb-82a1-4986-b25e-dd4889dc6c42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79abd0fb-82a1-4986-b25e-dd4889dc6c42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a187bd82-0f02-4a05-8ae5-9ef0987e1b22' class='xr-var-data-in' type='checkbox'><label for='data-a187bd82-0f02-4a05-8ae5-9ef0987e1b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[29.37973312, 29.43390339, 29.23139299, ..., 29.32269827,\n",
       "        29.4431175 , 29.48909796],\n",
       "       [29.41698464, 29.43541981, 29.46071327, ..., 29.61732269,\n",
       "        29.31474574, 29.54253852],\n",
       "       [29.46318888, 29.43119263, 29.55240956, ..., 29.69275288,\n",
       "        29.52520168, 29.21762176],\n",
       "       [29.3425946 , 29.58506231, 29.36450509, ..., 29.43371506,\n",
       "        29.3930364 , 29.58169149]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Apache Spark pool</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>85.08 55.86 95.17 ... 111.6 45.33</div><input id='attrs-e0f15829-7b7a-4823-a41b-cd02d7959739' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0f15829-7b7a-4823-a41b-cd02d7959739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0c22e5a-fd63-4c42-b832-d4153c4b8dcf' class='xr-var-data-in' type='checkbox'><label for='data-d0c22e5a-fd63-4c42-b832-d4153c4b8dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 85.07728413,  55.86405096,  95.17448157, ...,  84.57782022,\n",
       "         27.33078433,  61.19429832],\n",
       "       [ 37.78876869,  94.7761767 ,  54.91780477, ...,  42.35723135,\n",
       "         93.15058759,  42.66579596],\n",
       "       [ 75.00200854,  50.22413554,  86.76504179, ...,  21.563252  ,\n",
       "         35.15801368,  80.62024265],\n",
       "       [102.82893385,  47.12177679,  99.92243319, ...,  43.77752801,\n",
       "        111.57748449,  45.32864017]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>App Service Environment</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.469e+03 1.464e+03 ... 1.467e+03</div><input id='attrs-0cb6f36c-9bd9-4a7a-b749-48605ad7ea2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0cb6f36c-9bd9-4a7a-b749-48605ad7ea2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15a754a4-230b-4a7c-837d-74af6ebc6926' class='xr-var-data-in' type='checkbox'><label for='data-15a754a4-230b-4a7c-837d-74af6ebc6926' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1469.42317878, 1464.22128915, 1465.42858925, ..., 1466.27239875,\n",
       "        1467.56960946, 1466.01490436],\n",
       "       [1467.23360825, 1464.36783929, 1466.41668234, ..., 1465.79664491,\n",
       "        1468.75005415, 1464.4914036 ],\n",
       "       [1467.29716774, 1466.52858915, 1466.99876519, ..., 1466.59923711,\n",
       "        1465.182076  , 1466.78040751],\n",
       "       [1464.66675069, 1467.93074268, 1466.27530105, ..., 1468.20333818,\n",
       "        1466.68946056, 1467.13251197]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>App Service plan</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.4 498.6 499.1 ... 501.8 498.9</div><input id='attrs-2827a622-479b-4d78-a07d-bbf09a8b4024' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2827a622-479b-4d78-a07d-bbf09a8b4024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd635d50-7bc9-4b5a-977f-ca95e7ca7374' class='xr-var-data-in' type='checkbox'><label for='data-fd635d50-7bc9-4b5a-977f-ca95e7ca7374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[500.41419944, 498.56649621, 499.07359366, ..., 498.7854756 ,\n",
       "        501.99232804, 499.21771813],\n",
       "       [499.5500036 , 500.53437948, 499.60649635, ..., 498.1910282 ,\n",
       "        500.62603302, 498.21790216],\n",
       "       [499.5320685 , 499.3576748 , 499.90747004, ..., 498.78685964,\n",
       "        500.57507722, 499.41924353],\n",
       "       [500.21358939, 499.07118373, 499.31937735, ..., 498.32342745,\n",
       "        501.77096901, 498.8708941 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Application gateway</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>158.9 162.5 162.3 ... 160.2 162.5</div><input id='attrs-36b7ea8e-1cb8-4741-a3bc-5855c6af23c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36b7ea8e-1cb8-4741-a3bc-5855c6af23c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b55f0f-7bc6-42ad-81ad-a0c6c75fb729' class='xr-var-data-in' type='checkbox'><label for='data-e6b55f0f-7bc6-42ad-81ad-a0c6c75fb729' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[158.93879467, 162.5307539 , 162.2761562 , ..., 162.51011847,\n",
       "        160.06794892, 160.24719755],\n",
       "       [161.6812028 , 160.44193679, 162.15685472, ..., 159.24144637,\n",
       "        162.7534059 , 160.2218702 ],\n",
       "       [161.50881812, 161.27191239, 161.0968046 , ..., 158.60953156,\n",
       "        161.86194749, 159.64644499],\n",
       "       [160.15155917, 163.03876857, 159.57375806, ..., 160.78149453,\n",
       "        160.18774627, 162.51463301]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Azure Database for MySQL flexible server</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>201.3 201.4 200.6 ... 202.1 200.6</div><input id='attrs-26e89811-61bb-44ca-a640-40683871f5f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26e89811-61bb-44ca-a640-40683871f5f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2336a1e5-e820-444a-8931-7be88f6e5006' class='xr-var-data-in' type='checkbox'><label for='data-2336a1e5-e820-444a-8931-7be88f6e5006' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[201.26163868, 201.38626906, 200.56882628, ..., 201.66210296,\n",
       "        199.64204231, 200.85174758],\n",
       "       [200.51347781, 201.48322471, 199.07254027, ..., 199.86839273,\n",
       "        200.51770755, 200.08582366],\n",
       "       [200.87392113, 199.87704429, 200.71935882, ..., 200.89300476,\n",
       "        202.17337079, 199.45951235],\n",
       "       [201.0284658 , 200.07922264, 199.90526836, ..., 199.21796884,\n",
       "        202.07281665, 200.60295964]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Azure Machine Learning workspace</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>126.5 124.5 125.9 ... 127.9 123.3</div><input id='attrs-8e5463df-469c-40bf-83c3-22587ce2a2bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e5463df-469c-40bf-83c3-22587ce2a2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fa2b16d-08f0-422a-83a4-d2b1869cc9ca' class='xr-var-data-in' type='checkbox'><label for='data-1fa2b16d-08f0-422a-83a4-d2b1869cc9ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[126.53926497, 124.45976714, 125.8619774 , ..., 126.52027671,\n",
       "        125.16825208, 125.09956609],\n",
       "       [124.71672588, 126.32765846, 123.71169035, ..., 124.85953383,\n",
       "        126.85860873, 124.00967658],\n",
       "       [124.46009565, 125.82102574, 125.09634348, ..., 125.19101053,\n",
       "        123.71655668, 126.97435487],\n",
       "       [124.03686654, 126.85168493, 124.87212096, ..., 123.86738288,\n",
       "        127.8743298 , 123.25242621]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Container instances</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.3 44.4 44.38 ... 44.7 43.28</div><input id='attrs-1b6aeb99-e8fb-4df3-8e96-6a9f663089f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b6aeb99-e8fb-4df3-8e96-6a9f663089f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4602986-d4fe-4e76-b2ab-5100fbbf63b7' class='xr-var-data-in' type='checkbox'><label for='data-e4602986-d4fe-4e76-b2ab-5100fbbf63b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[46.29521147, 44.39522401, 44.37705831, ..., 43.58137136,\n",
       "        45.638849  , 45.41943604],\n",
       "       [43.38081312, 46.28276323, 45.00232699, ..., 45.21114045,\n",
       "        46.10076332, 44.60725663],\n",
       "       [44.98407035, 45.29915121, 44.85424703, ..., 44.64324849,\n",
       "        46.10831862, 43.5856041 ],\n",
       "       [45.14502937, 44.79431448, 44.81510804, ..., 46.66583893,\n",
       "        44.7019877 , 43.27951636]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Data Explorer pool</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>636.6 635.6 636.9 ... 636.5 635.9</div><input id='attrs-20ce6369-b7f0-43fc-9140-925b1b8a6039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20ce6369-b7f0-43fc-9140-925b1b8a6039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc10e90-e1d3-436a-a352-05322cf496a9' class='xr-var-data-in' type='checkbox'><label for='data-3dc10e90-e1d3-436a-a352-05322cf496a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[636.6326314 , 635.58607015, 636.86639091, ..., 637.64357158,\n",
       "        637.59582179, 636.2133568 ],\n",
       "       [637.31050417, 636.13388356, 636.67646185, ..., 636.37517073,\n",
       "        635.93985695, 636.13590345],\n",
       "       [636.40202911, 636.05134911, 636.38261729, ..., 635.63963119,\n",
       "        636.49800144, 636.30848139],\n",
       "       [635.4326578 , 636.71849994, 635.5783473 , ..., 633.68016996,\n",
       "        636.45966477, 635.92075537]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Data factory (V2)</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.032e+03 1.033e+03 ... 1.033e+03</div><input id='attrs-fbfafa58-a6ae-4886-bffe-9011b291d762' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbfafa58-a6ae-4886-bffe-9011b291d762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59fc66d1-e674-44bf-bd0b-b208934a3cdd' class='xr-var-data-in' type='checkbox'><label for='data-59fc66d1-e674-44bf-bd0b-b208934a3cdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1032.13946099, 1033.30711904, 1032.98725756, ..., 1032.00359877,\n",
       "        1032.32608362, 1033.97569732],\n",
       "       [1030.41699612, 1032.6325945 , 1031.72713995, ..., 1031.96869521,\n",
       "        1031.63655666, 1033.55572822],\n",
       "       [1031.7899893 , 1033.05318504, 1031.99274472, ..., 1032.44431461,\n",
       "        1032.37499069, 1031.28979696],\n",
       "       [1031.88804327, 1033.4091139 , 1031.85333931, ..., 1031.71430468,\n",
       "        1032.25948673, 1032.80348852]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Defender for Cloud</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>12.69 11.45 13.01 ... 12.01 13.4</div><input id='attrs-ce3fac90-68a5-4ca8-baa6-cb9932e6e5d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce3fac90-68a5-4ca8-baa6-cb9932e6e5d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9abbb30a-7229-42c5-8516-d9ef51793a83' class='xr-var-data-in' type='checkbox'><label for='data-9abbb30a-7229-42c5-8516-d9ef51793a83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[12.68539447, 11.45151095, 13.00504213, ..., 10.28138798,\n",
       "        12.94493257, 11.77103395],\n",
       "       [13.07820472, 12.54148823, 11.02907864, ..., 11.12521597,\n",
       "        12.05530755, 12.02759187],\n",
       "       [ 9.3299055 , 11.85960363, 11.52542602, ..., 10.90708162,\n",
       "        11.48502518, 13.13093012],\n",
       "       [11.58270616, 12.08986356, 11.73726523, ..., 11.32933235,\n",
       "        12.00768179, 13.39732822]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Disk</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.515e+03 1.514e+03 ... 1.514e+03</div><input id='attrs-9fce060e-f645-4bf8-8cc9-517dc1738f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fce060e-f645-4bf8-8cc9-517dc1738f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8595851-beae-4dff-b4ca-1e47d7a212a5' class='xr-var-data-in' type='checkbox'><label for='data-a8595851-beae-4dff-b4ca-1e47d7a212a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1514.84098956, 1513.60593772, 1511.80732019, ..., 1514.74029098,\n",
       "        1514.03517718, 1515.73028415],\n",
       "       [1514.31896954, 1513.56318582, 1514.94478286, ..., 1514.67343917,\n",
       "        1514.09920394, 1514.56392203],\n",
       "       [1514.42035708, 1513.69845217, 1515.09396696, ..., 1514.02543738,\n",
       "        1515.48435746, 1512.79625375],\n",
       "       [1514.5376221 , 1514.59497654, 1513.57470521, ..., 1514.42200507,\n",
       "        1514.06443265, 1513.96126343]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ExpressRoute circuit</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>78.24 77.74 78.52 ... 78.58 77.71</div><input id='attrs-d2d99931-b01e-4599-87ce-44222c628465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2d99931-b01e-4599-87ce-44222c628465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8376f6a-16a6-41da-8535-ca74673360f0' class='xr-var-data-in' type='checkbox'><label for='data-c8376f6a-16a6-41da-8535-ca74673360f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[78.24209045, 77.7368943 , 78.52419713, ..., 77.46486947,\n",
       "        77.84705671, 80.93695144],\n",
       "       [77.64284213, 78.77289571, 77.6318965 , ..., 79.81642585,\n",
       "        77.15953025, 78.13972202],\n",
       "       [79.99988381, 76.40518432, 80.38310929, ..., 77.81661265,\n",
       "        78.47487618, 78.13352091],\n",
       "       [76.92112471, 80.10899173, 76.24455692, ..., 78.6693364 ,\n",
       "        78.58165595, 77.71192378]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fabric Capacity</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.073e+04 2.073e+04 ... 2.073e+04</div><input id='attrs-98961733-8a10-4834-9c1c-b7257572025a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98961733-8a10-4834-9c1c-b7257572025a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddeade9a-b7e0-49eb-9728-c50fde5b87dd' class='xr-var-data-in' type='checkbox'><label for='data-ddeade9a-b7e0-49eb-9728-c50fde5b87dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[20730.69871315, 20730.24316411, 20731.03928544, ...,\n",
       "        20729.88280441, 20729.7065117 , 20730.18852937],\n",
       "       [20730.30997276, 20730.83349648, 20731.68727461, ...,\n",
       "        20731.56852724, 20730.0051284 , 20730.94402031],\n",
       "       [20729.39669493, 20731.42073314, 20729.62596983, ...,\n",
       "        20729.44340553, 20731.10536864, 20730.52316628],\n",
       "       [20730.43836343, 20730.45255666, 20730.45703238, ...,\n",
       "        20731.42245527, 20730.03476535, 20731.09052852]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Kubernetes service</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>348.7 345.5 347.8 ... 346.2 347.5</div><input id='attrs-734cdefe-fdeb-4d4d-83f6-01bd58d977fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-734cdefe-fdeb-4d4d-83f6-01bd58d977fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d7fdd5c-3365-4a3e-a703-fc0c058e799e' class='xr-var-data-in' type='checkbox'><label for='data-3d7fdd5c-3365-4a3e-a703-fc0c058e799e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[348.67775485, 345.49167213, 347.75443264, ..., 346.538986  ,\n",
       "        347.11782333, 347.38661758],\n",
       "       [345.78734233, 345.52500725, 347.49977475, ..., 348.3253767 ,\n",
       "        345.27178062, 348.18157071],\n",
       "       [347.84169384, 345.25853412, 347.77907127, ..., 346.88761977,\n",
       "        346.6811882 , 348.11116294],\n",
       "       [346.57882453, 347.08197865, 346.60662034, ..., 346.05072764,\n",
       "        346.22317475, 347.51093341]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Load balancer</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>111.6 113.8 113.4 ... 111.0 113.6</div><input id='attrs-132fde01-96e2-4e7d-b304-9f71329755c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-132fde01-96e2-4e7d-b304-9f71329755c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4f23321-fea4-473b-aa77-aa01bd89c108' class='xr-var-data-in' type='checkbox'><label for='data-d4f23321-fea4-473b-aa77-aa01bd89c108' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[111.58637032, 113.81080778, 113.42068654, ..., 113.20036489,\n",
       "        113.74570622, 111.55929645],\n",
       "       [112.71253704, 112.27036587, 113.50986979, ..., 113.24928699,\n",
       "        112.01967928, 112.89544318],\n",
       "       [111.39687025, 113.78130168, 111.86628148, ..., 114.88507577,\n",
       "        112.62234855, 112.12888094],\n",
       "       [112.14131343, 112.83438005, 112.85626282, ..., 113.70698984,\n",
       "        110.98763079, 113.59254785]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Log Analytics workspace</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>147.0 150.0 147.8 ... 148.0 150.8</div><input id='attrs-6ed7ce15-be56-48b7-992a-32fdf6d2dafa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ed7ce15-be56-48b7-992a-32fdf6d2dafa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51eecfa7-9cad-406e-b093-1ff1e2d27b47' class='xr-var-data-in' type='checkbox'><label for='data-51eecfa7-9cad-406e-b093-1ff1e2d27b47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[147.02486685, 149.97504497, 147.82520385, ..., 147.8026898 ,\n",
       "        146.57516964, 150.51375256],\n",
       "       [148.23439522, 149.92505028, 148.31246006, ..., 149.45446043,\n",
       "        148.10440587, 149.6624064 ],\n",
       "       [149.42985295, 148.52489404, 150.41191278, ..., 147.13610261,\n",
       "        150.10625076, 148.84085837],\n",
       "       [148.75724102, 149.60535148, 148.37061795, ..., 151.3860837 ,\n",
       "        148.00860585, 150.76841392]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Microsoft.Compute/capacityReservationGroups/capacityReservations</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>46.69 45.45 46.06 ... 43.3 45.84</div><input id='attrs-38055710-121b-45d9-8367-34527440bf4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38055710-121b-45d9-8367-34527440bf4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5121190a-12bf-49f0-ad69-08aca60a6a67' class='xr-var-data-in' type='checkbox'><label for='data-5121190a-12bf-49f0-ad69-08aca60a6a67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[46.69172586, 45.44714276, 46.05703344, ..., 45.39704066,\n",
       "        45.5678298 , 45.90752689],\n",
       "       [44.895898  , 44.36706819, 45.56548058, ..., 46.37105367,\n",
       "        44.98519497, 46.21222356],\n",
       "       [44.24213672, 47.79763038, 43.3881498 , ..., 45.19354835,\n",
       "        44.50313774, 45.55454013],\n",
       "       [44.1527223 , 46.90337105, 44.418436  , ..., 47.10937784,\n",
       "        43.29821575, 45.83537633]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Private endpoint</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.36 13.02 13.05 ... 12.19 14.2</div><input id='attrs-ac5286a9-2e07-4678-a46a-e54351568ef6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac5286a9-2e07-4678-a46a-e54351568ef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51867096-3f6c-4988-88ac-80afaebf58e1' class='xr-var-data-in' type='checkbox'><label for='data-51867096-3f6c-4988-88ac-80afaebf58e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[13.36432381, 13.02474327, 13.04881696, ..., 14.41059114,\n",
       "        12.47721656, 14.49029351],\n",
       "       [13.114315  , 15.01582812, 12.97385655, ..., 13.25362885,\n",
       "        13.51404648, 13.46626883],\n",
       "       [15.30680107, 12.44607259, 14.3253482 , ..., 13.83853224,\n",
       "        14.5126161 , 11.36996062],\n",
       "       [13.19901093, 13.62234785, 13.46653026, ..., 13.65513187,\n",
       "        12.18707844, 14.19735434]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Public IP address</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>122.8 121.2 123.1 ... 122.0 120.7</div><input id='attrs-8dc8d41b-def0-4a01-82d4-26f0c398382d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dc8d41b-def0-4a01-82d4-26f0c398382d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425928fd-b609-4f2f-be09-0abd36adfc94' class='xr-var-data-in' type='checkbox'><label for='data-425928fd-b609-4f2f-be09-0abd36adfc94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[122.76969806, 121.20827664, 123.13316265, ..., 120.84483805,\n",
       "        122.58557289, 121.14293963],\n",
       "       [121.81675234, 121.15616434, 122.98122349, ..., 123.06351504,\n",
       "        120.27344579, 121.64979978],\n",
       "       [122.0979265 , 121.20023925, 122.40828982, ..., 121.10198096,\n",
       "        122.3610067 , 122.18203651],\n",
       "       [121.78241007, 121.63184525, 121.78578951, ..., 122.42489783,\n",
       "        122.04911493, 120.68397233]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Reservation order</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.92 19.31 20.64 ... 20.02 20.65</div><input id='attrs-a208c927-2e4f-4426-ae52-b0cb87c41f91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a208c927-2e4f-4426-ae52-b0cb87c41f91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f469f6d-df8d-49d5-b505-805df77aa9d6' class='xr-var-data-in' type='checkbox'><label for='data-6f469f6d-df8d-49d5-b505-805df77aa9d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[21.91616605, 19.31358891, 20.642288  , ..., 21.83439271,\n",
       "        21.46950138, 20.56428659],\n",
       "       [21.2985072 , 21.34158111, 19.98827199, ..., 20.08136412,\n",
       "        21.02723723, 20.24732932],\n",
       "       [19.96489433, 21.15938103, 19.96783936, ..., 19.85654772,\n",
       "        22.04097874, 17.91325679],\n",
       "       [20.63505511, 20.60953617, 21.3501756 , ..., 20.22741297,\n",
       "        20.02279716, 20.65094822]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SQL database</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.673e+03 2.674e+03 ... 2.675e+03</div><input id='attrs-ce920b74-c3e8-4b5a-8996-ff09e61f4a69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce920b74-c3e8-4b5a-8996-ff09e61f4a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a480e83-1c5f-4fa1-b105-d9aee2e12580' class='xr-var-data-in' type='checkbox'><label for='data-0a480e83-1c5f-4fa1-b105-d9aee2e12580' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2673.260115  , 2674.4107865 , 2674.42104873, ..., 2671.41404754,\n",
       "        2671.54421837, 2674.07263627],\n",
       "       [2675.61541242, 2671.79978799, 2675.30014472, ..., 2673.64059654,\n",
       "        2673.94129562, 2674.04476063],\n",
       "       [2674.95227239, 2672.84111114, 2675.42474384, ..., 2674.38562087,\n",
       "        2673.27742382, 2674.78686555],\n",
       "       [2673.86221678, 2673.65872426, 2674.13968091, ..., 2674.24270923,\n",
       "        2673.8297238 , 2675.2789555 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SQL elastic pool</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.405e+03 1.406e+03 ... 1.405e+03</div><input id='attrs-99cdf0f1-5851-43a0-9dd8-1a4fb87cab67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99cdf0f1-5851-43a0-9dd8-1a4fb87cab67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b263db-93ce-4a3e-8379-7cba691df8c5' class='xr-var-data-in' type='checkbox'><label for='data-48b263db-93ce-4a3e-8379-7cba691df8c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1405.30125584, 1406.4870463 , 1407.78319566, ..., 1406.12022682,\n",
       "        1405.9183595 , 1407.09409815],\n",
       "       [1405.77649766, 1407.59649829, 1405.18251976, ..., 1406.84720846,\n",
       "        1405.14777632, 1406.97858519],\n",
       "       [1406.30720061, 1405.48142934, 1406.74453721, ..., 1405.08257785,\n",
       "        1406.38363116, 1404.26389191],\n",
       "       [1405.80701489, 1406.21206777, 1405.88396138, ..., 1404.86816675,\n",
       "        1407.17284748, 1404.90749951]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SQL managed instance</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>433.1 434.1 434.0 ... 433.2 433.4</div><input id='attrs-72e5e896-399d-4f4c-bbcc-605f738bf603' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72e5e896-399d-4f4c-bbcc-605f738bf603' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-757d2a7a-85ca-4cda-822a-064e0ad3e0f4' class='xr-var-data-in' type='checkbox'><label for='data-757d2a7a-85ca-4cda-822a-064e0ad3e0f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[433.13867669, 434.11490215, 434.03800273, ..., 433.93999339,\n",
       "        435.95261689, 430.46246079],\n",
       "       [433.70105686, 432.22765163, 433.22589761, ..., 433.00090923,\n",
       "        435.22465505, 431.83636173],\n",
       "       [433.68125824, 433.44157333, 433.1508517 , ..., 432.9651635 ,\n",
       "        433.62534848, 432.64297243],\n",
       "       [432.9899301 , 433.56766432, 433.56679103, ..., 433.83126341,\n",
       "        433.20573177, 433.44145257]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SQL server</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>62.31 63.49 62.81 ... 64.39 61.91</div><input id='attrs-a87635bd-d457-4029-a950-935a15788ca5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a87635bd-d457-4029-a950-935a15788ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b3b9a56-5ffd-4adf-8f60-bce2abe37320' class='xr-var-data-in' type='checkbox'><label for='data-7b3b9a56-5ffd-4adf-8f60-bce2abe37320' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[62.31137106, 63.48840021, 62.81168228, ..., 61.59459352,\n",
       "        62.27472739, 63.01975435],\n",
       "       [63.32497054, 64.10554913, 60.81119072, ..., 62.42112219,\n",
       "        64.11304178, 61.86177156],\n",
       "       [63.7193069 , 61.7425074 , 64.39235813, ..., 62.78152866,\n",
       "        61.73033247, 63.93493473],\n",
       "       [63.03955484, 63.3434668 , 62.69129915, ..., 62.44933667,\n",
       "        64.38563819, 61.90713416]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Search service</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>45.7 44.82 47.24 ... 45.58 44.72</div><input id='attrs-5954ef0d-8bf4-4a27-abb6-fe2a658e3901' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5954ef0d-8bf4-4a27-abb6-fe2a658e3901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2a6d2e7-d3fc-4fa6-bf84-0591cffc83ac' class='xr-var-data-in' type='checkbox'><label for='data-c2a6d2e7-d3fc-4fa6-bf84-0591cffc83ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[45.69824042, 44.81614692, 47.24401663, ..., 44.99514732,\n",
       "        46.03399941, 45.32406382],\n",
       "       [45.37267496, 47.66805503, 42.11142418, ..., 46.38558512,\n",
       "        43.77312719, 46.58245517],\n",
       "       [47.3538601 , 42.85439096, 47.75252657, ..., 46.04173411,\n",
       "        46.43784004, 43.34309742],\n",
       "       [45.13086651, 45.13907026, 45.30216051, ..., 45.34587863,\n",
       "        45.57588765, 44.72229648]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Storage account</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>325.1 324.2 324.8 ... 324.2 324.9</div><input id='attrs-8f3f58b3-786a-47a1-b723-829acf049d49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f3f58b3-786a-47a1-b723-829acf049d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe04d4f-c0e6-4c5b-805f-eea13e6d21d8' class='xr-var-data-in' type='checkbox'><label for='data-7fe04d4f-c0e6-4c5b-805f-eea13e6d21d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[325.07661923, 324.16641872, 324.7509944 , ..., 325.49379727,\n",
       "        327.03397069, 325.79091663],\n",
       "       [325.21713078, 323.49293754, 326.31585769, ..., 324.66440542,\n",
       "        324.74962155, 324.2036809 ],\n",
       "       [322.54440858, 326.82643033, 323.13001945, ..., 325.17770744,\n",
       "        326.22836457, 324.68508038],\n",
       "       [324.60541706, 324.45436533, 324.30622134, ..., 324.76052995,\n",
       "        324.19814437, 324.91601422]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Storage account (classic)</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>25.51 24.4 25.97 ... 25.86 25.1</div><input id='attrs-d642eb11-4e80-45bc-a7fa-f99dd85cbcc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d642eb11-4e80-45bc-a7fa-f99dd85cbcc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b17a66-4fd6-4f2a-82f6-11540ed4c2e5' class='xr-var-data-in' type='checkbox'><label for='data-b1b17a66-4fd6-4f2a-82f6-11540ed4c2e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[25.50873226, 24.39704401, 25.97012732, ..., 24.95199186,\n",
       "        23.42792843, 26.37576043],\n",
       "       [25.93755568, 23.7436811 , 25.4684566 , ..., 24.45654262,\n",
       "        26.28930826, 23.62800414],\n",
       "       [23.55826209, 25.26196358, 24.59353388, ..., 24.38256243,\n",
       "        24.10028   , 25.42705442],\n",
       "       [23.98796429, 25.84870091, 24.10525971, ..., 24.36880727,\n",
       "        25.85800371, 25.09893563]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Virtual machine</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.728e+03 1.73e+03 ... 1.728e+03</div><input id='attrs-ce01093d-8de0-4373-a858-124427e6edcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce01093d-8de0-4373-a858-124427e6edcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa515315-f864-474c-b102-89e7c594b958' class='xr-var-data-in' type='checkbox'><label for='data-aa515315-f864-474c-b102-89e7c594b958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1727.50621743, 1729.81888853, 1729.44554268, ..., 1727.84238435,\n",
       "        1728.56919604, 1726.17979465],\n",
       "       [1728.75728994, 1728.11980288, 1729.88355019, ..., 1727.99505924,\n",
       "        1729.2623608 , 1728.68645257],\n",
       "       [1729.74210154, 1727.70384716, 1729.26708053, ..., 1728.36642207,\n",
       "        1729.2442748 , 1729.48907838],\n",
       "       [1729.22120472, 1728.05273404, 1729.29758979, ..., 1727.83543218,\n",
       "        1729.43726209, 1727.78450152]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Virtual machine scale set</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>651.5 650.5 651.2 ... 651.2 652.2</div><input id='attrs-a5d20111-7898-40bf-a686-1473a58b78b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5d20111-7898-40bf-a686-1473a58b78b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4196224-a448-4979-8792-430b56907a08' class='xr-var-data-in' type='checkbox'><label for='data-b4196224-a448-4979-8792-430b56907a08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[651.48053818, 650.49460518, 651.21614876, ..., 649.49405756,\n",
       "        649.40439811, 652.9712855 ],\n",
       "       [650.59706527, 650.95433533, 650.6434094 , ..., 651.05670158,\n",
       "        650.69151011, 651.42774409],\n",
       "       [651.218012  , 650.79127797, 650.55366097, ..., 649.14261243,\n",
       "        653.06532068, 650.58316373],\n",
       "       [651.36952291, 651.87748895, 650.29729541, ..., 649.72697367,\n",
       "        651.19021352, 652.21281252]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Virtual network gateway</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>643.9 645.9 644.5 ... 643.8 645.6</div><input id='attrs-77f60311-cb44-4e86-a11e-da355fd54213' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77f60311-cb44-4e86-a11e-da355fd54213' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39d6d658-b4dd-4248-9819-1282c7c11039' class='xr-var-data-in' type='checkbox'><label for='data-39d6d658-b4dd-4248-9819-1282c7c11039' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[643.91966638, 645.93138135, 644.5087398 , ..., 646.18055961,\n",
       "        643.45126574, 645.3515746 ],\n",
       "       [645.99224237, 646.07446849, 643.78354141, ..., 644.83784295,\n",
       "        646.61913035, 643.66893512],\n",
       "       [646.35414919, 644.10344067, 646.10278331, ..., 645.66041074,\n",
       "        643.58667541, 645.3918549 ],\n",
       "       [644.84993019, 645.56487481, 644.75349121, ..., 646.42291893,\n",
       "        643.76204287, 645.60490937]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Total Cost</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.543e+04 3.539e+04 ... 3.538e+04</div><input id='attrs-963908d8-3800-4528-996f-e6ea212efd38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-963908d8-3800-4528-996f-e6ea212efd38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ab1bfc-4aa3-430d-a6bd-7e56eee1df32' class='xr-var-data-in' type='checkbox'><label for='data-49ab1bfc-4aa3-430d-a6bd-7e56eee1df32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[35427.80092355, 35389.18534643, 35440.2926921 , ...,\n",
       "        35417.30448803, 35364.45801558, 35404.55687746],\n",
       "       [35375.51071787, 35433.51077539, 35387.59867296, ...,\n",
       "        35379.77487385, 35433.97510702, 35374.44294808],\n",
       "       [35414.17120592, 35381.51525813, 35431.25326291, ...,\n",
       "        35349.14112747, 35380.23019997, 35408.53427134],\n",
       "       [35430.32450655, 35395.85472643, 35426.69130137, ...,\n",
       "        35379.92093598, 35449.06763126, 35384.56261865]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8727f92e-f519-47c2-8fea-45accd515abd' class='xr-section-summary-in' type='checkbox'  ><label for='section-8727f92e-f519-47c2-8fea-45accd515abd' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8b12f02-7f24-471a-9875-2ece97753ddc' class='xr-index-data-in' type='checkbox'/><label for='index-b8b12f02-7f24-471a-9875-2ece97753ddc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36da3f4d-1698-4aa9-9794-4051dca0ae16' class='xr-index-data-in' type='checkbox'/><label for='index-36da3f4d-1698-4aa9-9794-4051dca0ae16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ee87d31-995b-4727-accc-b07ac604ccf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6ee87d31-995b-4727-accc-b07ac604ccf0' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-14T16:36:57.156808</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.1</dd><dt><span>sampling_time :</span></dt><dd>40.21694207191467</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsa96387d0-c97c-454e-8d7b-70d2cc099c5b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsa96387d0-c97c-454e-8d7b-70d2cc099c5b\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "Data variables: (12/17)\n",
       "    smallest_eigval        (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "    perf_counter_diff      (chain, draw) float64 0.003518 0.003521 ... 0.003536\n",
       "    perf_counter_start     (chain, draw) float64 7.905e+04 ... 7.906e+04\n",
       "    reached_max_treedepth  (chain, draw) bool False False False ... False False\n",
       "    step_size              (chain, draw) float64 0.7163 0.7163 ... 0.6476 0.6476\n",
       "    lp                     (chain, draw) float64 -48.38 -41.27 ... -48.91 -43.98\n",
       "    ...                     ...\n",
       "    energy                 (chain, draw) float64 59.19 69.94 ... 62.03 60.77\n",
       "    energy_error           (chain, draw) float64 0.5699 -0.7431 ... -0.5511\n",
       "    process_time_diff      (chain, draw) float64 0.003519 0.003522 ... 0.003536\n",
       "    max_energy_error       (chain, draw) float64 0.6448 1.597 ... -0.484 -0.5511\n",
       "    largest_eigval         (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "    step_size_bar          (chain, draw) float64 0.6826 0.6826 ... 0.6713 0.6713\n",
       "Attributes:\n",
       "    created_at:                 2024-10-14T16:36:57.173268\n",
       "    arviz_version:              0.17.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.16.1\n",
       "    sampling_time:              40.21694207191467\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-83c6047a-e765-4fb1-9732-42d15be32ea3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83c6047a-e765-4fb1-9732-42d15be32ea3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35b21585-df94-47b5-9286-3ffd48aa7b3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35b21585-df94-47b5-9286-3ffd48aa7b3a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-ed4e9c51-d05a-4ec5-8a1f-e2de9c2d9d80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed4e9c51-d05a-4ec5-8a1f-e2de9c2d9d80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55ae6efd-c168-4a15-b341-7b08ad2a7e24' class='xr-var-data-in' type='checkbox'><label for='data-55ae6efd-c168-4a15-b341-7b08ad2a7e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-6b9f3167-ff43-497a-b567-c0610dfc6b35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b9f3167-ff43-497a-b567-c0610dfc6b35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a60c6978-10ee-499b-9dbd-d6b57b321efb' class='xr-var-data-in' type='checkbox'><label for='data-a60c6978-10ee-499b-9dbd-d6b57b321efb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cfbc8fda-dd7d-4613-9c88-f855911aa943' class='xr-section-summary-in' type='checkbox'  ><label for='section-cfbc8fda-dd7d-4613-9c88-f855911aa943' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-58182c24-4f5d-4607-8f9f-02a678f7687f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58182c24-4f5d-4607-8f9f-02a678f7687f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b3b819-a4b0-4b2a-ba81-e4cde59e47dd' class='xr-var-data-in' type='checkbox'><label for='data-f1b3b819-a4b0-4b2a-ba81-e4cde59e47dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003518 0.003521 ... 0.003536</div><input id='attrs-02f7e40c-1606-4e0c-9726-d529c25d9627' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02f7e40c-1606-4e0c-9726-d529c25d9627' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f1adeb-14bd-4fcd-868b-3053926c4e1e' class='xr-var-data-in' type='checkbox'><label for='data-81f1adeb-14bd-4fcd-868b-3053926c4e1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00351767, 0.00352142, 0.00352288, ..., 0.00353275, 0.00351663,\n",
       "        0.00352767],\n",
       "       [0.00346562, 0.00338488, 0.00331   , ..., 0.00327142, 0.00329512,\n",
       "        0.00331162],\n",
       "       [0.00351592, 0.00352354, 0.00352733, ..., 0.00339892, 0.00341538,\n",
       "        0.00341825],\n",
       "       [0.00349633, 0.00348004, 0.003488  , ..., 0.0036005 , 0.00371   ,\n",
       "        0.00353629]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.905e+04 7.905e+04 ... 7.906e+04</div><input id='attrs-f12b39c8-502b-405f-8411-f7e65e7df78e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f12b39c8-502b-405f-8411-f7e65e7df78e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc1f8a06-8429-470c-a4df-b2e04203597d' class='xr-var-data-in' type='checkbox'><label for='data-cc1f8a06-8429-470c-a4df-b2e04203597d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[79052.07106058, 79052.07496371, 79052.07887308, ...,\n",
       "        79055.97396367, 79055.97787721, 79055.98176942],\n",
       "       [79052.28725121, 79052.291096  , 79052.29484025, ...,\n",
       "        79056.17377967, 79056.17740608, 79056.18104812],\n",
       "       [79052.13113725, 79052.13503379, 79052.13893504, ...,\n",
       "        79056.03602075, 79056.03978283, 79056.04356067],\n",
       "       [79052.09993783, 79052.10381379, 79052.10767121, ...,\n",
       "        79055.98607892, 79055.99865604, 79056.00275779]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0ae3de1f-3938-443c-905d-a695f8fc1999' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ae3de1f-3938-443c-905d-a695f8fc1999' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f96b7663-276c-466c-984b-1f468b9a1c3e' class='xr-var-data-in' type='checkbox'><label for='data-f96b7663-276c-466c-984b-1f468b9a1c3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7163 0.7163 ... 0.6476 0.6476</div><input id='attrs-b5921670-cadd-493d-867b-22e1f9e98be9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5921670-cadd-493d-867b-22e1f9e98be9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9423d79b-9949-4a83-b53b-feb426dcfb3a' class='xr-var-data-in' type='checkbox'><label for='data-9423d79b-9949-4a83-b53b-feb426dcfb3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.71629884, 0.71629884, 0.71629884, ..., 0.71629884, 0.71629884,\n",
       "        0.71629884],\n",
       "       [0.64440342, 0.64440342, 0.64440342, ..., 0.64440342, 0.64440342,\n",
       "        0.64440342],\n",
       "       [0.81704613, 0.81704613, 0.81704613, ..., 0.81704613, 0.81704613,\n",
       "        0.81704613],\n",
       "       [0.64760373, 0.64760373, 0.64760373, ..., 0.64760373, 0.64760373,\n",
       "        0.64760373]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-48.38 -41.27 ... -48.91 -43.98</div><input id='attrs-2aa51dea-bed2-4e87-8a08-b8e703f9bffe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2aa51dea-bed2-4e87-8a08-b8e703f9bffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9768b56-3dc9-49f4-b3eb-7a64d4200753' class='xr-var-data-in' type='checkbox'><label for='data-e9768b56-3dc9-49f4-b3eb-7a64d4200753' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-48.37804029, -41.26555925, -46.23382341, ..., -46.06591874,\n",
       "        -54.98137155, -50.84219822],\n",
       "       [-40.83870718, -49.85704648, -48.1736438 , ..., -41.66148804,\n",
       "        -46.48422654, -42.93252802],\n",
       "       [-48.31886183, -44.74364835, -46.91374012, ..., -47.38254475,\n",
       "        -46.7301731 , -51.83640703],\n",
       "       [-39.16730842, -38.30496544, -37.77252623, ..., -47.47231998,\n",
       "        -48.91320378, -43.9845125 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 5 -2 2 -6 -1 ... -4 -7 -5 6 -3 -3</div><input id='attrs-1050095c-d542-41bd-b8a9-718180ba35d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1050095c-d542-41bd-b8a9-718180ba35d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02486543-efa1-42ff-8133-b01a171dc5db' class='xr-var-data-in' type='checkbox'><label for='data-02486543-efa1-42ff-8133-b01a171dc5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 2,  5, -2, ...,  1, -2,  3],\n",
       "       [ 7, -6, -6, ...,  3, -5,  4],\n",
       "       [ 5, -5, -4, ...,  2, -6,  3],\n",
       "       [ 5,  4,  4, ...,  6, -3, -3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6865 0.6551 ... 0.9099 0.9837</div><input id='attrs-3f818c38-0d0e-4e47-aad6-e7bb7c8814f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f818c38-0d0e-4e47-aad6-e7bb7c8814f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f072b75b-4fd7-400f-950f-faf6d6310905' class='xr-var-data-in' type='checkbox'><label for='data-f072b75b-4fd7-400f-950f-faf6d6310905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.68652543, 0.65511805, 0.68068015, ..., 1.        , 0.59934815,\n",
       "        1.        ],\n",
       "       [0.97066967, 0.48306324, 0.8842961 , ..., 0.9476086 , 0.56202593,\n",
       "        0.80057623],\n",
       "       [0.93622727, 0.99819629, 0.74785656, ..., 0.74321927, 0.75238275,\n",
       "        0.66024731],\n",
       "       [0.77758769, 0.88658317, 0.63941226, ..., 0.90596802, 0.90985889,\n",
       "        0.98370986]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fc5bfd33-3bd6-4e5b-9848-f84dfe031fa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc5bfd33-3bd6-4e5b-9848-f84dfe031fa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45fcede0-4874-4b33-9492-3cf5061ba7e4' class='xr-var-data-in' type='checkbox'><label for='data-45fcede0-4874-4b33-9492-3cf5061ba7e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.0 7.0 7.0 7.0 ... 7.0 7.0 7.0 7.0</div><input id='attrs-e61d87ae-6c3c-440f-b0c5-0b8858234544' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e61d87ae-6c3c-440f-b0c5-0b8858234544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1808f78e-91d8-4112-ab77-62eb067245c5' class='xr-var-data-in' type='checkbox'><label for='data-1808f78e-91d8-4112-ab77-62eb067245c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7., 7., 7., ..., 7., 7., 7.],\n",
       "       [7., 7., 7., ..., 7., 7., 7.],\n",
       "       [7., 7., 7., ..., 7., 7., 7.],\n",
       "       [7., 7., 7., ..., 7., 7., 7.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3</div><input id='attrs-857ea26e-f15b-4024-b9b0-b8798a8cef47' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-857ea26e-f15b-4024-b9b0-b8798a8cef47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf9af7c6-56e3-414b-8e95-bfe2bbd55118' class='xr-var-data-in' type='checkbox'><label for='data-bf9af7c6-56e3-414b-8e95-bfe2bbd55118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>59.19 69.94 56.56 ... 62.03 60.77</div><input id='attrs-69494205-4393-4ab6-aeee-7b25064f3137' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69494205-4393-4ab6-aeee-7b25064f3137' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5b8199-19b3-49e8-8ebb-a899b548910d' class='xr-var-data-in' type='checkbox'><label for='data-0a5b8199-19b3-49e8-8ebb-a899b548910d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[59.19202727, 69.94264678, 56.55935925, ..., 62.76138727,\n",
       "        71.74375446, 72.92791546],\n",
       "       [50.00147736, 63.04086445, 63.88374561, ..., 54.33553284,\n",
       "        59.15881199, 63.30703891],\n",
       "       [59.38345517, 59.39593001, 61.68370096, ..., 61.73980239,\n",
       "        66.19459146, 67.4609925 ],\n",
       "       [51.43672896, 48.65681784, 52.55825758, ..., 67.93725468,\n",
       "        62.026976  , 60.77177842]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5699 -0.7431 ... 0.1798 -0.5511</div><input id='attrs-81f7a55e-06db-41ac-a866-93d6de036f75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81f7a55e-06db-41ac-a866-93d6de036f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62aaaf29-3bee-4dcd-96e7-46f817c68186' class='xr-var-data-in' type='checkbox'><label for='data-62aaaf29-3bee-4dcd-96e7-46f817c68186' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.56988345, -0.74307471,  0.64494552, ..., -0.62994084,\n",
       "         0.9264314 , -0.30539705],\n",
       "       [ 0.05717807,  1.00389958, -0.21639577, ..., -0.19345674,\n",
       "         0.74384727, -0.35734524],\n",
       "       [ 0.160213  , -0.55991049,  0.25552156, ...,  0.31086255,\n",
       "        -0.01798309,  0.5654037 ],\n",
       "       [-0.20080498, -0.11774909, -0.0673971 , ..., -0.21628112,\n",
       "         0.17975537, -0.55106967]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003519 0.003522 ... 0.003536</div><input id='attrs-1d87791a-d339-429a-a292-ba955365bb5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d87791a-d339-429a-a292-ba955365bb5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e58e17f-4db2-4e4a-8221-e33e64546509' class='xr-var-data-in' type='checkbox'><label for='data-1e58e17f-4db2-4e4a-8221-e33e64546509' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.003519, 0.003522, 0.003523, ..., 0.003532, 0.003516, 0.003528],\n",
       "       [0.003465, 0.003385, 0.003311, ..., 0.003272, 0.003295, 0.003312],\n",
       "       [0.003517, 0.003524, 0.003527, ..., 0.003399, 0.003415, 0.003418],\n",
       "       [0.003497, 0.003479, 0.003489, ..., 0.003601, 0.003709, 0.003536]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6448 1.597 ... -0.484 -0.5511</div><input id='attrs-7cf27fd3-02bd-4e11-86de-a186cf3dd0ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cf27fd3-02bd-4e11-86de-a186cf3dd0ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258db855-3d2b-4a06-8de1-c445dc77b176' class='xr-var-data-in' type='checkbox'><label for='data-258db855-3d2b-4a06-8de1-c445dc77b176' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.64475054,  1.59677021,  0.69909448, ..., -0.95808979,\n",
       "         0.9264314 , -0.39342496],\n",
       "       [-0.42491043,  1.39718993, -1.11322135, ..., -0.33167632,\n",
       "         1.12510684,  0.69621888],\n",
       "       [-0.81763962, -0.71601024,  0.58037489, ...,  0.54881897,\n",
       "         0.99311105,  0.86077905],\n",
       "       [ 0.64866572,  0.28491416,  0.97443069, ...,  0.2723347 ,\n",
       "        -0.48399006, -0.55106967]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-19291282-aba5-44ad-8bab-c1e108bf19f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19291282-aba5-44ad-8bab-c1e108bf19f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a42af9e-653d-458b-b1b0-777bf24bc1bb' class='xr-var-data-in' type='checkbox'><label for='data-0a42af9e-653d-458b-b1b0-777bf24bc1bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6826 0.6826 ... 0.6713 0.6713</div><input id='attrs-93cdd560-d211-4e5c-88a4-dbc92030ffc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93cdd560-d211-4e5c-88a4-dbc92030ffc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8baba2ee-4fbc-4ca3-b16d-d52073f1f09d' class='xr-var-data-in' type='checkbox'><label for='data-8baba2ee-4fbc-4ca3-b16d-d52073f1f09d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.68256776, 0.68256776, 0.68256776, ..., 0.68256776, 0.68256776,\n",
       "        0.68256776],\n",
       "       [0.70198675, 0.70198675, 0.70198675, ..., 0.70198675, 0.70198675,\n",
       "        0.70198675],\n",
       "       [0.693402  , 0.693402  , 0.693402  , ..., 0.693402  , 0.693402  ,\n",
       "        0.693402  ],\n",
       "       [0.67133419, 0.67133419, 0.67133419, ..., 0.67133419, 0.67133419,\n",
       "        0.67133419]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ce1bdf1-703c-4f66-9bbb-3c83a3188240' class='xr-section-summary-in' type='checkbox'  ><label for='section-2ce1bdf1-703c-4f66-9bbb-3c83a3188240' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f0bccbbf-5c73-4985-8213-891a971c8846' class='xr-index-data-in' type='checkbox'/><label for='index-f0bccbbf-5c73-4985-8213-891a971c8846' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cfc36492-7dc4-43aa-a6f2-1079febba9e3' class='xr-index-data-in' type='checkbox'/><label for='index-cfc36492-7dc4-43aa-a6f2-1079febba9e3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e9c713c-13fc-4683-b6bb-5797f79f40a2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e9c713c-13fc-4683-b6bb-5797f79f40a2' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-14T16:36:57.173268</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.1</dd><dt><span>sampling_time :</span></dt><dd>40.21694207191467</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022d4f72-1066-4a2e-a485-869717b4f9fc",
   "metadata": {},
   "source": [
    "### Example Output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad21848-fab2-4667-9805-24dbffa4a074",
   "metadata": {},
   "source": [
    "The simple example below shows how to extract the sampled values for Total Cost and calculated the expected budget value (mean), as well as the 99% percentile “worst-case” scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d811fb0-c6cf-4078-ac3a-0cc61eb4e46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fold the the monte carlo chains into a single dimension and save the 'Total Cost' chain as a variable\n",
    "total_cost_samples = az.extract(trace)['Total Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a94e4ad-a4b2-47ef-90cb-49b9765d9ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the mean and 99th percentile\n",
    "mean_value = np.mean(total_cost_samples)\n",
    "percentile_99 = np.percentile(total_cost_samples, 99)\n",
    "\n",
    "# Plot the histogram\n",
    "plt.hist(total_cost_samples, bins=30, alpha=0.7, color='lightblue')\n",
    "\n",
    "# Add vertical lines for the mean and 99th percentile\n",
    "plt.axvline(mean_value, color='b', linestyle='--', label=f'Mean: {mean_value:.2f}')\n",
    "plt.axvline(percentile_99, color='r', linestyle='--', label=f'99th Percentile: {percentile_99:.2f}')\n",
    "\n",
    "# Add labels and legend\n",
    "plt.title('Histogram with Mean and 99th Percentile')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f60f2251-25cf-437f-9f45-c6a46058f5b3",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}