{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import uproot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.insert(0, \"/home/belle/zhangboy/B2SW/2025_VirginiaTech/sysvar/src/\")\n",
    "\n",
    "# Load reconstructed events from 1/ab generic MC\n",
    "df_nominal = uproot.concatenate(['/home/belle/zhangboy/B2SW/2025_VirginiaTech/B2Denu_MC.root:nominal_tree'],library=\"pd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>index</th>\n",
       "      <th>ell_charge</th>\n",
       "      <th>ell_p</th>\n",
       "      <th>ell_theta</th>\n",
       "      <th>ell_PDG</th>\n",
       "      <th>ell_mcPDG</th>\n",
       "      <th>B0_Mbc</th>\n",
       "      <th>B0_deltaE</th>\n",
       "      <th>B0_CMS3_weMissM2</th>\n",
       "      <th>template</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.205724</td>\n",
       "      <td>0.972190</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-2212.0</td>\n",
       "      <td>5.180572</td>\n",
       "      <td>-2.300313</td>\n",
       "      <td>4.148000</td>\n",
       "      <td>bkg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.299124</td>\n",
       "      <td>0.815572</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.037718</td>\n",
       "      <td>-2.107326</td>\n",
       "      <td>1.837669</td>\n",
       "      <td>bkg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.415534</td>\n",
       "      <td>1.193000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.114793</td>\n",
       "      <td>-1.812358</td>\n",
       "      <td>1.463991</td>\n",
       "      <td>bkg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.362656</td>\n",
       "      <td>1.274201</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.152764</td>\n",
       "      <td>-2.625380</td>\n",
       "      <td>5.461838</td>\n",
       "      <td>bkg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.451889</td>\n",
       "      <td>1.221380</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>5.275530</td>\n",
       "      <td>-1.387965</td>\n",
       "      <td>1.775905</td>\n",
       "      <td>bkg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>272603</th>\n",
       "      <td>272603</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.984586</td>\n",
       "      <td>0.495626</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.173876</td>\n",
       "      <td>-1.208834</td>\n",
       "      <td>0.248509</td>\n",
       "      <td>$D^\\ast\\ell\\nu$</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272604</th>\n",
       "      <td>272604</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.054710</td>\n",
       "      <td>1.565140</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>5.200073</td>\n",
       "      <td>-1.620619</td>\n",
       "      <td>1.685410</td>\n",
       "      <td>$D^\\ast\\ell\\nu$</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272605</th>\n",
       "      <td>272605</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.768823</td>\n",
       "      <td>0.861458</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>5.248800</td>\n",
       "      <td>-0.455493</td>\n",
       "      <td>-0.224378</td>\n",
       "      <td>$D^\\ast\\ell\\nu$</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272606</th>\n",
       "      <td>272606</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.313779</td>\n",
       "      <td>2.005175</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.085501</td>\n",
       "      <td>-1.568251</td>\n",
       "      <td>0.339968</td>\n",
       "      <td>$D^\\ast\\ell\\nu$</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>272607</th>\n",
       "      <td>272607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684441</td>\n",
       "      <td>1.459065</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>5.010036</td>\n",
       "      <td>-2.053994</td>\n",
       "      <td>1.337592</td>\n",
       "      <td>$D^\\ast\\ell\\nu$</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272608 rows × 11 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         index  ell_charge     ell_p  ell_theta  ell_PDG  ell_mcPDG    B0_Mbc  \\\n",
       "0            0        -1.0  1.205724   0.972190     11.0    -2212.0  5.180572   \n",
       "1            1        -1.0  1.299124   0.815572     11.0       11.0  5.037718   \n",
       "2            2        -1.0  1.415534   1.193000     11.0       11.0  5.114793   \n",
       "3            3        -1.0  0.362656   1.274201     11.0       11.0  5.152764   \n",
       "4            4         1.0  1.451889   1.221380    -11.0      -11.0  5.275530   \n",
       "...        ...         ...       ...        ...      ...        ...       ...   \n",
       "272603  272603        -1.0  1.984586   0.495626     11.0       11.0  5.173876   \n",
       "272604  272604         1.0  1.054710   1.565140    -11.0      -11.0  5.200073   \n",
       "272605  272605         1.0  2.768823   0.861458    -11.0      -11.0  5.248800   \n",
       "272606  272606        -1.0  1.313779   2.005175     11.0       11.0  5.085501   \n",
       "272607  272607         1.0  0.684441   1.459065    -11.0      -11.0  5.010036   \n",
       "\n",
       "        B0_deltaE  B0_CMS3_weMissM2         template  channel  \n",
       "0       -2.300313          4.148000              bkg        0  \n",
       "1       -2.107326          1.837669              bkg        0  \n",
       "2       -1.812358          1.463991              bkg        0  \n",
       "3       -2.625380          5.461838              bkg        0  \n",
       "4       -1.387965          1.775905              bkg        0  \n",
       "...           ...               ...              ...      ...  \n",
       "272603  -1.208834          0.248509  $D^\\ast\\ell\\nu$        0  \n",
       "272604  -1.620619          1.685410  $D^\\ast\\ell\\nu$        0  \n",
       "272605  -0.455493         -0.224378  $D^\\ast\\ell\\nu$        0  \n",
       "272606  -1.568251          0.339968  $D^\\ast\\ell\\nu$        0  \n",
       "272607  -2.053994          1.337592  $D^\\ast\\ell\\nu$        0  \n",
       "\n",
       "[272608 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nominal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose a color-blind-friendly color palette\n",
    "import seaborn as sns\n",
    "friendly_c = sns.color_palette(\"colorblind\")\n",
    "friendly_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the fitting variable\n",
    "def plot_fit_var(var, bins):\n",
    "    # Group fitting variable by categories\n",
    "    category_order = ['bkg', r'$D^\\ast\\ell\\nu$', r'$D\\ell\\nu$']\n",
    "    groups = [df_nominal[df_nominal['template'] == cat][var] for cat in category_order]\n",
    "    \n",
    "    # Plot\n",
    "    plt.hist(groups, bins=bins, stacked=True, label=category_order, color=friendly_c[:3])\n",
    "    plt.xlabel('$M_{miss}^2$  [GeV^2/c^4]')\n",
    "    plt.ylabel('Count')\n",
    "    plt.title('Stacked Histogram of fitting variable by category')\n",
    "    plt.legend(title='Category')\n",
    "    plt.grid()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose the fitting variable to be the missing mass square, plot it\n",
    "b1 = np.linspace(-2,8,21)\n",
    "plot_fit_var(var='B0_CMS3_weMissM2',bins=b1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: The histograms above are from MC only, i.e. they don't take the PID Data/MC discrepency into account. We need to correct them before using as the fitting model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a. Load LID correction weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import yaml\n",
    "\n",
    "# eID_eff_yaml_path = '/home/belle/zhangboy/B2SW/2025_VirginiaTech/sysvar/configs/MC15ri/eID_eff.yaml'\n",
    "# eID_fake_yaml_path = '/home/belle/zhangboy/B2SW/2025_VirginiaTech/sysvar/configs/MC15ri/eID_fake.yaml'\n",
    "\n",
    "# with open(eID_eff_yaml_path, 'r') as file:\n",
    "#     eID_eff_yaml = yaml.safe_load(file)\n",
    "# with open(eID_fake_yaml_path, 'r') as file:\n",
    "#     eID_fake_yaml = yaml.safe_load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eID_eff_yaml['table_paths'] = '/home/belle/zhangboy/B2SW/2025_VirginiaTech/LID_tables_MC15ri'\n",
    "# eID_fake_yaml['table_paths'] = '/home/belle/zhangboy/B2SW/2025_VirginiaTech/LID_tables_MC15ri'\n",
    "\n",
    "# # Normal theta binning: /group/belle2/users2022/unok/leptonid/combination/perf/PID/methods/moriond_2024/proc13prompt/MC15ri/v0_coarse\n",
    "# # Dense theta binning: /group/belle2/users2022/unok/leptonid/combination/perf/PID/methods/moriond_2024/proc13prompt/MC15ri/v0_dense\n",
    "\n",
    "# # Save to the same file or a new file\n",
    "# with open(eID_eff_yaml_path, 'w') as file: # or 'new_config.yaml'\n",
    "#     yaml.dump(eID_eff_yaml, file, default_flow_style=False, sort_keys=False)\n",
    "# with open(eID_fake_yaml_path, 'w') as file: # or 'new_config.yaml'\n",
    "#     yaml.dump(eID_fake_yaml, file, default_flow_style=False, sort_keys=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ⚠️ Try to install `particle` library if you get an error complaining about it\n",
    "`!pip install --user particle`\n",
    "### If the sysvar library is not found, you can install it by following the instruction [here](https://gitlab.desy.de/itsaklid/sysvar), the `sys.path.insert` should be changed to where your installation is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING : load_covariance_matrix: 275 :  Explicit covariance matrix was not found in config under None or cov_matrix_path and will be built from the specified uncertainties.\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "INFO : add_weights_to_dataframe: 84 :  ell_eID_eff_weight does not exist. Adding it to dataframe\n",
      "WARNING : load_covariance_matrix: 275 :  Explicit covariance matrix was not found in config under None or cov_matrix_path and will be built from the specified uncertainties.\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "INFO : add_weights_to_dataframe: 84 :  ell_eID_fake_weight does not exist. Adding it to dataframe\n"
     ]
    }
   ],
   "source": [
    "# Load the PID weights saved from systematic correction framework\n",
    "from sysvar import add_weights_to_dataframe\n",
    "\n",
    "add_weights_to_dataframe(\n",
    "    df = df_nominal,\n",
    "    systematic= \"eID_eff\",\n",
    "    MC_production= \"MC15ri\",\n",
    "    prefix= \"ell\",\n",
    "    weightname =\"eID_eff_weight\",\n",
    "    #overwrite: False,\n",
    "    #Nvar: 0\n",
    ")\n",
    "\n",
    "add_weights_to_dataframe(\n",
    "    df = df_nominal,\n",
    "    systematic= \"eID_fake\",\n",
    "    MC_production= \"MC15ri\",\n",
    "    prefix= \"ell\",\n",
    "    weightname =\"eID_fake_weight\",\n",
    "    #overwrite: False,\n",
    "    #Nvar: 0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample 360/fb MC as data to be fitted later\n",
    "df_data = df_nominal.sample(frac=0.36).reset_index(drop=True)\n",
    "\n",
    "# if multiple correction weights exist, combine them\n",
    "# df_nominal[\"total_weight\"] = df_nominal[[\"ell_eID_eff_weight\", \"ell_eID_fake_weight\"]].product(axis = 1)\n",
    "df_nominal[\"total_weight\"] = df_nominal[\"ell_eID_eff_weight\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b. Build the nominal model with MC stat uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare the input for model bin counts and staterror (MC stat)\n",
    "category_order = ['bkg', r'$D^\\ast\\ell\\nu$', r'$D\\ell\\nu$']\n",
    "fit_var_col = 'B0_CMS3_weMissM2'\n",
    "weight_var_col = 'ell_eID_eff_weight'\n",
    "b1 = np.linspace(-2,8,21)\n",
    "groups = [df_nominal[df_nominal['template'] == cat][[fit_var_col, weight_var_col]] for cat in category_order]\n",
    "\n",
    "# calculate the histogram with PID correction weights (consider eff correction only)\n",
    "hists = {}\n",
    "MC_stat_error = {}\n",
    "for i, name in enumerate(category_order):\n",
    "    counts, edges = np.histogram(groups[i][fit_var_col],bins=b1, weights=groups[i][weight_var_col])\n",
    "    staterr_squared, _ = np.histogram(groups[i][fit_var_col],bins=b1, weights=groups[i][weight_var_col]**2)\n",
    "    hists[name] = counts.round(1)\n",
    "    MC_stat_error[name] = np.sqrt(staterr_squared).round(1)\n",
    "\n",
    "data, _ = np.histogram(df_data[fit_var_col],bins=b1, weights=df_data[weight_var_col])\n",
    "# print(hists)\n",
    "# print(MC_stat_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the pyhf workspace to store the model\n",
    "channels = []\n",
    "observations = []\n",
    "measurements = [{\"name\": \"D_ell_nu\", \"config\": {\"poi\": \"$D\\\\ell\\\\nu$_norm\", \"parameters\": []}}]\n",
    "version = \"1.0.0\"\n",
    "\n",
    "####### Store 360/fb MC as real data to be fitted ######\n",
    "observations.append({\n",
    "    'name': f'channel_1',\n",
    "    'data': data.round(0).tolist()  # Extract nominal values from uncertainties\n",
    "})\n",
    "\n",
    "###### Store 1/ab MC as the model #######\n",
    "# Initialize channel structure\n",
    "channels.append({\n",
    "    'name': f'channel_1',\n",
    "    'samples': []\n",
    "})\n",
    "\n",
    "# Loop over each fitting component in the channel\n",
    "for sample_index, sample_name in enumerate(category_order):\n",
    "    # Add the nominal template data for the sample\n",
    "    channels[0]['samples'].append({\n",
    "        'name': sample_name,\n",
    "        'data': hists[sample_name].tolist(),\n",
    "        'modifiers': [\n",
    "            {\n",
    "                'name': sample_name+'_norm',\n",
    "                'type': 'normfactor',\n",
    "                'data': None  # Normalization factor modifier\n",
    "            }\n",
    "        ]\n",
    "    })\n",
    "\n",
    "    # Add uncertainty modifiers for MC statistical errors\n",
    "    channels[0]['samples'][sample_index]['modifiers'].append({\n",
    "        'name': 'MCstat_ch1',\n",
    "        'type': 'staterror',\n",
    "        'data': MC_stat_error[sample_name].tolist()\n",
    "    })\n",
    "\n",
    "    # Define parameter bounds based on whether it's a background or signal sample\n",
    "    if sample_name.startswith('bkg'):\n",
    "        par_config = {\"name\": sample_name+'_norm', \"bounds\": [[0, 2]], \"inits\": [1.0], \"fixed\":False}\n",
    "    else:\n",
    "        par_config = {\"name\": sample_name+'_norm', \"bounds\": [[-5, 5]], \"inits\": [1.0]}\n",
    "\n",
    "    # Add parameter configuration if it doesn't already exist\n",
    "    if par_config not in measurements[0]['config']['parameters']:\n",
    "        measurements[0]['config']['parameters'].append(par_config)\n",
    "\n",
    "# Construct the workspace dictionary (input to pyhf)\n",
    "spec = {\n",
    "    'channels': channels,\n",
    "    'measurements': measurements,\n",
    "    'observations': observations,\n",
    "    'version': version\n",
    "}\n",
    "# cabinetry.workspace.save(spec, 'workspace_1.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c.  Prepare the sysvar config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'output_filepath': './test_output.root',\n",
       " 'reco_channel_id_column': 'channel',\n",
       " 'reco_channels': {'channel1': [0], 'channel2': [1]},\n",
       " 'template_id_column': 'template',\n",
       " 'templates': ['signal', 'bkg'],\n",
       " 'total_weight': 'total_weight',\n",
       " 'MC_prod': 'MC15ri',\n",
       " 'Nvar': 500,\n",
       " 'bins': {'channel1': {'fit_variable1': [0, 0.2, 0.4, 0.6, 0.8, 1],\n",
       "   'fit_variable2': [1, 2, 3, 4]},\n",
       "  'channel2': {'fit_variable1': [0, 0.2, 0.4, 0.6, 0.8, 1],\n",
       "   'fit_variable2': [1, 2, 3, 4]}},\n",
       " 'systematics': {'charged_slow_pi': {'weight': 'charged_weight',\n",
       "   'prefices': 'slow_pi',\n",
       "   'reco_channels': {'include': None, 'exclude': None}},\n",
       "  'neutral_slow_pi': {'weight': 'weight',\n",
       "   'prefices': 'slow_pi',\n",
       "   'reco_channels': {'include': None, 'exclude': None}}}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a template config file\n",
    "\n",
    "from sysvar.utils import read_yaml\n",
    "\n",
    "settings = read_yaml(\"study_setup\", \"MC15ri\")\n",
    "settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'output_filepath': './test_output.root',\n",
       " 'reco_channel_id_column': 'channel',\n",
       " 'reco_channels': {'channel1': [0]},\n",
       " 'template_id_column': 'template',\n",
       " 'templates': ['$D\\\\ell\\\\nu$', '$D^\\\\ast\\\\ell\\\\nu$', 'bkg'],\n",
       " 'total_weight': 'total_weight',\n",
       " 'MC_prod': 'MC15ri',\n",
       " 'Nvar': 500,\n",
       " 'bins': {'channel1': {'B0_CMS3_weMissM2': array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,\n",
       "           3.5,  4. ,  4.5,  5. ,  5.5,  6. ,  6.5,  7. ,  7.5,  8. ])}},\n",
       " 'systematics': {'eID_eff': {'weight': 'eID_eff_weight',\n",
       "   'prefices': 'ell',\n",
       "   'reco_channels': {'include': None, 'exclude': None},\n",
       "   'templates': ['$D\\\\ell\\\\nu$', '$D^\\\\ast\\\\ell\\\\nu$', 'bkg']},\n",
       "  'eID_fake': {'weight': 'eID_fake_weight',\n",
       "   'prefices': 'ell',\n",
       "   'reco_channels': {'include': None, 'exclude': None},\n",
       "   'templates': ['$D\\\\ell\\\\nu$', '$D^\\\\ast\\\\ell\\\\nu$', 'bkg']}}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the relevant systematics to study\n",
    "\n",
    "settings['reco_channels'] = {'channel1':[0]}\n",
    "settings['templates'] = [r'$D\\ell\\nu$', r'$D^\\ast\\ell\\nu$', 'bkg']\n",
    "settings['bins'] = {'channel1': {'B0_CMS3_weMissM2': b1} }\n",
    "settings['systematics'] = {'eID_eff': {'weight': 'eID_eff_weight', 'prefices': 'ell', \n",
    "                                       'reco_channels': {'include': None, 'exclude': None},\n",
    "                                       'templates': settings['templates'] },\n",
    "                           'eID_fake': {'weight': 'eID_fake_weight', 'prefices': 'ell', \n",
    "                                        'reco_channels': {'include': None, 'exclude': None},\n",
    "                                        'templates': settings['templates'] }}\n",
    "settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sysvar import save_nominal_templates\n",
    "# save_nominal_templates(df_nominal, settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## c. Eigendecomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING : load_covariance_matrix: 275 :  Explicit covariance matrix was not found in config under None or cov_matrix_path and will be built from the specified uncertainties.\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "WARNING : get_lid_queries: 1428 :  If negative weights or extremely large weights are present in the table, these will be excluded. The arbitrarily selected 'physical range' is (0 < data_MC_ratio < 10)\n",
      "INFO : get_lid_queries: 1435 :  The following cutstring has been applied to the provide LID table: (working_point == 'FixedThresh09') and (is_best_available == True) and (not ((theta_min == 0.56 and theta_max == 2.23) or (theta_min == 0.22 and theta_max == 2.71) or (p_min == 0.2 and p_max == 7) or (p_min == 0.2 and p_max == 5))) and (variable == 'pidChargedBDTScore_e') and (0 < data_MC_ratio < 10)\n",
      "INFO : create_templates: 184 :  ########## Reco channel: channel1 ##########\n",
      "INFO : create_templates: 215 :  Building Template1D for $D\\ell\\nu$ from 93639 events\n",
      "INFO : create_templates: 215 :  Building Template1D for $D^\\ast\\ell\\nu$ from 59945 events\n",
      "INFO : create_templates: 215 :  Building Template1D for bkg from 119021 events\n",
      "INFO : vary_templates: 144 :  ########## Reco channel: channel1 ##########\n",
      "INFO : vary_templates: 158 :  Adding variations to $D\\ell\\nu$ template\n",
      "INFO : vary_templates: 158 :  Adding variations to $D^\\ast\\ell\\nu$ template\n",
      "INFO : vary_templates: 158 :  Adding variations to bkg template\n",
      "WARNING : max_differences: 113 :  Only the first 60 eigendirections will be considered to find the maximum number of eigenvariations.\n",
      "Building partial covariances: 100%|███████████| 60/60 [00:00<00:00, 6882.87it/s]\n",
      "INFO : find_important_eigendimension_indices: 181 :  Found that 4 eigendirections matter for 0.01 per cent precision\n"
     ]
    }
   ],
   "source": [
    "# Generate toys, construct covariance matrix, eigendecompose it\n",
    "# consider only the efficiency correction and uncertainty\n",
    "\n",
    "from sysvar import eigendecompose\n",
    "\n",
    "egd_eff = eigendecompose(\n",
    "    df = df_nominal,\n",
    "    settings = settings,\n",
    "    syst_effect = \"eID_eff\",\n",
    "    #criterion: \"max_differences\",\n",
    "    prc= 0.01,\n",
    "    #save_variations: False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cvmfs/belle.cern.ch/el9/externals/v02-03-00/Linux_x86_64/common/lib/python3.11/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[:, None]\n",
      "/cvmfs/belle.cern.ch/el9/externals/v02-03-00/Linux_x86_64/common/lib/python3.11/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n",
      "  c /= stddev[None, :]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation matrix from the toys\n",
    "\n",
    "from sysvar import plot_analysis_corr_matrix\n",
    "plot_analysis_corr_matrix(egd_eff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# egd_eff.save_template_variations()\n",
    "\n",
    "# bkg_up_1 = uproot.open('/home/belle/zhangboy/B2SW/2025_VirginiaTech/test_output.root:channel1/bkg/eID_eff_var1_up')\n",
    "# bkg_up_1.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the determination of the important eigenvectors\n",
    "\n",
    "from sysvar import register_saving_info\n",
    "\n",
    "si = {\"top_dir\": \"./\" }\n",
    "register_saving_info(egd_eff, si)\n",
    "\n",
    "from sysvar import plot_cov_diff\n",
    "plot_cov_diff(egd_eff, save=False, filename = \"test_figure_eff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_794295/2817293268.py:16: RuntimeWarning: invalid value encountered in divide\n",
      "  corr_trun = np.where(rr!=0,cov_trun/norm_variation,0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use only n eigenvectors to construct the truncated correlation matrix, plot it\n",
    "\n",
    "def trunEigenVec(EigenVec, n):\n",
    "    vec_to_keep = EigenVec[:n]\n",
    "    leftover = EigenVec[n:]\n",
    "    agg_leftover = np.sqrt(np.sum(leftover**2, axis=0)) # sum the rest to be an 'uncorrelated' vector\n",
    "    return vec_to_keep, agg_leftover\n",
    "\n",
    "category_order = ['bkg', r'$D^\\ast\\ell\\nu$', r'$D\\ell\\nu$']\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,5))\n",
    "corr_vecs,uncorr_vec =trunEigenVec(egd_eff.eigen_variations,3)\n",
    "cov_trun = np.transpose(corr_vecs).dot(corr_vecs) +np.identity(len(uncorr_vec))*uncorr_vec**2\n",
    "\n",
    "diag = np.diag(cov_trun)\n",
    "norm_variation = np.sqrt(diag[np.newaxis,:]*diag[:,np.newaxis])\n",
    "corr_trun = np.where(norm_variation!=0,cov_trun/norm_variation,0)\n",
    "im = ax.imshow(corr_trun,vmin=0,vmax=0.1,cmap='Blues')\n",
    "# divider = make_axes_locatable(ax[1])\n",
    "# cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "fig.colorbar(im)\n",
    "ax.set_xticks(np.arange(0,60,20))\n",
    "ax.set_xticklabels(category_order, rotation=45) #45\n",
    "ax.set_yticks(np.arange(0,60,20))\n",
    "ax.set_yticklabels(category_order, rotation=0)\n",
    "ax.set_xlabel('Bin number')\n",
    "ax.set_ylabel('Bin number')\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.patch.set_facecolor('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## d. Input eigenvectors and eigenvalues to our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the slices of eigenvectors to be the input for pyhf\n",
    "\n",
    "def chunk_dict_from_list(vectors, nkeep, chunk_size):\n",
    "    \"\"\"\n",
    "    Split the first `nkeep` elements of vectors (a list of lists) into `chunk_size` chunks and store in a dict.\n",
    "    \n",
    "    Returns:\n",
    "        dict with keys as category and values as list of lists (nkeep elements, each with chunk_size numbers)\n",
    "    \"\"\"\n",
    "    result = {}\n",
    "    for i in range(0, len(vectors), chunk_size):\n",
    "        # Extract slice [i : i + chunk_size] from the first nkeep lists in vectors\n",
    "        chunk = [row[i:i+chunk_size] for row in vectors[:nkeep]]\n",
    "        result[category_order[i // chunk_size]] = chunk\n",
    "    return result\n",
    "\n",
    "# keep the first 4 eigenvectors and eigenvalues\n",
    "eigenvectors = chunk_dict_from_list(egd_eff.eigen_vectors, nkeep=4, chunk_size=20)\n",
    "eigenvalues = egd_eff.eigen_values[:4]\n",
    "\n",
    "# add the eigenvalues and eigenvectors to the pyhf model\n",
    "\n",
    "for sample in spec['channels'][0]['samples']:\n",
    "    name   = sample['name']\n",
    "    yields = sample['data']\n",
    "    uncer_vecs = eigenvectors[name]\n",
    "    \n",
    "    for i, vec in enumerate(uncer_vecs):\n",
    "        sample['modifiers'].append(\n",
    "            {   \n",
    "                'name': f'pid_{i}',\n",
    "                'type': 'normsys',\n",
    "                'data': {\n",
    "                        \"hi\": 1 + np.sqrt(eigenvalues[i])/sum(yields),\n",
    "                        \"lo\": 1 - np.sqrt(eigenvalues[i])/sum(yields)\n",
    "                }\n",
    "            }\n",
    "        )\n",
    "        \n",
    "        sample['modifiers'].append(\n",
    "            {   \n",
    "                'name': f'pid_{i}',\n",
    "                'type': 'histosys',\n",
    "                'data': {\n",
    "                        'hi_data': (np.array(yields)+np.sqrt(eigenvalues[i])*np.array(vec)).tolist(),\n",
    "                        'lo_data': (np.array(yields)-np.sqrt(eigenvalues[i])*np.array(vec)).tolist()\n",
    "                }\n",
    "            }\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install --user pyhf\n",
    "# !pip install --user cabinetry\n",
    "\n",
    "import cabinetry\n",
    "import pyhf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : __init__: 312 :  Validating spec against schema: workspace.json\n",
      "INFO : __init__: 768 :  Validating spec against schema: model.json\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_0 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_1 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_2 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_3 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier $D\\ell\\nu$_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier $D^\\ast\\ell\\nu$_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier bkg_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier MCstat_ch1 (20 new nuisance parameters)\n",
      "INFO : _save_and_close: 27 :  saving figure as figures/modifier_grid.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 510.526x393.75 with 2 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the model and systematics setup\n",
    "cabinetry.visualize.modifier_grid(pyhf.Workspace(spec).model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : __init__: 312 :  Validating spec against schema: workspace.json\n",
      "INFO : __init__: 768 :  Validating spec against schema: model.json\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_0 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_1 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_2 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier pid_3 (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier $D\\ell\\nu$_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier $D^\\ast\\ell\\nu$_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier bkg_norm (1 new nuisance parameters)\n",
      "INFO : _create_and_register_paramsets: 478 :  adding modifier MCstat_ch1 (20 new nuisance parameters)\n"
     ]
    }
   ],
   "source": [
    "# Load model and data from workspace\n",
    "model, data = cabinetry.model_utils.model_and_data(spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : _save_and_close: 27 :  saving figure as figures/channel_1_prefit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot model and data before fit\n",
    "model_pred_prefit = cabinetry.model_utils.prediction(model)\n",
    "pre_plot = cabinetry.visualize.data_mc(model_pred_prefit, data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : fit: 478 :  performing maximum likelihood fit\n",
      "INFO : _fit_model_pyhf: 108 :  Migrad status:\n",
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 66.25                      │              Nfcn = 955              │\n",
      "│ EDM = 0.00019 (Goal: 0.0002)     │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│      No parameters at limit      │           Below call limit           │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│             Hesse ok             │         Covariance accurate          │\n",
      "└──────────────────────────────────┴──────────────────────────────────────┘\n",
      "INFO : print_results: 35 :  fit results (with symmetric uncertainties):\n",
      "INFO : print_results: 38 :  pid_0                =  0.1880 +/- 0.7805\n",
      "INFO : print_results: 38 :  pid_1                = -0.3097 +/- 0.8533\n",
      "INFO : print_results: 38 :  pid_2                =  0.0311 +/- 0.9838\n",
      "INFO : print_results: 38 :  pid_3                = -0.0180 +/- 0.9901\n",
      "INFO : print_results: 38 :  $D\\ell\\nu$_norm      =  0.3628 +/- 0.0043\n",
      "INFO : print_results: 38 :  $D^\\ast\\ell\\nu$_norm =  0.3604 +/- 0.0077\n",
      "INFO : print_results: 38 :  bkg_norm             =  0.3575 +/- 0.0028\n",
      "INFO : print_results: 38 :  MCstat_ch1[0]        =  1.0009 +/- 0.0226\n",
      "INFO : print_results: 38 :  MCstat_ch1[1]        =  0.9898 +/- 0.0126\n",
      "INFO : print_results: 38 :  MCstat_ch1[2]        =  1.0030 +/- 0.0066\n",
      "INFO : print_results: 38 :  MCstat_ch1[3]        =  0.9988 +/- 0.0044\n",
      "INFO : print_results: 38 :  MCstat_ch1[4]        =  1.0015 +/- 0.0039\n",
      "INFO : print_results: 38 :  MCstat_ch1[5]        =  0.9982 +/- 0.0045\n",
      "INFO : print_results: 38 :  MCstat_ch1[6]        =  1.0003 +/- 0.0059\n",
      "INFO : print_results: 38 :  MCstat_ch1[7]        =  1.0016 +/- 0.0072\n",
      "INFO : print_results: 38 :  MCstat_ch1[8]        =  1.0010 +/- 0.0083\n",
      "INFO : print_results: 38 :  MCstat_ch1[9]        =  1.0012 +/- 0.0094\n",
      "INFO : print_results: 38 :  MCstat_ch1[10]       =  1.0029 +/- 0.0101\n",
      "INFO : print_results: 38 :  MCstat_ch1[11]       =  0.9980 +/- 0.0107\n",
      "INFO : print_results: 38 :  MCstat_ch1[12]       =  1.0010 +/- 0.0116\n",
      "INFO : print_results: 38 :  MCstat_ch1[13]       =  0.9989 +/- 0.0124\n",
      "INFO : print_results: 38 :  MCstat_ch1[14]       =  0.9996 +/- 0.0133\n",
      "INFO : print_results: 38 :  MCstat_ch1[15]       =  0.9917 +/- 0.0146\n",
      "INFO : print_results: 38 :  MCstat_ch1[16]       =  1.0018 +/- 0.0156\n",
      "INFO : print_results: 38 :  MCstat_ch1[17]       =  1.0047 +/- 0.0167\n",
      "INFO : print_results: 38 :  MCstat_ch1[18]       =  0.9928 +/- 0.0192\n",
      "INFO : print_results: 38 :  MCstat_ch1[19]       =  1.0006 +/- 0.0218\n",
      "INFO : _goodness_of_fit: 408 :  calculating goodness-of-fit\n",
      "INFO : _goodness_of_fit: 427 :  p-value for goodness-of-fit test: 99.15%\n"
     ]
    }
   ],
   "source": [
    "# perform the fit\n",
    "fit_results = cabinetry.fit.fit(model=model, data=data, goodness_of_fit=True,\n",
    "                                init_pars=[1]*4+[0.36]*3+[1]*20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : _save_and_close: 27 :  saving figure as figures/channel_1_postfit.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the model and data after the fit\n",
    "model_pred_postfit = cabinetry.model_utils.prediction(model, fit_results=fit_results)\n",
    "p = cabinetry.visualize.data_mc(model_pred_postfit, data=data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the pulls for fitting parameters\n",
    "cabinetry.visualize.pulls(fit_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO : _save_and_close: 27 :  saving figure as figures/correlation_matrix.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 810x610 with 2 Axes>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot correlation matrix for fitting parameters\n",
    "cabinetry.visualize.correlation_matrix(fit_results, pruning_threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Belle2)",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}