ep04-higgs-search.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hsf-training/hsf_matplotlib_notebooks/blob/main/ep04-higgs-search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Run this cell only if running on Google Colab\n",
        "Else you can skip this cell below"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "5PC1H69UoFpF",
        "tags": []
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      "outputs": [],
      "source": [
        "! git clone https://github.com/hsf-training/hsf_matplotlib_notebooks.git\n",
        "! cd hsf_matplotlib_notebooks/; pip install -r requirements.txt \n",
        "% cd hsf_matplotlib_notebooks/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2AnUm2Ma9xzZ"
      },
      "source": [
        "# Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-01-28T06:26:23.519939Z",
          "iopub.status.busy": "2022-01-28T06:26:23.519669Z",
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      "source": [
        "#### In this episode, we will go through a first HEP analysis where you will be able to apply your knowledge of matplotlib and learn something new.\n",
        "\n",
        "As we mentioned before the goal is to reveal the decay of the Standard Model Higgs boson to two Z bosons and subsequently to four leptons (H->ZZ->llll), this is called as a “golden channel”.\n",
        "\n",
        "For this tutorial we will use the [ATLAS data](http://opendata.atlas.cern/samples-13tev/) collected during 2016 at a center-of-mass energy of 13 TeV, equivalent to 10fb⁻¹ of integrated luminosity.\n",
        "Here we will use the available 4 leptons final state samples for simulated samples (Monte Carlo \"MC\") and data, that after a selection, we will reveal a narrow invariant mass peak at 125 GeV, the Higgs.\n",
        "\n",
        "First we need to import numpy and the matplotlib.pyplot module under the name plt, as usual"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWILMHAUny05"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5eCPVh99xze"
      },
      "source": [
        "<font color=yellow>Auto-show in jupyter notebooks:</font> The jupyter backends (activated via %matplotlib inline, %matplotlib notebook, or %matplotlib widget), call show() at the end of every cell by default. Thus, you usually don’t have to call it explicitly there. "
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-DgelY89xzg"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpA9UlnGny06"
      },
      "source": [
        "# Samples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqKM5vKzny09"
      },
      "source": [
        "In the following dictionary, we have classified the samples we will work on, starting with the “data” samples, followed by the “higgs” MC samples and at the end the “zz” and “others” MC background components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GFSqyv2kny0-"
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      "outputs": [],
      "source": [
        "samples_dic= {'data': [['data', 'data_A'], \n",
        "                       ['data', 'data_B'], \n",
        "                       ['data', 'data_C'], \n",
        "                       ['data', 'data_D']],\n",
        "              'higgs': [['mc', 'mc_345060.ggH125_ZZ4lep'], \n",
        "                        ['mc', 'mc_344235.VBFH125_ZZ4lep']], \n",
        "              'zz': [['mc', 'mc_363490.llll']], \n",
        "              'other': [['mc', 'mc_361106.Zee'], \n",
        "                        ['mc', 'mc_361107.Zmumu']]}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCwCk0Jx9xzn"
      },
      "source": [
        "We will use uproot to read the content of our samples that are in ROOT format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QdB6Udew9xzo",
        "outputId": "de71691c-ff82-4626-c7a5-97952a03f65b"
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      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='./data-ep04-higgs-search/tree_plot.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6yL_3wncny0-"
      },
      "outputs": [],
      "source": [
        "import uproot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGRnh-Ac9xzr"
      },
      "source": [
        "<font color=yellow>Uproot tutorial:</font> If you want to learn more of the Uproot python Module you can take a look to the tutorial also given by the HEP software foundation in the following [link.](https://hsf-training.github.io/hsf-training-uproot-webpage/index.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCxBB18uny1A"
      },
      "source": [
        "For each sample of the above `samples_dic`, we will return another dictionary that will contain all the \"branches\" or \"variables\"\". "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B0z3xyiMny1A"
      },
      "outputs": [],
      "source": [
        "processes = samples_dic.keys()\n",
        "Tuples={}\n",
        "samples=[]\n",
        "for p in processes:\n",
        "    for d in samples_dic[p]:\n",
        "        # Load the dataframes\n",
        "        sample = d[1] # Sample name\n",
        "        samples.append(sample)\n",
        "        DataUproot = uproot.open(f'./data-ep04-higgs-search/OpenData_Atlas_{sample}.root')\n",
        "        Tuples[sample] = DataUproot['myTree']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAdnQhZYny1D"
      },
      "source": [
        "Let's take a look to the \"branches\" stored in out data samples, taking \"data_A\" as example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MDERm8YMny1E",
        "outputId": "d59efbdc-2ad1-4a33-f9db-e5632fde02ca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<TTree 'myTree' (18 branches) at 0x7f29d88fd670>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Tuples['data_A']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0r2YsQteny1G",
        "outputId": "381c5405-fdb4-449c-8c08-00d6cdb11c64"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['lep_n',\n",
              " 'goodlep_pt_2',\n",
              " 'trigM',\n",
              " 'm4l',\n",
              " 'weight',\n",
              " 'lep_charge',\n",
              " 'sum_good_lep',\n",
              " 'trigE',\n",
              " 'goodlep_pt_0',\n",
              " 'goodlep_pt_1',\n",
              " 'goodlep_sumtypes',\n",
              " 'lep_type',\n",
              " 'channelNumber',\n",
              " 'lep_pt',\n",
              " 'good_lep',\n",
              " 'XSection',\n",
              " 'lep_E',\n",
              " 'sum_lep_charge']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list(Tuples['data_A'].keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zcbl-pz0ny1I"
      },
      "source": [
        "Let's access one of these \"branches\" and make a simple plot:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5GRRmEWzny1J"
      },
      "outputs": [],
      "source": [
        "branches={}\n",
        "for s in samples:\n",
        "    branches[s] = Tuples[s].arrays()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3gocEfEny1K"
      },
      "source": [
        "Using the pyplot `hist` function we can visualize the distribution the mass of the 4 leptons \"m4l\" for example fot the \"data_A\" sample."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqaHYqIMny1K",
        "outputId": "a7ff60d7-21f6-4f2c-f77b-8318c6f9e56d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"First look at samples\")\n",
        "plt.hist(branches['data_A']['m4l'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AdWhbtAny1M"
      },
      "source": [
        "Tip: In the previous plot the numbers in the axis are very small, we can change the font size (and font family) for all the following plots, including in our code:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSloLwSDny1M"
      },
      "outputs": [],
      "source": [
        "import matplotlib\n",
        "matplotlib.rcParams.update({'font.size': 20, 'font.family': 'serif'})#'sans-serif', 'monospace'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6o8pXrbHny1M"
      },
      "source": [
        "Note that this changes the global setting, but it can still be overwritten later.\n",
        "<br>\n",
        "Let's do the plot again to see the changes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h4YUvChNny1O",
        "outputId": "0ee6788e-ae9a-40e2-c2ba-39f5ef4c7797"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 3., 13.,  3.,  5.,  4.,  2.,  0.,  0.,  1.,  1.]),\n",
              " array([ 19.312525,  82.30848 , 145.30444 , 208.3004  , 271.29636 ,\n",
              "        334.2923  , 397.28827 , 460.2842  , 523.28015 , 586.2761  ,\n",
              "        649.2721  ], dtype=float32),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title(\"First look at samples\")\n",
        "plt.hist(branches['data_A']['m4l'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Htsdbe5kny1P"
      },
      "source": [
        "### Exercise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBjdj0F89xz6"
      },
      "source": [
        "Make the histogram of the variable m4l for sample mc_363490.llll.\n",
        "\n",
        "In the range [0,200].\n",
        "\n",
        "With bins=50.\n",
        "\n",
        "Include a legend “llll”.\n",
        "\n",
        "Include the axis labels “Events” and “m4l”, in the axis y and x, respectively."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jkRCfP_hny1Q"
      },
      "outputs": [],
      "source": [
        "#Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myM4vJ3p9xz7"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQBziq-pny1R"
      },
      "source": [
        "# Selection criteria"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1dYWVWjny1S"
      },
      "source": [
        "Is very important to include some selection criteria in our samples MC and data that we are analyzing. These selections are commonly know as “cuts”. With these cuts we are able to select only events of our interest, this is, we will have a subset of our original samples and the distribution are going to change after this cuts. This will help out to do some physics analysis and start to search for the physics process in which we are interested. In this case is the 4 lepton final state.\n",
        "\n",
        "To do this, we will select all events, from all the samples, that satisfy the following criteria:\n",
        "\n",
        "    - the leptons need to activate either the muon or electron trigger,\n",
        "    - number of leptons in the final state should be 4,\n",
        "    - the total net charge should be zero,\n",
        "    - the sum of the types (11:e, 13:mu) can be 44 (eeee), 52 (mumumumu) or 48 (eemumu),\n",
        "    - good leptons with high transverse momentum\n",
        "\n",
        "Let’s visualize some of these requirements. For now, let us continue working with the “data_A” sample. We can see how many good leptons we have in the event, by printing:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3IFvjH0ny1S",
        "outputId": "de7a0b80-a5ba-4eef-d12f-70d9519ac03d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Array [[1, 1, 1, 1], [1, ... 1], [1, 0, 1, 1]] type='32 * var * int32'>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "branches['data_A']['good_lep']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aq1EDhZ6ny1S",
        "outputId": "54c4c52d-46c7-4f63-84ad-26189a4c6ef0",
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Array [4, 4, 3, 4, 4, 4, ... 4, 4, 2, 2, 4, 3] type='32 * int32'>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "branches['data_A']['sum_good_lep']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "drkg_MqGny1U",
        "outputId": "86b5a58b-8b38-4dc6-fb86-a3c499ec2f25",
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Array [True, True, False, ... True, False] type='32 * bool'>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "branches['data_A']['sum_good_lep'] == 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjlHrncMny1U",
        "tags": []
      },
      "outputs": [],
      "source": [
        "sum_leptons_test = branches['data_A']['sum_good_lep'] == 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOv2RZZ2ny1V"
      },
      "source": [
        "### Exercise\n",
        "\n",
        "Make a histogram of the variable “sum_good_lep” for the sample “data_A” or another sample.\n",
        "\n",
        "Try to represent in you histogram what are the events that we want to keep using lines, arrows or text in you plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M8vZ_2Yuny1V"
      },
      "outputs": [],
      "source": [
        "#Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Bn2J2Bqny1V"
      },
      "source": [
        "Finally, let’s save in a dictionary for all the samples the requirements mentioned above as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SNZuPRafny1W"
      },
      "outputs": [],
      "source": [
        "selection_events={}\n",
        "for s in samples:\n",
        "    # trigger\n",
        "    trigger = ((branches[s]['trigM'] == True) | (branches[s]['trigE'] == True))\n",
        "    sum_leptons = branches[s]['sum_good_lep'] == 4\n",
        "    sum_charge = branches[s]['sum_lep_charge'] == 0\n",
        "    sum_types_ee = branches[s]['goodlep_sumtypes'] == 44\n",
        "    sum_types_mm = branches[s]['goodlep_sumtypes'] == 52\n",
        "    sum_types_em = branches[s]['goodlep_sumtypes'] == 48\n",
        "    sum_types_goodlep = (sum_types_ee | sum_types_mm | sum_types_em)\n",
        "    sum_lep_selection = (sum_leptons & sum_charge & sum_types_goodlep)\n",
        "    # Select leptons with high transverse momentum\n",
        "    pt_0_selection = branches[s]['goodlep_pt_0'] > 25000\n",
        "    pt_1_selection = branches[s]['goodlep_pt_1'] > 15000\n",
        "    pt_2_selection = branches[s]['goodlep_pt_2'] > 10000\n",
        "    high_pt_selection = (pt_0_selection & pt_1_selection & pt_2_selection)\n",
        "    final_selection = trigger & sum_types_goodlep & sum_lep_selection & high_pt_selection\n",
        "    selection_events[s] = final_selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qcu-KzGYny1X"
      },
      "source": [
        "Moreover, we can compare, by printing, the number of initial and final events after the previous selection ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whzVpFH5ny1Y",
        "outputId": "710444aa-0e94-4b03-9a22-9a111094229c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "data_A       Initial events:  32\n",
            "data_B       Initial events:  108\n",
            "data_C       Initial events:  174\n",
            "data_D       Initial events:  277\n",
            "mc_345060.ggH125_ZZ4lep       Initial events:  163316\n",
            "mc_344235.VBFH125_ZZ4lep       Initial events:  189542\n",
            "mc_363490.llll       Initial events:  547603\n",
            "mc_361106.Zee       Initial events:  244\n",
            "mc_361107.Zmumu       Initial events:  148\n"
          ]
        }
      ],
      "source": [
        "for s in samples:\n",
        "    print(s,'      Initial events: ', len(branches[s]['m4l']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kGKVqZ-kny1Y",
        "outputId": "523176e1-8840-4dea-81cb-a2d14072034a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "data_A       After selection:  18  ( 56.25 %)\n",
            "data_B       After selection:  52  ( 48.15 %)\n",
            "data_C       After selection:  93  ( 53.45 %)\n",
            "data_D       After selection:  158  ( 57.04 %)\n",
            "mc_345060.ggH125_ZZ4lep       After selection:  141567  ( 86.68 %)\n",
            "mc_344235.VBFH125_ZZ4lep       After selection:  161106  ( 85.0 %)\n",
            "mc_363490.llll       After selection:  454732  ( 83.04 %)\n",
            "mc_361106.Zee       After selection:  27  ( 11.07 %)\n",
            "mc_361107.Zmumu       After selection:  16  ( 10.81 %)\n"
          ]
        }
      ],
      "source": [
        "for s in samples:\n",
        "    final_len=len(branches[s]['m4l'][selection_events[s]])\n",
        "    print(s,'      After selection: ',final_len, ' (', round(final_len*100/len(branches[s]['m4l']),2), '%)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tboRb1N4ny1Z"
      },
      "source": [
        "Notice that the background events are reduced meanwhile we try to keep most of the signal."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7ARVSsa9x0F"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9A8Qf4kny1Z"
      },
      "source": [
        "# MC samples\n",
        "\n",
        "To make a plot with all the MC samples we will do a stack plot. We will merge the samples following the classification we introduced at the beginning, that is:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GkJumZPFny1h",
        "outputId": "31e34fa3-8a6a-47fc-af00-d343b2b6dec9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['higgs', 'zz', 'other']\n"
          ]
        }
      ],
      "source": [
        "mc_samples=list(processes)[1:]\n",
        "print(mc_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5DV_9ITny1h"
      },
      "source": [
        "Remember that our variable of interest is the mass of the 4 leptons (m4l), then, let’s append in stack_mc_list_m4l the values of this variable for the 3 merged samples corresponding to the processes above.\n",
        "\n",
        "On the other hand, a very important aspect of the MC samples is the weights, these weights include important information that modifies the MC samples to be more like real data, so we will save them in stack_weights_list. Notice that the weights should be of the same shape variable that we want to plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m5XpE1j1ny1i"
      },
      "outputs": [],
      "source": [
        "stack_mc_list_m4l = []\n",
        "stack_weights_list = []\n",
        "for s in mc_samples:\n",
        "    list_sample_s = []\n",
        "    list_weight_s = []\n",
        "    for element in samples_dic[s]:\n",
        "        sample_s=element[1]\n",
        "        mc_selection_values = branches[sample_s]['m4l'][selection_events[sample_s]]\n",
        "        list_sample_s += list(mc_selection_values)\n",
        "        mc_selection_weight = branches[sample_s]['weight'][selection_events[sample_s]]\n",
        "        list_weight_s += list(mc_selection_weight)\n",
        "    stack_mc_list_m4l.append(list_sample_s)\n",
        "    stack_weights_list.append(list_weight_s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YPBxj97ny1i"
      },
      "source": [
        "We can check the lengths, to see that everything is ok."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4BWPimaAny1j",
        "outputId": "ecb43b6c-933c-4ef9-ce1b-37a542d1bfb5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "302673 302673\n",
            "454732 454732\n",
            "43 43\n"
          ]
        }
      ],
      "source": [
        "for k in range(0,3):\n",
        "    print(len(stack_mc_list_m4l[k]), len(stack_weights_list[k]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WU2hH_59ny1j"
      },
      "source": [
        "And then make a plot, actually, let’s make 2 plots, with matplotlib we can add sub-plots to the figure, then, we will be able to compare the MC distribution without and with weights.\n",
        "\n",
        "In order to do this, we will use the `subplot` function. Notice that in this case, we are creating a figure and defining the axes of the figure directly, the syntax of the functions that we call for these axes change a bit with respect to the ones using only pyplot. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yeU8qVXSny1k"
      },
      "outputs": [],
      "source": [
        "var_name = 'm4l'\n",
        "units = ' [GeV]'\n",
        "rangos = [[80,170]]\n",
        "bines = 24 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-78xGv3ny1k",
        "outputId": "6591c93f-8846-48a4-eb74-54fa43af3303"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f29d31705b0>"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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sa8Bk5szMPAFYneL3zA8oBulrAFMi4t9b2T5Jjm9azPFNYTiMb3rqsnK/a7nfHbi9DMhUp1df8y111tCpfj+f78H72dNAavXnqdEYYiiMH97KzJ9l5niKWwyPo7itaQxwDDVrOak5BmU0XP2a4pFxUCyueWFmPtJF/nrOpRgYLMyC95LWFRGXRsTjEXFgk+cCioXsMvOyzPwUxaPmnqL4RVv9H6EJ5f7mrFkUbghZscHxjqp//7OHdd1d9e91G2WKiC0i4uCIqDuTo4d921OXl/tPRMRKFI/VvLQqvfLv3SJiTYpvsC7rxXkGw9Sqf3c0yFN9fGpN2txyv8A3IOX05mV727Am9dd7Un19XX3ePhoR/1Z+U90Tlc/7Mo0+ozT+uRl0WfhzZh5F8f7fUCZ9vmWNkgSOb1rN8U1hOIxveupKihkk74liPaDdqX/Nm0exjtyu1L/m+3n7NqCu3s/1y/ezpwv9Vn+e1miQZ8iMH2BewPAkiuDMd8vDu0dEo/arGwZlNCyVU1Mr3xwlxVoJzdbxIkVkF+C46OapMxGxOcUv6sVo4g9URDwVETvXHs9i1faLy5fVC28tVLOvtXpPzz2APtTgeGU67P09nepaDjYrU0d36iLrGcB/UrXyey/6tqeu5e17kL9BcY/5X6rSL6f43K1HMbX88cy8vbaSHqpMlZ43XTsiloiIPSNivQZlmnEzxaMKoXg0Zz2V40+z4CKHlW+K6vXje2k8+waqpoFX7k8v/71TRGzVRbl6+us9uZm3F7yr+3kr77W/EDgia56i1IX/4e1pw1s3yNPdUz/6ot7nKMrP0QfK17+OiJNqC5a/C08pX/Z2EUBJ/cDxTcs5vhk+45seKWes3FK+/DLFY60vq0r/B0XAJShu712dOj8HWTyt8aryZVfv50kUT3Gc00We6nrv5+1A7IQG2QZy/ABvr8lU/V5NiIiPlf/+YkT8obZQ+fvqG1WHHEP0kkEZDWfnAV8EJmVmT7+1mE9mnkXxmLl3ARdGRN1pg+XiX7+m+EN1WGbOaOI0KwF7NUirRNqr/+BV/lP84YhYtaYdiwMHNHHugXJY7ZTosq2TypffbLK+Yyj+Q/upetNxI+IAim9rvldOv61otm97pLxlpfIUmn+juO84q9Kf4O3F+Q7m7W+eeqMS9KiecfJ+ikde9vmPcGbOBb5WvjwqIsZWp5ffzE0uX/5H1jwimrcHMltVB1ZKk5l/ccVaz1WlL1ueb2GKhXQP6eElAP33npT9UXmk6eHlIoe1jgWW5+3bCHrSvkeB88uXR9fOsImITYCP9rS+Xqh8jqrf33EUn6Ndy9fLAnvE/I9drej1z4ukfuf4pnUc3wyT8U2TLiv3/wb8b52fq0r6wRRr6DxIfcdTrPvyoYj4RG1iRGxL8YSmszPzuSbaV/lcHRgR8z0hrPw8fraJunqj3nt1Fm+Pl5akuOZ6M4Qqx2YD/xiY5o0AmenmNuQ2igj++hS/DBLYvny9RA/KrljmPbAsW11+mQZlPk/xy+Qhiij61hSLnO0O/IjiF/BLwH69uJZKG86n+EX9AeBjwJTy+D3A0lX5x1D8UkuKezUnUixIuhfFYx5fLNNuK69pzZo+u6xMP636mst/b1/VnvWBdWv6rLbsig2upRO4C9izvJ5PU0T5k+IPUb338rQy/bLy9ao1+Q6l+FZlBsVTBzYDPkLxxIm5FAuuLdyXvm3yfTu4qv4d6qR/pSp9uwZ1rFte621lvq/Wfg6BJcprfoNiqvqWFAOmVyi+rVmC+T/PT9S878v0sH//s6pP9i77ak/g7+Xx7zS4hoXL9zopBlIfBbYAfgz8hOLJTVm2b31gqZryvy3Tv1u+p/9Vvt6zFe9JVd4Ty3yPA5+h+HnfofzsvAWc3uznuHwv/lam/6asbxOKwdTDZR8kcEOD31m1da9Yc+6uPkdB8SjOpPj5+SDF/d1vAZuVef5Qpt8M7EPxJIqtKRaPfBWYCWzSm58XNze3nm04vnF84/imX8c3Pbzmd1Vd07fqpG9Rlf6Nbur6eHkNrwJfL8v+C0XA5mXgr1SNhaquo/Y6162p9+wy/f7yc/cBYA+Kn40TKu2rKdNfY8Qzy/Sfl5/Po8vXXyzTjy9fP0IxpvkQsDnF+OkRirHGIQP1e3MkbC1vgJtbva3qj07tNqEHZTsblE2Kb50alVulLPsXim/4K39Aby5/Ga3Uy2tZj2Jq35/KX1yvUQyAbqf4Rn6BgVj5S/R7FIOoueUv+dspBleH1FzTDd302aQyvV7atG76rLOmXZXjHRSDhBso/iM3m2I19n8Doofv5ZQ6170RxTeEj5XXPYvim7WDgYX6o2+beN9WpJjlMQsY1eDcWX5GFm5Qx7SefA6BrYA/l/34IsXslO3KtAkN6qi875Oa6N8JFN+IPlX279MUf6A/0k1frFS+j8+UffxP4CiKQEDtNe5SU/ad5TlnUAxg/gn8e6vek5r8/0Ixa+cpisUTXwB+B+zRxO+kKTX5lqAI+DxY9tXTFMGs9wAHlWV+28PfWZ09+dmuqud9FEGwlygGjHcBn6pKX4FidtP/UAz6XqGYXv2/FP85W6O3Py9ubm4927r4eZ7Qg7KNflc4vnF808z71nbjmx5e9z1l+c3rpAXFrc0JjO9BXWsBp1N8jufw9uf4C8CiNXkbXce0Om04qKxndvn5u6H8PK5VlplbU6Zf+pDiiVzn8PY470GKQNDCZfqiFIGiSyjW5nuBYtz0OMUC1B/q7efRrdii7GhJ6lZEVH5hrJmZ01rZFmm4iYjPU/xn5MLM3K/V7ZEkFRzfaCgrb327A5iemat2l1/Dj2vKSJLUDyLigxHx/7rIUllP4JYu8kiSpBEkIlaMiBMjYoEnXZYcP7Q5gzKSJPWPTYEfRERHbUJErE3xaNoZFPdsS5IkQXFL2XHUWdy5fHpa5fHnPxrMRmnwLNzqBkga2srHA7+75vC6EbEkxQr2r7egWdJQtQhwfUR8C7ibYvG7D1IsnAjw6cyc2aK2SZJKjm80BP2k/BLnRoo1hNalWG9pQ4onZN7QwrZpALmmjKQuld/6P9wg2XuvpVL5uPE9gE9SLJS4MkWQ5gng98B3M/OB1rVQklTh+EZDRRkg3IliRu2mFOOHpYDnKRbo/mFmXt+6FmqgGZSRJEmSJElqAW9fahPLL798dnR0tLoZkiQNKXfcccdzmblCq9sxUvT3eGT69OmMGzeu3+pT9+zzwWV/Dy77e3DZ32/rajziTJk2MX78+Lz99ttb3QxJkoaUiLgjM8e3uh0jRX+PRyICx6qDyz4fXPb34LK/B5f9/bauxiM+fUmSJEmSJKkFDMpIkiRJkiS1gEEZSZIkSZKkFjAoI0mSJEmS1AIGZSRJkjQkHX/88a1uwohjnw8u+3tw2d+Dy/7uGZ++1CZ8+pIkSQvy6UuDy/GIJEkL8ulLkiRJkiRJQ4xBGUmSJEmSpBYwKCNJkiRJktQCBmUkSZIkSZJawKCMJEmSJElSCxiUkSRJkiRJagGDMpIkSZIkSS1gUEaSJEmSJKkFDMpIkiRJkiS1gEEZSZKkESAilo+ISyIiI2JSN3kXiohDI+LPETEjImZHxIMR8fOI2GyQmixJUtszKCNJ0jD2l7/8hYiYt91www3dlpk7dy4bb7wxG2+8MXPnzh34RqrlImIP4F5g+x7kXRS4BvgccAKwKrAacDawH7DtwLVUkjQcOR7pvYVb3QBJkgZKxzFXt7oJXZr2rZ37XMfmm29OZtLZ2ckJJ5zQozKvv/46jz/++Lx/jxo1qs/t0NAVEYcBXwMOAvYCJnZT5GRgS+A9mflEeWw28M2I2Ah4fqDaKkntyPFIfY5HCgZlJEkaYZZYYgkeeuihef9W25sKvC8zZ0TEXl1ljIhVgcOBn1YFZObJzE8NUBslSSOM45GCQRlJqta5dD/UMavvdUgDbMyYMa1uggZJZt7URPa9KMaHfxyg5gxf/n2QpH7neMQ1ZSRJaiuZyfe+9z3WWmstFl10UdZbbz0uuuiieelTpkyZ757vadOmLVDHlVdeyQc/+EFGjx7Nsssuy2677cb9999PR0fHvHKTJk2ar8w555zDhhtuyKKLLspKK63ExIkTefLJJ+c7V2dn57z8v/nNb/jIRz7CiiuuyFJLLcVGG23E5z//ee66666B6Rj11Obl/pmIOCEiHoqI1yLiyYg4NyLWbGnrJEnDguORnjMoI0lSG/nWt77F3LlzufXWW/nf//1fVl11Vfbbbz9uu+02ACZNmkRmMnFi/WVFLrjgAj75yU+y5JJLcvfdd/Pkk09yyCGHsNtuu/Hiiy+yxhprkJlMmTJlXplvfvObHHTQQay//vo89NBDPPzww3z0ox9l552Le9S33nrrefeZA1xxxRXssMMObLjhhtx99908/fTTfOc73+Hiiy9m8uTJA9k96t7a5f6nwFbArsDSFOvR7ATcFhHrtqZpkqThwvFIzxmUkSSpjSy66KJ85StfYfnll6ejo4OTTz6ZzOSCCy7otuyLL77I4YcfzpJLLsmvfvUr1llnHRZddFF22mknJk+ezIwZMxYo89BDD3H88cez5pprcu655/LOd76TxRdfnH/9139lxx13rHueygDqxBNPZOWVV2bxxRdn++2358QTT+zTtatfVOaRLwt8MjOnZuaczLwW+DKwHPDjlrVOkjQsOB7pOYMykiS1kU9+8pPzvX73u98NwAMPPNBt2csvv5xZs2ax/fbbM3bs2PnS9t1337plLrzwQt544w123313FllkkR6ViQgALr744gXy92SwpkFxbWa+VHPsIuAt4CMRsVy9QtOnT59vini9qeKSpPY3UscjnZ2ddf8OAuMalTEoI0lSGxk3bv6/+UsuuSQAs2fP7rbsnXfeCbw9cKq2xBJLsOyyyzZVZvXVV697ns9+9rOMGjWKQw45hPHjx/P973+fadOmMXr0aFZdddVu26kBVfn68dHahMycAzwNBG/f5jSfcePGkZkLbAZlJGlkGanjkc7Ozrp/B4HpjcoYlJEkqY2MHj16vteVb4HKAUGXXnzxRaDxYymXWmqppsrUyw/wkY98hFtvvZVPfepT3HPPPXzhC19grbXWYocdduD+++/vtp0aUP8s94t0mQu6/0BJkkYsxyM95yOxJalKx5wL+1zHtL43Q2qJpZcuHvn7yiuv1E1/6aWXFhjYdFXmpZdq735520YbbcTFF1/Miy++yOWXX85pp53Gb37zGz784Q9z33331f0WTIPi98AkYIGnLEXEosBKwJtA9/PPJUnqhZE2HnGmjCRJAuADH/gAAPfdd98CaS+//DIvvPBCU2UefXSBO2AAuOmmm3jmmWcAGDNmDPvvvz8333wzH/3oR3n22Wf54x//2OtrUJ9dCjwBfCwilq9J24di7HhFZs4c7IZJkkaGkTYeMSgjSZIA+MQnPsEyyyzD7373uwWebHDRRRfVLbPvvvuyyCKL8Otf/5rXX399vrQLL6w/8+y4447jv//7v+c7FhG85z3vARac8qzBk5mzKWbKLARcEhHrRMSoiPgY8B2KyYBHtK6FkqR2N9LGIwZlJEkSUNxzffrpp/Pyyy+z11578eCDD/Laa69xzTXX8POf/5yVVlppgTJrrrkmJ554ItOmTWPixIk88cQTvPrqq1xwwQXcddddDc91wgkncOWVVzJr1ixeeuklrrjiCs477zw23HBDttlmmwG8ypEnIjoiIiMigYnl4XPKY9Nq82fm74HNgZeAvwAvA6cB5wPjM7PhYoWSJPXVSBuPDKmgTEQsHxGXlIOESU2U+35Z5oZu8u0QETdGxEsRMSMiroqITbrIv1BEHBkR90TEqxExPSLOiogVuygzJiL+KyIejYg5EXF/RBwXEQ0XzIuIdSPilxHxXES8EhF/jYhP9ejiJUkj2rRp04gITjjhBAC22WYbOjo6AJg0adK8hfVuvPFGIoIpU6YQEZx77rlAMYip5AfYZ599uPLKK3nppZdYf/31eec738lFF13EL37xCxZeeGFGjRq1QBuOPvpopkyZwtSpU1lrrbVYe+21+ctf/jLvcZK1ZX7wgx+w3377cdxxx9HR0cEqq6zCMcccwxFHHMENN9xQ9xzqvcyclpnRYOtoUObOzNw1M5fLzFGZuXZmfiEznx/k5kuShgHHI70XPVn9eDBExB4U38KMApYBDszMKT0oN57iW5x3ADdm5oQG+Q4Cfgb8EOgEFgd+AOwMfCwzb6hT5lzg08BngEuAdwMXAaOBD2bmUzX5xwB/BsZS3Hd9B7ADxTdLfwQ+nplv1pTZCPgT8DfgYOAZYDJwAvDVzPxmd30AMH78+Lz99tt7klVSFzqOubrPdUz71s790BJpaHnzzTdZbLHF2Gqrrbj++ut7VOaRRx6ho6ODAw88kLPPPnuAW1hfRNyRmeNbcvIRqK3HI51L90Mds/pehySNYO04HhkSM2Ui4jCKYMlBwOVNlFsY+ClwWzf5VgV+DNwKHJWZL2Tm48D+wAzgvPKJAtVl9gAOAE7JzHMyc3Zm3gnsB6xOEdCpdRKwPnBoZt6Uma9m5qXA8cCOFMGd6nMsBJxL8T7snZkPZuaLmfl14CrgxIhYv6f9IUlSX33lK1/hrLPOWuD4b37zG9544w122WWXBdIOOOAArrnmmgWOX311EeSsV0aSJKmRkTQeGRJBGWAq8L7MbPYr6i8DSwFf7ybfYcBiwDlZNTUoM1+hmAGzGrBnTZnPlfv5QmmZeQdwN7BnRLyzcjwilqKY6fIkcG1NXVOArKqzYltgI+CqzHymJu1sivfnqG6uTZKkfvPkk09yzDHHzJsyPGPGDK644go+85nPsMkmm3DYYYctUObRRx/liCOO4MYbb2T27Nk888wznH/++Xz1q19l5513Ztdddx38C5EkScPWSBqPLNzqBgBk5k3NlomIdYDjgE8Ab3STvXIvwS110m4BjizzXFDWPRbYEpiZmQs+U6sosyGwE3BmeWxbisDPX7PmnrDMfD4i7gfeHRHrZub9PWxXdR5Jkgbc4YcfzqKLLsqXv/xlpk+fzpw5c+jo6GD//ffn2GOPrfskguOOO45zzz2Xgw8+mCeffJI333yTddZZh2OOOYbPf/7zLLTQUPkOSJIkDQcjaTwyJIIyzYpilaCzgEsy8/cRMaGLvO8A3lu+nFYnS+XYBlXH3gdEg/yNymxQk1avzLvLfJWgTMMymflURMwBVomI5VxYT5I0GDbddFM23XTTpspst912bLfddgPUIkmSNNKMpPHIsAzKUNwm9B5g9x7kHUuxeHBmZr3V1WaW++rnaq1c7ud/KPrgl5lFMftmJcCgjCRJkiRJbWRozt/pQkSsAnybcsHeHhSpzGt6vUH63HK/+DApM09EHBoRt0fE7c8++2yDKiRJkiRJ0lA07IIywI+AmzPz4h7mf7XcL9IgvfLw8dnDpMw8mXlmZo7PzPErrLBCgyokSZIkSdJQNKyCMhGxK7A9xdOUemoGxYyTiIil66QvU+6frjr2VLkf26DOwSpTae/TDdIlSZIkSdIwNayCMsCuwJLAIxGRlQ34Q5m+ddXxToDMfBP4R5m+Zp06O8r91Kpj91I8wrqjNnMXZSr/rneOpstExMoU68k86SK/kiRJkiS1n2EVlMnMSZkZtRuwTZnlxqrjnVVFryn3m9epdouaPGTmDIpHUi8TEes1KJPAtVXHrgdeAzYrnw41T0QsB6wLPFT1OOym2yVJkiRJktrHsArK9MEZwBzgwOqASUQsAewNPA78qqbMKeX+oOqDEbEJsCHw68x8rHI8M18CfgasAuxYU9ckikdsn1Jz/DqK2TK7RMSKNWkHAW8BP+ju4iRJkiRJ0vAzIoIyZfDkSGAz4NSIWDYiVgXOB5YDJmXmnJoyvwQuACZHxIERsXhEbAz8nCKIc2SdUx1LcavUmRHx4YgYHRG7AZ3AbymCQ9XneAuYSDHr5hcRsXZEjImIrwG7AJ2ZeXc/dYMkSZIkSRpChkRQJiI6qtaHmVgePqc8Nq2LclMarSlTmzczzwJ2AjYGHqVYN2YxYMvMvK7BKQ4AvgR8EXiB4nalm4HxmTm9zjlmAVtSzLq5CJhJ8fjubwMfz8w36pS5E9gUeA64lWLx308A+2bmiY2uXZIkSZIkDW8Lt7oBAJk5jeL2nmbLTaK4Nain+a9l/nVgusv/FnBqufW0zCxgcrn1tMx9wJ49zS9JkiRJkoa/IRGUkSRpQHQu3eoWdK1zVqtbIEmSBprjEXVhSNy+JEmSJEmSNNIYlJEkSZIkSWoBgzKSJA1zHR0dRETDbfLkyUyZMqXLPBHBjBkzWn0pkiRpmHI80jsGZSRJagMPP/wwmTlve+SRR1h66aVZaqmlOPLIIwGYOHHifHkyk8mTJwNw+OGHM3bs2BZegSRJGu4cjzTPoIwkScPcmDFjWGiht/+kv/XWW+y///7MmjWLH/3oR6y11lqMGjWKxRdffL5yv/3tbzn11FN573vfy3e+853BbrYkSWojjkd6x6cvSZI0zN19993zvf7Wt77FH//4R/bee28OOOAAAPbdd1/23XffeXmef/55Jk2axCKLLMKFF17I6NGjB7XNkiSpvTge6R2DMpIktZHbb7+dzs5OVlttNX7yk580zHfwwQfz5JNP8t3vfpeNNtpoEFsoSZLaneORnvP2JUmS2sQrr7zCvvvuy5tvvsl5553HMsssUzffmWeeyWWXXcZHPvIRPv/5zw9uIyVJUltzPNIcgzKSJLWJyZMn88ADD/ClL32JCRMm1M1z//338/nPf55ll12Wc889l4gY3EZKkqS25nikOQZlJElqA5dddhk//elP2WSTTTjxxBPnHZ87dy4vvPACAK+//jr77bcfr7zyCmeddRarrrrqvHwvvPACc+fOHfR2S5Kk9uF4pHkGZSRJGuaefPJJDj74YBZffHEuuOACFllkkXlpN998M7vvvjsAxx9/PLfffjv/9m//Nu9Yxe67787NN988qO2WJEntw/FI77jQryRJw1hmMnHiRJ5//nlOPvlkVlppJWbOnDkv/eWXXwbgj3/8IyeffDKrrbYanZ2d8+UBeOONNwax1ZIkqZ04Huk9gzKSJA1jU6dO5Xe/+x0ARx99NEcfffQCebbeemtOOeUU3nrrLR577DFWW221wW6mJElqY45Hes+gjCSpfXXOanULBtyGG25IZra6GZIkqRHHI+qCa8pIkiRJkiS1gEEZSZIkSZKkFjAoI0mSJEmS1AIGZSRJkiRJklrAoIwkSZIkSVILGJSRJEmSJElqAYMykiRJI0BELB8Rl0RERsSkJsp9vyxzw8C1TpKkkWnhVjdAkiRJAysi9gBOA0Y1WW48cOSANEqSJDlTRpIkqZ1FxGHAD4GDgMubKLcw8FPgtgFqmiRJI55BGUmSpPY2FXhfZl7dZLkvA0sBX+//JkmSJPD2JUmSpLaWmTc1WyYi1gGOAz4BvNHvjZIkSYAzZSRJklQlIgI4C7gkM3/f6vZIktTOnCkjSZKkagcD7wF2b3VDJElqd86UkSRphJkyZQqdnZ0LHH/qqaeIiHnblClTBr1taq2IWAX4NnBUZr7QbPnp06fP9xmqbPU+b5Kkka0dxyOdnZ11/w4C4xqVcaaMJKltbXDuBq1uQpemTpzakvNOmTKFG2+8cYGB0Morr0xmMmXKFA488MCWtE0t9yPg5sy8uDeFx40bx/Tp0/u5SZI0vDkeqa8dxyOdnZ11A00R0fCPo0EZSZIkERG7AtsD72txUyRJGjG8fUmSJEkAuwJLAo9ERFY24A9l+tZVxztb1UhJktqJQRlJktrAueeey+abb86SSy7JkksuyRZbbMF55503X54pU6YQEdx4440APbpf+7zzzuO9730viy66KB0dHZxyyikN23Deeeex+eabs8QSS7DUUkvxoQ99iEsuuWS+PBdffPF8533ggQc4/vjjWXPNNVl44YVdf6SFMnNSZkbtBmxTZrmx6nhnC5sqSRqiHI80z6CMJEnD3GGHHcakSZP4+Mc/zmOPPcZjjz3GzjvvzMSJE/n3f//3efkmTZpEZrL11lsDkJnztkmTJi1Q7wUXXMAdd9zB73//ex5++GE+9KEP8bnPfY5f/epXC+Q94ogjmDhxIttuuy2PP/4406ZNY5tttmGfffbhG9/4xrx8++yzD5nJxIkTATjyyCMZM2YMt956K/fccw8rrrhiP/eOJEkaDI5HesegjCRJw9jll1/OGWecwac//Wm++tWvMnbsWMaOHctxxx3HPvvsw49+9COuuuqqXtX99NNPc+qppzJu3DjGjRs371upn//85/Plu/LKK/nxj3/MVlttxTe/+U3Gjh3Lcsstxze+8Q222morOjs7ue++++qe453vfCdf+MIXWGGFFVhvvfX4yle+wvrrr9+r9kqSpNZwPNJ7BmUkSRrGzjjjDAA+/elPL5BWOXbaaaf1qu5PfvKT871eYYUVWHbZZXnggQfmO3766acDcPDBBy9Qxz777MObb77J+eefX/cce+2113yvJ0+ezJ577tmr9qq+iOioWh9mYnn4nPLYtC7KTWm0pswAN1mSNMw4Huk9gzKSJA1jt912GwDrrbfeAmmVY5U8zRo3btwCx5Zccklmz54937Fbb70VgPe///0L5F9ttdUAuP322+ueo5KugZOZ0+qtFVNuHV2Uq7vGTLnOjCRJ8zge6T0fiS1J0jA2a9YsAJZYYokF0irHZs6c2au6R48evcCxiCBz/okSlTZstNFGDet6+umne3wOSZI0vDge6T1nykiSNIwts8wyALzyyisLpFWOjR07dlDa8MADD8y3WF/1dtdddw1oGyRJUus4Huk9gzKSJA1jm222GQD//Oc/F0irHKvkqYjo37tPPvjBDwIwbdq0uul/+ctfuPvuu/v1nJIkaehwPNJ7QyooExHLR8Ql5SJykxrkWTwiPhMRv42IZyPi9Yh4OiIujYgPdVP/DhFxY0S8FBEzIuKqiNiki/wLRcSREXFPRLwaEdMj4qyIaPh8rIgYExH/FRGPRsSciLg/Io6LiEW6KLNuRPwyIp6LiFci4q8R8amurkWSJIDPfvazAFx00UULpF188cVA8YjKapVvqubMmQPA97//ffbYY48+t2HKlCkLpD3xxBNMmDBhSH4zJUmS+ofjkd4bMkGZiNgDuBfYvpusVwBnAP8ANgOWAfYG3g38qYtgzkHAtcDfgTWADYC5wM0RMaHBuc4Bvgt8D1gO2BnYCrgtIlauc44xwJ+BvYB9gbHA0cAxwOUR8Y46ZTYCbgdWADYHVgGuBi6OiGO76AdJkth555054ogjuPjiiznppJOYMWMGM2fO5KSTTuLiiy/miCOOYOedd56vzPjx4wH4/e9/z3PPPce5557LUkst1es27LTTTkyePJkLL7yQo48+mv/7v/9j9uzZ3Hjjjey4445svfXWdZ/GIEmS2oPjkd6L2sVxWtKIiMOArwGHUAQ0JgIHZuaUOnlvAmZl5s41x9cD7gFeBVbNzBer0lYFHgTuBjbP8qIjYgngIYrgzDqZ+VpVmT2AXwHfycwvVx3fhCKI8svM3LumDT8EjgB2zsxrqo5/gSK4c3hmnlZ1fCHgb8C7gLUy85mqtCuBnYCNMvOeLjsQGD9+fDZaSVpSz3Ucc3Wf65j2rZ27z6RBscG5G7S6CV2aOnFqv9V1/vnnc9ppp82blrvBBhvw2c9+lgMOOGCBvC+99BL/7//9P37zm9/w+uuvs9VWW3HmmWcybty4ulOJM5POzk5OOOGE+Y4ff/zxdHZ2znt90UUX8eMf/5i77rqLhRZaiDXXXJP999+fww8/fN4CejfccAPbbLPNAuf4wx/+wIQJE/rQA/VFxB2ZOb7fK1ZdbT0e6Vy6H+qY1fc6JA07jkccj3Q1HhkqQZkPA/dm5oyImELXQZmfAr/NzF/USbuPYsbMxzLzt1XHvwF8FTgsM8+oKXMqcCTwr5l5QdXxm4APAe/JzPtqyvydYqbN6pn5eHlsKeAZYAZFUCir8i8HPAs8lJnrVB3fDvgdcElm7lNzjt2AXwM/zcxD6nZclbYeBEmDyKCM1F4Mygyuth6PGJSRJPVSV+ORIXH7UmbelJkzepj34HoBmdJL5b42rFb5H9ItdcrcUpOHiBgLbAnMrA3IVJUJipksFdsCiwF/zZpIV2Y+D9wPvCsi1u1tuyRJkiRJUvsYEkGZ/lCu17I2xe1Lt9Ucf2/5clqdopVj1XPK3kcRdKmXv1GZDWrS+lQmM58C5gCrlDNtJEmSJElSG2mboAywA8XCuj/JzBeqjo8FRgGZmfXmjM4s9ytVHass4tto9s5glam0d6UG6ZIkSZIkaZhqi6BMRIwCvk1xi9BXa5JHl/vXGxSfW+4XHyZl5omIQyPi9oi4/dlnn21QhSRJkiRJGoraIigD/IjikdK7ZObsmrRXy/0iDcqOKvfV5YZymXky88zMHJ+Z41dYYYUGVUiSJEmSpKFo2AdlIuJ4YE9gh8x8oE6WGRQzTiIi6i2bv0y5f7rq2FPlfmyD0w5WmUp7n26QLkmSJEmShqlhHZSJiK8CRwHbZebf6uXJzDeBf5Qv16yTpaPcVz+c/V4gq9J6Uqby73rnaLpMRKxM8TSnJ8unN0mSJEmSpDYybIMyEXEs8AXgo9UBmYjYISK2rMl+TbnfvE5VW9TkoXw89y3AMhGxXoMyCVxbdex64DVgs4iY75Hc5dOT1gUeysz7e9suSZIkSZLUPoZlUCYivgJ8iSIgc0dN8j7A9jXHzqB4vPSB1QGTiFgC2Bt4HPhVTZlTyv1BNefeBNgQ+HVmPlY5npkvAT8DVgF2rKlrEsUjtk+pOX4dxWyZXSJixZq0g4C3gB8gSZIkSZLazrALykTE0cA3gUeBL0XExdUbsHVtmTJ4ciSwGXBqRCwbEasC5wPLAZMyc05NmV8CFwCTI+LAiFg8IjYGfk4RxDmyTvOOpbhV6syI+HBEjI6I3YBO4LcUwaHqc7wFTKSYdfOLiFg7IsZExNeAXYDOzLy7Vx0lSZIkSZKGtIVb3QCAiOgAHq45fE5EnAM8kpkdVccPK/cblluPZOZZEfE4ReDkUeAN4GZgy8y8vUGxA4DbgC8CpwMzgauBYzNzgcV3M3NWeevUCcBFwIrlub4NnJyZb9Qpc2dEbAp8A7iV4jHZ9wL7ZuZFPb0+SZIkSZI0vAyJoExmTqO4vacneTv6cJ5rmX8dmO7yvwWcWm49LTMLmFxuPS1zH8UTpCRJkiRJ0ggx7G5fkiRJkiRJagcGZSRJkiRJklrAoIwkSZIkSVILGJSRJEmSJElqAYMykiRJkiRJLWBQRpIkSZIkqQUMykiSJEmSJLWAQRlJkiRJkqQWMCgjSZIkSZLUAgZlJEmSJEmSWsCgjCRJkiRJUgsYlJEkSZIkSWoBgzKSJEmSJEktYFBGkiRJkiSpBQzKSJIkSZIktYBBGUmSJEmSpBYwKCNJkjQCRMTyEXFJRGRETGqQZ/GI+ExE/DYino2I1yPi6Yi4NCI+NMhNliSp7RmUkSRJanMRsQdwL7B9N1mvAM4A/gFsBiwD7A28G/hTo2COJEnqHYMykiRJbSwiDgN+CBwEXN5N9sWAazJzcmY+nJmvZOaNwO7AW8API2LMwLZYkqSRw6CMJElSe5sKvC8zr+5B3vuAc2sPZuZ9wIPAksDm/ds8SZJGroVb3QBJkiQNnMy8qYm8B3eR/FK5j761SJIkVThTRpIkSV2KiHcAawOvAre1uDmSJLUNgzKSJEnqzg7AWOAnmflCqxsjSVK7MCgjSZKkhiJiFPBt4H7gq13lnT59OhGxwNbZ2TkYTZUkqaU6Ozvr/h0ExjUqY1BGkiRJXfkRsAKwS2bO7irjuHHjyMwFNoMykqSRoLOzs+7fQWB6ozIu9CtJkqS6IuJ4YE9gu8x8oNXtkSSp3ThTRpIkSQuIiK8CR1EEZP7W6vZIktSODMpIkiRpPhFxLPAF4KPVAZmI2CEitmxdyyRJai8GZSRJkjRPRHwF+BJFQOaOmuR9gO0Hv1WSJLUn15SRJEkSABFxNPBN4G7gS+UTI6p9EJg2yM1qH51L90Mds/pehyRpyDAoI0mS1MYiogN4uObwORFxDvBIZnZUHT+s3G9YbpIkaQAZlJEkSWpjmTkNWGDKS4O8HQPaGEmSNB/XlJEkSZIkSWoBgzKSJEmSJEktYFBGkiRJkiSpBQzKSJIkSZIktYBBGUmSJEmSpBYwKCNJkiRJktQCBmUkSZIkSZJawKCMJEmSJElSCxiUkSRJkiRJaoEhFZSJiOUj4pKIyIiY1E3edSPilxHxXES8EhF/jYhPdVNmh4i4MSJeiogZEXFVRGzSRf6FIuLIiLgnIl6NiOkRcVZErNhFmTER8V8R8WhEzImI+yPiuIhYpD+vRZIkSZIkDW9DJigTEXsA9wLb9yDvRsDtwArA5sAqwNXAxRFxbIMyBwHXAn8H1gA2AOYCN0fEhAanOgf4LvA9YDlgZ2Ar4LaIWLnOOcYAfwb2AvYFxgJHA8cAl0fEO/rjWiRJkiRJ0vA3JIIyEXEY8EPgIODybvIuBJxL0fa9M/PBzHwxM78OXAWcGBHr15RZFfgxcCtwVGa+kJmPA/sDM4DzImLRmjJ7AAcAp2TmOZk5OzPvBPYDVgd+UKd5JwHrA4dm5k2Z+WpmXgocD+wIfKav1yJJkiRJktrDkAjKAFOB92Xm1T3Iuy2wEXBVZj5Tk3Y2xTUdVXP8MGAx4JzMzMrBzHwFuARYDdizpsznquqkqswdwN3AnhHxzsrxiFgKOBh4kmJGTrUpQFbV2ZdrkSRJkiRJbWBIBGXKWSUzeph953J/S520W2ry9KpMRIwFtgRmZuZ9DcoEsFPVsW0pAj9/rQ78AGTm88D9wLsiYt0+XoskSZIkSWoDQyIo06QNyv202oTMfAqYA6wSEcsBlOu4vLdRmapjG1Qdex9F0KVe/kZlGrarN2XqXYskSZIkSWofwzEoU1lgt9HMmlnlfqVyPxYYBWRmzqqTf2ZN/p6cY7DK1F6LJEmSJElqE8MxKDO63L/eIH1uuV+8l/mHepl5IuLQiLg9Im5/9tlnG1QhSZIkSZKGouEYlHm13C/SIH1UuZ/dy/xDvcw8mXlmZo7PzPErrLBCgyokSZIkSdJQNByDMk+V+7EN0pcu90+X+xkUM04iIpauk3+Zmvw9Ocdglam9FkmSJEmS1CaGY1BmarlfszYhIlameALSk+UTj8jMN4F/NCoDdNTUC3AvxSOsO2ozd1GmYbt6U6betUiSJEmSpPYxHIMy15T7zeukbVGTp1dlysdz3wIsExHrNSiTwLVVx64HXgM2i4iozlw+PWld4KHMvL+P1yJJkiRJktrAcAzKXEcxw2SXiFixJu0g4C3gBzXHz6B4vPSB1QGTiFgC2Bt4HPhVTZlTquqkqswmwIbArzPzscrxzHwJ+BmwCrBjTV2TKB6xfUrN8d5ciyRJkiRJagPDLiiTmW8BEylmqvwiItaOiDER8TVgF6AzM++uKfMYcCSwGXBqRCwbEasC5wPLAZMyc05NmV8CFwCTI+LAiFg8IjYGfk4RxDmyTvOOpbhV6syI+HBEjI6I3YBO4LcUwaE+XYskSZIkSWoPQyIoExEdEZERkRRBCoBzymPTavNn5p3ApsBzwK0UC+Z+Atg3M0+sd47MPAvYCdgYeJRi3ZjFgC0z87oGTTsA+BLwReAFituVbgbGZ+b0OueYBWxJMevmImAm8O1y+3hmvtEf1yJJkiRJkoa/hVvdAIDMnEZxe08zZe4D9myyzLXMvw5Md/nfAk4tt56WmQVMLreelmn6WiRJkiRJ0vA2JGbKSJIkSZIkjTQGZSRJkiRJklrAoIwkSZIkSVILGJSRJEmSJElqAYMykiRJkiRJLWBQRpIkSZIkqQUMykiSJEmSJLWAQRlJkiRJkqQWMCgjSZIkSZLUAgZlJEmSJEmSWsCgjCRJ0ggQEctHxCURkRExqZu860bELyPiuYh4JSL+GhGfGqSmSpI0YhiUkSRJanMRsQdwL7B9D/JuBNwOrABsDqwCXA1cHBHHDmQ7JUkaaQzKSJIktbGIOAz4IXAQcHk3eRcCzqUYI+6dmQ9m5ouZ+XXgKuDEiFh/oNssSdJIYVBGkiSpvU0F3peZV/cg77bARsBVmflMTdrZFGPHo/q5fZIkjVgLt7oBkiRJGjiZeVMT2Xcu97fUSbulJo8kSeojZ8pIkiSpYoNyP602ITOfAuYAq0TEcoPZKEmS2pVBGUmSJFWsXO5nNEifVe5XGoS2SJLU9gzKSJIkqWJ0uX+9Qfrccr94vcTp06cTEQtsnZ2d/d1OSZKGnM7Ozrp/B4FxjcoYlJEkSVLFq+V+kQbpo8r97HqJ48aNIzMX2AzKSJJGgs7Ozrp/B4HpjcoYlJEkSVLFU+V+bIP0pcv904PQFkmS2p5BGUmSJFVMLfdr1iZExMrAYsCTmfn8oLZKkqQ2ZVBGkiRJFdeU+83rpG1Rk0eSJPXRwq1ugCS1nc6lu8/TbR2zus8jSf3vOorZMrtExIqZ+UxV2kHAW8APWtIySZLakDNlJEmSBEBmvgVMBBL4RUSsHRFjIuJrwC5AZ2be3dJGSpLURgzKSJIktbGI6IiIjIikCLgAnFMem1abPzPvBDYFngNupVj89xPAvpl54iA1W5KkEaHfbl+KiKWBuZn5areZJUmS1JTejrUycxoQTZa5D9izmTKSJKl5Tc2UiYhxEfEf5bZMeWzJiPgt8AIwMyJ+FBFN/eGXJEmSYy1JkkaaZm9f2h/4GrB21bHvANsBzwP/BA4DDuyX1kmSJI0sjrUkSRpBmg3KfBw4IjMnZubMchrtAcAjwLsy8/3AMcAh/dtMSZKkEcGxliRJI0iza8qsA1xc9frjwGjgR5n5YnnsHODL/dA2SRqWOuZc2Oc6pvW9GZKGJ8dakiSNIM3OlFkMeLPq9T7AW8w/eHi5zCdJkqTmONaSJGkEaTYo8xCwK0BEbAh8DPhTZj5RlWd94PF+aZ0kSdLI4lhLkqQRpNmgzE+BsyPiVuCmsvz3K4kR8W7gVODWfmuhJEnSyOFYS5KkEaTZNWXOoHgawCRgLnByZl4JEBGfBC4t8329vxooSZI0gjjWkiRpBGkqKJOZbwFfKLfatMtpfuaNJEmSSo61JEkaWZr6wx4Rq3eT/uWI2D8iom/NkiRJGnkca0mSNLI0+23Lw92kbwz8BDilV62RJEka2RxrSZI0gjQblOnyW5nM/DTwQWDvXrdIkiRp5HKsJUnSCNJsUCZ7kOcFYPFetEWSJGmkc6wlSdII0uVCvxExEZhYc+z6LoosBqwH3Nn3pkmSJLU3x1qSJI1s3T19qQOYUPU6a17XmgHcBhzVl0b1RERsB3weWB9YAXgC+Avwjcy8r07+dYGTgG2A0cA9wPcz85IuzrED8BXgA8AbwJ+B4zPzjgb5FwKOAA6leJzlDOBq4KuZ+UyDMmOAE4A9gBWBR4HzKB6B+XrXvSBJkoa5DoboWEuSJA28Lm9fyswTMnOhygZE9es623KZ+bF6QZH+FBGfB35H8W3RTsBywL7ABsDfI2JCTf6NgNspgjebA6tQBEsujohjG5zjIOBa4O/AGmXdc4Gba+uvcg7wXeB7ZZt2BrYCbouIleucYwxFoGevsv1jgaOBY4DLI+Id3XSFJEkaxobqWEuSJA2OZteUOXdAWtGEiBgFdFJ8k7RPZt6TmbMz81bgYGAUcHJV/oUo2r0QsHdmPpiZL2bm14GrgBMjYv2ac6wK/Bi4FTgqM1/IzMeB/Sm+oTovIhatKbMHcABwSmaeU7bpTmA/YHXgB3Uu5ySKmT6HZuZNmflqZl4KHA/sCHymD10lSZKGn5aPtSRJ0uBpKiiTmQf2JF9E/EvvmtMjY4GlgOfq3BJ0b7nfsOrYtsBGwFV18p9N0Qe1U4APo5iFc05mzltwLzNfAS4BVgP2rCnzuao6qSpzB3A3sGdEvLNyPCKWoggiPUkxI6faFIqg0+eQJEkjxhAZa0mSpEHS7EyZnvrDANVLZj4NTAeWj4gVa5LfV+6fqjq2c7m/pU51t9Tk6VWZiBgLbAnMbDCd+BaKR1zuVHVsW4rAz1+rAz8Amfk8cD/wrnItHEmSpGoDNtaSJEmDp7uFfhdQ3tqzB7AOxeMYo78b1QOTKGasXBwRRwIPUaz5claZ/qOqvBuU+2m1lWTmUxExB1glIpbLzOfLdVze26hM1bENqo69j6If6uVvVKZhu6qOv7vMd3+DPJIkqc0MkbGWJEkaBE0FZSLiI8DlFAOErmQ36X2Smb+LiC2B04CpVUn/C3w2M0+vOlZZYHdGg+pmUcxYWQl4nuL2qFHFaXJWnfwzy/1KTZyjv8pIkqQ2NlTGWpIkaXA0O1PmO8DDwNeBfwIvs+CgIChmrgyYiNiT4klHf6FYL+b/yv3hwNIR8Y7MfLPMPrrcN3q89NxyXxn8NJt/MMtIkqT2NiTGWpIkaXA0G5R5D/CBzPxnV5ki4o+9b1LXImJN4HyKGSafzMzZZdKfI+IJitkym1JM+wV4tdwv0qDKUeW+Uk+z+QezzHwi4lDgUIDVV1+9UTZJkjR8tHysJUmSBk+zC/0+DzzRXabM3KZ3zemRfShuN7qyKiBTOe80itkzu5e3N8Hbi/6ObVDf0uX+6XI/g2KWSkTE0nXyL1OTvyfn6K8y88nMMzNzfGaOX2GFFRplkyRJw8dQGGtJkqRB0mxQ5lwWfBT0AiLi+t41p0fWKPdPNkivHH9/ua+sObNmbcaIWJkiwPNk+cQjytue/tGoDNBRUy8Uj+LOqrSelGnYri7KSJKk9jYUxlqSJGmQNHv70jnA9yNiA+AKikdTv1qTJ4Ct+6FtjTxf7ldpkD6u3FfWarkGmAxsXifvFlV5ql1DEdTZHLiruzKZOSMibgG2jIj16jwWewuKoM21VceuB14DNouIqH4sdkQsB6wLPJSZPnlJkqSRYyiMtSRJ0iBpdqbM/cDOwJHA7ylmlDxcs/1ffzawjqvK/S4RMbo6ISLWAD4IvAX8oTx8HcVsk10iYsWaug4q8/6g5vgZwBzgwIiY9xjKiFgC2Bt4HPhVTZlTquqsbtMmwIbArzPzscrxzHwJ+BlFcGnHmromUQy4TkGSJI0kQ2GsJUmSBkmzM2UAzusmPYD9e1Fvj2TmLRFxOnAYcGlEfIlicLIhxSOyRwEnZeaDZf63ImIi8EfgFxHxb8CzwFHALsB/ZObdNed4LCKOBM4ETo2IToqnJf0QWA7YMTPn1JT5ZURcAEyOiH8ClwDvBn5OEcQ5ss7lHAtMAM6MiH2AO4AdgE7gtxTBIUmSNLK0dKwlSZIGT9NBmcw8sLs8EXFA75rT4zZ8NiL+DBwC/AlYEphJEdTYPTMvrcl/Z0RsCnwDuJUiwHIvsG9mXtTgHGdFxOMUgZNHgTeAm4EtM/P2Bk07ALgN+CJwetmmq4FjM3OBBXszc1a5IPEJwEXAiuW5vg2cnJlv9KhDJElS2xgKYy1JkjQ4mg3KfLqH+Qb8iQCZeQFwQRP576MHC+fVlLmW+deB6S7/W8Cp5dbTMrMo1ryZ3EzbJElSWxoyYy1JkjTwmlpTJjMv6WG+G3vXHEmSpJHLsZYkSSNLswv9AhAR60bEiRFxbUTcUR7bICL+NSLe0b9NlCRJGlkca0mSNDI0vaZMRBxLsRBtpWzlUc6LA2cB+0bEbpn5Wr+0UJIkaQRxrCVJ0sjR1EyZiNibYrHcW4DDgU9W0jLzr8C7gFWBz/ZjGyVJkkYEx1qSJI0szd6+dCTF46a3zszTM/PK6sTMfKLM4xMBJEmSmudYS5KkEaTZoMwGwPe7yXMrsGbvmiNJkjSiDZmxVkRsFxHXRMSjEfFqRDwYET+PiPUG+tySJI0UzQZl3gG81U2elXtRryRJkobIWCsiPg/8DlgM2AlYDtiXImj094iYMJDnlyRppGj2D/pU4Ivd5PkMcFevWiNJkjSytXysFRGjKBYaTmCfzLwnM2dn5q3AwcAo4OSBOr8kSSNJs09fOh2YEhEfBn4K/B0gItakmEY7ieJblP36sY2SJEkjxVAYa40FlgKezcxnatLuLfcbDuD5JUkaMZoKymTmeRGxGcWK//9SlfRguQ/glMy8pJ/aJ0mSNGIMhbFWZj4dEdOBVSJixZrAzPvK/VMDdX5JkkaSpu9HzswjKB7P+BvgBeBN4HngGmDnzPx8v7ZQkiRpBBkiY61JwEzg4ohYPyJGl8Gin5bpPxqENkiS1PaavX0JgPLxjFd2m1GSJElNa/VYKzN/FxFbAqdRrHNT8b/AZzPz9Na0TJKk9tLUTJmI+L+BaogkSdJIN1TGWhGxJ3AbxSydjSjWmPkw8Ddg6Yh4RwubJ0lS22j29qWOiDgxIt41IK2RJEka2Vo+1ioXFT4feAn4ZGbenZkvZ+afgWOBE4Bf1Cs7ffp0ImKBrbOzc9DaL0lSq3R2dtb9OwiMa1Sm6TVlKFb+/3tE/DEiJkXEEr1tsCRJkhbQ6rHWPsBiwJWZObs6ITOnAX8Bdi9vb5rPuHHjyMwFNoMykqSRoLOzs+7fQWB6ozK9Wej3XymiPBcC/w94MiLOjoitettwSZIkFYbAWGuNcv9kg/TK8fcPfFMkSWpvTQVlMnOhcj8rM8/IzM2BzSmeDPCLiHggIo6NiHcOQFslSZLa2hAZaz1f7ldpkF6Zgv36ALZBkqQRoTe3L80nM/+RmV8EdqX443wiMCQWqZMkSRruWjDWuqrc7xIRo6sTImIN4IPAW8AfBrANkiSNCM0+femAmtfLRsSREXEncDOwHvAicHb/NVGSJGlkGApjrcy8BTidYkbMpRGxQUQsERFbAJcBo4D/zMwHB6oNkiSNFAs3mf8c4LyI2AE4CPg4xR9mgOvL9F9n5pz+a6IkSdKIMSTGWpn52Yj4M3AI8CdgSWAmcAewe2ZeOpDnlyRppGg2KBMR8RjFNycBPAycC0zJzEf7u3GSJEkjzJAZa2XmBcAFg3lOSZJGmmaDMgBjgZ8D52TmDf3bHEmSpBHPsZYkSSNEb4IyK2fmy/3eEkmSJIFjLUmSRoxmn750oIMESZKkAeNYS5KkEaTLoExE/F/VtlhmnttF3hMj4s1ye6P/mypJktReHGtJkjSydXf7UgdwYPnvud3k/Rnwe2Ap4PK+NUuSJGlE6MCxliRJI1Z3QZms/sYmIt4CsibDO8r9NGBaRCzd342UJElqU461JEkawZpd6HcbisczXl/+W5IkSf3HsZYkSSNIU0GZzLwRICKy8m9JkiT1D8dakiSNLM0+fUmSJEmSJEn9wKCMJEmSJElSCxiUkSRJkiRJaoHu1pSJiHizQULd45IkSeoxx1qSJI1gPVnoN3pRb3afRZIkSTjWkiRpxOouKJOZ+Y5mKoyIZYDne90iSZKkkcOxliRJI1h3a8r8Xy/qfKOX5SRJkkYax1qSJI1gXc6Uycx1mq0wM18Gmi4nSZI00jjWkiRpZPPpS5IkSZIkSS1gUEaSJEmSJKkFDMpIkiRJkiS1gEEZSZIkSZKkFhjWQZmI2C4iroiIpyPitYh4LCKujohP18m7bkT8MiKei4hXIuKvEfGpburfISJujIiXImJGRFwVEZt0kX+hiDgyIu6JiFcjYnpEnBURK3ZRZkxE/FdEPBoRcyLi/og4LiIWaa43JEmSJEnScDJsgzIR0Qn8CrgCeA+wLPDvwFbAxJq8GwG3AysAmwOrAFcDF0fEsQ3qPwi4Fvg7sAawATAXuDkiJjRo1jnAd4HvAcsBO5ftuS0iVq5zjjHAn4G9gH2BscDRwDHA5RHxju76QZIkSZIkDU/DMigTEbsCxwMHZOZPM/OFzHwlMy8DTgAeq8q7EHAuxbXunZkPZuaLmfl14CrgxIhYv6b+VYEfA7cCR5X1Pw7sD8wAzouIRWvK7AEcAJySmedk5uzMvBPYD1gd+EGdSzkJWB84NDNvysxXM/PS8tp2BD7Tl36SJEmSJElD17AMygDfBO7LzCtqEzLze5l5SNWhbYGNgKsy85ma7GdT9MFRNccPAxYDzsnMrKr7FeASYDVgz5oyn6uqs7o9dwB3A3tGxDsrxyNiKeBg4EmKGTnVpgBZVackSZIkSWozC7e6Ac2KiPdT3K50Zg+L7Fzub6mTdktNnp6WObLMc0HZprHAlsDMzLyvQZkNgZ2q2r0tReDnr9WBH4DMfD4i7gfeHRHrZub9Da5NkiRJUpvZ4NwN+lzH1IlT+6ElkgbacJwps3m5fzQiDoiIO8pFdWdGxG8iYuua/JXfaNNqK8rMp4A5wCoRsRxAuY7LexuVqTpW/ZvyfUA0yN+oTMN2dVFGkiRJkiS1ieEYlFm73B8MnAh8GVieYkHdscB1EbF3Vf7KArszGtQ3q9yvVO7HAqOAzMxZdfLPrMnfk3P0VxlJkiRJktQmhmNQZky576BY6Pe6cpHfqcCnKWasnBERS5b5Rpf71xvUN7fcL97L/INZZj4RcWhE3B4Rtz/77LONskmSJEmSpCFoOAZlKp7OzBurD2TmQ8BfKGa7fLQ8/Gq5X6RBPaPK/exe5h/MMvPJzDMzc3xmjl9hhRUaZZMkSZIkSUPQcAzKVG73ebRB+iPlfp1y/1S5H9sg/9Ll/umq+ucCERFL18m/TE3+npyjv8pIkiRJkqQ2MRyDMv8s941mmFRUnmhUWXZ8zdoMEbEyxROQnszM5wEy803gH43KUNw2VV0vwL3l+TpqM3dRpmG7uigjSZIkSZLaxHAMylxHEQBZIyLqtX+Ncl95NPU15X7zOnm3qMlDzeselcnMGRSPvV4mItZrUCaBa6uOXQ+8BmwWEVGduXwS1LrAQz4OW5IkSZKk9jTsgjKZ+ThwGcVtPztVp0XEWhSBlOnA78rD11HMNtklIlasqe4g4C3gBzXHz6B4VPaB1QGTiFgC2Bt4HPhVTZlTquqsbtMmwIbArzPzsarreAn4GbAKsGNNXZMoFiw+BUmSJEmS1JaGXVCm9O/AY8CPI+JfImJURKwPXEQx++SAzJwDkJlvARMpZqr8IiLWjogxEfE1YBegMzPvrq68DJ4cCWwGnBoRy0bEqsD5wHLApEr9VWV+CVwATI6IAyNi8YjYGPg5RRDnyDrXcSzFrVJnRsSHI2J0ROwGdAK/pQgOSZIkSZKkNjQsgzKZ+QQwnuIWoguAl4HfAw8Bm2bmdTX57wQ2BZ4DbqVYZPcTwL6ZeWKDc5xFMRNnY4pFhe+lWH9my9r6qxwAfAn4IvACxe1KNwPjM3N6nXPMArakmHVzETAT+Ha5fTwz3+i+NyRJkiRJ0nC0cKsb0FuZ+QxwWLn1JP99wJ5NnuNa5l8Hprv8bwGnlltPy8wCJpebJEmSJEkaIYblTBlJkiQNvIjYLiKuiIinI+K1iHgsIq6OiE+3um2SJLUDgzKSJElaQER0UtxifQXwHmBZinX9tqJYr0+SJPXRsL19SZIkSQMjInYFjgc+mZlXVCVdFhEnAOu1pGGSJLUZgzKSJEmq9U3gvpqADACZ+b0WtEeSpLbk7UuSJEmaJyLeT3G70h9b3BRJktqeQRlJkiRV27zcPxoRB0TEHRHxakTMjIjfRMTWLW2dJEltxKCMJEmSqq1d7g8GTgS+DCxPscDvWOC6iNi7RW2TJKmtGJSRJElStTHlvgM4IDOvy8xXMnMq8GkggDMiYsnagtOnTyciFtg6OzsHrfGSJLVKZ2dn3b+DwLhGZQzKSJIkqZ6nM/PG6gOZ+RDwF4oZMx+tLTBu3Dgyc4HNoIwkaSTo7Oys+3cQmN6ojEEZSZIkVZtR7h9tkP5IuV9nENoiSVJbMygjSZKkav8s94t0ky8HuiGSJLU7gzKSJEmqdh1FwGWNiKg3Vlyj3N83eE2SJKk9LdzqBkiSJGnoyMzHI+IyYDdgJ+CqSlpErEXxyOzpwO9a0kBpBJj6cKO7ByW1G2fKSJIkqda/A48BP46If4mIURGxPnAR8BrFU5nmtLSFkiS1AYMykiRJmk9mPgGMB64BLgBeBn4PPARsmpnXtbB5kiS1DW9fkiRJ0gIy8xngsHKTJEkDwJkykiRJkiRJLWBQRpIkSZIkqQUMykiSJEmSJLWAQRlJkiRJkqQWcKFfSZIktbfOpVvdAkmS6nKmjCRJkiRJUgsYlJEkSZIkSWoBgzKSJEmSJEktYFBGkiRJkiSpBQzKSJIkSZIktYBBGUmSJEmSpBYwKCNJkiRJktQCBmUkSZIkSZJaYOFWN0CS+kXn0v1U0YX9VI8kSZIkdc2ZMpIkSZIkSS1gUEaSJEmSJKkFDMpIkiRJkiS1gEEZSZIkSZKkFjAoI0mSJEmS1AIGZSRJkiRJklrAR2JLagsdc3yUtSRJUsUG527Q5zqmTpzaDy2R1BVnykiSJEmSJLWAQRlJkiRJkqQWMCgjSZIkSZLUAm0TlImIj0dERkR2kWfdiPhlRDwXEa9ExF8j4lPd1LtDRNwYES9FxIyIuCoiNuki/0IRcWRE3BMRr0bE9Ig4KyJW7KLMmIj4r4h4NCLmRMT9EXFcRCzSs6uXJEmSJEnDTVsEZSJiKeC0bvJsBNwOrABsDqwCXA1cHBHHNihzEHAt8HdgDWADYC5wc0RMaHCqc4DvAt8DlgN2BrYCbouIleucYwzwZ2AvYF9gLHA0cAxweUS8o6vrkiRJkiRJw1NbBGWAk4HHGyVGxELAuRTXu3dmPpiZL2bm14GrgBMjYv2aMqsCPwZuBY7KzBcy83Fgf2AGcF5ELFpTZg/gAOCUzDwnM2dn5p3AfsDqwA/qNO8kYH3g0My8KTNfzcxLgeOBHYHPNN0bkiRJkiRpyBv2QZmI+BBwEHBIF9m2BTYCrsrMZ2rSzqboh6Nqjh8GLAack5nzbonKzFeAS4DVgD1rynyuqk6qytwB3A3sGRHvrGr7UsDBwJMUM3KqTQGyqk5JkiRJktRGhnVQJiJGAWcB38nMe7rIunO5v6VO2i01eXpVJiLGAlsCMzPzvgZlAtip6ti2FIGfv1YHfgAy83ngfuBdEbFunfokSZIkSdIwNqyDMsBxFNfwjW7ybVDup9UmZOZTwBxglYhYDqBcx+W9jcpUHdug6tj7KIIu9fI3KtOwXV2UkSRJkiRJbWDhVjegtyLivcCXgY9m5mvdZK8ssDujQfosihkrKwHPUyy2OwrIzJxVJ//Mcr9SE+forzKSJEmSJKkNDMuZMuXCvT8FpmTmn3pQZHS5f71B+txyv3gv8w9mGUmSJEmS1AaG60yZwykeUb1jD/O/Wu4XaZA+qtzP7mX+wSwzT0QcChwKsPrqqzeoQpIkSdJIM/XhR1vdBEk9MOxmykTEahSPkT6iwa1F9TxV7sc2SF+63D9d7mdQzFKJiFi6Tv5lavL35Bz9VWaezDwzM8dn5vgVVlihQRWSJEmSJGkoGnZBGeAjwFLAryMiq7dKhqpjN5SHppb7NWsri4iVKdaTebJ84hGZ+Sbwj0ZlgI6aegHupXiEdUdt5i7KNGxXF2UkSZIkSVIbGHZBmcyckplRb6vKUzk2oTx0TbnfvE6VW9TkoeZ1j8pk5gyKx14vExHrNSiTwLVVx64HXgM2i4iozlw+CWpd4KHMvL9OfZIkSYMiIj5e+yWYJEnqu2EXlOml6yhmm+wSESvWpB0EvAX8oOb4GRSPyj6wOmASEUsAewOPA7+qKXNKVZ1UldkE2BD4dWY+VjmemS8BPwNWYcH1cSZRPGL7FCRJklokIpYCTmt1OyRJakcjIiiTmW8BEylmqvwiItaOiDER8TVgF6AzM++uKfMYcCSwGXBqRCwbEasC5wPLAZMyc05NmV8CFwCTI+LAiFg8IjYGfk4RxDmyTvOOpbhV6syI+HBEjI6I3YBO4LcUwSFJkqRWOZliHCNJkvrZsA/KRMSkLtaUmVA5lpl3ApsCzwG3Uiyy+wlg38w8sV7dmXkWsBOwMfAoxboxiwFbZuZ1DZp0APAl4IvACxS3K90MjM/M6XXOMQvYkmLWzUXATODb5fbxzHyjRx0hSZLUzyLiQxQzgA9pdVskSWpHw/WR2PNk5hRgSg/z3gfs2WT91zL/OjDd5X8LOLXcelpmFjC53CRJklouIkYBZwHfycx7apa/kyRJ/WDYB2UkSZI0II6jmFX9jVY3RBouNjh3g36px0evSiOHQRlJkiTNJyLeC3wZ+Ghmvtbq9kiS1K6G/ZoykiRJ6j8RsRDwU2BKZv6pmbLTp08nIhbYOjs7B6StkiQNJZ2dnXX/DgLjGpVxpowkSZKqHQ6sAezYbMFx48YxffoCzzWQ1KSOORf2uY5pi+3bDy2R1IzOzs66X0RERMM/jgZlJEmSBEBErAacBEwsH0QgSZIGkLcvSZIkqeIjwFLAryMiq7dKhqpjN7SslZIktQmDMpIkSQIgM6dkZtTbqvJUjk1oYVMlSWoLBmUkSZIkSZJawKCMJEmSJElSCxiUkSRJUl0RMamLNWUmtK5lkiS1B4MykiRJqqurNWYy84ZWt0+SpOHOoIwkSZIkSVILGJSRJEmSJElqAYMykiRJkiRJLWBQRpIkSZIkqQUMykiSJEmSJLWAQRlJkiRJkqQWMCgjSZIkSZLUAgZlJEmSJEmSWsCgjCRJkiRJUgsYlJEkSZIkSWoBgzKSJEmSJEktYFBGkiRJkiSpBQzKSJIkSZIktYBBGUmSJEmSpBYwKCNJkiRJktQCBmUkSZIkSZJaYOFWN0CSJElSD3Uu3U/1zOqfeiRJfeJMGUmSJEmSpBZwpowkSZIk9YOpDz/aL/V09EstkoYDZ8pIkiRJkiS1gEEZSZIkSZKkFjAoI0mSJEmS1AIGZSRJkiRJklrAoIwkSZIkSVILGJSRJEmSJElqAYMykiRJkiRJLbBwqxsgSZIkSdJA2+DcDfpcx9SJU/uhJdLbnCkjSZIkSZLUAgZlJEmSJEmSWsDblzT0dS7dD3XM6nsdkiRJ0gji7T7SwBt2QZmICGAXYD9gS2BlYDZwN3BWZp7foNy6wEnANsBo4B7g+5l5SRfn2gH4CvAB4A3gz8DxmXlHg/wLAUcAhwJrAzOAq4GvZuYzDcqMAU4A9gBWBB4FzgNOzszXG3aEJEmSJGnEMmjWHobj7UtfBa4AlgM+CSwDbEERADkvIs6uLRARGwG3AysAmwOrUARLLo6IY+udJCIOAq4F/g6sAWwAzAVujogJDdp2DvBd4Htl+3YGtgJui4iV65xjDEWgZy9gX2AscDRwDHB5RLyjq46QJEmSJEnD13AMyiwGPA3slpl3ZubszPwnRWDj/4ADI2LbSuZy9sq5FNe6d2Y+mJkvZubXgauAEyNi/eoTRMSqwI+BW4GjMvOFzHwc2J+3gz+L1pTZAzgAOCUzzynbdSfFjJ7VgR/UuZaTgPWBQzPzpsx8NTMvBY4HdgQ+06eekiRJkiRJQ9awu30JeAI4NzNfrj6YmXMj4ncUgYztgOvLpG2BjYBL6txCdDbFrVBHAYdUHT+MIvhzTmZm1TleiYhLgCOBPYELqsp8rqrO6nbdERF3A3tGxDvL4A4RsRRwMPAkxYycalOA75R1nta4KyRJkiRpQR1zLuxzHUtxTD+0pH/0x6060lA07GbKZObpmXl0g+SXyn1UHdu53N9SJ/8tNXl6VSYixlKsbzMzM+9rUCaAnaqObUsR+PlrdeAHIDOfB+4H3lWuhSNJkiRJktrMcJwp05VKAOOPVccqIdVptZkz86mImAOsEhHLZebz5Tou721UpupYdaj2fRRBl3r5G5Vp2K6q4+8u893fII8kSVK/6u1DFSRpJOivGTsusKuKYTdTppGIWBb4GHAn8D9VSZUFdmc0KFp5VvJK5X4sMArIzKz3HOWZNfl7co7+KiNJkjTQmn6ogiRJ6p12minzbSCBA2puBxpd7hs9XnpuuV+8l/kHs8x8IuJQisdvs/rqqzfKJkmS1IzqhypU1vD7Z0TsBfyT4qEKP8/M6xvWIKktTH340VY3oW25Ro4q2mKmTETsB0wC9svMe2qSXy33izQoPqrcz+5l/sEsM5/MPDMzx2fm+BVWWKFRNkmSpGY0fKgC8Lvy5XaD3ipJktrQsJ8pExEfBX5K8VjpX9fJ8hTFmi9jG1SxdLl/utzPoJilMioilq5zC9MyNfkr56CLc/RXGUmSpAGVmad3kVzvoQqSJKmXhnVQJiK2Ay4DDs/MRvc3TwU+AqxZp/zKFFN0nyyfeERmvhkR/wDeX5a5q6ZYR1W9FfdS3DrVQX31ylT+vUC7uigjSZLUSvUeqiBJw0J/3I61wZrttWxEf9xG5aLFfTNsb1+KiI9QBGSOrA7IRMT7IuJTVVmvKfeb16lmi5o8vSqTmTMoHnu9TESs16BMAtdWHbseeA3YrHzKwTwRsRzFoOehzPTJS5IkqeW6eKiCJEnqpWE5UyYitgUuByZn5s9qkjelWF/mkvL1dRSzTXaJiBUz85mqvAcBbwE/qKnjDODzFAvZ/aSycHBELAHsDTwO/KqmzCkUj408CPhyVVs3ATYE/jszH6scz8yXIuJnwGeBHZk/MDSJYlrwKV10gyRJ0mBq9FAFqS30y4yBfmiHpN4bjjN/hl1QJiK2Aa6ieJT1duUtTNXW5O1FdMnMtyJiIsU0219ExL8BzwJHAbsA/5GZd1dXkJmPRcSRwJnAqRHRSfG0pB9SPB5yx8ycU1PmlxFxATA5Iv5JERR6N/BziiDOkXUu51hgAnBmROwD3AHsAHQCv6UIDkmSJLVU1UMV9q7zUIV5pk+fTs0EYACOP/54Ojs7B6x9ktqfT4JST7T6c9LZ2ckJJ5xQL2lcozLDLigDTKQIkIwGPtUgz43VLzLzzojYFPgGcGtZ9l5g38y8qF4FmXlWRDxOETh5FHgDuBnYMjNvb3DeA4DbgC8CpwMzgauBYzNzgQV7M3NWRGwJnABcBKxYnuvbwMmZ+UaD80iSJA2KHjxUYZ5x48Yxffr0wWmYJLVAf/2nv93WplGhs7Oz7pcQEdHwj+OwC8pk5iSKb2qaLXcfsGeTZa5l/nVgusv/FnBqufW0zCxgcrlpoHQu3X2eHtVT+zAuSZLaVw8fqiBJknpp2AVlJEmSNPC6eqgCsH5mXtKorCTN019fjrYZnwSlCoMykiRJmk+TD1WQJLVA2wV2RmgAz6CMJEmS5mn2oQpSu2j1AqHSsDVCgyn9xaCMJEmSqjX9UAVJktQ7BmUkSZI0T28fqiBJGn6cIdZ6C7W6AZIkSZIkSSORQRlJkiRJkqQWMCgjSZIkSZLUAq4pI0mSpKHJJ3pIktqcQRlJkiRJTdvg3A36XMfUiVP7oSWSNHwZlJHUev3yTeiF/VCHJEmSJA0e15SRJEmSJElqAYMykiRJkiRJLeDtS5IkSZKGtX5Z36Yf2iFJzXKmjCRJkiRJUgs4U0ZSy3XMcZFeSZIkSSOPQRlJkiRJ6gd+0SSpWQZlJEmSpJGmc+m+17Hm6n2vQ5JGONeUkSRJkiRJagGDMpIkSZIkSS1gUEaSJEmSJKkFXFNGkoai/rjXH6BzVv/UI0nSEDb14Udb3QRJ6hVnykiSJEmSJLWAM2UkSZIkNc3ZKZLUdwZlJEmSJLVGf92uK0nDlEEZDSz/0EqSJEmSVJdrykiSJEmSJLWAQRlJkiRJkqQWMCgjSZIkSZLUAq4pI0lDUMecC/ulnmn9UoskSZKkgeBMGUmSJEmSpBYwKCNJkiRJktQC3r4kqW/65bHn/XOrjiRJkiQNJ86UkSRJkiRJagFnykjN6I9ZIZ2z+l6HJEmSJGnYc6aMJEmSJElSCxiUkSRJkiRJagFvX5LUJx1zXKRXkiRJknrDmTKSJEmSJEkt4EwZaSTzcdbtz8WpJUmSpCHLoIwkqWv9ErzTAvor2NVO748BQEkt5C3ZklrBoMwQEBFjgBOAPYAVgUeB84CTM/P1ljSqnQb5Q419Kwn8XaAhb0iOTyRJajMGZVqsHPD8GRgL7APcAewAnA9sGREfz8w3W9hEtTG/EZIk1TNUxiedN8yhc8JiA30aVbHPB9fMmy5gmQ/v1+pmjBh+vgeX/d0zBmVa7yRgfWDnzLypPHZpRBwPfBf4DHBaqxonSZJGpCExPjnhxrkO6AeZfT64Zv35oiEdlOmPL/CmLbZvP7Skf/j5Hlz2d8/49KUWioilgIOBJ4Fra5KnAAl8bpCbJUmSRjDHJ5IkDR5nyrTWtsBiwF8zM6sTMvP5iLgfeHdErJuZ97ekhRqSvO1IkjSAHJ9o2HFsJGm4MijTWhuU+2kN0qcB7y7zOegZAvyDL0kaARyfaNA4tpI00hmUaa2Vy/2MBukzy/1KA9+U+fkHUmoP7XYvuKRBMWTHJxqMMdoujgMlaRAZlGmt0eW+0WMl55b7xeslRsShwKHly5cj4n/7sW3LA8/1Y33qnn0+uOzvHor+qcb+HlzDs79P6KdP2/zWGIhK21yvxyd33HHH7IioN255Epjei7aMixNe7E25NrbLQJ9g3CMn72KfD5627+8B+c3ee/5OGVzDs7/7Nh4ZB6xS5/h7GhUwKNNar5b7RRqkjyr3s+slZuaZwJn93SiAiLg9M8cPRN2qzz4fXPb34LK/B5f9rT7q9fgkM5cYkBZJktSmfPpSaz1V7sc2SF+m3D898E2RJEkCHJ9IkjRoDMq01tRyv2aD9I6afJIkSQPN8YkkSYPEoExrXQ+8BmwWEfPduBYRywHrAg+16HGTA3JblLpknw8u+3tw2d+Dy/5WXwzK+CQitouIayLi0Yh4NSIejIifR8R6DfKvGxG/jIjnIuKViPhrRHyqL21oVxGxfERcEhEZEZO6ydt0v0bEDhFxY0S8FBEzIuKqiNikXy9iGOlJf0fE4hHxmYj4bUQ8GxGvR8TTEXFpRHyom/rt7yrNfL5ryn2/LHNDN/ns7xpN/k5ZKCIOjYg/l/03u+r3+2YNyozoPjco00KZ+RLwM4qFgHasSZ5EsS7WKYPbqkK5Xo0GkX0+uOzvwWV/Dy77W30xGOOTiPg88DtgMWAnYDlgX4rHbP89IibU5N8IuB1YAdi8bNvVwMURcWxf2tJuImIP4F5g+x7kbbpfI+Ig4Frg7xQLaW9AsfjzzbXv20jQRH9fAZwB/APYjOI2wL0pHi//py6COfZ3lWY+3zXlxgNH9iCf/V2jyd8piwLXAJ8DTgBWBVYDzgb2A7atU2bE93lkZqvbMKJFxNLAzcDSwD7AHcAOwHnl8Z0z843WtVCSJI00Azk+iYhRFE8HWxJYOTOfqUrbFLgVuDUzP1geWwj4G/AuYK2a/FdSBHU2ysx7etOedhIRhwFfAw4B9gImAgdm5pQ6eZvu14hYFXgQuBvYPMv/SETEEsBDFP+RWiczXxuQCxximuzvm4BZmblzzfH1gHsoFtheNTNfrEqzv6s009815RamCD6+ShF8vDEzJ9TJZ3/XaLbPI+IU4CDgPZn5RE3aJcDvM/OsqmP2Oc6UabnMnAVsCfwKuAiYCXy73D4+EAGZcLrwgGlyap/ThfuoJ/0dThfuV818xmvKOWW4F5r8neJ0YfWbAR6fjAWWAp6rDgSU7i33G1Yd2xbYCLiqTv6zKcazR/WhPe1kKvC+zLy6B3l706+HUcxuOqfynyeAzHwFuITiG/E9e9n24aiZ/r4POLf2YGbeR/Gf0iUpAgbV7O/5NdPf1b5M8Tvn693ks78X1OM+LwMshwMX1AZkADLzU9UBmZJ9jkGZISEzZ2Xm5MxcLTMXzcx1MvPEzJzb3+cKpwsPmCan9jlduI+a6G+nC/eTZj7jNeWcMtwLTf5Ocbqw+t1AjU8y82lgOrB8RKxYk/y+cv9U1bHKzIJb6lR3S02eES0zb8rMGT3M3pt+9b2o0kx/Z+bBmfmLBskvlfuoOW5/V2ny8w1ARKwDHAd8hmKmTFfs7xpN9vlewMLAH5s4hX2OQZkRJYrpwp1AAvtk5j2ZOTszbwUOBkYBJ1flX4gior8QsHdmPpiZL2bm14GrgBMjYv3Bvo6hqJza90OK6XqXd5O36X4tI88/ppjSfVRmvpCZjwP7AzOA88r/lI0IzfQ3RQDymvI/Fg9n5iuZeSOwO/AW8MOIGFNTv/1do8k+ry63MPBT4LZu8tnnVXrR3ydTzGrYLjN/W/5ufz4zvwn8Ani+pn77W602iWL2zcURsX5EjC5ndP20TP9RVd4Nyv202koy8ylgDrBKFIsQq+ea6teIeAfw3kZlqo5tUCdNDZT9ujZFwOC2muP2dx9ERABnAZdk5u+7yWt/911lptczEXFCRDwUEa9FxJMRcW5EzPdEP/v8bQZlRhanCw8cpwsPLqcLDz6nDA8upwurrWXm7ygCiVB83mcDfwUWBT6bmd+ryr5yuW/0be2scr9Sf7ezzTXbr2MpvsDL8va2WjNr8qtndqDo259k5gtVx+3vvjsYeA/whR7ktb/7bu1y/1NgK2BXinXJDqK4Q+O2iFi3Kr99XjIoM4I4XXjgOF14cDldePA5ZXhwOV1Y7S4i9qSYFfAmxRcVSwEfplh4dunyG9SK0eX+9QbVVW6nWnwAmtrOmu1X34d+Vs5i/zZwP/DVmmT7uw8iYhWKvj2qJtjViP3dd5WZ58sCn8zMqZk5JzOvpfiSbjmKWboV9nnJoMzIMwmnC7ea04WHAKcLDyynDA8qpwtrWCk/k+dTBMY/mZl3Z+bLmfln4FiKdZGqg+mVoO4iDaocVe5nD0R721iz/er70P9+RLG+4C6ZWdtv9nff/Ai4OTMv7mF++7v/XJuZL9Ucu4hi2YCPVP3f0T4vGZQZYZwuPCQ4XXhocLrwwHLK8OBxurCGm30obp+7svY/opk5DfgLsHtEVMYrlVm8YxvUt3S5f7qf29numu3XGRTfXEcUj0yvtUxNfnUhIo6nuE10h8x8oE4W+7uXImJXikXyD2uimP3dd5X/2zxam5CZcyj6Lnh73GKflwzKjDBOFx4SnC7cYk4XHlhOGR50ThfWcLNGuX+yQXrl+PvL/dRyv2ZtxohYmSLA82RmPl+bri411a+Z+SbFkwzrlgE6aupVAxHxVYr1A7fLzL/Vy2N/98muFGsGPhIRWdmAP5TpW1cd7wT7u5/8s9w3mvlSkWCfVzMoM4I4XXjIcLpw6zldeGA5Zbg1nC6s4aISPFmlQfq4cl8JHF5T7msXZQfYoiaPeq43/ep70UcRcSzFLNKPVgdkImKHqtlhFfZ3L2TmpMyM2g3YpsxyY9Xxzqqi9nffVG5XrxfoXZRiBu6bQPXMMPscgzIjjdOFhwanC7eQ04UHllOGW8Lpwhpurir3u0TE6OqEiFgD+CBFMLHyrfZ1FN+U7lLnQQUHlXl/MHDNbVu96dczKNa+O7BcOwyAiFgC2Bt4HPjVgLV4mIuIrwBfogjI3FGTvA/F389q9vfgsr/75lLgCeBjEbF8Tdo+FLGHKzJzZtVx+xyDMiON04WHBqcLt4jThQfFrjhleLA5XVjDSmbeApxOMSPm0ojYICKWiIgtgMsoZmv9Z2Y+WOZ/C5hI8Rn+RUSsHRFjIuJrwC5AZ2be3YprGc5606+Z+RhwJLAZcGpELBsRq1LMxF4OmFQGg1UjIo4GvkkRQP9SRFxcvQFb15axvweX/d035Zf+kyhiDJdExDoRMSoiPgZ8h+JBAkfUlLHPMSgz0jhdeGhwunALOF14cDhluCWcLqxhJzM/C/wrxRcRf6JY5P5K4Blg98w8rib/ncCmwHPArRSzTj8B7JuZJw5i04e0iOioCoZPLA+fUx6bVpu/N/2amWdRLCK+MUWA4V6K93HLzLyuny9pSGuyvyszSDcEPlVn66h3Dvv7bc1+vqvKTWn0BVFtXvt7fr34nfJ7irHFSxR3YbwMnEYRZBmfmdPrlBnxfR6ZC3wW1abKb6BuBqYD78rMV6vS1qBY9HRh4N2Z+WBELATcBawFrJWZz1Tlv5Lyh8dvp+YXEVMofmkdmJlT6qQ33a8RsRrF+3M3sHmWP7jl1L4HgTeAdUZCJLlWd/1d5vkKxYKn29VOFy7LT6sOENjfXetJn9cpM4FiMHRjZk6ok26fN9CD3ymLU/TdcsBqmflcVdpEYApwaWbuXnXc/pYkSRoCnCkzgjhdeGhwuvDgcrrw8GCf957ThSVJkoYvZ8qMQBGxH3AIxdoxSwIzgTuAMzLz0jr51wO+QXH7wWiKKWXfz8yLBqnJQ15EdAAPN0h+JDM76pRpul8jYkeKJ2VtTPFN9s3Af2Tm7X1p/3DTTH+XUyvXaJC34oSaW2kqZe3vUm8+42W5Kbw93XU+5W1Ntfntc3r9O2Vj4HhgK2Ap4DGKgPs3G639ZX9LkiS1lkEZSZIkSZKkFvD2JUmSJEmSpBYwKCNJkiRJktQCBmUkSZIkSZJawKCMJEmSJElSCxiUkSRJkqRBEhFHRkSWT4isTbuhTMuIuKGJOqvLZUTUfYpeFHaPiF9GxKMRMSciZkfEgxFxaURMjoi1+nBt02rakRHRWSffhNp85fFf1Ryf1tu2SMOFQRlJkiRJGgQRsTpwUqP0zJyQmdGHU6yZmZGZ4+uce0XgRuBs4FZgG2Bp4F3AvwPLA/8FPBQRH+zNyTOzA9iyfPkasGxmdtbJd0N5nXcBh1WuOTP3LNvflz6QhhWDMpIkSZI0OE4H7hnsk0bEEsB1wMbAv2TmdzLzocx8LTOnZ+a1FEGa68oii/T2XJl5C3A/sCiwTxdtWh94D3BJb88ltQODMpJGDKcLO11YkqRWiYh9gS2AyS04/deB9YFvZ+bd9TJk5hvA0f10vnPL/cQu8hwAXJWZM/rpnNKwZFBG0ojgdOF5+ZwuLElSAxGxec2XFBMi4ovlFyovR8T1EbFhmXdCRNwWEa+WX7L8axf1LgecAnwJeHpwrmbeuZcA/l/58tyu8mbmHcBXgEfr1PPeiLg4Ip6JiNfKaz45IpauU9X5wFvAByPi3XXqWgjYFzivycuR2o5BGUkjhdOF326T04UlSaojM/9SfkFxQnnoaOB1YCPgw8CawO8iYnPgU8BuFF+yPAScFxELfDlT+i/gnsz82UC2v4EPA4sDT2fmAsGWWpn5rdp8EbE1xRdLa1GMW8YARwAHAn8sxzvVdTwG/KF8eUCd02xHMV65trlLkdqPQRlJbc/pwgtwurAkST3zSmaempkzMvMu4IfAihSzX/89Mx/PzCeArwIB7FdbQUR8FNgT+MzgNXs+65b7x3tTOCIWAy4ERgP7Zea95ZdL/wMcA2wIHFunaGVMsn9E1M7E3R+4KDNf702bpHZiUEbSkOB0YacLS5I0BF1d8/qBcn9b+aVKxf3lft3qzBGxOPAT4OuZ+QCtMabcv9rL8p8AxgE31rmGyqzbSXXK/TfwMrAaxewaACJiSYoZRo5FJAzKSBoinC7sdGFJkoagJ2tev1TveGa+WP5z8Zr8JwIvAt/t/6b1WKVtoxtlKB9AUP3l2A1VyZuV+7tqy2XmK8ALwLiIWKUmbTbwq/Jl9Qze3YFHM7PuwxGkkcagjKShyunCTheWJKnV5jR5fN7f3YjYhOKLlINrZtUMtsosntUbZcjMxcovx7apk1yZmfu5Ok96TGDZMn2lOmWnlPs9qr5IOgBnyUjzGJSRNFQ5XdjpwpIkDWcfB0YBt9UEMR4u09eoOj5lANtxE/AKsEJErN2L8jPL/UmVJzU22O6qU/aPwDRgCYrAzDuBrYGf96IdUlsyKCNpqHK6sNOFJUkatjKzs17wguKWbIBHqo5PGsB2vAKcUb48qBdV/LXcd9RLjIjVIuJjdWbnkpnJ218GTaSY2XxjZvZqFrH+f3t3zGNTEIYB+P1CNKLwD1axv4BQyRKFTi9R6FU0FGK3UPkBNArREiEaGhQKkSgkGhFBSaPRKGQUZzbW5t7d7Lpy7PU8yW3unDlzTjf55p0zzCNFGeBfJS4sLgwAzMZykjdJLlTV4Q2u2zXhv4cZtmKfqqr9E9qvJ7nYCzCTrM49jic5F3MR+I2iDDCPxIXFhQGArqdlTiR5leRJVV2uqsWq2lNV+6rqcFVdTXInyY8kL9b0/Z7kdIYFsEf9xMy9VXWgqm5kONjg/AZjv88wJ6oMC0r3/tJrwo6kKAPMHXFhcWEA2I6qWugLOcv9r6dV9bG3PcuvEw6X++LOQlXd6n2SZKn/f3bCvVc2WCRaWB1jwr1WZvFurbUvSY5mOMHxYJJnGbaHf86w7flQkmtJFltrl9b1fd7b3yW5n2EL9ZMMW7SPtNZebzL86iEE91pr32bwOjA3do/9AABzbjnJyQxx4QettZdTrts0Ltxa+7qu/XqSva21x1PueTvJlQxx4cUkl7f89ADwH2mtfcyaLdHr2o5N6XY2kz+8v77/SpKVTa6ZNsZM9EWbO/231b5vk5zZ5rg3k9zcTl+Yd5IyAH+RuDAAADCNogzwTxAXFhcGAP7Yhz6H2ZEnLVbV3TXfAoT/Qk3/SDYAm+kFo6UkB3rkecfrE6FPrbWFsZ8FAADmmaQMAAAAwAgUZQBmQ1wYAADYEtuXAAAAAEYgKQMAAAAwAkUZAAAAgBEoygAAAACMQFEGAAAAYASKMgAAAAAjUJQBAAAAGIGiDAAAAMAIfgIMpK3ThktWMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, (plot_1, plot_2) = plt.subplots(1,2, figsize=[18,8])\n",
        "plot_1.set_title(\"MC samples without weights\")\n",
        "plot_1.hist(stack_mc_list_m4l, range=rangos[0], label=mc_samples, stacked=True, bins=bines)\n",
        "plot_1.set_ylabel('Events')\n",
        "plot_1.set_xlabel(var_name+units)\n",
        "plot_1.legend(frameon=False)\n",
        "plot_2.set_title(\"MC samples with weights\")\n",
        "plot_2.hist(stack_mc_list_m4l, range=rangos[0], label=mc_samples, stacked=True, weights=stack_weights_list, bins=bines)\n",
        "plot_2.set_ylabel('Events')\n",
        "plot_2.set_xlabel(var_name+units)\n",
        "plot_2.tick_params(which='both',direction='in',top=True,right=True, length=6, width=1)\n",
        "plot_2.legend(frameon=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InXDLSy49x0M"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3l2uWwhsny1l"
      },
      "source": [
        "# Data samples\n",
        "\n",
        "Let’s append in stack_dara_list_m4l the values of the m4l variable for all the data samples A,B,C and D."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Engr-Ruyny1l"
      },
      "outputs": [],
      "source": [
        "stack_data_list_m4l=[]\n",
        "for element in samples_dic['data']:\n",
        "    sample_d=element[1]\n",
        "    #print(sample_d)\n",
        "    data_list = list(branches[sample_d]['m4l'][selection_events[sample_d]])\n",
        "    stack_data_list_m4l += data_list\n",
        "    #print(sample_d,len(data_list))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiI2KyXlny1l"
      },
      "source": [
        "We can print the length of the list to check again that everything is fine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Yk1rA_Cny1l",
        "outputId": "0b18753a-cbfe-443f-d642-9b0eab62da0c",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "321"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(stack_data_list_m4l)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHnJLUUbny1m"
      },
      "source": [
        "To make more easy the data vs. MC final plot, we can define the following helper function that makes a histogram of the data and calculates the poisson uncertainty in each bin.\n",
        "\n",
        "When we want to make a plot that includes uncertainties we need to use the plt.errorbar function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yr3W_O5Rny1m"
      },
      "outputs": [],
      "source": [
        "def plotData(data_var, range_ab, bins_samples):\n",
        "    data_hist,bins = np.histogram(data_var, range=range_ab, bins=bins_samples) \n",
        "    print(data_hist, bins)\n",
        "    data_hist_errors = np.sqrt( data_hist )\n",
        "    bin_center=(bins[1:]+bins[:-1])/2\n",
        "    h0=plt.errorbar(x=bin_center, y=data_hist, yerr=data_hist_errors,fmt='kv', label='Data')#, capsize=3,, ms=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqITtKQ_9x0R"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSwwzRjnny1n"
      },
      "source": [
        "# Data vs. MC plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4oIZA4S9x0T"
      },
      "source": [
        "Finally, we can include the MC and data in the same figure, and see if they are in agreement :)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HbqbNvLSny1n",
        "outputId": "bca663dc-554a-4663-acc8-4f965d7992a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 1  6 20  7  4  3  1  0  2  1  7  9  7  3  3  1  4  1  2  4  2  1  4  2] [ 80.    83.75  87.5   91.25  95.    98.75 102.5  106.25 110.   113.75\n",
            " 117.5  121.25 125.   128.75 132.5  136.25 140.   143.75 147.5  151.25\n",
            " 155.   158.75 162.5  166.25 170.  ]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f29d310da00>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plotData(stack_data_list_m4l, rangos[0], bines)\n",
        "h1=plt.hist(stack_mc_list_m4l, range=rangos[0], label=mc_samples, stacked=True, weights=stack_weights_list, bins=bines)\n",
        "plt.ylabel('Events')\n",
        "plt.xlabel(var_name+units)\n",
        "plt.ylim(0,30)\n",
        "plt.legend(fontsize=18,frameon=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUHDoG_Iny1o",
        "tags": []
      },
      "source": [
        "### Exercise\n",
        "Modify a bit the previous code to include the ticks and text, in the text and axis labels use latex to achieve the final plot."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IXxzhkvlny1q"
      },
      "outputs": [],
      "source": [
        "#Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K19lVUEF9x0V"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yq_DT-yhny1o"
      },
      "source": [
        "# Final Plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI1SMaBP9x0W"
      },
      "source": [
        "### You can see at 125 GeV the component corresponding at the Higgs boson."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U-KBqTuSny1p",
        "outputId": "fa4613d2-b256-4676-9bd5-242c21672b63",
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "Image(filename='./data-ep04-higgs-search/Finalplot.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRF0Xnzt9x0Y"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIs0xHJN9x0Y"
      },
      "source": [
        "**Bonus**: If you are more curious about other HEP analysis tools, you can take a look at this same example developed with the ROOT framework [here](https://root.cern.ch/doc/v622/df106__HiggsToFourLeptons_8py.html).\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ep04-higgs-search.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python [conda env:hepML]",
      "language": "python",
      "name": "conda-env-hepML-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}