jabrantley/test_bag_of_skittles.ipynb

## test_bag_of_skittles.ipynb
{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install numpyro"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjjQPnixcKh1",
        "outputId": "58d8ec9e-9d2c-4b7e-86f2-a96a7f6d724a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting numpyro\n",
            "  Downloading numpyro-0.14.0-py3-none-any.whl (330 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/330.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.4/330.2 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m330.2/330.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: jax>=0.4.14 in /usr/local/lib/python3.10/dist-packages (from numpyro) (0.4.26)\n",
            "Requirement already satisfied: jaxlib>=0.4.14 in /usr/local/lib/python3.10/dist-packages (from numpyro) (0.4.26+cuda12.cudnn89)\n",
            "Requirement already satisfied: multipledispatch in /usr/local/lib/python3.10/dist-packages (from numpyro) (1.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from numpyro) (1.25.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from numpyro) (4.66.2)\n",
            "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.14->numpyro) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.14->numpyro) (3.3.0)\n",
            "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from jax>=0.4.14->numpyro) (1.11.4)\n",
            "Installing collected packages: numpyro\n",
            "Successfully installed numpyro-0.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "96832185-6147-40a7-99e5-e29d409f247c",
          "showTitle": false,
          "title": ""
        },
        "id": "JmcIiHPbb0CP"
      },
      "outputs": [],
      "source": [
        "# Data\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Pymc\n",
        "import pymc as pm\n",
        "import pymc.sampling.jax as pmjax\n",
        "import xarray as xr\n",
        "import arviz as az\n",
        "import pytensor as pt\n",
        "\n",
        "RANDOM_SEED = 101\n",
        "rng = np.random.default_rng(RANDOM_SEED)\n",
        "\n",
        "# Viz\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "debd3e5c-168b-4916-8a6d-407290f08a15",
          "showTitle": false,
          "title": ""
        },
        "id": "4F0jI3M7b0CP"
      },
      "source": [
        "# Simulate data\n",
        "\n",
        "We will start by simulating the total number of skittles, which is defined by the size of the bag and the facility multiplier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "4c1b550a-2f4a-444a-8edb-1f2eccaa9da4",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "ya7uldYqb0CQ",
        "outputId": "6f936b8c-a9de-4c14-efec-69ed2886abbc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ea83993d420>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1604.11x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define size of each package\n",
        "fun_size = 15\n",
        "normal_size = 55\n",
        "share_size = (4/2.17) * normal_size # based on size of bag in ounces\n",
        "\n",
        "# Randomly chosen multiplier for each facility. This implies that the size from facility A results in (size*facility_A) skittles, on average.\n",
        "facility_A = 1.25\n",
        "facility_B = 0.75\n",
        "facility_C = 1\n",
        "\n",
        "# Sigma for normal\n",
        "sigma = 2\n",
        "\n",
        "# Sample values for each combination - probably a cleaner way to do this (maybe zipping and one single call to pm.draw but this was quick and dirty)\n",
        "out = []\n",
        "for a, aa in zip([fun_size, normal_size, share_size], [\"fun\", \"normal\", \"share\"]):\n",
        "    for b, bb in zip([facility_A, facility_B, facility_C], [\"A\", \"B\", \"C\"]):\n",
        "        # Draw from normal and round. Might not be acceptable for discrete data but works more or less\n",
        "        counts = pm.draw(pm.Normal.dist(mu=a*b, sigma=sigma),100)\n",
        "        # Add data with name and size\n",
        "        out.append(pd.DataFrame({\"total\": np.round(counts)}).assign(bag_size=a, bag_size_name=aa, facility=b, facility_name = bb))\n",
        "out = pd.concat(out)\n",
        "\n",
        "# Visualize data\n",
        "sns.displot(data=out,x=\"total\", col=\"bag_size_name\", hue=\"facility_name\",bins=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "ea932e7b-f2c1-457f-94fb-eb5aee7b2ac6",
          "showTitle": false,
          "title": ""
        },
        "id": "HiExN0cRb0CQ"
      },
      "source": [
        "We will now define the true probability for each color. We use the multinomial distribution to sample the number of each skittle given the true probability and the total observed for each bag (given size and facility).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "c3228620-18f1-48fb-8efc-f5a1b1666b2d",
          "showTitle": false,
          "title": ""
        },
        "id": "hGhpyAyQb0CQ"
      },
      "outputs": [],
      "source": [
        "# True probability of each skittle color\n",
        "skittles_p = np.array([.25, .08, .07, .2, .4])\n",
        "\n",
        "# Draws given total number\n",
        "skittles_draws = pm.draw(pm.Multinomial.dist(n=out.total, p=skittles_p))\n",
        "colors = [\"red\", \"orange\", \"yellow\", \"green\", \"purple\"]\n",
        "\n",
        "out[colors] = skittles_draws\n",
        "out[[c + \"_percent\" for c in colors]] = np.divide(skittles_draws,out.total.values.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "bb7f211d-0395-489c-93ab-54151eddda99",
          "showTitle": false,
          "title": ""
        },
        "id": "Ww9jM3G-b0CR"
      },
      "source": [
        "Visualize KDE of each combination."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ada9bd31-ba5e-42d8-bd6a-3b8438dd091e",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "QggTzQELb0CR",
        "outputId": "48e77102-5297-4bfb-e962-cbc175a3fcdb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(len([fun_size, normal_size, share_size]), len([facility_A, facility_B, facility_C ]),sharex=True, sharey=True)\n",
        "fig.set_size_inches(10,10)\n",
        "\n",
        "for  ii, s in enumerate([fun_size, normal_size, share_size]):\n",
        "    for jj, f in enumerate([facility_A, facility_B, facility_C]):\n",
        "            _local = out.loc[(out.facility==f) & (out.bag_size==s), :]\n",
        "            for c, clr in zip(colors, colors):\n",
        "                sns.kdeplot(ax=ax[ii,jj], data=_local, x = c + \"_percent\",color=c)\n",
        "                ax[ii,jj].set_title(f\"Size: {_local.bag_size_name.unique().item()}; Facility: {_local.facility_name.unique().item()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "f6f1a4b3-4306-45cc-bda0-6bacf47db807",
          "showTitle": false,
          "title": ""
        },
        "id": "3E2wh1gqb0CR"
      },
      "source": [
        "# Preprocessing\n",
        "\n",
        "Not much preprocessing required here. Mainly just factorize the facility and size names for indexing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "85410d09-04cf-4802-ba32-c8431a0a796b",
          "showTitle": false,
          "title": ""
        },
        "id": "oZl4nGxib0CR"
      },
      "outputs": [],
      "source": [
        "def factorize_columns(data, columns):\n",
        "    # Empty dict for storing unique entries in column\n",
        "    entry_dict = {}\n",
        "    # for each column\n",
        "    for col in columns:\n",
        "        # Factorize\n",
        "        column_factorized, column_entries = pd.factorize(data[col])\n",
        "        # Save data in original df\n",
        "        data = data.assign(temp=column_factorized).rename(columns={\"temp\": col + \"_factorized\"})\n",
        "        # add unique values to dict\n",
        "        entry_dict[col] = column_entries.tolist()\n",
        "    return data, entry_dict\n",
        "\n",
        "data, factorized_dict = factorize_columns(out,[\"facility_name\", \"bag_size_name\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "3817cfff-fa16-4b44-a5fb-f26e768cc1d7",
          "showTitle": false,
          "title": ""
        },
        "id": "qqErWZIkb0CR"
      },
      "source": [
        "\n",
        "# Define pooled model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "953d6ce2-3926-49ea-b49c-4400e426b63a",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "7ZzAq8pzb0CS",
        "outputId": "ed29d5cb-1389-4602-bfd7-88f490597ff8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<graphviz.graphs.Digraph at 0x7ea7de5f23b0>"
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Combine outcomes with factorized columns dict\n",
        "coords = dict({\"colors\": colors, \"observations\": np.arange(data.shape[0])}, **factorized_dict)\n",
        "\n",
        "# PyMC model\n",
        "with pm.Model(coords=coords) as model:\n",
        "    frac   = pm.Dirichlet(\"frac\", a=np.ones(len(colors)), dims=\"colors\")\n",
        "    conc   = pm.Lognormal(\"conc\", mu=1, sigma=3)\n",
        "    counts = pm.DirichletMultinomial(\"counts\", n=data.total.values, a=frac*conc, observed=data[colors].values,dims=(\"observations\", \"colors\"))\n",
        "    # _a     = pm.Deterministic(\"_a\", frac*conc)\n",
        "    # counts = pm.DirichletMultinomial(\"counts\", n=data.total.values, a=_a, observed=data[colors].values,dims=(\"observations\", \"colors\"))\n",
        "\n",
        "pm.model_to_graphviz(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "2a2c7da9-1a47-4f8e-a837-8bc7e331b4b6",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f9d128b45c86432eb504fa454feac70a",
            "a0a6cd3858864f82b0ad1bb02d73aec3",
            "85266704e3e44153b051b880c19889f9",
            "9adf6f4055e84508ab1b7013d207a573",
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            "805ec019e3f54f75b6a2278d014791b4",
            "278236dca4a74dbc833ad7da8118cdaf",
            "e240fa2b431e4862a2b2de17104864a1",
            "e60df96897d14a6dbfadee953f692615",
            "392d2d44dba04f30b706417ee2ec11ab",
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            "29b79c7db7654fdeae0b104c53174262",
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            "7ba244706e9f4788aaf228898f83a522",
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            "385810c7a556498db6654a7d807c9d08",
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            "8b57599685924d1f9ff6d5f8349674a0",
            "1bb6cabb578d42cb8718a6c781c941f2",
            "9f94e66942fc4048a72dfdf03859a7ef",
            "230a63c26b3e4cdaa437a4544c86c9dd",
            "954c358a32fb4730ba6eb5fbf6ee2bbd",
            "6c758a11c48b449bbf1b859d9f5b56b0",
            "3149f2c070554b92b71aaa87fdf8f2d5",
            "49de6eedbea64df98ab2a788588f9186",
            "faa92b17d8db45798574428a8452b71b",
            "a0a2cb24812140259b4b2b2c220c6723",
            "272a973d8e5b43a0b2c2d3b2227632ac",
            "c4d995bcc21746e1b813e2559baff030",
            "19a7ac9ac5054884816b21549a754162",
            "d0f1a789863e422b980fccff9d4d40dd",
            "76cf871f697541719aab4d28f1cbba7b"
          ]
        },
        "id": "TgCEO4dkb0CS",
        "outputId": "113915db-8c39-4f66-898d-643edb178ddc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9d128b45c86432eb504fa454feac70a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ed4319858514c55967de0e5ae5af6e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29b79c7db7654fdeae0b104c53174262"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "954c358a32fb4730ba6eb5fbf6ee2bbd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "with model:\n",
        "    idata_model = pmjax.sample_numpyro_nuts(2000,chains=4, idata_kwargs=dict(log_likelihood = True), target_accept=0.9, random_seed=RANDOM_SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "4e09ce0e-8fc3-44c6-982b-2bdd9150a560",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "HrUVEmjUb0CS",
        "outputId": "22db7143-b6b1-4fee-9c49-bc81ad748326"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/arviz/utils.py:184: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
            "  numba_fn = numba.jit(**self.kwargs)(self.function)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              true       mean         sd   hdi_3%    hdi_97%  mcse_mean  \\\n",
              "frac[red]      NaN      0.250      0.002    0.246      0.254      0.000   \n",
              "frac[orange]  0.25      0.081      0.001    0.079      0.084      0.000   \n",
              "frac[yellow]  0.08      0.069      0.001    0.066      0.071      0.000   \n",
              "frac[green]   0.07      0.203      0.002    0.200      0.207      0.000   \n",
              "frac[purple]  0.20      0.397      0.002    0.393      0.401      0.000   \n",
              "conc          0.40  16265.254  60787.879  709.656  41347.015   1300.087   \n",
              "\n",
              "              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
              "frac[red]       0.000    7495.0    5979.0    1.0  \n",
              "frac[orange]    0.000    6663.0    5360.0    1.0  \n",
              "frac[yellow]    0.000    5640.0    5114.0    1.0  \n",
              "frac[green]     0.000    7183.0    5615.0    1.0  \n",
              "frac[purple]    0.000    9542.0    5402.0    1.0  \n",
              "conc          919.423    4530.0    2357.0    1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e43e0ee-7b3f-4f4d-af53-170df91c3933\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>true</th>\n",
              "      <th>mean</th>\n",
              "      <th>sd</th>\n",
              "      <th>hdi_3%</th>\n",
              "      <th>hdi_97%</th>\n",
              "      <th>mcse_mean</th>\n",
              "      <th>mcse_sd</th>\n",
              "      <th>ess_bulk</th>\n",
              "      <th>ess_tail</th>\n",
              "      <th>r_hat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>frac[red]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.246</td>\n",
              "      <td>0.254</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7495.0</td>\n",
              "      <td>5979.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[orange]</th>\n",
              "      <td>0.25</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>6663.0</td>\n",
              "      <td>5360.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[yellow]</th>\n",
              "      <td>0.08</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>5640.0</td>\n",
              "      <td>5114.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[green]</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.203</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.200</td>\n",
              "      <td>0.207</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7183.0</td>\n",
              "      <td>5615.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[purple]</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.397</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.393</td>\n",
              "      <td>0.401</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>9542.0</td>\n",
              "      <td>5402.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc</th>\n",
              "      <td>0.40</td>\n",
              "      <td>16265.254</td>\n",
              "      <td>60787.879</td>\n",
              "      <td>709.656</td>\n",
              "      <td>41347.015</td>\n",
              "      <td>1300.087</td>\n",
              "      <td>919.423</td>\n",
              "      <td>4530.0</td>\n",
              "      <td>2357.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e43e0ee-7b3f-4f4d-af53-170df91c3933')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6e43e0ee-7b3f-4f4d-af53-170df91c3933 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6e43e0ee-7b3f-4f4d-af53-170df91c3933');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62b90add-4a44-4bab-94b9-047597f0db94\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62b90add-4a44-4bab-94b9-047597f0db94')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62b90add-4a44-4bab-94b9-047597f0db94 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"true\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13583077707206126,\n        \"min\": 0.07,\n        \"max\": 0.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.08,\n          0.4,\n          0.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6640.180490896364,\n        \"min\": 0.069,\n        \"max\": 16265.254,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.25,\n          0.081,\n          16265.254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24816.5470294769,\n        \"min\": 0.001,\n        \"max\": 60787.879,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.002,\n          0.001,\n          60787.879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_3%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 289.6355303818921,\n        \"min\": 0.066,\n        \"max\": 709.656,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.246,\n          0.079,\n          709.656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_97%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16879.765152266293,\n        \"min\": 0.071,\n        \"max\": 41347.015,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.254,\n          0.084,\n          41347.015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 530.7582952042923,\n        \"min\": 0.0,\n        \"max\": 1300.087,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1300.087,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 375.3528679631563,\n        \"min\": 0.0,\n        \"max\": 919.423,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          919.423,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_bulk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1712.0092776228366,\n        \"min\": 4530.0,\n        \"max\": 9542.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7495.0,\n          6663.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1313.112853743602,\n        \"min\": 2357.0,\n        \"max\": 5979.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5979.0,\n          5360.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r_hat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "summary = az.summary(idata_model)\n",
        "pd.concat([pd.DataFrame({\"true\": np.insert(skittles_p, 0, np.nan)},index=summary.index), summary],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "7945d0e4-5904-4363-9047-7ffdad27f063",
          "showTitle": false,
          "title": ""
        },
        "id": "pHXwYGocb0CS"
      },
      "source": [
        "This model samples well and correctly recovers the parameters."
      ]
    },
    {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "97a150ca-3551-416f-b12d-36b184882395",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "posterior = idata_model[\"posterior\"]\n",
    "posterior[\"conc_frac\"] = posterior[\"conc\"].expand_dims(dim={\"colors\": 5},axis=-1)**posterior[\"frac\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "d41393e8-ab58-4464-adcc-40141d2eb1e0",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'conc_frac\\nred'}>,\n",
       "        <Axes: title={'center': 'conc_frac\\norange'}>,\n",
       "        <Axes: title={'center': 'conc_frac\\nyellow'}>],\n",
       "       [<Axes: title={'center': 'conc_frac\\ngreen'}>,\n",
       "        <Axes: title={'center': 'conc_frac\\npurple'}>, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "output_type": "display_data",
     "data": {
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amqoff/xRc+bM0aJFi/TBBx+oV69euueee3TzzTerT58+gW3LqlwqKg/9xaFguummm/TZZ59p7dq1Wrdunaqrq9WpUyddcMEFuu666zRq1KhQhwgAAJqhtrHR0qVL1bFjR/Xu3VvXXXddrWOjw2ZcqIrsNfpywfxax0aJMTYVOJwqrXTV9dStZt26dVq2bFmNZXl5ecrLy6vzMRdccIFefvllrVq1SkVFRUpPT9c555yjW265RUOHDm3hiAEAAAAAQCiZjHAtkwqSz9ft0WUv/qQjuyXr/aubNlfFUfd+ob2lVfrgmqM1pGtycAMEAABoQ9weQ33+NF+StPzPx6lDXNRB20z+x9fKyi/X21eO1cieHVo7RAAAAAAAgCZrN3Oc1aWo3ClJSo6xNXkf3TrESpK2F1QEJSYAAIC2yunyBH62W2sfSib6xlVtoVUjAAAAAABAY7T7xFlxhfeCTlJzEmcpMZKk7YXlh9gSAAAgvFVWuwM/15k4i/YlzipC36oRAAAAAACgMUic+RJnybFNT5x1TfFXnJE4AwAAka3KV3FmMZtktdRVceadRpeKMwAAAAAAEG7afeKsqNyXOGtWq0Z/xRmtGgEAQGTzt2qsq9pM2r/ijMQZAAAAAAAILyTOfBd0EpvVqtFbcbaDijMAABDhnG5v4iyqvsRZYI4zWjUCAAAAAIDw0u4TZ/taNUY1eR/dOvgSZ4UV8niMoMQFAADQFvkrzmx1tGmUpMRoX6tGKs4AAAAAAECYIXFW7pTUvFaNnZKiZTGb5HR7tLe0KlihAQAAtDnV/oqz+hJngYozEmcAAAAAACC8tPvEmb9VY1Js0xNnVotZnZKiJUnbC2nXCAAAIldDWjUmBCrOaNUIAAAAAADCS7tPnAVaNTaj4kzaN8/ZduY5AwAAEaw60KrRVOc2idFUnAEAAAAAgPDUrhNnHo8RSJw1p+JMkrp1iJEkbS+oaHZcAAAAbVVVAyrO/K0aSyupOAMAAAAAAOGlXSfOSitdMgzvz0nBqjijVSMAAIhg+yrO6kmc+SrO/DcoAQAAAAAAhIt2nTjzX8yJsVlkt1qata9uHWjVCAAAIl9gjrN6EmfxvjnOyqqoOAMAAAAAAOGlXSfOiiqckqTkZrZplPa1atxRSKtGAAAQuaob0Kox3u5NnDldHlW53K0SFwAAAAAAQDC078RZuW9+s2a2aZT2tWrcVVwRuKAEAAAQaZyuBlSc+RJnkuSoInEGAAAAAADCR7tOnPlbNQYjcZaeYJfdapbHkHYWUXUGAAAik9PtnSC2vjnOLGaTYqO8bbDLKmnXCAAAAAAAwke7TpwVBTFxZjKZ1DXF265xewGJMwAAEJkCFWf1tGqUpDg785wBAAAAAIDw064TZ6WV3sRZQnTzE2eS1K2Dt13j9sLyoOwPAACgrfG3pK6v4kySEkicAQAAAACAMNSuE2cO34WchGjrIbZsGP88ZztInAEAgAjV0Iqz+Gh/4qy6xWMCAAAAAAAIlnadOPPPubH/BPbN0a0DrRoBAEBk81ecRVlM9W4XF+UdX5UyxxkAAAAAAAgj7TtxVuWWtG8OjubyV5zRqhEAAESqxlacOXzjLQAAAAAAgHDQzhNn3tZB8cFq1eif44yKMwAAEKGcjZ7jjFaNAAAAAAAgfLTrxJn/Duh4uyUo++ua4m3VmFdWpQond1cDAIDI0+g5zmjVCAAAAAAAwki7TpyVVvnnOLMFZX9JMbbA3dU7aNcIAAAiUHUDK878rbD94y0AAAAAAIBw0K4TZ2WVvlaNQZrjzGQyBdo1bisgcQYAACJPtduQJNkPVXFm989xRuIMAAAAAACEj3adONvXqjE4iTNJ6h6Y54zEGQAAiDz+Vo2HnOPM36qRxBkAAAAAAAgj7Tpx5r+Q45+DIxi6p/orziqCtk8AAIC2wulu2BxncVG+Vo3McQYAAAAAAMJIu02ceTyGHE7vhZw4uyVo+6VVIwAAiGQNrTiLp+IMAAAAAACEoXabOCuvdsvwTtGhBLstaPulVSMAAIhk1Q2sOEtgjjMAAAAAABCG2m3irMzXNshskqJtwTsM3VJiJHkrzgx/Zg4AACBC+CvOoiymercLVJzRqhEAAAAAAISR9ps4889vZrfKZKr/wk9jdEmJkckkVVS7lVfmDNp+AQAA2oKGVpzF+SrOSqk4AwAAAAAAYaTdJ84SooPXplGS7FaLOiVGS2KeMwAAEHmqGjjH2f6tGqnCBwAAAAAA4aLdJs78823E2S1B33c35jkDAAARyl9xdqjEmb9Vo8fwVuIDAAAAAACEg3abOCut3NeqMdi6kzgDAAARytnAVo0xNovMvm7YzHMGAAAAAADCRbtNnO2rOGu5xBmtGgEAQKSpdnnbLkYdouLMZDIxzxkAAAAAAAg77TZxtm+OsxZInKWSOAMAAJGpoRVnUs15zgAAAAAAAMJBu0+ctUSrRuY4AwAAkara1bA5zqR985zRqhEAAAAAAISLdp84a8lWjbtKKlVZ7Q76/gEAAEKlqhEVZ7RqBAAAAAAA4ab9Js58dz4ntEDiLDUu0h43BgAAbfdJREFUSgnRVhmGlJXvCPr+AQAAQsEwDFW7/RVnpkNu76/sp+IMAAAAAACEi3abOHO0YMWZyWRSv4x4SdKmPWVB3z8AAEAouD2GDMP7c1QDWjX655J1OEmcAQAAAACA8NBuE2f+lkH+uTeCrV9GgiRp014SZwAAIDK4PEbgZ2sDEmdxUb5WjVScAQAAAACAMNFuE2f+irP4Fqg4k6R+Hb0VZ7/tLW2R/QMAALQ2f5tGSbKaG9Cq0XeDUhlznAEAAAAAgDDRbhNnZS2cOOtLq0YAABBhXO59FWe2hrRqZI4zAAAAAAAQZtpv4qyypSvOvK0at+Y5atydDQAAEK6qPd4xjckkWRpRceag4gwAAAAAAISJ9ps4813AiWuhxFnnpGjFRVnk8hjKzne0yHMAAAC0Jn/Fmc3csCGkf5xVSuIMAAAAAACEiXafOEuIbpnEmclkol0jAACIKP7EmdVy6GozSUqItkmSSiurWywmAAAAAACAYGqXiTO3x1C50y2p5SrOJKlvhrdd46a9JM4AAED487dqtDagTaO07walUuY4AwAAAAAAYaJdJs4czn0Xb1pqjjNJ6tfRV3FG4gwAAESAQKtGS8OGkIm+irMSKs4AAAAAAECYaJ+JM1+bRpvFJLu15Q5BP1+rxo27S1vsOQAAAFpLtdtXcdbAVo2JvoqzkgoqzgAAAAAAQHhol4mzMl+7oDi7VSZTwy78NMWgzkmSpE17SwPJOgAAgHDl8vjmODM3sOIsZt8cZ4ZhtFhcAAAAAAAAwdI+E2e+JFZcVMu1aZSkzKRoZSZGy2NIv+QUt+hzAQAAtDSXr+LM1uCKM2/izGNIDt/8sgAAAAAAAG1Zu0yclfsu3MTZLS3+XEd281adrdpR1OLPBQAA0JKqfXOcWRs4x1m0zSyr2ZtkK2WeMwAAAAAAEAbadeIspoUrziRpaLcUSdLK7UUt/lwAAAAtyeXxzXFmbljFmclkCrRrZJ4zAAAAAAAQDtpp4sx74SbW1ooVZ9tp1QgAAMKby1dxZmtgxZkkJUZ7b1QqoeIMAAAAAACEgXaZOKvwVZzFRrV84mxwlySZTFJOUYX2lla2+PMBAAC0FKdvjjNrA+c4k6QE3zxntGoEAAAAAADhoF0mzva1amz5xFlCtE39MuIlUXUGAADCW6DizNyIirMYX8UZrRoBAAAAAEAYaJeJs4rq1qs4k6QjuyZLklZuL2yV5wMAAGgJgTnOGlFxluirOKNVIwAAAAAACAftMnHmn+MsphXmOJOkod2TJUkrtxe1yvMBAAC0hGpfxZm1EXOcJfjmOCutpOIMAAAAAAC0fe00ceZv1Whtlecb1i1FkrdVo9tjtMpzAgAABJvLN8eZzdyEirMKKs4AAAAAAEDb1y4TZxXO1m3V2L9jvGKjLCqrcum3vWWt8pwAAADBVu3xV5w1PHGWEGjVSMUZAAAAAABo+9pl4qy8lRNnVos5MM/Zim3McwYAAMKTv+KsMa0aE2O8Ff7McQYAAAAAAMJBu0ycVVT7WzW2TuJMkob55jlbsa2o1Z4TAAAgmFy+Oc5o1QgAAAAAACJV+0yctXLFmSQN6+6d52zFdirOAABAeKr2NL7iLCHaW3FWSqtGAAAAAAAQBtpl4qzc6b1wE2OzttpzDu2WLEnatLeMVkUAACAsBSrOGjHHWWKMf44zxj8AAAAAAKDta6eJs9avOEtPsKtbhxgZhrR6e3GrPS8AAECwBOY4MzdijjNfq0YqzgAAAAAAQDhol4kz/xxnrZk4k6Sh3bztGlftKGrV5wUAAAiGao+/4qzxrRqZ4wwAAAAAAISDdpk481ecxbRy4uzwTomSpPW7S1v1eQEAAILBX3HWlFaNVS6PqlzuFokLAAAAAAAgWNpl4qwi0Kqx9eY4k6SBnRIkSet3lbTq8wIAAARDtW+OM2sjEmcJdqtMvs1p1wgAAAAAANq6dpc4MwxD5U7vRZvWbtV4WKa34mxLnkOV1dxxDQAAwovL0/g5zsxmk+KjaNcIAAAAAADCQ7tLnFW5PPJNz9HqrRo7JtqVEmuT22Pot71lrfrcAAAAzeVy++c4a3jFmbSvXSMVZwAAAAAAoK1rd4kzf5tGSYq1tW7izGQyaUCmt13jBuY5AwAAYWZfq8bGDSETon0VZ5VUnOH/27vv+Crr8//j7/vMnOxAAmGHJchQRBEUFRygrX7dWxG0VftTW1tra22lUq3aWrW1ltZaVKxWWwe1WrG4QIaAA0RkyN4BEsjOyckZ9++PMyAQICHjPuP1fDx4AGfd152cyMfzvq/rAwAAAABAfEu54Kw2MiLRZbc1+0Of1tCnIFOStKGUjjMAAJBY9o1qbGbHWVq446zSS8cZAAAAAACIbykXnHkj+5u195jGqD75GZKkDSU1lhwfAADgaO0b1di8JWS2J9xxVkXHGQAAAAAAiHMpF5zVRkY1plsUnPXtFOk4IzgDAAAJxh+MdJw1c4+zrGjHGcEZAAAAAACIcykbnFnVcdY3PxycbdxTo2DItKQGAACAoxGIrF2ctmZ2nEX2OKvwEpwBAAAAAID4lnLBmdfijrNueR65HDbVB0LaUe61pAYAAICjcbQdZznpLkkEZwAAAAAAIP6lXHAWG9XodFhyfLvNUM8O6ZKkTXsY1wgAABJHdI8zRzP3OMv1hEc1ltcSnAEAAAAAgPiWgsFZQJJ1oxolqUeeR5K0dS8dZwAAIHEEQuGOM6eteR1nuenh4IyOMwAAAAAAEO9SLjjz+q0d1ShJPSIdZ1vLai2rAQAAoLn8R9txlk7HGQAAAAAASAwpF5xFRzVa23EWCc72EpwBAIDEEe04a/YeZ57wHmfl3vpWrwkAAAAAAKA1pWxwZm3HWWRUYxmjGgEAQOKI7nHmtNFxBgAAAAAAklPKBWfeyB5n6S6HZTV0j3ScbaPjDAAAJBB/8Og6znI94eCsqi6gQOQ1AAAAAAAA4lHKBWexUY1O6/c421NTrxpfwLI6AAAAmiMQinScNXtUozP258o61j4AAAAAACB+pVxw5o2DUY05Hqey3OGOt+IKxjUCAIDEEB3V6GjmqEaH3RZb+5TXss8ZAAAAAACIXykXnMXDHmeS1DknTZK0s8JnaR0AAABNdbSjGiUpJ7LPWYWXfc4AAAAAAED8Sr3gzB8Z1WjhHmeSVJgdCc4q6yytAwAAoKn2jWps/hIyNxKclROcAQAAAACAOJZywZm3PryvhuUdZ5HgbBfBGQAASBCxjjNb8zvOcj0uSVJFLcEZAAAAAACIXykXnEVHNXosDs66xEY1EpwBAIDEEN3j7Gg6zqKjGtnjDAAAAAAAxLOUC8680T3OnHGyxxkdZwAAIEEEQke/x1muh1GNAAAAAAAg/qVccBbtOEuPkz3OGNUIAAASgWma8kc6zhy2FuxxxqhGAAAAAAAQx1IwOAvvcWb1qMZocMaoRgAAkAiCITP2Z+dRdZxF9jij4wwAAAAAAMSxlAvOvP5ox5nVoxrdkqSSap/8wZCltQAAABxJYL/gzMEeZwAAAAAAIEmlVHDmD4ZiI4asDs7yM9xy2AyZplRS5bO0FgAAgCPZ/0Ifh409zgAAAAAAQHJKqeAsur+ZZP2oRpvNUKescNfZTvY5AwAAcS4Q3H9U49HscRYZ1cgeZwAAAAAAII6lVHDmjQRndpsh11F84NPaOueE9znbxT5nAAAgzvlD4Y4zwwivpZorN52OMwAAAAAAEP+sT4/aUW19QJKU7rTLMJr/gU9rK8wOB2d0nAEAgHgX7Thz2o5u+Rgb1Vhbr9B++6UBAAAAAADEkxQLzsIdZ1aPaYwqzCE4AwAAiSEanDnsR3fxUXYkOAuZUnXkYiYAAAAAAIB4k1LBmdcfDs7S4yU4y2ZUIwAASAzRUY2OoxjTKElpTrs8zvAajH3OAAAAAABAvEqp4Gxfx5nD4krC6DgDAACJIjaqsQX7xMb2OSM4AwAAAAAAcSqlgjNvdI+zOOk46xzd44yOMwAAEOf8wUjH2VGOapSknMi4xrLa+lapCQAAAAAAoLWlVHAW7TiLt+Bsd5XP4koAAAAOLxCK7HFma4WOMy8dZwAAAAAAID6lZHAW3V/Dap2y3JLCddX4AhZXAwAAcGiBSMeZswUdZ3npLklSOR1nAAAAAAAgTqVUcOaNs46zDLcjVgtdZwAAIJ75I3ucOVq0x1k4ONtbQ3AGAAAAAADiU0oFZ7GOM5fD4kr2KYh0nZUQnAEAgDgWCEX2OLO1pOMsMqqxllGNAAAAAAAgPqVWcOYPj0OMl44zad+4RoIzAAAQzwKRjjNnCzrOoqMayxjVCAAAAAAA4lRKBWfxNqpR2tdxtruqzuJKAAAADs0f2ePM0YI9znIjHWdldJwBAAAAAIA4lVLB2b5RjfETnHXKSpNExxkAAIhvgVCk48zW8o6zcjrOAAAAAABAnEqp4CzWceaMn+BsX8cZwRkAAIhfrdFxlpcR7TgjOAMAAAAAAPEptYIzf/x1nBWwxxkAAEgA0T3OHC3Y4yw32nFWw6hGAAAAAAAQn1IqOKutD0iSPC6HxZXsQ8cZAABIBNGOM6etBR1nkeCsyheIvR4AAAAAAEA8SangLB5HNXai4wwAACQAfyjacXb0wVmOxykj8vTyWrrOAAAAAABA/Emp4Kw2GpzF4ajGPTU+BbjyGgAAxKlAbI+zo18+2m2GstPC+5yVs88ZAAAAAACIQykZnMXTHmcdM9yyGZJpSntr+AAJAADEp+geZy0Z1ShJeenh4KyMjjMAAAAAABCHUio48/qjHWfxs8eZ3WaoYyb7nAEAgPjmD7W840ySciP7nJXRcQYAAAAAAOJQSgVntfUBSfE1qlFinzMAABD/Yh1nLdjjTNrXccaoRgAAAAAAEI9SJjgLhUzV+cNXSsfTqEZp3z5nBGcAACBexfY4s7Vs+ZgX6zhjVCMAAAAAAIg/KROcRcc0SvHbcba7qs7iSgAAABrnD4U7zhwt7DhjVCMAAAAAAIhnKROc1dbvC87SHPEVnNFxBgAA4l2048zZwj3OYqMaa+g4AwAAAAAA8SdlgjNvJDjzOO2y2Vp2pXRr65SVJknaTXAGAADilD+yx5mjheuo3Aw6zgAAAAAAQPxKmeCs1h+QFH9jGiU6zgAAQPwLhCJ7nLVWxxl7nAEAAAAAgDiUOsFZtOMsDoOzfXucEZwBAID4FIh0nLlauMdZHnucAQAAAACAOJYywVl0VGO8d5yZpmlxNQAAAAeLjWpsYcdZbqTjrIyOMwAAAAAAEIdSJjjb13HmsLiSg0WDM68/qJpInQAAAPEkNqqxhXucRTvOymvruWAIAAAAAADEnRQKziJ7nDnjr+Ms3eVQpjsc6O2urLO4GgAAgINFRzU6W7zHWTg4C4RMVfsCLa4LAAAAAACgNaVMcBbPoxqlhuMaAQAA4o0/GOk4a+EeZx6XXS5HeAla4WVcIwAAAAAAiC8pE5ztG9UY38HZboIzAAAQhwKhSMeZreXLxxxPeJ+zSi8dZwAAAAAAIL6kTHDm9dNxBgAAcLRaq+NMkrLTwiOqK+voOAMAAAAAAPElZYKz2B5nLofFlTSuEx1nAAAgjkX3OHO0cI8zScqOdZwRnAEAAAAAgPiSQsFZfI9q7JydJknaXVlncSUAAAAHC4TCHWdOW2t0nIWDM/Y4AwAAAAAA8SZlgjNvJDhLd8ZncNYlJxyc7ajwWlwJAADAwfxt0XFWxx5nAAAAAAAgvqRMcBbvHWddcjySpJ0VdJwBAID4E+04a9U9zug4AwAAAAAAcSblgrN43eMs2nFWXFEn0zQtrgYAAKCh6B5nTlvLl485sY4zgjMAAAAAABBfUiY48/rDo4DS47TjLLrHmS8QUlktHyIBAID44g+2YsdZNDjzMqoRAAAAAADEl5QJzuJ9VKPLYVN+pluStKOcfc4AAEB8CYQiHWetMqqRjjMAAAAAABCfUiY488ZGNcZncCZJXXPDXWfscwYAAOJNdFSjoxVGNWZ72OMMAAAAAADEp5QJzmrqo6Ma43OPM0kqzI7uc0bHGQAAiC+tOqox0nFWQXAGAAAAAADiTMoEZ9GOswx3PHeceSRJxXScAQCAOLNvVGNrdJyFg7OqOvY4AwAAAAAA8SVlgrMaX2RUozOOO85yoh1nBGcAACC+xDrObK3RccaoRgAAAAAAEJ9SIjgLhUx5/ZHgLI47zrrkMKoRAADEp+geZ63aceYLKBjpZAMAAAAAAIgHKRGcRUMzScqI4z3OuuQwqhEAAMSnQKj19ziTpGrGNQIAAAAAgDiSEsFZTX34AxnDkNKc8XvKXfYb1WiaXH0NAADig2ma8kc6zhy2lq+lXA6bPM7wFIDKOsY1AgAAAACA+BG/KVIr8tZH9zezyzBafpV0W+mcnSbDkOoDIe2tqbe6HAAAAElqME7R2QodZ5KU7QlPAahgnzMAAAAAABBHUiI4q/FF9zeL3zGNUvjq6/xMtyTGNQIAgPgR2C84c7TCHmfSvnGNlQRnAAAAAAAgjqREcOb1h0c1prvsFldyZPuPawQAAIgH/mAo9meHrbU6ziLBGaMaAQAAAABAHEmJ4CzWceaK744zSeqa45EkbSurtbgSAACAsEBw/1GNrdVxFl6XVXoDrfJ6AAAAAAAArSElgrPa+sTpOOvVMV2StGUvwRkAAIgP/lC448wwJDsdZwAAAAAAIImlSHAW7TiL/+CsR4dIcLaH4AwAAMSHaMeZ09Z6S8ccD3ucAQAAAACA+JMSwVlNJDjLSIBRjdGOs810nAEAgDgRDc4c9tbpNpOk7LRoxxmjGgEAAAAAQPxIieDMm0ijGjtkSAqPagyFzCM8GgAAoO1FRzU6WmlMoyRle6J7nNFxBgAAAAAA4kdKBGc1vsioRnf8B2ddc9NktxmqD4S0u8pndTkAAAD7RjXaW2/pGO04qyA4AwAAAAAAcSQlgrPaWMdZ/I9qdNht6pbrkSRt3lNjcTUAAACSPxjpOGvNUY3RPc7qCM4AAAAAAED8SJHgLNJxlgCjGiX2OQMAAPElEBkf7bC1fsdZpZc9zgAAAAAAQPxIqeAsIwE6ziSpZ4dwcLZlD8EZAACwXiDSceZs1Y6zyB5ndJwBAAAAAIA4kiLBWfhKZk+CdJzFgjM6zgAAQBzwR/Y4c7TiHmc50VGN7HEGAAAAAADiSIoEZ5GOM3diBGeMagQAAPEkEIrscWZrxY6zyKjGmvpgrKMNAAAAAADAaikVnKUnzKjGDEnSlj01FlcCAAAgBSIdZ85W7DjLStu3LquqY58zAAAAAAAQH1IiOKvxhT+MSU+UUY2RjrOyWj/7fgAAAMv5Ix1hjlbc48xhtykjsjarYFwjAAAAAACIEykRnCVax1mm26H8TJckaXMp4xoBAIC1AqFIx5mtdZeO2dF9zrhQCAAAAAAAxIkUC84So+NMknrnh8c1biittrgSAACQ6tqi40zat89ZpZdRjQAAAAAAID6kSHAW/jAmI0E6ziSpT36mJGl9CfucAQAAa0X3OHO04h5nkpTtCa/N6DgDAAAAAADxIumDs1DIlNcf6ThzJ07HWZ+CSMdZCR1nAADAWoFQuOPMaWvdjrOc6KhG9jgDAAAAAABxIumDs7pAUGb4IumEGtXYpyDccbaBjjMAAGAxf6zjrI1GNdJxBgAAAAAA4kTSB2c1vnC3mWFIaY5ECs7CHWcbS2sUCpkWVwMAAFJZILbHWWuPamSPMwAAAAAAEF+SPjjz1oeDM4/TLlsrjxdqSz07pMthM+T1B7Wzss7qcgAAQAoLRC7iae1Rjdlp4T3OKhjVCAAAAAAA4kTSB2c19eErmNNdDosraR6n3aaeHdIlMa4RAABYa9+oxjbqOGNUIwAAAAAAiBNJH5zVRjrOMtyJM6YxKjqucUNptcWVAACAVOaPjGp0ttUeZ3ScAQAAAACAOJECwVm448zjTMTgLFMSHWcAAMBasT3ObK3dccaoRgAAAAAAEF+SPjir8UU7zhJrVKMk9ckPd5ytL6HjDAAAWMcfio5qbN2Os7x0lySpvJbgDAAAAAAAxIekD868/nDHmb90i6677jp16dJFbrdbRUVFuuOOO1RaWtrk1/rOd74jwzBkGIbmz59/0P2hUEi//OUv1bVrV3k8Ho0dO1ZfffVVo68VCAQ0dOhQnXrqqTJNs9HHHK7jLFrH4UyfPl2GYWjSpEmN3r7/r4yMDHXt2lVjx47VPffcoxUrVjT7dQEAQHxatWpVi9ZBgWBIpTOf1JQLh7TqOuj/XfVtmaapstr6Zp0P6yAAAAAAANBWkj44q/EF5d28TO/++ka9/PLLys3N1QUXXCC3262pU6fqhBNO0LZt2474OrNnz9Zzzz132A9pfvvb3+rBBx9UTk6Oxo0bp4ULF+qcc85RVVXVQY996qmntHLlSk2dOvWQrxnd42xHhVd1/mATz7jp+vbtq4kTJ2rixIm66KKLNGTIEK1YsUKPPvqohgwZouuvv16VlZWtflwAANB+PvroI5100kktWgetWbpYNcvfb/V10GO//6MMw1C5169gqPELidoK6yAAAAAAANCYpA/OyiurVfr27xSor9Mvf/lLrVq1Sm+88YZWr16tu+++W9u2bdN3vvOdw75GXV2dbr31Vg0ePFinnHJKo4/x+/169NFHdfzxx+vLL7/UW2+9peeee04lJSX661//2uCxu3bt0pQpU3TrrbfqhBNOOORxO2a4lJ3mkGlKG0tbf5+z0047TdOnT9f06dP18ssv67333tPu3bv19ttvq6ioSP/4xz904YUXyu9nfBIAAImotrZW1157rWpra1u0Dvrvn38lZ35P9Tp2WKOPOdp10OmnjJAkmWb773PGOggAAAAAADQm6YOzRR/NVKimXB26Fen++++P3W4Yhh5++GEVFRXpvffe07Jlyw75Gg8++KDWrVunp59+Wk6ns9HHbNq0SeXl5br66qvldrslSddcc43S0tL05ZdfNnjsT3/6UzmdTv36178+bO2GYRx2XGNbMAxDF1xwgRYvXqyuXbvq448/1l/+8pd2OTYAAGhdM2bM0K5duzRgwIAWrYP27tyiDufeLoejdddBTrtN2WnhfWj31jRvXGNbYB0EAAAAAACSPjjbuPprSVKfISNkszU8XafTqdGjR0uS/vOf/zT6/OXLl+t3v/udbrrpJp122mmHPE5ZWZkkKS8vL3abzWZTTk5O7D5J+uSTT/Tiiy/qkUceUYcOHY5Yf3Rc44aS6iM+tjV16tRJDzzwgCTpj3/8Y7seGwAAtI4vvvhCknTGGWe0aB00ZOzFSus+WIea1NiSdVCHDFf4NZq5z1lbYh0EAAAAAEDqSvrgrLY23KmVlZPT6P0dO3aUpEavtA6FQrrllluUm5urRx999LDH6dmzpyRpzZo1sdvKyspUUlISuy8UCumOO+7QiSeeeMSxSFF9ox1nbTCq8UiuvPJK2Ww2rV+/vkn7nwAAgPhSUxNeP+wfaO2vqeugU6/9gSQdco+zlqyD8iLBWTx0nO2PdRAAAAAAAKkp6YMzZ0Y4MKsuLW70/o0bN0qSNm/efNB9U6dO1aJFi/TYY48dsTussLBQw4cP1/PPP6/58+errKxMd911l0KhkM4//3xJ0tNPP60vv/xSU6dOPeiq70Ppkx/uOFvfzh1nkpSVlaU+ffpIklauXNnuxwcAAC1TUFAgqfF1jtT0dZDDky1JOkTDWYvWQR3Sw8FZeRx1nEmsgwAAAAAASFUOqwtoax36Hi9JWr5ojkpLS5Wfnx+7b/v27Xr//fclSVVVVQ2et23bNv3iF7/Q2LFjdcMNNzTpWI8//rjOPfdcnX766bHbvv3tb+uCCy7Qnj17NHnyZN100006+eSTY/fX1dXJ5XIdMkjr3znccbZ+d7VCIVM2W8OPrA515Xdryc/P17p16xqMWQIAAInhjDPO0MMPP6x33nmnReug+X//XJIOOapROvp1UE5sjzN/s8+PdRAAAAAAAGhtSR+c5fQ/Sa7OfVW3a72+9a1vaerUqRo0aJCWL1+uW2+9VYFAQJIOCq5uv/12+Xy+Zm0IP3bsWC1ZskQvvviiysvLNXLkSE2YMEGSdO+998o0Tf3mN7+RJH344Yf6wQ9+oJUrV8rj8WjChAl68sknlZaW1uA1e3XMkNNuqKY+qO3lXvXokN7g/okTJx6ynnXr1mnBggVNrr8xpmlKavsPpgAAQOsbP368hg8friVLlrRoHRQIhdcDtsOsB452HeR0p8k9cKx2j3qk2efHOggAAAAAALS2pA/OauqDKrjkF3J++Ft9/vnnGjlyZOy+zp07a8qUKbrvvvsa7P3xxhtv6K233tLkyZM1cODAZh1v8ODBsQ+Foj7//HM9++yz+uMf/6j8/Hxt375d//d//6chQ4bojTfe0MqVKzVlyhRlZGToiSeeaPBcp92mvgWZWr2zSmt2VR0UnE2fPv2QtUyfPr3FHxiVlpZK0hFHVQIAgPhjGIZmzJih888/v0XrIH8wFHm9wx/vaNZBz78zT/99/o96e1q+7r/4xWadH+sgAAAAAADQ2pI+OKuuC8iR00mvzJyrrUs/1ieffCKv16vBgwfruuuu04wZMySFP+iJevvttyVJ77//vubOndvg9b788ktJ0ve//33l5ORo0qRJmjRp0iGPb5qmbr/9dh133HH63ve+Jym8Z0hdXZ1effVVFRUV6dJLL9W6des0depU/frXv1Z6esNw7JjOWZHgrFpnH9u5pV+SJqusrNSGDRskSYMGDWq34wIAgNbTq1cvffnll/r3v/991OuglTsqVVnnl6N8i6TWXQf5e5yk2Z8u19JZ/1Jt7V8PWgdZhXUQAAAAAACpKfmDM19QkpSTkaZRV1yhK664osH9n3zyiaTweKEDLVq06JCvGw3QGnve/p577jl99tlnmjdvnux2uyRp9erVys/PV1FRUexxJ598sl544QWtW7dOxx13XIPXOCayz9maXQ33H2lrr776qkzT1DHHHKOuXbu267EBAEDrcTgcuqIV1kG+yO+tuQ7qlJ0md5djVPP1h42ug6zCOggAAAAAgNRkO/JDElu1L7zRfKb74Ixw586dev3119WxY0ddeumlsdunT58u0zQb/TVmzBhJ0rx582SapqZMmXLIY5eXl+vee+/VhAkTNHr06Ab3eb3eBn+vqamRdPAeI1K440ySvtnZfsHZ7t279ctf/lKSdOedd7bbcQEAQPtozjrogj/OU697/qvjR5wqqXXXQZ2y3Ar56yQ1vg6yAusgAAAAAABSV3x8OtFGAsGQ6vwh1ZdsklOBBvdt27ZNF110kaqqqvT444/L4/G0+vHvu+8++Xw+Pfroow1uHzx4sKqrq/Wf//xHkuT3+/Xaa6/J7Xarb9++B73OgMJwcLaupFrBkNnqde7PNE3NnDlTI0eOVHFxsc466yzdcsstbXpMAADQdr7++mvV1dU1uK2566Cm7nG2v6augzp47Kr9Zr5kd6pXUe+mH6ANsA4CAAAAAABJPaqxJjKmsfLTf2tA759r+PDh6tKli3bv3q358+fL5/Np8uTJmjhxYqsfe9myZXr66af12GOPqXPnhvuS3X777frDH/6gq666Sueee67WrVunlStX6mc/+1mjH1z1yEtXmtOmOn9Im/fUqE9BZqvUOH/+/Ni+JPX19dqzZ4+WLFmi0tJSSdKECRM0depUORxJ/TYBACCpPfbYY/r3v//donVQfSQ4szUxOWvuOqi+eK2yR12umqBdWc0/xaPCOggAAAAAADQmqT8JqIqMacwZeIqGdnNo2bJlWrBggfLy8nTeeefphz/84RH35jha3//+93XsscfqjjvuOOi+wsJCzZo1S3fffbf+97//KTc3V3fffbceeOCBRl/LZjPUv1OWlm+v0JpdVa0WnK1fv17r16+XJHk8HuXm5mrQoEEaNWqUbrjhBg0ePLhVjgMAAKxz8cUXa+fOnS1aBzW346y566Aup18h58jrtKuyToU5aU09tRZhHQQAAAAAABpjmKbZtrP/LLR6Z6XO+8M85We69Pl946wup0V+/OoyvbFkm+4ad4x+cHZ/q8sBAAApZNTDH2pnZZ3++/3TNKRbTqu//kVTF2jZ1nI9M+FEjR9c2OqvDwAAAAAA0FRJvcdZjS+8r1mGO/Eb6wYUhrvMVhVXWlwJAABINdGOM5ejbZaOnbLckqTdVb42eX0AAAAAAICmSurgrKouHJxlJkFwFr26+6ttFRZXAgAAUk10jzOnvW2Wjp2zI8FZZV2bvD4AAAAAAEBTJXVwVp1EHWdDu+XIMKTt5V6VcDU2AABoR/5YcNbETc6aqVNWeF8zOs4AAAAAAIDVkjo4i45qzEqC4Cwrzal+BeFxjV9tK7e2GAAAkFL8wfCWuK427jjbRccZAAAAAACwWFIHZ7FRjWmJH5xJ0nHdcyVJyxjXCAAA2kkwZCoYCgdnbTWqkY4zAAAAAAAQL5I6OEumUY2SNKxHeJ+zZVvLrS0EAACkjOiYRklyOtooOIt1nBGcAQAAAAAAayV1cJZMoxqlfR1nX20rl2ma1hYDAABSQv3+wVkb7XHWOTvccbanxqfAfscDAAAAAABob0kdnEU7zjKTJDgb2CVLLrtNZbV+bd3rtbocAACQAvyB/YIzW9ssHTuku+SwGTJNqbS6vk2OAQAAAAAA0BRJHZxF9zhLllGNboddx3bJkiR9ua3c2mIAAEBK8AfDXe4OmyGbrW06zmw2QwVZ0XGNdW1yDAAAAAAAgKZI6uAsOqoxMy05gjNJOqFnniTps417La4EAACkgugeZ6422t8sqlMkONtdxT5nAAAAAADAOkkdnFUn2R5nknRK346SpIUb9lhcCQAASAXRPc6c9jYOziL7nNFxBgAAAAAArJTUwVmyjWqUpFG9O8owpHW7q7W7ig+WAABA2/K3U3DWOZtRjQAAAAAAwHpJHZxVJ+Goxpx0pwZ1yZYkLVxP1xkAAGhb/kB4jzOXvW32N4vqkuORJO0oJzgDAAAAAADWSergLLbHWRJ1nEnSKX3C4xoXMa4RAAC0sdioxjbe46xrbnhUY3GFt02PAwAAAAAAcDhJG5yZprmv4yzJgrNT+0X2OaPjDAAAtLH2GtVYmB3uOCuuoOMMAAAAAABYJ2mDM18gJH8wPFoomUY1StKIog6y2wxt2lOrHeVclQ0AANpOewVn0Y6zHeVemabZpscCAAAAAAA4lKQNzqJjGiUpw5VcwVlWmlPHdc+RJM1dU2JxNQAAIJlFg7O23uOsMCccnPkCIZXX+tv0WAAAAAAAAIeStMFZdExjussuu61tP+ixwpkDOkmSPlq92+JKAABAMqsPhLu/2rrjzO2wKz/TJUnawT5nAAAAAADAIkkbnFXVJef+ZlFnDQwHZwvWlcoXCFpcDQAASFb17TSqUZK65ET2OStnnzMAAAAAAGCNpA3OoqMakzU4G9QlWwVZbtXUB/XZxjKrywEAAEnKH4gEZ462XzZ2yw0HZ1v21rb5sQAAAAAAABqTtMFZdFRjZlpyBmc2m6EzBxRIYlwjAABoO+21x5kk9S7IkCRtLK1p82MBAAAAAAA0JvmDsyTtOJP2jWuc/Q3BGQAAaBv+dhzV2Duf4AwAAAAAAFgraYOzyiTf40ySRvfLl8tu08bSGq3ZVWV1OQAAIAnVB01J7ROc9aXjDAAAAAAAWCx5gzOvX5KU43FaXEnbyUpz6vT++ZKkd74qtrgaAACQjNq34yxTkrS93Ks6f7DNjwcAAAAAAHCgpA3OKlIgOJOkbw/tIkmauZzgDAAAtD5/ILLHmaPtl4156c7Y2o2uMwAAAAAAYIXkDc5qUyM4O2dQZznthtburtZaxjUCAIBWFu04c9mNNj+WYRga0DlLkrRsa3mbHw8AAAAAAOBAyRucRTvO0pM7OMvxOHV6/wJJ0jt0nQEAgFbWnnucSdKovh0lSZ+s39MuxwMAAAAAANhf8gdnSd5xJu0b1/ifL3fINE2LqwEAAMkktsdZO4xqlKRT9wvOWNcAAAAAAID2lvTBWXYKBGfnDSlUptuhjaU1WrCOq7MBAEDriQVntrYf1ShJJ/TMVZrTptJqn5YyrhEAAAAAALSzpA/OUqHjLNPt0KXDu0mSXly0ydpiAABAUqkPhIMzt9PeLsdzO+yxbvqXFm5ul2MCAAAAAABEJW1wVplCwZkkXT+qlyTp/ZW7VFzhtbgaAACQLHzR4KydRjVK0g2nFEmS/vtVsSrr/O12XAAAAAAAgKQMzoIhU1W+gKTUCc6O6ZylUX06KGRKf+fqbAAA0Ep8gaCk9g3OhvXIVd+CDNUHQ5q9ene7HRcAAAAAACApg7Not5mUOsGZJN00urckafqCTdpdVWdxNQAAIBn4/NGOs/YZ1Rh13pBCSdKsFTvb9bgAAAAAACC1JWVwFt3fLN1ll9OelKfYqHGDOmtYj1x5/UE9/M4qq8sBAABJIDaq0dm+a6pzB4eDs4+/KVEwZLbrsQEAAAAAQOpKylQpuhdGKnWbSZJhGJpy4WDZDOnNL3fo/ZW7rC4JAAAkOCtGNUrS4K45ynQ7VFMf1JpdVe16bAAAAAAAkLqSMjiLdpylWnAmhfcEufmMPpKkn/97ucpq6i2uCAAAJLJYx1k7j2q02wwd3yNHkrRkS1m7HhsAAAAAAKSupA7OslMwOJOkH51zjPoWZKikyqefvP6VTJPxRgAA4Ojs2+Os/ZeNw3vmSZKWbC5v92MDAAAAAIDUlNTBWSp2nElSmtOuJ68+QS67TR+s2qW/L9xsdUkAACBBxUY1tvMeZ9K+4OzLrXScAQAAAACA9pHUwVl2WmoGZ5I0pFuO7v32QEnSQzNXaVNpjcUVAQCARGTVqEZJGtQ1W5K0sbRGdf5gux8fAAAAAACknqQOzlK14yxq0qlFOq1fvuoDIT00c5XV5QAAgAS0Lzhr/2Vjpyy38tKdCpnSut3V7X58AAAAAACQepIyOKskOJMkGYahKRcOkt1m6P2Vu7RiR4XVJQEAgATji3R6WdFxZhiGBhRmSZJW76xq9+MDAAAAAIDUk5TB2b6OM4fFlVivX6csnT+0iyTpmbkbLK4GAAAkmljHmQV7nEnSwMLwuMbVxZWWHB8AAAAAAKSW5A7O0lO74yzqljP6SJLeXb5T5bX1FlcDAAASRSAYUiBkSrJmVKMkDYx0nH2zi44zAAAAAADQ9pI7OEvxUY1RQ7rl6Ngu2aoPhvT2sh1WlwMAABJEfTAU+7MVoxolaWCXcMfZqmKCMwAAAAAA0PYIzlLEZcO7SZLe/JLgDAAANI3Pvy84c1nUcXZM50wZhlRa7VNptc+SGgAAAAAAQOpIzuCsluDsQN+O7HO2ZEsZHzoBAIAmie5v5rQbstsMS2pIdznUq0O6JOmbnXSdAQAAAACAtpV0wVkoZKrKF5AkZROcxXTN9WhIt2yZpvTRqt1WlwMAABKALxCUZN2YxqgBkX3OVhOcAQAAAACANpZ0wVlVXUBmeA97Os4OMO7YQknSh6t3WVwJAABIBNGOM7dFYxqjBhaG9zlbXVxpaR0AAAAAACD5JV1wFt3fLM1ps/zq6HhzxjH5kqSF6/coGDItrgYAAMS76B5n1gdn4Y6zNbvoOAMAAAAAAG0r6YKzcm+9JCnX47K4kvgztFuOstwOVdYFtGJHhdXlAACAOBcb1ei09mKkY2LBWbVCXPwDAAAAAADaUNIFZ3trwsFZhwyCswM57DaN7NNRkrRg3R6LqwEAAPEuXkY1FnXMkMthk9cf1Ja9tZbWAgAAAAAAkhvBWYoZ3S8cnH2yvtTiSgAAQLyLdZxZHJzZbYb6d8qUJH3DuEYAAAAAANCGCM5SzOh+4X3OPtu0N/ZhGAAAQGP27XFm/b6xAyLjGr/ZSXAGAAAAAADaDsFZiunfKVMFWW7V+UNasrnc6nIAAEAci41qdFq/ZBzQORKc0XEGAAAAAADakPWfgrSyslqCs8MxDEOn9mVcIwAAOLJ4GdUo0XEGAAAAAADah/WfgrSyPdUEZ0cyum94XOMn6/dYXAkAAIhnsY6zOBjVOLAwW5K0sbSGcdMAAAAAAKDNJF1wRsfZkZ0S6ThbtrVc1b6AxdUAAIB4tW+PM+uXjJ2z3cpOcygYMrV+d43V5QAAAAAAgCRl/acgrWwPe5wdUY8O6erRwaNAyNRnm/ZaXQ4AAIhTdf5wZ5crDoIzwzBiXWerd1ZaXA0AAAAAAEhW1n8K0srKCM6aJDqucSHjGgEAwCHURoIzj8v6UY2SNKhrODj7cmu5tYUAAAAAAICklVTBWTBkqtzrl0RwdiTRcY0L1pVaXAkAAIhX3vpwcJYeJ8HZiKIOkqTPN5VZXAkAAAAAAEhWSRWcldXWyzQlw5ByPU6ry4lr0eBsZXFlrEsPAABgf7X14b1Q010OiysJO6koT1J4VGNVnd/iagAAAAAAQDJKquCspMonSeqQ7pLDnlSn1uo6ZaXpmM6ZMk1pHl1nAACgEbWRjjOPMz46zjpnp6l7nkchU/piM11nAAAAAACg9SVVuhQNzgqy3BZXkhjOHNBJkjR79W6LKwEAAPEo3kY1Svv2aZ3zTYnFlQAAAAAAgGSUVMFZaTXBWXOcNTAcnM35ZreCIdPiagAAQLyJdZzFUXB21rHh9cuHq3fJNFm/AAAAAACA1pVUwVm04yw/k+CsKU7slaccj1NltX59unGv1eUAAIA4U+uPdpzFxx5nknRav3y57DZt3evV+pJqq8sBAAAAAABJJimDMzrOmsZht+m8wYWSpLeW7bC4GgAAEG+89QFJ8TWqMcPt0Ki+HSVJH65i3DQAAAAAAGhdyRWcVUc7zlwWV5I4LhzWVZL07tfFqotcVQ4AACDF56hGSTp7YHRcI8EZAAAAAABoXUkVnLHHWfON6tNR3XI9Kq/103UGAAAa8NZHRzXGV3AW3af1i81lqqj1W1wNAAAAAABIJkcVnD3xxBO69NJL1b9/f+Xk5MjtdqtXr1664YYbtHz58hYXtXbtWnk8HhmGoXPOOafJz4uNasxMa3ENqcJuMzThlF6SpGfnbVQwZLZ7DR9//LEuu+wyFRYWyu12q2vXrvrWt76lt956q91rAQAg3nzxxRf6zW9+o0svvVTdu3eXYRgyDOOwz9m6dav+/Oc/a9KkSTr22GNls9lkGIbmzJnTrGNHO87SnfGzx5kk9eiQrmM6ZyoYMvXx2pI2O87mzZv14x//WGeccYa6d++utLQ0ZWZm6oQTTtBDDz2kmpqag55TXl6ul19+Wddcc4169+4tl8ulrKwsjRw5Uk8++aT8foI+AAAAAADi2VF9CvLwww+rpqZGxx13nIYOHSpJWrFihV588UX985//1IwZM3TBBRccdVG33HKLfD5fs58XDc7ysxjV2BzXjOipqbPX6ZtdVXr186265uSe7XbsKVOm6Fe/+pXcbrdGjx6tTp06afv27Zo3b566deumCy+8sN1qAQAgHj344IP6z3/+06znvPHGG/rRj37UouOGQqa8/vgc1ShJZw3srDW7qvXRql268PiubXKM5cuX64knnlBhYaEGDhyo008/XWVlZVq0aJHuu+8+vfLKK5o3b57y8vJiz3nsscf00EMPyTAMDRs2TCNHjlRJSYkWLFigTz/9VK+//rpmzZql9PT0NqkZAAAAAAC0zFEFZ//5z3904oknKi2tYWfXn//8Z91+++367ne/q23btsnhaP7LP/vss5ozZ45uueUWPfPMM01+Xp0/qLLIqJ5OWXScNUdOulM/POcYPfjflXronVU6uXcH9S3IbPPjTp8+Xb/61a80cuRIvf766+revXvsvtraWm3YsKHNawAAIN6dcsopOu644zRixAiNGDFCRUVFR7zAqE+fPvrhD38Ye84dd9yh9957r1nHrQvs2/s03kY1StLZx3bS0x+v15w1JQoEQ3LYW38C+Yknnqivv/5agwcPbnB7ZWWlLr30Un344Yd66KGH9Nhjj8Xuy8jI0E9/+lPdfvvt6tlz38VIa9eu1TnnnKP58+fr17/+tR5++OFWrxcAAAAAALScYZpmq87m69evn9avX69ly5bpuOOOa9Zzd+3apWOPPVYnnXSSfv7zn+vMM8/U2WefrQ8++OCIz92yp1Zn/G623A6bVj943hFHGKEhfzCk6/62WJ9u2quCLLeeuPJ4nd6/oM2O5/V61aNHD9XX12vNmjUqLCxss2MBAJBM0tLS5PP51Jwl3HnnnadZs2Zp9uzZGjt2bJOeU1rt00m/Dq/BNjz8bdls8bW2CgRDOumhD1Re69dr3ztFI4o6tOvx58+fr9NPP10nnHCClixZ0qTnvPLKK7r22mtVVFSkjRs3tnGFAAAAAADgaLT6pblOp1OS5HI1f1zinXfeKa/Xqz//+c/Nfm5xhVeS1CUnjdDsKDjtNk29brgGFmappMqnCc9+qhue+1RrdlW1yfFmzJihPXv26IorriA0AwAgDnkj+5ulOW1xF5pJksNu05hjwhf5fLhqd7sf/2jWvMcff7wkaceOHW1SEwAAAAAAaLlWDc5efPFFffPNN+rfv7/69+/frOfOnDlT//rXv/Tzn/9c/fr1a/axd1bWSZIKcxjTeLQKstx67XunaNKpRXLaDc1dU6JvPTlPD/53peoDoVY91kcffSRJOvXUU1VeXq6pU6fq//2//6e77rpLr7/+ugKBQKseDwAANE9tJDhLdx3VZO92cdbATpKkj1bvatfj1tbW6qGHHpIknX/++U1+XnQMNRcNAQAAAAAQv1r0Scjvfvc7rVixQjU1NVq1apVWrFihrl276pVXXpHd3vS9MGpqanTbbbdpwIABuueee46qlp0V4eCsS47nqJ6PsKw0p6ZcOFg3ji7SwzNXadaKXXp2/kat3FGp5yaNkKeV9jhZuXKlJKmkpESDBg1ScXFx7L7f//73Gjp0qN555x316NGjVY4HAACap7Y+fBGLxxl/+5tFjT2mk2yGtGZXtbaV1ap7XnqbHKesrEw/+tGPJIXXLosXL9aePXt08cUX6+67727y6zz55JOSpIsuuqhN6gQAAAAAAC3Xoo6zWbNm6YUXXtDrr7+uFStWqFevXnrllVd04oknNut17rvvPm3evFlPP/30UY14lKTiCjrOWlOvjhn664ST9OzEk5Tpdmjhhj264+UlCoZaZ0u8srIySdLkyZPVoUMHzZs3T5WVlVq8eLGGDx+u5cuX67LLLmvW/i0AAKD1eGMdZ/EbnOWkO3VirzxJ0uxvStrsODU1NXrhhRf0wgsvaObMmdqzZ4+uvPJKPfPMM/J4mnbR1tNPP60PPvhAubm5+tnPftZmtQIAAAAAgJZpUXD2wQcfyDRNlZWVae7cuerfv7/GjBkTG13TFJ9//rn++Mc/6oYbbmjyZvWN2ddxRnDWms4+trOm3zhCbodNH67erSfe/6ZVXjcUCo9+tNvtevfdd3XaaacpKytLJ598st59911lZGTos88+0wcffNAqxwMAAM1TmwDBmSSNHRAZ17iq7cY1du/eXaZpKhQKacuWLXr22Wc1d+5cDR06VEuWLDni8+fNm6c777xThmHoueeeU9euXdusVgAAAAAA0DKtssdZbm6uTj/9dM2cOVMnnniiJk+erM8+++yIzwsEArr55puVm5urxx57rEU1FEf3OMsmOGttJxV10KOXHydJmjp7vd5dXnyEZxxZZmamJOnss88+aBxjp06dYvuFfPzxxy0+FgAAaL5afzg4a60xzW1l/KDOkqR5a0tVWu1r02MZhqEePXropptu0ltvvaXS0lLdeOONh+2Q//rrr3XRRRepvr5eTz75pC655JI2rREAAAAAALRMq+727nQ6ddVVV+mLL77Q22+/rREjRhz28du2bdOXX36pwsJCXXHFFQ3uKy8vlyR98cUXsU60OXPmHPK1dpR7JbHHWVu5aFg3Ld9WoWnzN+rHry1T74IMDSzMlhT+2v/zs63avKdGx3TO0tUjeqhjpvuwr9erVy8tXbpURUVFjd4fvX337t2teRoAAKCJanzhPc4yXK26XGx1/Ttn6fjuOVq2rUJvLt2u757ep12OO2LECA0YMEBfffWVNm7cqD59Dj7uxo0bNX78eJWVlWnKlCn6/ve/3y61AQAAAACAo9fqn4Tk5+dLCm+c3lQ7d+7Uzp07G72vvLz8iF1Hdf6gSqrCVxj36EBw1lZ+9q2BWllcqU/W79GVTy/UhFN6af3uGr2/aleDvc9e+GSTnp04QkO75xzytU444QS9+eabsb3ODrR3715J+zrTAABA+6r0+iVJOR6nxZUc2eUn9dCybRWaNm+jrhvZq9265PZf9x4YnBUXF2vcuHEqLi7WnXfeqfvvv79dagIAAAAAAC3TKqMa9xcNufr27XvExxYVFck0zUZ/zZ49W1J4lF/0tkPZVlYrScpyOxLiw51E5bDbNPXa4RreM1eVdQFNnb1e/1uxU8GQqVF9OujH445R34IM7a7y6fpnF2vtrqpDvtaFF14oSfrkk0/k9/sb3BcKhTR//nxJ4YANAAC0v8q68L/P2QmwtrrixO7qluvRzso6/Wn22nY5ZmVlpZYuXSrDMNS7d+8G95WVlencc8/V+vXrdeONN+r3v/99u9QEAAAAAABartnB2YIFC/S///1PoVCowe1+v19PPfWUXnzxRXk8Hl111VUN7v/Tn/6kgQMH6t57721ZxY3Yujc8prF7h3QZhtHqr4998jJceuWWUXrk0qG69IRuuuWMPvrfD0/XP285Rd8/u7/+c8dpOqFnriq8fn3vpS/krQ82+jrDhg3TuHHjtHnzZt13330NgtFf//rXWr16tTp16qRLL720vU4NAADspyLScZadFt+jGiUpzWnX5AuOlST9Zc56fbF5b6u87rRp07Rhw4aDbt++fbuuvfZaVVVV6fzzz1enTp1i99XW1ur888/X8uXLdeWVV+pvf/sb61MAAAAAABJIsz8JWbt2rW688Ubl5+frxBNPVMeOHVVaWqrly5eruLhYaWlpmj59unr06NHgeaWlpfrmm29UXFzcasVHbdkb7jjrkceYxvbgdth1zck9dc3JPQ+6L9Pt0LQbTtK3npyn9SU1evCdlXr4kqGNvs6zzz6rU045RY8++qjefPNNDR06VKtWrdLKlSvl8Xj0j3/8QxkZGW19OgAAxLV33nlHDz74YOzv9fX1kqRRo0bFbps8ebLOP//82N+Li4t1ySWXxP6+evVqSdJtt92m7OzwHqXnn3++Jk+efMjjVnrDe5wlQseZJJ03pIsuHd5NM5Zs131vrtB/v3+a7LaWBVYvvfSSbr75Zg0aNEgDBw6U0+nU1q1b9cUXX8jn82nw4MF65plnGjznF7/4hRYuXCi73S6Hw6HvfOc7jb729OnTW1QbAAAAAABoG80OzsaMGaOf//zn+vjjj/XVV1+ptLRULpdLRUVFuvzyy/WDH/xA/fr1a4taD2lrNDjrkN6ux0XjOma69furhun6Zxfr5cVbNOaYAp07uPCgx/Xo0UNLly7Vr371K7399tt66623lJeXp6uvvlq/+MUvNGTIEAuqBwAgvpSUlGjx4sUH3b7/bQfuLevz+Rp9zqpVq2J/Hjhw4GGPG+s4S5DgTJLuO3+QPli5S6uKK/X6F1t11YiDL/Jpjp/85Cfq27evFi1apNmzZ6uqqko5OTkaNWqULrvsMt1yyy1yu90NnhPdvzUYDOrll18+5GsTnAEAAAAAEJ8M83CbhyWIW1/8XLNW7NKU/xukSaN7H/kJaBePzFylv87doA4ZLs364RkqyHIf+UkAACAuXPLnBVq6pVx/nXBioxfAxKtp8zbo1++sUrdcjz66e4zcDrvVJQEAAAAAgATS7D3O4tGm0nDHWa+OjPWLJ3eNP0YDC7O0t6Ze9874SkmQ0QIAkDIqY3ucJU7HmSRdP6qXOmW5tb3cq1c/22p1OQAAAAAAIMEkfHAWDJnaWFojSepbkGlxNdif22HXH64eJpfdpg9W7darn/PhFQAAiaIitsdZsyd7WyrNadftZ4bHhv9p9jrV+AIWVwQAAAAAABJJwgdnW/fWqj4YktthU7c8j9Xl4AADC7N197nHSJJ+9fZKfbOzyuKKAABAU1TWhTvOchJoj7Ooq0/uoW65Hu2q9Omnr3+lQDCkxRv26IKn5um4KbP00DsrVR8IWV0mAAAAAACIQwkfnK3bXS1J6lOQKbvNsLgaNOY7p/XR6H4dVVsf1E3TP9PWvbVWlwQAAA6jzh+MBUvZCRicuR12PXn1MNltht5ZXqxRj3yoa6ct1tfbK1VZF9Df5m3Unf9cqkCQ8AwAAAAAADSU8MHZ+pJwcNa3gP3N4pXdZuhP1wxXn/wMbS/36pI/L9D/vt7JnmcAAMSp6P5mNkPKdCXWqMaok4o66M/XDVe6y67S6noFQ6YuPL6rHrx4iFx2m979eqdunP6ZdlfVWV0qAAAAAACII4n5Sch+1u6OBmfsbxbP8jJceuWWUZr43KdavbNK33vpCx3fPUc3ju6tcwcXyuOyW10iAACIiI5pzEpzypbAHf3nDi7UvJ+eqUUb9qpXx3QN6ZYjSeqak6Y7Xl6qeWtLNe6JufrpeQN0xYk95HIk/DVlAAAAAACghQwzwdt+vv3kPK0srtTT1w/XeUO6WF0OjqDOH9RTH63Vc/M3yesPSpLSXXaN7N1BI/t01HHdczSkW46y0xJvLBQAAMnii817ddlfFqpHB4/m/fQsq8tpE6uKK/WT15fp6+2VksJh2q1j+uqqET2U5uSCHgAAAAAAUlVCB2e+QFCDfzlLgZCp+fecqe556VaXhCYqrfbpxYWbNWPpNm3d6z3o/lF9Ouju8QN0UlEHC6oDACC1vb9yl27+++ca2i1Hb3//NKvLaTOBYEh/X7hZf/l4vUqqfJKk/Ey3Hrl0qMYN6mxxdQAAAAAAwAoJHZx9ta1cF/5pgXLTnVo6eZwMI3FHCaWqUMjUyuJKLdqwR0u2lGnZ1gptLw8HaTZDuu/8QbrptN4WVwkAQGp5ceEmTf7PCo0b1Fl/u+Ekq8tpc3X+oF77YpuenrNe28u9stsMTbvhJJ05sJPVpQEAAAAAgHaW0HucRUfrDO2WQ2iWoGw2Q0O65cT2HJGk7eVePf7eN5qxZLse+O9KeVx2XXNyTwurBAAgtRRX1EkKjy9MBWlOuyaM6qWrR/TQPa9/pRlLt+unb3yl9390hnLTXVaXBwAAAAAA2lFC74C+dEuZJDUIXZD4uuV69PgVx+v2M/tKkia/+bU+WVdqcVUAAKSOnZHgrDDHY3El7ctpt+nhS4eqb0GGSqp8euDtlVaXBAAAAAAA2llCB2eLNu6RJI3szT5YycYwDN09foAuGtZVgZCp//ePJdpQUt3oYytq/frH4s26d8ZXmvLWCs3+ZrcSeAIpAACW21ERHpvcJUU6zvaX5rTrd1ccL5shzVi6XY/N+kbVvoDVZQEAAAAAgHaSsKMat5XVauve8B4UI4oIzpKRYRj67WXHafOeWn25tVzffeFzvXLLKHXODn+IV+cPavonm/Tn2etUWbfvA63pn2zSiKI8/fay49SnINOq8gEASFjRjrNUDM4kaXjPPP14/AD9btY3+tPsdZr+ySb93/FddN3IXkw6AAAAAAAgyRlmgrbmvPr5Vv309a90Qs9c/fu20VaXgza0u6pOl0z9RNvLveqc7da1J/eS1x/Uv5du065KnySpf6dMnTekUGW19Xrji+3y+oNKc9r003MHatKpRbLZ2AMPAICmME1TAyf/T75ASHN/cqZ6dky3uiTLvLl0u/744VptKK2RJBmGdPf4Abr9zH4WVwYAAAAAANpKwgZnNz7/qWZ/U6IfnXOM7jynvySppKTE4qoQVVBQ0Kqvt2VPrSY9/2nsg6uobrke3TXuGF18QjfZI+HY9nKv7nn9K82P7It2clEH/fCc/hrVpyMBGgAAR7C3pl7DH3xfkjT/zhPldtgtrqjtNGW9YpqmFm7Yo5cXb9F/vyqWJE29drjOP65LW5cHAAAAAAAskJDBWVlNvUY89IECIVMf3DVG/TqFx/EZBqFIvGiLt1WdP6hXP9+qLzaXKc1h16n9Ouq8IYWNfqBnmqZe/nSLHnpnlWrrg5KkztlujerTUUO75ahTdppM01Sl168Kr19ef1CFOR6N6V+Q0lfWAwAwf22prn92sTpnu/XpL8ZZXU6bau565ZGZq/TXuRvUIcOlD+8ao7wMVxtVBgAAAAAArJKQwdkzc9fr4ZmrNbAwS//74Rmx2wnO4ke8vK227q3Vn+es03+/KlbVfvugHc7AwixdO7KnLjmhm7LSnG1cIQAA8eVnb3ylf362Vdec3FO/uew4q8tpU81dr/iDIZ3/x3las6ta5x/XRX+65gQZhiHTNBUMmXLYbYd9/u7KOpV7/SrMSVM2awwAAAAAAOJSwgVn1b6Axv5ujkqrfXr08uN05Uk9YvcRnMWPeHtb1fmD+nxTmT7dtFcbSqq1u8onu2Eox+NUjscpt9Omtbuq9emmvQqGwrWnu+y6+IRuuuC4LhreM09pzuQdVQUAgCTVB0Ia8dAHqvD69fLNIzW6X+uOXo43R7NeWbqlTFc8vVCBkKlzju2sDLddC9btUWm1T30LMvT9s/rromFdY+tSXyCoN5du17PzN2rNrurY63TL9ejYLtk6pW9HXX5id+V4CNIAAAAAAIgHCRWcrSqu1ANvr9TCDXvUo4NHH/14rJz7XdlLcBY/Euht1UB5bb3eXLpdLy7arPUl+/ZTczlsOqFHro7tkq2eHdKVmeZQptshu81Q+FRNmaZksxnKz3SpIDNNnXPcSb0vDAAg+Xyzs0pXPbNQTrtNi+49+4gdVInuaNcrf1+4Sfe/tUKHevr4QZ31w3OO0ariSj3x/hptL/dKkmyGlO1xqrzW3+Dx2WkOPXn1CTpzYKejqgcAAAAAALSehAnO1u2u0jlPzJUUDjH+ecsoDe+Z1+AxJSUlVpSGRhQUJPYV6qZpatGGvXrt862av65Uu6t8zX4NmyEVdcxQv06ZGt4rT6f1y9egLtmy2Qh4AQDxyx8MafOeGvXrlJX0a6uWrFe+3Fqu/329U067oVP6dlTv/Ay99vk2PfXRWvmDDZfXhdlp+s5pvXXliB7K8ThVUevX6p2V+mpbhV77YqvW7KqWYUj3fmugbj69jwzDUGWdX4bE2GgAAAAAANpZwgRnknTpnxeoMCdNt5zRV8N65FpdDlKEaZpaX1KjJZvLtL6kWtvKvarxBVTjCygQMmVIshmGDEPyB03tqfFpd6VPvkDooNfKS3fqlL4ddWKvDurVIV0dM11yO+xyO21Kc9rVOct9yKv7vfVBfbOrStV1AZky1S3Xo255HrraAACIIyt3VOrRWas1f22pctNduum0It00uvchRz7XB0K6/62v9cqnWyWF91qt9gW0rSzcpXZCz1z94Kz+GjugQDX1QX38TYnqg0Gd0CNPRfkZKqup198Xbtb/VuxUSVWd0px29chL17hBnXX5Sd3ZSw0AAAAAgGZKqOAsEAwl/cggJAfTNLW7yqe1u6q1emelFm3Yo0Ub9qraFzjs81wOm/rkZ6h/5ywd0ylTHpddm/fU6qtt5Vqxo1KBUMMfV8OQOmW51SkrTW6HTUHTlLc+qJr6gGp94d/thqGCLLcKstzqmutRrw7p6tkxQ0Ud09WzY7oKMt0tGnNqmqYCIVMh05TTZjtiR523PqjdVXXaU1Mvm2HI47SrU5ZbuelOxq0CAJKGaZpN/nfNNE298MkmPfjOqtheqwfq1TFdOyvqGlyY06ODRyVVPtX5D75YRwrv13rRsG4a2buD+nXKVN+C8NriUDaV1mhHuVfHFGYpP9PdpNoBAAAAAEg2CRWcAYnMHwzpq23l+mTdHn29o0Lbyryq8PrlC4Tk8wfl9QcPGu10oPxMt/IzXQqZpraVeVVbH2xxXekuuwpz0pTrcSo33aUcj1NpTrvcDpvcTpvcdpvckb877TbtralXcYVXO8rrtKPCq+LyOnn94TpsRnikVI5n3y+Xw6by2nrtralXaXX9IcNDl92mTtludc5OU+fscBiY7XEqy+1QZppDGW5H7M+Z7v1+pTka7HV4ON76oHZUeFVS5VN5bb0qvH6ZpuS02+SwG3I7bMp0O5WZ5lBW2r7jeZz2w374aZqmauqDKqsJn+fe2nqFQmb4+5UV/p4d2BlomqbKa/2xr2FxZZ18/qBMU8pKcygn8v3okOFSXoZTeemuJp8nUo9pmqryBVRS5VOa065uuR6rSwJwFDaV1mjZtnJ1zHDruB45qqsP6tn5G/X8J5tUHwnMijqmKz/TraVby2Mh27FdsnXz6b01sDBbXn9QX20r1yufbtGaXdUNXt8wpO55HjntNu2qqFO626H+nTLVIy9dX++o0IodlZIkp93Qhcd307hBnbW93KtlW8u1sbRGLodN6S67PE67MtwOdc5OU9+CDLkcNm0r82pjaY02ldYoaJo6oUeeTirK0/CeeQqZpnZW1mlnRZ3Ka/1y2A11SHdpYJcsdcpK096aem0v96q02ien3VCm26kuOWkqzElTMGRqR3l43bG1rFbrdldrVXGltpV51SHDpXOO7aRzBnVWp6w0bSur1c6KOmW4w/+O2m2GymrrVVbjj613uuZ4lO1xxP5dD4ZMlVT55HbYjvoiHtM0VVsfjKwlDg4mK7x+7an2KdvjVMcM1yGPYZqmymr92rK3VsFQSJ2z09QlxyP7IS5KCoVMbSit0brd1XI7bRrQOUtdctK4EAloY3uqfTIMQx0yXFaXAgAAgDZCcAbEiWDI1PYyr9burtLa3dVas6tK9YGQenRI18DCLJ3YK0/dcj2xD0NM01Rpdb12VtRpd1Wd/MGQDMNQusuudFc4VEp32RUImSqt9mlXZZ22lXm1eU+NNu+p1eY9tSqu8OoQF7a3KY/Tro6ZLpmmVFsfUFmtv0WvFw68IgFb5Nw9LrtMST5/uPOuuDzc5XY0HDZD2ZEgMDvNIZfDpjp/SHX+oCrr/Cqr8as+2PjV/lF56U51zHTLZkh1/pB2VtbFPgRtCZshOew2uSLhn9Nuk8cZ/lDTE/lwM91lV5rLLkcL9tczTSkQCsnnD8kXCJ+7LxCSLxD53R+SKVMOm01OuyGH3SaHLVxP9O9Ou7Hvftu+evcdY9+b0Tzg2I3f3vjj1eDx5sGv0cj9hz9O4y/e3Lqa8vgGR2pCnTX1QZVW+VRS7Yu9nyae0ku/umhIo68JIDHtrKjTih0V6pLj0bFdsmQYhspr67V0a7kKMt0a3DX7oLAkul/rO8t3aM2uaq3dVXXEf2/tNkOF2WnaXu5ty9NpMsM48L/BrSPdFe52D4RM7ayoi3X0Z6c51LNjurrkeOSy22QYkmEYkX+7g6r0BlRZ51dVXXhkt6nwhVE1vkBsPZXldqh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AAAAAAAAAAABJBGcAAAAAAAAAAACAJIIzAAAAAAAAAAAAQBLBGQAAAAAAAAAAACCJ4AwAAAAAAAAAAACQRHAGAAAAAAAAAAAASJL+Pw8atdTiUWsPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2208x1104 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(posterior, var_names=\"conc_frac\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "5791f1d4-c8b3-4bb6-a3f8-0356fc223908",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "red       14.531111\n",
       "orange     4.617778\n",
       "yellow     4.037778\n",
       "green     11.582222\n",
       "purple    22.466667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[colors].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "7945d0e4-5904-4363-9047-7ffdad27f063",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "This model samples well and correctly recovers the `skittles_p` parameter well. However, when we try to recover the values of conc**frac, we do not get the correct number for each color. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3056dbf8-1851-41fd-81a2-11dc3ef9e4d6",
     "showTitle": false,
     "title": ""
    }
   },
      "source": [
        "# Hierarchical model\n",
        "\n",
        "Here we look at introducing the facility name and bag size as levels in the hierarhical structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "762fb303-3587-4683-bb35-6552a7b30a81",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "17MoPFStb0CS",
        "outputId": "f9ab296e-4bde-4c79-8fcb-bbd4981d01b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              total    bag_size  facility    red  orange  \\\n",
              "facility_name bag_size_name                                                \n",
              "A             fun             18.62   15.000000      1.25   4.59    1.53   \n",
              "              normal          68.74   55.000000      1.25  16.75    5.32   \n",
              "              share          126.95  101.382488      1.25  31.77   10.20   \n",
              "B             fun             11.42   15.000000      0.75   2.93    0.98   \n",
              "              normal          41.45   55.000000      0.75  10.64    3.29   \n",
              "              share           76.14  101.382488      0.75  19.42    6.19   \n",
              "C             fun             15.10   15.000000      1.00   3.72    1.37   \n",
              "              normal          55.03   55.000000      1.00  13.38    4.50   \n",
              "              share          101.11  101.382488      1.00  25.39    8.46   \n",
              "\n",
              "                             yellow  green  purple  red_percent  \\\n",
              "facility_name bag_size_name                                       \n",
              "A             fun              1.28   3.64    7.58     0.245423   \n",
              "              normal           5.25  14.00   27.42     0.243756   \n",
              "              share            8.40  26.43   50.15     0.250242   \n",
              "B             fun              0.83   2.13    4.55     0.253852   \n",
              "              normal           2.61   8.53   16.38     0.257262   \n",
              "              share            5.20  15.26   30.07     0.255099   \n",
              "C             fun              0.94   3.01    6.06     0.246739   \n",
              "              normal           3.87  11.31   21.97     0.243449   \n",
              "              share            6.89  20.32   40.05     0.251168   \n",
              "\n",
              "                             orange_percent  yellow_percent  green_percent  \\\n",
              "facility_name bag_size_name                                                  \n",
              "A             fun                  0.082872        0.067590       0.197126   \n",
              "              normal               0.077265        0.076307       0.203697   \n",
              "              share                0.080298        0.066133       0.208189   \n",
              "B             fun                  0.085369        0.072764       0.184959   \n",
              "              normal               0.079309        0.063052       0.205560   \n",
              "              share                0.081176        0.068364       0.200603   \n",
              "C             fun                  0.090498        0.063337       0.196393   \n",
              "              normal               0.081777        0.070212       0.205235   \n",
              "              share                0.083725        0.068170       0.200926   \n",
              "\n",
              "                             purple_percent  facility_name_factorized  \\\n",
              "facility_name bag_size_name                                             \n",
              "A             fun                  0.406990                       0.0   \n",
              "              normal               0.398976                       0.0   \n",
              "              share                0.395138                       0.0   \n",
              "B             fun                  0.403055                       1.0   \n",
              "              normal               0.394816                       1.0   \n",
              "              share                0.394758                       1.0   \n",
              "C             fun                  0.403032                       2.0   \n",
              "              normal               0.399327                       2.0   \n",
              "              share                0.396010                       2.0   \n",
              "\n",
              "                             bag_size_name_factorized  \n",
              "facility_name bag_size_name                            \n",
              "A             fun                                 0.0  \n",
              "              normal                              1.0  \n",
              "              share                               2.0  \n",
              "B             fun                                 0.0  \n",
              "              normal                              1.0  \n",
              "              share                               2.0  \n",
              "C             fun                                 0.0  \n",
              "              normal                              1.0  \n",
              "              share                               2.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20c11a1a-1d25-4be2-850c-51448f83b13e\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>total</th>\n",
              "      <th>bag_size</th>\n",
              "      <th>facility</th>\n",
              "      <th>red</th>\n",
              "      <th>orange</th>\n",
              "      <th>yellow</th>\n",
              "      <th>green</th>\n",
              "      <th>purple</th>\n",
              "      <th>red_percent</th>\n",
              "      <th>orange_percent</th>\n",
              "      <th>yellow_percent</th>\n",
              "      <th>green_percent</th>\n",
              "      <th>purple_percent</th>\n",
              "      <th>facility_name_factorized</th>\n",
              "      <th>bag_size_name_factorized</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>facility_name</th>\n",
              "      <th>bag_size_name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
              "      <th>fun</th>\n",
              "      <td>18.62</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.25</td>\n",
              "      <td>4.59</td>\n",
              "      <td>1.53</td>\n",
              "      <td>1.28</td>\n",
              "      <td>3.64</td>\n",
              "      <td>7.58</td>\n",
              "      <td>0.245423</td>\n",
              "      <td>0.082872</td>\n",
              "      <td>0.067590</td>\n",
              "      <td>0.197126</td>\n",
              "      <td>0.406990</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal</th>\n",
              "      <td>68.74</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.25</td>\n",
              "      <td>16.75</td>\n",
              "      <td>5.32</td>\n",
              "      <td>5.25</td>\n",
              "      <td>14.00</td>\n",
              "      <td>27.42</td>\n",
              "      <td>0.243756</td>\n",
              "      <td>0.077265</td>\n",
              "      <td>0.076307</td>\n",
              "      <td>0.203697</td>\n",
              "      <td>0.398976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>share</th>\n",
              "      <td>126.95</td>\n",
              "      <td>101.382488</td>\n",
              "      <td>1.25</td>\n",
              "      <td>31.77</td>\n",
              "      <td>10.20</td>\n",
              "      <td>8.40</td>\n",
              "      <td>26.43</td>\n",
              "      <td>50.15</td>\n",
              "      <td>0.250242</td>\n",
              "      <td>0.080298</td>\n",
              "      <td>0.066133</td>\n",
              "      <td>0.208189</td>\n",
              "      <td>0.395138</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
              "      <th>fun</th>\n",
              "      <td>11.42</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.93</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.83</td>\n",
              "      <td>2.13</td>\n",
              "      <td>4.55</td>\n",
              "      <td>0.253852</td>\n",
              "      <td>0.085369</td>\n",
              "      <td>0.072764</td>\n",
              "      <td>0.184959</td>\n",
              "      <td>0.403055</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal</th>\n",
              "      <td>41.45</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>0.75</td>\n",
              "      <td>10.64</td>\n",
              "      <td>3.29</td>\n",
              "      <td>2.61</td>\n",
              "      <td>8.53</td>\n",
              "      <td>16.38</td>\n",
              "      <td>0.257262</td>\n",
              "      <td>0.079309</td>\n",
              "      <td>0.063052</td>\n",
              "      <td>0.205560</td>\n",
              "      <td>0.394816</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>share</th>\n",
              "      <td>76.14</td>\n",
              "      <td>101.382488</td>\n",
              "      <td>0.75</td>\n",
              "      <td>19.42</td>\n",
              "      <td>6.19</td>\n",
              "      <td>5.20</td>\n",
              "      <td>15.26</td>\n",
              "      <td>30.07</td>\n",
              "      <td>0.255099</td>\n",
              "      <td>0.081176</td>\n",
              "      <td>0.068364</td>\n",
              "      <td>0.200603</td>\n",
              "      <td>0.394758</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
              "      <th>fun</th>\n",
              "      <td>15.10</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.72</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.94</td>\n",
              "      <td>3.01</td>\n",
              "      <td>6.06</td>\n",
              "      <td>0.246739</td>\n",
              "      <td>0.090498</td>\n",
              "      <td>0.063337</td>\n",
              "      <td>0.196393</td>\n",
              "      <td>0.403032</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal</th>\n",
              "      <td>55.03</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>13.38</td>\n",
              "      <td>4.50</td>\n",
              "      <td>3.87</td>\n",
              "      <td>11.31</td>\n",
              "      <td>21.97</td>\n",
              "      <td>0.243449</td>\n",
              "      <td>0.081777</td>\n",
              "      <td>0.070212</td>\n",
              "      <td>0.205235</td>\n",
              "      <td>0.399327</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>share</th>\n",
              "      <td>101.11</td>\n",
              "      <td>101.382488</td>\n",
              "      <td>1.00</td>\n",
              "      <td>25.39</td>\n",
              "      <td>8.46</td>\n",
              "      <td>6.89</td>\n",
              "      <td>20.32</td>\n",
              "      <td>40.05</td>\n",
              "      <td>0.251168</td>\n",
              "      <td>0.083725</td>\n",
              "      <td>0.068170</td>\n",
              "      <td>0.200926</td>\n",
              "      <td>0.396010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40.14730003375072,\n        \"min\": 11.42,\n        \"max\": 126.95,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          55.03,\n          68.74,\n          76.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bag_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.43873253964217,\n        \"min\": 15.0,\n        \"max\": 101.38248847926269,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15.0,\n          55.0,\n          101.38248847926269\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"facility\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21650635094610965,\n        \"min\": 0.75,\n        \"max\": 1.25,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.25,\n          0.75,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.063200010158024,\n        \"min\": 2.93,\n        \"max\": 31.77,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          13.38,\n          16.75,\n          19.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orange\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.239847390095884,\n        \"min\": 0.98,\n        \"max\": 10.2,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4.5,\n          5.32,\n          6.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yellow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.72523597347296,\n        \"min\": 0.83,\n        \"max\": 8.4,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.87,\n          5.25,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"green\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.302534418945685,\n        \"min\": 2.13,\n        \"max\": 26.43,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          11.31,\n          14.0,\n          15.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purple\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.837146032175255,\n        \"min\": 4.55,\n        \"max\": 50.15,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          21.97,\n          27.42,\n          30.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005092787620728321,\n        \"min\": 0.2434491986674017,\n        \"max\": 0.2572622817303413,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.2434491986674017,\n          0.24375558338117226,\n          0.25509919294715955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orange_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0038494811872226316,\n        \"min\": 0.0772654514120045,\n        \"max\": 0.09049835557459396,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.0817765851927177,\n          0.0772654514120045,\n          0.08117550108363168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yellow_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004256011768979921,\n        \"min\": 0.06305226704709582,\n        \"max\": 0.07630666978063394,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.07021203728832841,\n          0.07630666978063394,\n          0.06836381028496628\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"green_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006954985318633631,\n        \"min\": 0.1849591682826977,\n        \"max\": 0.20818946135824148,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.205234822473813,\n          0.20369676543331824,\n          0.20060325995938938\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"purple_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0044140370130275036,\n        \"min\": 0.39475823572485313,\n        \"max\": 0.40698996045346497,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.39932735637773925,\n          0.39897552999287106,\n          0.39475823572485313\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"facility_name_factorized\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8660254037844386,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bag_size_name_factorized\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8660254037844386,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Empirical averages just for checking\n",
        "data.groupby(by=[\"facility_name\", \"bag_size_name\"]).agg(\"mean\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {},
          "inputWidgets": {},
          "nuid": "47b60820-60e7-4699-b75b-caa8a85dc700",
          "showTitle": false,
          "title": ""
        },
        "id": "fxNB4tDxb0CS"
      },
      "source": [
        "There is not a true hierarchical structure implemented within the simulated data, so the empirical averages look quite similar to the pooled means. Nonetheless, I would like to see if we can still get close to recovering the original probabilities."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "10c0edf8-d2eb-4de7-9ed3-265e2b3ba36c",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "q5tm5bKpb0CS",
        "outputId": "22b7d5b2-0048-4d1e-97c9-7651376e59af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"436pt\" height=\"260pt\"\n viewBox=\"0.00 0.00 436.00 259.91\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 255.91)\">\n<title>%3</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-255.91 432,-255.91 432,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>clustercolors (5)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M20,-129.95C20,-129.95 104,-129.95 104,-129.95 110,-129.95 116,-135.95 116,-141.95 116,-141.95 116,-231.91 116,-231.91 116,-237.91 110,-243.91 104,-243.91 104,-243.91 20,-243.91 20,-243.91 14,-243.91 8,-237.91 8,-231.91 8,-231.91 8,-141.95 8,-141.95 8,-135.95 14,-129.95 20,-129.95\"/>\n<text text-anchor=\"middle\" x=\"81.5\" y=\"-137.75\" font-family=\"Times,serif\" font-size=\"14.00\">colors (5)</text>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>clusterfacility_name (3) x bag_size_name (3) x colors (5)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M136,-129.95C136,-129.95 408,-129.95 408,-129.95 414,-129.95 420,-135.95 420,-141.95 420,-141.95 420,-231.91 420,-231.91 420,-237.91 414,-243.91 408,-243.91 408,-243.91 136,-243.91 136,-243.91 130,-243.91 124,-237.91 124,-231.91 124,-231.91 124,-141.95 124,-141.95 124,-135.95 130,-129.95 136,-129.95\"/>\n<text text-anchor=\"middle\" x=\"272\" y=\"-137.75\" font-family=\"Times,serif\" font-size=\"14.00\">facility_name (3) x bag_size_name (3) x colors (5)</text>\n</g>\n<g id=\"clust3\" class=\"cluster\">\n<title>clusterobservations (900) x colors (5)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M47,-8C47,-8 229,-8 229,-8 235,-8 241,-14 241,-20 241,-20 241,-109.95 241,-109.95 241,-115.95 235,-121.95 229,-121.95 229,-121.95 47,-121.95 47,-121.95 41,-121.95 35,-115.95 35,-109.95 35,-109.95 35,-20 35,-20 35,-14 41,-8 47,-8\"/>\n<text text-anchor=\"middle\" x=\"148.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">observations (900) x colors (5)</text>\n</g>\n<!-- frac -->\n<g id=\"node1\" class=\"node\">\n<title>frac</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"62\" cy=\"-198.43\" rx=\"45.92\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"62\" y=\"-209.73\" font-family=\"Times,serif\" font-size=\"14.00\">frac</text>\n<text text-anchor=\"middle\" x=\"62\" y=\"-194.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"62\" y=\"-179.73\" font-family=\"Times,serif\" font-size=\"14.00\">Dirichlet</text>\n</g>\n<!-- counts -->\n<g id=\"node3\" class=\"node\">\n<title>counts</title>\n<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"138\" cy=\"-76.48\" rx=\"94.51\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"138\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">counts</text>\n<text text-anchor=\"middle\" x=\"138\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"138\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">DirichletMultinomial</text>\n</g>\n<!-- frac&#45;&gt;counts -->\n<g id=\"edge1\" class=\"edge\">\n<title>frac&#45;&gt;counts</title>\n<path fill=\"none\" stroke=\"black\" d=\"M82.77,-164.65C91.14,-151.44 100.92,-135.99 109.92,-121.8\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"112.99,-123.48 115.39,-113.16 107.08,-119.74 112.99,-123.48\"/>\n</g>\n<!-- conc -->\n<g id=\"node2\" class=\"node\">\n<title>conc</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"189\" cy=\"-198.43\" rx=\"56.64\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"189\" y=\"-209.73\" font-family=\"Times,serif\" font-size=\"14.00\">conc</text>\n<text text-anchor=\"middle\" x=\"189\" y=\"-194.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"189\" y=\"-179.73\" font-family=\"Times,serif\" font-size=\"14.00\">LogNormal</text>\n</g>\n<!-- conc&#45;&gt;counts -->\n<g id=\"edge2\" class=\"edge\">\n<title>conc&#45;&gt;counts</title>\n<path fill=\"none\" stroke=\"black\" d=\"M173.97,-162.09C168.77,-149.85 162.87,-135.96 157.37,-123.03\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"160.58,-121.65 153.45,-113.81 154.14,-124.38 160.58,-121.65\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.graphs.Digraph at 0x7ea7d01176d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# PyMC model\n",
        "with pm.Model(coords=coords) as model2:\n",
        "    frac   = pm.Dirichlet(\"frac\", a=np.ones(len(colors)), dims=(\"colors\"))\n",
        "    conc   = pm.Lognormal(\"conc\", mu=1, sigma=3, dims=(\"facility_name\", \"bag_size_name\",\"colors\"))\n",
        "    # _a     = pm.Deterministic(\"_a\", frac.T*conc[data.facility_name_factorized, data.bag_size_name_factorized])\n",
        "    counts = pm.DirichletMultinomial(\"counts\", n=data.total.values.astype(\"int\"), a=frac.T*conc[data.facility_name_factorized, data.bag_size_name_factorized], observed=data[colors].values,dims=(\"observations\", \"colors\"))\n",
        "\n",
        "pm.model_to_graphviz(model2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "c9093427-7444-4884-a6bb-579e6a3618f1",
          "showTitle": false,
          "title": ""
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "199433720367471ab474c4280fef7154",
            "6701fbc7c5b1415dbb32c88181058344",
            "b9907f586c6d4981a08180a0611d0e57",
            "219fc57b95764f2895a364b5f6e880fc",
            "ebd834203b864100841c555140feccec",
            "556fa578b5084594ac05380aa30f42fe",
            "1c66e2d684fe49da9e9ff3240c572818",
            "bffcb42d07954b8a937abd79ee4c2a34",
            "8c49d709f3104b809f31e471cfdd4d9e",
            "0a770aa0390d49cbab2fb5e2ca09f816",
            "a592254ba4914d2dbbb376bff18f4cf6",
            "e1b1f896737142e4ab52fa83a0e26df2",
            "05708d82769840aaafd0a77d24426e4b",
            "08ba53cef03e43fd97598eeda1211c1c",
            "ea2ded389bb6472794f5136eee1a7a30",
            "d45f4ed625064545a6b5c8ec5562ab30",
            "1c8cb296ff92430bad1a8118c4b2e2f7",
            "1cb73f2f41334942a5e2bcbc65b66c8b",
            "4592e7cee2674f35ac5d97392b597fe1",
            "15813d5543cf4e5fbf515987b504e488",
            "9597704219664f879cc0236b1aa61fad",
            "792f65baec4e4de88cc532938e2169ee",
            "84f3d093e03541f0b64d5c5eddb9ba7d",
            "8534b356c73b4fceb6bb6c1ec78972f4",
            "08413cf6bfe944809a4ca5fcd10451a3",
            "50629642ebeb4f0183cafc318b4df87c",
            "2546152fd78247d89c332e1e9adf35d9",
            "34bd63824a4141ecb85c965799efebc1",
            "9ce96cd2e97342689f6b29ca730b16ce",
            "91d069915674419fbf6212d1530355f3",
            "f70a90dc869242babf167a5aa01eba0b",
            "49b6d82d5aa445d1a9cd4c00bbbc2a8b",
            "d6bf5d92a75440cbb16f6eee9a9d58fb",
            "d13965e9bde340c3a9b7163ca28f4017",
            "2ecd99c846a745e98ddafce507dadbab",
            "098dd2804139439b8fa3aa6e3a60d09e",
            "885085b4a5884a9b9b97bc7a7e656dc3",
            "2bb5dadbcac2453f8c32023a3f0f5c14",
            "430ded0acc424d7e949f3f28b38ce021",
            "4b4654c9b50644a587a59f0d1926325c",
            "c194d7dafced4e9e970bf69cc0012206",
            "aa8672dd43974962a3634a60fbfd0ee4",
            "88b0d73f579a4760b95849ce1e0c6968",
            "ba29d7eab8454d63b64fffd424422e36"
          ]
        },
        "id": "J-WVli1Zb0CS",
        "outputId": "84a5eb13-73d6-42a0-d091-5761630948c8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "199433720367471ab474c4280fef7154",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e1b1f896737142e4ab52fa83a0e26df2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "84f3d093e03541f0b64d5c5eddb9ba7d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d13965e9bde340c3a9b7163ca28f4017",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with model2:\n",
        "    idata_model2 = pmjax.sample_numpyro_nuts(2000,chains=4, idata_kwargs=dict(log_likelihood = True), random_seed=RANDOM_SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9e06f533-5c6d-40ea-9cde-890904c42058",
          "showTitle": false,
          "title": ""
        },
        "id": "KMy5hIk4b0CS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6a8c74a8-9d7e-4d00-b848-077ab00aa8c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             mean        sd   hdi_3%   hdi_97%  mcse_mean  \\\n",
              "frac[red]                   0.210     0.069    0.088     0.339      0.001   \n",
              "frac[orange]                0.181     0.065    0.067     0.302      0.001   \n",
              "frac[yellow]                0.175     0.064    0.062     0.295      0.001   \n",
              "frac[green]                 0.206     0.069    0.080     0.332      0.001   \n",
              "frac[purple]                0.227     0.070    0.102     0.361      0.001   \n",
              "conc[A, fun, red]         236.907   196.612   38.480   534.505      3.023   \n",
              "conc[A, fun, orange]       95.788    93.349   14.107   226.807      1.491   \n",
              "conc[A, fun, yellow]       81.446    73.871   13.062   188.230      1.108   \n",
              "conc[A, fun, green]       194.013   165.797   31.310   436.584      2.538   \n",
              "conc[A, fun, purple]      360.165   316.653   71.462   806.478      4.998   \n",
              "conc[A, normal, red]      856.379   600.199  175.380  1827.331      8.161   \n",
              "conc[A, normal, orange]   324.938   248.041   66.485   711.343      3.746   \n",
              "conc[A, normal, yellow]   332.949   248.838   58.732   733.908      3.619   \n",
              "conc[A, normal, green]    740.752   546.221  166.066  1645.602      8.122   \n",
              "conc[A, normal, purple]  1279.139   843.449  280.168  2743.073     11.693   \n",
              "conc[A, share, red]      2127.844  1490.532  403.163  4695.067     21.528   \n",
              "conc[A, share, orange]    825.509   666.255  159.119  1822.178     10.796   \n",
              "conc[A, share, yellow]    696.007   527.516  131.382  1549.495      8.062   \n",
              "conc[A, share, green]    1832.335  1352.197  339.607  3916.809     20.447   \n",
              "conc[A, share, purple]   3095.044  2226.884  643.036  6781.915     32.733   \n",
              "conc[B, fun, red]         352.204   353.817   41.927   840.994      5.552   \n",
              "conc[B, fun, orange]      139.763   132.883   19.551   343.977      2.151   \n",
              "conc[B, fun, yellow]      122.676   121.052   15.995   297.089      1.820   \n",
              "conc[B, fun, green]       265.568   288.194   30.738   649.266      4.752   \n",
              "conc[B, fun, purple]      504.386   507.086   68.870  1208.526      8.229   \n",
              "conc[B, normal, red]      848.661   693.656  157.566  1902.929     11.235   \n",
              "conc[B, normal, orange]   321.661   327.298   50.708   756.047      5.782   \n",
              "conc[B, normal, yellow]   257.902   222.796   39.132   591.541      3.515   \n",
              "conc[B, normal, green]    710.144   620.161   99.405  1627.134     10.130   \n",
              "conc[B, normal, purple]  1200.339   963.777  223.764  2704.109     15.121   \n",
              "conc[B, share, red]      1174.378   877.921  212.040  2604.250     13.570   \n",
              "conc[B, share, orange]    446.166   348.516   76.831   991.494      5.393   \n",
              "conc[B, share, yellow]    388.829   298.686   61.144   863.291      4.680   \n",
              "conc[B, share, green]     947.303   720.007  176.936  2074.485     10.602   \n",
              "conc[B, share, purple]   1673.352  1285.466  329.049  3588.726     19.195   \n",
              "conc[C, fun, red]         194.938   163.956   35.547   442.321      2.463   \n",
              "conc[C, fun, orange]       86.847    79.519   13.574   197.300      1.299   \n",
              "conc[C, fun, yellow]       60.583    53.410    9.490   138.749      0.802   \n",
              "conc[C, fun, green]       161.839   135.259   29.061   370.277      2.150   \n",
              "conc[C, fun, purple]      293.020   257.564   62.395   663.757      4.137   \n",
              "conc[C, normal, red]      525.867   362.492  114.419  1116.151      5.319   \n",
              "conc[C, normal, orange]   212.539   156.421   42.586   457.717      2.355   \n",
              "conc[C, normal, yellow]   188.468   150.118   38.278   408.748      2.395   \n",
              "conc[C, normal, green]    458.964   321.514   99.001   983.511      4.972   \n",
              "conc[C, normal, purple]   791.819   535.005  191.381  1661.942      7.700   \n",
              "conc[C, share, red]       694.675   454.773  192.362  1406.675      6.647   \n",
              "conc[C, share, orange]    277.228   184.835   69.595   573.161      3.007   \n",
              "conc[C, share, yellow]    232.823   154.723   55.602   484.447      2.294   \n",
              "conc[C, share, green]     569.501   342.937  140.590  1148.678      5.079   \n",
              "conc[C, share, purple]   1006.226   618.315  282.953  2028.422      9.225   \n",
              "\n",
              "                         mcse_sd  ess_bulk  ess_tail  r_hat  \n",
              "frac[red]                  0.001    5712.0    4971.0    1.0  \n",
              "frac[orange]               0.001    4679.0    3853.0    1.0  \n",
              "frac[yellow]               0.001    5008.0    4173.0    1.0  \n",
              "frac[green]                0.001    4947.0    4556.0    1.0  \n",
              "frac[purple]               0.001    5964.0    4405.0    1.0  \n",
              "conc[A, fun, red]          2.157    5854.0    4591.0    1.0  \n",
              "conc[A, fun, orange]       1.054    5465.0    4336.0    1.0  \n",
              "conc[A, fun, yellow]       0.783    5807.0    4596.0    1.0  \n",
              "conc[A, fun, green]        1.795    5664.0    4902.0    1.0  \n",
              "conc[A, fun, purple]       3.534    6437.0    4620.0    1.0  \n",
              "conc[A, normal, red]       5.771    6422.0    5551.0    1.0  \n",
              "conc[A, normal, orange]    2.649    5593.0    4913.0    1.0  \n",
              "conc[A, normal, yellow]    2.559    5657.0    4962.0    1.0  \n",
              "conc[A, normal, green]     5.812    6081.0    4958.0    1.0  \n",
              "conc[A, normal, purple]    8.387    6643.0    5375.0    1.0  \n",
              "conc[A, share, red]       15.331    6328.0    4667.0    1.0  \n",
              "conc[A, share, orange]     8.243    5465.0    4286.0    1.0  \n",
              "conc[A, share, yellow]     5.701    5687.0    4719.0    1.0  \n",
              "conc[A, share, green]     15.381    6279.0    4576.0    1.0  \n",
              "conc[A, share, purple]    23.862    6554.0    5031.0    1.0  \n",
              "conc[B, fun, red]          3.926    5996.0    4788.0    1.0  \n",
              "conc[B, fun, orange]       1.521    5445.0    4576.0    1.0  \n",
              "conc[B, fun, yellow]       1.287    5946.0    4763.0    1.0  \n",
              "conc[B, fun, green]        3.360    5760.0    4338.0    1.0  \n",
              "conc[B, fun, purple]       6.007    6192.0    4649.0    1.0  \n",
              "conc[B, normal, red]       8.135    5667.0    4232.0    1.0  \n",
              "conc[B, normal, orange]    4.089    5040.0    3487.0    1.0  \n",
              "conc[B, normal, yellow]    2.486    5653.0    4347.0    1.0  \n",
              "conc[B, normal, green]     7.240    5422.0    4381.0    1.0  \n",
              "conc[B, normal, purple]   10.693    6023.0    3934.0    1.0  \n",
              "conc[B, share, red]       10.704    6122.0    4859.0    1.0  \n",
              "conc[B, share, orange]     4.014    5744.0    4526.0    1.0  \n",
              "conc[B, share, yellow]     3.328    5491.0    4511.0    1.0  \n",
              "conc[B, share, green]      7.497    5966.0    4558.0    1.0  \n",
              "conc[B, share, purple]    13.574    6010.0    4239.0    1.0  \n",
              "conc[C, fun, red]          1.742    6167.0    4438.0    1.0  \n",
              "conc[C, fun, orange]       0.919    5376.0    4233.0    1.0  \n",
              "conc[C, fun, yellow]       0.567    5778.0    4828.0    1.0  \n",
              "conc[C, fun, green]        1.520    5621.0    4668.0    1.0  \n",
              "conc[C, fun, purple]       2.925    5590.0    4455.0    1.0  \n",
              "conc[C, normal, red]       3.959    6422.0    4962.0    1.0  \n",
              "conc[C, normal, orange]    1.698    5694.0    4939.0    1.0  \n",
              "conc[C, normal, yellow]    1.693    5603.0    4550.0    1.0  \n",
              "conc[C, normal, green]     3.539    5963.0    4517.0    1.0  \n",
              "conc[C, normal, purple]    5.694    7079.0    4763.0    1.0  \n",
              "conc[C, share, red]        5.161    6778.0    4981.0    1.0  \n",
              "conc[C, share, orange]     2.148    5225.0    4292.0    1.0  \n",
              "conc[C, share, yellow]     1.622    5679.0    4902.0    1.0  \n",
              "conc[C, share, green]      3.591    5488.0    5117.0    1.0  \n",
              "conc[C, share, purple]     6.827    6802.0    4346.0    1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2a3b610a-cf9f-4ed0-8b38-986f0e7e8166\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>sd</th>\n",
              "      <th>hdi_3%</th>\n",
              "      <th>hdi_97%</th>\n",
              "      <th>mcse_mean</th>\n",
              "      <th>mcse_sd</th>\n",
              "      <th>ess_bulk</th>\n",
              "      <th>ess_tail</th>\n",
              "      <th>r_hat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>frac[red]</th>\n",
              "      <td>0.210</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.088</td>\n",
              "      <td>0.339</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>5712.0</td>\n",
              "      <td>4971.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[orange]</th>\n",
              "      <td>0.181</td>\n",
              "      <td>0.065</td>\n",
              "      <td>0.067</td>\n",
              "      <td>0.302</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>4679.0</td>\n",
              "      <td>3853.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[yellow]</th>\n",
              "      <td>0.175</td>\n",
              "      <td>0.064</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>5008.0</td>\n",
              "      <td>4173.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[green]</th>\n",
              "      <td>0.206</td>\n",
              "      <td>0.069</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.332</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>4947.0</td>\n",
              "      <td>4556.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frac[purple]</th>\n",
              "      <td>0.227</td>\n",
              "      <td>0.070</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>5964.0</td>\n",
              "      <td>4405.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, fun, red]</th>\n",
              "      <td>236.907</td>\n",
              "      <td>196.612</td>\n",
              "      <td>38.480</td>\n",
              "      <td>534.505</td>\n",
              "      <td>3.023</td>\n",
              "      <td>2.157</td>\n",
              "      <td>5854.0</td>\n",
              "      <td>4591.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, fun, orange]</th>\n",
              "      <td>95.788</td>\n",
              "      <td>93.349</td>\n",
              "      <td>14.107</td>\n",
              "      <td>226.807</td>\n",
              "      <td>1.491</td>\n",
              "      <td>1.054</td>\n",
              "      <td>5465.0</td>\n",
              "      <td>4336.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, fun, yellow]</th>\n",
              "      <td>81.446</td>\n",
              "      <td>73.871</td>\n",
              "      <td>13.062</td>\n",
              "      <td>188.230</td>\n",
              "      <td>1.108</td>\n",
              "      <td>0.783</td>\n",
              "      <td>5807.0</td>\n",
              "      <td>4596.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, fun, green]</th>\n",
              "      <td>194.013</td>\n",
              "      <td>165.797</td>\n",
              "      <td>31.310</td>\n",
              "      <td>436.584</td>\n",
              "      <td>2.538</td>\n",
              "      <td>1.795</td>\n",
              "      <td>5664.0</td>\n",
              "      <td>4902.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, fun, purple]</th>\n",
              "      <td>360.165</td>\n",
              "      <td>316.653</td>\n",
              "      <td>71.462</td>\n",
              "      <td>806.478</td>\n",
              "      <td>4.998</td>\n",
              "      <td>3.534</td>\n",
              "      <td>6437.0</td>\n",
              "      <td>4620.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, normal, red]</th>\n",
              "      <td>856.379</td>\n",
              "      <td>600.199</td>\n",
              "      <td>175.380</td>\n",
              "      <td>1827.331</td>\n",
              "      <td>8.161</td>\n",
              "      <td>5.771</td>\n",
              "      <td>6422.0</td>\n",
              "      <td>5551.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, normal, orange]</th>\n",
              "      <td>324.938</td>\n",
              "      <td>248.041</td>\n",
              "      <td>66.485</td>\n",
              "      <td>711.343</td>\n",
              "      <td>3.746</td>\n",
              "      <td>2.649</td>\n",
              "      <td>5593.0</td>\n",
              "      <td>4913.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, normal, yellow]</th>\n",
              "      <td>332.949</td>\n",
              "      <td>248.838</td>\n",
              "      <td>58.732</td>\n",
              "      <td>733.908</td>\n",
              "      <td>3.619</td>\n",
              "      <td>2.559</td>\n",
              "      <td>5657.0</td>\n",
              "      <td>4962.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, normal, green]</th>\n",
              "      <td>740.752</td>\n",
              "      <td>546.221</td>\n",
              "      <td>166.066</td>\n",
              "      <td>1645.602</td>\n",
              "      <td>8.122</td>\n",
              "      <td>5.812</td>\n",
              "      <td>6081.0</td>\n",
              "      <td>4958.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, normal, purple]</th>\n",
              "      <td>1279.139</td>\n",
              "      <td>843.449</td>\n",
              "      <td>280.168</td>\n",
              "      <td>2743.073</td>\n",
              "      <td>11.693</td>\n",
              "      <td>8.387</td>\n",
              "      <td>6643.0</td>\n",
              "      <td>5375.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, share, red]</th>\n",
              "      <td>2127.844</td>\n",
              "      <td>1490.532</td>\n",
              "      <td>403.163</td>\n",
              "      <td>4695.067</td>\n",
              "      <td>21.528</td>\n",
              "      <td>15.331</td>\n",
              "      <td>6328.0</td>\n",
              "      <td>4667.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, share, orange]</th>\n",
              "      <td>825.509</td>\n",
              "      <td>666.255</td>\n",
              "      <td>159.119</td>\n",
              "      <td>1822.178</td>\n",
              "      <td>10.796</td>\n",
              "      <td>8.243</td>\n",
              "      <td>5465.0</td>\n",
              "      <td>4286.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, share, yellow]</th>\n",
              "      <td>696.007</td>\n",
              "      <td>527.516</td>\n",
              "      <td>131.382</td>\n",
              "      <td>1549.495</td>\n",
              "      <td>8.062</td>\n",
              "      <td>5.701</td>\n",
              "      <td>5687.0</td>\n",
              "      <td>4719.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, share, green]</th>\n",
              "      <td>1832.335</td>\n",
              "      <td>1352.197</td>\n",
              "      <td>339.607</td>\n",
              "      <td>3916.809</td>\n",
              "      <td>20.447</td>\n",
              "      <td>15.381</td>\n",
              "      <td>6279.0</td>\n",
              "      <td>4576.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[A, share, purple]</th>\n",
              "      <td>3095.044</td>\n",
              "      <td>2226.884</td>\n",
              "      <td>643.036</td>\n",
              "      <td>6781.915</td>\n",
              "      <td>32.733</td>\n",
              "      <td>23.862</td>\n",
              "      <td>6554.0</td>\n",
              "      <td>5031.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, fun, red]</th>\n",
              "      <td>352.204</td>\n",
              "      <td>353.817</td>\n",
              "      <td>41.927</td>\n",
              "      <td>840.994</td>\n",
              "      <td>5.552</td>\n",
              "      <td>3.926</td>\n",
              "      <td>5996.0</td>\n",
              "      <td>4788.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, fun, orange]</th>\n",
              "      <td>139.763</td>\n",
              "      <td>132.883</td>\n",
              "      <td>19.551</td>\n",
              "      <td>343.977</td>\n",
              "      <td>2.151</td>\n",
              "      <td>1.521</td>\n",
              "      <td>5445.0</td>\n",
              "      <td>4576.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, fun, yellow]</th>\n",
              "      <td>122.676</td>\n",
              "      <td>121.052</td>\n",
              "      <td>15.995</td>\n",
              "      <td>297.089</td>\n",
              "      <td>1.820</td>\n",
              "      <td>1.287</td>\n",
              "      <td>5946.0</td>\n",
              "      <td>4763.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, fun, green]</th>\n",
              "      <td>265.568</td>\n",
              "      <td>288.194</td>\n",
              "      <td>30.738</td>\n",
              "      <td>649.266</td>\n",
              "      <td>4.752</td>\n",
              "      <td>3.360</td>\n",
              "      <td>5760.0</td>\n",
              "      <td>4338.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, fun, purple]</th>\n",
              "      <td>504.386</td>\n",
              "      <td>507.086</td>\n",
              "      <td>68.870</td>\n",
              "      <td>1208.526</td>\n",
              "      <td>8.229</td>\n",
              "      <td>6.007</td>\n",
              "      <td>6192.0</td>\n",
              "      <td>4649.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, normal, red]</th>\n",
              "      <td>848.661</td>\n",
              "      <td>693.656</td>\n",
              "      <td>157.566</td>\n",
              "      <td>1902.929</td>\n",
              "      <td>11.235</td>\n",
              "      <td>8.135</td>\n",
              "      <td>5667.0</td>\n",
              "      <td>4232.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, normal, orange]</th>\n",
              "      <td>321.661</td>\n",
              "      <td>327.298</td>\n",
              "      <td>50.708</td>\n",
              "      <td>756.047</td>\n",
              "      <td>5.782</td>\n",
              "      <td>4.089</td>\n",
              "      <td>5040.0</td>\n",
              "      <td>3487.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, normal, yellow]</th>\n",
              "      <td>257.902</td>\n",
              "      <td>222.796</td>\n",
              "      <td>39.132</td>\n",
              "      <td>591.541</td>\n",
              "      <td>3.515</td>\n",
              "      <td>2.486</td>\n",
              "      <td>5653.0</td>\n",
              "      <td>4347.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, normal, green]</th>\n",
              "      <td>710.144</td>\n",
              "      <td>620.161</td>\n",
              "      <td>99.405</td>\n",
              "      <td>1627.134</td>\n",
              "      <td>10.130</td>\n",
              "      <td>7.240</td>\n",
              "      <td>5422.0</td>\n",
              "      <td>4381.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, normal, purple]</th>\n",
              "      <td>1200.339</td>\n",
              "      <td>963.777</td>\n",
              "      <td>223.764</td>\n",
              "      <td>2704.109</td>\n",
              "      <td>15.121</td>\n",
              "      <td>10.693</td>\n",
              "      <td>6023.0</td>\n",
              "      <td>3934.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, share, red]</th>\n",
              "      <td>1174.378</td>\n",
              "      <td>877.921</td>\n",
              "      <td>212.040</td>\n",
              "      <td>2604.250</td>\n",
              "      <td>13.570</td>\n",
              "      <td>10.704</td>\n",
              "      <td>6122.0</td>\n",
              "      <td>4859.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, share, orange]</th>\n",
              "      <td>446.166</td>\n",
              "      <td>348.516</td>\n",
              "      <td>76.831</td>\n",
              "      <td>991.494</td>\n",
              "      <td>5.393</td>\n",
              "      <td>4.014</td>\n",
              "      <td>5744.0</td>\n",
              "      <td>4526.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, share, yellow]</th>\n",
              "      <td>388.829</td>\n",
              "      <td>298.686</td>\n",
              "      <td>61.144</td>\n",
              "      <td>863.291</td>\n",
              "      <td>4.680</td>\n",
              "      <td>3.328</td>\n",
              "      <td>5491.0</td>\n",
              "      <td>4511.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, share, green]</th>\n",
              "      <td>947.303</td>\n",
              "      <td>720.007</td>\n",
              "      <td>176.936</td>\n",
              "      <td>2074.485</td>\n",
              "      <td>10.602</td>\n",
              "      <td>7.497</td>\n",
              "      <td>5966.0</td>\n",
              "      <td>4558.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[B, share, purple]</th>\n",
              "      <td>1673.352</td>\n",
              "      <td>1285.466</td>\n",
              "      <td>329.049</td>\n",
              "      <td>3588.726</td>\n",
              "      <td>19.195</td>\n",
              "      <td>13.574</td>\n",
              "      <td>6010.0</td>\n",
              "      <td>4239.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, fun, red]</th>\n",
              "      <td>194.938</td>\n",
              "      <td>163.956</td>\n",
              "      <td>35.547</td>\n",
              "      <td>442.321</td>\n",
              "      <td>2.463</td>\n",
              "      <td>1.742</td>\n",
              "      <td>6167.0</td>\n",
              "      <td>4438.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, fun, orange]</th>\n",
              "      <td>86.847</td>\n",
              "      <td>79.519</td>\n",
              "      <td>13.574</td>\n",
              "      <td>197.300</td>\n",
              "      <td>1.299</td>\n",
              "      <td>0.919</td>\n",
              "      <td>5376.0</td>\n",
              "      <td>4233.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, fun, yellow]</th>\n",
              "      <td>60.583</td>\n",
              "      <td>53.410</td>\n",
              "      <td>9.490</td>\n",
              "      <td>138.749</td>\n",
              "      <td>0.802</td>\n",
              "      <td>0.567</td>\n",
              "      <td>5778.0</td>\n",
              "      <td>4828.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, fun, green]</th>\n",
              "      <td>161.839</td>\n",
              "      <td>135.259</td>\n",
              "      <td>29.061</td>\n",
              "      <td>370.277</td>\n",
              "      <td>2.150</td>\n",
              "      <td>1.520</td>\n",
              "      <td>5621.0</td>\n",
              "      <td>4668.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, fun, purple]</th>\n",
              "      <td>293.020</td>\n",
              "      <td>257.564</td>\n",
              "      <td>62.395</td>\n",
              "      <td>663.757</td>\n",
              "      <td>4.137</td>\n",
              "      <td>2.925</td>\n",
              "      <td>5590.0</td>\n",
              "      <td>4455.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, normal, red]</th>\n",
              "      <td>525.867</td>\n",
              "      <td>362.492</td>\n",
              "      <td>114.419</td>\n",
              "      <td>1116.151</td>\n",
              "      <td>5.319</td>\n",
              "      <td>3.959</td>\n",
              "      <td>6422.0</td>\n",
              "      <td>4962.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, normal, orange]</th>\n",
              "      <td>212.539</td>\n",
              "      <td>156.421</td>\n",
              "      <td>42.586</td>\n",
              "      <td>457.717</td>\n",
              "      <td>2.355</td>\n",
              "      <td>1.698</td>\n",
              "      <td>5694.0</td>\n",
              "      <td>4939.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, normal, yellow]</th>\n",
              "      <td>188.468</td>\n",
              "      <td>150.118</td>\n",
              "      <td>38.278</td>\n",
              "      <td>408.748</td>\n",
              "      <td>2.395</td>\n",
              "      <td>1.693</td>\n",
              "      <td>5603.0</td>\n",
              "      <td>4550.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, normal, green]</th>\n",
              "      <td>458.964</td>\n",
              "      <td>321.514</td>\n",
              "      <td>99.001</td>\n",
              "      <td>983.511</td>\n",
              "      <td>4.972</td>\n",
              "      <td>3.539</td>\n",
              "      <td>5963.0</td>\n",
              "      <td>4517.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, normal, purple]</th>\n",
              "      <td>791.819</td>\n",
              "      <td>535.005</td>\n",
              "      <td>191.381</td>\n",
              "      <td>1661.942</td>\n",
              "      <td>7.700</td>\n",
              "      <td>5.694</td>\n",
              "      <td>7079.0</td>\n",
              "      <td>4763.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, share, red]</th>\n",
              "      <td>694.675</td>\n",
              "      <td>454.773</td>\n",
              "      <td>192.362</td>\n",
              "      <td>1406.675</td>\n",
              "      <td>6.647</td>\n",
              "      <td>5.161</td>\n",
              "      <td>6778.0</td>\n",
              "      <td>4981.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, share, orange]</th>\n",
              "      <td>277.228</td>\n",
              "      <td>184.835</td>\n",
              "      <td>69.595</td>\n",
              "      <td>573.161</td>\n",
              "      <td>3.007</td>\n",
              "      <td>2.148</td>\n",
              "      <td>5225.0</td>\n",
              "      <td>4292.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, share, yellow]</th>\n",
              "      <td>232.823</td>\n",
              "      <td>154.723</td>\n",
              "      <td>55.602</td>\n",
              "      <td>484.447</td>\n",
              "      <td>2.294</td>\n",
              "      <td>1.622</td>\n",
              "      <td>5679.0</td>\n",
              "      <td>4902.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, share, green]</th>\n",
              "      <td>569.501</td>\n",
              "      <td>342.937</td>\n",
              "      <td>140.590</td>\n",
              "      <td>1148.678</td>\n",
              "      <td>5.079</td>\n",
              "      <td>3.591</td>\n",
              "      <td>5488.0</td>\n",
              "      <td>5117.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>conc[C, share, purple]</th>\n",
              "      <td>1006.226</td>\n",
              "      <td>618.315</td>\n",
              "      <td>282.953</td>\n",
              "      <td>2028.422</td>\n",
              "      <td>9.225</td>\n",
              "      <td>6.827</td>\n",
              "      <td>6802.0</td>\n",
              "      <td>4346.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a3b610a-cf9f-4ed0-8b38-986f0e7e8166')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2a3b610a-cf9f-4ed0-8b38-986f0e7e8166 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2a3b610a-cf9f-4ed0-8b38-986f0e7e8166');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d4c1e8c3-6b6c-4ec3-b2b7-dd7490617d5c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4c1e8c3-6b6c-4ec3-b2b7-dd7490617d5c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d4c1e8c3-6b6c-4ec3-b2b7-dd7490617d5c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"az\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 608.8546438037005,\n        \"min\": 0.175,\n        \"max\": 3095.044,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          740.752,\n          293.02,\n          1174.378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 437.099864616835,\n        \"min\": 0.064,\n        \"max\": 2226.884,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          843.449,\n          184.835,\n          342.937\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_3%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125.39976586459929,\n        \"min\": 0.062,\n        \"max\": 643.036,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          166.066,\n          62.395,\n          212.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdi_97%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1323.4760182688176,\n        \"min\": 0.295,\n        \"max\": 6781.915,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          1645.602,\n          663.757,\n          2604.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.470528482627195,\n        \"min\": 0.001,\n        \"max\": 32.733,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          4.972,\n          15.121,\n          13.57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mcse_sd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.726091162204701,\n        \"min\": 0.001,\n        \"max\": 23.862,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          3.539,\n          10.693,\n          10.704\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_bulk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 492.10313171620635,\n        \"min\": 4679.0,\n        \"max\": 7079.0,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          5422.0,\n          5603.0,\n          5653.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ess_tail\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 369.8729511602599,\n        \"min\": 3487.0,\n        \"max\": 5551.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          4939.0,\n          3487.0,\n          4347.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"r_hat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "az.summary(idata_model2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "672566e0-8777-41ab-938f-7afd2dfe1f49",
          "showTitle": false,
          "title": ""
        },
        "id": "zlJJiTdOb0CT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "1b259f2e-e6f9-4c42-cedd-5f1eff97c490"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/arviz/utils.py:184: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
            "  numba_fn = numba.jit(**self.kwargs)(self.function)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 4 Axes>"
            ],
            "image/png": 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          },
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        }
      ],
      "source": [
        "az.plot_trace(idata_model2,compact=True)\n",
        "plt.tight_layout()"
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    },
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        "While the model seems to sample well, it seems that we are unable to correctly recover the original probabilties for each color..."
      ]
    }
  ],
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