Repository: bambinos/kulprit (language: python)

Issue:
ADVI projections
## Description

Implement ADVI inference for the large optimisation refactor.

## Checklist

- [x] Tests covering the new functionality have been added
- [x] Documentation has been updated OR the change is too minor to be documented
- [ ] Changes are listed in the `CHANGELOG.md` OR changes are insignificant



Discussion / hints:

Fix patch:
diff --git a/.flake8 b/.flake8
index c0f0e2f..3fc240b 100644
--- a/.flake8
+++ b/.flake8
@@ -1,5 +1,5 @@
 [flake8]
-ignore = B903, E203, E266, E501, W503, F403, F401, F811
+ignore = B903, E203, E266, E501, W503, F403, F401, F811, C417
 max-line-length = 89
 max-complexity = 18
 select = B,C,E,F,W,T4,B9
diff --git a/.github/actions/python-poetry-env/action.yml b/.github/actions/python-poetry-env/action.yml
index 59284e8..58d9852 100644
--- a/.github/actions/python-poetry-env/action.yml
+++ b/.github/actions/python-poetry-env/action.yml
@@ -5,7 +5,7 @@ inputs:
   python-version:
     required: false
     description: 'Python version'
-    default: '3.7'
+    default: '3.8'
 outputs: {}
 runs:
   using: 'composite'
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d14bd40..ee27ad7 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -20,36 +20,13 @@ repos:
         entry: poetry run autoflake -r -i --remove-unused-variables
         language: system
         types: [ python ]
-      # - id: isort
-      #   name: isort
-      #   entry: poetry run isort
-      #   language: system
-      #   types: [python]
       - id: black
         name: black
         entry: poetry run black
         language: system
         types: [python]
-      # - id: pyupgrade
-      #   name: pyupgrade
-      #   entry: poetry run pyupgrade --py37-plus
-      #   language: system
-      #   types: [python]
-      # - id: mypy
-      #   name: mypy
-      #   entry: poetry run mypy .
-      #   require_serial: true
-      #   language: system
-      #   types: [python]
-      #   pass_filenames: false
       - id: flake8
         name: flake8
         entry: poetry run flake8
         language: system
         types: [python]
-      - id: kacl-verify
-        name: kacl-verify
-        entry: poetry run kacl-cli verify
-        language: system
-        files: 'CHANGELOG.md'
-        pass_filenames: false
diff --git a/README.md b/README.md
index 5ac0747..8abf9cd 100644
--- a/README.md
+++ b/README.md
@@ -5,18 +5,15 @@ _(Pronounced: kuːl.prɪt)_
 [![PyPI](https://img.shields.io/pypi/v/kulprit?style=flat-square)](https://pypi.python.org/pypi/kulprit/)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kulprit?style=flat-square)](https://pypi.python.org/pypi/kulprit/)
 [![PyPI - License](https://img.shields.io/pypi/l/kulprit?style=flat-square)](https://pypi.python.org/pypi/kulprit/)
-[![Backend - PyTorch](https://img.shields.io/badge/backend-PyTorch-red?style=flat-square)](https://pytorch.org/)
 
 [Getting Started](https://colab.research.google.com/github/yannmclatchie/kulprit/blob/main/docs/notebooks/quick-start.ipynb) | [Documentation](https://yannmclatchie.github.io/kulprit) | [Contributing](https://github.com/yannmclatchie/kulprit/blob/main/CONTRIBUTING.md)
 
 ---
 
-Kullback-Leibler projections for Bayesian model selection in Generalised Linear Models.
+Kullback-Leibler projections for Bayesian model selection in Python.
 
 ## Example workflow
 
-🚧 **WIP** 🚧
-
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yannmclatchie/kulprit/blob/main/docs/notebooks/quick-start.ipynb)
 
 ```python
@@ -29,28 +26,32 @@ import kulprit as kpt
 import arviz as az
 import matplotlib.pyplot as plt
 
-# define model data
-data = data = bmb.load_data("my_data")
+# define the data
+x = np.random.normal(0, 0.25, 121)
+y = np.random.normal(0, 1, 121)
+z = np.random.normal(2 * x + 3, 0.1)
+data = pd.DataFrame({"x": x, "y": y, "z": z})
 
-# define and fit model with MCMC
-model = bmb.Model("y ~ x + z", data, family="gaussian")
-num_draws, num_chains = 2_000, 2
-idata = model.fit(draws=num_draws, chains=num_chains)
+# fit the reference model
+model = bmb.Model("z ~ x + y", data, family="gaussian")
+idata = model.fit()
 
 # build reference model object and perform the search procedure
 ref_model = kpt.ReferenceModel(model, idata)
 ref_model.search()
 
-# compare submodels found in the search
-# setting option `plot=True` will return a comparison plot axes object
-cmp, _ = ref_model.loo_compare()
+# compare the projected posterior densities of the submodels
+ax = ref_model.plot_densities();
+
+# compare submodels found in the search by LOO-CV ELPD
+cmp, ax = ref_model.loo_compare(plot=True);
 cmp
 
 # project the reference model onto a chosen submodel size
 submodel = ref_model.project(1)
 
 # visualise projected parameters
-az.plot_posterior(submodel.idata)
+ax = az.plot_posterior(submodel.idata, var_names=submodel.model.term_names);
 ```
 
 ## Installation
diff --git a/docs/notebooks/quick-start.ipynb b/docs/notebooks/quick-start.ipynb
index aa7c9c0..74f84ec 100644
--- a/docs/notebooks/quick-start.ipynb
+++ b/docs/notebooks/quick-start.ipynb
@@ -35,16 +35,7 @@
    "execution_count": 1,
    "id": "dd10a114",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/yannmclatchie/Desktop/kulprit/.venv/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
-      "  import pandas.util.testing as tm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -75,26 +66,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# define model data\n",
-    "data = data = bmb.load_data(\"my_data\")"
+    "# define the data\n",
+    "x = np.random.normal(0, 0.25, 121)\n",
+    "y = np.random.normal(0, 1, 121)\n",
+    "z = np.random.normal(2 * x + 3, 0.1)\n",
+    "data = pd.DataFrame({\"x\": x, \"y\": y, \"z\": z})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 3,
    "id": "43305558",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1296x432 with 3 Axes>"
+       "<Figure size 1800x600 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -152,8 +144,8 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 4 jobs)\n",
-      "NUTS: [y_sigma, z, x, Intercept]\n"
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [Intercept, x, y, z_sigma]\n"
      ]
     },
     {
@@ -168,6 +160,9 @@
        "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
        "        background-size: auto;\n",
        "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
        "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
        "        background: #F44336;\n",
        "    }\n",
@@ -185,8 +180,8 @@
       "text/html": [
        "\n",
        "    <div>\n",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:20<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:06&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -201,15 +196,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 30 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 30 seconds.\n"
      ]
     }
    ],
    "source": [
-    "# define and fit model with MCMC\n",
-    "model = bmb.Model(\"y ~ x + z\", data, family=\"gaussian\")\n",
-    "num_draws, num_chains = 2_000, 2\n",
-    "idata = model.fit(draws=num_draws, chains=num_chains)"
+    "# fit the reference model\n",
+    "model = bmb.Model(\"z ~ x + y\", data, family=\"gaussian\")\n",
+    "idata = model.fit()"
    ]
   },
   {
@@ -249,27 +243,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 6,
    "id": "cd0aafa2",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perform the search procedure\n",
+    "ref_model.search()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "272f5a38",
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "                        Terms  Distance from reference model\n",
-       "Model size                                                  \n",
-       "0                 [Intercept]                       1.070913\n",
-       "1              [Intercept, z]                       1.032097\n",
-       "2           [Intercept, z, x]                       1.005195"
+       "<Figure size 2208x552 with 3 Axes>"
       ]
      },
-     "execution_count": 24,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "ref_model.search(method=\"gradient\")"
+    "# compare the projected posterior densities of the submodels\n",
+    "ax = ref_model.plot_densities();"
    ]
   },
   {
@@ -282,30 +284,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 8,
    "id": "70dcd723",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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GYVyzDQAAHQlfNiPprrvu0rFjx67ZBgCAzoKAl+Tz+VjpDgBwFKboAQBwIEbwAACYeOCBB+wuIS4EPAAAJnr37m13CXFhih4AABOnTp3SqVOn7C7DMgIeAAATZWVlKisrs7sMywh4AAAciIAHAMCBCHgAAByIgL9CIBDQ5MmTNWrUKE2ePFmBQMDukgAAsMxywG/ZskWDBw9Wenq6srKyNH36dJ07dy4RtSVVc3OzCgsLlZOTo0WLFunNN9/UokWLlJOTo8LCQjU3N9tdIgDABj6fTz6fz+4yLLMU8B9//LFGjBih/Px8VVZWaunSpVq9erWeeeaZRNWXNGPGjNH69etb3bd+/XqNGTMmyRUBADqCkSNHauTIkXaXYZml74P/xS9+oZ07d2r37t2RbStXrtS4ceN06NAhfetb37rma1j5LttkCQQCysnJuWa7uro6ZWdnJ6EiAABaspKhlu5kt23bNo0dOzZq26hRoyL7Wgv4cDiscDgcVVwy5efnX7PN0aNHY3qt/v37q0+fPqZtKioqYnotAEDnsHHjRknSiBEjbK7EGktT9HV1dS1Gum63W263W7W1ta0+Z/78+fJ6vZGH3++Pv9oE+fLLL9u1HQDAOWpqalRTU2N3GZa1y73oDcNQSkpKq/tmzZqlGTNmRH4OhUJJDflYRtSTJ0/WokWLrtlu3Lhxevnll9ujLAAAEsrSCD4nJ0d1dXVR2xoaGtTY2HjVc9gul0sejyfq0dHMmTOnXdsBAGA3SwFfUFAQORdxSXl5eWRfZ5Wdna3Ro0ebthk9ejQL7AAAnYalgH/uued04MAB/frXv9aePXv01ltv6dlnn9W4ceNiWkHfka1bt+6qIT969GitW7cuyRUBABA/S5fJSdLmzZv1u9/9Tp988om8Xq8effRRvfjii8rIyIjp+R3xMrnLBQIB/elPf1JNTY38fr/mzJnDyB0AurBgMChJuummm2yuxFqGWg74turoAQ8AQEdlJUO5Fz0AACaOHz+u48eP212GZQQ8AAAmysvLIwvKOxMCHgAAByLgAQBwIAIeAAAHIuABAHCgdrkXPQAATnXzzTfbXUJcCHgAAEz88Ic/tLuEuDBFDwCAAxHwAACYWLt2rdauXWt3GZYxRQ8AgIlL96LvbBjBAwDgQAQ8AAAORMADAOBABDwAAA7EIjsAAExMmDDB7hLiQsADAGDia1/7mt0lxIUpegAATBw8eFAHDx60uwzLGMEDAGBiy5YtkjrfPekZwQMA4EAEPAAADkTAAwDgQAQ8AAAOxCI7AABM3HHHHXaXEBcCHgAAE/fcc4/dJcSFKXoAAByIgAcAwMTy5cu1fPlyu8uwjCl6AABMnD171u4S4sIIHgAAByLgAQBwIAIeAAAHSvo5eMMwJEmhUCjZhwYAwLJz585J6hi5damGS1lqJsWIpVU7OnbsmPx+fzIPCQCAo9TU1KhXr16mbZIe8M3NzaqtrVVmZqZSUlKSeeh2EwqF5Pf7VVNTI4/HY3c5jkP/Jh59nFj0b2J15f41DEP19fXy+XxKTTU/y570KfrU1NRr/tXRWXg8ni735kom+jfx6OPEon8Tq6v2r9frjakdi+wAAHAgAh4AAAci4OPgcrk0d+5cuVwuu0txJPo38ejjxKJ/E4v+jU3SF9kBAIDEYwQPAIADEfAAADgQAQ8AgAMR8AAAOBABb+LPf/6zCgoK5PF4lJKSos8++6zVdlu2bNHgwYOVnp6urKwsTZ8+PXLv4kvOnj2radOmKSsrSxkZGRo8eLD+/e9/J+G36Fxyc3OVkpIS9Wjtxkix9Dlaot/ax7Jly1q8T1NSUvTPf/4z0obPfOx2796tsWPH6jvf+Y5SU1P12GOPtWgTa3/yHv8fAt5EOBzW6NGj9Yc//OGqbT7++GONGDFC+fn5qqys1NKlS7V69Wo988wzUe2eeuoplZWVaenSpaqoqNCAAQP0wAMPqLq6OtG/RqczZcoU1dXVRR5VVVVR+2Ptc0Sj39rfkSNHot6r48ePj+zjMx+7s2fPqnfv3pozZ47uvPPOVtvE0p+8x69g4JreffddQ5Kxd+/eFvsmTJhg9OvXL2rbihUrDEnGoUOHDMMwjIMHDxqSjNdffz2qXV5envH4448nrvBOqE+fPsZvf/tb0zax9Dlaot/aT2lpqSHJOHfuXKv7+czHb9iwYcajjz4atS3W/uQ9Ho0RfBtt27ZNDz74YNS2UaNGRfZd/s8r240cOVJbt25NQpWdS2lpqW666Sbl5eXpqaeeUk1NTdT+WPocLdFv7a9fv37KysrSvffeq3/961+Rr/DkM9++Yu1P3uPRkv5lM3Y6ffq0AoGAaZsbbrhB3/72t2N+zbq6OuXk5ERtc7vdcrvdqq2tjbTJzMxUt27dotrl5ORE2jiV1T6fOnWq7r77bmVlZenQoUN6/vnnNWDAAO3atUvf/OY3JcXW52iJfms/eXl5Ki0t1R133KGLFy+qvLxcTz/9tPbu3asXXnihS3/mEyHW/uQ9Hq1LBfzy5cs1depU0zZ9+vTRkSNH2nwswzAiX4drXOVmgZe3cSqrfT5z5szI9ttuu01Dhw5Vbm6uFi9erHnz5pm+Tlfoz0Sg36wbMmSIhgwZEvl50KBBampq0sKFCzV79uwu/ZlPhLb2Z1ft9y41RT9lyhQZhmH6sBruOTk5qquri9rW0NCgxsbGyF+SPp9P9fX1amxsjGoXCARa/LXpNG3t8+7du+u73/2uDh8+HNkWS5+jJfotse655x6Fw2HV1dV16c98IsTan7zHo3WpgE+EgoICbdy4MWpbeXl5ZN/l/2yt3X333ZeEKjuvUCikgwcPqnfv3pFtsfQ5WqLfEquyslJpaWnKzs7mM9/OYu1P3uNXSNJivk7p6NGjRlVVlfGPf/zDkGSsW7fOqKqqMo4ePRpps2vXLiMtLc2YPHmysXv3bqO8vNzIzs42xo0bF/VajzzyiJGTk2O89dZbxu7du41JkyYZLpfL+OSTT5L9a3VYH3zwgfHiiy8alZWVxpEjR4wtW7YYQ4cONbxer3H48OFIu1j7HNHot/Yzd+5c48033zT2799v7Nmzx1i4cKHhcrmM6dOnR9rwmY9dOBw2qqqqjKqqKmPgwIHG8OHDIz+Hw2HDMGLrT97j0Qh4E0888YQhqcXjiSeeiGq3adMmIz8/30hLSzN69uxpTJkyxTh79mxUm8bGRmPKlClGz549jbS0NGPQoEHGli1bkvjbdHyVlZXG97//faN79+7GDTfcYPTp08cYP368sX///hZtY+lztES/tY8ZM2YYffv2NdLT0w2v12sMHDjQ+Pvf/240NTVF2vCZj93hw4db/X+tpMgf97H2J+/x/+HrYgEAcCDOwQMA4EAEPAAADkTAAwDgQAQ8AAAORMADAOBABDwAAA5EwAMA4EAEPOBwxcXFSklJUVNTk92lAEgiAh4AAAci4AEAcCACHuji6uvrNXXqVPl8PrlcLt1yyy166aWXWrTbuXOnCgoKlJ6eLr/frxdeeEFz585Vbm5u8osGcE3X210AAPs0Nzdr1KhR2rFjh55//nndfvvt2rBhg5599lkFg0E9//zzkqRgMKgf/ehH8vl8evXVV5WWlqaSkhIdPnxYqamME4COiIAHurCNGzfqvffeU2lpqZ588klJ0v3336/6+nr95S9/UVFRkbp3766SkhI1Njbq7bffVq9evSRJw4cPV25urjIyMmz8DQBcDX96A13Y1q1blZqaqp///OdR2ydMmKBwOKyPPvpIkvThhx9qyJAhkXCXpIyMDP3kJz9Jar0AYkfAA13YiRMn1L17d7lcrqjt2dnZkqSTJ09Kkurq6vSNb3yjxfOzsrISXySAuBDwQBfWo0cPnTx5UhcuXIjaHggEJEndu3eXJOXk5Oi///1vi+f/5z//SXyRAOJCwANd2LBhw9Tc3KyysrKo7a+99ppcLpfuueceSdKQIUP04Ycf6tixY5E2586dU3l5eVLrBRA7FtkBXcTatWtbrHj3+XwaNmyYJk2apC+++EL9+vVTeXm5li1bptmzZ+vGG2+UJBUVFenll1/W8OHDNXfuXLlcLv3tb3+Ty+ViFT3QQRHwQBfx6KOPttj2s5/9TBs2bNDvf/97LViwQCdOnFDfvn1VUlKiZ599NtLupptu0ubNmzVt2jQ9/vjj6tGjhyZNmqRgMKitW7cm8bcAEKsUwzAMu4sA0PlcvHhRd999twYNGqQlS5bYXQ6AKzCCBxCT2bNn6+abb1afPn0UDAa1ePFiffbZZ3r11VftLg1AKwh4ADGbN2+eamtrdf3112vgwIF65513dNddd9ldFoBWMEUPAIADsfwVAAAHIuABAHAgAh4AAAci4AEAcCACHgAAByLgAQBwIAIeAAAHIuABAHAgAh4AAAf6P1nv7rfJ+ZHPAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "cmp, axes = ref_model.loo_compare(plot=True);"
+    "# compare submodels found in the search by LOO-CV ELPD\n",
+    "cmp, ax = ref_model.loo_compare(plot=True);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 9,
    "id": "422e1ae1",
    "metadata": {},
    "outputs": [
@@ -343,13 +344,13 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>2</th>\n",
        "      <td>0</td>\n",
-       "      <td>-74.457921</td>\n",
-       "      <td>2.385010</td>\n",
+       "      <td>115.897211</td>\n",
+       "      <td>4.011590</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000e+00</td>\n",
-       "      <td>4.996276</td>\n",
+       "      <td>8.037039</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
@@ -357,24 +358,24 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>-75.372881</td>\n",
-       "      <td>3.876921</td>\n",
-       "      <td>0.914960</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>5.227423</td>\n",
-       "      <td>1.420981</td>\n",
+       "      <td>113.209995</td>\n",
+       "      <td>6.609141</td>\n",
+       "      <td>2.687216</td>\n",
+       "      <td>3.053818e-10</td>\n",
+       "      <td>8.616916</td>\n",
+       "      <td>0.853040</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>0</th>\n",
        "      <td>2</td>\n",
-       "      <td>-77.125790</td>\n",
-       "      <td>5.547887</td>\n",
-       "      <td>2.667869</td>\n",
-       "      <td>6.439294e-15</td>\n",
-       "      <td>5.058074</td>\n",
-       "      <td>1.562105</td>\n",
+       "      <td>-104.858972</td>\n",
+       "      <td>0.166653</td>\n",
+       "      <td>220.756183</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>7.852922</td>\n",
+       "      <td>11.030949</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
@@ -383,18 +384,18 @@
        "</div>"
       ],
       "text/plain": [
-       "   rank        loo     p_loo     d_loo        weight        se       dse  \\\n",
-       "0     0 -74.457921  2.385010  0.000000  1.000000e+00  4.996276  0.000000   \n",
-       "1     1 -75.372881  3.876921  0.914960  0.000000e+00  5.227423  1.420981   \n",
-       "2     2 -77.125790  5.547887  2.667869  6.439294e-15  5.058074  1.562105   \n",
+       "   rank         loo     p_loo       d_loo        weight        se        dse  \\\n",
+       "2     0  115.897211  4.011590    0.000000  1.000000e+00  8.037039   0.000000   \n",
+       "1     1  113.209995  6.609141    2.687216  3.053818e-10  8.616916   0.853040   \n",
+       "0     2 -104.858972  0.166653  220.756183  0.000000e+00  7.852922  11.030949   \n",
        "\n",
        "   warning loo_scale  \n",
-       "0    False       log  \n",
+       "2    False       log  \n",
        "1    False       log  \n",
-       "2    False       log  "
+       "0    False       log  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -413,24 +414,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 10,
    "id": "0e293770",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Intercept', 'z']"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "submodel = ref_model.project(1)\n",
-    "submodel.structure.term_names"
+    "# project the reference model onto a chosen submodel size\n",
+    "submodel = ref_model.project(1)"
    ]
   },
   {
@@ -443,50 +433,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 11,
    "id": "477f3ad4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1987.2x331.2 with 4 Axes>"
+       "<Figure size 1472x552 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# visualise projected parameters\n",
-    "az.plot_posterior(submodel.idata);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "4d4449e2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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QD0eh1fEaKRHZNkmS8PiKozhdp8aHt4w8p+gAAAqFwItXDoWHkx2e/PkYdDx2EpkMCw9ktp5dnYKjhTV447p4JEZ6d+s1TvZKvHvjCNgpBR74/gg07QtQExERGdrK5CK8vz0bN44Jx/3Toy/5/FvHRSIh3BOv/JGGxhaNCRISEZE5e2FtKtYeP41/zovDAzNiujUbzt/NEcsXD8bxolr8nHTKBCmJiMzX70eLsSm1DI/PGYAR4V4XfJ6Xiz2eWjAQR0/VYs3xEhMmJLJtLDyQWdqaXoZv9xfgr1OietwyKdjTCf93xVCcKKrDBztyjJSQiIhsWdrpOjy+4hhG9/PGskWDu3WxSKEQeHrhIJTVt+BDjk9ERDbtiz15+HhXLm4dF4G7J/es5/iiYUEYHuaJ1zdkQN2mNVJCIiLzVtnQgmdWpSA+zBO3T+h3yedfHh+C2ABXvLEpgzepEpkICw9kdmqb2vDkz8cQG+CKh2fF9mob84cGYd6QQPxvSyZO16oNnJCIiGyZuk2LB747AjdHO7xzwwjYq7r/cWpEuBfmDA7AZ7tz0cBZD0RENmlfTiWeWXUSMwf6Y2k3i9dnEkLgyblxOF2nxorDnPVARLbplT/SUa9uwytXD4NS0b2bgB6eFYuc8kasTC42QUIiYuGBzM4r69NQ0dCKV68ZDgeVstfb+df8gdDqJLy5KcOA6YiIyNa9uDYVGaUNeO3a4fBzc+jx6++dGo06tQbf7S8wQjoiIjJn5fUteOC7I4j0ccGbSxK6dbHsfMZGeWNYqAc+3ZXLfuVEZHOOn6rFj4cLcfuEfogNcOv26+YMDsTgYHe8vTWLx04iE2DhgcxKRmk9vjtQgJvHRmBYqGefthXm7YxbxkXix0OFyCitN0xAIiKyaVvTyvDF3nzcMaEfpsT69Wob8WGeGBvljc9253KaNxGRDZEkCQ//mIza5ja8c+MIuF5kIelLEULgrklRyKloxJa0MgOmJCIyb5IkYfmqk/Bxscffu7HO2pmEELhnSn/kVjRiazqPnUTGxsIDmZX/W5MKVwcVHpwRY5Dt/X1aNFwcVHh9A2c9EBFR3zS2aPDUr8cRG+CKx+cO6NO2bh0XieJaNballxsoHRERmbtv9hdgZ2YF/r1wEAYGufd5e/OGBMLPzQHfHyw0QDoiIsvw+9FiHM6vxuNz4uDuaNfj188dEoggD0d8ujvXCOmI6EwsPJDZ2JlZju0Z5XhgRgy8XOwNsk0vF3vcOi4S61NOI7u8wSDbJCIi2/TW5kwU16rxwhVD4WjX+1aAADBzUAD83Bzw7QG2WyIisgWFVU14YW0qJkb74qYx4QbZpp1SgStHhGBrehnK6riuHRFZv6ZWDV5cm4ahIR64emRor7Zhp1TglnGR2J1VidSSOgMnJKIzsfBAZkGSJLyxMQPBHo64eVyEQbd924RI2CsV+HB7jkG3S0REtiP9dD0+2ZWL6xLDkBjp3eft2SkVWDIqDFvTy3CquskACYmIyFzpdBIe/ekolELg5auH9Xgx6Yu5NjEMWp2EX44UGWybRETm6t2t2Thdp8byxYOg6OUaOQBw/egwONkp8RlnPRAZFQsPZBZ2ZFYgqaAG902P7tOC0ufj6+qAaxJD8euRIpTyTiAiIuohSZLw9G8n4OaowpPz4gy23etGhQEAfmCLDCIiq/bl3jzsz63C0wsHIcTTyaDb7u/nivgwT6w5VmLQ7RIRmZv8ykZ8uDMHl8cHY2RE324E8nS2x1UjQ/BbcjEqGloMlJCIzsbCA8lOkiS8uUk/2+GakWFG2cfdk/pDo9Phq735Rtk+ERFZr82pZTiQV4VH5wwwWCtAAAj1csakGD/8eqQIkiQZbLtERGQ+imqa8cr6dEyJ9cM1ib1rC3Ip84cG4nhRLQqrOIOOiKzXc6tTYKcQ+Of8gQbZ3m3j+6FVo8O3+9n6lMhYWHgg2e3IrMCR9tkO9irj/EqG+zhj2gB/fH+wEK0anVH2QURE1kenk/DqhnRE+jjj2kTDF8cvjw/GqepmHM6vNvi2iYhIfstWnoQkAc9fPsSgLZbONG9IEABg3QnOeiAi67QlrRSbUsvw4MwYBLg7GmSb0f6umBLrh6/25fM6EZGRsPBAsnt7SyZCPJ2MNtuhw01jI1DR0IINKaeNuh8iIrIeq44VI+10PR6ePQB2SsN/bJo9OBCOdgr8yt7cRERWZ/3J09iUWop/zIxBmLez0fYT5u2MISHuWHuc5zlEZH3UbVo8syoF/f1ccNv4fgbd9u0TIlFe34I1x4sNul0i0mPhgWR1pKAaB/OqcefEfkab7dBhcqwfQr2c8PU+tlsiIqJL0+kkvLU5E3GBblg4NMgo+3B1UGH2oECsOV7CO62IiKxIQ4sGy1aeRFygG+6YaNgLZeczb0gQkgtrUFTTbPR9ERGZ0sc7c5Bf2YTliwcb/LrR5Bg/RPm54LPdeWx9SmQELDyQrD7emQs3RxWuHWXc2Q4AoFQI3DAmHPtyqpBVVm/0/RERkWXbnlGO7PJG/HVKFBQK47THAIDLE4JR09SGHRnlRtsHERGZ1msb0lFar8YLVw41yoy5s80bEggA2HiSsx6IyHoU1TTj7a1ZmDckEJNi/Ay+fYVC4PYJ/XDsVC1bnxIZAQsPJJvCqiasO1GCG8aEw9VBZZJ9XpsYBjulwNf7uHgQERFd3Ec7cxDo7oiFw4KNup9JMX7wdrHHb8lst0REZA1OFNXiiz15uHFMOEaEe5lkn1F+roj0ccY2FrGJyIo8vzoFAPDvhYOMto+rRoTA3VGF97fnGG0fRLaKhQeSzSe7cqEQArcbuEffxfi6OmDukCD8eqQILRqtyfZLRESW5WRxLfZkV+K2CZFGv1PVTqnAgqFB2JhSisYWjVH3RURExiVJEp5bnQJPZ3s8NifOpPueOsAfe7MroW7jeQ4RWb6dmeVYd+I0/j4tGiGeTkbbj7O9CndOjMKm1FIcP1VrtP0Q2SIWHkgWtU1t+PFQIRYPD0agh6NJ9331yFDUNrdha1qZSfdLRESW44s9eXC2V+L60eEm2d/i+GC0aHTYzLGJiMiibUwpxf7cKjw0KxYeTnYm3ffUAX5o0eiwL6fSpPslIjK0Vo0Oy38/iQgfZ9w1Kcro+7tjYiQ8ne3w+sZ0o++LyJaw8ECy+PZAAZpatSYZQM42ob8P/N0csOIwW1oQEdG56tVtWHW0BIuGBZvsotHIcC8EuDtgzbFik+yPiIgMr02rw0vr0hDt74rrTbCG3dnGRvnAQaXAtnS2WyIiy/bZ7lxklzdi2aJBcLRTGn1/bo52uHtyFLaml+NwfpXR90dkK1h4IJNr1ejw+Z5cTIj2waBgd5PvX6VU4PKEEGxLL0NlQ4vJ909ERObt96PFaG7TYslo0100UigE5g0Jwtb0cjSw3RIRkUX69UgRcioa8eTcOKhMsKD02RztlBjX3wfb0jl7jogs1+laNd7anImZA/0xPS7AZPu9dVwkAtwd8PRvJ6HR6ky2XyJrxsIDmdzqY8UorWuRZbZDhytHhECjk7DqKO8sJSKirr4/UIi4QDfEh3madL8LhwWhVaPD5tRSk+6XiIj6TqPV4Z2tWRgS4o4ZA/1lyzFtgD/yKpuQX9koWwYior54cV0q2nQSnjbigtLn4+KgwvJFg5FSUocPdnChaSJDYOGBTEqSJHy4Iwcx/q6YGusnW464QHcMDnbHz0lst0RERH86WVyL40W1WDIqDEIIk+57RLgXAt0dsfpYiUn3S0REfff70WLkVzbh/ukxJh8/zjQh2hcAsCeb6zwQkeXZn1OJlcnFuGdyFCJ8XEy+/7lDArFwWBBe3ZCOjSm8GYior1h4IJPanVWJtNP1+MukKNk+kLe0tOCRRx7BzmVXYM0jMzF15hzk5eVd8nU//PADrrzySgQFBUEIgc8///yc52zbtg1CiHO+nnzyScO/ESIiMrjfk4uhUghcFh9i8G13jD/+/v5wcXHBggULuow/CoXA/KFB2J5ejnp1W5fXfvTRR4iJiYGjoyNGjhyJzZs3n7P9Xbt2Ydy4cXB0dERwcDCeeuopaDRd2zZNnTr1vOOUWq02+PslIrIVOp2Ed7ZmIS7QDbMGmq4tCHDu2PLg7dfBXVONvRcpPNTV1WHZsmUYPXo0PDw8EBgYiCuuuAIZGRldntfa2orHHnsMkyZNgpOTk6wFFSKyfjqdhOfWpCDE0wn3To2WJYMQAv+5ejiGhnjg3q8P4+t9+dDpJINs+7nnnsPMmTPh7u4OIUS3rkMRWToWHsikPtqZA19XB1yWECxbhgceeACff/45XnjpZQRc8S9kF5Zg1qxZl7zosmLFCuTl5WHhwoWX3Mc333yDvXv3dn7dd999hopPRERGomtvwTc51g9eLvYG337H+PPqq69ixYoVqKioOGf8WTAsCK1aHTad0W7pu+++wz333INbbrkF69atw+DBg7Fw4UKcOHGi8zm5ubmYNWsWAgIC8Ouvv+Kf//wn/vvf/+LRRx89J8e0adO6jFF79+6Fg4ODwd8vEZGt2JNdiezyRvxlUhQUCtNenD/f2FLwzb+wK60EknT+i2UFBQX46KOPMGfOHKxYsQIffPABSkpKMGbMGBQWFnY+r6mpCR9//DGcnZ0xfvx4U70lIrJRa0+U4ERRHR6ZHQsne+MvKH0hTvZKfH3XGIzr74N//3YCs97YjqUrT+C1Del4fWMGXlybiv9tzsTGlNIerc32wQcfQKPRYNq0aUZMT2ReVHIHINuRUVqP7RnleGRWLBxU8gwip06dwieffIJPP/0Ut9xyC444HERSZCxOvnELvv76a9x1110XfO0PP/wAhUKBhoYGfPzxxxfdz7BhwzBkyBBDxyciIiNKKqhGca0aj8+NM/i2zx5/AP1Y0a9fvy7jT0KYJ4I9HLHmWAmuSAgFACxfvhy33nornn76aQDAlClTcOTIEbz00kv4+uuvAQAvvfQSgoKCsGLFCqhUf368e/jhh/HEE08gKCio8zFvb2+MHTvW4O+RiMhWfb0vH17OdlgwLOjSTzagC40tEZH9kH/gD2SXT0K0v9s5r+vXrx+ys7Ph5OTU+dikSZMQHh6OTz/9FMuWLQMAeHp6oqqqCkIIvP3229iyZYtp3hgR2RyNVofXN2QgNsDVKDOPe8rd0Q5f3D4aa46X4Jv9+fglqQiNrRpIEmCvUqBVo1982tPZDg/PisXNYyMuOSusoKAACoUCq1evxu+//26Kt0EkO854oPO67bbbkJiYiDVr1mDQoEFwdnbGggULUFVVhaysLEybNg0uLi5ITEzEsWPHOl+n0+nw0ksvITo6Gg4ODoiNjcUXX3wBAPh4Zw4c7RTwrzmJWbNmwd/fH+7u7hg7diw2bNjQZf/Lly+Hr68vjhw5grFjx8LZ2RkJCQnYuXNnn95Xx36uvPJK/f+OCEW1cMOQEWOwbt26i75WoeCfCxGRsRlj/OmwZs2ai44/q44Wo37Pt7hz1nCjjz8AEBISgokTJ3YZfzraLe3IqEBtcxtycnKQkZGBa6+99oznKHDNNdd0eV1ycjKmTp3apegwe/ZsaDSac8ZYIiJbY8yx5esff8WXS+9C5uvXw9/HS9ZzG0A/toweOw7qnMMXXOfBxcWlS9EB0BelIyIiUFxc3OVxtlciIlP4JakIORWNwPZ3MWb0KFnOBYCux+vx48fhunHRSH/vb3hvpjNyX1yA3BfnI+P5eUh7bi6+/csYDAn2wNKVJ/HMqpQLzjLrwGtKZIv4W08XVFBQgKVLl+L555/Hhx9+iD179uDuu+/GkiVLsGTJEqxYsQIajQZLlizpPMDef//9eP7553H33XdjzZo1uOKKK3DHHXfg259+xW/JxbhqRCjKS05h0aJF+Oqrr/Dzzz9j/PjxmDdvHnbv3t1l/01NTbj11lvx17/+FT///DMcHBxw5ZVXoqmpqfM5Op0OGo3mol9arbbz+WlpaQgNDYWrqysAYMZAf7g5qKDzCEZaWprB/u2mT58OpVKJyMhIPP/8810yEBHRxRly/Fm9enXndnNzcy84/mi0Oqw5XoIoXxc0m2D86TBw4MBzxp/OdksppZ0/i4uLO+d1VVVVKC8vBwCo1WrY23dtD9XxfWpqapfHN2zYAGdnZzg7O2POnDldTtqIiKyVscaWX3ccgWP/0Xjnw09lP7fpED90MKSaoouu83C28vJyZGVlITY2tkf/rkREfaXTSXh/ezYGB7sj2NNJlnOBM13seN1RjLVXCoyO8MSnt4zAbePC8NmubHy0PeuCx2simyVJkiG/yErceuutklKplLKysjofe+yxxyQA0hdffNH52Jo1ayQAUkpKipSZmSkJIaTPP/+8y7ZuvvlmKSx2iBTxxGops7Suy8+0Wq3U1tYmzZ49W7r99ts7H1+2bJkEQNq8eXPnY0eOHJEASOvWreuSE8BFv6ZMmdL5/LvuuksaPnx4lwyP/pgs+U5cIgUGBXXr36a+vl4CIH322Wfn/CwpKUl68sknpTVr1kgbN26UHnzwQUmhUEgPPPBAt7ZNNsvQx2JL/iIbZ+jxJzEx8bz7OXv82ZlRLkU8sVq68d6HTTb+SJIkPfXUU1LQWeOPTqeTxr+4Wbr9swPS119/LQGQqquruzxn48aNEgApPT1dkiRJuvLKK6WRI0d2ec73338vAZD+8pe/dD62dOlS6dNPP5V27NghffXVV1JcXJzk7u4u5ebmnvffiSyC3Mdtc/oiOi9jjS06nU6a9PIW6YaP9kqSZB7nNpKkH1tcvPyk+GfWS1qtrlv/RjfffLPk7e0tVVRUnPfn//vf/yT95QOyQnIfu83pi2Twx4kSKeKJ1dLK5CLZzgU6GOt4faZVq1ZJAPj5m6zJBY+rXOOBLigyMhL9+/fv/D46OhqA/m7+sx8rKipCdnY2FAoFrrjiCmg0fy6wM2XqNHz9zbe4Mdob0f5uOHXqFJ566ils2rQJJSV/Lno2YcKELvu3t7fH1KlTO78fNGgQAH0v0w7Lly/H3//+94u+Dze3c/uanumy+BB8qJWga+/R1xcJCQlISEjo/H7mzJlwcHDA66+/jqeffhq+vr593gcRkbUz1PgzY8YMfPfdd9BqtVAqlRcdf1YfK4argwqRPi4mG38uRAiBBcOC8NnuXIzr370F6+655x7Mnj0bzz33HO69915kZWXhySefhFKp7DKt+5lnnun8/5MmTcLMmTMRFxeHN998E2+++Wav8hIRWQJjjC0nS+qRk1cAsX0VQpbtNYtzm879KQWqm9qQdroeg4LdL/rc9957D19//TV+/vln+Pj4dGv7RESG8uGOHIR6OWH+kED8AnnOBc7Um+N1g1qDe74+jBAvR7x05TAIIXp9LkBkTVh4oAvy9PTs8n1Hy4YzH+94TK1Wo6KiAlqtFh4eHufd3uJYZ+h0OixevBj19fV49tlnER0dDRcXFyxduhRlZWVdnu/m5tblYsmZ++oQHh6O0NDQi76PM/uSenl5oba2tsvPx/X3gYO2CRp7l4tup7euvvpqvPLKKzh27FiXgZKIiM7P0ONPSUkJgoODLzr+bEwpxbQ4f6jSFSYbfwCguroaXl5e5zy+YGgQPtyRg5x6/fe1tbVd3n91dXXndgFg1qxZeP755/Hcc89h6dKlsLOzw9KlS/HWW28hMDDwghkDAwMxYcIEJCUlXfS9EBFZOmOMLb8n16Lil+fg4AqzObcB9GOEn68PWgDszam8aOHh999/x/3334+XX34ZV1xxxUX3TURkaEkF1TicX41nFg+GSqk/RspxLnCm3h6vnxL++PfKEyhVBWLu0CCukUMEFh7IgLy9vaFSqbB79+7Og7QkSXjox2S0tOmweEwcsrKycOTIEaxbtw5z587tfG1zc3Ov9nnHHXecs2DQ2aZMmYJt27YB0PfILiwsRGNjI1xc9IUGpULAVV2GMtdA1Da3wcPJrldZLqRjsOGgQ0RkHOcbf87k7+9/0fGnqVWDpsZWzBoUgKT07u3TEOMPoO/Pffb6DQAwLNQDoV5OyFC7dz4vIiKiy+u8vb3h5+fX+dhTTz2FBx98ELm5uQgNDYVWq8XTTz+NsWPHXjSnEIJjFBHRWS41tvj6+uHnrZvRUpqNdz43n3MbQD9GDBk0EOXezjiQW4k7J/Y777Z2796NJUuW4J577sFjjz3Wq8xERH3x/YECuNgrcdXIixddL6av5wK9cbHj9aL/6P/3zOM1ka1i4YEMZvr06dBqtaitrcWsWbMAAAfzqnBKWYbnrxoCBweHzoO6g4ND5+vy8/Oxe/duDBs2rMf77Ol05NmzZwMAfv31V9x0000AgOLiYhSmJsF9xj1Yf/I0rk0M63GOi1mxYgVUKlWv3h8REV3a+cafs11s/PGPiIWjQmDqAD90975/Q40/O3fuxLvvvnvOa4UQWDA0CJ/sUiM6JgY//fQT5syZA0C/+OhPP/2EefPmnfM6V1dXDB06FIC+rVJERARmzpx5wYynT5/Grl27cMcdd1ziHRMR2ZZLjS2H86tQWl0HwPzObTrGlhR3L2xPL4ckSecUmE+ePIlFixZh7ty5eOutt3qclYiorxpaNFh9rASLhgXD1aH3lyf7ei5gyOP1HydO452tWfi/K4ZgwkDDXlsiskQsPJDBDBgwAPfccw+WLFmCxx9/HImJiXht3Qm0HD2OTWU/46axnyIuLg6hoaF45JFH8Nxzz6G+vh7Lli1DSEhIr/YZGRmJyMjIbj8/NDQUd955J/7xj39AkiT4+flh+fLliIyIQMDEBfg9uRjXJobh2WefxbPPPtulP2BKSgpSUlI6p9cdOnQIrq6u8PPzw5QpUwAA9957L/z8/DBq1CjY29tj7dq1ePvtt/GPf/yD/VKJiIzkfOOPWq3GyZMnkZGRgY8//vii40+lug1To3zg7tj9GW+GGn8iIiI6LxYB6DL+LBgWhA925OCyWx7A28seRGRkJCZMmIAvvvgCmZmZ+Pbbbztfl5WVhW+//RajR4+GRqPB6tWr8emnn2LNmjVQqfQf944dO4Z//vOfuOaaaxAREYGCggK8+OKLUCgU+Mc//tHt90JEZAsuNbb0u+JhOPtFICTE/M5tOsaWX4+V4ZekIjz85NP432svdZ7blJWVYe7cuXB1dcUDDzyAAwcOdG7T3d29s585AKxbtw6NjY1ITk4GoL+pCgBGjRrVZSYeEVFPrT5ajKZWLa4d1bcL9H09F+iNCx2vhwzX4tfCLdhT64k7Bgzo8rPt27ejvLwchw8fBqA/vvr5+WHQoEFdjrtE1oSFBzKod955B7Gxsfjoo4+wdOlSaJSOiOgfi+k33w9AX13+5ZdfcN999+Hqq69GaGgonnrqKWzbtg0nTpwwSca33noLLi4uePjhh9HU1IQpU6bgu+++wy+ZrXhnaxbK6tTQ6XTQarVdXvfjjz92WZTznXfewTvvvNNl+tzAgQPx8ccf44033kBrayuio6Px2muv4cEHHzTJeyMislVnjz8dF07uvPNOABcef1b9sRGrtx/ArEEBRs94ofHH0dGx8zlnjj9DQzwQ5u2Eat/ReP/99/Hyyy/jueeew+DBg7F69WoMGTKk83X29vbYtGkTXnvtNWg0GowaNQqbN2/GpEmTOp/j4+MDSZLwz3/+E5WVlXBzc8PUqVPx22+/ITw83Ojvn4jI0lxsbPkyvRzjYgPwwK/meW7j6OiIUZHeAIBTVY1dzm1SUlI6F0mdNm1al+2d3Rrk3nvvRX5+fuf311xzDQDgs88+w2233Wakd0VEtuCHQ4WI8XfFiHDPPm+rt+cChj5eO9opcU1iGD7amYPSOjUC3P/8nL9s2TJs37698/u//e1vnY8vX77cYBmIzInoWMXdQAy6MbJsL6xNxSe7crHz8WkI9nSSO84lZZXVY+brO7B04SDccYE+qERGwubqf+I4Qib1wfZsvLguDbufnI4QMxyrXlqXho925uDwv2fC09le7jhknjiG/IljCJlEYVUTJr2yFU8vHHTB9RPMgSRJGPHcRswYGIBXrxkudxwyXxxH/sRxxERyyhsw/bXteGr+QPxlcpTccQwqr6IRU1/dhkdnx+Lv02PkjkNkChccR85ddYXIABpbNPjuQAHmDgm0iKIDAET7u2FQkDt+P1osdxQiIjKRjSmlGBzsbpZFBwCYNyQQWp2EjSmlckchIqJ229LLAADTBvjJnOTihBBIjPTGobwquaMQEXWx+lgJAGDh8CCZkxhepK8LxkX54MdDp2Dgm72JLA4LD2QUPyedQr1agzsmmO8dQOdzWXwwkgtrkF/ZKHcUIiIysoqGFhwuqMbMgcZvs9Rbw0I9EOLphD9OnJY7ChERtduWXo5wb2f083WRO8oljYr0Ql5lE8rq1XJHISLqtPpYMUZFeiHIwzxv/umryxOCUVDVhBNFdXJHIZIVCw9kcDqdhM9252F4mKdBevWZ0sLhwQCA35M564GIyNptSS2DJMEk6zv0lhACcwYHYmdmBerVbXLHISKyeeo2LXZnV2DaAD8IYf4dahLb13k4nFctcxIiIr2M0npklDZgUfv1F2s0e1AgVAqBNcdL5I5CJCsWHsjg9mRXIreiEbeNj7CID+NnCvF0wuhIb/yWXMQpcUREVm5DSilCPJ0wONhd7igXNW9oIFq1OmxJK5M7ChGRzTuYVwV1mw5TB/jLHaVbhgR7wNFOgYMsPBCRmVh9tBgKAcwbYn1tljp4udhjfLQv1hwv5rUlsmksPJDBfXsgH17OdhY7iCyOD0Z2eSNSSjgljojIWjW3arErqxwzB/qbfZF8ZLgX/Nwc2G6JiMgM7M2uhEohMLqft9xRusVepUB8mCcOcp0HIjITa46XYGyUD/zcHOSOYlQLhwahsKoZx4tq5Y5CJBsWHsigyurV2HCyFFeNCIWjnVLuOL0yf2gQVArBdktERFZsV1YF1G06zDTjNksdFAqBOYMDsC29HM2tWrnjEBHZtL05lRgW6gEXB5XcUbptVKQ3ThbXoqFFI3cUIrJxeRWNyC5vxGwL+AzeV7MHB+jbLR1juyWyXSw8kEH9dOgUNDoJ148JlztKr3m72GNyrB9+P1oMnY5T4oiIrNHGlNNwc1BhTD8fuaN0y7whQWhu02J7BtstERHJpaFFg2OnajE2yjLGjg6Jkd7QSUByQY3cUYjIxnW0Dp0eZ/2FB09ne4yN8sGm1FK5oxDJhoUHMhidTsL3BwswNsob/f1c5Y7TJ5fFB6OkVo1D+eyFSkRkbbQ6CZtTyzA1zh/2Ksv4KDSmnze8nO2wju2WiIhkcyivClqdhHH9LavwMCLcEwoBtlsiItltTitFjL8rwn2c5Y5iEtPi/JFd3oiCyia5oxDJwjLOtski7MyqQGFVM24YEyF3lD6bOTAATnZKrEwukjsKEREZWHJhNSobWzFzoGUsDAoAKqUCswYFYEtqGdq0OrnjEBHZpL05lbBTCiRGWMb6Dh3cHO0wMMidhQciklW9ug37c6ow3YI+g/fV9Dj9e92azlnLZJtYeCCD+fnwKXg622HOYMufMufioMLMQQFYc7wErRpe4CEisiYbUkqhUghMHWBZJz0zBgagvkXDC0dERDLZl12J+DBPONlb3lp2oyK9caSghsVrIpLNzswKaHQSZthAm6UO/Xxd0M/XpbPFFJGtYeGBDKKhRYMNKaexcFgQHFSW90H8fC4bHoyapjbsyiqXOwoRERnQxpRSjI3ygYeTndxRemRitC/slQps5YkLEZHJNbRocLzI8tZ36JAY6YXmNi1SiuvkjkJENmprWhk8nOwwItxT7igmNW2AP/bmVKKpVSN3FCKTY+GBDGLd8RKo23S4IiFU7igGMznWDx5OdliZXCx3FCIiMpDs8gbklDdi1iDLu9PKxUGFMVHevGOKiEgGyQU10En6mQOWqCM3Z80RkRwkScKurApMjPaFSmlblyKnx/mjVaPDnqxKuaMQmZxt/bWT0fx6pAiRPs5WVbm2Vykwf2gQNpwsZWWaiMhKbEopBQDMsNDestMGcIE6IiI5HM6vhhBAvIWe7wS4OyLc25mFByKSRW5FI0pq1RgfbZmzxvpidD9vuNgrsYXrPJANYuGB+qykthl7cypxeUIIhBByxzGoy+KD0dymxcb2C1VERGTZNqaUYlCQO0K9nOWO0isdC9RtSeO4RERkSkkF1Yj1d4O7o2W16TtTYqQXDuVVQ5IkuaMQkY3ZnVUBQN861NbYqxSYGOOLrWllPP6SzWHhgfrstyPFkCTgioQQuaMY3OhIbwS6O2LVUbZbIiKydBUNLThcUG2RbZY6RPq6IMrXBVvSuf4QEZGp6HQSkgqqMSLCS+4ofTI60huVja3ILm+UOwoR2ZhdWRUI9XJCuLdl3vzTV9MG+KOkVo2ssga5oxCZFAsP1Gcrk4swItwTET4uckcxOIVCYHF8MLall6O6sVXuOERE1Af6u4yAmQMtt/AAANPi/LGPC9QREZlMVnkD6tUajLTwwsOofvp1Hg6x3RIRmZBWJ2FvdiUmRvtaXZeM7poYo5/psTOzQuYkRKbFwgP1SV5FI9JO12PBsGC5oxjN4uHB0OgkrDtxWu4oRETUB1vSyhDg7oAhIe5yR+mTjgXqdnOBOiIikzicXw0AFl94iPJ1gY+LPQ6w8EBEJnSiqBZ1ag3G22CbpQ6hXs7o5+uCXVksPJBtYeGB+mT9Sf3F+DmDLfvu0YsZHOyOKD8XrEwukjsKERH1UqtGhx0Z5ZgeF2Dxd1olRnrB0U7R2SuXiIiM63B+Nbxd7BHpY9ktQoQQSIz04gLTRGRSHRfbx/e3vYWlzzQh2gf7cirRqtHJHYXIZFh4oD754+RpDA3xsNhFOrtDCIHLhofgQF4Vimua5Y5DRES9cCC3Co2tWsxoX5zZkjmolBjdz4d3TBERmUhSfjVGhHtZfOEaAEZFeqOwqhmna9VyRyEiG7E7qwIDg9zh6+ogdxRZTYz2Q1OrFkcKquWOQmQyLDxQr5XWqXGkoMaqZzt0WBwfDEkCVh/jItNERJZoc1opHFQKTLCSKd4To32QVdbAC0dEREZW1diKnIpGi2+z1GF0+zoPbLdERKagbtPiUH41Jtj4bAcAGNffBwoB3jxENoWFB+q1De1tluYOCZQ5ifH183XB8FAPrExm4YGIyNJIkoTNqWUY398HTvZKueMYREcBhe2WiIiMq+POVGspPAwKcoeLvRIHc1l4ICLjO1pYg1aNDmOiWHjwcLLD8DBPLjBNNoWFB+q1P06eRn8/F0T7u8kdxSQWx4fgZHEdssoa5I5CREQ9kF3eiIKqJkwfaD0z9AYGusPbxZ6FByIiIzucXw2VQmBYqIfcUQxCpVRgRATXeSAi0+g41oyKtI7ibV9NivbFsVM1qG1qkzsKkUmw8EC9UtPUin05VTYx26HDomFBEAL4/ShnPRARWZItaaUAgOlWsL5DB4VCYHx//ToPkiTJHYeIyGodzq/G4BAPONpZx4w5QL/OQ3ppPS98EZHRHcirxoAAN3g628sdxSxMjPGDTgL25vDmIbINLDxQr+zIrIBWJ2GGFd09ein+7o4YF+WD35OLeJGHiMiCbE4tQ1ygG0I8neSOYlATon1RVt+C7HLOxCMiMoY2rQ5HT9VgRLin3FEMKjHSC5IEHC7grAciMh6NVofDeVUY1Y+zHTokhHvCxV7JdktkM1h4oF7Znl4OT2c7DA/1lDuKSV0WH4y8yiYcO1UrdxQiIuqG2qY2HMqvxoyB1jPbocOYjgVCc6tlTkJEZJ1SS+qgbtNZzfoOHRLCvGCnFBw/iMioUkvq0diqxeh+XN+hg51SgbFRPlxgmmwGCw/UY5IkYUdmOSZG+0KpEHLHMam5g4Ngr1RwkWkiIguxLaMMWp2E6XHWN0Ovn68LfF3t2aebiMhIkvKta2HpDk72SgwJ8eD4QURGdaD9GDM60lvmJOZlYowv8iubUFjVJHcUIqNj4YF6LLWkHuX1LZgS6yd3FJPzcLbD1AF+WHWsGFod2y0REZm7LWll8HGxR3yYp9xRDE4IgdH9vHEglxeOiIiM4XBBDYI8HBHkYV2t+gD9hcBjp2qgbtPKHYWIrNSB3EqEeTsh0MNR7ihmZVKMLwCw3RLZBBYeqMd2ZJYDACbbYOEBAC6LD0F5fQv25VTKHYWIiC5Co9VhW3o5pg7wt9oZeqMivVFU04xT1bxjiojI0JLyqzHCymY7dBgV6Y02rYTkwhq5oxCRFZIkCYfyqjE6km2WztbfzxWB7o7YlVUudxQio2PhgXpse3o54gLdEOBum1XrGQP94WKvxO9st0REZNaSCmpQ29xmles7dBjdvs4D22UQERnW6Vo1imqaMTLcOgsPiZH693WQs+aIyAiyyxtR2diK0VxY+hxCCEyM8cXurEp20iCrx8ID9UhjiwaH8qtsss1SB0c7JeYMCcTaEyVo0XBqMhGRudqcVgqVQnROZ7ZGcYHucHNUsd0SEZGBJRXo13ew1hkPns72iA1wxcF8LjBNRIbX8dl0FNd3OK9JMb6obW7DiaJauaMQGRULD9Qje7Mr0aaVbLrwAACLhwejXq3BtnROjSMiMldbUsswJsobbo52ckcxGqVCIDHCi4UHIiIDS8qvhoNKgUFB7nJHMZpRkd5Iyq/mHbdEZHAH86rg6+qAfr4uckcxSxOi9TdG7criOg9k3Vh4oB7ZmVkOJzslRkZa550/3TUh2hc+LvZst0REZKYKKpuQWdaA6XEBckcxutH9fJBd3oiKhha5oxARWY2kgmoMC/WAvcp6T5lH9/NGQ4sGqSV1ckchIitzILcKo/t5QQjrXGetr3xdHTAwyB07M3kzK1k36/0URUaxL6cKiZFecFAp5Y4iKzulAguGBWFTainq1W1yxyEiorNsSSsFAMyIs971HTp09M5ln24iIsNo0WhxoqgOI6x0fYcOHS1Q9nP8ICID6lwjJ4Jtli5mcowvDudXo7FFI3cUIqNh4YG6rbKhBeml9Rgb5SN3FLNwWXwwWjQ6bDhZKncUIiI6y+a0MkT5uSDSBqZ3Dw3xhINKgYN57NNNRGQIJ4rq0KrVWe36Dh2CPZ0Q4eOMvdmVckchIivSsUbOSCs/hvbVxBhftGkltkwlq8bCA3Vbx8GQhQe9EeFeCPVywsqjbLdERGROGlo02J9TZROzHQDAXqXA0BAPJBey8EBEZAhJ7QsuW/uMBwAY398H+3MqodHq5I5CRFbCFtbIMYRRkd6wVymwM5PrPJD1YuGBum1fTiWc7JQYFuohdxSzIITA4uHB2J1Vwb7aRERmZFdmOVq1OswYaP3rO3RICPfEiaI6tGi0ckchIrJ4SQXVCPN2gp+bg9xRjG58f1/Ut2hwvKhW7ihEZCWSCqoxNMS618gxBEc7JUZHemNXFtd5IOvFowB1W8f6DnZK/tp0uCw+BFqdhLXHS+SOQkRE7TanlsHdUWVT07tHhHuhVatDSjEXCCUi6gtJkpBUUI2RNjDbAQDG9dfPZt/DdktEZACda+TY0OfwvpgY44uM0gaU1qnljkJkFLyCTN1S1djK9R3OY0CgGwYEuGFlMtstERGZA51Owtb0MkwZ4G9ThfKE9gtkRwpq5A1CRGThimqaUVrXYjMXzXxdHRAX6IY92Wz1QUR9d7K4fY2ccE+5o1iESTG+AMB2S2S1bOeMnPrkQK7+DpixUd4yJzE/i+ODcTi/GoVVTXJHISKyeceKalHR0Goz6zt0CPRwRJCHI44U1sgdhYjIoiW1F3BtYX2HDuP7++JQXjXUbWzXR0R9Y0tr5BjCwEB3+LjYY1cm2y2RdWLhgbplX04VnOyUGBriKXcUs7N4eDAA4HcuMk1EJLstqaVQCGBKrJ/cUUxuRLhX58keERH1TlJ+NZzslIgLdJM7ismM7++DFo0OSQUcQ4iob44U1CDE0wn+7o5yR7EICoXAhGhf7MqqhCRJcschMjgWHqhb9uVUIjHSi4sDnUeYtzNGhHti9TGu80BEJLfNaWUYGeEFLxd7uaOYXEK4J4pqmlHGHrFERL2WVFCN4WEeUNlQu74xUd5QKgT2ZHGdByLqm8P51TbTqs5QJsb4oqKhBWmn6+WOQmRwtvNpinqttrkNaafrMSqSbZYuZP7QIKSW1CG3olHuKERENut0rRoni+swPS5A7iiySGjvpct2S0REvdPcqkVKcZ3NtQhxc7TDsFAP7OY6D0TUB8U1zThdp+b6Dj305zoPbLdE1oeFB7qk5PYLGCNZtb6geUODAABrj3PWAxGRXLaklQEAZgy0rfUdOgwO9oCdUnCBaSKiXjp2qgYanWST5z2Ton1xtLAGtU1tckchIgvV0a7NFo+hfRHk4YRof1cuME1WiYUHuqTD+dVQCGB4mKfcUcxWiKcT4sM8se4ECw9ERHLZklaKUC8nxPi7yh1FFo52SgwK9mCPbiKiXupYWDrBxmY8AMDkWD/oJGBXFi98EVHvJOXXwNFOgYFB7nJHsTgTo31xILcK6jat3FGIDIqFB7qkIwXVGBDoDlcHldxRzNr8oYE4UVSHgsomuaMQEdkcdZsWu7IqMHNgAIQQcseRTUKYp/6OXa1O7ihERBYnqaAa/Xxd4G2D6wTFh3nCzVGF7RllckchIguVVFCNYSGesLOhNXIMZVKML1o0OhzK4w1EZF14NKCL0ukkJBfUsEdfN8wb0t5uibMeiIhMbk92BdRtOkyPs802Sx1GRHhB3abj4nRERD0kSRKS8qttbn2HDiqlApNifLE9oxySJMkdh4gsjLpNi5PFtUiI8JQ7ikUaG+UDe6WCxV+yOiw80EVlljWgvkVjsx/AeyLM2xnDQz24zgMRkQw2p5bB2V6JMVHeckeRVUJ7W8QjbLdERNQjBVVNqGxsxQgbvmg2JdYPpXUtyChtkDsKEVmYk8W1aNNKvHbUSy4OKozu541t6VxgmqwLCw90UYfz9RcuRnBxoG6ZNzQIx07VorCK7ZaIiExFkiRsSSvDpBhfOKiUcseRVaiXE3xd7XGksEbuKEREFqXzvMeGL5pNjvUDAN5xS0Q9lpRfA8C2j6F9NXWAHzLLGng9iawKCw90UUkF1fB2sUekj7PcUSzC/PZ2S1xkmojIdFJL6lFSq8aMuAC5o8hOCIH4MC8ks/BARNQjB/Oq4eaoQmyAm9xRZBPk4YQBAW7YnsE7bomoZ5IKqhHm7QQ/Nwe5o1isae0tY7fxGExWhIUHuqikgmqMCPe06YU6eyLcxxlDQtyx5vhpuaMQEdmMLWmlAICpcX4yJzEPCeGeyClvRG1Tm9xRiIgsxsG8KiRGeEGpsO3znsmxvjiYW43GFo3cUYjIQkiS1H7tiLMd+iLK1wXh3s7YlsZZZ2Q9WHigC6ppakVOeSMSOHj0yPyhQThaWINT1ZweR0RkCpvTyjA81AP+bo5yRzEL8e3rPBw9VSNrDiIiS1HV2IqssgYkRtr2OkEAMHWAP1q1OuzOqpA7ChFZiOJaNUrrWlh46CMhBKYN8MPu7Aqo27RyxyEyCBYe6IKOFNQAYI++nupot/THCc56ICIytoqGFiQX1mA62yx1GhbqASHAdktERN10MK8KADC6HwsPoyK94eagwqbUUrmjEJGFSOIaOQYzdYA/1G067M+tkjsKkUGw8EAXdDi/GkqFwPAwD7mjWJRIXxcMCnLH2uNc54GIyNi2pZdDkoAZA/3ljmI23BztEO3nysIDEVE3Hcqrgr1KgWGhPO+xVykwNc4fm1PLoNVJcschIguQVFANRzsF4oJsd40cQxkb5QMHlQJb2W6JrAQLD3RBSQXVGBjkBmd7ldxRLM6CYUFIKqhBcU2z3FGIiKza5tRSBLg7YHCwu9xRzEp8mCeSC2sgSbxoRER0KQfyqhEf6gkHlVLuKGZh5kB/VDa2IrmwWu4oRGQBkgpqMCzUE3ZKXmLsKyd7Jcb398HmtFJ+jierwKMCnZdWJ+FoYQ2nyvXSvCGBANhuiYjImFo0WuzIKMf0uAAIYduLgZ4tPtwTVY2tKKjiekNERBfT1KrByaJaJEbyvKfD1AH+UCkENqSw3RIRXZy6TYuU4lpeOzKg2YMDUVjVjLTT9XJHIeozFh7ovNJP16OxVcvBo5ei/FwRF+jGdktEREZ0ILcKja1azGSbpXN0LDDNdktERBd3pKAGGp2EUVzfoZOHkx3GRvlgEwsPRHQJJ4pq0aaVMCLcU+4oVmPGQH8IAWw4yWMwWT4WHui8Dhfop9WOjGDhobfmDw3CofxqnK5Vyx2FiMgqbU4tg4NKgfH9feWOYnYGBLjByU6JIwU1ckchIjJrB/OqIATPe842c6A/sssbkVPeIHcUIjJjSe3XjkbwGGow/m6OGBHuhQ0p7KBBlo+FBzqvI/nV8HV1QKiXk9xRLNb8oUEAgD9OcNYDEZGhSZKETamlmBjtCyd79uQ+m0qpwNAQD854ICK6hIN5VRgY6A53Rzu5o5iVmYMCAAAbOeuBiC4iKb8GET7O8HV1kDuKVZk9KAAni+twqpptU8mysfBA55VUUI0R4Z7smd0H0f6uiA1wxdrjrFITERlaRmkDTlU3Y8bAALmjmK34cE+kFNehRaOVOwoRkVlq0+qQlF+DUVzf4RyhXs4YFOSOTaksPBDR+UmShMMF1WzRbQSzB+vXDWXxlywdCw90jsqGFuRVNnGqnAHMHxqEg/lVKKtjuyUiIkPanKb/ED49jus7XEh8mCdatTqklnBhOiKi8zlZXIfmNi3Xd7iAmYMCcDi/GpUNLXJHISIzdKq6GeX1LVzfwQj6+bogNsCV6zyQxWPhgc6R1N4Pmn1O+27B0CBIEvDHSc56ICIypM2pZRgS4o5AD0e5o5itzgWm23vvEhFRV4fyqgAAoyNZeDifOYMDoJOADbzjlojOo2N9hwTOeDCK2YMCcSCvCtWNrXJHIeo1Fh7oHEkF1VApBIaGeMgdxeLFBLgh2t8Va49znQciIkOpbGhBUkE1ZsSxzdLFBHk4wt/Nges8EBFdwIHcKkT4OMPfnUXs8xkU5I4IH2eeyxDReR0pqIGzvRJxgW5yR7FKswcHQKuTsDmtTO4oRL3GwgOdIym/GoOD3eFox8U6DWH+0CAcyK1CeT2nKBMRGcK29HJIEjCT6ztclBAC8WGeLDwQEZ2HJEk4lF+NUZztcEFCCMwfGoQ92ZWo4h23RHSWpIJqDAv1gErJS4vGMDTEA0EejljPDhpkwXh0oC40Wh2Onarl+g4GNH9oIHQSOFgQERnI5rRS+Ls5YHCwu9xRzF58uCfyKps4RZuI6CzZ5Q2oamzlwtKXsGBoELQ6CRt4LkNEZ1C3aZFSXMeFpY1ICIG5QwKxPaMcdeo2ueMQ9QoLD9RF2ul6NLdpOXgY0IAAN0T5uXCKMhGRAbRqdNiRUYEZA/2hUAi545i9hDD9eJ58qkbeIEREZuZgnr43OWc8XNzgYHeEeztjDc9liOgMx07VQqOTeO3IyBYND0arRoeNXGSaLBQLD9TF4Xz9B3DOeDAcIQTmDwnCvpxKVDaw3RIRUV8cyK1CQ4uG6zt007BQDygEkFxQI3cUIiKzciC3Cr6u9ujn6yJ3FLN2Zrslzp4jog5/LiztKW8QK5cQ5okQTyesPlYsdxSiXmHhgbpIKqhGgLsDgj24wJohzR8a1N5uiVVqIqK+2JRaCgeVAhOifeWOYhFcHFSIDXDjOg9ERGeQJAn7cioxJsoHQnD23KV0tltKYbslItJLyq9GpI8zfFwd5I5i1YQQWDgsCDszK1DTxOIvWR4WHqiLpIJqjAj34gdwAxsY5IZ+vi5Yd4JTlImIekuSJGxOK8WEaF842SvljmMx4sM8cfRUDSRJkjsKEZFZKKhqQkmtGmOjfOSOYhGGhLgjwscZq47yXIaI9J/Jkwqq2SnDRBYND4ZGJ3HdULJILDxQp7I6NQqrmjGSg4fBCSEwb0gg9mRXoopTlImIeiWzrAGFVc2YMdBf7igWJT7MEzVNbcirbJI7ChGRWdiXUwkAGBfF9R26QwiBy+JDsDu7AqV1arnjEJHMCqqaUNHQymtHJjI42B2RLP6ShWLhgTp19Ojj4GEc8zumKLNKTUTUK5tTywCA6zv0UHx7790j7eM8EZGt25ejX9+hv5+r3FEsxuXxwZAk4Pdk9hknsnUda4MmRrB4awr6dkvB2JNdgfJ6rhtKloWFB+p0OL8a9ioFBgd7yB3FKg0Odke4tzPWHGeVmoioNzanlmJwsDsCuQ5Rj8T4u8HFXsl1HoiIwPUdeivKzxXDQz3w65EiuaMQkcwO51fDzUGFGH8Wb03l8oQQ6CRgZTKPwWRZWHigTofyqzE81AP2Kv5aGIMQAouHB2N3VgXK6jlFmYioJ6oaW5FUUI0ZAznboaeUCoGhoR4sPBARges79MXlCSFIKalDRmm93FGISEaH86uREOEFhYLFW1OJ9ndFfJgnVhw+xXXbyKLwCjMBANRtWpwoquXiQEZ2eUIwdJyiTETUY9vSy6CTgJlc36FX4sO8kFpSB3WbVu4oRESy4voOvbdwWDCUCoHfOOuByGbVqduQXlqPkeG8dmRqV40MRdrpepwsrpM7ClG3sfBAAIATRbVo00rs0Wdk0f5uGBrigd84PY6IqEc2p5bBz80BQ9gOsFfiwzzRppV4okJENo/rO/Sen5sDJkb7YmVyMXQ63nFLZIuSC2ogSVwbVA6LhgXBXqnAisOn5I5C1G0sPBCAPxcHGtG+ACUZzxUJIThRVIdMTlEmIuqWVo0O2zPKMSPOn1O6eymhfXxnuyUismWSJGE/13fokysSQlBU04xD7eePRGRbDudXQyGA4WG8GcjUPJ3tMWtQAH4/WoxWjU7uOETdwsIDAdAPHv18XeDj6iB3FKu3aLh+ijIXZiMi6p6DeVVoaNFwfYc+CHB3RJCHIwsPRGTTCquaUcz1Hfpk9uAAONsreS5DZKOSCqoxINAdbo52ckexSVeNDEFVYyu2ppfJHYWoW1h4IEiShKSCaoxgjz6T8HNzwKQYTlEmIuquTamlsFcpMCGaF4r6Ij7ME8mFvEOViGwX13foO2d7FeYMDsSaY8Vo0XDdICJbotVJOFJQg0S2WZLN5Bg/+Lo64Ge2WyILwcIDIb+yCRUNrezRZ0IdU5QP5FXJHYWIyKxJkoTNqWWY0N8HzvYqueNYtPgwTxRWNaOyoUXuKEREstidXQFfVweu79BHl8UHo06twda0crmjEJEJZZTWo6FFw2tHMlIpFbgiIRhb0spQXs/P9GT+WHigzvUdOHiYzqxB7VOUkzhFmYjoYrLKGlBQ1cQ2SwYQH+YJgOs8EJFt0ukk7M6qwMRoru/QVxOjfeHrao+VyTyXIbIlh3jtyCxcNyocGp2EHw8Vyh2F6JJYeCAcLqiGm6MKMf6888dUnO1VmDskEGuPl0DdxinKREQXsjlN3790xkB/mZNYvqGhHlAqBAsPRGSTUk/XoaKhFZNi/OSOYvFUSgUWDQ/G5tQy1Da3yR2HiEwkKb8afm4OCPVykjuKTYv2d8W4KB98u78AWrbvJjPHwgPhcF41EsK9oFDwzh9TuiIhBPUtGmxJ46JAREQXsjm1FIOC3BHkwROcvnK2VyE2wI2FByKySbsyKwAAE2N8ZU5iHS6PD0GrVod1x0vkjkJEJnI4vxojw704a8wM3DQ2AkU1zdiewetJZN5YeLBxVY2tSC+tx5h+XGDN1Mb394W/mwN+YbslIqLzqm5sxeH8aszkbAeDSQj3RHJhDXS8O4qIbMyurArEBrgiwN1R7ihWYVioByJ9nPH70WK5oxCRCZTVq1FQ1cQ2S2Zi9uAA+Lk54Ot9BXJHIbooFh5s3IHcSgDA2CgfmZPYHqVC4LL4YGxLL0NVY6vccYiIzM62jDLoJHB9BwOKD/NEvVqDnIpGuaMQEZmMuk2LA7lVmBjNNkuGIoTA4vgQ7M2pRFmdWu44RGRkSfk1AIARLDyYBTulAktGhWFrehkKq5rkjkN0QSw82Li92ZVwslNiWKiH3FFs0uUJIdDoJKw5xjuFiIjOtim1DH5uDhgawjHKUBK4wDQR2aCDeVVo0egwiW2WDGrx8GBIErD6GNstEVm7w/lVsFcpMCTEXe4o1O760eEQAL47wFkPZL5YeLBx+3KqkBjpBTslfxXkMCjIHQMC3PDrEbZbIiI6U6tGhx3p5Zg+wJ9rEBlQfz9XuDmocKSgWu4oREQmsyuzAnZKgTFRbC9rSNH+rhgU5I6VbLdEZPUO51djWIgHHFRKuaNQu2BPJ0yPC8CPhwrRqtHJHYfovHi12YZVNrQgvbSebZZkJITA5QkhSCqoQS7bXhARdTqUV4X6Fg1mcH0Hg1IoBIaFeXDGAxHZlJ2ZFRgZ4QVne5XcUazOZfHBOFpYg/xKnssQWSt1mxbHi2q5voMZumlsOCoaWvHHydNyRyE6LxYebNj+3CoAwLj+LDzI6YqEECgE8EvSKbmjEBGZjU2pZbBXKTCRbTEMLj7ME2mn69HcqpU7ChGR0Z2uVSOlpA6TY7m+gzEsHB4MAFjFWQ9EViupoBptWomzxszQ5Bg/RPg448s9eXJHITovFh5s2L6cSjjbK9k7W2aBHo6YGOOHX5KKoNNJcschIpKdJEnYnFaKcVE+vDvVCOLDvKDVSThRXCt3FCIio9uaXgYAmBEXIHMS6xTi6YRRkV5YmVwMSeK5DJE1OpBbBSGAkREsPJgbhULg1nGROJRfjaOc0UxmiIUHG7YvpxKJkd5c38EMXD0yFEU1zdiXUyl3FCIi2eVUNCK/sgkz2WbJKOI7FpguqJE1BxGRKWxOLUWolxNiA1zljmK1FseHILOsAWmn6+WOQkRGsD+nCoOC3OHhZCd3FDqPa0eFwc1BhU925codhegcvOJsoyoaWpBR2oCxnCpnFmYPCoCboworDrPdEhHR1jT93anT4lh4MAY/NweEeDpxnQcisnrqNi12ZVVgRpw/hBByx7Fa84cEQiGAdSfYY5zI2rRqdEgqqMbofrx2ZK5cHVS4dlQY1h4vQUlts9xxiLpg4cFG7cnW31k/jgtLmwVHOyUWDgvG2hMlqFe3yR2HiEhWm1PLMCDADaFeznJHsVrx4Z4sPBCR1dubXQl1mw7TB7LNkjH5uDpgVKQ31rPwQGR1jp2qQYtGhzH9eO3InN02PhI6ScIXe/LljkLUBQsPNmp7ejk8ne0wLNRT7ijU7uqRoVC36bDuOD+wE5HtqlO34WBeFaazzZJRJYR5oqimGWX1armjEBEZzabUUjjbKzGGd+oa3ZzBgUgvrUduRaPcUYjIgPbnVgEAZzyYuTBvZ8wZHIjvDhSgqVUjdxyiTiw82CCdTsL2jHJMivGDUsEpx+ZiRLgnonxd2G6JiGzazowKaHQSZrDNklFxnQcisnZanYT1J0sxJdYPjnZKueNYvTlDAgEA60/yJioia7I/twqxAa7wdrGXOwpdwp0T+6G2uQ0/85oSmREWHmxQSkkdKhpaMDXWT+4odAYhBK4aGYoDeVXIr+SdQkRkmzanlcLT2Q4J4V5yR7FqQ0I8oFIIHGG7JSKyUvtzK1HR0IKFw4LljmITQjydMDTEA3+w3RKR1dBodTicV8XZDhZiZIQXhod64LPdedDpJLnjEAFg4cEmbc8oBwBMivWVOQmd7coRIRAC+DmpSO4oREQmp9VJ2JZejqmxnJFnbI52SgwJ8cCB9unzRETWZs2xEjjZKTEtjjdbmcrcIYFILqzB6Vq28SOyBieL69DYquX6DhZCCIE7JvZDTkUjtqSVyR2HCAALDzZpe0Y5Bge7w9/NUe4odJYgDydMjPbFz4dPsUJNRDYnubAGVY2tXATURMb198HRwho0trAPLBFZF41Whz9OnMaMgf5wtlfJHcdmzBmsH783pHDWA5E16LhBhevkWI75Q4MQ4umE97dnyx2FCAALDzantrkNSfnVmMI2S2br6pGhKKppxr7cSrmjEBGZ1Na0MigVAlNiOEaZwvj+PtDoJBzM46wHIrIu+3KqUNnYyjZLJhbt74YoPxdsOFkqdxQiMoD9uVXo5+sCf3fetGop7JQK3DWpHw7lV+MQP+OTGWDhwcZsSSuFRidh1iDeTWquZg8KhJuDiotME5HN2ZxWhpERXvBwtpM7ik1IjPCGnVJgbw4L3URkXX49UgRXBxWmDmAh29RmxPnjQG4VZ9MRWTitTsKB3ErOdrBA140Kg6ezHd7fniN3FCIWHmzNHydOI8DdAcNDPeWOQhfgZK/EwuFB+OPEaX5gJyKbUVzTjNSSOsyI85c7is1wslciPswT+7JZeCAi61GvbsPa4yVYNDwYjnZKuePYnGkD/NGq1WEPxxYii3aiqBZ1ag3G9ef6DpbG2V6FW8ZFYlNqKTJL6+WOQzaOhQcb0tyqxfaMcsweFAgFF+00a1ePDEVTqxZrj5fIHYWIyCS2pusXQJsxkIUHUxoX5YPjRbWoU7fJHYWIyCBWHS1Bc5sW140KkzuKTUqM9IaLvbJzXCciy7Q7uwIAML6/r8xJqDduGx8JRzsFPtzBWQ8kLxYebMj2jHKo23SYOyRQ7ih0CSPCvdDP14XtlojIZmxJLUO4tzP6+7nKHcWmjOvvC50EHMhhD1gisg4/HipEbIArhod6yB3FJtmrFJgQ7Yvt6eWQJEnuOETUS3uyKhEX6AY/Nwe5o1AveLvY47rEMPyWXISS2ma545ANY+HBhmw4eRoeTnYYzR59Zk8IgatHhmJ/bhUKKpvkjkNEZFTqNi32ZFdi2gA/CMEZeaaUEO4Je5WC6zwQkVVIP12P5MIaXJsYxvFERlMH+KOophmZZQ1yRyGiXlC3aXEwr4qzHSzcXZOioJOAT3flyh2FbBgLDzZC3abFxpRSzBoUADsl/7NbgisSQiAE8HMSZz0QkXU7lFeN5jYtpnARUJNztFNiZLgX9rIXNxFZgc/35MJBpcCVI0LljmLTOhb13sZ2S0QWKSm/Gi0aHSZEc30HSxbm7YwFQ4Pw7f4C1DazrSrJg1egbcTGlFLUt2hwZUKI3FGom4I9nTAx2hcrDp+CTsdpykRkvXZklsNeqcDYKJ7cyGFCtA9SSupQXt8idxQiol6rbGjBz0lFuHJEKLxd7OWOY9OCPZ0wIMANW9PK5Y5CRL2wO7sCSoVgtwwr8NcpUWhs1eLrfflyRyEbxcKDjfgl6RSCPRx5UcfCXJMYhqKaZuzhnahEZMW2p5cjMdILzvYquaPYpKkD9At6885UIrJk3+wvQKtGhzsnRsodhQBMjfPDwbwqNLRo5I5CRD20O6sSw0M94OZoJ3cU6qPBwR6YFOOLz3bnQd2mlTsO2SAWHmxAeX0LdmRW4LKEECgU7HVqSWYPCoCHkx1+OFQodxQiIqM4XatGemk9psSyzZJcBge7I8DdAVtZeCAiC6Vu0+LLvfmYEuuHaH83ueMQgCmxftDoJOzjDVREFqVO3YZjp2owMZrrO1iLe6f0R0VDC349UiR3FLJBLDzYgN+PFkOrkwzaZum3337DsGHD4ODggH79+uH111+/6PMfeughCCHw6KOPdnk8LS0NY8aMgYeHB5YsWYKGhq4LkO3YsQMhISHnPH4+n3/+OYQQ533u8uXL4ev758CZl5cHIUTnl4uLC/r3748bb7wRO3fuPOf1t912GxITEy+ZwdAc7ZS4IiEE60+eRk1Tq8n3T0RkbDsy9G0YJhuh8MCxqnuEEJg2wB87MyrQptX1+PVERHL77kABKhpa8NcpUb16PccLwxsZ4QUnOyV2ZrLdEpEl2Z9TBZ0EjDeDwgOPzYYxrr8PhoS446MdOWzjTSbHwoOVkyQJ3x0owPAwT8QEGObun927d+PKK6/E6NGjsWrVKtxxxx144okn8Oabb573+SkpKfjkk0/g7u5+zs9uu+02REdH48cff0RKSgpeeOGFzp/pdDo8+OCDePHFF+Hq6mqQ7Gd79dVXsXfvXqxduxZPP/00KisrMXnyZDzzzDNG2V9vXJMYilaNDr+xOk1EVmh7Zjn83RwQF2jYO1Q5VvXMtDh/1LdocDCvymDbJCIyheZWLd7Zmo2xUd4Y37/nF8o4XhiHg0qJsVHe2JlZYfJ9E1Hv7c6qgKOdAgnhnvLm4LHZYIQQ+Ovk/sipaMTG1FKT7JOoA5spW7m92ZXIKmvAq9cMN9g2n332WUyYMAEff/wxAGD27NmoqanBs88+i7/97W+wt++6mNv999+PBx98EF999VWXxxsaGrB//36sWrUKfn5+qKmpwauvvto5CHz66aews7PDzTffbLDsZxswYADGjh0LAJgyZQpuu+02LF26FMuXL8eUKVMwdepUo+27uwYHe2BIiDt+PHQKt03oJ3ccIiKD0eok7MqswKxBARDCsK0AOVb1zMRoX9grFdiaVtarC3dERHL5al8eKhpa8N5NI3r1eo4XxjMpxg9b01NQWNWEMG9nk+6biHpnd1YFRkV6w0GllDUHj82GNW9IIMK8nfDB9mzMGRxo9P0RdeCMByv35d58eDnbYeGwIINtMzk5GbNmzery2OzZs1FdXY29e/d2eXzFihVIS0vDk08+ec52Wlv1rYOcnJwAAM7Ozp2P1dXV4d///jf++9//Gvxi1KUsW7YMwcHBeP/9902634u5LjEMKSV1OFFUK3cUIiKDOXqqBrXNbUZZ34FjVc+4OKgwtr8PNqSUQpI4BZuILENDiwbvb8/BpBhfjIr07tU2OF4Yz6QYfSF7VxZnPRBZguKaZmSWNZjF2ms8NhuWSqnAXROjkFRQg0Oc4UwmxMKDFSupbcbG1FJcNyocjnaGq1ar1epzqssd36empnY+1tzcjEceeQQvvfQSXFxcztmOt7c3IiMj8b///Q9VVVX48MMPO/vZPffcc5g5cybGjRvX43xarRYajabLl07X/Z7VSqUS06dPx759+3q8b2NZHB8CB5UCPxzkItNEZD22p5dDCBhl8TqOVT23YGgg8iubcLK4zmDbJCIypi/25KGqsRUPz4rt9TY4XhhPtL8rAt0duc4DkYXYlq7/WzWHwgOPzYZ3TWIovJzt8P72HJPtk4itlqzY57vzIEkSbhwTbtDtRkdH4+DBg10eO3DgAACgqurPyumLL76IoKAg3HTTTRfc1rvvvotrrrkG//rXvxATE4N33nkHWVlZ+Pjjj3H8+PFe5fP09Dzv4z4+Pt3eRmhoKEpLzaf3nYeTHeYOCcRvyUV4asFAgxaSiIjksiOzHMNDPeHlYn/pJ/cQx6qemz0oEE/9egKrj5VgSIiHwbZLRGQMdeo2fLgjB9Pj/JEQ7tXr7XC8MB4hBCbF+GL9ydPQ6iQoFaa9o5iIemZ7RhlCPJ0Q7W+ctQ56gsdmw3O2V+HmcZF4a3MmssoazOK/M1k/zniwUjVNrfh6Xz4WDQ82eD/Ne+65B7/99hs++ugjVFdXY/369Xj99dcBAAqF/lcqNzcXr7766iWnrM2bNw9lZWVIT09HamoqwsPD8fDDD+Ohhx5CaGgo3nnnHYSHhyM8PBzvvvtut/Lt2LEDBw8e7PL1l7/8pUfv0RzbTFyXGIZ6tQbrT56WOwoRUZ/VNLXiaGENJhvpjiqOVT3n5WKPCdG+WHO82CzHQSKiM326Kxe1zW19mu0AcLwwtkmxfqhTa3DsVI1sGYjo0lo1OuzOqsTkWD+Ttx06Hx6bjePWcRFwUCnw0Q7OeiDT4IwHK/XFnnw0tmpx79T+Bt/2HXfcgaNHj+Lee+/F3XffDWdnZ7z88su4//77ERioX6TmySefxLx58zBgwADU1NQAAHQ6HVpaWlBTUwMPD4/OgcHZ2RmxsfoTho0bN+Lo0aP44YcfcPToUTz99NPYs2cPAGDcuHGYOHEihg0bdtF8CQkJcHXtWrldvXp1j95jUVERAgICevQaYxsb5YMwbyf8cLAQl8WHyB2HiKhPdmVVQCcZbyo3x6reWTAsCI+vOIbjRbUYFupp0G0TERlKTVMrPtmZizmDA/o8Q4vjhXFNjPaFEMCuzIo+zUwhIuNKKqhGQ4sGUwfI32YJ4LHZWHxcHXBtYhh+OFiIR+bEwt/N0aT7J9vDGQ9WqKFFg8/25GLmwADEBbobfPtKpRJvv/02ysvLcezYMZSWlmLs2LEA0Pm/6enp+OWXX+Dl5dX5VVhYiLfffhteXl4oKio6Z7tarRYPPfQQXnnlFTg5OWHbtm2YPn064uLiEBcXhxkzZmD79u0Gfz9n02g02LJlS6/6/BmTQiFw7cgw7MmuREFlk9xxiIj6ZHt6OdwdVRgeapyWPhyremfOoEDYKQVWJhcbdLtERIb08c5c1Ldo8FAfZzsAHC+MzdvFHkOCPbAzkwtME5mzbenlUCkExvfvfqsgY+Kx2XjumNgPrVodvtvPNUTJ+DjjwQp9uD0bNU1tuH96tFH303FgB/Q988aPH4+4uDgAwMcff4yGhoYuz1+yZAmmTJmCe++9F35+51bR33vvPXh5eeG6667rfKyp6c8L7I2NjSaZivbss8+iuLgY99xzj9H31VNXjQzFG5sy8OOhQjw6Z4DccYiIekWSJOzILMekGD+olMa9B4JjVc94ONthRlwAfjtShCfmxsFexXtUiMi8VDW24rPduVgwLMigN1lxvDCeSTG++HBHDurVbXBztJMtBxFd2PaMciRGepnd3yiPzYbXz9cFUwf44Zv9+bh3an9+3iejYuHBypTUNuPDnTlYNDwYw8M8jbKPffv2YdeuXYiPj0ddXR2+++47rF+/Hrt27ep8TmJi4jmvc3R0RFhYGKZOnXrOz6qqqvDMM89g/fr1nY9NnjwZjz/+OD799FNIkoQtW7bgpZdeMuh7SU9Ph6+vL1pbW5Gbm4vvv/8ef/zxB5YvX44pU6YYdF+GEOzphKkD/PHDoUI8ODMGdka+YEdEZAwZpQ0orWvB5Fhfo+2DY1XvXTcqDH+cPI0taWWYOyTQ4NsnIuqLD3fkoKlNi4dmxhhkexwvjG9SjB/e3ZaNfTlVmDXIvNrZEhFQWqdGakkdnpgbJ3eUTjw2G9et4yJx++cHsf7kaSwaHixLBrINLDxYmVfXZ0CnAx434t3wdnZ2+OGHH7B8+XIoFApMmjQJu3fvxtChQ3u9zeXLl2Px4sUYMWJE52MJCQl45ZVX8NRTTwEAXn31VQwfPrzP+c/06KOPAtAPTkFBQRg3bhx27NiBSZMmGXQ/hnTD6HDc9eUhbE4txdwhQXLHISLqse0ZZQBgtIWlAY5VfTEpxhcB7g746VAhCw9EZFaqGlvx5d48LBoWjGh/N4Nsk+OF8Y2I8ISzvRI7M8tZeCAyQ9szygHAbNZ3AHhsNrYpsX6I8HHGF3vyWHggoxIGnkJk+iXZqdOJolosensX7p4UhX/OHyh3HDISjVaHSa9sRbS/K766c4zcccgwhNwBzAjHERtw08f7UVavxoaHzG9mGem98kca3t+ejb3/nIEAdy46Z+Y4hvyJY4iV+8/6NLy7LRsb/jEZMQGGKTyQadz+2QHkVTZh66NT5Y5C5+I48iebHEfu+yYJh/KrsO+fMzoXYybr9/HOHDy/JhWr75+IISHGWXePbMYFDxzs02IlJEnC/61JhaeTHf42zbhrO5C8VEoFrhsVhp2ZFVxkmogsTlOrBgdyqzA5xnzuqKJzXZMYBp0EfHegQO4oREQAgJqmVnyxJx/zhwax6GCBJsX4IbeiEYVVPH8hMicarQ47M8sxJdaPRQcbc01iGJzslPhyb57cUciKsfBgJbaklWFvTiX+MTMWHk7mtRgQGd6SUeFQKgS+O8gLQkRkWfblVKJVq8MUM5rKTefqWHTu630FaNFo5Y5DRIRPd+WioUWDB6YbZm0HMq2OdZ12ZlbInISIzpRcWIM6tQZTYv3ljkIm5uFkhytGhGBlcjGqG1vljkNWioUHK9Cm1eGFtamI8nXBDWPC5Y5DJhDo4Yjpcf746VAhWjU6ueMQEXXb1rRyONkpMbqft9xR6BJun9APFQ0tWHOsRO4oRGTjGlo0+HxPHuYODsSAQM52sET9/VwR5OGInZnlckchojNsSy+HUiEwMcZX7igkg1vGRaBFo8MPhwrljkJWioUHK/D9gQJklzfiyXlxsFPyP6mtuGFMOCoaWrExpVTuKERE3SJJErZllGFCtA8cVEq549AlTI7xRX8/F3y2Ow8GXhOMiKhHfjxYiDq1Bn+dEiV3FOolIQQmxfhid1YFtDqOKUTmYntGORLCPNk5w0bFBbpjTD9vfL0vHzoem8kIeJXawtWp2/DGpkyM6eeNWYMC5I5DJjQ5xg8hnk749kC+3FGIiLolp6IRhVXNmDKAU7ktgRACt03oh+NFtTicXy13HCKyURqtDp/uzsWoSC8khHvJHYf6YFKMH+rUGhw7VSN3FCICUFavxvGiWkxlC1SbdtPYCJyqbsZ2zkgjI2DhwcK9ty0bVY2t+PeCQVwIyMYoFQLXjw7D7qxK5FU0yh2HiOiStqaVAQCmxvLkxlJcNSIE7o4qfLIrV+4oRGSj/jh5Gqeqm3HXJM52sHQTon0hBNd5IDIX29L0F5qnx/EmVls2Z3AgfF0d8M0+3tRKhsfCgwU7Vd2ET3bl4sqEEAwN9ZA7Dsng2sQwqBQC3x3gItNEZP62Z5Qj2t8VYd7OckehbnK2V+GmsRH44+Rp5LLITUQmJkkSPtqRg36+Lpg5kBfGLJ23iz2GhnhwnQciM7E5rRRBHo4YGMS1c2yZvUqB60aFYktaGYpqmuWOQ1aGhQcL9p/16RAAHp0zQO4oJBN/d0fMHBiAnw6fQotGK3ccIqILamrVYH9OFWc7WKDbJ/SDvVKBD7Znyx2FiGxMUkE1jp6qxR0T+0Gp4OxuazApxhdJBTWoV7fJHYXIprVotNiZWYHpcf7snkG4fnQ4JADf7edNrWRYLDxYqOTCGqxMLsZdk/oh2NNJ7jgkoxvGhKOqsRXrT3KRaSIyX3uyKtGq1WFaHNd3sDR+bg64NjEMPyedwulatdxxiMiGfLOvAK4OKlyZECJ3FDKQidF+0Ook7M2ulDsKkU3bn1OFplYtZgzkZ3MCQr2cMX2AP74/WIhWjU7uOGRFWHiwQJIk4f/WpMDX1R73To2WOw7JbGK0L8K9nfHtfvbjIyLztS2jDM72SiRGcmFQS3T35CjoJOCTXTlyRyEiG1Hd2IrVx0twRUIIXBxUcschAxkR4QlneyXXeSCS2Za0MjjaKTC+v6/cUchM3Dg2HBUNLdiYwptayXBYeLBA60+W4mBeNR6aFQtXfgi3eQqFwJLRYdiXU4Wssga54xARnUOSJGxLL8eEaF84qJRyx6FeCPN2xqJhQfh2fwFqmlrljkNENuDnpFNo1ehww5hwuaOQATmolBgb5cN1HohkJEkSNqeVYkJ/Xzja8bM56U2J9UeIpxO+5iLTZEAsPFiYVo0OL61LRYy/K65LDJM7DpmJa0ZykWkiMl9ZZQ04Vd2MqQO4voMlu2dqfzS2avHlXp6MEJFxSZKEb/cXYGSEFwYGucsdhwxsUowv8iqbUFjVJHcUIpuUVdaAwqpmzBgYIHcUMiNKhcANY8KxN6eSN7WSwbDwYGG+3pePvMom/GvBQKiU/M9Hen5uDpgzOBA/J52Cuo2LTBORednQPl13Jk9uLFpcoDtmxPnjs925aGrVyB2HiKzY3pxK5FQ04obRnO1gjSbF6G9EYLslInlsTisDAEzn2mt0lutGhcFOKfANW3mTgfDKtQWpbWrDW1syMTHaF1NjedcodXXzuAjUNLXhtyNFckchIurijxOnkRDuiQB3R7mjUB/dO7U/qpva8N2BQrmjEJEV+2Z/ATyc7LBgWJDcUcgI+vu5INjDETsy2G6JSA5bUsswONgdgR78bE5d+bo6YO6QIPx8+BSaW3lTK/UdCw8W5M3NGahrbsO/5g+EEELuOGRmxvTzxqAgd3y6OxeSJMkdh4gIAFBU04zjRbWYMzhQ7ihkAImR3hgb5Y0Pd2Rzhh0RGUV5fQvWnziNq0eGsve4lRJCYMoAP+zKquBYQmRi1Y2tOJRfhRmc7UAXcNOYcNSpNVh1rFjuKGQFWHiwEBml9fhybz6uHx2OQcHsc0rnEkLg9gmRyChtwJ7sSrnjEBEBADacPA0ALDxYkQemx6C0rgU/HT4ldxQiskI/HS6ERidxUWkrN2dwIBpaNNjFdktEJrU9oxw6CZjOFqh0AaP7eSPG3xXfcJFpMgAWHiyAJEl4ZtVJuDqo8MjsAXLHITO2aHgwfFzs8dnuXLmjEBEB0LdZGhDghn6+LnJHIQMZ198HI8I98f62bLRpdXLHISIrotPpF5UeG+WN/n6ucschIxrf3xfujiqsO3Fa7ihENmVzWhl8Xe0xLMRD7ihkpoQQuHFMOI6eqsXxU7VyxyELx8KDBVh/shS7syrxyOxYeLvYyx2HzJijnRI3jo3A5rQy5FU0yh2HiGxcZUMLDuZVYc5g3lFlTYQQuH9GDIpqmvFrEtcVIiLD2ZFZjlPVzbhxTITcUcjI7FUKzBwUgI0pp9GqYRGbyBTatDpsTy/DtAH+UCjYvpsu7MqRoXCyU+JrznqgPmLhwcyp27R4fk0K4gLdcMNoTjemS7tpbDhUCoHP9+TJHYWIbNzm1DLoJGA22yxZnamxfhga4oF3tmVBw1kPRGQg3+wvgI+LPdvz2Yj5Q4JQp9Zgbw7bxBKZwuH8atSpNZgxkOs70MW5O9rhsvhgrDxahNrmNrnjkAVj4cHMfbgjB6eqm7F00SColPzPRZfm7+aIRcOCseLwKdSrOUAQkXxWHStGuLczBnNtIqsjhMDfp0cjv7IJq4+VyB2HiKxASW0zNqeW4tpRYbBX8bzHFkyM8YWrgwqrj3IBUyJT2JJWBnulAhNj/OSOQhbgprERULfp8O3+ArmjkAXjJzozVlzTjHe3ZWHB0CCM7+8rdxyyILdP6IeGFg1+OFgodxQislFldWrszqrA5fHBEIJTua3RrIEBGBDghre3ZkGnk+SOQ0QW7vsDhdBJwPWjOMvbVjjaKTF3SCDWnTiNplaN3HGIrN6m1FKMifKGq4NK7ihkAYaEeGBSjC8+2ZUDdZtW7jhkoVh4MGPPrDoJAPjn/DiZk5ClGRrqgdH9vPHJrlz2TCUiWfx+tBg6CbgsIUTuKGQkCoV+1kNWWQP+OMnFQYmo99q0Onx/sACTY/0Q7uMsdxwyoatHhqKhRYP1HEeIjCqrrAE55Y2YPYhrr1H3/X1aNCoaWvH9Ac56oN5h4cFMrT95GutPluLBGbEI9eKHb+q5+6ZFo6RWjd+OcOFPIjK935KLMCzUA/39XOWOQkY0f2gQovxc8L8tWZAkznogot7ZnFqK0roW3DSGsx1szehIb4R7O2PF4VNyRyGyahtS9MW9mSw8UA+MifLBqEgvfLAjBy0aznqgnmPhwQzVq9uwbOVJxAW64a5J/eSOQxZqcowvBge7473t2dCyBQYRmVBWWT1OFNXhsnjOdrB2SoXAfVOjkVpSh82pZXLHISIL9fW+AgR7OGJ6HBc8tTUKhcBVI0KxJ7sSp6qb5I5DZLU2nCzF8FAPBHk4yR2FLMyDM2JRUqvGl3vy5Y5CFoiFBzP02oYMlNar8eKVQ2HHBaWpl4QQuG9aNHIrGvHHCU5dJiLT+eFgIVQKgUXDg+SOQiZwWXwwwryd8L8tmZz1QEQ9llvRiF1ZFbh+dDhUPPexSVeO0N+o8P0Brk9HZAyldWokF9ZgFmc7UC9MjPHFlFg//G9LJqobW+WOQxaGn+zMzL6cSnyxNw+3jI1AQriX3HHIws0ZHIgoPxcu/ElEJqNu02LF4VOYNSgA/m6OcschE1ApFfjb1GgcPVWLbenlcschIgvz7f58qBQC140OkzsKySTM2xkzBwbgm/35aG5lKw8iQ9uUWgoAmD04UOYkZKn+NX8gGlo0+O/mTLmjkIVh4cGMNLZo8NiKowj3dsbjc7mgNPWdUiHw92n6FhjrOOuBiExg/cnTqG5qww3s021TrhoRinBvZ7yyPp2FbiLqNnWbFj8dPoU5gwNZrLZxd03sh+qmNvxyhGs9EBnahpOliPRxRow/116j3hkQ6IYlo8Px5d48JBfWyB2HLAgLD2bkhbWpOFXdjFevGQ4XB5XccchKXBYfgtgAV7y6IR1tWp3ccYjIyn2zvwDh3s6Y0N9X7ihkQvYqBR6ZHYvUkjqsOlYsdxwishBrjpWgpqkNN45lsdrWje7njaEhHvh0Vy4L2EQGVK9uw57sCsweHAghhNxxyII9OS8OAe6OeOTHZKjbODuNuoeFBzOxJa0U3+wvwF0T+2FUpLfccciKKBUCj82JQ25FI1Yc5h1ERGQ8aafrcCC3CtePDodCwRMbW7NoWDDiAt3w+sYMFrqJ6JIkScInu3IR4++KcVE+cschmQkhcNekfsgub8QfJzlTm8hQtqWXo00rYTbXd6A+cne0w8tXDUN2eSNeWpcmdxyyECw8mIFT1U146IejGBTkjkdmD5A7DlmhmQP9MSLcE//dlMnKNBEZzYc7cuBsr8T17NNtkxQKgSfmxiG/sgk/HOQCoUR0cXuyK5FSUoe/TIriXbgEAFg4LBixAa74z3rO1CYylA0ppfB1tecaomQQk2P9cMeEfvh8Tx6+P1AgdxyyACw8yKxVo8Pfvz0CrU7CuzeOgKOdUu5IZIWE0F8MOl2nxoc7cuSOQ0RWqLimGb8nF+PaxDB4OtvLHYdkMnWAH0ZHeuO/mzPR1KqROw4RmbEPd+TA19UBlyUEyx2FzIRSIfB4+0xtFrCJ+q65VYvNqaWYNSgASs5GJgP51/w4TI71w79/O4GdmeVyxyEzx8KDzF5al4bkwhq8cvUwRPq6yB2HrNiYKB8sHBaEt7dmIb+yUe44RGRlPtudCwnAnRP7yR2FZCSEwBPzBqC8voWFbiK6oPTT9dieUY7bxkfAQcUbr+hPMwb6Y1SkF97clInapja54xBZtK3pZWhq1WLRMBZ4yXBUSgXeviEB0f6uuOuLQ9iRweIDXRgLDzL640QJPt2di9vGR2L+0CC545ANeHrhINgrFVi68iQkiYu2EZFhVDW24tv9BVgwNAhh3s5yxyGZjYzwxoJhQXh/ezZKapvljkNEZujjnTlwslPixjERckchMyOEwLJFg1Hd1IoX1qbKHYfIoq06WgxfVweM4To6ZGDujnb49i9j0d/PFXd9eQhrj5fIHYnMFAsPMsmvbMRjPx3D8DBP/Gv+QLnjkI0IcHfEI7NjsT2jHGuPc9E2IjKMD7Zno6lNi/unR8sdhczEk3PjoJOAl7nwHBGdpbCqCb8lF+HaxFB4ubA1H51rSIgH/jIpCj8cKsSuzAq54xBZpHp1G7aklWHhsCC2WSKj8Haxxzd3jcHQEA/87ZskfLA9mze40jlYeJCBuk2Lv32TBIVC4O3rE2Cv4n8GMp2bx0ZgSIg7lq48gbI6tdxxiMjCldWr8cXePFweH4KYADe545CZCPN2xt2TovBbcjGSCqrljkNEZuR/WzIhhMA9U/vLHYXM2D9mxiDK1wWPrTiK6sZWueMQWZxNqaVo0eiwcBi7a5DxeLUXHxYMC8KL69Lw1G8noNHq5I5FZoRXvGXw3OoUnCyuw+vXDmdLCjI5lVKBN6+LR2OrBg//eBQ6HSvSRNR7727NRptWwoMzYuSOQmbm3qn94efmgGdXpfDuJyICAORVNOLnpCLcMDocQR5OcschM+Zop8R/lySgsqEVj/zEcxainlp9tATBHo4YEe4ldxSyco52SvxvSQLundof3+4vwJ1fHEJDi0buWGQmWHgwsZXJRfhmfwH+OjkKMwYGyB2HbFS0vxuWLRqMXVkVeHtrltxxiMhCZZXV4+t9+bg2MQyRvi5yxyEz4+KgwuNzBiC5sAYrk4vljkNEZuCtzZmwUwr8bRpnO9ClDQ31wFMLBmJLWhne3cZzFqLuqmlqxY7MciwcHgwF2yyRCSgUAk/MjcOLVw7FrqwKXPv+XpSywwaBhQeTyiprwD9/OY7ECC88OmeA3HHIxi0ZFYYrE0Lw+sYMrDnGhYCIqGckScKzq1PhZK/Eo7Nj5Y5DZuqqEaEYGuKBF9el8s4nIhuXVVaP35KLcPPYCPi7OcodhyzELeMicFl8MF7dkMHFS4m6aWVyMdq0Ei6LD5Y7CtmY60eH45NbE5Ff2YjL39mN7PIGuSORzFh4MJHmVi3u+yZJPwXphgTYKflPT/ISQuDFq4YiMcILD/+YjP05lXJHIiILsjm1DDsyyvGPmbHwcXWQOw6ZKYVCYPniwSita8H/NmfKHYeIZPT8mlS42KtwzxTOdqDuE0Lg5auGYUS4Jx76IRlHC2vkjkRk9n44WIghIe4YHOwhdxSyQVMH+OPHe8ahTavDkg/3IbO0Xu5IJCNe/TaRpStPIKOsHm9eF89+pmQ2HFRKfHDzSIR6OeG2zw5iT3aF3JGIyALUq9vw9MoTiA1wxS3jIuSOQ2ZuZIQXrk0MxSe7cnniQWSjtqaVYVt6OR6cGcNiNfWYo50SH92SCH93B9z15SEU1TTLHYnIbJ0oqkVKSR2uSwyTOwrZsMHBHvj+7rEAgCUf7kP6aZ4D2CoWHkzgx0OF+OnwKdw/LRqTY/3kjkPUhY+rA76/exzCvJ1w+2cHsfoY+3AT0cW9tC4NpXVqvHL1cM7go255Ym4cnO2VWLryJBeaJrIxLRotnludgig/F9wyLlLuOGShfFwd8Omto6Bu1eLOzw+yfR/RBfxwsBAOKgUWx4fIHYVsXLS/G364eyzslApc/9E+pJ2ukzsSyYBXC4ws7XQdlq48gXFRPnhwJntgk3nyc9MXH4aGeODv3x7B6xvSodXxwhARnWtXZgW+2V+AOyf2Q3yYp9xxyEL4uDrgsblx2JtTidVcV4jIpryzNRs5FY1Ytmgw7FU8/aTeiwlww7s3jUBmWQMe/O4Iz1eIztLYosFvyUWYNyQQHk52cschQpSfK37461jYKxW48aP9nP1sg2zik58kSXjhhRcQFhYGJycnTJ48GcnJyZd83caNG3H99dcjMjISQggsX778nOfk5eVBCHHO15IlS1Db3Ia/fnUYbo52+O/18VAqhOHfHJGB7Ny0Dmnv/BWnXr8ST94wCxP/+jyKezCNuaioCK6urhBCoKHhzwWESkpK8Nhjj2H48OFwdXVFWFgYbr31VhQXc2YFkSGYeoybFOuH5vWv4eFZAwz/Zsiq3TA6HENC3PH8mhR8v+IXDB06FI6Ojhg0aBB++OGHS75+xYoVGD9+PHx8fODo6IgBAwbg+eefR2tra5fndfxOn/kVGBhorLdFJJveHv8BYOXKlRf9G1y+fPl5z3GEEHjxxRc7n9fa2opnn30W0dHRcHJyQnR0NJYtW4aWlhYAQGpJHd7dmoUrE0IwhTO/qQ86fmdnDQ2D+rsH8fuvK/DyH2ndfv2FzlUAjhtkeCkpKZgxYwacnZ0RHByMpUuXQqvVXvJ1hw4dwuzZs+Ht7Q1vb2/MnDkT+/fvv+DzV65cCSEEEhMTAQA/HSpEvVqDW8dHGuqtEPXJxo0b8eTf78Kp9+9A0tLZmHL9fcgqu/SC0z/99BMWL16MkJAQuLq6YuTIkfjuu+9MkJgMzSYKDy+99BKee+45PPHEE1i1ahVcXV0xc+ZMnD59+qKv++OPP3Ds2LHOAeNiXn31Vezdu7fz69lnn8PDPySjqLoZ7904Av5ujoZ8S0QGtWvXLlx11VWYPn0aNq7/A/Pmz8e+j5dh4v1v4o8TF/876fDYY4/B1dX1nMcPHz6MX3/9Fddffz1WrVqF//znP9i/fz/Gjx9/zod+Iuo5U41xu3fvwYJ/f4LQ217H52+/Bid7pSHfBtkApULgucuGoCAlCTdcdy2mTZuGdevWYcGCBbj++uuxYcOGi76+srIS06dPx8cff4x169bhjjvuwP/93//h4YcfPue5N9xwQ5fPZWvXrjXW2yKSTW+P/x2f+y72N3jXXXd1+Rvau3cvnnjiCQDAvHnzOp/35JNP4qWXXsLf/vY3rF27Fvfeey9eeeUVPP7442jV6PD4imPwcLLD0wsHGecfgWzC2b+zN1x9OSpW/Qf//WIFfjhY0K1tXOhcpQPHDTKU6upqzJw5E0IIrFy5EkuXLsVrr72GZcuWXfR1hYWFmDlzJjQaDb766it89dVX0Gg0mDVrFvLz8895vlqtxkMPPYSAgAAAgFYn4dPdeRgR7omEcC+jvDeinuo455wzayacnJwBSLjho33IrWi86Otef/11uLq64o033sDvv/+OadOm4YYbbsD//vc/0wQnw5EkyZBfZqe5uVlyd3eXnnnmmc7HGhoaJF9fX+mpp5666Gu1Wm3n//fx8ZGWLVt2znNyc3MlANKqVau6PP7mxgwp4onV0ue7c/uUn8gUZs+eLU2bNq3LY1NnzJa8ooZKEU+slpatPCGp2zQXfP327dslLy8v6T//+Y8EQKqvr+/8WXV1tdTW1tbl+enp6RIA6fPPPzfsG7Fchj4WW/IX9YApx7g3NqZzXCOD6B8/XnIIHybty67ofGzevHnShAkTerytf/3rX5KHh4ek0+k6H4uIiJAeeeQRg2S1EHIft83py2b05fh/vs993fkbnD9/vhQXF9flsYCAAOnhhx/u8thDDz0k+fv7Sy+sTZEinlgtrTte3J23RHRB5/udnTt3ruQXPUzq/8810t4zxpPzudi5iiTZ5LhxNrmP3eb01WcvvPCC5OnpKdXW1nY+9vLLL0tOTk5dHjvbe++9JykUCqmmpqbzsaqqKkmhUEjvvvvuOc9/9tlnpYkTJ0q33nqrNHLkSGnd8RIp4onV0ppjPOaS+Tj7nPPvjzwpJTy7QRr7wiYpv6Lxgq8rLy8/57Hrr79eioyMNEpO6rMLHletfsbDnj17UFdXh2uvvbbzMRcXFyxatAjr1q276GsVit7982xNL8ObmzNwRUIIbhkX0attEJlKS0sLtm7d2uVvBABuv+VG1OadxA3xvvh8Tx6ufX8vCquaznm9VqvF/fffj6VLl8LX1/ecn3t6ekKlUnV5LDY2Fs7Ozmy3RNRHphrjkvKr8eamTFw5guMa9U1LSwsKTh5E6MjpePznY2hu1bcdWLJkCfbu3Yva2toebc/Hx+ecVktEtqC3x/8Lfe671N9gZWVlZ4u+M7W1tcHDw6PLY56enmjV6PDB9hzcMCYcc4cE9fTtEXW60O/s9ddfj8qcEwhx1uGerw8jv/L8d89e6lyFyNDWrVuHOXPmwN3dvfOxJUuWoLm5Gdu3b7/g69ra2qBSqeDi4tL5mKurK1QqFSSp63omBQUFeOWVV/Df//6387H3t2cjxNMJswcFGPDdEPXN2eecPq4O+PrOMWhu0+L6j/ad9xoTgPMerxMSEngNyQJZfeEhLS0NSqUSMTExXR4fOHAg0tK63xPyUm6//XYolUr4BwTimtvuQYy3A164YiiE4LoOZN6ys7PR1taGuLi4Lo8PHDgQOp0OV/RX4IObRyKnohHz39qJbellXZ73/vvvo6WlBffdd1+393ns2DE0NTUhNpYLrhP1hanGuGcf/zvyX1mM7x6ch0ceeQTNzd1f/4XoTB1jzj+umor8yia8uiEdwJ9jTkZGxiW3odVq0dTUhF27duGtt97Cvffee87nrU8++QT29vbw8PDA1Vdffd4WBUSWrLfH/0t97rvQ3+DPP/+Mtra2cwoPd911Fz744APs3r0bDQ0N2LlzJ9559z04x89HjL8rnl7AFkvUN5f6nX1kjDskCbj7y8NobNGc8/runqtw3CBDSUtLO+f3NTw8HM7Ozhc9Pl911VVwdnbGI488grKyMpSVleGhhx6Cl5cXrrnmmi7PfeSRR3DttddixIgRAIA6dRuSC2vw9+nRUCmt/jIfWbhBwe74+s4xaGjR4Ip39+BoYU23Xrd3715eQ7JAqks/xbJVV1fD1dUVSmXXXtReXl5oampCa2sr7O3te719BwcH3HfffZg9ezbsnVzw99e+Rs7mbwHPVjg9NrOv8YmMrrq6GoD+7rQzeXl5df58zqhRGBTkjr9+dRh3fXEIL101DFePDEVlZSWefvppfP3117Czs+vW/nQ6HR588EHExMRg8eLFBn0vRLbG2GNcRZMW/mMWwyNmJF66biyOHdqDl19+GdnZ2Vi5cmVf45MN6hhzJg6OQImzEp/uzsWcwYHwPmPMuRQXF5fOhWtvueUW/Oc//+ny88suuwxjx45FaGgoUlNT8cwzz2DSpEk4fvz4OXdmE1mq3h7/u/O573y+//57jBgx4pxCx0svvYTm5mZMnDix87GoyVfCbvwSvHV9AtcDoj671O+so6TG2zck4NZPD+CxFUfxzg0jOovR3T1X4bhBhlRdXX3O7yug/5292Oec4OBgbN26FQsXLsRbb70FAAgKCsL69evh5+fX+bwtW7Zgw4YNnYViSQJKa1sQ7+OMq0eGGvbNEBnJkBAP/HzvONz22UFc9+FevHFtPOYNvfAMyc2bN+O3337Dp59+asKUZAhWUwqVJAkajabzS6vVmmS/QUFBePvtt7Fw4SL8UuIBXcLVeOCp5/D/7d15fFTV/f/x15lMdpJAWBIgEBAEZNcoixW3akHc6kJdK2j9drHYfmv159cvKtj61VpsXWvrUhXXKlrbgihKBaWCC7IIsi9hCWFJIPs2y/n9MZOFQJAMk8zk5v18PM5jkjt3Juczd3I+595z77kfvv8uq1atapU6iLSGXulJvPGTMYw5oTO3z17FnxdtYdq0aYwZM4aJEyce8/vcddddLF26lJdffvmYBytE2rtI5Lj9pdXc/u4OMi/4OXNm/orLL/oeM2bM4I9//CP/+te/lOPkuN018SR6pyfxy7+toLji2KdLWrJkCYsXL+YPf/gD//znP5k6deohzz/22GNcc801jBs3jh//+MfMnz+f3bt388ILL4Q7BJEWF6l9nIby8/P5+OOPD7vaAWDmzJm88sorPPHEEyxatIjv3nQn2z6fz8h9H3BS99QjvJtI+I07sSt3XXAS81bv4alFW+qWH+u+ivKGRIP8/HwmTZpETk4O7733Hu+99x45OTlceOGF7NgRuIm61+vlF7/4BdOmTau7qfTOgxVUeX3cdv4AYnW1g7Qh/bul8I+ff4eTuqfys1eX85s5a6nx+g9bLzc3l2uvvZZLL72UKVOmtH5F5bg4plX6+OOPiY2NrSvf/e53gcCocllZ2WGd9IMHD5KUlHRcZ4I29ND89Xywdi93XziYaT+/EYCvvvoqLO8t0pJqzxZqPKdv7dkYtc8DpCTE8vyU07hkRA9++/J8nvvr89x7770UFRVRVFRERUVF3XsdaSqWp556ipkzZzJr1ixGjx7dUiGJOE5r57gD5TXc8PwX7C2p5vkppzEwM6XuuSuvvBJQjpPQNMw5HeLdPHnNKRSW1TDjrS8Pef5oTjnlFM444wxuu+02Hn/8cf785z+zZcuWJtcfOnQoAwcOZPny5eEJQqQVhbv9b06/r9abb76JtZarrrrqkOUFBQXcfffdPPTQQ0ydOpUtMb3Z3HUcV/zkDt5+/kn27dt32HuJNNexfmdvHteXS0f24OEPNrBw/T6++eYbnn++efsqtZQ35Hh06tTpiPfLOXjw4FH7OTNnzsTj8fDWW28xYcIEJkyYwNtvv01MTAwPP/wwAM8++yzFxcVMmTKFoqIitu/ez8rcAhJiDON6J+HxeFosLpGW0KVDPH/78RimnN6H5z/dxpV/WcLmfaV1zx84cIALLriA7OxsXn311QjWVELlmKmWcnJy+PLLL+t+T0kJHCQZNGgQPp+PzZs3M3DgwLrnjzTvXqje+HIHT3+8letG9+bG7/ShsLAQQPd3kDahX79+xMbGsn79es4666y65evXr8flch02h16c28UjV41k27KFzPV6GDt27GHvmZWVxY9+9COee+65umVvv/02t956K7///e8P23EVkaNrzRy3v7Sa6577jO2FFTw3+VRysg/dQarNbcpxEorGOWdYVhrTLjyJXz+wAHOEnPNtauc23rZtG/369WtyPWOMvrPSJoW7/W9uvw8C0yydccYZ9OrV65DlW7duxePxMHLkSBas3cv9767le4MzuGnABN56fAbbt2+nW7duoYYuAhz7d9YYw+8uH86mvWX84m8r+HnfA3g8x76v0pjyhoRq0KBBh93LYefOnVRUVBy1fV6/fj1Dhgw5ZFaAuLg4hgwZUneCxYYNG9i1a1fd1Q4Nde6czssvv8z1118fpkhEWke8O4YZlwxhbL/O3Pn210x87D9MPbc/N5zWnYsuuoiamhrmzp1LUlJSpKsqIXDMFQ8pKSmceuqpdaW2A3766aeTmprK7Nmz69atqKhgzpw5XHDBBcf9d5dsKWDaO2sYd2IXZlwyBGMMb731FhDYURCJdvHx8ZxzzjmH/I8AvPHGG4wdO/aI85rGuAzP/r/r+f7dz5BxzQPc8fhrLFy4kDvvvBOAefPmcccdd9Stv2jRIq677jpuvfVWbr/99pYNSMSBWivH7S2p4upnlrLzQCUvTDmNcSd2PWwd5Tg5HkfKOTeMzaZD3hfE9RjEst1VzXq/Tz/9FIC+ffs2uc6aNWtYv369vrPSJoW7/W9uvy83N5fPPvvsiNMsZWdnA/D2/E/4+WvLGdYzjUeuGsmKFYGzxPv06RNSzCINNec7mxgXwzM35BAb4+K17Um8O/9DFi5cWFea2ldpTHlDjscFF1zA/PnzKS2tP2v7jTfeIDEx8ZDBs8ays7NZs2YNNTX1009WV1ezZs2auvZ06tSpdd/nR2b9nYxrHuDEnDMYMGAACxcu5Pzzz2+xuERa2vghmSy47SwmDM3kD/PXceKY81m3YSPvv/++TmRoy6y14SxR6YEHHrCJiYn2ySeftAsWLLATJ060nTt3tnv27KlbZ9asWTYmJsbm5ubWLcvNzbWzZ8+2s2fPtikpKXbSpEl29uzZdt68edZaa1fvKrJdz7zO9j7rB/al196wH374ob3nnntsQkKCvfzyy1s9TpFQLV682MbExNhf/vKXduHChfaOO+6wxhg7f/78unVyc3NtTEyMnTVrVt0yj9dnf/LSMpt951z7+ufb7QsvvGABW1paWrfO2rVrbVpamh0xYoRdsmSJXbp0aV3ZvHlzq8YZxcLdFrflIs0Urhw38ZLL7KDrptte1/zGfr610Fpr7fTp0+1tt91m3377beU4CZumcs6onz1sB9/znl2TV3TEnDN+/Hg7c+ZMO2/ePDt//nx777332uTkZHvVVVfVrTN37lx79dVX21deecV+9NFH9qmnnrI9evSwffv2tcXFxZEItzVEut2OptKuhNr+H0u/r9aDDz5o3W633b9//xHrcO74C60rPtmeeOlU+49359s//vGPNjU11U6aNCn8AUu71dx9lU8377cn3PWuvXnWl9bn89etc6R9lXaaNxqLdNsdTeW4HThwwGZmZtrzzjvPfvjhh/bpp5+2ycnJdtq0aYes169fP3vTTTfV/b5s2TLrdrvtxIkT7dy5c+2cOXPshAkTrNvttitXrjzktTsKy+2I++bb8Y98bK//4Q02JycnHFUXCbtvO65au07jfv/ESddbwKaf92N740Ov2AWLFtcdR6qqqopEKHJ0TbarUddItwS/32/vv/9+27NnT5uQkGDPOOMMu3z58kPWqe2EbNu27bBljUt2drbdtLfUnvybD+yAq6fZ4SNPtqmpqTY2Ntb269fP3nPPPfpHkDbnnXfesUOGDLFxcXF24MCB9vXXXz/k+W3btlnAvvDCC4csr/b47OTnP7d9/meuvXXGHw/rzDf1fwTYyZMnt0JkbUKkO9jRVKSZwp3jumf1qlvn9ddftzk5OcpxEnZHyjl7iivtmAcW2JzffmA/XrbmsJxz99132yFDhtjk5GSblpZmTz75ZPv444/bmpqaunVWrVplzz33XNulSxfrdrttRkaGnTx5ss3Ly4tAlK0m0u12NJV2JdT239pv7/fVGjFihB0/fvwRn9u6v8yOnPaO7TFuks3u09cmJCTYfv362TvuuMOWlJSEJUaRWs3dV3n+P1tt9p1z7SMfbqhb50gDD+00bzQW6bY7mkpYfPPNN/acc86xCQkJNjMz0959993W6/Uesk52dvZh+8MLFiyw48aNs506dbKdOnWyZ555pl24cOEh65RXe+yFj39ih05/327bX2YnT56sgQeJWkc7rlrrSMeasrOzmzyO1LhPI1GhyXbVWGvDegFFON8sWm0rKOfaZz/D47PM/ulY+nZJjnSVRCKqyuPjxhe+5IvcA/zp2lOYMDQz0lVqazSBbL12kUeiyZxVu/n17FX07JjIizeeRnZn5TSJnC37y7jq6aXExrh48ydj6ZWuuVyPgXJIPeWQVpIb3B+q9vp586dj6de1Q6SrJHIIay23z/6at5fv4pkf5vC9Ido/OQrlkXpRnUeqvT5unrWMTzcX8MwPT+W8wYff60HEaVbuLOKuv69mXX4J3xucwX2XDqF7WmKkqyWHajKPaOChmTbuLeW65z7H57e8evNoTuqeGukqiUSF8movP/zr56zOK+bZG07l7IGag68Z1Nmv5/g8Ei28Pj8zP9jA0x9v5dTsTjx7w6l0So6LdLVEWJdfwtXPfEZKgptXfjSaPjrB49soh9RTDmkFm/eVce2zn+H1W1750WgG99D+kESnKo+Pq55eyuZ9Zfzj59/hxIyUSFcpWimP1IvaPFLt9TH1tRV8uHYvM68czqRTe0W6SiKtxuPz89f/bOPRBRtxu1zc/r0B/HBsH2Jcar6ihAYewuHrXUVMeeFLYlyG124erY6LSCPFlR6uffYzNu0r4/GrRzJhaPdIV6mtULas5+g8Ei0OlNfwi9dX8J/NBVw3ujf3XjyYeHdMpKslUufrXUVMfv4LYlyGF28cxdCead/+ovZLOaSeckgLW72rmBtf/AIwvHrzaAZman9Iolt+cSUXP/EfUhJieeeW0+mYpJMsjkB5pF5U5pGyai8/eXkZn24u5L5LhjD59D6RrpJIROworGDaP1azeFMBI3p15HeXD9MJ4dFBAw/Ha97qfG57cyWdk+N55ebRml5JpAkHy2u4adaXrNpZxP9dNoxrRvWOdJXaAnX26zk2j0SLFTsOMvW1Fewvreb+7w/lB6fpbCmJTpv3lXHDXz+npMrLE9eezDm6kq4pyiH1lENa0Idr9/KL11eQnhzHrJtG0b+bpleStuHL3ANc99znDMpM4ZWbR5OaEBvpKkUb5ZF6UZdHdh2s4McvfcWGvaU8dMVwrszJinSVRCLKWsu/Vu3mN3PWUlTp4b/GncDUc/vTId4d6aq1Zxp4CJXPb3n835t47N+bOKV3R57+4al0TYmPdLVEolpFjZdbXl3Oog37+a9xfblzwiDcMa5IVyuaqbNfz3F5JFp4fH6e/GgzTy7cTGZqAn+67hRG9uoY6WqJHFV+cSU3vbiM9XtK+NV5A5h6Tn9cuqS6MX0g9ZRDWoDfb3n6k638fv56hvdM49nJp9ItJSHS1RJpln+v28tPXv6KEb068tJNo0jWAaqGlEfqRVUeWbKlgKmvrcDj8/P4NToJQ6ShoooaHpi3jjeX7aJjUiw3facvV53Wi4xU9VEiQAMPodh5oIJfvbGSZdsPcvkpPXngsmEkxGoqCpFj4fH5uW/ON7zy2Q5G9Unn4Ukj6N1ZNwltgjr79RyVR6LFpr2l3P7W16zaWcTlJ/dkxqVDdLaftBmVNT7u+vvX/GPlbs47KYOHJw3XVBmHUg6ppxwSZvtKq/j1m6tYvKmAC4d35+ErR5AYp/0haZvmrc5n6mvLGdmrI89NPo103duqlvJIvajII36/5flPt/Hge+vp2yWZZ36YwwlddZWZyJGs3FnEkx9tYsG6fbgMnN6vC6f378zovukMyEghRfu9rUEDD81R4/Xz0tJcHl2wCQP89vtDuXRkD4xRPhZprndW7OLud9bgs5ZfnTeAH53RV1c/HE6NSz1H5JFoUVnj44mPNvHMJ1vpkODm/74/jAuH694r0vZYa5m1JJf7311Hp+Q4HrxsGOcNzoh0taKFckg95ZAw8fktby7bye/fX0+lx8f0i4dw9Wm9tD8kbd57q/P55Rsr6ZGWwIs3jqKPplAG5ZGGIp5HCsqquX32KhZt2M/4IRk8PGmEDpyKHINtBeW89dVOPvhmL5v2ldUt79Ihjl7pSfTsmEjPTolkBR97dEykZ8dE/X+FhwYejoXfb/lg7R5+P38DW/eXc/bArtz//aFkddJZ2iLHY3dRJff+8xsWrNtL3y7J/Pd5J3LhsO4agKinzn69Np1HooXX5+fvy/N4ZMFG8ouruDIni7suGETnDpoqUNq2NXnF3D57Fev3lHLR8O7cOWEQvdLbfT9NOaSecshxstbyn80F/P79DazOK2ZUn3T+77KhnJihm0iLc3y1/QA3z1qGBR66Yjjjh2RGukqRpjxSL6J5ZPGm/fzqjVWUVHm458KTuH5MtgZ8RUJQUFbNV9sPsnV/ObkF5ew8WEFeUSX5RVXU+PyHrNstJZ7T+qQzqm86Y/t15sRuHfR/13waeDia0ioPc7/O59nFW9m6v5wTuiRzz0WDOWeQ5s8TCRdrLQvW7eMPH2xg/Z5SenZM5LoxvZmU00v3TVFnv6E2mUeihcfnZ+7Xu/nTwi1s3lfGiF4dmTbxJEb1TY901UTCpsbr56lFm/nLx1vw+2Hy6dnccnZ/OrXfKTOUQ+oph4TI6/OzYN0+/vLxFlbuLKJ7WgL/c8EgLhmhq77FmXILypn6+nLW5JVwZU4W/3PBILq03xM09E9eLyJ5pLjSw0Pvr+e1z3dwYrcOPHHtyQzKTI1EVUQcze+37C+rJq+okryDleQVVbI+v4Qvth1gd3EVAL3Tkzh/cAbnD87gtD7pxOj+csdCAw+NVdb4WLKlgH+s3M0H3+yh2utnSI9UfnpWPy4YmqkzsUVaSODKor28tDSXJVsKMQZG9Uln/JBMxvbrzMCMlPZ449B2F/BRtJk8Ek1Kqzy8/dUunl28jbyiSgZmpPDf553IhKGZOmAkjrWnuIo/fLCBt5bvIt7t4rKTs5hyeh8GZra7M7P1T15POaSZdhRW8NZXO/nblzvZV1pNVqdEfnZ2P67MySLerXs5iLPVeP08smAjz36ylcS4GH5y5glcPya7Pd5HSHmkXqvmEb/f8q9Vu3lg3joKyqq56Tt9+fX3BupeOiIRsOtgBZ9sLODDtXv4dEshNV4/nZPjOH9wBuOHZnJ6v87qGzVNAw9+v2XL/jKWbClk4YZ9LN1SSLXXT8ekWC4e3oPLTunJyb06tsoBmhkzZnDfffe1+N8RaWz69OnMmDEj0tWos3lfKXNW5TNvdX7dHHxpibGc1iedEVlpDMhMYVBmClmdkpw+yuzo4JopavNINFqTV8yrn+/gnyvzqKjxkZPdiVvO7sc5A7tFbABPOU5qtVbO2bi3lOf/s413VuRR7fVzSu+OXDKiBxcO79FerqhTDqmnHPItfH7LuvwSFq7fx3tr9rA2vwRj4OwBXbl2dDbnDOza4idgKU9IUyK1r7J5XxkPzlvHv9fvIzE2hguGZnLxyB6M6du5vRwAVh6p1yp5xO+3/Ht9/WwAQ3um8uBlwxmWldYif0/trrS0aDvWdLzKq718vHE/76/Zw0fr91FW7SUl3s25J3Vj/JBMxpzQmfT2e7X1kTSZR9ytWYvWVFHj5etdxXy1/SDLcg+wfEcRxZUeAPp2Seba0b05d1A3RvftTJxbVzeIREL/bin86vwUfnX+AHYeqOCLbQcCJfcAC9btrVsvNsbQs2MivdKT6N2g9O2aTJ/OySTEtosdAhEASqo8vLc6n9e+2MmqnUUkxLq4ZEQPrh2dzYisNF3hIO3OgIwUfnfFcO6cMIg3lu3kHyvymDFnLb+Zu5YxJ3Tm3EHdOGtAV/prvlZpZ6wNTCewcU8Za/OL6/pZJVVeAE7pHZiOb+Lw7vTsmBjh2opETv9uHfjrlNNYv6eEFz/N5d3V+fx9RR5xMS5G9u7ImBM6MyIrjaE90+iWEq9cIiHbU1zFv1bl8cpnO9hxoII+nZN4/JqTuWhY9/Z41b9I1EqOdzNxWHcmDutOlScwY877a/bw4dq9/HPlbiAwJdNJ3QMnynZPSyApzk1CrAt3jAuXgRhjMMYQ4zK4DLhchliXi+4dE+jZMbHdHMdq0wMPfr+lsLyGvSVVbC+sYMOeEjbsLWXDnlK2H6ig9mKO/t06MGFIJjl9OjGqTzp9uiRHtuIicphe6Un0Sk/iipwsIDDCvHFvKRv3lpJbWMGOAxXsPFDBvNX5HKzw1L3OGOiRlsgJXZPp2yVQ+nRJpm/nZLI6JWraNHGEaq+PxRsLeGdFHh+u20uN10//bh2YfvFgLj85i7Sk2EhXUSTiOiXH8dOz+vHTs/qxcW8pc1btZt7qfO5/dx33v7uO7mkJnHliV0b1TScnuxPZnZN08EgcwVrL/tJqthaUs2lfGRv3lNb1oRr2mfp2SWbisO6MPiGd0/t1ISM1IYK1Fok+gzJT+d0Vw7nv0iEs2VLIZ1sKWbKlkCc+2lR3bKFLh3iG9kxlaI80hvZMZUiPNLI6JSqfyGFKqjzkHaxk874yVuwoYtn2A3y9qxiA0/p04o7xA5kwNJNY7a+KRLWE2BjOHZTBuYMy8Pr8LN9RxPIdB1m5o4gt+8v4ZGMBlR5fs9+3e1oCg7unMiwrjRFZHRmWlebIew1FxVRL81bns7uoEp/f4vVb/MFHn9/i8fkpr/FSXu2jtMpLebWX8hovhWU17CutwuOr/5MuA326JDMwI4UBGSkMz0rjlN6d2vPNBkUcqaTKw47CCrYWlLNtfznbCsrqfi6t9tat53YZeqcn0b1jAp2S4uicHEfHpDjiY13ExbiIjXHhjjGYMFxdfOnIHiTHhzyWqz2Vepomg8BBpJ0HKvlsayH/Xr+XxZsKqKjxkZ4cx8XDu3PZKVm6ukHkGOUVVfLJxv18vGE/n24poDR4tnd6chwDMjrQv1sHTujSgW6p8aQnx9EpKY44twu3K3CGUl0xBrfLhcsFMS4TyCEuEw3/hxGvQBRxbA6x1lJU4SG3sJzcwkCfZ2tBOdsKysktKKe8pn6HNyXezYDMFAZkdGBARkpg3ygzxZE7syKtobzay7r8EtbkFbNmd+Bx074yfP5Ak5OWGMugzBQG1paMFLI7J9OlQ1w05Ihj0SYq2UpCyiNvLtvJy0u3U+nxUVnjo6TSc8h+aUKsi2E90zh7YDfGD8mgf7d2dz8qEcey1lJS6aXS46PK48Prt1hr8dnAcW1rA1Nd+q2l2usnv7iSHYWV5BaWszqvmC37y+oGt3ukJTAsK43hWR0Z1jON4VlpbeW+Q9F9j4cf/GUpX+QeOGx57Y5eh3g3yfExJMe5SUlwkxzvJj0pjoy0BDJTE8hITSCrUyL9u3VoN5eqiMjhrA1cBbWtwY74toJy9pZUcbDCQ2FZdd30AuG29K5z6Z4W8jQF6uzXCymPvLw0F3dMcEDJHXiMj3UR73YR744JPgZ/Dg48+WxgcNvjtdT4/Hh8fmq8fqq9gccan6/u99pl1V4/1tq6/OQy9QckXS5DbEz9wchYt4tYV2BwKzbGRWxM4KBlnDvw6I4xeH2WokoPRRU17C6qCgyi7S/n67xi9pdWA4HOx7kndeO7J2VwRv8uOitK5Dj4/JbN+8r4avtBVu48yOZ9ZWzeVxZybnCZwFlQte1LQmx9OxPvdh3yXHxsoG3w+cEf3BGpLekd4njgsmGhhqUcUi+kHLK3pIp5q/OJd8cQV5cvXMTHxpAQ3I6BEvy5wTb2+QM5pNrjpyaYRypqfFR6fFTUeKny+AK/B5dV1vio8vjx+v2HnGhVewKWz2fx+AO/V3v87C+rZl9pFXtLqqnx+uvq7DKQ1Smp7mrP2is/T+jagR5pCW3lYKdIm1Xl8bF+Tylr8or5ZncxG/aUsnFvGWUNDjbHuV10Dx6zSE2MJSXBTUp84HhG/QB3oI9Y27dseIDK5w8cuKpd5vVbvA3aC6/PH3y0nJLdkatO6x1qOGow6oWUR+as2s07K/JIjA3kh9SEWHp0TKBnxySyOycxMDNFfXgROaKyai9r8or5elcRq/NKWL2riNzCirrne6UnckKXDmR3Dkw53rNjIh2T4uiYFEunpDhSE93EuIInSBkwxuD1BY5dVHl8dY9VHn/d4EjDfmmlJ1DOGtCVk7qnhhpGdA88lFR5sJa6M8tqH9VhFpFwq93BD5TAz+HQpUP88dwAW41dvWbnEb/fcsL/zmuJurS6eLeLvl2SGZSZQk6fdEb1SWdAhualF2lJ1loOlNdQUFZDYXk1RRWeQw4Ee32BAz/+BgMFPmvx1A1MBjryDR8bdvSrPX6qvD6qgweb666aCA5Yul2G7mmJzLppVKghqIGoF9K+yGdbC7n6mc/CXZejMgZiXa66fR93TOAAZO3P7uBVNV1T4umWEk+31AS6pcST3TkwwNArPZF4t064Eokm1lp2F1exYU8JOw9Usruokt3FVewprqS0yktplZey6kCpvVqiuerbCFfw6rvAsZOLhvfgnosGh1p15ZF6jr1yTkTajuIKD2t2F/P1rmLW7C5me2E52wsr6q7cPhqXgVBSzIOXD+OaUeEfwI6KgQcRkXZOnf16zc4jtQcNa+quXggc7PP4LNW1B/6CBwerPfU/1x7Uia27SiLwe+0Zr3HBKycanv0a53ZhMIGz0GoPRAbPSPP7weMPDGp5fbZucMvr8+PxBw5Sev31A15eX+DKiU7JsaQlxpGRGk+PtETdWE5EmkuNRr2Q9kW8Pj/l1b66QaPGg0m1Z4lVeXxUNVhW7fXjdpm6PFGbN5Li3CTGuUiMdZMYF0NSXAyJsTEkBh8TYmOO52QFEXGAhtNLe/31g90uU38T0hhTf4Wty9CSJ2eqQaqnY1oiEpWstRRXethdVEVRZQ1FFR6KKjyUVHnqTo6qvX1BnNtVfyW2OzAbRG0fNCnOHeyXBq7kre2jJrhjjudYhAYeRESimDr79ZRHRESaRzmknnKIiEjzKY/UUx4REWm+JvOIJpkTEREREREREREREZGw0cCDiIiIiIiIiIiIiIiEjQYeREREREREREREREQkbDTwICIiIiIiIiIiIiIiYaOBBxERERERERERERERCRsNPIiIiIiIiIiIiIiISNho4EFERERERERERERERMJGAw8iIiIiIiIiIiIiIhI2GngQEREREREREREREZGwMdbasL3Zfffd9z7QpdHiHsDusP2R6OPk+JwcGzg7PifHBs6Lr2D69OkTIl2JaNBEHmlLnPbdVDzRTfFEt9aKRzkkqBVyiJO+o4ol+jglDlAs0aqpWJRHgqJ4X8RJ38PWpM8tNPrcQtdeP7um84i1tkXLjBkzbEv/jUgWJ8fn5NicHp+TY2sP8am03eK076biie6ieKK7OC0eFWdtU8USfcUpcSiW6C1OiqW9FW07fW763NpG0Wd3eNFUSyIiIiIiIiIiIiIiEjatMfBwXyv8jUhycnxOjg2cHZ+TYwPnxydtl9O+m4onuime6Oa0eMRZ21SxRB+nxAGKJVo5KZb2RtsuNPrcQqPPLXT67BoJ6z0eRERERERERERERESkfdNUSyIiIiIiIiIiIiIiEjYaeBARERERERERERERkbDRwIOIiIiIiIiIiIiIiISNBh5ERERERERERERERCRswj7wYIyJN8Y8YYwpMMaUG2P+ZYzJasbr7zLGWGPMk+Gu2/EKJbZgPF8aY0qMMfuNMXOMMUNbq85HY4y5xRizzRhTZYz5yhgz7lvWPyu4XpUxZqsx5qetVddQNCc+Y8zlxpgPgtuo1BjzuTHmktasb3M0d9s1eN0ZxhivMWZNS9fxeITw3Ywzxvwm+JpqY8wOY8wvWqu+0n45Lee19TzntLzmtDzmtNylXOV8Tmnj23Lb7qR23UltupPac6e05SHEca0xZqUxpsIYs8cY84oxJrO16iv1tO2azxhzZjCX5QXz7JRjeM0wY8zHxpjK4OvuNcaYVqhuVGnuZ2eMOdsY809jTH7wO/e1MeamVqpu1AjlO9fgtScGc3lZC1YxarXEFQ+PAlcA1wDjgFRgrjEm5tteaIwZA/wY+LoF6hUOj9L82M4GngJOB84FvMACY0x6i9b0WxhjrgIeAx4ATgaWAO8ZY3o3sX5fYF5wvZOBB4EnjDFXtE6Nm6e58QFnAR8BFwbXnwe8c6yd6NYUQmy1r+sEvAT8u8UreRxCjO9vwAQC7cdAYBLR246IszyKs3Leo7TRPOe0vOa0POa03KVc1W48ijPa+Edpg227k9p1J7XpTmrPndKWh/C/8h3gZWAWMAT4PjAYeLU16iv1tO1C1gFYA/wSqPy2lY0xqcCHwF7gtODr7gBua8E6RqtmfXYE+gGrgSuBocCfgWeMMde2WA2jU3M/NyAwWE0gb3zSQvWKftbasBUgDagBrmuwrBfgB8Yfw2u3AOcAi4Anw1m3SMbW6H06AD7g4gjH8znwbKNlm4AHm1j/IWBTo2XPAUsjvW3CEV8T7/EF8IdIxxKu2IC/A9OBGcCaSMcRrviA7wHFQJdI112lfRWn5by2nueclteclseclruUq5xfnNLGt+W23UntupPadCe1505py0OI43Zge6NlNwJlkY6lvRVtu7B8hmXAlG9Z52dACZDYYNndQB5gIh1DNH92TbzuTeDtSNe/LXxuwCPAC8CU9vp/Gu4rHnKAWOCD2gXW2p3AOgKjZEfzDPCWtXZhmOsULscTW0MpBK40ORjW2jVDcMQthwaxBH1A07GMPcL684FTjTGx4a3h8QkxviNJIYLb6UhCjc0YcwuQAdzfcrU7fiHG933gS+A2Y8wuY8wmY8zjxpgOLVdTEcB5Oa/N5jmn5TWn5TGn5S7lqnbDKW18m2zbndSuO6lNd1J77pS2PMQ4PgW6G2MuNgFdgKsJXFkjrUTbrlWNBRZbaxueqT4f6AH0iUiN2rZUomAfI9oZYy4ELgJujXRdIincAw+ZBM6EKWi0fG/wuSMyxvwX0J/AiGO0Cim2I3gMWAksDU+1QtIFiCFQ94aOFktmE+u7g+8XTUKJ7xDGmJ8DWQQuY4wmzY7NGDOMwNlF11trfS1bveMWyrY7ATgDGEFgGoGpBC5/frFlqihSx2k5ry3nOaflNaflMaflLuWq9sEpbXxbbdud1K47qU13UnvulLa82XFYa5cSOFj9KoErovYDBpjcctWUI9C2az1N5Yfa5+QYGWMuAr5L4CQLaYIxpgfwLIHc1y7v7VDrmAYejDH3B2+ecbRydigVMMYMJDCf3bXWWk8o73E8WjK2I/ytPxLoqFwRZZ0uaSA4D+xMAt/J7ZGuz/EwxsQDbwC3W2u3Rbo+LcQFWALb63Nr7XwCOwFXGGMyIls1aYuclvOU59qftp7HHJq7lKuihFPaeLXt7UdbbtMd2J47oi03xgwGngB+S+CM+wkEDr4+Hcl6ybfTtpNIMoF7jLwG/MJa+0Wk6xPlXgb+bK39PNIViTT3Ma73KPDKt6yzAxhDYMS2C4GR11oZwOImXjc2uP43pv6G8jHAmcaYnwLJ1trqY6xnKB6l5WKrY4x5hMDI9DnW2q0h1TR8Cgic+dS4c5QB7GniNXuaWN/L4WdQRVoo8QFgjLmSwE3PbrDWzmmZ6h2X5sbWHTgJeMEY80JwmQswxhgvMNFa2/jSzkgKZdvlA3nW2uIGy9YFH3tz+JkNIt/mUZyV8x7F+XnOaXnNaXnMablLuaptexRntPGP4uy23UntupPadCe1505py0OJ4y7gC2vtzODvXxtjyoHFxpj/tdbuapmqSiPadq2nqfxQ+5x8C2PMGQSm9LrXWvvnSNenDTgXOMsYMz34uwFcwdx3i7W23VwxckwDD9baAo6hs2aM+QrwAOcTGAXDGJNFoLOxpImX/QNY1mjZCwRuqPMAgcvHWkwLx1b72seAqwh02Ncfb52Pl7W2JhjP+cDsBk+dD7zdxMuWApc1WnY+sCwSV6ocTYjxYYz5ATALmGytfatlaxmaEGLLA4Y1WnZLcP3LgNwWqGbIQtx2nwKTjDEdGlzCNiD42KbOCpPo4LSc1x7ynNPymtPymNNyl3JV2+aUNt7pbbuT2nUntelOas+d0paHGEcSgQPeDdX+Hu7puKUJ2natainwkDEmwVpbFVx2PrCbKDsmEo2MMWcC7wLTrbWPRrg6bUXj3HcpMA0YRSA3th/hvls18GdgF3AecDKwkMB8oDEN1lkPTD3KeywCngx33SIRG/AnoITAaFdmg9IhwrFcRWDn52YCOx6PEbgze3bw+ZeAlxqs3xcoJ3B21UnB19UQuOQ64tsmDPFdTWCn7JeNtlN6pGM53tiO8PoZwJpIxxHGbdcB2EmgszYE+A6wBpgd6VhUnF+clvPacp5zWl5zWh5zWu5SrmofxSltfFtt253UrjupTXdSe+6UtjyEOKYEv18/I3Dfiu8QuGn2V5HeJu2taNuF/Ll1AEYGSwVwb/Dn3sHnHwT+3WD9NAJXNvwNGApcTiDH/TrSsbSBz+5sArl1ZqN81DXSsUTz53aE108ByiIdR0Q+uxbYGPEE5pwrDG6MOUCvRutYYMZR3mMRUXIQ5nhjC/5+pNJk/K0Yzy0ERnerga+AMxttg0WN1j8LWB5cfxvw00jHEK74gr8faTstau16t8S2a/TaGURJZz9c8QEDgQ+C/5d5BHaWUyIdh4rzi9NyXlvPc07La07LY07LXcpVzi9OaePbctvupHbdSW26k9pzp7TlIcRxK/BNMI58Ajcrzop0HO2xaNuF9Jmd3UQb+WLw+ReB3EavGQZ8AlQFP7fpgIl0LNH+2QV/P9L6uZGof1v53I7w+im004EHE/wAREREREREREREREREjpvmgBMRERERERERERERkbDRwIOIiIiIiIiIiIiIiISNBh5ERERERERERERERCRsNPAgIiIiIiIiIiIiIiJho4EHEREREREREREREREJGw08iIiIiIiIiIiIiIhI2GjgQUREREREREREREREwkYDDyIiIiIiIiIiIiIiEjb/H3Qv0SXihTX4AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1987.2x331.2 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compared to reference model\n",
-    "az.plot_posterior(idata, var_names=[\"Intercept\", \"x\", \"z\", \"y_sigma\"]);"
+    "ax = az.plot_posterior(submodel.idata, var_names=submodel.model.term_names);"
    ]
   },
   {
@@ -496,20 +460,11 @@
    "source": [
     "For more detailed explanations of the projection predictive model selection paradigm, and associated examples of `kulprit`, please visit [the Tutorials section](tutorials/intro.md)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7a291cb",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9b2b7df85e895e1c4316290f5aaf26982dcc1b162585921f2ab63d2757844866"
-  },
   "kernelspec": {
-   "display_name": "Python 3.7.2 ('.venv': poetry)",
+   "display_name": "Python 3.8.13 ('kulprit')",
    "language": "python",
    "name": "python3"
   },
@@ -523,7 +478,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e258224d959b44f4182f17a74d14a0d47bab7e050ff9dffaf26beb16ea41a16f"
+   }
   }
  },
  "nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index e5e2832..4ce452b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -29,46 +29,6 @@ numpy = ">=1.17.0"
 scipy = ">=0.14"
 typing_extensions = "*"
 
-[[package]]
-name = "appnope"
-version = "0.1.3"
-description = "Disable App Nap on macOS >= 10.9"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[[package]]
-name = "argon2-cffi"
-version = "21.3.0"
-description = "The secure Argon2 password hashing algorithm."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-argon2-cffi-bindings = "*"
-typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
-
-[package.extras]
-dev = ["pre-commit", "cogapp", "tomli", "coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "sphinx-notfound-page", "furo"]
-docs = ["sphinx", "sphinx-notfound-page", "furo"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
-
-[[package]]
-name = "argon2-cffi-bindings"
-version = "21.2.0"
-description = "Low-level CFFI bindings for Argon2"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-cffi = ">=1.0.1"
-
-[package.extras]
-dev = ["pytest", "cogapp", "pre-commit", "wheel"]
-tests = ["pytest"]
-
 [[package]]
 name = "arviz"
 version = "0.12.1"
@@ -93,7 +53,7 @@ all = ["numba", "bokeh (>=1.4.0,<3.0)", "ujson", "dask", "zarr (>=2.5.0)"]
 
 [[package]]
 name = "astroid"
-version = "2.11.5"
+version = "2.11.7"
 description = "An abstract syntax tree for Python with inference support."
 category = "dev"
 optional = false
@@ -118,7 +78,7 @@ six = ">=1.6.1,<2.0"
 
 [[package]]
 name = "atomicwrites"
-version = "1.4.0"
+version = "1.4.1"
 description = "Atomic file writes."
 category = "dev"
 optional = false
@@ -126,17 +86,17 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "attrs"
-version = "21.4.0"
+version = "22.1.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.5"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
 docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
-tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"]
 
 [[package]]
 name = "autoflake"
@@ -149,14 +109,6 @@ python-versions = "*"
 [package.dependencies]
 pyflakes = ">=1.1.0"
 
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-category = "dev"
-optional = false
-python-versions = "*"
-
 [[package]]
 name = "bambi"
 version = "0.9.0"
@@ -190,7 +142,7 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "22.3.0"
+version = "22.6.0"
 description = "The uncompromising code formatter."
 category = "dev"
 optional = false
@@ -201,7 +153,7 @@ click = ">=8.0.0"
 mypy-extensions = ">=0.4.3"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
 typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\" and implementation_name == \"cpython\""}
 typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
@@ -213,7 +165,7 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "5.0.0"
+version = "5.0.1"
 description = "An easy safelist-based HTML-sanitizing tool."
 category = "dev"
 optional = false
@@ -224,8 +176,8 @@ six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0)"]
-dev = ["pip-tools (==6.5.1)", "pytest (==7.1.1)", "flake8 (==4.0.1)", "tox (==3.24.5)", "sphinx (==4.3.2)", "twine (==4.0.0)", "wheel (==0.37.1)", "hashin (==0.17.0)", "black (==22.3.0)", "mypy (==0.942)"]
+css = ["tinycss2 (>=1.1.0,<1.2)"]
+dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"]
 
 [[package]]
 name = "cached-property"
@@ -245,7 +197,7 @@ python-versions = "~=3.7"
 
 [[package]]
 name = "cffi"
-version = "1.15.0"
+version = "1.15.1"
 description = "Foreign Function Interface for Python calling C code."
 category = "dev"
 optional = false
@@ -264,11 +216,11 @@ python-versions = ">=3.6.1"
 
 [[package]]
 name = "cftime"
-version = "1.6.0"
+version = "1.6.1"
 description = "Time-handling functionality from netcdf4-python"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 
 [package.dependencies]
 numpy = ">1.13.3"
@@ -295,7 +247,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "colorama"
-version = "0.4.4"
+version = "0.4.5"
 description = "Cross-platform colored terminal text."
 category = "dev"
 optional = false
@@ -314,7 +266,7 @@ logical-unification = ">=0.4.0"
 
 [[package]]
 name = "coverage"
-version = "6.4.1"
+version = "6.4.4"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -334,22 +286,6 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
-[[package]]
-name = "debugpy"
-version = "1.6.0"
-description = "An implementation of the Debug Adapter Protocol for Python"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[[package]]
-name = "decorator"
-version = "5.1.1"
-description = "Decorators for Humans"
-category = "dev"
-optional = false
-python-versions = ">=3.5"
-
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
@@ -371,7 +307,7 @@ graph = ["objgraph (>=1.7.2)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.4"
+version = "0.3.5"
 description = "Distribution utilities"
 category = "dev"
 optional = false
@@ -407,7 +343,7 @@ multipledispatch = "*"
 
 [[package]]
 name = "fastjsonschema"
-version = "2.15.3"
+version = "2.16.1"
 description = "Fastest Python implementation of JSON schema"
 category = "dev"
 optional = false
@@ -418,7 +354,7 @@ devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "fastprogress"
-version = "1.0.2"
+version = "1.0.3"
 description = "A nested progress with plotting options for fastai"
 category = "main"
 optional = false
@@ -426,15 +362,15 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "filelock"
-version = "3.7.1"
+version = "3.8.0"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"]
-testing = ["covdefaults (>=1.2.0)", "coverage (>=4)", "pytest (>=4)", "pytest-cov", "pytest-timeout (>=1.4.2)"]
+docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"]
+testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pytest-cov (>=3)", "pytest-timeout (>=2.1)"]
 
 [[package]]
 name = "flake8"
@@ -452,7 +388,7 @@ pyflakes = ">=2.3.0,<2.4.0"
 
 [[package]]
 name = "flake8-bugbear"
-version = "22.4.25"
+version = "22.7.1"
 description = "A plugin for flake8 finding likely bugs and design problems in your program. Contains warnings that don't belong in pyflakes and pycodestyle."
 category = "dev"
 optional = false
@@ -505,7 +441,7 @@ pycodestyle = "*"
 
 [[package]]
 name = "flake8-eradicate"
-version = "1.2.1"
+version = "1.3.0"
 description = "Flake8 plugin to find commented out code"
 category = "dev"
 optional = false
@@ -514,7 +450,7 @@ python-versions = ">=3.6,<4.0"
 [package.dependencies]
 attrs = "*"
 eradicate = ">=2.0,<3.0"
-flake8 = ">=3.5,<5"
+flake8 = ">=3.5,<6"
 
 [[package]]
 name = "flake8-logging-format"
@@ -526,7 +462,7 @@ python-versions = "*"
 
 [[package]]
 name = "fonttools"
-version = "4.33.3"
+version = "4.36.0"
 description = "Tools to manipulate font files"
 category = "main"
 optional = false
@@ -598,7 +534,7 @@ typing-extensions = {version = ">=3.7.4.3", markers = "python_version < \"3.8\""
 
 [[package]]
 name = "identify"
-version = "2.5.1"
+version = "2.5.3"
 description = "File identification library for Python"
 category = "dev"
 optional = false
@@ -609,7 +545,7 @@ license = ["ukkonen"]
 
 [[package]]
 name = "importlib-metadata"
-version = "4.11.4"
+version = "4.12.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
@@ -622,11 +558,11 @@ zipp = ">=0.5"
 [package.extras]
 docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
 perf = ["ipython"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
 
 [[package]]
 name = "importlib-resources"
-version = "5.7.1"
+version = "5.9.0"
 description = "Read resources from Python packages"
 category = "dev"
 optional = false
@@ -636,8 +572,8 @@ python-versions = ">=3.7"
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
 
 [[package]]
 name = "iniconfig"
@@ -647,89 +583,6 @@ category = "dev"
 optional = false
 python-versions = "*"
 
-[[package]]
-name = "ipykernel"
-version = "6.13.1"
-description = "IPython Kernel for Jupyter"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[package.dependencies]
-appnope = {version = "*", markers = "platform_system == \"Darwin\""}
-debugpy = ">=1.0"
-ipython = ">=7.23.1"
-jupyter-client = ">=6.1.12"
-matplotlib-inline = ">=0.1"
-nest-asyncio = "*"
-packaging = "*"
-psutil = "*"
-tornado = ">=6.1"
-traitlets = ">=5.1.0"
-
-[package.extras]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
-
-[[package]]
-name = "ipython"
-version = "7.34.0"
-description = "IPython: Productive Interactive Computing"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
-colorama = {version = "*", markers = "sys_platform == \"win32\""}
-decorator = "*"
-jedi = ">=0.16"
-matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
-pygments = "*"
-traitlets = ">=4.2"
-
-[package.extras]
-all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.17)", "pygments", "qtconsole", "requests", "testpath"]
-doc = ["Sphinx (>=1.3)"]
-kernel = ["ipykernel"]
-nbconvert = ["nbconvert"]
-nbformat = ["nbformat"]
-notebook = ["notebook", "ipywidgets"]
-parallel = ["ipyparallel"]
-qtconsole = ["qtconsole"]
-test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.17)"]
-
-[[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-description = "Vestigial utilities from IPython"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[[package]]
-name = "ipywidgets"
-version = "7.7.0"
-description = "IPython HTML widgets for Jupyter"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-ipykernel = ">=4.5.1"
-ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""}
-ipython-genutils = ">=0.2.0,<0.3.0"
-jupyterlab-widgets = {version = ">=1.0.0", markers = "python_version >= \"3.6\""}
-nbformat = ">=4.2.0"
-traitlets = ">=4.3.1"
-widgetsnbextension = ">=3.6.0,<3.7.0"
-
-[package.extras]
-test = ["pytest (>=3.6.0)", "pytest-cov", "mock"]
-
 [[package]]
 name = "isort"
 version = "5.10.1"
@@ -744,21 +597,6 @@ requirements_deprecated_finder = ["pipreqs", "pip-api"]
 colors = ["colorama (>=0.4.3,<0.5.0)"]
 plugins = ["setuptools"]
 
-[[package]]
-name = "jedi"
-version = "0.18.1"
-description = "An autocompletion tool for Python that can be used for text editors."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-parso = ">=0.8.0,<0.9.0"
-
-[package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
-
 [[package]]
 name = "jinja2"
 version = "3.1.2"
@@ -783,7 +621,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "jsonschema"
-version = "4.6.0"
+version = "4.14.0"
 description = "An implementation of JSON Schema validation for Python"
 category = "dev"
 optional = false
@@ -793,32 +631,17 @@ python-versions = ">=3.7"
 attrs = ">=17.4.0"
 importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
 importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
+pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2"
 typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
-format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
-
-[[package]]
-name = "jupyter"
-version = "1.0.0"
-description = "Jupyter metapackage. Install all the Jupyter components in one go."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-ipykernel = "*"
-ipywidgets = "*"
-jupyter-console = "*"
-nbconvert = "*"
-notebook = "*"
-qtconsole = "*"
+format-nongpl = ["webcolors (>=1.11)", "uri-template", "rfc3986-validator (>0.1.0)", "rfc3339-validator", "jsonpointer (>1.13)", "isoduration", "idna", "fqdn"]
+format = ["webcolors (>=1.11)", "uri-template", "rfc3987", "rfc3339-validator", "jsonpointer (>1.13)", "isoduration", "idna", "fqdn"]
 
 [[package]]
 name = "jupyter-client"
-version = "7.3.3"
+version = "7.3.4"
 description = "Jupyter protocol implementation and client libraries"
 category = "dev"
 optional = false
@@ -837,27 +660,9 @@ traitlets = "*"
 doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
 test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
 
-[[package]]
-name = "jupyter-console"
-version = "6.4.3"
-description = "Jupyter terminal console"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-ipykernel = "*"
-ipython = "*"
-jupyter-client = ">=7.0.0"
-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
-pygments = "*"
-
-[package.extras]
-test = ["pexpect"]
-
 [[package]]
 name = "jupyter-core"
-version = "4.10.0"
+version = "4.11.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "dev"
 optional = false
@@ -878,17 +683,9 @@ category = "dev"
 optional = false
 python-versions = ">=3.7"
 
-[[package]]
-name = "jupyterlab-widgets"
-version = "1.1.0"
-description = "A JupyterLab extension."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
 [[package]]
 name = "kiwisolver"
-version = "1.4.2"
+version = "1.4.4"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
@@ -905,6 +702,14 @@ category = "dev"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "llvmlite"
+version = "0.39.0"
+description = "lightweight wrapper around basic LLVM functionality"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "logical-unification"
 version = "0.4.5"
@@ -917,6 +722,20 @@ python-versions = ">=3.6"
 multipledispatch = "*"
 toolz = "*"
 
+[[package]]
+name = "lxml"
+version = "4.9.1"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["beautifulsoup4"]
+source = ["Cython (>=0.29.7)"]
+
 [[package]]
 name = "markdown"
 version = "3.3.7"
@@ -941,7 +760,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "matplotlib"
-version = "3.5.2"
+version = "3.5.3"
 description = "Python plotting package"
 category = "main"
 optional = false
@@ -956,18 +775,7 @@ packaging = ">=20.0"
 pillow = ">=6.2.0"
 pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=4"
-
-[[package]]
-name = "matplotlib-inline"
-version = "0.1.3"
-description = "Inline Matplotlib backend for Jupyter"
-category = "dev"
-optional = false
-python-versions = ">=3.5"
-
-[package.dependencies]
-traitlets = "*"
+setuptools_scm = ">=4,<7"
 
 [[package]]
 name = "mccabe"
@@ -1010,7 +818,7 @@ python-versions = "*"
 
 [[package]]
 name = "mkdocs"
-version = "1.3.0"
+version = "1.3.1"
 description = "Project documentation with Markdown."
 category = "dev"
 optional = false
@@ -1021,7 +829,7 @@ click = ">=3.3"
 ghp-import = ">=1.0"
 importlib-metadata = ">=4.3"
 Jinja2 = ">=2.10.2"
-Markdown = ">=3.2.1"
+Markdown = ">=3.2.1,<3.4"
 mergedeep = ">=1.3.4"
 packaging = ">=20.5"
 PyYAML = ">=3.10"
@@ -1045,7 +853,7 @@ mkdocs = ">=1.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "8.3.3"
+version = "8.4.1"
 description = "Documentation that simply works"
 category = "dev"
 optional = false
@@ -1156,7 +964,7 @@ python-versions = "*"
 
 [[package]]
 name = "nbclient"
-version = "0.6.4"
+version = "0.6.6"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "dev"
 optional = false
@@ -1174,7 +982,7 @@ test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "i
 
 [[package]]
 name = "nbconvert"
-version = "6.5.0"
+version = "6.5.3"
 description = "Converting Jupyter Notebooks"
 category = "dev"
 optional = false
@@ -1188,6 +996,7 @@ entrypoints = ">=0.2.2"
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
+lxml = "*"
 MarkupSafe = ">=2.0"
 mistune = ">=0.8.1,<2"
 nbclient = ">=0.5.0"
@@ -1232,7 +1041,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "netcdf4"
-version = "1.5.8"
+version = "1.6.0"
 description = "Provides an object-oriented python interface to the netCDF version 4 library."
 category = "main"
 optional = false
@@ -1244,41 +1053,24 @@ numpy = ">=1.9"
 
 [[package]]
 name = "nodeenv"
-version = "1.6.0"
+version = "1.7.0"
 description = "Node.js virtual environment builder"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
 
 [[package]]
-name = "notebook"
-version = "6.4.12"
-description = "A web-based notebook environment for interactive computing"
-category = "dev"
+name = "numba"
+version = "0.56.0"
+description = "compiling Python code using LLVM"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.dependencies]
-argon2-cffi = "*"
-ipykernel = "*"
-ipython-genutils = "*"
-jinja2 = "*"
-jupyter-client = ">=5.3.4"
-jupyter-core = ">=4.6.1"
-nbconvert = ">=5"
-nbformat = "*"
-nest-asyncio = ">=1.5"
-prometheus-client = "*"
-pyzmq = ">=17"
-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
-
-[package.extras]
-docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"]
-json-logging = ["json-logging"]
-test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium", "pytest-cov", "requests-unixsocket"]
+importlib-metadata = {version = "*", markers = "python_version < \"3.9\""}
+llvmlite = ">=0.39.0dev0,<0.40"
+numpy = ">=1.18,<1.23"
 
 [[package]]
 name = "numpy"
@@ -1327,18 +1119,6 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
-[[package]]
-name = "parso"
-version = "0.8.3"
-description = "A Python Parser"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["docopt", "pytest (<6.0.0)"]
-
 [[package]]
 name = "pathspec"
 version = "0.9.0"
@@ -1349,46 +1129,35 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
 [[package]]
 name = "pep8-naming"
-version = "0.13.0"
+version = "0.13.2"
 description = "Check PEP-8 naming conventions, plugin for flake8"
 category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 
 [package.dependencies]
 flake8 = ">=3.9.1"
 
-[[package]]
-name = "pexpect"
-version = "4.8.0"
-description = "Pexpect allows easy control of interactive console applications."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-ptyprocess = ">=0.5"
-
-[[package]]
-name = "pickleshare"
-version = "0.7.5"
-description = "Tiny 'shelve'-like database with concurrency support"
-category = "dev"
-optional = false
-python-versions = "*"
-
 [[package]]
 name = "pillow"
-version = "9.1.1"
+version = "9.2.0"
 description = "Python Imaging Library (Fork)"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinx-rtd-theme (>=1.0)", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "pkgutil-resolve-name"
+version = "1.3.10"
+description = "Resolve a name to an object."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "platformdirs"
 version = "2.5.2"
@@ -1418,7 +1187,7 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pre-commit"
-version = "2.19.0"
+version = "2.20.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 category = "dev"
 optional = false
@@ -1433,47 +1202,6 @@ pyyaml = ">=5.1"
 toml = "*"
 virtualenv = ">=20.0.8"
 
-[[package]]
-name = "prometheus-client"
-version = "0.14.1"
-description = "Python client for the Prometheus monitoring system."
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.extras]
-twisted = ["twisted"]
-
-[[package]]
-name = "prompt-toolkit"
-version = "3.0.29"
-description = "Library for building powerful interactive command lines in Python"
-category = "dev"
-optional = false
-python-versions = ">=3.6.2"
-
-[package.dependencies]
-wcwidth = "*"
-
-[[package]]
-name = "psutil"
-version = "5.9.1"
-description = "Cross-platform lib for process and system monitoring in Python."
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-
-[package.extras]
-test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
-
-[[package]]
-name = "ptyprocess"
-version = "0.7.0"
-description = "Run a subprocess in a pseudo terminal"
-category = "dev"
-optional = false
-python-versions = "*"
-
 [[package]]
 name = "py"
 version = "1.11.0"
@@ -1482,17 +1210,6 @@ category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
-[[package]]
-name = "pychalk"
-version = "2.0.1"
-description = "Color printing in python"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-six = ">=1.11.0"
-
 [[package]]
 name = "pycodestyle"
 version = "2.7.0"
@@ -1519,23 +1236,26 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "pygments"
-version = "2.12.0"
+version = "2.13.0"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "dev"
 optional = false
 python-versions = ">=3.6"
 
+[package.extras]
+plugins = ["importlib-metadata"]
+
 [[package]]
 name = "pylint"
-version = "2.14.1"
+version = "2.14.5"
 description = "python code static checker"
 category = "dev"
 optional = false
 python-versions = ">=3.7.2"
 
 [package.dependencies]
-astroid = ">=2.11.5,<=2.12.0-dev0"
-colorama = {version = "*", markers = "sys_platform == \"win32\""}
+astroid = ">=2.11.6,<=2.12.0-dev0"
+colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
 dill = ">=0.2"
 isort = ">=4.2.5,<6"
 mccabe = ">=0.6,<0.8"
@@ -1634,22 +1354,6 @@ pytest = ">=4.6"
 [package.extras]
 testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
 
-[[package]]
-name = "python-box"
-version = "6.0.2"
-description = "Advanced Python dictionaries with dot notation access"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.extras]
-pyyaml = ["pyyaml"]
-all = ["ruamel.yaml (>=0.17)", "toml", "msgpack"]
-msgpack = ["msgpack"]
-"ruamel.yaml" = ["ruamel.yaml (>=0.17)"]
-toml = ["toml"]
-yaml = ["ruamel.yaml (>=0.17)"]
-
 [[package]]
 name = "python-dateutil"
 version = "2.8.2"
@@ -1661,22 +1365,6 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 [package.dependencies]
 six = ">=1.5"
 
-[[package]]
-name = "python-kacl"
-version = "0.2.30"
-description = "Python module and CLI tool for validating and modifying Changelogs in \"keep-a-changelog\" format\""
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-click = "*"
-gitpython = "*"
-pychalk = "*"
-python-box = "*"
-pyyaml = "*"
-semver = "*"
-
 [[package]]
 name = "pytkdocs"
 version = "0.16.1"
@@ -1695,7 +1383,7 @@ numpy-style = ["docstring_parser (>=0.7)"]
 
 [[package]]
 name = "pytz"
-version = "2022.1"
+version = "2022.2.1"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
@@ -1703,7 +1391,7 @@ python-versions = "*"
 
 [[package]]
 name = "pyupgrade"
-version = "2.34.0"
+version = "2.37.3"
 description = "A tool to automatically upgrade syntax for newer versions."
 category = "dev"
 optional = false
@@ -1720,14 +1408,6 @@ category = "dev"
 optional = false
 python-versions = "*"
 
-[[package]]
-name = "pywinpty"
-version = "2.0.5"
-description = "Pseudo terminal support for Windows from Python."
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
 [[package]]
 name = "pyyaml"
 version = "6.0"
@@ -1749,7 +1429,7 @@ pyyaml = "*"
 
 [[package]]
 name = "pyzmq"
-version = "23.1.0"
+version = "23.2.1"
 description = "Python bindings for 0MQ"
 category = "dev"
 optional = false
@@ -1759,42 +1439,6 @@ python-versions = ">=3.6"
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 py = {version = "*", markers = "implementation_name == \"pypy\""}
 
-[[package]]
-name = "qtconsole"
-version = "5.3.1"
-description = "Jupyter Qt console"
-category = "dev"
-optional = false
-python-versions = ">= 3.7"
-
-[package.dependencies]
-ipykernel = ">=4.1"
-ipython-genutils = "*"
-jupyter-client = ">=4.1"
-jupyter-core = "*"
-pygments = "*"
-pyzmq = ">=17.1"
-qtpy = ">=2.0.1"
-traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
-
-[package.extras]
-doc = ["Sphinx (>=1.3)"]
-test = ["flaky", "pytest", "pytest-qt"]
-
-[[package]]
-name = "qtpy"
-version = "2.1.0"
-description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[package.dependencies]
-packaging = "*"
-
-[package.extras]
-test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
-
 [[package]]
 name = "scikit-learn"
 version = "1.0.2"
@@ -1826,27 +1470,6 @@ python-versions = ">=3.7,<3.11"
 [package.dependencies]
 numpy = ">=1.16.5,<1.23.0"
 
-[[package]]
-name = "semver"
-version = "2.13.0"
-description = "Python helper for Semantic Versioning (http://semver.org/)"
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-
-[[package]]
-name = "send2trash"
-version = "1.8.0"
-description = "Send file to trash natively under Mac OS X, Windows and Linux."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.extras]
-nativelib = ["pyobjc-framework-cocoa", "pywin32"]
-objc = ["pyobjc-framework-cocoa"]
-win32 = ["pywin32"]
-
 [[package]]
 name = "setuptools-scm"
 version = "6.4.2"
@@ -1887,22 +1510,6 @@ category = "dev"
 optional = false
 python-versions = ">=3.6"
 
-[[package]]
-name = "terminado"
-version = "0.15.0"
-description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[package.dependencies]
-ptyprocess = {version = "*", markers = "os_name != \"nt\""}
-pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
-tornado = ">=6.1.0"
-
-[package.extras]
-test = ["pre-commit", "pytest-timeout", "pytest (>=6.0)"]
-
 [[package]]
 name = "threadpoolctl"
 version = "3.1.0"
@@ -1952,7 +1559,7 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "tomlkit"
-version = "0.11.0"
+version = "0.11.4"
 description = "Style preserving TOML library"
 category = "dev"
 optional = false
@@ -1960,34 +1567,23 @@ python-versions = ">=3.6,<4.0"
 
 [[package]]
 name = "toolz"
-version = "0.11.2"
+version = "0.12.0"
 description = "List processing tools and functional utilities"
 category = "main"
 optional = false
 python-versions = ">=3.5"
 
-[[package]]
-name = "torch"
-version = "1.11.0"
-description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-category = "main"
-optional = false
-python-versions = ">=3.7.0"
-
-[package.dependencies]
-typing-extensions = "*"
-
 [[package]]
 name = "tornado"
-version = "6.1"
+version = "6.2"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "dev"
 optional = false
-python-versions = ">= 3.5"
+python-versions = ">= 3.7"
 
 [[package]]
 name = "traitlets"
-version = "5.2.2.post1"
+version = "5.3.0"
 description = ""
 category = "dev"
 optional = false
@@ -2014,7 +1610,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "typing-extensions"
-version = "4.2.0"
+version = "4.3.0"
 description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
@@ -2022,26 +1618,25 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "virtualenv"
-version = "20.14.1"
+version = "20.16.3"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+python-versions = ">=3.6"
 
 [package.dependencies]
-distlib = ">=0.3.1,<1"
-filelock = ">=3.2,<4"
-importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
-platformdirs = ">=2,<3"
-six = ">=1.9.0,<2"
+distlib = ">=0.3.5,<1"
+filelock = ">=3.4.1,<4"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.8\""}
+platformdirs = ">=2.4,<3"
 
 [package.extras]
-docs = ["proselint (>=0.10.2)", "sphinx (>=3)", "sphinx-argparse (>=0.2.5)", "sphinx-rtd-theme (>=0.4.3)", "towncrier (>=21.3)"]
-testing = ["coverage (>=4)", "coverage-enable-subprocess (>=1)", "flaky (>=3)", "pytest (>=4)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.1)", "pytest-mock (>=2)", "pytest-randomly (>=1)", "pytest-timeout (>=1)", "packaging (>=20.0)"]
+docs = ["proselint (>=0.13)", "sphinx (>=5.1.1)", "sphinx-argparse (>=0.3.1)", "sphinx-rtd-theme (>=1)", "towncrier (>=21.9)"]
+testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"]
 
 [[package]]
 name = "watchdog"
-version = "2.1.8"
+version = "2.1.9"
 description = "Filesystem events monitoring"
 category = "dev"
 optional = false
@@ -2050,14 +1645,6 @@ python-versions = ">=3.6"
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
-[[package]]
-name = "wcwidth"
-version = "0.2.5"
-description = "Measures the displayed width of unicode strings in a terminal"
-category = "dev"
-optional = false
-python-versions = "*"
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -2066,17 +1653,6 @@ category = "dev"
 optional = false
 python-versions = "*"
 
-[[package]]
-name = "widgetsnbextension"
-version = "3.6.0"
-description = "IPython HTML widgets for Jupyter"
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-notebook = ">=4.4.1"
-
 [[package]]
 name = "wrapt"
 version = "1.14.1"
@@ -2128,20 +1704,20 @@ test = ["hypothesis", "pytest", "pytest-cov"]
 
 [[package]]
 name = "zipp"
-version = "3.8.0"
+version = "3.8.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
 
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7.2, <3.10"
-content-hash = "d5e67c8df156b494dedffa532aff18a47988944b34e5e0cf0b786370807ac939"
+content-hash = "e2aafe88e6e2dfce23d47dc71f98d2f721552f58e69784168f794ba89702f056"
 
 [metadata.files]
 aeppl = [
@@ -2150,64 +1726,20 @@ aeppl = [
 aesara = [
     {file = "aesara-2.6.6.tar.gz", hash = "sha256:c02ed45bf8f7d4d1caac7ffc2d20b530dd1f26dbedbd435ac7672446e26d1950"},
 ]
-appnope = [
-    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
-    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
-]
-argon2-cffi = [
-    {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
-    {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
-]
-argon2-cffi-bindings = [
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@@ -3651,7 +2560,4 @@ xarray-einstats = [
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diff --git a/poetry.toml b/poetry.toml
index ab1033b..084377a 100644
--- a/poetry.toml
+++ b/poetry.toml
@@ -1,2 +1,2 @@
 [virtualenvs]
-in-project = true
+create = false
diff --git a/pyproject.toml b/pyproject.toml
index ac9b309..9160c36 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "kulprit"
 version = "0.0.1"
-description = "Kullback-Leibler projections for Bayesian model selection in Generalised Linear Models"
+description = "Kullback-Leibler projections for Bayesian model selection"
 authors = [
     "Yann McLatchie <Your email address (eq. you@example.com)>",
 ]
@@ -30,41 +30,35 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-arviz = ">=0.11.2"
 bambi = "0.9.0"
-numpy = "^1.21.5"
-pandas = "^1.3.5"
-pymc = "4.0.0"
 python = ">=3.7.2, <3.10"
-scipy = "1.7.3"
-torch = "^1.11.0"
 scikit-learn = "1.0.2"
+numba = "^0.56.0"
 
 [tool.poetry.dev-dependencies]
-autoflake = "*"
-black = "*"
-flake8 = "*"
-flake8-bugbear = "*"
-flake8-builtins = "*"
-flake8-comprehensions = "*"
-flake8-debugger = "*"
-flake8-eradicate = "*"
-flake8-logging-format = "*"
-isort = "*"
-jupyter = "1.0.0"
-mknotebooks = "*"
-mkdocstrings = "<0.19"
-mkdocs-material = "*"
-mypy = "*"
-pep8-naming = "*"
+autoflake = "^1.4"
+black = "^22.3.0"
+flake8 = "^3.9.2"
+flake8-bugbear = "^22.4.25"
+flake8-builtins = "^1.5.3"
+flake8-comprehensions = "^3.10.0"
+flake8-debugger = "^4.1.2"
+flake8-eradicate = "^1.2.1"
+flake8-logging-format = "^0.6.0"
+isort = "^5.10.1"
+mknotebooks = "^0.7.1"
+mkdocstrings = "0.18.1"
+mkdocs-material = "^8.3.3"
+mypy = "^0.961"
+nbconvert = "^6.5.2"
+pep8-naming = "^0.13.0"
 pip = "^22.0.3"
-pre-commit = "*"
-pymdown-extensions = "*"
-pytest = "*"
-pytest-cov = "*"
-python-kacl = "*"
-pyupgrade = "*"
-tryceratops = "*"
+pre-commit = "^2.19.0"
+pymdown-extensions = "^9.5"
+pytest = "^7.1.2"
+pytest-cov = "^3.0.0"
+pyupgrade = "^2.34.0"
+tryceratops = "^0.1"
 pylint = "^2.13.8"
 
 [build-system]
diff --git a/setup.cfg b/setup.cfg
index a501114..54e3d92 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -35,7 +35,3 @@ per-file-ignores =
 max-complexity = 10
 
 exclude = .git,__pycache__,old,build,dist,.venv,.eggs,.tox
-
-[options]
-install_requires =
-    torch
diff --git a/src/kulprit/data/__init__.py b/src/kulprit/data/__init__.py
index 3c52008..47d1d32 100644
--- a/src/kulprit/data/__init__.py
+++ b/src/kulprit/data/__init__.py
@@ -1,5 +1,1 @@
 """Top-level model data module."""
-
-from kulprit.data.data import ModelData
-from kulprit.data.structure import ModelStructure
-from kulprit.data.submodel import SubModelStructure, SubModelInferenceData
diff --git a/src/kulprit/data/data.py b/src/kulprit/data/data.py
deleted file mode 100644
index 1444dec..0000000
--- a/src/kulprit/data/data.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Module for handling data throughout the procedure."""
-
-import dataclasses
-
-import torch
-from arviz import InferenceData
-
-from kulprit.data.structure import ModelStructure
-
-
-@dataclasses.dataclass(order=True)
-class ModelData:
-    """Data class for handling model data.
-
-    This class serves as the primary data container passed throughout the
-    procedure, allowing for more simple and legible code. Note that this class
-    supports ordering, and we choose distance to reference model as our sorting
-    index. Naturally, this value is set to zero for the reference model,
-    providing a known global minimum.
-
-    Attributes:
-        structure (kulprit.data.ModelStructureData): ModelStructureData object
-            built from a Bambi model
-        idata (arviz.InferenceData): InferenceData object of the fitted Bambi
-            model
-        dist_to_ref_model (torch.tensor): The distance from this model to the
-            reference model being used in the procedure as defined by the loss
-            function
-        sort_index (int): Sorting index attribute used in forward search method
-    """
-
-    structure: ModelStructure
-    idata: InferenceData
-    dist_to_ref_model: torch.tensor
-    sort_index: int = dataclasses.field(init=False)
-
-    def __post_init__(self):
-        # use the distance from the reference model as the ordering index
-        self.sort_index = self.dist_to_ref_model
-
-        # add MCMC draws dimension to the structure object
-        self.structure.num_draws = (
-            self.idata.posterior.dims["chain"] * self.idata.posterior.dims["draw"]
-        )
-
-        if self.structure.predictions is None:
-            # make insample predictions for the model and append to structure
-            self.structure.predictions = self.structure.predict(
-                idata=self.idata, inplace=False, kind="pps"
-            ).posterior_predictive[self.structure.response_name]
diff --git a/src/kulprit/data/structure.py b/src/kulprit/data/structure.py
deleted file mode 100644
index 383eb0f..0000000
--- a/src/kulprit/data/structure.py
+++ /dev/null
@@ -1,105 +0,0 @@
-"""Bambi model structure data classes."""
-
-import torch
-
-from bambi.models import Model
-from pymc.util import is_transformed_name, get_untransformed_name
-
-
-class ModelStructure:
-    def __init__(self, model: Model) -> None:
-        """Initiate ModelStructure object from Bambi model.
-
-        Args:
-            model (bambi.models.Model): A Bambi model whose structure to define
-
-        Attributes:
-            X (torch.tensor): Model design matrix
-            y (torch.tensor): Model variate observations
-            backend (pymc.Model): The PyMC model backend
-            design (formulae.matrices.DesignMatrices): The formulae design matrix
-                object underpinning the GLM
-            link (bambi.families.Link): GLM link function object
-            term_names (list): List of model covariates in their order of
-                appearance **not** including the `Intercept` term
-            common_terms (list): List of all terms in the model in order of
-                appearance (includes the `Intercept` term)
-            response_name (str): The name of the response given to the Bambi model
-            num_obs (int): Number of data observations
-            num_terms (int): Number of variables observed, and equivalently the
-                number of common terms in the model (including intercept)
-            model_size (int): Number of common terms in the model (terms not
-                including the intercept)
-            has_intercept (bool): Flag whether intercept included in model
-            predictions (arviz.InferenceData): In-sample model predictions
-        """
-
-        # build structure
-        self.structure_factory(model)
-
-    def structure_factory(self, model: Model) -> None:
-        """Build structure variables for a given Bambi model.
-
-        Args:
-            model (bambi.models.Model): A Bambi model whose structure to define
-        """
-
-        # store the prediction function
-        self.predict = model.predict
-
-        # log the underlying backend model
-        self.backend = model.backend
-
-        pymc_model = model.backend.model
-        # log the transformations
-        self.transforms = get_transforms(pymc_model)
-
-        # log the compiled model log probability
-        self.model_logp = pymc_model.compile_logp(
-            sum=False, vars=pymc_model.observed_RVs
-        )
-
-        # define model architecture
-        self.architecture = "glm" if len(model.group_specific_terms) == 0 else "glmm"
-
-        # define the link function and family of the reference model
-        self.link = model.family.link
-        self.family = model.family.name
-
-        # extract covariate and variate names
-        self.term_names = list(model.term_names)
-        self.common_terms = list(model.common_terms.keys())
-        self.response_name = model.response.name
-        self.design = model._design
-        self.has_intercept = model.intercept_term is not None
-
-        # extract data from the fitted bambi model
-        self.X = torch.from_numpy(model._design.common.design_matrix).float()
-        self.y = torch.from_numpy(model._design.response.design_matrix).float()
-        self.predictions = None
-
-        # extract some key dimensions needed for optimisation
-        self.num_obs, self.num_terms = model._design.common.design_matrix.shape
-        self.model_size = len(self.common_terms)  # does not include intercept
-
-
-def get_transforms(model):
-    """Generate dict with information about transformations
-
-    Args:
-        backend (pymc.Model) : PyMC model
-    Returns:
-        Dictionary with keys unstransformed variable name
-        and values (tranformation name, forward transformation)
-    """
-    transforms = {}
-    for var in model.value_vars:
-        name = var.name
-        transform_name = ""
-        transform_function = None
-        if is_transformed_name(name):
-            name = get_untransformed_name(name)
-            transform_name = var.tag.transform.name
-            transform_function = var.tag.transform.forward
-        transforms[name] = (transform_name, transform_function)
-    return transforms
diff --git a/src/kulprit/data/submodel.py b/src/kulprit/data/submodel.py
index ad7c018..65046c0 100644
--- a/src/kulprit/data/submodel.py
+++ b/src/kulprit/data/submodel.py
@@ -1,260 +1,30 @@
-"""Submodel factory class."""
-
-from abc import ABC, abstractmethod
-from copy import copy
-
-from typing import Optional, List
-from pymc import Model
-
-import arviz as az
-from arviz import InferenceData
-
-import numpy as np
-import torch
-
-from kulprit.data.data import ModelData
-from kulprit.data.structure import ModelStructure
-
-
-class SubModel(ABC):
-    """Abstract base class for submodel data classes."""
-
-    @abstractmethod
-    def create(self):  # pragma: no cover
-        pass
-
-
-class SubModelStructure(SubModel):
-    def __init__(self, data: ModelData) -> None:
-        """Submodel object used to create submodels from a reference model.
-
-        Args:
-            data (kulprit.data.ModelData): The reference model from which
-                to build the submodel
-        """
-
-        # log reference model ModelData
-        self.data = data
-
-    def generate(self, var_names: List[str]) -> ModelStructure:
-        """Generate new ModelStructure class attributes for a submodel.
-
-        Args:
-            var_names (list): The names of the parameters to use in the r
-                estricted model
-
-        Returns:
-            Tuple: Structure attributes of the resulting submodel
-        """
-
-        if len(var_names) > 0:
-            # extract the submatrix from the reference model's design matrix
-            X_res = torch.from_numpy(
-                np.column_stack(
-                    [self.data.structure.design.common[term] for term in var_names]
-                )
-            ).float()
-            # manually add intercept to new design matrix
-            X_res = torch.hstack((torch.ones(self.data.structure.num_obs, 1), X_res))
-        else:
-            # intercept-only model
-            X_res = torch.ones(self.data.structure.num_obs, 1).float()
-
-        # update common term names and dimensions and build new ModelData object
-        _, num_terms = X_res.shape
-        submode_term_names = ["Intercept"] + var_names
-        model_size = len(var_names)
-
-        return X_res, num_terms, model_size, submode_term_names, var_names
-
-    def create(self, var_names: List[str]) -> ModelStructure:
-        """Build a submodel from a reference model containing specific terms.
-
-        Args:
-            var_names (list): The names of the parameters to use in the r
-                estricted model
-
-        Returns:
-            ModelData: The resulting submodel `ModelData` object
-        """
-
-        # test that the submodel is indeed a submodel
-        full_set = set(self.data.structure.term_names)
-        projection_set = set(var_names)
-        if not projection_set.issubset(full_set):
-            raise UserWarning(
-                "Please ensure that the submodel you wish to build contains "
-                + "only terms from the larger model."
-            )
-
-        # copy and instantiate new ModelStructure object
-        submodel_structure = copy(self.data.structure)
-        (
-            submodel_structure.X,
-            submodel_structure.num_terms,
-            submodel_structure.model_size,
-            submodel_structure.term_names,
-            submodel_structure.common_terms,
-        ) = self.generate(var_names)
-
-        # ensure correct dimensions
-        assert submodel_structure.X.shape == (
-            self.data.structure.num_obs,
-            submodel_structure.model_size + 1,
-        )
-        return submodel_structure
-
-
-class SubModelInferenceData(SubModel):
-    def __init__(self, data: ModelData) -> None:
-        """Submodel object used to create submodels from a reference model.
-
-        Args:
-            data (kulprit.data.ModelData): The reference model ModelData object
-                from which to build the submodel
-        """
-
-        # log reference model ModelData
-        self.data = data
-
-    def create(
-        self,
-        submodel_structure: SubModelStructure,
-        theta_perp: torch.tensor,
-        disp_perp: Optional[torch.tensor] = None,
-    ) -> InferenceData:
-        """Convert some set of pytorch tensors into an ArviZ idata object.
-
-        Args:
-            submodel_structure (kulprit.data.SubModelStructure): The restricted
-                model's structure object
-            theta_perp (torch.tensor): Restricted parameter posterior projections,
-                including the intercept term
-            disp_perp (torch.tensor): Restricted model dispersions parameter
-                posterior projections
-
-        Returns:
-            arviz.InferenceData: Restricted model idata object
-        """
-
-        # reshape `theta_perp` so it has the same shape as the reference model
-        num_chain = len(self.data.idata.posterior.coords.get("chain"))
-        num_draw = int(submodel_structure.num_thinned_draws / num_chain)
-        num_terms = submodel_structure.num_terms
-        num_obs = self.data.structure.num_obs
-
-        theta_perp = torch.reshape(theta_perp, (num_chain, num_draw, num_terms))
-
-        transforms = self.data.structure.transforms
-
-        # build posterior dictionary from projected parameters
-        posterior = {
-            term: theta_perp[:, :, i]
-            for i, term in enumerate(submodel_structure.term_names)
-        }
-        posterior_ = posterior.copy()
-        if disp_perp is not None:
-            # reshape `disp_perp` if present
-            disp_perp = torch.reshape(disp_perp, (num_chain, num_draw))
-            # update the posterior draws dictionary with dispersion parameter
-            response_name = f"{self.data.structure.response_name}_sigma"
-            disp_dict = {response_name: disp_perp}
-            posterior.update(disp_dict)
-            # check for transformed variables
-            transform_name, transform_function = transforms[response_name]
-            if transform_name:
-                disp_dict = {
-                    f"{response_name}_{transform_name}__": transform_function(
-                        disp_perp.numpy()
-                    ).eval(),
-                }
-                posterior_.update(disp_dict)
-
-        # build points data from the posterior dictionaries
-        points = self.posterior_to_points(posterior_)
-
-        # compute log-likelihood of projected model from this posterior
-        log_likelihood = self.compute_log_likelihood(self.data.structure.backend, points)
-
-        # reshape the log-likelihood values to be inline with reference model
-        log_likelihood.update(
-            (key, value.reshape(num_chain, num_draw, num_obs))
-            for key, value in log_likelihood.items()
-        )
-
-        # add observed data component of projected idata
-        observed_data = {
-            self.data.structure.response_name: self.data.idata.observed_data.get(
-                self.data.structure.response_name
-            )
-            .to_dict()
-            .get("data")
-        }
-
-        # build idata object for the projected model
-        idata = az.data.from_dict(
-            posterior=posterior,
-            log_likelihood=log_likelihood,
-            observed_data=observed_data,
-        )
-        return idata
-
-    def posterior_to_points(self, posterior: dict) -> list:
-        """Convert the posterior samples from a restricted model into list of dicts.
-
-        This list of dicts datatype is referred to a `points` in PyMC, and is needed
-        to be able to compute the log-likelihood of a projected model, here
-        `res_model`.
-
-        Args:
-            posterior (dict): Dictionary of posterior restricted model samples
-
-        Returns:
-            list: The list of dictionaries of point samples
-        """
-        initial_point = self.data.structure.backend.model.initial_point(seed=None)
-
-        # build samples dictionary from posterior of idata
-        samples = {
-            key: (
-                posterior[key].flatten()
-                if key in posterior.keys()
-                else np.zeros((self.data.structure.num_thinned_draws,))
-            )
-            for key in initial_point.keys()
-        }
-        shapes = [val.shape for val in initial_point.values()]
-        # extract observed and unobserved RV names and sample matrix
-        var_names = list(samples.keys())
-        obs_matrix = np.vstack(list(samples.values()))
-
-        # build points list of dictionaries
-        points = [
-            {
-                var_names[j]: np.full(shape, obs_matrix[j, i])
-                for j, shape in zip(range(obs_matrix.shape[0]), shapes)
-            }
-            for i in range(obs_matrix.shape[1])
-        ]
-
-        return points
-
-    def compute_log_likelihood(self, backend: Model, points: list) -> dict:
-        """Compute log-likelihood of some data points given a PyMC model.
-
-        Args:
-            backend (pymc.Model) : PyMC model for which to compute log-likelihood
-            points (list) : List of dictionaries, where each dictionary is a named
-                sample of all parameters in the model
-
-        Returns:
-            dict: Dictionary of log-likelihoods at each point
-        """
-        log_likelihood_dict = {
-            var.name: np.array(
-                [self.data.structure.model_logp(point) for point in points]
-            )
-            for var in backend.model.observed_RVs
-        }
-
-        return log_likelihood_dict
+"""Submodel dataclass module."""
+
+from dataclasses import dataclass
+
+import arviz
+import bambi
+
+
+@dataclass
+class SubModel:
+    """Submodel dataclass.
+
+    Attributes:
+        model (bambi.Model): The submodel's associated Bambi model, from which
+            we can extract a built pymc model.
+        idata (InferenceData): The inference data object of the submodel
+            containing the projected posterior draws and log-likelihood.
+        loss (float): The final loss (negative log-likelihood) of the submodel
+            following projection predictive inference
+        size (int): The number of common terms in the model, not including the
+            intercept
+        term_names (list): The names of the terms in the model, including the
+            intercept
+    """
+
+    model: bambi.models.Model
+    idata: arviz.InferenceData
+    loss: float
+    size: int
+    term_names: list
diff --git a/src/kulprit/families/__init__.py b/src/kulprit/families/__init__.py
deleted file mode 100644
index 17905f6..0000000
--- a/src/kulprit/families/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""Distribution families module."""
-
-from abc import ABC, abstractmethod
-
-
-class BaseFamily(ABC):
-    """Base family class."""
-
-    @abstractmethod
-    def solve_dispersion(self):  # pragma: no cover
-        pass
diff --git a/src/kulprit/families/continuous.py b/src/kulprit/families/continuous.py
deleted file mode 100644
index 6dd10cc..0000000
--- a/src/kulprit/families/continuous.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""Continuous distribution families."""
-
-from kulprit.data.data import ModelData
-from kulprit.families import BaseFamily
-
-import numpy as np
-import torch
-
-
-class GaussianFamily(BaseFamily):
-    def __init__(self, data: ModelData) -> None:
-        # initialise family object with necessary attributes
-        self.data = data
-        self.has_dispersion_parameters = True
-        self.name = "gaussian"
-
-    def solve_dispersion(self, theta_perp: torch.tensor, X_perp: torch.tensor):
-        """Analytic projection of the model dispersion parameters.
-
-        Args:
-            theta_perp (torch.tensor): A PyTorch tensor of the restricted
-                parameter draws
-            X_perp (np.ndarray): The design matrix of the restricted model we
-                are projecting onto
-
-        Returns:
-            torch.tensor: The restricted projections of the dispersion parameters
-        """
-
-        def _dispersion_proj(
-            theta_ast: torch.tensor,
-            theta_perp: torch.tensor,
-            sigma_ast: torch.tensor,
-        ) -> np.ndarray:
-            """Analytic solution to the point-wise dispersion projection.
-
-            We separate this solution from the primary method to allow for
-            vectorisation of the projection across samples.
-
-            Args:
-                theta_ast (torch.tensor): Reference model posterior parameter
-                    sample
-                theta_perp (torch.tensor): Submodel projected parameter sample
-                sigma_ast (torch.tensor): Reference model posterior dispersion
-                    parameter sample
-
-            Returns:
-                np.ndarray: The sample projection of the dispersion parameter in
-                    a Gaussian model according to the analytic solution
-            """
-
-            f = X_ast @ theta_ast
-            f_perp = X_perp @ theta_perp
-            sigma_perp = torch.sqrt(
-                sigma_ast**2
-                + 1 / self.data.structure.num_obs * (f - f_perp).T @ (f - f_perp)
-            )
-            sigma_perp = sigma_perp.numpy()
-            return sigma_perp
-
-        # extract parameter draws from both models
-        theta_ast = torch.from_numpy(
-            self.data.idata.posterior.stack(samples=("chain", "draw"))[
-                self.data.structure.term_names
-            ]
-            .to_array()
-            .transpose(*("samples", "variable"))
-            .values
-        ).float()
-        sigma_ast = torch.from_numpy(
-            self.data.idata.posterior.stack(samples=("chain", "draw"))[
-                self.data.structure.response_name + "_sigma"
-            ]
-            .transpose()
-            .values
-        ).float()
-
-        # thin the parameter draws to match the optimisation
-        theta_ast = theta_ast[self.data.structure.thinned_idx]
-        sigma_ast = sigma_ast[self.data.structure.thinned_idx]
-
-        # extract the design matrix of the reference model
-        X_ast = self.data.structure.X
-
-        # project the dispersion parameter
-        vec_dispersion_proj = np.vectorize(
-            _dispersion_proj,
-            signature="(n),(m),()->()",
-            doc="Vectorised `_dispersion_proj` method",
-        )
-        sigma_perp = (
-            torch.from_numpy(vec_dispersion_proj(theta_ast, theta_perp, sigma_ast))
-            .flatten()
-            .float()
-        )
-
-        # assure correct shape
-        assert sigma_perp.shape == sigma_ast.shape
-        return sigma_perp
diff --git a/src/kulprit/families/discrete.py b/src/kulprit/families/discrete.py
deleted file mode 100644
index 4a467d7..0000000
--- a/src/kulprit/families/discrete.py
+++ /dev/null
@@ -1,1 +0,0 @@
-"""Discrete distribution families."""
diff --git a/src/kulprit/families/family.py b/src/kulprit/families/family.py
deleted file mode 100644
index 902dc62..0000000
--- a/src/kulprit/families/family.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""Core family class to be accessed"""
-
-import torch
-
-from kulprit.data.data import ModelData
-from kulprit.families import BaseFamily
-from kulprit.families.continuous import GaussianFamily
-
-
-class Family:
-    def __init__(self, data: ModelData) -> None:
-        """Family factory constructor.
-
-        Args:
-            data (kulprit.data.ModelData): Reference model dataclass object
-        """
-
-        # log model data and family name
-        self.data = data
-        self.family_name = data.structure.family
-
-        # define all available family classes
-        self.family_dict = {
-            "gaussian": GaussianFamily,
-        }
-
-        # test family name
-        if self.family_name not in self.family_dict:
-            raise NotImplementedError(
-                f"The {self.family_name} family has not yet been implemented."
-            )
-
-        # build BaseFamily object
-        self.family = self.factory_method()
-
-    def factory_method(self) -> BaseFamily:
-        """Choose the appropriate family class given the model."""
-
-        # return appropriate family class given model variate family
-        family_class = self.family_dict[self.family_name]
-        return family_class(self.data)
-
-    def solve_dispersion(self, theta_perp: torch.tensor, X_perp: torch.tensor):
-        """Analytic projection of the model dispersion parameters.
-
-        Args:
-            theta_perp (torch.tensor): A PyTorch tensor of the restricted
-                parameter draws
-            X_perp (np.ndarray): The design matrix of the restricted model we
-                are projecting onto
-
-        Returns:
-            torch.tensor: The restricted projections of the dispersion parameters
-        """
-
-        # test whether or not the family has dispersion parameters
-        if not self.family.has_dispersion_parameters:  # pragma: no cover
-            return None
-
-        # compute the solution and return
-        solution = self.family.solve_dispersion(theta_perp=theta_perp, X_perp=X_perp)
-        return solution
diff --git a/src/kulprit/plotting/.gitkeep b/src/kulprit/plotting/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/src/kulprit/plotting/__init__.py b/src/kulprit/plotting/__init__.py
deleted file mode 100644
index 0917005..0000000
--- a/src/kulprit/plotting/__init__.py
+++ /dev/null
@@ -1,1 +0,0 @@
-"""Plotting module."""
diff --git a/src/kulprit/plotting/visualise.py b/src/kulprit/plotting/visualise.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/kulprit/projection/architecture.py b/src/kulprit/projection/architecture.py
deleted file mode 100644
index 385e037..0000000
--- a/src/kulprit/projection/architecture.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""Model architecture module."""
-
-import torch
-import torch.nn as nn
-
-from kulprit.data.submodel import SubModelStructure
-
-
-class BaseArchitecture(nn.Module):
-    """Base optimiser class."""
-
-    def __init__(self):
-        super(BaseArchitecture, self).__init__()
-
-
-class Architecture:
-    """Architecture factory class."""
-
-    def __init__(self, submodel_structure: SubModelStructure) -> None:
-        """Architecture factory constructor.
-
-        Args:
-            submodel_structure (SubModelStructure): The submodel stucture data
-                object
-        """
-
-        # log model data
-        self.submodel_structure = submodel_structure
-
-        # define all available KL divergence loss classes
-        self.architecture_dict = {
-            "glm": GLMArchitecture,
-        }
-
-        if (
-            self.submodel_structure.architecture not in self.architecture_dict
-        ):  # pragma: no cover
-            raise NotImplementedError(
-                f"The {self.submodel_structure.architecture} architecture has "
-                + "not yet been implemented."
-            )
-
-        # build architecture class
-        self.architecture = self.factory_method()
-
-    def factory_method(self) -> BaseArchitecture:
-        """Choose the appropriate architecture class given the model."""
-
-        # return appropriate divergence class given model variate family
-        architecture_class = self.architecture_dict[self.submodel_structure.architecture]
-        return architecture_class(self.submodel_structure)
-
-
-class GLMArchitecture(BaseArchitecture):
-    """Core optimisation solver class.
-
-    This class solves the general problem of Kullback-Leibler divergence
-    projection onto a submodel using a PyTorch neural network architecture
-    for efficiency. The procedure might use this class to project the
-    learned full parameter samples onto a submodel that uses a restricted
-    dataset to define which parameters to include/exclude.
-
-    Attributes:
-        inv_link (function): The inverse link function of the GLM
-        num_obs (int): Number of observations in the GLM
-        num_terms (int): Number of parameters in the submodel
-        num_draws (int): Number of MCMC posterior samples
-        lin (torch.nn module): The linear transformation module
-    """
-
-    def __init__(self, submodel_structure: SubModelStructure) -> None:
-        """GLM architecture class for forward propagation in optimisation.
-
-        Args:
-            submodel_structure (kulprit.data.SubModelStructure): The structure object of
-                the submodel
-        """
-        super(GLMArchitecture, self).__init__()
-
-        # assign data shapes and GLM inverse link function
-        self.num_obs = submodel_structure.num_obs
-        self.num_terms = submodel_structure.num_terms
-        self.num_draws = submodel_structure.num_thinned_draws
-        self.inv_link = submodel_structure.link.linkinv
-        # build linear component of GLM without intercept
-        self.lin = nn.Linear(self.num_terms, self.num_draws, bias=False)
-
-    def forward(self, X: torch.tensor) -> torch.tensor:
-        """Forward method in learning loop.
-
-        Args:
-            X (torch.tensor): Design matrix (including intercept) of shape
-                (num_obs, num_terms)
-
-        Returns:
-            torch.tensor: Model outputs of shape (num_obs, num_draws)
-        """
-
-        # perform forward prediction step
-        out = self.inv_link(self.lin.forward(X).T)
-        return out
diff --git a/src/kulprit/projection/likelihood.py b/src/kulprit/projection/likelihood.py
new file mode 100644
index 0000000..b036e2b
--- /dev/null
+++ b/src/kulprit/projection/likelihood.py
@@ -0,0 +1,20 @@
+import numpy as np
+import numba as nb
+
+
+@nb.njit
+def gaussian_log_pdf(y, mu, sigma):  # pragma: no cover
+    return -np.log(sigma) - 0.5 * np.log(2 * np.pi) - 0.5 * ((y - mu) / sigma) ** 2
+
+
+@nb.njit
+def gaussian_neg_llk(points, mu, sigma):  # pragma: no cover
+    llk = []
+    for y, m in zip(points, mu):
+        llk.append(gaussian_log_pdf(y, m, sigma))
+    return -sum(llk)
+
+
+LIKELIHOODS = {
+    "gaussian": gaussian_neg_llk,
+}
diff --git a/src/kulprit/projection/losses/__init__.py b/src/kulprit/projection/losses/__init__.py
deleted file mode 100644
index e571594..0000000
--- a/src/kulprit/projection/losses/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""Losses module."""
-
-import torch.nn as nn
-
-
-class Loss(nn.Module):
-    """Base loss class."""
-
-    def __init__(self):
-        super(Loss, self).__init__()
diff --git a/src/kulprit/projection/losses/kld.py b/src/kulprit/projection/losses/kld.py
deleted file mode 100644
index cf33f86..0000000
--- a/src/kulprit/projection/losses/kld.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from kulprit.projection.losses import Loss
-
-
-class KullbackLeiblerLoss(Loss):
-    """Kullback-Leibler (KL) divergence loss module.
-
-    This class computes some KL divergence loss for observations seen from the
-    the reference model variate's family. The KL divergence is the originally
-    motivated loss function by Goutis and Robert (1998).
-    """
-
-    def __init__(self) -> None:
-        """Loss module constructor.
-
-        Note that our architecture consists of a PyTorch KL divergence loss
-        module with setting ``reduction="batchmean"``. This setting is chosen
-        as a result of the PyTorch internals leading to this option being the
-        mathematically correct one. For more information, please refer to the
-        [PyTorch docs on the subject](https://pytorch.org/docs/stable/generated/
-        torch.nn.KLDivLoss.html).
-        """
-        super(KullbackLeiblerLoss, self).__init__()
-
-        self.loss = nn.KLDivLoss(reduction="batchmean", log_target=True)
-
-    def forward(self, input: torch.tensor, target: torch.tensor) -> torch.tensor:
-        """Forward method in learning loop.
-
-        This method computes the sample Kullback-Leibler divergence between the
-        reference model posterior predictive log probabilities, and those of the
-        submodel we wish to optimise. We initially input the raw draws, before
-        converting them into log probabilities.
-
-        Args:
-            input (torch.tensor): Tensor of the submodel posterior predictive
-                draws
-            target (torch.tensor): Tensor of the reference model posterior
-                predictive draws
-
-        Returns:
-            torch.tensor: Tensor of shape () containing sample KL divergence
-        """
-
-        # transform samples to log probabilities
-        input = F.log_softmax(input, dim=-1)
-        target = F.log_softmax(target, dim=-1)
-
-        # compute the KL divergence between the two predictive posteriors
-        kl = self.loss(input, target)
-        return kl
diff --git a/src/kulprit/projection/projector.py b/src/kulprit/projection/projector.py
index 8fc6599..871c544 100644
--- a/src/kulprit/projection/projector.py
+++ b/src/kulprit/projection/projector.py
@@ -2,19 +2,26 @@
 
 from typing import Optional, List, Union
 
-from kulprit.data.data import ModelData
-from kulprit.data.submodel import SubModelStructure, SubModelInferenceData
-from kulprit.families.family import Family
+import arviz as az
+import bambi as bmb
+import xarray as xr
+from xarray_einstats.stats import XrContinuousRV
+
+from scipy import stats
+
+import numpy as np
+
+from kulprit.data.submodel import SubModel
+from kulprit.projection.likelihood import LIKELIHOODS
 from kulprit.projection.solver import Solver
 
 
 class Projector:
     def __init__(
         self,
-        data: ModelData,
+        model: bmb.Model,
+        idata: az.InferenceData,
         path: Optional[dict] = None,
-        num_iters: Optional[int] = 400,
-        learning_rate: Optional[float] = 0.01,
     ) -> None:
         """Reference model builder for projection predictive model selection.
 
@@ -29,32 +36,34 @@ def __init__(
             path (dict): An optional search path dictionary, initialised to None
                 and assigned by the ReferenceModel parent object following a
                 search for efficient submodel retrieval
-            num_iters (int): Number of iterations over which to run backprop
-            learning_rate (float): The backprop optimiser's learning rate
+            num_steps (int): Number of iterations to run VI for
+            obj_n_mc (int):
         """
 
-        # log reference model data object
-        self.data = data
+        # log reference model and reference inference data object
+        self.model = model
+        self.idata = idata
 
-        # build model family
-        self.family = Family(self.data)
+        # log properties of the reference model
+        self.response_name = self.model.response.name
+        self.ref_family = self.model.family.name
+        self.priors = self.model.family.likelihood.priors
 
-        # set optimiser parameters
-        self.num_iters = num_iters
-        self.learning_rate = learning_rate
+        # build solver
+        self.solver = Solver(model=self.model, idata=self.idata)
 
-        # initialise search path
+        # log search path
         self.path = path
 
     def project(
         self,
         terms: Union[List[str], int],
-    ) -> ModelData:
+    ) -> SubModel:
         """Wrapper function for projection method.
 
         Args:
             terms (Union[List[str], int]): Either a list of strings containing
-                the names of the parameters to include the submodel, or the
+                the names of the parameters to include in the submodel, or the
                 number of parameters to include in the submodel, **not**
                 including the intercept term
 
@@ -65,7 +74,7 @@ def project(
         # project terms by name
         if isinstance(terms, list):
             # test `terms` input
-            if not all([term in self.data.structure.term_names for term in terms]):
+            if not set(terms).issubset(set(self.model.common_terms)):
                 raise UserWarning(
                     "Please ensure that all terms selected for projection exist in"
                     + " the reference model."
@@ -85,10 +94,7 @@ def project(
             # project onto the search path submodel with `terms` number of terms
             return self.path[terms]
 
-    def project_names(
-        self,
-        term_names: List[List[str]],
-    ) -> ModelData:
+    def project_names(self, term_names: List[str]) -> SubModel:
         """Primary projection method for GLM reference model.
 
         The projection is defined as the values of the submodel parameters
@@ -98,41 +104,128 @@ def project_names(
 
         Args:
             term_names (List[str]): The names of parameters to project onto the
-                submodel, **not** including the intercept term
+                submodel
 
         Returns:
             kulprit.data.ModelData: Projected submodel ``ModelData`` object
         """
 
-        # build restricted model object
-        structure_factory = SubModelStructure(self.data)
-        submodel_structure = structure_factory.create(term_names)
-
-        # build solver
-        self.solver = Solver(
-            data=self.data,
-            family=self.family,
-            num_iters=self.num_iters,
-            learning_rate=self.learning_rate,
+        # copy term names to avoid mutating the input
+        term_names_ = term_names.copy()
+
+        # if projecting onto the reference model, simply return it
+        if set(term_names_) == set(self.model.common_terms.keys()):
+            return SubModel(
+                model=self.model,
+                idata=self.idata,
+                loss=0,
+                size=len(self.model.common_terms),
+                term_names=term_names_,
+            )
+
+        # build restricted bambi model
+        new_model = self._build_restricted_model(term_names=term_names_)
+
+        # extract the design matrix from the model
+        if new_model._design.common:
+            X = new_model._design.common.design_matrix
+
+            # Add offset columns to their own design matrix
+            # Remove them from the common design matrix.
+            if hasattr(new_model, "offset_terms"):  # pragma: no cover
+                for term in new_model.offset_terms:
+                    term_slice = new_model._design.common.slices[term]
+                    X = np.delete(X, term_slice, axis=1)
+
+        # build new term_names (add dispersion parameter if included)
+        term_names_ = self._extend_term_names(
+            new_model=new_model, term_names=term_names_
         )
 
-        # solve the parameter projections depending on method
-        theta_perp, final_loss = self.solver.solve(submodel_structure)
+        # compute projected posterior
+        projected_posterior, loss = self.solver.solve(term_names=term_names_, X=X)
 
-        # extract restricted design matrix
-        X_perp = submodel_structure.X
+        # add observed data component of projected idata
+        observed_data = {
+            self.response_name: self.idata.observed_data.get(self.response_name)
+            .to_dict()
+            .get("data")
+        }
 
-        # project dispersion parameters in the model, if present
-        disp_perp = self.solver.solve_dispersion(theta_perp, X_perp)
+        # build idata object for the projected model
+        new_idata = az.data.from_dict(
+            posterior=projected_posterior,
+            observed_data=observed_data,
+        )
 
-        # build the complete restricted model posterior
-        idata_factory = SubModelInferenceData(self.data)
-        sub_model_idata = idata_factory.create(submodel_structure, theta_perp, disp_perp)
+        # compute the log-likelihood of the new submodel and add to idata
+        log_likelihood = self.compute_model_log_likelihood(
+            model=new_model, idata=new_idata
+        )
+        new_idata.add_groups(
+            log_likelihood={self.response_name: log_likelihood},
+            dims={self.response_name: [f"{self.response_name}_dim_0"]},
+        )
 
-        # finally, combine these projected structure and idata into `ModelData`
-        sub_model = ModelData(
-            structure=submodel_structure,
-            idata=sub_model_idata,
-            dist_to_ref_model=final_loss,
+        # build SubModel object and return
+        sub_model = SubModel(
+            model=new_model,
+            idata=new_idata,
+            loss=loss,
+            size=len(new_model.common_terms),
+            term_names=term_names,
         )
         return sub_model
+
+    def compute_model_log_likelihood(self, model, idata):
+        # extract observed data
+        response_dims = model.response.name + "_obs"
+        obs_array = xr.DataArray(model.data[model.response.name], dims=response_dims)
+
+        # make insample latent predictions
+        preds = model.predict(idata, kind="mean", inplace=False).posterior[
+            f"{model.response.name}_mean"
+        ]
+        linear_preds = model.family.link.link(preds)
+
+        if model.family.name == "gaussian":
+            # initialise probability distribution object
+            dist = XrContinuousRV(
+                stats.norm, linear_preds, idata.posterior[f"{model.response.name}_sigma"]
+            )
+
+        # compute log likelihood of model
+        log_likelihood = dist.logpdf(obs_array).transpose(*("chain", "draw", ...))
+        return log_likelihood
+
+    def _build_restricted_formula(self, term_names: List[str]) -> str:
+        """Build the formula for the restricted model."""
+
+        formula = (
+            f"{self.response_name} ~ " + " + ".join(term_names)
+            if len(term_names) > 0
+            else f"{self.response_name} ~ 1"
+        )
+        return formula
+
+    def _build_restricted_model(self, term_names: List[str]) -> bmb.Model:
+        """Build the restricted model in Bambi."""
+
+        new_formula = self._build_restricted_formula(term_names=term_names)
+        new_model = bmb.Model(new_formula, self.model.data, family=self.ref_family)
+        return new_model
+
+    def _extend_term_names(
+        self, new_model: bmb.Model, term_names: List[str]
+    ) -> List[str]:
+        """Extend the model term names to include dispersion terms."""
+
+        # add intercept term if present
+        if new_model.intercept_term:
+            term_names.insert(0, "Intercept")
+
+        # add the auxiliary parameters
+        if self.priors:
+            aux_params = [f"{self.response_name}_{str(k)}" for k in self.priors.keys()]
+            term_names += aux_params
+        return term_names
diff --git a/src/kulprit/projection/solver.py b/src/kulprit/projection/solver.py
index ce54f33..f69bbcc 100644
--- a/src/kulprit/projection/solver.py
+++ b/src/kulprit/projection/solver.py
@@ -1,120 +1,202 @@
-"""Optimisation solver module."""
+"""Optimisation module."""
 
-from typing import Optional
+from typing import List, Optional
 
-import torch
+from kulprit.data.submodel import SubModel
+from kulprit.projection.likelihood import LIKELIHOODS
 
-from kulprit.data.data import ModelData
-from kulprit.families.family import Family
-from kulprit.projection.architecture import Architecture
-from kulprit.projection.losses.kld import KullbackLeiblerLoss
+import arviz as az
+import bambi as bmb
+
+import numpy as np
+
+from scipy.optimize import minimize
 
 
 class Solver:
+    """The primary solver class, used to perform the projection."""
+
     def __init__(
         self,
-        data: ModelData,
-        family: Family,
-        num_iters: Optional[int] = 400,
-        learning_rate: Optional[float] = 0.01,
-    ):
-        """Initialise solver object.
+        model: bmb.Model,
+        idata: az.InferenceData,
+    ) -> None:
+        """Initialise the main solver object."""
+
+        # log the reference model and inference data objects
+        self.ref_model = model
+        self.ref_idata = idata
+
+        # log the reference model's response name and family
+        self.response_name = self.ref_model.response.name
+        self.ref_family = self.ref_model.family.name
+
+        # define sampling options
+        self.num_chain = self.ref_idata.posterior.dims["chain"]
+        self.num_samples = self.num_chain * 100
+
+        try:
+            # define the negative log likelihood function of the submodel
+            self.neg_log_likelihood = LIKELIHOODS[self.ref_family]
+        except KeyError:
+            raise NotImplementedError from None
+
+    @property
+    def pps(self):
+        # make in-sample predictions with the reference model if not available
+        if "posterior_predictive" not in self.ref_idata.groups():
+            self.ref_model.predict(self.ref_idata, kind="pps", inplace=True)
+
+        pps = az.extract_dataset(
+            self.ref_idata,
+            group="posterior_predictive",
+            var_names=[self.response_name],
+            num_samples=self.num_samples,
+        )[self.response_name].values.T
+        return pps
+
+    def linear_predict(
+        self,
+        beta_x: np.ndarray,
+        X: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """Predict the latent predictor of the submodel.
 
         Args:
-            data (ModelData): The data object containing the model data of
-                the reference model
-            family (Family): The family object of the reference model
-            num_iters (int, optional): The number of iterations to run the
-                optimisation for, defaults to 200
-            learning_rate (float, optional): The learning rate for the optimisation
-                algorithm, defaults to 0.01
+            beta_x (np.ndarray): The model's projected posterior
+            X (np.ndarray): The model's common design matrix
+            x_offset (np.ndarray): Offset terms in the model is included
+
+        Return
+            np.ndarray: Point estimate of the latent predictor using the single draw
+                from the posterior and the model's design matrix
         """
 
-        # log reference model data and family
-        self.data = data
-        self.family = family
+        linear_predictor = np.zeros(shape=(X.shape[0],))
 
-        # log gradient descent parameters
-        self.num_iters = num_iters
-        self.learning_rate = learning_rate
+        # Contribution due to common terms
+        if X is not None:
 
-    def solve(self, submodel_structure: torch.tensor) -> tuple:
-        """Perform projection by gradient descent.
+            if len(beta_x.shape) > 1:  # pragma: no cover
+                raise NotImplementedError(
+                    "Currently this method only works for single samples."
+                )
 
-        Args:
-            submodel_structure (SubModelStructure): The structure of the submodel
-                being projected onto
+            # 'contribution' is of shape:
+            # * (obs_n, ) for univariate
+            contribution = np.dot(X, beta_x.T).T
+            linear_predictor += contribution
 
-        Returns
-            tuple: A tuple of the projection solution along with the final loss
-                value of the gradient descent
-        """
+        # return the latent predictor
+        return linear_predictor
 
-        # build architecture and loss methods for gradient descent
-        self.architecture = Architecture(submodel_structure)
-        self.loss = KullbackLeiblerLoss()
+    def _init_optimisation(self, term_names: List[str]) -> List[float]:
+        """Initialise the optimisation with the reference posterior means."""
+        init = np.hstack(
+            [
+                self.ref_idata.posterior.mean(["chain", "draw"])[term].values
+                for term in term_names
+            ]
+        )
+        return init
 
-        # extract submodel design matrix
-        X_perp = submodel_structure.X
+    def _build_bounds(self, init: List[float]) -> list:
+        """Build bounds for the parameters in the optimimsation.
 
-        # extract thinned reference model posterior predictive samples
-        y_ast = torch.from_numpy(
-            self.data.structure.predictions.stack(samples=("chain", "draw"))
-            .transpose(*("samples", f"{self.data.structure.response_name}_dim_0"))
-            .values
-        ).float()
-        y_ast = y_ast[self.data.structure.thinned_idx]
+        This method is used to ensure that dispersion or other auxiliary
+        parameters present in certain families remain within their valid regions.
 
-        # project parameter samples and compute distance from reference model
-        theta_perp, final_loss = self.optimise(X_perp, y_ast)
-        return theta_perp, final_loss
+        Args:
+            init (List[float]): The list of initial parameter values
 
-    def optimise(self, X_perp, y_ast):
-        """Optimisation loop in projection.
+        Returns
+            List[Tuple(float)]: The upper and lower bounds for each initialised
+                parameter in the optimisation
+        """
+
+        # build bounds based on family
+        if self.ref_family in ["gaussian"]:
+            # account for the dispersion parameter
+            bounds = [(None, None)] * (init.size - 1) + [(0, None)]
+        return bounds
+
+    def objective(
+        self,
+        params: np.ndarray,
+        obs: np.ndarray,
+        X: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """Variational projection predictive objective function.
+
+        This is negative log-likelihood of the restricted model but evaluated
+        on samples of the posterior predictive distribution of the reference model.
+        Formally, this objective function implements Equation 1 of mean-field
+        projection predictive inference as defined [here](https://www.hackmd.io/
+        @yannmcl/H1CZPjE1i).
 
         Args:
-            X_perp (torch.tensor):
-            y_ast (torch.tensor):
+            params (list): The optimisation parameters mean values
+            obs (list): One sample from the posterior predictive distribution of
+                the reference model
 
         Returns:
-            Tuple[torch.tensor, torch.tensor]: A tuple of the projected
-                parameter draws as well as the final loss value (distance from
-                reference model)
+            float: The negative log-likelihood of the reference posterior
+                predictive under the restricted model
         """
 
-        # build optimisation framework
-        solver = self.architecture.architecture
-        solver.zero_grad()
-        optim = torch.optim.Adam(solver.parameters(), lr=self.learning_rate)
-
-        # run optimisation loop
-        for _ in range(self.num_iters):
-            optim.zero_grad()
-            y_perp = solver(X_perp)
-            loss = self.loss.forward(y_perp, y_ast)
-            loss.backward()
-            optim.step()
-
-        # extract projected parameters and final loss function value
-        theta_perp = list(solver.parameters())[0].data
-        final_loss = loss.item()
-        return theta_perp, final_loss
-
-    def solve_dispersion(
-        self, theta_perp: torch.tensor, X_perp: torch.tensor
-    ) -> torch.tensor:
-        """Analytic projection of the model dispersion parameters.
+        # Gaussian observation likelihood
+        if self.ref_family == "gaussian":
+            mu = self.linear_predict(beta_x=params[:-1], X=X)
+            neg_llk = self.neg_log_likelihood(points=obs, mu=mu, sigma=params[-1])
+
+        # TODO: implement more observation model likelihoods
+        return neg_llk
+
+    def solve(self, term_names: List[str], X: np.ndarray) -> SubModel:
+        """The primary projection method in the procedure.
+
+        The projection is performed with a mean-field approximation rather than
+        concatenating posterior draw-wise optimisation solutions as is suggested
+        by Piironen (2018). For more information, kindly read [this tutorial](h
+        ttps://www.hackmd.io/@yannmcl/H1CZPjE1i).
 
         Args:
-            theta_perp (torch.tensor): A PyTorch tensor of the restricted
-                parameter draws
-            X_perp (np.ndarray): The design matrix of the restricted model we
-                are projecting onto
+            term_names (List[str]): The names of the terms to project onto in
+                the submodel
+            X (np.ndarray): The common term design matrix of the submodel
 
         Returns:
-            torch.tensor: The restricted projections of the dispersion parameters
+            SubModel: The projected submodel object
         """
 
-        # compute the solution and return
-        solution = self.family.solve_dispersion(theta_perp=theta_perp, X_perp=X_perp)
-        return solution
+        # initialise the optimisation
+        init = self._init_optimisation(term_names=term_names)
+
+        # build the optimisation parameter bounds
+        bounds = self._build_bounds(init)
+
+        # perform mean-field variational projection predictive inference
+        res_posterior = []
+        objectives = []
+        for obs in self.pps:
+            opt = minimize(
+                self.objective,
+                args=(obs, X),
+                x0=init,  # use reference model posterior as initial guess
+                bounds=bounds,  # apply bounds
+                method="powell",
+            )
+            res_posterior.append(opt.x)
+            objectives.append(opt.fun)
+
+        # compile the projected posterior
+        res_samples = np.vstack(res_posterior).T
+        posterior = {term: samples for term, samples in zip(term_names, res_samples)}
+        posterior.update(
+            (key, value.reshape(self.num_chain, 100)) for key, value in posterior.items()
+        )
+
+        # compute the average loss
+        loss = np.mean(objectives)
+
+        return posterior, loss
diff --git a/src/kulprit/reference.py b/src/kulprit/reference.py
index 98a3937..a21f2ef 100644
--- a/src/kulprit/reference.py
+++ b/src/kulprit/reference.py
@@ -1,14 +1,16 @@
 """Core reference model class."""
 
-from typing import Union, Optional, List
+from typing import Tuple, Union, Optional, List
 from typing_extensions import Literal
 
-from arviz import InferenceData
-from bambi.models import Model
+import matplotlib
+
+import arviz as az
+import bambi as bmb
+
 import pandas as pd
-import torch
 
-from kulprit.data import ModelData, ModelStructure
+from kulprit.data.submodel import SubModel
 from kulprit.projection.projector import Projector
 from kulprit.search.searcher import Searcher
 
@@ -20,11 +22,8 @@ class ReferenceModel:
 
     def __init__(
         self,
-        model: Model,
-        idata: Optional[InferenceData] = None,
-        num_iters: Optional[int] = 400,
-        learning_rate: Optional[float] = 0.01,
-        num_thinned_draws: Optional[int] = 400,
+        model: bmb.models.Model,
+        idata: Optional[az.InferenceData] = None,
     ) -> None:
         """Reference model builder for projection predictive model selection.
 
@@ -37,52 +36,46 @@ def __init__(
         choices from previous papers on the topic.
 
         Args:
-            model (bambi.models.Model): The referemce GLM model to project
-            idata (arviz.InferenceData): The arViz InferenceData object
+            model (bmb.models.Model): The referemce GLM model to project
+            idata (az.InferenceData): The ArviZ InferenceData object
                 of the fitted reference model
             num_iters (int): Number of iterations over which to run backprop
             learning_rate (float): The backprop optimiser's learning rate
-            num_thinned_draws (int): The number of draws to use in optimisation
+            num_thinned_samples (int): The number of draws to use in optimisation
         """
 
         # test that the reference model has an intercept term
         if model.intercept_term is None:
             raise UserWarning(
-                "The procedure currently requires reference models to have an, "
+                "The procedure currently requires reference models to have an"
                 + " intercept term."
             )
 
+        # test that the reference model does not admit any hierarchical structure
+        if model.group_specific_terms:
+            raise NotImplementedError("Hierarchical models currently not supported.")
+
         # build posterior if not provided
         if idata is None:
             idata = model.fit()
 
-        # build model data class
-        structure = ModelStructure(model)
-        self.data = ModelData(
-            structure=structure, idata=idata, dist_to_ref_model=torch.tensor(0)
-        )
+        # test compatibility between model and idata
+        if not test_model_idata_compatability(model=model, idata=idata):
+            raise UserWarning("Incompatible model and inference data.")
 
-        # define thinning
-        self.data.structure.num_thinned_draws = min(
-            num_thinned_draws, self.data.structure.num_draws
-        )
-        self.data.structure.thinned_idx = torch.randperm(self.data.structure.num_draws)[
-            : self.data.structure.num_thinned_draws
-        ]
+        # log reference model and inference data
+        self.model = model
+        self.idata = idata
 
         # instantiate projector, search, and search path classes
-        self.projector = Projector(
-            data=self.data,
-            num_iters=num_iters,
-            learning_rate=learning_rate,
-        )
+        self.projector = Projector(model=self.model, idata=self.idata)
         self.searcher = Searcher(self.projector)
         self.path = None
 
     def project(
         self,
         terms: Union[List[str], int],
-    ) -> ModelData:
+    ) -> SubModel:
         """Projection the reference model onto a variable subset.
 
         Args:
@@ -92,7 +85,7 @@ def project(
                 including the intercept term
 
         Returns:
-            kulprit.data.ModelData: Projected submodel ``ModelData`` object
+            kulprit.data.SubModel: Projected submodel object
         """
 
         # project the reference model onto a subset of covariates
@@ -103,7 +96,8 @@ def search(
         self,
         max_terms: Optional[int] = None,
         method: Literal["forward", "l1"] = "forward",
-    ) -> dict:
+        return_path: Optional[bool] = False,
+    ) -> Optional[dict]:
         """Model search method through parameter space.
 
         If ``max_terms`` is not provided, then the search path runs from the
@@ -116,6 +110,8 @@ def search(
             method (str): The search method to employ, either "forward" to
                 employ a forward search heuristic through the space, or "l1" to
                 use the L1-regularised search path
+            return_path (bool): Whether or not to return the search path as a
+                dictionary object to the user
 
         Returns:
             dict: The model selection procedure search path, containing the
@@ -124,18 +120,25 @@ def search(
 
         # set default `max_terms` value
         if max_terms is None:
-            max_terms = self.data.structure.num_terms - 1
+            max_terms = len(self.model.common_terms)
 
         # test `max_terms` input
-        if max_terms > self.data.structure.num_terms:
+        if max_terms > len(self.model.common_terms):
             raise UserWarning(
                 "Please ensure that the maximum number to consider in the "
                 + "submodel search is between 1 and the number of terms in the "
                 + "reference model."
             )
 
-        self.path = self.searcher.search(max_terms=max_terms, method=method)
-        return self.path
+        # perform search through the parameter space
+        self.path = self.searcher.search(
+            max_terms=max_terms,
+            method=method,
+        )
+
+        # return the path dictionary if specified
+        if return_path:  # pragma: no cover
+            return self.path
 
     def loo_compare(
         self,
@@ -148,7 +151,7 @@ def loo_compare(
         scale: Optional[Literal["log", "negative_log", "deviance"]] = None,
         var_name: Optional[str] = None,
         plot_kwargs: Optional[dict] = None,
-    ) -> pd.DataFrame:
+    ) -> Tuple[pd.DataFrame, matplotlib.axes.Axes]:
 
         # perform pair-wise predictive performance comparison with LOO
         comparison, axes = self.searcher.loo_compare(
@@ -163,3 +166,54 @@ def loo_compare(
             plot_kwargs=plot_kwargs,
         )
         return comparison, axes
+
+    def plot_densities(
+        self,
+        var_names: Optional[List[str]] = None,
+        outline: Optional[bool] = False,
+        shade: Optional[float] = 0.4,
+    ) -> matplotlib.axes.Axes:
+        """Compare the projected posterior densities of the submodels"""
+
+        # set default variable names to the reference model terms
+        if not var_names:
+            var_names = self.model.term_names
+
+        axes = az.plot_density(
+            data=[submodel.idata for submodel in self.path.values()],
+            group="posterior",
+            var_names=var_names,
+            outline=outline,
+            shade=shade,
+            data_labels=[submodel.model.formula for submodel in self.path.values()],
+        )
+        return axes
+
+
+def test_model_idata_compatability(model, idata):
+    """Test that the Bambi model and idata are compatible with vanilla procedure.
+
+    In the future, this will be extended to allow for different structures and
+    covariates, testing instead only that the observation data are the same.
+
+    Args:
+        model (bmb.models.Model): The reference Bambi model object
+        idata (az.InferenceData): The reference model fitted inference data obejct
+
+    Returns:
+        bool: Indicator of whether the two objects are compatible
+    """
+
+    # test that the variate's name is the same in reference model and idata
+    if not model.response.name == list(idata.observed_data.data_vars.variables)[0]:
+        return False
+
+    # test that the variate has the same dimensions in reference model and idata
+    if not (
+        idata.observed_data[model.response.name].to_numpy().shape
+        == model.data[model.response.name].shape
+    ):
+        return False
+
+    # return default truth
+    return True
diff --git a/src/kulprit/search/forward.py b/src/kulprit/search/forward.py
index b294861..92bc988 100644
--- a/src/kulprit/search/forward.py
+++ b/src/kulprit/search/forward.py
@@ -3,7 +3,7 @@
 from typing import List
 
 import pandas as pd
-from kulprit.data.data import ModelData
+from kulprit.data.submodel import SubModel
 from kulprit.projection.projector import Projector
 from kulprit.search import SearchPath
 
@@ -12,23 +12,23 @@ class ForwardSearchPath(SearchPath):
     def __init__(self, projector: Projector) -> None:
         """Initialise search path class."""
 
-        # log the names of the terms in the reference model
-        self.ref_terms = projector.data.structure.term_names
-
         # log the projector object
         self.projector = projector
 
+        # log the names of the terms in the reference model
+        self.ref_terms = list(self.projector.model.common_terms)
+
         # initialise search
         self.k_term_idx = {}
         self.k_term_names = {}
         self.k_submodel = {}
-        self.k_dist = {}
+        self.k_loss = {}
 
     def __repr__(self) -> str:
         """String representation of the search path."""
 
         path_dict = {
-            k: [submodel.structure.term_names, submodel.dist_to_ref_model]
+            k: [submodel.term_names, submodel.loss]
             for k, submodel in self.k_submodel.items()
         }
         df = pd.DataFrame.from_dict(
@@ -42,14 +42,14 @@ def add_submodel(
         self,
         k: int,
         k_term_names: list,
-        k_submodel: ModelData,
-        k_dist: float,
+        k_submodel: SubModel,
+        k_loss: float,
     ) -> None:
         """Update search path with new submodel."""
 
         self.k_term_names[k] = k_term_names
         self.k_submodel[k] = k_submodel
-        self.k_dist[k] = k_dist
+        self.k_loss[k] = k_loss
 
     def get_candidates(self, k: int) -> List[List]:
         """Method for extracting a list of all candidate submodels.
@@ -63,12 +63,15 @@ def get_candidates(self, k: int) -> List[List]:
                 submodels
         """
 
-        prev_subset = self.k_term_names[k]
+        prev_subset = self.k_term_names[k - 1]
         candidate_additions = list(set(self.ref_terms).difference(prev_subset))
         candidates = [prev_subset + [addition] for addition in candidate_additions]
         return candidates
 
-    def search(self, max_terms: int) -> dict:
+    def search(
+        self,
+        max_terms: int,
+    ) -> dict:
         """Forward search through the parameter space.
 
         Args:
@@ -84,18 +87,21 @@ def search(self, max_terms: int) -> dict:
         k = 0
         k_term_names = []
         k_submodel = self.projector.project(terms=k_term_names)
-        k_dist = k_submodel.dist_to_ref_model
+        k_loss = k_submodel.loss
 
         # add submodel to search path
         self.add_submodel(
             k=k,
             k_term_names=k_term_names,
             k_submodel=k_submodel,
-            k_dist=k_dist,
+            k_loss=k_loss,
         )
 
         # perform forward search through parameter space
         while k < max_terms:
+            # increment submodel size
+            k += 1
+
             # get list of candidate submodels, project onto them, and compute
             # their distances
             k_candidates = self.get_candidates(k=k)
@@ -103,24 +109,19 @@ def search(self, max_terms: int) -> dict:
                 self.projector.project(terms=candidate) for candidate in k_candidates
             ]
 
-            # identify the best candidate by distance from reference model
-            best_submodel = min(
-                k_projections, key=lambda projection: projection.sort_index
-            )
-            best_dist = best_submodel.sort_index
+            # identify the best candidate by loss (equivalent to KL min)
+            best_submodel = min(k_projections, key=lambda projection: projection.loss)
+            best_loss = best_submodel.loss
 
             # retrieve the best candidate's term names and indices
-            k_term_names = best_submodel.structure.common_terms
-
-            # increment number of parameters
-            k = len(k_term_names)
+            k_term_names = best_submodel.term_names
 
             # add best candidate to search path
             self.add_submodel(
                 k=k,
                 k_term_names=k_term_names,
                 k_submodel=best_submodel,
-                k_dist=best_dist,
+                k_loss=best_loss,
             )
 
         # toggle indicator variable and return search path
diff --git a/src/kulprit/search/l1.py b/src/kulprit/search/l1.py
index 7c86470..87f3232 100644
--- a/src/kulprit/search/l1.py
+++ b/src/kulprit/search/l1.py
@@ -17,13 +17,21 @@ def __init__(self, projector: Projector) -> None:
         # log the projector object
         self.projector = projector
 
-        # log the model data object of the reference model
-        self.data = projector.data
+        # test whether the model includes categorical terms, and if so raise error
+        if (
+            sum(
+                [
+                    self.projector.model.terms[term].categorical
+                    for term in self.projector.model.terms.keys()
+                ]
+            )
+            > 0
+        ):
+            raise NotImplementedError("Group-lasso not yet implemented")
 
         # initialise search
         self.k_term_names = {}
         self.k_submodel = {}
-        self.k_dist = {}
 
         self.search_completed = True
 
@@ -31,7 +39,7 @@ def __repr__(self) -> str:
         """String representation of the search path."""
 
         path_dict = {
-            k: [submodel.structure.term_names, submodel.dist_to_ref_model]
+            k: [submodel.term_names, submodel.loss]
             for k, submodel in self.k_submodel.items()
         }
         df = pd.DataFrame.from_dict(
@@ -82,22 +90,21 @@ def compute_path(self) -> None:
         """
 
         # extract reference model data and latent predictor
+        self.common_terms = list(self.projector.model.common_terms)
         X = np.column_stack(
-            [
-                self.data.structure.design.common[term]
-                for term in self.data.structure.common_terms
-            ]
+            [self.projector.model._design.common[term] for term in self.common_terms]
+        )
+        eta = self.projector.model.family.link.link(
+            np.array(self.projector.model._design.response)
         )
-        eta = self.data.structure.link.link(self.data.structure.y.numpy())
 
         # compute L1 path in the latent space
         _, coef_path, _ = lasso_path(X, eta)
         cov_order = self.first_non_zero_idx(coef_path)
-
         return cov_order
 
-    def search(self, max_terms: int) -> None:
-        """Perform L1 search through the parameter."""
+    def search(self, max_terms: int) -> dict:
+        """Perform L1 search through the parameter space."""
 
         # compute L1 path for each model size
         coef_path = self.compute_path()
@@ -106,16 +113,14 @@ def search(self, max_terms: int) -> None:
         cov_lasso = {
             k: v for k, v in sorted(coef_path.items(), key=lambda item: item[1])
         }
-        sorted_covs = [self.data.structure.common_terms[k] for k in cov_lasso]
-        sorted_covs = [["Intercept"] + sorted_covs[:i] for i in range(max_terms + 1)]
+        sorted_covs = [self.common_terms[k] for k in cov_lasso]
 
         # produce submodels for each model size
-        self.k_term_names = {len(terms) - 1: terms for terms in sorted_covs}
+        self.k_term_names = {k: sorted_covs[:k] for k in range(max_terms + 1)}
 
         # project the reference model on each of the submodels
         for k, term_names in self.k_term_names.items():
-            self.k_submodel[k] = self.projector.project(term_names)
-            self.k_dist[k] = self.k_submodel[k].dist_to_ref_model
+            self.k_submodel[k] = self.projector.project(terms=term_names)
 
         # toggle indicator variable and return search path
         self.search_completed = True
diff --git a/src/kulprit/search/searcher.py b/src/kulprit/search/searcher.py
index 753c499..ff619cd 100644
--- a/src/kulprit/search/searcher.py
+++ b/src/kulprit/search/searcher.py
@@ -99,6 +99,9 @@ def loo_compare(
 
         # make dictionary of inferencedata objects for each projection
         self.idatas = {k: submodel.idata for k, submodel in self.path.k_submodel.items()}
+        self.idatas.update(
+            {len(self.projector.model.common_terms): self.projector.idata}
+        )
 
         # compare the submodels by some criterion
         comparison = az.compare(
@@ -112,8 +115,8 @@ def loo_compare(
             var_name=var_name,
         )
 
-        axes = None
         # plot the comparison if requested
+        axes = None
         if plot:
             axes = az.plot_compare(comparison, **plot_kwargs)
 


Test patch:
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 168e607..a7a0c57 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -42,7 +42,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7"]
+        python-version: ["3.8"]
     steps:
       - uses: actions/checkout@v2
       - uses: ./.github/actions/python-poetry-env
diff --git a/tests/conftest.py b/tests/conftest.py
index f6b33e1..7fd6f05 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -3,18 +3,14 @@
 import kulprit as kpt
 import bambi as bmb
 
-import numpy as np
-
-import torch
-
 import pytest
 
 # define model fitting options
-NUM_DRAWS, NUM_CHAINS = 50, 2
+NUM_DRAWS, NUM_CHAINS = 500, 4
 
 
 @pytest.fixture(scope="session")
-def bambi_model():
+def bambi_model():  # pragma: no cover
     """Return a bambi model."""
 
     # define model data
@@ -25,7 +21,7 @@ def bambi_model():
 
 
 @pytest.fixture(scope="session")
-def bambi_model_idata(bambi_model):
+def bambi_model_idata(bambi_model):  # pragma: no cover
     """Return a bambi model fitted inference data."""
 
     # fit model with MCMC
@@ -34,7 +30,7 @@ def bambi_model_idata(bambi_model):
 
 
 @pytest.fixture(scope="session")
-def ref_model(bambi_model, bambi_model_idata):
+def ref_model(bambi_model, bambi_model_idata):  # pragma: no cover
     """Initialise a Gaussian reference model for use in later tests."""
 
     # build and return reference model object
@@ -42,38 +38,7 @@ def ref_model(bambi_model, bambi_model_idata):
 
 
 @pytest.fixture(scope="session")
-def sub_model(ref_model):
+def sub_model(ref_model):  # pragma: no cover
     """Initialise a standard submodel projection."""
 
     return ref_model.project(["x"])
-
-
-@pytest.fixture(scope="session")
-def disp_proj_data(ref_model, sub_model):
-    """Produce data needed for dispersion projection tests."""
-
-    # extract reference model data and parameters
-    X_perp = sub_model.structure.X
-    theta_perp = torch.from_numpy(
-        sub_model.idata.posterior.stack(samples=("chain", "draw"))[
-            sub_model.structure.term_names
-        ]
-        .to_array()
-        .values.T
-    ).float()
-
-    # extract reference dispersion parameter
-    sigma_ast = torch.from_numpy(
-        ref_model.data.idata.posterior.stack(samples=("chain", "draw"))[
-            ref_model.data.structure.response_name + "_sigma"
-        ].values.T
-    ).float()
-
-    return X_perp, theta_perp, sigma_ast
-
-
-@pytest.fixture(scope="session")
-def draws():
-    """Define some random Gaussian draw tensor to test loss methods."""
-
-    return torch.from_numpy(np.random.normal(0, 1, 100)).float()
diff --git a/tests/test_dispersion.py b/tests/test_dispersion.py
deleted file mode 100644
index cb06dd9..0000000
--- a/tests/test_dispersion.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from kulprit.families.family import Family
-
-import pytest
-
-from tests import KulpritTest
-
-
-class TestDispersion(KulpritTest):
-    """Test the dispersion parameter projection methods."""
-
-    def test_gaussian_disp_proj_shape(self, ref_model, disp_proj_data):
-        """Test that the shape of the projected dispersion parameter is correct."""
-
-        # build dispersion projection object
-        family = Family(ref_model.data)
-
-        # extract necessary data
-        X_perp, theta_perp, sigma_ast = disp_proj_data
-
-        # project dispersion parameter
-        sigma_perp = family.solve_dispersion(theta_perp, X_perp)
-        # test equivalent shapes
-        assert sigma_perp.shape == sigma_ast.shape
diff --git a/tests/test_family.py b/tests/test_family.py
deleted file mode 100644
index b354cab..0000000
--- a/tests/test_family.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from kulprit.families.family import Family
-
-import pytest
-
-from tests import KulpritTest
-
-
-class MyStructure:
-    def __init__(self):
-        self.family = "unimplemented-family"
-
-
-class MyData:
-    def __init__(self):
-        self.structure = MyStructure()
-
-
-class TestFamily(KulpritTest):
-    """Test the family module used in the procedure."""
-
-    def test_not_implemented_family(self):
-        """Test that unimplemented families raise a warning."""
-
-        with pytest.raises(NotImplementedError):
-            bad_data = MyData()
-            Family(data=bad_data)
diff --git a/tests/test_likelihood.py b/tests/test_likelihood.py
new file mode 100644
index 0000000..346590b
--- /dev/null
+++ b/tests/test_likelihood.py
@@ -0,0 +1,25 @@
+import numpy as np
+
+from scipy import stats
+
+import pytest
+
+from kulprit.projection.likelihood import gaussian_neg_llk
+
+
+class TestLikelihood:
+    """Test the likelihood mehtods implemented."""
+
+    def test_gaussian_likelihood(self):
+        # produce random samples
+        data = list(np.random.random((10,)))
+
+        # define the parameters of the Gaussian
+        mus = list(np.random.random((10,)))
+        sigma = 1.0
+
+        # compute the log likelihood using scipy
+        scipy_llk = np.log(np.product(stats.norm.pdf(data, mus, sigma)))
+
+        # test that kulprit produces similar results
+        assert -gaussian_neg_llk(data, mus, sigma) == pytest.approx(scipy_llk)
diff --git a/tests/test_loss.py b/tests/test_loss.py
deleted file mode 100644
index 7101dfc..0000000
--- a/tests/test_loss.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from kulprit.projection.losses.kld import KullbackLeiblerLoss
-
-import pytest
-
-from tests import KulpritTest
-
-
-class TestLoss(KulpritTest):
-    """Test the loss functions used in the procedure."""
-
-    def test_gaussian_kl(self, draws, ref_model):
-        """Test that KLD(P, P) == 0."""
-
-        loss = KullbackLeiblerLoss()
-        div = loss.forward(draws, draws)
-        assert div == pytest.approx(0.0)
-
-    def test_gaussian_kl_shape(self, draws, ref_model):
-        """Test the shape of the loss output for compatibility with PyTorch."""
-
-        loss = KullbackLeiblerLoss()
-        div = loss.forward(draws, draws)
-        assert div.shape == ()
diff --git a/tests/test_model_data.py b/tests/test_model_data.py
deleted file mode 100644
index 415c715..0000000
--- a/tests/test_model_data.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from kulprit.data.submodel import SubModelStructure
-
-import pytest
-
-from tests import KulpritTest
-
-
-class TestData(KulpritTest):
-    """Test the methods used for building ModelData objects."""
-
-    def test_has_intercept(self, ref_model):
-        """Test that the reference model has an intercept."""
-
-        assert ref_model.data.structure.has_intercept
-
-    def test_intercept_only_submodel(self, ref_model):
-        """Test that the submodel is correctly constructed when only intercept is used."""
-
-        # build restricted model object
-        structure_factory = SubModelStructure(ref_model.data)
-        sub_model_structure = structure_factory.create([])
-
-        assert sub_model_structure.X.shape == (
-            ref_model.data.structure.num_obs,
-            1,
-        )
-
-    def test_build_submodel(self, ref_model):
-        """Test that the submodel is correctly constructed."""
-
-        # build restricted model object
-        structure_factory = SubModelStructure(ref_model.data)
-        sub_model_structure = structure_factory.create(["x"])
-
-        assert sub_model_structure.X.shape == (ref_model.data.structure.num_obs, 2)
-        assert sub_model_structure.model_size == 1
-
-    def test_build_too_large_submodel(self, ref_model):
-        """Test that an error is raised when a submodel is too large."""
-
-        with pytest.raises(UserWarning):
-            # build restricted model object
-            structure_factory = SubModelStructure(ref_model.data)
-            structure_factory.create(["x", "a", "b", "c"])
diff --git a/tests/test_project.py b/tests/test_project.py
index f46ef1a..e1b71a6 100644
--- a/tests/test_project.py
+++ b/tests/test_project.py
@@ -1,10 +1,9 @@
-import torch
+import numpy as np
+import pandas as pd
 import bambi as bmb
-import arviz as az
 
 import kulprit as kpt
 from kulprit import ReferenceModel
-from kulprit.projection.architecture import GLMArchitecture
 
 import pytest
 import copy
@@ -15,35 +14,92 @@
 class TestProjector(KulpritTest):
     """Test projection methods in the procedure."""
 
+    NUM_DRAWS, NUM_CHAINS = 500, 4
+
     def test_idata_is_none(self, bambi_model):
         """Test that some inference data is automatically produced when None."""
 
         no_idata_ref_model = ReferenceModel(bambi_model)
-        assert no_idata_ref_model.data.structure.num_draws is not None
+        assert no_idata_ref_model.idata is not None
 
     def test_no_intercept_error(self):
         """Test that an error is raised when no intercept is present."""
 
-        # load baseball data
-        df = bmb.load_data("batting").head(10)
-        # build model without an intercept
-        bad_model = bmb.Model("p(H, AB) ~ 0 + playerID", df, family="binomial")
-        bad_idata = az.from_json("tests/data/binomial.json")
+        # define model data
+        data = bmb.load_data("my_data")
+        # define model
+        bad_model = bmb.Model("z ~ 0 + x + y", data, family="gaussian")
+        bad_idata = bad_model.fit(draws=self.NUM_DRAWS, chains=self.NUM_CHAINS)
 
         with pytest.raises(UserWarning):
             # build a bad reference model object
             kpt.ReferenceModel(bad_model, bad_idata)
 
-    def test_architecture_forward(self, ref_model):
-        """Test that the architecture forward method works."""
+    def test_hierarchical_error(self):
+        """Test that an error is raised when model is hierarchical."""
 
-        architecture = GLMArchitecture(ref_model.data.structure)
-        y = architecture.forward(ref_model.data.structure.X)
+        # define model data
+        data = bmb.load_data("my_data")
+        # define model
+        bad_model = bmb.Model("z ~ (x|y)", data, family="gaussian")
 
-        assert y.shape == (
-            ref_model.data.structure.num_draws,
-            ref_model.data.structure.num_obs,
-        )
+        with pytest.raises(NotImplementedError):
+            # build a bad reference model object
+            kpt.ReferenceModel(bad_model)
+
+    def test_unimplemented_family(self):
+        """Test that an error is raised when an unimplemented family is used."""
+
+        # define model data
+        data = bmb.load_data("my_data")
+        # define model
+        bad_model = bmb.Model("z ~ x + y", data, family="t")
+        bad_idata = bad_model.fit(draws=self.NUM_DRAWS, chains=self.NUM_CHAINS)
+
+        with pytest.raises(NotImplementedError):
+            # build a bad reference model object
+            kpt.ReferenceModel(bad_model, bad_idata)
+
+    def test_different_variate_name(self, bambi_model_idata):
+        """Test that an error is raised when model and idata aren't compatible."""
+
+        # define model data
+        a = np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839])
+        b = np.array([59, 60, 62, 56, 63, 59, 62, 60])
+        y = np.array([6, 13, 18, 28, 52, 53, 61, 60])
+        data = pd.DataFrame({"a": a, "b": b, "y": y})
+
+        # define model
+        formula = "y ~ a + b"
+        bad_model = bmb.Model(formula, data, family="gaussian")
+
+        with pytest.raises(UserWarning):
+            # build a bad reference model object
+            kpt.ReferenceModel(bad_model, bambi_model_idata)
+
+    def test_different_variate_dim(self, bambi_model_idata):
+        """Test that an error is raised when model and idata aren't compatible."""
+
+        # define model data
+        z = np.array([1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839])
+        x = np.array([59, 60, 62, 56, 63, 59, 62, 60])
+        y = np.array([6, 13, 18, 28, 52, 53, 61, 60])
+        data = pd.DataFrame({"z": z, "x": x, "y": y})
+
+        # define model
+        formula = "z ~ x + y"
+        bad_model = bmb.Model(formula, data, family="gaussian")
+
+        with pytest.raises(UserWarning):
+            # build a bad reference model object
+            kpt.ReferenceModel(bad_model, bambi_model_idata)
+
+    def test_no_term_names_error(self, ref_model):
+        """Test that an error is raised when no term names are provided."""
+
+        with pytest.raises(UserWarning):
+            # build a bad reference model object
+            ref_model.project(terms=None)
 
     def test_project(self, ref_model):
         """Test that the analytic projection method works."""
@@ -51,77 +107,25 @@ def test_project(self, ref_model):
         # project the reference model to some parameter subset
         sub_model = ref_model.project(terms=["x"])
 
-        assert sub_model.structure.X.shape == (ref_model.data.structure.num_obs, 2)
-        assert sub_model.structure.num_terms == 2
-        assert sub_model.structure.model_size == 1
-
-    def test_projected_idata_dims(self, ref_model, bambi_model_idata):
-        """Test that the dimensions of the projected inference data are correct."""
-
-        # extract dimensions of projected idata
-        sub_model = ref_model.project(terms=ref_model.data.structure.term_names)
-        sub_model_idata = sub_model.idata
-        print(sub_model_idata)
-        print(sub_model_idata.observed_data)
-
-        num_chain = len(sub_model_idata.posterior.coords.get("chain"))
-        num_draw = len(sub_model_idata.posterior.coords.get("draw"))
-        num_obs = len(
-            sub_model_idata.observed_data.coords.get(
-                f"{ref_model.data.structure.response_name}_dim_0"
-            )
-        )
-        disp_shape = sub_model_idata.posterior.get(
-            f"{ref_model.data.structure.response_name}_sigma"
-        ).shape
-
-        # ensure the restricted idata object has the same dimensions as that of the
-        # reference model
-        assert num_chain == len(bambi_model_idata.posterior.coords.get("chain"))
-        assert num_draw == len(bambi_model_idata.posterior.coords.get("draw"))
-        assert num_obs == len(
-            bambi_model_idata.observed_data.coords.get(
-                f"{ref_model.data.structure.response_name}_dim_0"
-            )
-        )
+        response_name = list(ref_model.idata.observed_data.data_vars.keys())[0]
         assert (
-            disp_shape
-            == bambi_model_idata.posterior.data_vars.get(
-                f"{ref_model.data.structure.response_name}_sigma"
-            ).shape
+            sub_model.idata.observed_data.dims[f"{response_name}_dim_0"]
+            == ref_model.idata.observed_data.dims[f"{response_name}_dim_0"]
         )
+        assert sub_model.size == 1
 
-    def test_reshaping(self, ref_model):
-        """Test that the reshaping is correct."""
-
-        # extract the parameters from the reference model
-        theta = torch.from_numpy(
-            ref_model.data.idata.posterior.stack(samples=("chain", "draw"))[
-                ref_model.data.structure.term_names
-            ]
-            .to_array()
-            .transpose(*("samples", "variable"))
-            .values
-        ).float()
-
-        # extract dimensions of the reference model idata object
-        num_chain = len(ref_model.data.idata.posterior.coords.get("chain"))
-        num_draw = len(ref_model.data.idata.posterior.coords.get("draw"))
-        num_terms = ref_model.data.structure.num_terms
-
-        # reshape torch tensor back to desired dimensions
-        reshaped = torch.reshape(theta, (num_chain, num_draw, num_terms))
-
-        # achieve similarly shaped tensor using xarray transposition
-        transposed = torch.from_numpy(
-            ref_model.data.idata.posterior[ref_model.data.structure.term_names]
-            .to_array()
-            .transpose(*("chain", "draw", "variable"))
-            .values
-        ).float()
-
-        # ensure that these two methods both behave well
-        assert (reshaped == transposed).all()
+    def test_project_categorical(self):
+        """Test that the projection method works with a categorical model."""
+
+        data = bmb.load_data("carclaims")
+        data = data[data["claimcst0"] > 0]
+        model_cat = bmb.Model(
+            "claimcst0 ~ C(agecat) + gender + area", data, family="gaussian"
+        )
+        fitted_cat = model_cat.fit(draws=100, tune=2000, target_accept=0.9)
+        ref_model = kpt.ReferenceModel(model=model_cat, idata=fitted_cat)
+        sub_model = ref_model.project(terms=["gender"])
+        assert sub_model.size == 1
 
     def test_project_one_term(self, ref_model):
         """Test that the projection method works for a single term."""
@@ -130,7 +134,7 @@ def test_project_one_term(self, ref_model):
         ref_model_copy = copy.copy(ref_model)
         ref_model_copy.search()
         submodel = ref_model_copy.project(terms=1)
-        assert submodel.structure.num_terms == 2
+        assert submodel.size == 1
 
     def test_project_too_many_terms(self, ref_model):
         """Test that the projection method raises an error for too many terms."""
@@ -156,3 +160,24 @@ def test_project_wrong_term_names(self, ref_model):
         with pytest.raises(UserWarning):
             # project the reference model to some parameter subset
             ref_model.project(terms=["spam", "ham"])
+
+    def test_build_restricted_model(self, bambi_model, bambi_model_idata):
+        """Test that restricted model building works as expected."""
+
+        # build restricted model which is the same as the reference model
+        solver = kpt.projection.projector.Projector(
+            model=bambi_model, idata=bambi_model_idata
+        )
+        new_model = solver._build_restricted_model(["x", "y"])
+
+        # perform checks
+        assert new_model.formula == bambi_model.formula
+        assert new_model.data.shape == bambi_model.data.shape
+        assert np.all(
+            new_model.data.loc[:, new_model.data.columns != solver.response_name]
+            == bambi_model.data.loc[:, new_model.data.columns != solver.response_name]
+        )
+        assert new_model.family.name == bambi_model.family.name
+        assert new_model.term_names == bambi_model.term_names
+        assert set(new_model.common_terms.keys()) == set(bambi_model.common_terms.keys())
+        assert new_model.response.name == bambi_model.response.name
diff --git a/tests/test_search.py b/tests/test_search.py
index ce25fc6..10ca2ed 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -30,7 +30,6 @@ def test_l1(self, ref_model):
     def test_l1_utils(self, ref_model):
         """Test that L1 utility methods return expected result."""
 
-        copy.copy(ref_model)
         proj = ref_model.projector
         searcher = L1SearchPath(proj)
         arr = np.array(
@@ -38,6 +37,20 @@ def test_l1_utils(self, ref_model):
         )
         assert searcher.first_non_zero_idx(arr) == {0: 1, 1: 2, 2: np.inf}
 
+    def test_l1_categorical_error(self):
+        """Test that an error is raised when no search path is found."""
+
+        data = bmb.load_data("carclaims")
+        data = data[data["claimcst0"] > 0]
+        model_cat = bmb.Model(
+            "claimcst0 ~ C(agecat) + gender + area", data, family="gaussian"
+        )
+        fitted_cat = model_cat.fit(draws=100, tune=2000, target_accept=0.9)
+
+        with pytest.raises(NotImplementedError):
+            ref_model = kpt.ReferenceModel(model=model_cat, idata=fitted_cat)
+            ref_model.search(method="l1")
+
     def test_bad_search_method(self, ref_model):
         """Test that an error is raised when an invalid search method is used."""
 
@@ -77,7 +90,7 @@ def test_forward_repr(self, ref_model):
         """Test the string representation of the forward search path."""
 
         ref_model_copy = copy.copy(ref_model)
-        path = ref_model_copy.search()
+        path = ref_model_copy.search(method="forward")
         assert type(ref_model_copy.searcher.__repr__()) == str
         assert type(path.__repr__()) == str
 
@@ -89,9 +102,14 @@ def test_l1_repr(self, ref_model):
         assert type(ref_model_copy.searcher.__repr__()) == str
         assert type(path.__repr__()) == str
 
-    def test_plotting(self, ref_model):
+    def test_plot_comparison(self, ref_model):
         """Test the LOO compare plotting method."""
 
         ref_model_copy = copy.copy(ref_model)
         ref_model_copy.search()
         ref_model_copy.loo_compare(plot=True, plot_kwargs={"insample_dev": False})
+
+    def test_plot_densities(self, ref_model):
+        ref_model_copy = copy.copy(ref_model)
+        ref_model_copy.search()
+        ref_model_copy.plot_densities()