diff --git a/.DS_Store b/.DS_Store
index f5b8af8..3a60761 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.ipynb_checkpoints/occupancy-checkpoint.ipynb b/.ipynb_checkpoints/occupancy-checkpoint.ipynb
index bad52b9..9f15ec6 100644
--- a/.ipynb_checkpoints/occupancy-checkpoint.ipynb
+++ b/.ipynb_checkpoints/occupancy-checkpoint.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "raw",
+   "id": "d6a0ccf7",
    "metadata": {},
    "source": [
     "---\n",
@@ -19,6 +20,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b02dbedd",
    "metadata": {},
    "source": [
     "In this notebook, I demonstrate how to fit static site-occupancy models in PyMC [@royle2008, Chapter 3]. The standard site-occupancy model models binary detection/non-detection data $y_{j,k}$ for repeated surveys $k=1,2,\\dots,K$ at sites $j=1,2,\\dots,J.$ The species is present at the sites when $z_j=1,$ and absent otherwise. We assume that our probability of detecting the species given that the site is occupied is  $P(y_{j,k}|z_j=1)=p,$ and zero when the site is unoccupied. The probability of occurrence, which is typically the parameter of interest, is $P(z_{j}=1)=\\psi.$ As such, we can think of this as a zero-inflated binomial model, where\n",
@@ -36,12 +38,13 @@
     "\n",
     "# Simulated examples\n",
     "\n",
-    "To start, I demonstrate how to simulate this zero-inflated model using numpy. Throughout this section, I use hyperparameter values similar to those of @kery2011."
+    "To start, I demonstrate how to simulate this zero-inflated model using numpy. Throughout this section, I use hyperparameter values similar to those of @kery2011.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "c0922406",
    "metadata": {},
    "outputs": [
     {
@@ -60,26 +63,28 @@
     }
    ],
    "source": [
+    "# relevant libraries\n",
     "import numpy as np\n",
     "import pymc as pm\n",
     "import arviz as az\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "# plotting styles\n",
     "plt.style.use('fivethirtyeight')\n",
     "plt.rcParams['axes.facecolor'] = 'white'\n",
     "plt.rcParams['figure.facecolor'] = 'white'\n",
-    "# pal = sns.color_palette('Set2')\n",
-    "# sns.set_palette(pal)ß\n",
     "\n",
     "def scale(x):\n",
+    "    '''Scale x: 0 is the mean and 1 is one standard deviation from the mean.'''\n",
     "    return (x - np.nanmean(x)) / np.nanstd(x)\n",
     "\n",
     "def invlogit(x):\n",
+    "    '''Compute inverse logit of x.'''\n",
     "    return 1 / (1 + np.exp(-x))\n",
     "\n",
     "def sim_y(p, z, site_count, visit_count):\n",
-    "    \n",
+    "    '''Simulate detections given detection probability p and occurrence state z.'''\n",
     "    ones = np.ones((site_count, visit_count))\n",
     "    p_array = p * ones \n",
     "\n",
@@ -122,16 +127,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "89ea7a9d",
    "metadata": {},
    "source": [
     "## Estimating parameters with PyMC\n",
     "\n",
-    "Next, I use PyMC to train the occupancy model with the simulated data. First, similar to JAGS and Stan, the model must be specified using the PyMC syntax. This is done using a [context manager](https://peps.python.org/pep-0343/) in Python, essentially, a `with` statement. This creates a `Model` object. "
+    "Next, I use PyMC to train the occupancy model with the simulated data. First, similar to JAGS and Stan, the model must be specified using the PyMC syntax. This is done using a [context manager](https://peps.python.org/pep-0343/) in Python, essentially, a `with` statement. This creates a `Model` object. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "443704e8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,14 +155,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e1402c00",
    "metadata": {},
    "source": [
-    "In JAGS, the prior for $p$ would be specified as `p ~ dunif(0, 1).` The PyMC equivalent is `p = pm.Uniform('p', 0, 1)`. This could, alternatively, be specified as `p = pm.Uniform('detection probability', 0, 1)`. For the likelihood, I use PyMC's built-in `ZeroInflatedBinomial` distribution. We tell PyMC that this is an observed random variable by supplying data to the `observed` argument. PyMC also has handy tools for visualizing the model."
+    "In JAGS, the prior for $p$ would be specified as `p ~ dunif(0, 1).` The PyMC equivalent is `p = pm.Uniform('p', 0, 1)`. This could, alternatively, be specified as `p = pm.Uniform('detection probability', 0, 1)`. For the likelihood, I use PyMC's built-in `ZeroInflatedBinomial` distribution. We tell PyMC that this is an observed random variable by supplying data to the `observed` argument. PyMC also has handy tools for visualizing the model.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "fig-pdot",
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +225,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x153be0bd0>"
+       "<graphviz.graphs.Digraph at 0x141d50410>"
       ]
      },
      "execution_count": 3,
@@ -225,7 +234,7 @@
     }
    ],
    "source": [
-    "#| fig-cap: Visual representation of model $p(\\cdot)\\psi(\\cdot).$ `MarginalMixture` refers to the zero-inflated binomial distribution. \n",
+    "#| fig-cap: Visual representation of model $p(\\cdot)\\psi(\\cdot).$ `MarginalMixture` refers to the zero-inflated binomial distribution.\n",
     "#| label: fig-pdot\n",
     " \n",
     "pm.model_to_graphviz(constant)"
@@ -233,14 +242,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f3449a5f",
    "metadata": {},
    "source": [
-    "Now I can sample from the posterior. Again, I use the context manager, this time referring to the model by name. It's typical to name the output with `idata` because, by default, PyMC returns an object of class `InferenceData` from the Arviz package. Arviz is similar to the coda package for R."
+    "Now I can sample from the posterior. Again, I use the context manager, this time referring to the model by name. It's typical to name the output with `idata` because, by default, PyMC returns an object of class `InferenceData` from the Arviz package. Arviz is similar to the coda package for R.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "2cbe57a7",
    "metadata": {},
    "outputs": [
     {
@@ -312,19 +323,21 @@
   },
   {
    "cell_type": "markdown",
+   "id": "807c203f",
    "metadata": {},
    "source": [
-    "PyMC will try to use the No-U-Turn Sampler (NUTS) whenever possible. As you can see, it samples the posterior quickly. I can plot the output using the `az.plot_trace()`, supplying the true values for $p$ and $\\psi$ for comparizon. I can also look at a tabular summary using `az.summary()`."
+    "PyMC will try to use the No-U-Turn Sampler (NUTS) whenever possible. As you can see, it samples the posterior quickly. I can plot the output using the `az.plot_trace()`, supplying the true values for $p$ and $\\psi$ for comparizon. I can also look at a tabular summary using `az.summary()`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "fig-pdot_trace",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x400 with 4 Axes>"
       ]
@@ -347,6 +360,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "2ec0897f",
    "metadata": {},
    "outputs": [
     {
@@ -383,27 +397,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>psi</th>\n",
-       "      <td>0.800</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>0.723</td>\n",
-       "      <td>0.882</td>\n",
-       "      <td>0.001</td>\n",
+       "      <th>p</th>\n",
+       "      <td>0.499</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>0.448</td>\n",
+       "      <td>0.558</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>1966.0</td>\n",
-       "      <td>1708.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>2150.0</td>\n",
+       "      <td>2559.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p</th>\n",
-       "      <td>0.499</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.443</td>\n",
-       "      <td>0.556</td>\n",
+       "      <th>psi</th>\n",
+       "      <td>0.799</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.724</td>\n",
+       "      <td>0.881</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>1835.0</td>\n",
-       "      <td>2235.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2438.0</td>\n",
+       "      <td>2499.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -412,12 +426,12 @@
       ],
       "text/plain": [
        "      mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "psi  0.800  0.043   0.723    0.882      0.001    0.001    1966.0    1708.0   \n",
-       "p    0.499  0.030   0.443    0.556      0.001    0.000    1835.0    2235.0   \n",
+       "p    0.499  0.029   0.448    0.558      0.001    0.000    2150.0    2559.0   \n",
+       "psi  0.799  0.042   0.724    0.881      0.001    0.001    2438.0    2499.0   \n",
        "\n",
        "     r_hat  \n",
-       "psi    1.0  \n",
-       "p      1.0  "
+       "p      1.0  \n",
+       "psi    1.0  "
       ]
      },
      "execution_count": 6,
@@ -431,16 +445,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e06c45ad",
    "metadata": {},
    "source": [
     "## Adding site covariates\n",
     "\n",
-    "Next, I add in some realism by simulating a site-level covariate $x$ that affects the occurrence probability. I model this effect with a logit-linear model, i.e., $\\psi_j=\\text{logit}^{-1}(\\beta_0 + \\beta_1 x_j).$ "
+    "Next, I add in some realism by simulating a site-level covariate $x$ that affects the occurrence probability. I model this effect with a logit-linear model, i.e., $\\psi_j=\\text{logit}^{-1}(\\beta_0 + \\beta_1 x_j).$ \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "27750e3d",
    "metadata": {},
    "outputs": [
     {
@@ -492,14 +508,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1dcde573",
    "metadata": {},
    "source": [
-    "Again, I specify the model with PyMC. Like JAGS, the random variables can be manipulated, as in a linear model with $x_j.$ These behave like numpy arrays, meaning that vectorized operations and broadcasting are available. To monitor the output of these manipulations, use the `pm.Deterministic` class. In this case, I am monitoring the site level occurrence probability $\\psi_j.$"
+    "Again, I specify the model with PyMC. Like JAGS, the random variables can be manipulated, as in a linear model with $x_j.$ These behave like numpy arrays, meaning that vectorized operations and broadcasting are available. To monitor the output of these manipulations, use the `pm.Deterministic` class. In this case, I am monitoring the site level occurrence probability $\\psi_j.$\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "fig-psix",
    "metadata": {},
    "outputs": [
     {
@@ -520,11 +538,11 @@
        "<path fill=\"none\" stroke=\"black\" d=\"M20,-8C20,-8 168,-8 168,-8 174,-8 180,-14 180,-20 180,-20 180,-223.23 180,-223.23 180,-229.23 174,-235.23 168,-235.23 168,-235.23 20,-235.23 20,-235.23 14,-235.23 8,-229.23 8,-223.23 8,-223.23 8,-20 8,-20 8,-14 14,-8 20,-8\"/>\n",
        "<text text-anchor=\"middle\" x=\"161.88\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">200</text>\n",
        "</g>\n",
-       "<!-- beta1 -->\n",
+       "<!-- beta0 -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>beta1</title>\n",
+       "<title>beta0</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"44\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"44\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta1</text>\n",
+       "<text text-anchor=\"middle\" x=\"44\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta0</text>\n",
        "<text text-anchor=\"middle\" x=\"44\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"44\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
@@ -536,14 +554,28 @@
        "<text text-anchor=\"middle\" x=\"94\" y=\"-193.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"94\" y=\"-176.93\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
-       "<!-- beta1&#45;&gt;psi -->\n",
+       "<!-- beta0&#45;&gt;psi -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>beta1&#45;&gt;psi</title>\n",
+       "<title>beta0&#45;&gt;psi</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M59.96,-277.99C65.5,-265.22 71.72,-250.87 77.31,-237.97\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"80.47,-239.49 81.23,-228.92 74.04,-236.7 80.47,-239.49\"/>\n",
        "</g>\n",
-       "<!-- p -->\n",
+       "<!-- beta1 -->\n",
        "<g id=\"node2\" class=\"node\">\n",
+       "<title>beta1</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"144\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta1</text>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"144\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta1&#45;&gt;psi -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>beta1&#45;&gt;psi</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M128.04,-277.99C122.5,-265.22 116.28,-250.87 110.69,-237.97\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"113.96,-236.7 106.77,-228.92 107.53,-239.49 113.96,-236.7\"/>\n",
+       "</g>\n",
+       "<!-- p -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
        "<title>p</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"233\" cy=\"-198.48\" rx=\"44.72\" ry=\"40.66\"/>\n",
        "<text text-anchor=\"middle\" x=\"233\" y=\"-209.93\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
@@ -564,20 +596,6 @@
        "<path fill=\"none\" stroke=\"black\" d=\"M200.41,-170.44C183.51,-156.42 162.5,-138.99 143.63,-123.33\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"145.95,-120.71 136.02,-117.02 141.48,-126.09 145.95,-120.71\"/>\n",
        "</g>\n",
-       "<!-- beta0 -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>beta0</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"144\" cy=\"-315.79\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"144\" y=\"-327.24\" font-family=\"Times,serif\" font-size=\"14.00\">beta0</text>\n",
-       "<text text-anchor=\"middle\" x=\"144\" y=\"-310.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"144\" y=\"-294.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- beta0&#45;&gt;psi -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>beta0&#45;&gt;psi</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M128.04,-277.99C122.5,-265.22 116.28,-250.87 110.69,-237.97\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"113.96,-236.7 106.77,-228.92 107.53,-239.49 113.96,-236.7\"/>\n",
-       "</g>\n",
        "<!-- psi&#45;&gt;y -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>psi&#45;&gt;y</title>\n",
@@ -588,7 +606,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x15d90b790>"
+       "<graphviz.graphs.Digraph at 0x143d46290>"
       ]
      },
      "execution_count": 8,
@@ -597,7 +615,7 @@
     }
    ],
    "source": [
-    "#| fig-cap: Visual representation of model $p(\\cdot)\\psi(x).$ `MarginalMixture` refers to the zero-inflated binomial distribution. \n",
+    "#| fig-cap: Visual representation of model $p(\\cdot)\\psi(x).$ `MarginalMixture` refers to the zero-inflated binomial distribution.\n",
     "#| label: fig-psix\n",
     "with pm.Model() as psix:\n",
     "\n",
@@ -624,6 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "c0fec524",
    "metadata": {},
    "outputs": [
     {
@@ -696,11 +715,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "psix_trace",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 6 Axes>"
       ]
@@ -724,6 +744,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "314c0dff",
    "metadata": {},
    "outputs": [
     {
@@ -761,38 +782,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>beta0</th>\n",
-       "      <td>-1.243</td>\n",
-       "      <td>0.262</td>\n",
-       "      <td>-1.730</td>\n",
-       "      <td>-0.745</td>\n",
+       "      <td>-1.238</td>\n",
+       "      <td>0.265</td>\n",
+       "      <td>-1.733</td>\n",
+       "      <td>-0.747</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>2604.0</td>\n",
-       "      <td>3121.0</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>3195.0</td>\n",
+       "      <td>2625.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta1</th>\n",
-       "      <td>2.860</td>\n",
-       "      <td>0.416</td>\n",
-       "      <td>2.131</td>\n",
-       "      <td>3.680</td>\n",
+       "      <td>2.853</td>\n",
+       "      <td>0.405</td>\n",
+       "      <td>2.152</td>\n",
+       "      <td>3.629</td>\n",
        "      <td>0.008</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>2818.0</td>\n",
-       "      <td>2636.0</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>2887.0</td>\n",
+       "      <td>2656.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p</th>\n",
-       "      <td>0.745</td>\n",
-       "      <td>0.031</td>\n",
+       "      <td>0.744</td>\n",
+       "      <td>0.032</td>\n",
        "      <td>0.683</td>\n",
        "      <td>0.801</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>3913.0</td>\n",
-       "      <td>3134.0</td>\n",
+       "      <td>3981.0</td>\n",
+       "      <td>2902.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -801,9 +822,9 @@
       ],
       "text/plain": [
        "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "beta0 -1.243  0.262  -1.730   -0.745      0.005    0.004    2604.0    3121.0   \n",
-       "beta1  2.860  0.416   2.131    3.680      0.008    0.006    2818.0    2636.0   \n",
-       "p      0.745  0.031   0.683    0.801      0.001    0.000    3913.0    3134.0   \n",
+       "beta0 -1.238  0.265  -1.733   -0.747      0.005    0.003    3195.0    2625.0   \n",
+       "beta1  2.853  0.405   2.152    3.629      0.008    0.005    2887.0    2656.0   \n",
+       "p      0.744  0.032   0.683    0.801      0.001    0.000    3981.0    2902.0   \n",
        "\n",
        "       r_hat  \n",
        "beta0    1.0  \n",
@@ -822,16 +843,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "21604454",
    "metadata": {},
    "source": [
     "## Adding visit covariates\n",
     "\n",
-    "Finally, I add in visit-level covariate $w_{j,k}$ that affects detection."
+    "Finally, I add in visit-level covariate $w_{j,k}$ that affects detection.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
+   "id": "d7ae2a3b",
    "metadata": {},
    "outputs": [
     {
@@ -886,6 +909,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8f5f3921",
    "metadata": {},
    "source": [
     "Our PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state $z_j.$ The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit painfully slowly.\n",
@@ -898,12 +922,13 @@
     "    P(\\mathbf{y}_j | z_j = 1)\\; \\psi_j,  & \\text{otherwise}\n",
     "\\end{cases}\n",
     "$$\n",
-    "where $P(\\mathbf{y}_j | z_j = 1) = \\prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}$ [@royle2008]. To do this in PyMC, I rely on the `pm.math.switch` function, which is similar to `ifelse()` in R or `np.where()`."
+    "where $P(\\mathbf{y}_j | z_j = 1) = \\prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}$ [@royle2008]. To do this in PyMC, I rely on the `pm.math.switch` function, which is similar to `ifelse()` in R or `np.where()`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
+   "id": "1dc6e65b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -931,14 +956,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c012a6d1",
    "metadata": {},
    "source": [
-    "Then, I simply provide this function as an argument to the `CustomDist` class in our PyMC model. Note that we have to tell `CustomDist` how many dimensions each parameter has via the `ndims_params` argument. In this case, `p` has the same number of dimensions of `Y` (2), while `psi` has one dimension since it is vector of length `site_count`."
+    "Then, I simply provide this function as an argument to the `CustomDist` class in our PyMC model. Note that we have to tell `CustomDist` how many dimensions each parameter has via the `ndims_params` argument. In this case, `p` has the same number of dimensions of `Y` (2), while `psi` has one dimension since it is vector of length `site_count`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
+   "id": "fig-pw",
    "metadata": {},
    "outputs": [
     {
@@ -964,11 +991,11 @@
        "<path fill=\"none\" stroke=\"black\" d=\"M205.01,-8C205.01,-8 331.01,-8 331.01,-8 337.01,-8 343.01,-14 343.01,-20 343.01,-20 343.01,-215.82 343.01,-215.82 343.01,-221.82 337.01,-227.82 331.01,-227.82 331.01,-227.82 205.01,-227.82 205.01,-227.82 199.01,-227.82 193.01,-221.82 193.01,-215.82 193.01,-215.82 193.01,-20 193.01,-20 193.01,-14 199.01,-8 205.01,-8\"/>\n",
        "<text text-anchor=\"middle\" x=\"314.39\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">200 x 3</text>\n",
        "</g>\n",
-       "<!-- beta1 -->\n",
+       "<!-- beta0 -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>beta1</title>\n",
+       "<title>beta0</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-296.48\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"41.01\" y=\"-307.93\" font-family=\"Times,serif\" font-size=\"14.00\">beta1</text>\n",
+       "<text text-anchor=\"middle\" x=\"41.01\" y=\"-307.93\" font-family=\"Times,serif\" font-size=\"14.00\">beta0</text>\n",
        "<text text-anchor=\"middle\" x=\"41.01\" y=\"-291.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"41.01\" y=\"-274.93\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
@@ -980,9 +1007,9 @@
        "<text text-anchor=\"middle\" x=\"132.01\" y=\"-186.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"132.01\" y=\"-169.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
-       "<!-- beta1&#45;&gt;psi -->\n",
+       "<!-- beta0&#45;&gt;psi -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>beta1&#45;&gt;psi</title>\n",
+       "<title>beta0&#45;&gt;psi</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M67.58,-265.29C77.71,-253.77 89.37,-240.53 99.95,-228.5\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"102.46,-230.95 106.44,-221.13 97.21,-226.32 102.46,-230.95\"/>\n",
        "</g>\n",
@@ -995,7 +1022,7 @@
        "<text text-anchor=\"middle\" x=\"259.01\" y=\"-274.93\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- p -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>p</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"313.14,-219.82 222.89,-219.82 222.89,-162.32 313.14,-162.32 313.14,-219.82\"/>\n",
        "<text text-anchor=\"middle\" x=\"268.01\" y=\"-202.52\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
@@ -1022,22 +1049,22 @@
        "<path fill=\"none\" stroke=\"black\" d=\"M332.45,-265.29C322.32,-253.77 310.66,-240.53 300.07,-228.5\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"302.82,-226.32 293.59,-221.13 297.56,-230.95 302.82,-226.32\"/>\n",
        "</g>\n",
-       "<!-- beta0 -->\n",
+       "<!-- beta1 -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>beta0</title>\n",
+       "<title>beta1</title>\n",
        "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-296.48\" rx=\"41.01\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"141.01\" y=\"-307.93\" font-family=\"Times,serif\" font-size=\"14.00\">beta0</text>\n",
+       "<text text-anchor=\"middle\" x=\"141.01\" y=\"-307.93\" font-family=\"Times,serif\" font-size=\"14.00\">beta1</text>\n",
        "<text text-anchor=\"middle\" x=\"141.01\" y=\"-291.43\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
        "<text text-anchor=\"middle\" x=\"141.01\" y=\"-274.93\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
-       "<!-- beta0&#45;&gt;psi -->\n",
+       "<!-- beta1&#45;&gt;psi -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>beta0&#45;&gt;psi</title>\n",
+       "<title>beta1&#45;&gt;psi</title>\n",
        "<path fill=\"none\" stroke=\"black\" d=\"M137.53,-255.5C136.84,-247.55 136.12,-239.22 135.43,-231.3\"/>\n",
        "<polygon fill=\"black\" stroke=\"black\" points=\"138.94,-231.26 134.58,-221.6 131.96,-231.87 138.94,-231.26\"/>\n",
        "</g>\n",
        "<!-- y -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>y</title>\n",
        "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"268.01\" cy=\"-81.16\" rx=\"67\" ry=\"40.66\"/>\n",
        "<text text-anchor=\"middle\" x=\"268.01\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
@@ -1060,7 +1087,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x15eab9850>"
+       "<graphviz.graphs.Digraph at 0x1434f7510>"
       ]
      },
      "execution_count": 14,
@@ -1107,6 +1134,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
+   "id": "808baa62",
    "metadata": {},
    "outputs": [
     {
@@ -1152,7 +1180,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1179,11 +1207,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
+   "id": "fig-pw_trace",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAAIhCAYAAABaC+xGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d7gk11nn/zmnQueb09zJyrKSFSxnyQFLcpIDxhgvLCwmGPCCCctv8xJ2F9gFr2HBu2CSwSw44YDlJGcbB1mycpYmz9wc+nasdM7vj1Pdt9ONczUaaerzPPNIM91dXVVdVec97/m+31dorTUJCQkJCQkJCQkJCT2RT/cOJCQkJCQkJCQkJJzNJAFzQkJCQkJCQkJCwjokAXNCQkJCQkJCQkLCOiQBc0JCQkJCQkJCQsI6JAFzQkJCQkJCQkJCwjokAXNCQkJCQkJCQkLCOiQBc0JCQkJCQkJCQsI6JAFzQkJCQkJCQkJCwjokAXNCQkJCQkJCQkLCOiQBc8KO8Ru/8RsIIXZkW5/4xCd473vfe1rbePDBB7npppvI5XIMDw/zkz/5kywtLe3I/iUkJCScqyTP+oRzkSRgTjgrOd2H6KlTp3jZy16G53l87GMf40/+5E+4/fbbufXWW0m6wSckJCScHSTP+oRnCvbTvQMJCU8F//N//k+iKOJTn/oU/f39AExOTnLjjTfyT//0T9x6661P8x4mJCQkJJwuybM+4UyRZJgTdpwHHniAl770pWQyGfbt28d73vOettcPHz7M29/+dkZHR0mn01xzzTV86lOfar7+Ez/xE3zgAx/g6NGjCCEQQvCyl70MgLm5OX7mZ36GCy64gEwmw4EDB/jpn/5pFhcX277jU5/6FK9//eubD1CAG264gX379vHJT37yqTv4hISEhHOE5FmfcC6RBMwJO84b3/hGbr31Vj7xiU9wyy238Ku/+qv8xV/8BQDHjx/n+c9/Pg899BB/+Id/yCc/+UmuuOIK3vjGN3LbbbcB8J/+03/iNa95DRMTE3z729/m29/+Nu973/sAWFxcJJPJ8Hu/93t8/vOf53d/93e55557uOWWW5rfX6vVOHz4MJdffnnXvl122WU89NBDZ+AsJCQkJDy7SZ71CecUOiFhh/gv/+W/aEC/5z3vafv31772tXrv3r06iiL9kz/5k3p8fFwvLS21vecVr3iFvuaaa5p///Ef/3G9f//+Db8zDEN97NgxDei77rpLa631yZMnNaDf//73d73/7W9/u77ooou2fnAJCQkJCVrr5FmfcG6SZJgTdpy3vOUtbX9/61vfyvHjxzl58iSf+9zneO1rX0s+nycMw+afV7/61dx9992Uy+V1t6215o//+I+58soryefz2LbNvn37AHjsscea7wF6VnHvVGV3QkJCwrlO8qxPOJdIiv4SdpyxsbG2v4+PjwOmmnl2dpa//Mu/5C//8i97fnZxcZF8Pr/mtv/oj/6Id7/73fzKr/wKN998M4ODgyileMELXkC9XgdgcHAQIUSX1g1gaWmJoaGh7R5aQkJCQkJM8qxPOJdIAuaEHWd2dpa9e/c2/z4zMwOYyuXh4WFe9rKX8Wu/9ms9PzsxMbHutj/84Q/zyle+kj/4gz9o/tvhw4fb3pPNZjlw4AAPPvhg1+cffPBBXvnKV276WBISEhISepM86xPOJZKAOWHH+ehHP8ov//IvN//+4Q9/mL1797J7925uueUWvvvd73L55ZeTTqfX3EYqlWpmEVqpVqv09fW1/dvf/M3fdL3v1ltv5W//9m9ZWVlpvv+b3/wmR48eTWyGEhISEnaA5FmfcC6RBMwJO8773vc+wjDkqquu4mMf+xi33XYbf/7nf46Ukt/6rd/i+uuv58Ybb+QXfuEX2L9/P0tLS9x///0cO3aM97///QBceuml/Nmf/Rl/8Rd/wZVXXklfXx8XX3wxt9xyC7/3e7/Hf//v/53rr7+er3zlK3z0ox/t2od/82/+DR/84Ae59dZb+Xf/7t+xvLzMr//6r/PCF74weYgmJCQk7ADJsz7hnOLprTlMeDbRqJx+4IEH9Ete8hKdTqf17t279R/8wR+0ve/48eP6He94h56cnNSO4+iJiQn9qle9Sv/d3/1d8z0rKyv6LW95i+7r69OAvvHGG7XWWlerVf3Od75Tj4yM6Hw+r1/72tfqQ4cOaUD/1V/9Vdv33HffffqVr3ylzmQyenBwUP/ET/yEnp+ff6pPQ0JCQsKzmuRZn3AuIrROekcmJCQkJCQkJCQkrEViK5eQkJCQkJCQkJCwDknAnJCQkJCQkJCQkLAOScCckJCQkJCQkJCQsA5JwJyQkJCQkJCQkJCwDuvayimlUEq1/ZsQImk5mZCQ8KxDa01nDbSUEinPjbxC8rxPSEg4V9jO837DgLlSqezM3iUkJCQ8w8jlcudUwJw87xMSEs5VNnrenxsjQUJCQkJCQkJCQsI2SQLmhISEhISEhISEhHVIAuaEhISEhISEhISEdVhXw9yr2ONs0fRVq1Uef/xxLrzwQrLZ7NO9O89oknO5syTnc+c4k+eyl4b3XCp4O5uf92eKc/nePZePHc7t4z8Xj307z/stB8xnS9W4EIIoihBCnBX780wmOZc7S3I+d46n+1ye6wHz2fK8P1M83dfb08m5fOxw7h3/w4seDy545B3Jc/v1OXXsa3FaAXNCQkJCQkJCQsKzh1qouGe2DsBSFPGQiuh7mvfpmcC5O5VIACAMQz784Q/z+c9/njAMn+7dSUhISEhISHgKOboStP+9lIz9myEJmBMSEhLOAd73vvdx8OBB0uk01157Ld/4xjee7l1KSEhIeMaQSDISnrVorblnrs63p2o8tOgxX4twpOCiQZfrxtK8cl8e1zp3NKoJ5y4f+tCHePe738373vc+XvziF/Onf/qnvPrVr+ahhx5i3759T/fuJZylnCoHPL7sM5CyuHw4hSWT52XCuUsSMCc8q3nTP53gaCno+dpgSvIvLx3g168bZjLvnOE9S0g4c7znPe/hHe94Bz/1Uz8FwHvf+14+//nP83/+z//hd37nd57mvTt70FrzREVw7FiNfUOSq0fT52yQWA8VXz9ZRWs4VQ5JWYJLhlJP924l7AB647ck9CAJmBOe0SitOboS8MCCx9GVgFqoCJRmMGWxO+/wA/ty/MWDyz0/u+Qp/vCeRd7/wBK/8+Ix3vXcIeQ55IqQcG7g+z533XUX//bf/tu2f7/pppv41re+te5nq9XqWe0UEinNQ0sBK77ign6H8ax1Wts7vlzlyapktFyn6Cuy2md/4eweJgOleWQpINKaSIMjBVcOu1veTq1Wa/vvAws+tfpqsuHbxz32pnI7s9NnIZ3H/2ymWg2oe37z74Hvgzg3jr2B1lufNpzdT4KEhB4s1iM+8eQKtx0u86VjFYq+Oq3tVUPNL31thk88WeIjr93DcCa5LRKePczPzxNFEePj423/Pj4+zvT09Lqfffzxx4mi6KncvdPiUFXweMWU4twJvHw4wjmNypx/XpSAYG5+DoCPn4KbR8/O4y+HUI4EBUvzzaXViUJagjO8/X0+cuQIAI+VBadq7Sfz4eDsPBc7SeP4n82cqAlOlTtulNFz49gbWJbFeeedt6XPJJFBwjMCrTXfmarxf+9f4kOPreBF21tUuno0zUWDLl8/WWWq0l4Z/JUTVZ7/D4f5zBv3cdFgsvSY8OyiM1Ostd4we3zhhRee8QzzYj3igcUAR8JzR1wy9toR8P1PVJjsX/27PeRw6dDWs6sNHn18mfLULKMjoziu2c6lF5x9WdW5WsSDp+ooDUULJjOrr6UswaUHt958olarceTIEQ4cOEAmk2GoGhGeqjdfH81ILt2dWWcLz2w6j//ZzKSvWDq2mk3OiQD8qXPi2BtorbecDEgC5oSzmhUv4u8eKfJ/71/ivnnvtLd391yde+bq/NTlA1w+nOK3vjvPQn31pnmyGHDDR47wtbcc4OJEr5fwLGBkZATLsrqyybOzs11Z506y2ewZbWSgteaL02WqkQ0RPFqxePHk2sFfOtU+6XVSKXK59La/33WrZjuuSzpl7v9c7uwLmL86W8J1V59P6ZaR3LHEae1zJpMhl8tRECHp1GpiIpuxz8pzsdM0jv/ZTGhHpFOrK7MuEvxz49gbKKUolUpb+kxiK5dwVnL3bI2f/eIpJv/8MX7+K9ObCpYvHHD5yef085+vH+Zg39pFfBp4/wPL/PYd8/zhjePctK/9ATFTjXjv3YunewgJCWcFruty7bXXcvvtt7f9++23386LXvSip2mvelP0FNVgdSA/ttK7YHctzmK59Y6y4q0tQ4vUzpR0ddY6qm1oPhPOTiwhGEpbDKUthjMWfW4SCm6GJMOccNYwXwv5h0dX+MDDy9w5U9/4Ax08vuzz+LLPeLb9AVBwJFeNpvjmqfaChvlaxI9+/hS/cs0Q5/W7/N/7lwD4oQv7+KOXTZzewSQknEX8yq/8Cj/2Yz/Gddddxwtf+EL+7M/+jGPHjvHOd77z6d61Nk6vGuH02Veweazl7xcNbl/e8XSh9ObkNhvRWQC9Q3F4wllA3pXcfCDf/HulUuHhJEe0IUnAnPC04oWK246U+ZuHitx2pES4jRHTlrR9bqYaMVM1MosX7crwLy/t52evHOKrxyv84temub8jW/2e7y/yol1pfubyAcqB4gM378Y+R62kEp6d/PAP/zALCwv81m/9FlNTU1x++eV85jOfYf/+/U/3rrWx1buuPyUptmRb+08zU9Zpy97p036iFHCqEjKRtdm3zirWdij7ivvn60gBV42mSa+j3d4IpbuPZat0Z5hPb3sJCc90koA54YyjteaO6RofeLjIPzxaZGmd5cWN2Ju3Gc3afH+2d0b6W1M1vjVV40vHq/yvG8e560fO4713L/AfvjVLy8ov35qqc6oc8qlb9yXBcsKzkp//+Z/n53/+55/u3dhR+lyrLWB2drgRUevW5msh3zhpNM5PLvuk7Rxj2c0NoUeKPg8ueuRsyfN3ZXoWMn7zVJWluJ6iFmpetnf7WtJIw+kZ7HVPXrZjw5WQcKaphYqvHq80vaaztjyte6mVJGBOOGMcXfH54CNF/ubhIo8t+eu+V8R/1gulc7bgJy4b4Ob9Oe6e9fjOdI3PHSm3FfE1+MjjK3z+aJk/uGGcX7t2mBt2Z3n5x45SC1cHgSOlkJd+5AifeP3eHbvBEhISnhpWvAi/I+15ujFd6+dLfsShos/egskk33600vbeO6ZrvO68wobb9CPNd6drKG20xw8ueFw33u5EEES6GSwDXQ4+nVw46PL4Os/QSGu2nq9vZ7kjkdH596cbrTWnKiFam7oUAewpPHsbUAWRZtmLmoGgIwWD6e1Ni46tBHx/toYQAgGMO+FpT7DWwgsV356qsehFHOxzuHrsqXXh0Lr9Wt2moVZPkoA54SmlFire+/0F/uTeJU5uMAi0oundjSjvSCayFj92yQC/dM0Q/Slzm794Mse7MAUvnzlS5o/uWeSLx9oHuBVf8dNfnOLTh8q847L+tmC5QdFX3PKJY3z0tXvaBsNHFj0GUhYTueSWSUjYiLtmajy25DOQktywJ0fudMyRe/DNk1WOr9HBcycoB4rHSh5FX1GPdM+iqNIm/d+PrvhtcobHl/yugDnaQqTvhYojRZ+FesTwGgFTtAOxbXU7+rgziNLw9RPV5t9dSzyrA+YVP2ob08ayFq/cl1/nE2sTah2Pf+a68yzN1o0IN8cTRb85AXxk0Wd/n8vQNgP9zSDEaoHqTjciS0b/hKeEoys+v3fnPH/5QBFvA/HbYEqyt+C0OWF06pLTluAdlw/wH68fXTdotaTg9ecVeP15Bf75VJVf/8YM35pqL/b75KESd83W+LfXDfO7dy50bcOLNG/69HH+5qbdvO3iPv7ywWV+8avTvGQyy+fetO+s7nyWkPBUEymN0mZgEhita+s9sViPmitIy57i3rk6EzmbtCU2bEHvdMih+lPdgepCLXzKguXGk+rISkhD1BAp+O5Ubdve7J2NkDqDhaV6xNdPVrh7rs55fQ79KYulesTtR8tkHcm1Y6t6Zq01XzhW4dEln+lKyMF+h1257nOqd6D58U4o05bqEdVQsStnbzl4qYUKLzRHorQm58g2XXfn5noNM7VQsViPGEpb6/p57zQlP+KbJ6tUQs3lw6nTainuhYo7Zmqs+IqHFz1cS3B+v3tav3Dn/OypGtGqgeLTh8qkLMFoRjJdifjoY0XeelE/A+sEzTOVkIV6xJ68TV9qa8H1E8s+352uIYXgogF3RyfrScCcsKMcWwn47Tvm+KsHlze9FPKagwXGMlZbwHzdWIbvTNcYSEl+/sohfvG5Q4xvMbv74sks33zrAf7iwWV+9eszrLRkhE6UQ9579yI/c/kAXqS5ajTNr3x9pvl6qODtnzvJe76/wJ2xPvoLxyr8xYPL/NTlg1vaj4SEZxMffmyl7e99KclrD66uxjy53C4V+PThElcMG2/k546luXSd4GEzgV6joLf350+PxjMrbQv8ECpxocPpFOB1BiOdgehdszUW64paoHii6HP1aJpHl32z3F6LcKXgeRMmI32qElL2VXObh4tBV8B8x3SNaqjIxvt8zVi66Sl/bCVgyYs4EAfm66G1xo80juxuerMRSms+8thKM4gdSElefXBj+UorDy54bbKT6ycynD+w6lrSGSCHHf9QDRSfO1rGCzWuJbh5f558y0pBqDQPL3r4kV73mtwO9897TVnA3bN1DvQ5276GIg0nSiHlQLFUj0jbpx/e1jpWD54ohlxx2lttR2vNF46WWfEjVjzFybKgHmoO9Dt89kiZvpSk4EieN9Gu6Z+phHz5uMmkP7AAt55X2PS5i5Tm+7N1tDarNkdWgh1ddUgC5oQdoR4q/vv35vm9OxfwtyAaetW+HK8/mOfm/Xn+7fNGyDmSjC14cMHj21M1/sUl/ac1QxRC8FOXD3Lz/jw/+rmTfP3k6hJePdL82QPL/NcXjfLuq4cYyVj8qy+cagv07+woJvzVr8/w6gN5dm+QKUtIOFfoDFxaY6taqCi16Anvma3veHDSSsmPmKkYbWenw0UnfqSRgrYi34bLRiPTbcX/PZ0klRdpnjeRYV/BwZZmUK8Gimy80blq1FxC9kLNTDVCtZzUJ5b9ZsBc3kAGUo90V+C47JkJxtEVn2/F1pqPLnm88fy+Nc9RLVR841SNe2fr9KUk5/W7nKqE3D1b47Lh9IbndroStl0Xy55ivhYyktl8yNH5DUprHlvymK6E7M477NoggXL/fB0vlt35kea++TovammC8/3ZenNyN1MNuXFs6z/yYj3ivrk6jiW4ZizdDPyOdviHHy8FXHia3WMtCf1pi4wl2JWz6eux+uJHmlPlgP6Uta6++UyUb54sh9RCzUjaxpYRD8x7XDjo8mTR58hKwL6Cw568Q2bea17fAN+dXl0RjpSZOF07vjndczlovz9qodrRY00C5oTT5jtTVX7iC6d4dINCvl785GUD/PDF/V3/fsVImitGtt+xq5O9BYcv/+B+fvO7c/z2d+fbXvuP35pjsR7x+y8dp+BIfvizJ9cM+ld8xc99eYpPvn5vIs1ISGD95dytTJ4BCq7F2y7u41Ax4BNPrnB4RXPvnMdEzuZHLu5bN9NUjzRfOFphPGuTcyQ378+RWuP9jyx63DNXxxKCF01m1p4AN9eut3evN7TcAIeKPtePZ/jqiQq1ULOnYPPS3aa4uDW41FpT2MAer/XRo7Tm8aLPQjXkiWKAawkmCzYHCiYb29j0t061ByKPLnltz1itNYergsqsR2jpZgOZFU/xnekq+/Iujyz61ELdFnj24t65bteioqcY2UK9V6eG+mgpYC5eXThZDnnhZPvGOgPIY6Ww4+8BrW16WldCip6itPXhi088sULRM0HZ0ZWAN19gsqGuJdqu/fxp2B02fuuMJblsKEXe7e36oLTmc0fKVAKFEHDjnmxPuc5azPvmnO9UuXs9Pv6xrMVUJcRXmqMrAdVAsa/gcGwlYCxrt00IYXVVp8FijyL+Tsq+YsmLkAK8DgF/Ndg5LX4SMCdsG6U17/n+Av/un2e35Z8M8F++M8dbLuw7I1ZulhT81gvHuHw4xb/8/Cm8lgfae76/SD3U/O+XT3DbG/byxn86TiXoPdj/06EyH35spWegn5BwrtF56wrgVCWg5CvKgeJUJaTkRxTczWkRhRDcN19nqhI2M4TztRB1Ud+6nztW8slaEqU1DyzUma4G/MC+POf1tzcfCZXm7njlKNSab0/VeMuFTnPfe7HiRaz4EXlHMpmzNzVZrgSqzQ1ooRbxtZOVZrHxiVLIbNUEdUprFLoZOMs1noeV+EF7qrwaDC7UIxaqEUVfoRTUlea+2TojKYv8Oue8s2jxkeWAxyqS8kpIukWuESjNTDmiUV92dCXgRZMbHv5pc6Ij4J3rkOJ8f4PmVifKAbPVkD5Xsq/gYG3wm23VTaEaKJ4s+s2ui4eLPjfvz5G2JZcNp5rnVwhjbVYLV6U0lhA4luBUOeDRJb9Z9OlKwTVjmZ4BdjVUnCgHZG3JWMZkkFt19YeKQTPY1Nro7t94wWrAHCmNrzQZW8bOIpqT5ZCSr+i3Ih5dsLjjgRVeuk/S50om8/aaAbfWmkDFblbCPAPW06jXQoUfahZD8xuGSmNLwVI9YnwDa8b52voB83I94vZjlebKinoKa1WTgDlhW1QDxb/43Ek+8eTGvdgHUrKnJVHKErz+YB4v0mfU+/itF/Wzr+Dw+k8db7sZ33ffEoHS/Okrd/HXr5rkhz9zck1bu1/82jQ378+vW7iQkPBMZ6EWkXZ0W7D7nGGXb56q4UpB3pEc6GsPSCdyNkeKZkm6GirqoWK2tvmAGYyMofWJECpN0Ys4shI0g0xfaR5f9qkEiomszXw1Yk9BMl0NmSqHLNYjCk6N0YzV9t2dGaegR6SkW/5bjzR3ztY42OeyUItwpNiU//Lhos+KH3G8FGJLONjnUvQj+lv2pVG8WA0UR1YCwsgEH8Pp7u1rrXl0sTsNejg+161Z6sW64ptTNW7Zn6foRXxqE8/p+xfWLqT0dsCba6dlAIHSnNfvGEclTZsOdq4acmQlQClN2VfkHLnhb5beooe3FCBarlLXFs1j3J23WarHwSuaYysBDyys1ugc6HO4cNDlrx9axpGCyZaiyEjXeHlLBrkRBHuRZr4aARGH8gG7I90WMHdJWFr+f8WL+MqJKtVAsTtv0+9K5mpRs/X8TBgifMFSRfHFY2UuH06TskRXwBxERvddDiIeXw5w43E770pe32GxKDDXbKjMZLBRnyAQW+7oebzksyvndMUJSmv+8YkVpiohk3kjeXpwweNYfF+tF1ccLwUMbHE/koA5Ycss1EJe/6njfLvDfaKTsYxFLdI9g+W3XtjH798w3vQ4PdO8YFeWr73lAD/wj0fbPE/f/8AyC/WILx8rr3tTz1YjfuM7c7w3aaGd8Czm6yerBBpeMpllT8ExTYemajy0UEcDF/S7XDXaLp1KdQQenc4Xa7FYj7hzusoDCx4F16LeYvtYCRSfPVomY60GRdOVkGK8XHsiHiBPtLhnNIre7p6tc8OeHPO1kEgZe64lL2IgJdsCHlhd/tYtPsbHSz5BZAZnKQRPLPubCpgdCY8u+c2AXOBjSUGkYaij4O5Y2QTLAMW6wlchu/M2fS3B9exaxY7xPnee98V6SKg0jy75jPbQDrcGsHPVkCeLAWVPMBm/0hSjxJvtLBRrUAkUSuvmucw6q/uhtWaqElL0FWNZiwvior2Ziili21dwtt1sZjBt8fxdvaUhcx1ZyceX/K6VkE7ZxFZ16gIoei3fo2les1OVkLtaMuCdbi8azf97pMhUvFLgR5rJWJN9pOgT7c429fN+pHsWw3YmdDMdxYCjGYsgMqstd0zXcCxBxpKcLIcEWYsnOiSUc75gBJoZ814J4+/N1Di6EuBFikeXfK4cSbPiRxxZ8RnP2jx3NM3jyx5HVgJWPEXRV9w/V+fJYsB0JSJlC4ZS1qYnT7VIcfdMne9MVXnBriyXDae4cjTN40sed8/V+dzhMnWlGUxJFr2IK0fS8STdfIPdIi1q5WQ54B8eLfLOi7emK08C5oQtMVMJefnHjvBwj0xHKy+YyPC9mVrXMteevM1fvmqSV+3fnn/kTvKc4RRf/6EDvPJjR5szUoB/fGLjbAzAH9+7yDsuH9hRrXVCwtmG1qYL3dsu7ud4KeArJ6osxAHJ8VLAzQfa7+XtrhV9d6rKsqc4XAzIOe0Bz4ly2BV4n1jDWq4eKtK2bNq3PbzoIQRMV0PCyGgcH17wuH5XhtYY4+7ZGl86UuVbi5KU76OlTS1UTMT65lBBa5J8phryxaNlRrMWTyz7LNcjLh5KMZCyKLiSoq/asteN1Sw/0s2AuRGULHXoNJdqpkiq1TauHmszlTZL67aE1pC/VxASadMQpVfA3MALFV86XmGurljyBLtqEft6xBGtAXtj8gDw9ROVtqTI685bvR6WPZM5B7hvrs6VI2lKgeK7U6vFhwMpyfFSiBQCIeBFuzIb2g8CDKUkC7WQwbTVJQdQuv1saK27JHad52urJSm9ZDmN+VznK53yl7tm6m19AKYrIdMtiZvXHIwYy1p8e6rGfXN1Dq347FpjkrbiRTy44LHiKwquoM+1EPEqxV2zNQ4XAx6Y90jbRu4B3RrzUCkqEfRHikK6fcLz2JLPoaLPUMbitkMlKqEm7whKceOgRxZ9QqV5ctmn6EVtq7YNbXEQL38EjWxzx8kvehEPLHhEHQWrjXu88c8PLniMZW3unKnzxLLPyXjCESrNeEZQCdqL/MIIDq90Pye+cLS8KW10J0nAnLBpFmohr/r40XWDZVvAq/bn+OyRStdrbzq/wJ+/avIpNS3fKhcMuHz1Lft5yYePcGqNxio37s7yrakqnRPVSMO//so0X3nL/qQAMOFZTWOA66xCVxqCyCzVCgFWDy3jWtmkohdxx3QNSwokcN+8x754xakSKGxLEEY6zrBtHl9pGlPY2WrIE8s+D8x7DGcsLh5MdWlZU7ZgsR7xyKLPoWLIQiDI+RpbKqq+YiK/OkyGSjNbi7h/vs5MJeyy0Htgwee6iTS7cg6j6fasYsNbvjM4BrCFwLUEoTbOHZEGhW6zjVPaBNsPLNRZ8SIqoWZP3sZZZ4rSsNc6VgoIlcliLtQjjpYCHlu2OFEyuteHFlflAodWIvYNtMhSmqnm1V+hMXmYr5nMdON9fR3yuyeK7efnezO1tlbmRU8RKHNskVYIRNdvrbSOHRcUkzmHvCspB4qvnazy+HLAcMbiVftybc/gRsFZpEw3wFqo8bUmUrqZue0MqlvPYj1UTZ1xg/vn65wqh9hSkLEFlw2nyDiSWkM3jGms1YtQa+qBZq4WkXPEhjU/Smtmq1HTbaMWaJZ9xUWDKTSaF00aK7aSH3Hb4XLzc7VQYQtB1pHsydvcdtgExkKY7HclUCg0oYIrR9PGxcZXPFzz8TQcLUeUVUDelVw06PLoks/Hn1jh21M1QqWxhOBAn8NcTTW7U7a6srQFy4HxjZ6rRc3uu0rFGfOOTpRfP1nt6QDTqVsH+EpsOTdbDXFtgR9qinXFDbtTXVl2WF0FWKpH3DNXx5Y0ayO2ShIwJ2yKkh9xyyeOcX+LV3InaUvwwslMV7AsBfzui8f4tWuHz8rA8mC/y+1v3s8NHznCQj1CAP/9xaP86f3L/Kfnj/CvnjPAbYfLvPnTx7uC5q+drPKhx1Z4W1IAmHCOoFsCjTCC24+WeTgOuF62N9smm4DVtsWd+JFuDrBaa0qBagY6be9Tpvdx5zZyrqTSY5Bt6BYl8HhLQFsKFCt+RC6Waqh4x7Smuf+9htHWIqK5WkTZN0HGqWrISEoSatFs4tLnShZqEXvyDgf6Xa4aTfHggs+xUkCgFPtj54pAGanC7rzN92drTFVDZqpGMjKYklQDhVIWsuVUKm2Wks1ys8ALzZJ3U7vbsvOlQBH5mnvn6zy84LO7YJOzJctexHDaRinNkWLAfC3ieClg9yY87pfrqplZDpXxNn5g3uPRlmD7spFUW5awUx9e7RGoeHH0eN+8R8U3XsODaYvvTFdxpeBkedWi7rFln1fvz/Nk0WfAtXhk0cOLNNOVkNGM6XwnBU3/5qKvmpncqUrI0VLAef0uOvboXa5HDKYs9hRs6pHmnqLkjkdKzPoV8o7kXz5ngLGszalywD8+scJ8NUII47pUD8315LSMaY18/5JnCl4bRXG7sjafnyo3j6PVTxpMoDtXi5BCMJq1UBoOrbRPNhZrJoCO4oK7tAWXDqdj9wkfjZEAeaHmwgGXLxytxI0/JM+PXShevjfHPbM1Co7ZkbwjuXeuzKKnqEWi6ZCxUI24d65OxpZ8f3bVni/CBN1ZRzBViZrFir1UV0fi/a+G7RnlVqlVqDWnKgGTmEnhYj3CjzQjGatNf9xLApVxZNs1n7UFc9XI+FV7EbYlyLcE0P98qkrJV5womcYmQahgi/0Nk4A5YUMipXn7Z09y5zpVyX2u5DUHcvzDY+1yhn5X8qHX7Olatj3beM5wis+9aR83/eNR/vfLd/EvLunnV64ZafqNvu68Ah96zR7ectuJLt/Zd39tmtcdLJyWdVBCwjOBoysBy75qBnJKtQeZErGppW0vVNw3X+dEOSBnS6YqocmsttxcfY4gciQKyLqyK2BOWYLOdSyFxgs1WZuuCLvPlWRsye6CzVDRag7ySpvl5MeXfVZ66B0bi7zLXtS2tK4UzNUjtF79oowteHzJx5aC509kmK8rlr2Qk+WAQGn2FRwEgi8eK7PsKU6WA7Q2E49ImT+9JBafPWyWwk+UAyyxeo6X61GXHhpMVtmPNBVfNX2YLxp0qYea2apxMDFL6+bc97mSzBqPr16TiFCZ7d87X2O2FpKxBVlb4scrDWttR2sYz9nMtKzmpW3BQi0iaJmZRMoUOPbqGD5bC6n4in5XUom7AR5d8XFkikhrjpcCSkGE1rot45mxJffPe5zX73KyHHI8zt6WfEVfSnKyDN9clBRLNSYKKRaE4I7pGq87r8Cp8qqjidSCYysBJ0oBJ0shQxlJn2O1TQ4+9FgRvyUwrA+n2tujL/vN1RQwcqEgMmdprmqCzMmczbGVAI1mphaitXH+KDiSI65PxhEMpW2UhieLPpEy96clYDxrUQs0984ZH+1X7s2RsSWWEKy0SFPqoeJEKcQLVXz9aSJlrvlyYCZjKx01SIHSmDWhValEr2tkxVNI0e0drpp/NI8seGRsyWTO4VQ5aMp3ZqqyqzaiE6XbZ9GWEFRDxWwlNEF5qLGFpB5pFmrGDSRSiq+drFENFOE2+scnAXPChvz7f57l0y3LPp1cP57mn27dR6A13zxV40SsK9pXcPjMG/dy2fAzQ+N73XiGwz95YbMDVqc5/5su6ON9L9/FO7881fbvM9WI3/zuHP/zpeNnbF8TEs40QaS5b96L3QFM5sxXtHXQFKJdr6npXv786vEK983XuXu2zlQ1ZG/eIW0JhjPtxUAXDaaQ8SBYOVnl21NVUpbkwgGX/pTVM5g6XgrxIm10rZjAsR6agVVXNY4U7MnbXDzoNrOBs7WIuWrIcj1irhbROY4ux24HvYqXu3Sw8X8jpZmuhjy+5HGyHFINjNRiqhIxmbNZ8UygVw91s1ivM1Bu3fayp5iqBNhStB13/xrytmbxYtu+msI8LcwxNxpCSWGeYQfyHRFzx8EZ1wPzPyfKIQv1kM8cqrBYj/CU5mCfw/kDbnsLamGWxCuBpj8t2VPQXDGcis+/2c+/f7TI4eWAY6WAsYzR356qhnjKyAs6p0qtcobO2HyhFvG5oxUeWfA4Vg7QWjdXNdOWsTH7xskKXz5W4VgpIGMLRjM2jy/7RIEpfKujoRqxJ2dT8hWPLnm89+6FpjygPy0Zz5hAVaOZKoc4BYGUZiITRLotWAZzPVwynGru73JLsWCodRwsG6qB4nDR44rRNINpyWJdUws1QWSuF1t267MX6xFeqCl5CkuYCelUNURrwXw14pElj6tHM2a1poXGWF30FaVQUK0rPBUwlLa6CmIbzFYjaqFmb97hvH63WTT/wEKd0Yy9oU1c4zoq+WZ22HBgfmzZb8o7aqFpdNP8zlrYdM4RAiayNpcNpbhkMMWJUsDxUoAtzQpMK8t1xbGVgK+dMA3LTlXCpnws7fQ+vvVIAuaEdfl/jxT5H3ctrPn6i3Zl+Mwb9zWDzM++cR8v+fARzut3+fQb9m6qeONsYqN2sT975SDHywH/7Y725iefOVzid148dkbt8RISziSfOVLGkjTdHAAQINCkbBPISQFLcTAQoZmthszXw7YAYaoS8viSTzqu2i8FiuFM7+fEiXLAiXLATDWk6krGM4Ini36zeAlMpqoeaqqhZsWPVgMsIQgUzcIgMMHW/oLDXD1iPC6Ee2zJZzAlURhNZqCNRtW1JbtzNsv+6r7r2Cs5UJp9fY4Z+YVo2nM1grMT5ZDPHCpx92y9LcALlGYka3Gi3Hh/9zF3BoiPLXuMZmw0UAtNwJmvGKu6dIv8pd7iplCLA/TW8EhpozHfnXN4eGF1uX+zdmqiZXv3ztXb3IXQRlM6Ww05VPR5ougzV41YrIVMVY3M7aEFj/0Fl9GWgGq2GnJ42ZyLUBt9uJFmRCxUFZaE4YyF1XJONFD0IypLqqsL7GePlJurFF6kKfqKgZZnei3OqB5aMd3mMo6xOJvI2hR9TeNUVH1FmNUcLvrcPVdr09IW64pAmax6qKAcGo14OdD8w6PLTGS7r2VLiraVgCDSq1Z92mj2A2OODMBds3UuHExR9BQPLgb0OYKTnskCFxt2dfF7e/ULOLISUgoUg/F3TlVCrhrV1EMjq7lzpk5/Sq7KjVo2obQJwrOOYE+hd4hY8hW2jPjOVLWZWQ8izYrnNyfMM7WQTI9ry5YCjcaPwG3Z/IqvWGxZERjOrAa/M9WQoysBe/I2lhAMpS0GbXNsI1mjy5cidj3p+MqsY1YibCGakwBbCApJwJywkzy25PEzXzq15us37M7y6TfsbfM4vXwkzZd+cD8XDbpb8l0929Fa88FHirzton5+8IICH3x4maMtxvoPLfr8u3+eTbLMCc9aqoHqyjmNZCxeOJnlNQdXPVg/9FgRgKW6YsVXBJHpsnbFSLrd6UKYbVYDGE1bBLZos886VQ55YLHOUt005CjWFeMZo4F8ZMnjyIpPOdBN7eJKoJqa5rlaRHmm1gzeW4/hezM1TpZD3nyBaYSiVOz5rMFTJms+V40QlmY4bdHnSmYr8SRAm9dC7ZGSEtemGXib7w3ZlbObxXadxV0jGYvhtbLC8X8b56BxJuarEfPViLGc3czEe0p3FWAueRHTlQgpVzPBrdnoxr85UpB1BEoba7a01e2L+/Cix/n9blvwkbZMcWQ5DsS0NhOlho4V0SjcrHG8FFALjINCYzfnqhF3pKvctD/HnryDJQWPLnks+xFZW3Jen8tMLWS+FjKctsg7kowjqAa6LbhZqodMl0MUcMmgixCC2WrEil+j5CueM7xq8VELNQMpU3CZs02x4EDKYqYSNc9Nsa4YTmscYYQGXqRRKKYqISfjFYtOqr6i6ptATmmoBKAV3DFVbwZ6DSlPp3mh0pqw5YexpLGdO94ynviR5g/vXiRSJmgPlW66OvSlJJePrBavloPIZE7jjzcmEfVQ4zmKlJQs1SO+c6rGTNyyPIg9ncuhcUj2Ww6xFhp992sOyjZbw15UA9VV5NuQrhTrCjfb/nlLGtvBpXr3ZKdxzmqh7qmJXqpFLNUj+lOSkYzVzGSX/YjHlnykhOlK1LUyEmrNyXJAwWnfl171EhuRBMwJPamHih/+zMm22asA3n31EH90zyIviDPLvS76zfZ9f6bgR5qf/dIp/vqhIn9y7yLfn60TKNibtzheXh2Qf/+uBa4ZTXPr3uS2Snh20chddsogOse1+VrIY4s+s7Ww3bZJ0/RqbQzCnZ/1QxNgNbrjPba0thvPYi1ixTeFUgVXotFtBYBL9YiavSpfUNpkhjO2pBoYnfMjS55ZxvYVIxmruUOhNllDWxvN8DVjaS4aNC82CpkWa4q+FIBkxY+ohoqMJagG8ORywP4+TedildJxhzMhGM/aKODKkTSPLHrm7MbfP12NsIXoGvgbS9T3zntNZ4bmuVO6+XprkeJsy7J2Q3eesQV7Cy5Kac4fcHls2Ws22Wg9f4upEFeuehULAY8sedhCNIvsCo4wxVcYjbgrRdu2Omv8Hljw+MhjK/z8VUN4vuJbp2rMViKkNL9jLVSgBV6kuWjIoeBYPLbkM5636I8DnkeWfObrEaGCnB2wt+AghKAemqAybYuulb60LbHjAH2xHjXbbld9Tb9jguRAC/ocTQkjr6gGusvbGmAgLam26L/BBMtgGvWMSZslL+KhRY9ImyLOSqh4yWSWtCV4cMEj40hSsiHFEV1BedRyoxV9hduyG1KY45ECjqwE3DVbbwbLZnur91bRU4xlJIGChbrRQQ9lzGSnFCpOlALmqt1So0ab+SvX8v6O92OjkDNUml09CkrroaYeRoznLSI0tUjhR5qFetRcwZqrrsqGGqsFYPzVnyz6XDDg8vBCnTtm6mgNTyz7Pf2q/dA4jpyMwqZHeKA1tR4Sq41IRvaEnvz6N2e5p8OrUQN/+0iR33j+KLcfL7NUj3oGzM8mlusRP3jbcb583Gigvju9ek6OlyPGsxYzLQ+Vd3zxFF+6dYJnlhAlIWFt6pHm4cU6tcD4/x7sN1e3xgzs9855pG3JXDXg7rk605WQZU9RDVeXuEu+4ttTNfYVHCZztunc56umrpZ4W0s9BuggDh6cjkzWVDWk5sedxHqM3I3CtEqgqYQKrWEgFTKcsSkFZvm3FCgeXTZewL1QarWRQz0OrhuUfEUlUPhKGns0WzQlXacqYZdetImAvpTFZN4mbQuuHk9zohwQS62b50xjisGqsbesBiZzvTN+05UQr8MVM1I0tbG2NN3/6qFmOGNR9o1M4lDRb3P+CZTJEFqWWVLP27LpLTxbizhgy2Y09siyz0JNkY81ywMpi1BpTlZNYVp8qG0hjBcZa7VQab5xstoM7FSshTeBp+ZUOWSsJTs5U44gT7zPQXOjpUBRCTT5OKKshIqCY3HhoMvDS+2OTl6kWaorUlbU1KWnYxeFeqT5xlSdxYpEu6vOGvv72p/k5w04ZjIT41i06Y8BDi37BFozkraa1+WpSsg3T1U5VQ4JtWYia7cHkgIyLVn0eqRWHaW0ZrElBVwNFHdO1xhMWxwp+SzUOn74ltukocevhYoH5kMKboiQsDdns1hXlPze1+iKZ9pwXzDo9nw9inXT/a7snvm2sFFArRRYCO6YqplGO/G5TNmCUrAaX3RO1OuRmfB+9mil7fyPZCV9KYvFjgngJUMu9895zFRWW3NvhyRgTujiy8cr/O97Fnu+JoH/e/8SJyshN370CF/+wf3s7+t9Uz0b0Jil4bV40/l9fODh5eYDthZqfuQLs/zV5WdoBxMSnkKeKHqcKJlBph6ZFr97++ymdVzDwmmmEvKxx0tdWeGBtAm4lryIPtciUpp75z1CZfSUlVAznpFM5Cy+earWlh0FEwA1AtaBtGRfn8OJcohSmlrHYJ+Kg59GkaEXmUE9VCAkoGHZV/SnTKBbixRT5ZCKr3l40Viv9bmSyDOWXY4tGG4J2uqRasuYGtmF+ZO2jawh50pSlmAoLducIBpkbdmUfzy55HOg32EybvnbOiFo6L+PloK2cxIpzUDa7MRwatV6q+h1TzSEMJ3S+lMS1xJNfejDC16zgPF4KaR1pboSCfqkaE4MKpZqWnp1JltrHdrZpXrErpxNnyM52OcyWzMWbLagS5rymSNl7p2rU/IjIjRKwXwl4qJBt/ndmvbAZiZezcs5gkrLb7/sR02Hosaye8Pr2ZJmdaEcRBR9U3DZIklvnr+FatSceFUC3ZQ7FL2I4YxFNTCzGbuX6LwDDbjS6GUb56wSKO6crpOyBV7sh1wKFPviGh8J7cknbaz75uoR5Y7rXAgjXZirhs1z0kX8vQ1f4mLLORUKFjeRXS0Hqq2ZSiuR1lR9BS2poYmc3fb+ybxNOT7OBrakzXZyrhphSZguR21NzhwpiJTZB0d2d2G0heBYqce+adPIpZWRrIUXmftpzUnsJkkC5oQ2vFDxzg7d8mBKsuQphtJmMDgeB5CHigE3fuQod/zIwU21in0mMpi2+Mwb9/GCDx1mthpxoM8xPehTFn910ySvP6/AjXuy/MhnTzY/c7Qc8V8fc/nk5ad3cyYkPN2YoM8Muo3CveMrIYMZMwhVfdN4ot+VLHakOC1pdK+l1oKiFumEwPikVuOCvc5gGWiztKqHivl6SKAUx1u6/LU6TcBqkNCql24smc9VI6QwusaGZrQRME5XjF7V00AEIZrWcVqI3kV6GprFVdUwouKb4LTSw6LucDFAQ9NJwmR4uzPkJU9RDbrPidarRV+2EOQcSdoyEoT+tKRRN2YJE5j0pSQTWbttItP5XY3z12jkseQpbGk1D65xyAVH4imNLQW+Mi2HfdUITAWWFuQdyXicPU3bAkeKNt9dkyH2yDmCsq+o+MajtxhHsbO1sBlQeZFmobb6OzeyuQVHUmmJegUmsL56LM398x6ztYBTcVFlzpY4lmCmGsaNPhRuSye7sq8I4tbK0C0haVirNc7FfD1qFrcOpy1yjkQ75pyW4mtVCjN58CKzyhK1uGA0JnNa05S6NM7PfMvqyp2zdQ72OU0JUCflQDFdNvKTIBA4lpmUtMjJzfHHE4m0FNRUvE4hBI7szox3UvIV98zWuxr9rEUtNP7JDSu/RgOeVlu5jCPIxJdWo+PfYj2iGKx2AwTzmWORxl8OmMhZlDu7NGrNdDnqOobGu0Ss33csI1dqrHqdLs/OKCdh2/yPO+d5fHn1ISWA29+8j488vsLfP1pqayENcMuBPKOZZ09xXy8O9rv80637+I3vzPF3t+zm9mNlXjyZZXecHXjbxf3cNVvn91vcRL68aPNXj5T519ed3f7TCQnrcaoSMppZbQ3dYKmlmn3FM5mozqZEQsRNC1r/rWX9NtDaFNwJU6RnW2bQDZUJ4DqH6YmcQxCZwOLoympwrnV7M4TG0rYUHdFPTGNZtnO9uBKotsYaWplixZxjtLl5R/a0sivWFUVWM5pjcRFgL/0rGOuzehQwkbOZqYY8sRx0OVWYFavuL2v9l4VahNKadMY4B6QsE6gFkcl4p63exz9XD2nEMJHS1ALNQFpyvKzprIMK1eqXSiFMp7qCZLEeMVsLm0v+A2lJxhIoNDPVkPvnjX436IjOXSnQWjRdJxqyntaAMmMZvfViPcIS5prpS0kCZazVBKLZNXG+pih6iqNxx8L+lEW9Zd6WcyQjaYt6aIrMBlKS4axF1hVU48ytF2qmwwiBoiORaY6/RVfS6uKQtkwrZikEfa6kbjU+Y4o9Feb417L7bRZ19uj8qLTm8ErQ3MdOHl30EUIwkLKph4KRtI1GEyjNWMYmbUkiWmRMLTIfL1JoLUnZgnpl7aROEJlVpVyP7nm9rs6iF/GCXRnO6zdfVw0VX43t3BrUAo0lFK6EWqS7Vila8VsuRkua4NcS5trulFy0krIkOp4hBxE4Ak52ZKNtKbYly0gC5oQmh1YCfvO77XZpB/sdxrI2nzpU7gqW/9VzBnjfKybOyu59O831E6bIEeCtF/V3vf6G8wr873sW24o3/s23Fnn5gQEuH3lm+FAnJHQSRrDkRwy6xrGgHtGV9Qy1CXLTlkCK1QxmGEFJmSYnOUfiWLQVAlZD1QwI+lyTAQyVMm4F0jgQtOLEHsRPFv11xZG1QJOSRorgSIEQumc2rZAyRWaOFLh2e0OHBpEymtHQMoN0qze7kKuZ6+a/QdP71459edudKowOeq4eNjPcWhtv38aWOzslgrHf8kNz2OXAdJGzBITaYqEeMVuL2valHmrqtqb7SWWCoMZ5H81aZGxjyxdEGletBgWBVsa2rjXYChUztbCrmcVyXVG2FCyBpxSLNUWPukU0NPXNDez49269rmrBqivEQFpyIO2wUItobFFKaFh7NBL5jUdvZyB0qhIwX1OUAmNlpum+hsFkl71QtBVrOhac1+cghQlSvZY27Y8t+avNRmLx7Uw1RGDugbKv1gyWa6HmvPiLQq1Z7JARKN0enDeQcrU5UGOC0ijIFYArzcQi55hguRIqlF6Vz9RjBwpLwGTOxgtNwV0YweLaNbZd2EKAZSYEDVmL1qYN+YoXsSvn0OfKtsYxDcq+sQvs7HOwFgJB1hJkLYmURvpxJG7H3nkP9prQQrfM2hawttBybZKAOQEwF9ovfWO+K8NQ8hWv+sdjPNqhTfzRS/p5/w/s6rKUOVc52O903ZSegrd99iR3vO0g2Wd5cWTCsxXNQ/Me109kGE5ZLPfIhnmR5rszNSwEgykTwDU/rWHQtbCE0W6eqIdNG7bWgU4K0cwoepE2koiOrwqVxlOqGfwWXEk1VOaZ1SkzUJpJR5KyFERg2e1ZaDBL6P1piS0EoxmLWqjww4jWUrGSb4qf3NhiwhGCgbRkua56Ds6hMln1nGN0zNPtCbbYS1l1HVuxrhhISYqBIpORXYGAH4/ukVotPAz1qqSgM3AHkzk1xZURgTYFmJL2xiet/sKRVrghTEiBbQt8X5Bv6bC44keUA005CMn2yDqGEW3ezGsFL40NmtboRlfa58pmIR6YLGiD5boyGWtlLMfGchb9rtUMUsFkIJsBc8sXR1rTMENsOE9MlaNNdaMEI7toOG/M1aO1jylmOl69cC2xbsGb1nB4xacWOuYcbCJ6G81aTQ110Y+Yr0XUQtXuZCVgV85mIHbWq3Zca0Npsw3XFpyqhHiRyarbEgYcTSYjkbEcpyHt6YUlBJYw29iVMzZ9RU/xz1M1pDCtv68cTrNQX+fANpHgteRqwx0wE4lGsLxZZqoRSodm8rxeheImSALmBAC+vCD5RsY3I0tM3pEMpa2uYPmtF/bxVzdNYiVNOgDjWfpb35njl547xO91NHl5cMHj1785wx+/fNfTtHcJCdsniJeX52oRIxmrmb1rHXjKgRmUQzRe1CugVjjSohLrlBsDYGuMJ4XxQ0aKtsAJYCgjWawZ2cfulkYKBVeQd2x8pViotX+mHhh3jECbICvnCkayJihuLRiLlAmkj5dCLIwmuNix/53juitFz+xyg1JgsqvpwASnjewwmKCuuIaW0hICrYy2czhttWlaG1Q6qudqnaLbjtcOFYO2IM+SrJn1BPCUYKEecaDfZklpHlnw2wK1WtxCuRauv51OUraRUVR8TcmPSFumuYsXmYxnZzFdZzOORlYSMJ7Y6ww9brytFU9R9hU5R5C1BYE2E65mg4tNEGp4eCup15heDTQ68ULNTDXoKoiE3rHksmfsBtf7zbU2We7+lPnyzolZY4JU9ASOJZrXYqQVhILQV1iWoN/tdI/uxrUFfqh5dCloW3GIMBPA++aN/jnocTQi9r7rXIHpJFINXbk2KzemUxJ2LNXJ2bJNI130FZ290hrHbEvjYd1YZdiOk1USMCfw8FLAVxZs2LP6bwMpya6szcMdwfKbzi/wwVt2Jx3tYu6aqXHzx4+xUI842Nf7FvyTe5d40/kFXrkv0TMnPLNoxBVPFH2WPBPE2ZbJVDW6r62nQwSTFfIi1SwqawyAXtyi1pbgKxNkdo6tRrfYCNZMG2Iwle/n9bs8uWyyv+lYVtCKH+lmgF/xNWGPdfjGJ7zIFC82CoPsWCvp2qIrmKuGvTO6zW3GQXHcBLAte7iebLKx2D9TiYyrRw86reM2ojMY2SjI1UAl1G1tiVV8IEobFYQQWwuWXcusLqhY6nC0FLDsmczmcNpi2VOEWmNL8xtn7N4nqe07O96SsgUz1YDhjGxqhxuBVCVubhNExj97S2xd5rrpz2qg7GucHiVAxR4a3SCiZ/DZSaRgvmo8yteiGmqGYy/nxjXpKcxqjNL0b8L46kDBYaEeMTXffVFOVUKmKmt/NowgZa0fLLdS9NoL/EYyFqUgahZRNojU2hPSULX//gPu1mOYZJ34HGemEvK/7uvMqcDuXHew/LqDef7hNXtwNqk9erZz10yNV3zsaHMJ+vDK2ktFP3n7VJfdTULC2U7DEkqyWsUfRnQ5QKR6LNG3slg3rg9gZAULNZPm0doEAn4ICzXV9FxuEETtsoHG/gRK88iS8budrYVdwTKYQbs1wPRC3TXANv6mMBrMBqE2wVvWkl0uAa0ZrfWwJV3+zkOxhV4vSUBrIZNWJuue30b73u2SsqAx95mvqWYnQQ2sBBHT1RCtNh/kNGhkc1cL+1YLLBvPzkYzDmjXuW+Wqq9ZqClOlU0h5ELHNrbrj9BLg7vTnI6ssVdbbOjt5tJJPVptnd3+4fYVpAjT5KQcKiqhsWNs+Fcv1iN6SO43yfaPux5138tngiRgPodRWnOq0r0kNJiSPNixDHXz/hwfee2eTQv1zwUuGnS5ZA1j906OlQJ+5eszT/EeJSTsLIMpC8fqzihWOqr3C65kV85mKNM9pHQOa+WgW//b6EjXK7PW9r5YI1qsq2bL6K1kOxtkHMF5/Q6TOYtAa1O01iP46NU5bCt0tq9eqEUIxKaeo0pDOTDtp1O20USnbUHaFqcRpKxN53y+NSHvRbpref90CLVmxVd4kWaqEjJTiai3BGKd9MrC9uJUJWjretig14SqF+mnKCIaSMu2xiQNhDQdEkfWcZrazm+9GQOItYJttJlsztdDlr2Ikqcoe4qSZzzRK4FiKG0RaSOT6HR42UkaKy3dxaNnPliGJGA+Z7ljusaVHzzEO25v91xOW4KljiWNV+zN8vHX721mABIMBdfis2/az+XDqU29/y8eXOa2w6WneK8SEnYOL+rtMAEm89TICC/WI3yluoqGgbbRzpL0lHNtJ+g9HSwhWPZNMVkvrXCDWqRRaCI0odZdGfC1yDqiu80dqz6/BXfzz9KUJcz7RaOlsLEq20picqMVgJ4ImkWRp9nvoQvZI7sYRmsHtlspmn5k0WcgtT21aVp2f3/rsJeyTdHnRM5iImc12413ImTsORz/kcK4vLRKbTTGH9kWYt0AcDv3xmYVk/VIsdawHkRmlaWXZvpkOeCJZd+0stZ6w+tZSrpkRgLzPFjrOh5MSyYyNk6P669zwt5KxhEMZyTZbUguNiKJgM5BHl30eM0njvHggsf98+3tQz/6uj3csDvb/PuNu7N86tZ9q+btCW0MpS2+8OZ9nN+/uRKCn7p9altLjgkJTweVdeQHc7WoGWwqZSQVfqiNRdoaj4uCKzddcPVUUvYVjy55XQWGnRTritlqxGLNOCQsbfLejWK9ZGfvEkuYoj61hQh0xTPZ9E797VakEdsKHbSx1Cv6qqcNWyu9sqfrkd3i+1NSMJGzGM9Z62ZjwZwvtVXdSMxy2COQbzn2wZQkY5mCuPWK4rK2YMC1mn8CZfarVfuulAlIFz3jPuJYJhPdxRZ/vKF0r+lIbyqB7ll0uBH1SDNdifAjI7Ep+WrDCdxgSjabCpltKApub2/zoYxsk37mtzBhqgVGnrOWh/XpkERB5xgnywE3xUVqnfzQBTlee7DAZ964j5fvyfLS3Vk+/YZ97S07E7rYlXP44pv3szu3cVZjuhryS1+dPgN7lZDwFNNjPKqFmmK9PTDQHa+3Zsy2Kt+014mVhDBd/ja7hL1ZiYHWJjso2Tjb15AOeGtMCpbriplK1KZXzrliy+ehFdemp5QhZRtnkJGste0F7CDq9jVuYFuQdQU5V2xY+NlJw9Vka0G/QCLaOji2kraNy0kQSw2fKladj00mtzPLLGInkgjjyuEpteYk0ZKr2vogovcEbos/Xj3UzLSsmuRT3TfEZiUuvVgrebbWb+lI44KyWFdt2eowYk0d8mJNMVuJ2tpqnw0kkdA5xGIt5BUfPdrVgAQgJeG/Xj8ImCYDn37DPm57w17yW1g6PJc50O/yxR/cv2H2A+CDjxT5xBMrZ2CvEhKeOjYKdjK2WY5uHSQ7B8isI9bMRvdivSC335VdAflO0qlH7sVG7YZ7kbElw6fRLdUPTeDV1xEYWdJktG1BV8DW0EVvRqrRGSBJaYL8SDc6M3Z/ZiAtGctavTOmLaxsooAy78ouiUGvSVHKFjhCUAvVlt1ENst0JWr+WfYj0/RGirYAVMdWhZHSLNVNE5e1AsOdvFb743NtWeZcZBzBrpxNwZbN1xqcTuH+iqfWXJnp9XtLYSQpa02w18Pb5gmS8eR5p0mioXOEWqi49u8P89hyb0/Jm0ZCxrOrd33WkRTcZ3fL653mkqEUt79pPwObmGS888tTLNSeoqd6QsJZgAKIrdWk7B3kVPxVizbbMnrRvCvJddxDIg7S1uOpVHpoaGubvZPMVyPq8c5vt5ivHmpqHZFr1Tde1LVId01uGlnNzTgNdLqCKLX6u2nVO0tYCzX1SO+Y/ajfkeXudZ78yGjMd3rClHdlz8RRLTCtnethb53/U3W99CJjm054u3I2u7IOQykjBVmLqq+xpOl2uaP7YXVrh71IkznNwsCUJRjcQkWm0qvB+FjcCn0nSALmc4BQaV764SMcWcP27GDB5nkDZ9fSxzOV546l+eRrxshZ6z8sZ6oR/zqRZiQ8g1jLwWAtvLhgSGsTZK0XyDgWjKZtRjM2BUdS6NC4arV+oQ/Q7IC33ndsd+BsLUZ7KoqJyo0GEtt8DEeqd3Y70npbg7xtcVqri16om9rr06Xsmyxtw9pMoU3L7g5qgTaTj00Gqhsdn5AmY2quR7lmgd9a1ILuiUor68mLtkpTCx2q5n2g0Sx5EVPVsKf7TBRbF+7K2UxkLYYdTc4WpyXXqEW924GfjiTIeKR3+6yfFqK7CHEzJAHzsxytNf/isye4a7a+5nteOpnadFVtwsZcO5riDy/zyG0QYPz9oyt87PFEmpFw9uNYxmJuq0FzJ40gZC08pZirh0+J/23GlvQ7Vs+AYCRrUUhJNnN4wVlQtLhZaoGRBWyVMNq83zQYaU2nJKQTa41Vhs3Sam1W34E08kbHp5VxdKmEipUg2rDwcavspE0fmElKyVNMVUIW6kY2Ug/1utFqsW7eX/QiFKZYsb+XN/MmWa73lp8Ut3AtdbJYNxKQjeQbazFfj9rv2YaBzXbcR7a1BwnPGP79P8/y4cfXtzL70BNV5rx135KwRa7qU3z4ptENb7Cf+/IUc9VEmpHwzKexxNtZwJZqWY7daJBS2gQSDXXBZnSIUvYubGpFtGgaewUEtVAh2FwWfas65e1kEntpi1P29rJiZ4K40/H67xGn1zivQS3U+KH53XcyS9uLQJlM+UarG2cbW3WiqUWwFAjm6mrDlZrtsE3Tkh1Bda6+6O3vz1l6+yXsBO/9/gK/e+fChu+7dtRlaDuN1RPW5cbdGf78VbvWfc9cLeIXvpJIMxLOTnKxntgSAk+pNYMiIY1lnBTdg5Ej2xt19DJdkEJQDKKuArLN+B4rtSppWIvBtGx6/zpCMBq7R4xkLfrTkopvAqNezUtOl+1kEjuzdP3xhKDgmAKujbK5O4kljVXZRt+5kdNHGLU3QzndgnKl2HwEvs2FkY0a6ezkd50tWNKsuAymJQNpc72NZa3teXk/y0gC5mcp/++RIr+8ic5y/+55I7zj0sJT0jkqAf7VZYP89gtH133PRx5f4cOPdbcnT0h4uvEiRc4R1EPNYm3tZVFXCtKW6Jm5KQdGg9igV/bLCzVVX3ctk+/UsnU10EzXQmbiP4HSOEI0/2yF09F4rsd6z+CsLRlIWWTj4q7sGQxeImUarnS2Q2+lEugNPa076bXyv9WgbDMewo4Fu7I2uzZh+7ldGpl/sYYbRNreWtFaKxsVu+40VV83JVG2NG7Tc/XoaWlFfbaRhEnPQj5/pMyPf+Hkuu9xJHz2jXv5rReNPdMnxGc9//H5o/zPl4ytqxP/+S9PM1NJpBkJZxdas6ksnhdqqqFecym4NZBuFO88FbZPa1EPjauDUqtZzkBrfKUo+htH5a3Z1U5JRsYW7ER34F6yXCFhOCPZW7DJO5KZWhQH/me2+dGK17uY63RYqHVvMFS6pzXndhM6loSUJVFoVjahpdlubNqQGq21IFIPt6clB+hzrDMvxdGwFLefX+7wVW8g1unSt1W2utqw0fk43VqLtUgC5mcZd0zX+MFPH1935i0FfP2HDnDLgcKZ27FznF+7boQP3DS55uRkoR7xc1+eQj+dYq+EhA4iZWRDm2HFU1uqZM+58owGza0EyjgqLNTUpjTJKy2Sj76UxGnxPQ6U3hFLu06vXDCB2EJNcaoSsuyrppXbdgqW1mInVhfX20belZsOYCIFi173D5K2xbaulaG0RcGRhEpvSoe80VuyrmAolihM5Kw15RdbyZTnXOOH3WvlopFdPhuTWlrtkDZZ0GWJuJnvXou+lLnengoJSRIwP4t4ZNHj5o8fpbLBoPU7Lx7lBbuy674nYef50UsH+L+vnFjz9Y8/WeIfHk1cMxLOLnoNijvR0KjsK9wtDmo71UjpdIq4XAtGMjaTeZu+lOxKTlgybhW9xfF6Pb1spKC6RjFWLu62t1bQupED0ugGTVMG0t2+2K2kbMFYxl7zt6mGvSdSqTUUEr3cKDKWkaTsyhlpxXr+wa2BtRdpqpHakWJDsx+ClGU0/YK1qxjX6o7Yi4q/2umvM3NaCTSlYLU1+VOVOd0Krr31Vujrone2gUujsYoXalK22LId4HokAfOzhJPlgFf941GWNyh+uWYsza9cM3KG9iqhkzed37dupuRdX51mOpFmJJzFCLn5gGA9/WU91Gu2OV6LjazAzkhAER/6Wg2KImXs3NYKpvq2EfRr9JrPjYpvzmN/jyBSyt5FlptFiLgZRZyxayzBW9IEylnXdHGbroZ4kWrT8mYcQSEle54G14oDzk2yEkRELTO3Xp/MuZKJnIXVck2teIpiXVHcYFzcDhG62dSk4c0t5Gpr7O3QlTnV7dd8wZGs0Zl6XceQgbRpatKXMvubT0kyjtiWp7gfsuVW6Dsl3YC1J1q9kMB4xl7znG2VJGB+FrBYj7j548c4UV4/0LIE/PkP7Nqx7ksJW2c0a/OHN66dZV6sR7zzS4k0I+HsZStLsRsFRcEmo7lGgNZJZxC5o80N1kADQxkLOy50PBMESrc5jXTS6zSmbLHmAD+RM5KCXTkbgXENGU1LBu32DWltmlEESpNzBBNx8dxYxiZQplCz0aAjiFYDvrwrGXAt8rbsOXHwI928Mjajz/VD46fboPd1oxEIoh6v9QpgWwPdjRjKGMcIDZRDxaIXMVuJKPuKsq+o+trY5u2wXKYXa90yo2mboYxkKCObwbMVW+81TvGBPod9fQ4H+hzGMtYZu353cjjrbNW+HrXQdH/cqa9/6spGE84IlUDx2k8c48GFjY2Uf+3aYa4ey5yBvUpYj3dcPsAHHl7mn0/Ver7+yUMl/u6RIj966cCZ3bGEhHUQcjUYUNoUZ63VYCTnCCqBXtdZAVZlCBlbMNBim1CLVJvrQqToyhKNZSzqkaa2xeFQ9LC+m8hZVDdZmDWStgmVZq4WbauZwnaybYKNM8XVjqxfqDQFV3a5V6Ts7uxuPdLxMrZoulfY0rhQND5vS+hzaaZ312vkUYqbjAxlTEa0l068IZTQymRAG9KWzlWExu+llGlsI4VxYxlIybYVVSs+sb1OU8YRXVnRrC02lC/mXUneEezvcxAIjq4ELNZOfwXQ/AZbm+BlbIEtRc/roCHHScX6g9E1HDlsKRh0zGvDKQuF5tFFn2oAy775aTOOWUnwwq21GW9osDO2bNP8nw62NJaKjhRozHW65RWTHQzWkwzzMxg/0rzl08f5znR74JV3uvM6V46k+M0XrG9vlnBmkELw/h+YXHeZ6F1fmeZI0T9zO5WQsA5CwkSLTjVQ6w8ejaBws5klX2lqkaIStz/uZVHWams1mJJM5O019Ym9stGFlFmWHs+2r10LaTLh5/e7G3b6syQozFL/doOC9b5ivWfCeqcyiHRX1jVlG7mEEDT9dMEE0otexFJLcV0vm7rOWC7CdF1brJk/m2Fxg6LKPtf4O2dt2czWdtJ6DS3WFIv1qLmy0LjO0rYgZ0s81Vsv3UtCoGFDSVDOaZ9crJer3Oy1PhxPIrwtVopqoNhRENm4HyMF07WQ+XqIrxSlYPWP1zKzaejVfaWpxNryfX0O41lJn6MZz1oc7HPod6x1VzQ6Gc9ZjKRtRtI2Tsc1LMX2C0tDBSu+YqEeESqzKrGd+24obTzXGxn47RYEJgHzM5RQKd7+2RN87mil7d9v2pfjOUNu222dsgR/d8tuUjsl5Ek4bS4dSvEfnre2lrzoK97+uZPPqDa8CWcn/+2//Tde9KIXkc1mGRgY2N5GdOM/va9HxzJLvxlbrFuQBb2D2eG0RcoSlAOTmdwQYYKt/QWHkazFQNxkoUGnZ2xqE9ZvWVtQ2IS+uFW/vdGwm7YFadu0jR7LWYxlLQbWOT8Daat32zxhsvprUQu7f5mqb7yztTZZ4kY2P1Lm/HjxcWQdyXDa6tZk7/Cjpz8O2lPxeRZAzpHkbNMdcivDkwaWPXNstlwtbNyEQ2CTUOmmPDG/hja4ccoX64rDKz4rgcJZY11+JJ6IrReDZxyBK002fb0Aezgju6QqGVt0/VurZl8rc14CRXPyUfYVjRh7d95u7tuRYsDJUsiJUsjJUsiKb7LJK15ENb53pFjVZG/UTTNQxqLRj1cA2o4lbXVlhdfzMnes9lWYIDJ/1hsK16uVmK9FzFUj5qsRizWFH3U/HzZLEkE9A4mU5kUfOsLHnmhvef2CiQyXDKW4Y6ZdnvE7Lx7j8pH0mdzFhE3w768f5ZqxtX+Xb0/V+I3vzJ3BPUp4NuL7Pj/0Qz/Ez/3cz217G80l35aoczaWYwxnJCNpm9G0jWsLSt76rgS9CvMsIZAtubyULZruD70YzVhYQrDsRXiRJlTrN7EYSllkWtJcjUA2ZQtSctW6yxZ0ZZmFxGh8sxZD6c13Lcm5gsGUxWDKImdLIm10uCvrFC4K1m5+0Rpg9dL9rieR6IVWJsgbzVgIYbKpEhheIzByOyLBybzNRNbooDdj+Za1JFlLMpSy2FuwkUKQd41F22DK2nQJoIj3pXWSVA81U5WwZ4ZaSLhg0CHvtncrbPgMgzn2kR6/7ZIXsehFFL0IAfQ7FiOp9iYotjSZXkcYKcNmJAPrTX7AuGOMpC3yrplkjGYthtMWeUc2J1N5V3ZtJ4y6v7/iK6arISfLIY8trb1q6WtBNVptLNTnWExkbCYyNuMZm0I8scn2cMhYrCkW4j+dhEp3TQ7WW3UYTls9JxOBapdjpG1hZEIpSZ+z+U6EGxUOr0eiYX6GEUSaH//CSb43U2/79+cMpXjHZQP89Jem2v79DecVePfVQ2dyFxM2iWsJPnjLbq75u0PU15g+//fvzXPDniw378+f4b1LeLbwm7/5mwD89V//9ZY/W0hJMq6g7CsWvLBnEV+kIdIaS4gu94yULXpmcxr62AbVsD3Idi1hCsaAQIX4HbLRgmvFr7FxRlpAPVJEejVztSdvU+lcphdQ9BShhtbQKW0J7JaU12TO5kgp2DBAtVo+c+Ggw7FSyEI1ap7DnCupdAzeVpw577aYEwTKOGWkbEHGkkxVQvrSkpVNNsTIOkYDizbBx1jW4tUH8nztRBVXCiZzFtPLa2dIO2UbBXdVq7qVOvKJnEXBlbhS0B8LpuuRcZwo+WrD7n1ZR+JYAkuIZuv0tSYZYLKCWUsykYXlLQZLQQSOZa7tQspk4iUm49rQ2acso/vWaIbTa+v6wUhD6mG4oXxDA7YQFFqCU43JhOvM6utRjw31KohsvC1Uuk2K04tlL8JXmrQtKdgSL1IEuvdqykC6WyNf7qhbWNqihGI8a7MSmEJKWH2GVAPdVgzvKU0h1n+UQ4UUxic7JcW2m8RsRBIwP4OIlOZHP3eSDz/e7tXb70p++4Wj/Mjn2rv7ndfv8Nc3TSJ20tMlYUe5dCjF/3jpOL/41ek13/PaTxzj0E+cz77+1Bncs4QEqHo+ecdGqgjPWy3+yjoSWxjp0FLFLDX3uxaeH6Ij0KFAR5q+tMVsxwC9HKvIRCw90AqWq+3fa2uJimT8/1Gb3lNIkCpCRQodhejIRNNpW6xZRLUYB9yWNNnmtIBSR0WTjgRahc1t6jiNq6VARZp6ZAblkqcpWKAsjSsUllYgjESgNYh2hUXFD1nxFaNpTbEWgFJopdFRhKUlo2lByVfUAm1cGCIYsGE5Wp0huDZkJEQohIAohHIIUivGUy5BEDWDi/XIpkxWHmG2d/2oC1GEikJUpCh7IUEUxOeg+/M6kgzEkxghYMTVLFfNfmalJuPG2tigO+gdzlqoKGQoLclbEh1FzFd9jq2sapxTlqDfoZn1bSVtG9u8tCO5ZtjlO7N1tIJIKVSkELS7U0hpJh9BnHF1sFFRRBRE6B6VbDrSKK2g47cHGEiZfR9NOcjGiYkgCALzOSGYr2ukhLGMzXBKs+xFXW3dHSt2Eun47nxKYol2H24RX3OtKCkYzVhk4l2bq4bmuKP22WSgu506bAkqMvr7mVLYVsALoFSEiEK0CNGRJgg0thaUI9GmGRYyvk8i43GcAoQO2677tCsppE1xohfpLWmOM44AHVKQmkyqITPShIHJ8JvJnnmvjqAYmP/vT0tGU5LxrINrwXwNDheDtsm6lGYVJu+Yzo9sI6ZOAuZnCFpr3v216a5gOS3hf75kjJ/50lRbW9q0Jfjoa/cYTVzCWc0vXDXIpw+V+MKxSs/XIw0X/80hZn/6Qgrp5JZNOHPM3/klZgPPWIjF47ItoGIZTbAUUPQ1S8AUq8FAKV6aVq6g2COY63cF2ditYcmP6DQeWMJkBjXdAYYr4fGchRSCqWpIw7bckauDaS+ytsAWMCPAT0mWPEWfI3Ess6+PSsFi0ccLQuqWjYgrCpeABQmNcX8+lomMZyxqoWYl/tKSr9vG4MgVrPiaAVcwlbWZKYdUQo1vC8qhxrOF6RjoSEZscx7n4lReOc5+pyUoW1BJSxY9xbK3qlWWAjJZi1KgqIUapUxhXiuNc+JKiOz29YHjE2lOCDi8HFCPNF6o8Koec67LSmgyiq3nXqYlI2mLkm+KM6Ps6rMoUoq5loCv6GvSLVaADWFZ1ZXMWQIhBJUgYr6uaLjF9TmrPs+VQLcVHeZsQTojwZI8nBKcWgrQxA1dQh1rvM17XQmjGUnGEszWFBFwKGMxV4tYCTSS7lhJuQKtNPg+VWkTxmpVGb8GkOqzmzr44ZRFcdZjJb72G4TuqlPHyhqTmOG0YKFuXsvbgpX4N6q2HHDjfhpMiTbtbiljMZyWuJZgYSnAi3TX/dXr+PK24JQ0Y0nWFvS75tw00EpT9X0CbGq2NDroHveTJSByzGqHkoIaGKcRBQhTKBq5q9dZqNjQiaSxz3Z8rZ/KmondRQWLU9WISqCpVqK4SJOez5NC3uKwrzhpCYbiVYu8UsyXouZ11NinYnx+BnNpeO3bN9y3VpLR9xmA1prfu3OBP753qeu1uoJ//bWZ9gwM8MFbdicWcs8QpBD8zc27ufbvD3FyDS/teqS54u8OceQdF53hvUs4G/mN3/iNptRiLb73ve9x3XXXndb36NBnpephSxDx4FnXoKQJnKUwmZ7WMdEWEGAG17zSeNoM+o3EoSvB88CNNCkJdigIgu7vLlhQij+Tb+1CrKBW01QjQTlo+W5hsmtrUQzM/uYsqCuNHwi01jRUnZMZzTSSSAAqbNNZtu5eZL6KitLUlWgWVaHal61rdaMprSsoqzoqEISRCQ5SCpQPNUAGUBUmmGkUrTW+25ZQ8822FCYAaeQ+NVCvafxIYGFkJI2sZuc5CQAZ0raDS0s1hAA3gGIg8JWg3wapPcqRMMGPBif+ncM6VCLNom8CoopelQVqbVYVGrFMEMY66wh8tTrZqNahz4aMpalFgrq/+pvV1aq0I4zaf0tPQVVrQgnS03ie8USO9Oq5asR2KQvKFcilFaFvNL9VrQkCgY5Wbeoa5yhrmetUCKjbsOCHbd9tx/uV8cz5qoQwU4HQkwQdj2sv/pwtzPf1QsrV1+oq1sxrcIFKyzWXkpAKNUvh6nYqSqM9jQWIQBCGgihsD5AtYSZabjzJswBCCDHfY2tNLQTPb98/AYRRiK3NRCSKz2nGgobKZDStsRUs+IJitHo5paQJZv2o43eMfx8Z/y5enOV2hfl7Lc64K7OLyBAqWiMFLEWaDOBqiCw4WZd4dD9vACpVs20PCOrmN85ZmgEhmPGb8XxzQqY0BMHWS/iSgPkspx4qXvqRI9zZoVluIOm2p/kfLx3jBy/sOwN7l7BTjOdsPvraPdzwkSNrZsmOlkL+7P4lfuaKwTO7cwlnHe9617t429vetu57Dhw4cNrfM/b8V5MOVJt7hEBTCTRpWyCFkRRUQ40VZ/kytmAoJZjI2aQsyRPFgEqgmlnTPkc2W+te0O9Q9CIeWgraCnoEMJa1mImj7JG0bOvgdsWQy2wt5JGlgIYstbGYVu9Ms7ZgCxjOWBwo2BwphZzfZyOECQCvH0txwVKNLx0tkk5nmPd6y2L3xm4Du3PG8eJYOWSuR7FTzjbnRkrB+X0209WIE+WQvQW7eS78yJy3xrHtzVnM1SOOlyNcafTMy56iz5UmaLNls94hbQkmshaVQJFzjKb3yRUTwYVKk3cEs1VFLX7/aKY9QLjpwjxCGGcSW8Bi1ePo3BI608fhCpR8hR9phmMrstG0RX/K/J5jGautY2GkNJXQ2H55keZUJSRtCaQUbb/9YEoyHhewLdUj7JWwbf+kENQCxUqPmU82LblkwGFXzsKZ86mGGqXbvbOlMEvuWUfwn68b4BOHq6bLnxDM1UKKvqbPkRR9k7GcqylG0pKLB12iMOTJ+RL9TpqalvH2YSgl6U9Jbj6YBQR3z3v4CoYjxQMLfvN6y9miafOWswWHVnonPy4fdfDmAnMtxkWLChNsFn1FzoZ8rNG/oN/hieLqdO28Pps+V1IONM+x4LHlkCUvotRyvvKOIOdI+hzBSqC5oN9pvuZFilRc/Nq6XRVFVKo10iLFYMbGwmSXI2205Uv1iEDBdbvSBJHiaDlq3puwek9XAvMs6HclK76pS9jlSFyL2D/brJA0nDTmalFXkWIma2EJ6O9zuGTAod+VLNYjHikGKKU5XDLFnbYUzeMeylq0VmrZwjRpAZivhUQapqvGN70RLq3VqXM9koD5LGbFi7jhI0e4d77d9WIobbGvYHPPnNe19PLuq4f41WuGz9xOJuwYL9iV5Q9vnOBdX53mf79sgvP7HV77yeNtS3Lv+soUzxlK8ZLd2advRxOedkZGRhgZeepb3FuOg43GbfGBqoeKmlb0uxYCga0jbIymcSxrluxfdiDPVLxakk9LFvwQOx5tbFfixlrYdMolkBGjgdVWYS8FuI6NbZttLIe0ORNMDmTJZBTYPjPVkIWawombU9gbjGop1yaXdnDrAem0aUghBeRyWYZDgZIVQulirbEdx7WxhSCdttkzmMITPkqGzNdC8nGAm7YEE/0OQRw0pdIOkRcw2e8ymLbw42BDS8VQxiZnCxxLMJi2yGYUSq4GM46OsB2BI6Xp0tbvUA1MtzmJoKxCUikLRwoyno/SkJeCXTmLxcDDFuYBshTAUBygZRxJNp9DIphb8RnP2uTSLqq6wpSTwfUVto6IpMaNfdTclEUhazMc2oxkTWBf9FRzVpGyBQOuZKkaoS1JOl4atxxNKtQUPYXjSCzXJp2yOZgT1IRHKZ7xpCzBWNZmvhbh18JuDbBrYadcMhmHSMJ4wQTdu9z2983UQgYcm7uLFjO+TSElsYTACkNcrRjIWdRFhCMgEIqUI0inUvhWiLBqWKk0g9JiQGumqxHDeYcXT2bJ5UwAli5LZKSpehEHhlyKXsR8PaI/baQEfSnj+OLU/Z5d/9LpNGlXMJSxsBDM1FY1wLYNuYzElZKsK0mnbPYOOsyUY0eavEt/yiashFw+muJwrUxeKlKp1UHCkaa403ElLop0quUERbrZ3W//oNu8R0MrQvoBUrr0Z10GUxYnygEpaSbFfUJR9BWD+Qw5W5LNhKxM1ZrJHScl6UtbaC/CjkxTmciKcON9aSAxGXCBoBQopGV1WbU1rrd0ykW4DrmcQ01G5DwTBzmeRxBEZFyJHZ9gt8Pr77wBh+cMpXhsyWdPXPrj4RPUwuZKmb0NL+YkYD5LUVrzkg8f4f6ODn4pC377BaP8+2/NdH3mF587xHtuGE+K/J7B/NxVQ7xkd5YrYhvA77ztAC/98NFmVilQ8IZ/Os633nqAi4eSIsCEjTl27BiLi4scO3aMKIq45557ALjgggvI59d3X/FC3ZVmTdmCvhYTuNaXDxQcTlVCRjM2J8om6EvbsqvICEx7aTCDpyVFs7GG1hs3J3juaJq7ZusMp62425vCkYIaJvMdKOMrm3Pibm7xTmZs0dzfRsMSoPnMDFS8pG2bfVrusR8pS8Y2aILRtMXwrgxfPFam5EuCCKqBwhatJnkGKQTPG08zXw2b7X0XY7uyRpdDpU3Ak3dl0/4qUBrd3E/Y1+fgh5rlWbPqOJGzuW48TS3SRNo4Cci4uM+S7RZ0jexim3Qk9nA+Xgo5WRfIOCGZsyVetBq1jmUtnjeeafeGb/3x9eqSdz3U1GxFxpI8bzzNyUrIk8uB8aOOf/fz+11mqyF5WxIos2oxkJKmsUpdEG5gBD2asbDkapFgoxNehEVf2lrVrrYsw7fu60Tepj+tWWhxtej8RilNgmp33oRKs7WQBzoSWLYQDKclBVeScyRz1QgvMhOJ4YxEadqy4JYwDikNIz0pViUVaVuQksYTupF97ncs3IJxBLliJM2RlZDBtIVE4IcaWwjsFrvHiwZdlryop/NSqxd52hKM561mMC7i14diT/TRjEU51thkbEnKElw9mua5Yxne+/2Ftu1qbVxhUlKStox0qNH85Fg5aN77pjDS/P61jexQWM1Et65wyfi8rXiqy20HjIwpbUtGMnabjd5o1mLRC7fUvbCTJGA+SzlUDDha6hb2/cSlA7z769Ndy/bvumqQ996YBMvPBq5o8cy+bjzLR1+3h9d/8njzYb5Yj7j540d53cEC//76ETSwO+/03FZCwn/+z/+ZD3zgA82/X3311QB85Stf4WUve9mGn6+Gmv4U7OuzCRWxJ+3qYOxaJrMrhBngLCm4e67GQi1qWk6Npi0W6lFzsJLSLM9DPFAjyLaM5n6Pwb7x5sGUxcVDKcZzNn/54DJ78qZV9fkDDt+brmNLwUDKBCr98XdMVSKytmBX3mYyZ1NwJZYlmpm9Xk/NtEmFtUVRGVtwsN8hbUmKXoQUomczAwW4FuztM0Ps6w4WmK6ESClYqEXEK+48Z8jlyZal8cZ37cpZeGmLuVrIkLaagY7ABAxCtCzBu5KhjM3XT1R4cnl1Wwf7HfYXHHxTE9dmvxe16K0vHU7hh5qpakQxFAzHL4znbNKOaUPtSsF4pvczppAyxYCHi0Fb04rlumIZxXzNdI4by5hOcgMpi9G4YLDPtehzzaRguhIykrHNUv+6w9jqi0Mpq+nWMp6xcS2TucxagqG0NLKQjo2FSjOUsdhbcJivRc2A+1gpxFOrQZEQxpvZFqJ5XL0s2QquZCBlU4w70rXuoStl0/quwTVjaQ4XV+UaYRTbN9oCC8FI1mIw1T7hytiSIVtiS+Nb3e9KLhpM8bkj5WaGvvW9S56RnBxokWMMxv7Gy/ExOFLQLy36By38MGQp0ixYZqUibQmG+lzub5kcGN9siwsHzCpJyhJNCzvXWt1b3TJRGU5bHIsnzrakZ2OgvpRsepP3d3iAN5LTrf7vrT7QBUd22dYZL2xN2HLe9xbMecjaEkua6Wd2nWYna5E0LjkLmamE3Pzxo10G97fsz/GnDyx3Bcu/fPUQf/SyiSRYfpby2oMFfv+G8bZ/O1oK+ZP7lrjsb5/k8r99ks8eLq3x6YRznb/+67822sGOP5sJlrOuyXZeMOhwzViGvNMZfsBI2qIvJel3JcMZi7Ql2gIyMNmnhufuYEpyQb/b9Cm2pGAkI9EYay4hdJvvcQPXEpzf7zCZt9nf53DhQIofu3SA546muXDQxZWSkaxlOpRhso1jGZvJnMO1Y2muHE3z0sksr9ib5zlDaS4bXl2hafhHt4Y2QgjG0lZbW9/WboJr0Xi/xPglZyzJWNZmf7/bdk4KruSSoTSDsfhaylXZicAELbvzNqMZqy3o60tJXjyZpT9tMZazuXgwhepeCCDUxkt3KCXRGkprFEdcM5puC3ga3r8LtQhLmJbhewsOTkerxMZkY1fWbraYtkR7J0e7xaRpImuRcySOFE0HlAaOFOwtOFwylGLFUz3jZYHJ1Hb2NOxzLYZSpvmKJj4RwgSrV41lsC1B1hH0pwRSmpcHUpLxOGg/0O8wmjXNV3aldLNBS9aRTOZshDTZ0KwtmKt265L7U5JM3NSjdWLTOP5Ob3LXkuRbgrWxrI1r0cw4m3us+ww0nMr7XIvdBYfzB9w2mVKDiwZdCq65H18wkeE5wykuGkxx8ZCLJcHukElEaEJl3ERU/GywhOjy3e5PS153MI8tBZM5m9Gs3dYIpnWXhzMW5/c77O93cIRpEjSasUlbkpGMRaR1c/Kcs80kYCxrkY1vnoG0ZDxnN/3Wze8hOr9mTV9tpdob/YzEWe20Jel3Lfoci3zrxblJkgzzWciPfv4kh4rt2eUL+p2uNtgC+F83jvNLVyea5Wc7737uIB94aJn7OpYDG0vGr/nkcX7hqkF+/6XjpJMW6Ak7xOVDKZYDzXXj2ab2cV+fw0OLPmgziBbripwtybmShVrEYMqiHmr6UxZLdUXWEbiWGYBLvgnCGp6oYGRmx0qKuZal8daufGCCMBOQCK4bz8TZLsH1ExkuHXL59KFy873VwBS6mXbH7YN+wbW4+UCeuWrIF3vYOPZ1ZJ0sKRhOW+zK22htlvW9UJO2oH8Ny86BlNUVJAkBFw64nCyvPtcn4mDn0iEXW5qWyfUey9QDKYuiZzyYHcsE0vv7XC5rkWQprdlbcLh/bvX5sD/vsOCZrH4tDPEi1Wz0ACYody3RzOI15ihZW5J1FNX1LEcwGcvO7GY17AxmTah/84Ec/SmLEyUTcM7VejfvuHjQZV/BZnlh9VoYTMumpKFxDTgdjziFjkVColmkCPAD+3KcX3RY9hTHSgETuQgLWPEVh1Z8Hlvy6UtJ9uQdCo5gPKeYtiUzNeIg2+Ky4RR9cRHbXI+mJH6kydomEO1PWSZjLUxG9bw+lzuma833muvYTKTKsQnghQMOFw66lALNY0serhTs73OQQnC4uCop6HW+ejWMydiCA30utVCRtiWBNrKfkq/YlbO5cEDyqv15Dhd9vnGqyn1zdU6WIip1QUko+tPKZP5TFnv77Ka3+VjGbpEude/MYEpSjscjN9Y+n9/vMpa1WKw3OipKbtmf5x8eW2EgLanGk7jWPo97+2wyluT8/nZxep9rkbY1cy0elK5lVpMO9DscWQnaZmFCmG6Up8phV0HxxkZ3vUkC5rMArTXVUJOLnwLvvXGcl3/0aPPmlLRXtIK5Kf7+1Xt4w/mFM727CWcYrTW/+LWZrmC5ky8cLWPdMHGG9irhXMCSJjjxIoUfmUxbf0pyoM9BaSNbKNbNoN46dvelV7vAIQQrvsISZin+uok0e/Iud83U4s+JWItsPKYcKRjPWbzmYIGPPb7Spjm8aNDlxj3tBa+d2bisI8luoFBaa8DM2pILcorWfqlXjKS4dDhFwbGYrgQcKYVkHZO9bnDJYAovqptMr+5uT3xen8tA2uJle7KcKAUUXOMUAXDBQKqpo7VkeySYcyS2EM2s5+vOK/DKffmugFxpGHCtZle0rGOKyjxliu16cfVYmosG3WZyZn/BQpRN/nZ3zubx5fYxpxHMO5YwOuaOY7Sk0TmnPNHUr6ekJO9IDva5bUvnAymLsq8YjN0ywCy7XzGcYl+fy2NFn1poNM1pS1JoiZ0mcjaj8blXGP9pP4KU1GhlZADnDzi89mCevpTFSMbiS8cq7Cs47C3YLNYiVnyfWqBZ9ELAZrYaIoSxRxOAa4umC4gjBYFqyGFa9iNvJA4XD6bYU3C4Y6qKECaYbGu+0hHUduYzbGkmgS/ZneVjj6/gR0aDHijN4eLq+9pWPzBSGUsKapFqNujQwONLPgv1qFkj0DqJArhqNM1AyuL6iQyHigF3tgT0QFOjnrUlL9qV5XjJSG2eO7Y6QRvP2rHsSOBYxpUiY5k23kVPkXEE145ncKRgT8FhKG3ayl86mMKxJFlbEikz+bRjy8ggMvKijC25YMD84DlHUomD6nw8Ic87Fo7VmNgJ0Ob+GEpbLLZMaCQwmXNY9qJmbNX4OZKA+RlKEGne+eUpHl/2uf1N+0jZ5gHptYwSnY+7yZzNx1+/l+snEp/lc4FKoPlex0OtF4eKAX/54DI/e+XgGdirhHOBmVqEEIIH48na8VKIpywG3PbsatoW7MnbzUBxb96mntYEsb2Zr4xUw7FMEBVpoyNtDHCOFM1tajRjGZusLdmTtzleNi4CKUtwyXCqS6+fstqL5HpxvBwQRMbfNe9Krhgxg3+v9tQHMpq0bVOKBHvzDleOppvWnRM5h4mcw+683eaZfqDPZTJnU480rgXfmWq3AW3IGfYWXF65L8+Ty2aSMZS2eOFEhq+drFLyFbYQ7O93OLoSYEuToUtZkuMlH4TghfEzvzOz6Eeay0dSfG/aBDL9KcsUW7ZIYYIIlmOj57QluHDARYrV6UYt1CwEgmGt6U/bpOywbVl+KA7wLWF8nTulEW4sxRnN2IymbaQ0we1lwylyjuTCwRTXjadjP1zB546UyDmSI8I4e8xWQz7yeInBlGQkbZO1FFnH/K5C0PzjSmEC37oJFI+XTPbclSZ7e9GgS1/KZEgBRjI2N+/Ps1CP6HclH3+it3wtZUlu6Iu413ZpW8xty8CL+PeEvtg5Ju/IZkYVYDwO5hvneiRtMatDco4pRAsVDGZkMyHmWoJ+11jqXTSY4qG40L8zedwzw4yx8au1ZPY3KqYTwngmm2tDtv2KWbs9mLx2PMNwxjRseX5LvPHmC/q4c6bGfC3CEYLdBbt57OMZs5rS0B2npOD8kTT7Ct2z2IGUxXl9Jji+fleGWmjugYbEpeCuBsy1UBt7O62ajjzlQJN1jaXdS4YyzNeNdvvHL+nHloJ6ZGRence/3Yg5CZifRpbrEW+57QRfOm7uzp+8/RQfvGU3u3I2w2mbFb+76O+G3Vk+9Jo9zeW8hGc/eVfy5bcc4C2fPt4ly2kl0vDOL0/x0KLHH9ww3rUcnZCwVbQ2GbtGEZ4Qxpe2M4PqWGYpfC7OrAkh6EvJpgRAa818LWKhFuEIjzHP5sLBFIu1GrvzDrockLYdE0wBL9yV4WC/w7dOSQ4UHAKluWTIxemhbXYswUUDLt+f7e1VDybrF8YZrbK/Kk04v9/lvvk6Apr3S8aCF+9Kk06lmp/VaO6cqYEwQcqRFbvp89rgZXtz7O9z+f5sjYcXAiphxNEV8wz/zOESAymLV+3P87zxNAf6zDGNZ21sKXj1gTzT1ZCvn6iyO+ewK2fz/IkM35s2x3TBgNmXbLzfssd5uGYszR0jacqBYiLW5754MkOo4P4Fs51aoHEtgeuIpvdzY1O+0s3fdV/BoRYqIg0H+1zyjuT6iQyB0nx/1kzebWl+8z0FB83qMr1GM5o1LhV5W5JxjB2ebPRCj7lkKMVdM3UuGjTHthgXow1nbK4dTzNTiZfSc6bYruCazCTAZSMpvnFiNYngWoKUJfEjTcGVvGJPru3cDKQtBtLGs7pVY+2FYLfkncxqim7bT6VN4LbiRc1zlY9/h30Fp62QrT9lUfQigkg3HUmyjmRAmeC65CnunK4xlLHpTxvbtUuHUsj4tzi/310zYG5dVTB6baN7r3VIZ+bqETlbNlcwOtmVW5VWgMlSuxI8YfZ/JGMxnLW4aX+OCwZTXDWa7tpG1pH89OWD/NVDy7iWINeSNr9mLE26Q/9c6NDQpCxB67Tl/DjAtkX7Pqt4lqC0aU3fqDt43liGeqS5/ViFMNLUAsXRUsCevMNoxubgwGo2fLEeNVdR9vXZHIutHjsD6c2QRF1PE48vedz6qeM80mJ78v8eXWF/n8PXT1Y5vNIdLP/y1UP83kvGu4ovEp795BzJp27dx89+aYq/emh53ff+0T2LPLrk8f9evYehtMXhot/UxCUkbAVbwHjO4ng8yOzN202LMzB65lJgdMpz1YiMI6nFGaFWKYVGt+tdhclyvuH8AnO1iLQleHRpNVB4shjw5gv7ca0VwAzkE+voLLZ6aWcdyUWDLo8t+aZocMDl6rE0X3jS41TL++ZrIQ8veASx/KRB0YuA9v1p5GpVHG8pvdqFruSrpv2VEMZvuBVLCnbnHa4ZS3O0FDCasTiv320GzL2OcyJnMx33BZ/MmyDozRf08ZUTFeNsYQlesTfP/fN1JnM2h2OJRcYWbcVanTrkmZri0lGLF+zKtv2Gk3nzfSJe1HZifXcjczictrh/vk7WlhzsN1pkP9LctD/X89mzr+Dw/dl6LGHRbefEkcaX2uvQRDeW6rMdugaB4IJBh5wteddzh5jI9b5Wco5sWviBsRpr0OcK4+RgSabrq9+btQU37M7iK81jyz4nnRDHMr/Fnpas6aVDKfKu5HjJ2Oc51urYLlsy02CC8vGMzXDGapML5N3V6zJtCy4fMds8UQo50eKalXdMZlgSu0y0yF325h18pdu03BcOumYioOHFu1YlTRrTdMS3JZFlmp5cO55hLGMzsEbADXC8FIAwq91TlRBHrhZLpm3JdePpZrO1nCObha0NBtOW8dtWiqIfobRmpmqy8JkW68PnT2Qp+hFPLvscWQmM7V78+kWDDuVANeVMtUDz+JLflOs0eN6EyZJPVQKG0xa3+WVCBRPZpHHJM4IvHivz1ttOdNmh7CvYvP/+ZeY7WlVlbcFfvGqSt13cfyZ3M+Esw7EEf/GqXTx/IsMvfm16best4PNHK1zzd4f401dO8C+/cIqrRtL81U2Tif1cwpZoWKg1aOTeGldexpYdhWQGVwqyjuTioRSPLnp4oW7JUgtKnsK1jDPCXC3CtUTXikjelTx3NI0frXYVXIuN4uXOJhham+XmiwZNwV3DF/k5gy6PHOn+vKYj69jjO1olCpLVjOtQxtr0CvDFQ6l1/dVbj/PFk1keXvQQmGANjBXcq/blWPIUkzljswbmd8o0GrtIGEmvDv3VNdwzOlHaFF6tdbLzjuSWA3lmqyF9KWPHNpqxGMn0DjPStuSasTQPLnikLUmnokZrWPEjAg0lL2IyZzcz7CNpu+mGAiYDPJlz2JN31gyWG9ywO8PDi6bde4SmHpqCySFHgmeyoYNpWKpHSAnPHcs05R15RzYnTg2JxKVDKUYyFt84WQXgvLhY7eL42rpzps6DXmMyuOpAkrZEcwLU+htcO57hsuEUlhAcWfG5a7aOIwUXDq4KuRu5+n0Fl4xdx4sEXtyU5M0XFHAswb2xdvlV+/O8/rx8Txctrc21sRIoVkJIq+7JSC+OrgQcLwXcO1dnvhrhFlZ19gAXDppAvxponj+R4bsdksKrRlMorU1nQ0cyXQmxpGA8Z5OJO1IOpCzyriQfd7lslUABjGadTd1XUgguGHDZFV8XDcnMGpfluiQB8xlEa80f37vEL39tms5YZ0/e5nip26r9wgGXj79+D5cNdy+LJJx7CCH42SsHuWYszVtuO8GxHl7dDY6WAl79CePffPuxCpf/7ZP86St38daLkolXwubY3+cipWC6Iin5qmn/dbTHChiYQBlHsr/PoegphtMWGUcQYTSbjSBjMm939rxoI2UL/vlUlUjDiXJA2pbsKzjNwLCT1qxhz9fTctUTOms1A69ChxY71bF6t1YgrnsISudrEfv7TNFVK6aZyLq7tyaj2VWZy2Daagt6XEv0XC4fztgMt8gMhDAa8wHXos/VjGdWj7/xOsDBgoNTUuwacXjdeXm+N11ra3+sNewt2KQtQS0wzUmGsqYjnI5lCzfuyfK1E1W8UOOhm62i1+KiwVRTkvG1ExVOxUHRBf0uU5WQZU9RDzWVwGQi/chkMjOO5Icu7OMbp6pcNOiwK2/z5vP7mh0k18O1ZDMrfqQYMJSW7C041OMEVsERXDLkUAlMFv2igZaKw5YLQqEJlOI5sUxANRrGxIxnHV64K9Pms914ecWPSGfs5uY6k2QNp6MLB1Ps73N5eNFrSjUa2xlImcxtzpbNDPZY1mIoY7O34JC1JZVQcX6/u6blrAYyjmB/wWYh1G0Z9/Xo1K/32vqulomLJY00oj9l8d2pmvFrF4L9Bce0jxeCg31Os006wHXj68c8nbZ3DdZ6RuwEScB8hqgEip/78hR/+3Cx5+snyt3+jm84r8AHbp5sFm0kJDR43kSG77/9ID/2+VN89kh5zfe1PtYa1koJCZtl2YsYyThcNpKiFpgivtbgrxEoDaYsnLh46dUHC0zkbL54rMxc1diaWQgmcjZL9YjxrGV0yy3bkWK1MM4Sgt05m2MrAUJAPdLM1wNetifL2BoD+mjW5kcu6WcwbbFQC/nOlNEbW0IQKc1oxjRaONDncOPuXM9tQLe0Y606ws4A/YGFOrO1kCMrAXfM1KgGqhk8aW2e/53L0pvh+vGMkS0AV/cIjjfLYMriojhDebC/3b+3IQmwJFiYznWOFE1dbQOFRgjB+QMu87WQoqdYqkcci0/S+S2FXs3PbGGm8NLdWQ4XA2xpiuH/6VCpaWnmxR6EjUDtVDngB/blcSzjwHLxYIqsu9lgr53WPRYCrh1LcfcySKG5ejTdJoEUwEoQUQ81y3WF0ODEYtilesRwS9pSxLIcS6zKJhpbamh8Rcs1shauJZo+1637IYTguWNpvnmyQjlQ2FKQtWWzIPRAhy1bLxqFslIIbLPR5jleb9VmqxPA/X0u+/vM/+cdyaOLPl6kWahHTUlQwbV4br+DbQlTx9VyLq01suO7cnabM0bKNufkqSIJmM8Ajy56vOW2EzywsL4tWAMp4L++cIz/73nDie40YU2GMza3vWEv739gmV/9+gzlDZZW05bgurHEWSVh60iEaTMdqJ5SoJGM1cyKNWQABUcyR8T5Ay6PLvmMZWwuHHCbS9atWaqBlGm7DPCG8wvMVE3Q60rBlXHny+fvareT68W1Y2m+cbKCJQXn9TscjrN7AkG/azGZ727A0Upn0mo0Y3G8JHFi+7LBtMVzR1PYlqDir+7/iqe4ZNAUR4aRxlealBRk43bJaVv0dAnYiL6Uxcv2rh3gb4ZHF9sz3tPVsC1gvjh2ZnhwIeB4WZLyFB9/okQtVE2pCpgM4a6csQgzrhBh0xKuQWdgs5U2xDIOxgGOrvhtjVbCjkY4lVDjWGJT10QnncFepy3hWMbijcPZ5j61vVfAdLl3K+1KqOnsiKAxk5W+lGktnY0LIB9fNp/crIp2JGP8oHW8/w2Zy0DKuGv0tUwWtlrrPZlzKMiIbE0z3mettk9fJ/ZonJd9BYfpSrSlDnhjWRtXGuvBhztiIikF1/QYo0Yy7ZOhxj3c50pesMu8XwOvP29tm13XEuvKGDdDEjA/hWit+cBDRX7xa9NdhRVrMZmz+YfX7Oal62RBEhIaCCH4mSsGuWlfjn91+ym+eqK65nvrkeYVHzvKr147zG+/cDRpcJKwId3BRQ/EamZ2phry4EKdgmM1K9MHUxbXjqV5zlCKx5dXg7dI9c5AZh2zRP69mVoz4LpocONsGZhM85su6KO1g3FDIrG/z+F5G1hxDqYkrc38bCm4ciTFc4ZTlGL3iUuGUnxvukY1CJrnZ1+f06bBtoQpXNuTsxnJGs/bq5+myeqFg27TFxlgvCP4cC3Bq/bnOVGsokuKsfj1TieURvOJ8/od7pvzuq6FSOuuYG0rGeb2z0HOEazE8VR/3AClUTj5gtOwVM3YpmFLQ2Oesdt/N+gOlLU207t0xwG2/uYDHW2dBWaC9dqDheaKhNYmSz9dCfEjTTU+oLyz/rN4LGt3FYoCXW2/zfduLWIeTlvkhI1yTSFiQ4tcCVTTSrCT8/odTpQDLhhI8XjRb2bMLxzY3H3aSSM6CtYIaKUQvOmCQnPCAFAOlGlV03K4663Gv2gyw9fj8bE/bTGU2noyMgmYnyKmKyE/86VT/NOhtZfLO7lqJMUX3ry/542RkLAeB/pdvvSD+/nAQ0V+9evTZG3JyUq3zEcDv3/XAp8+VOL/vGIXL9ubQ2vNE8s+Fw4+ddqvhGcm6Y6Oe0K0yxZyzXa15r+z1ZCHFz0KTkdGSAoyTvcANdwRvDUCEFsKXjKZ5aFFj6wtuXx489emEIJX7svxwIKH0prFrGmOMZa1uqzgen32mv6IIy3/dsVomuvG2wO0F05meWHL3x9cqHNfXGRljvPsmYxeNpziu1MmUMg5qy2hWxlKW1w84FBfWP23ztClK/btCo67A80tJJjbyNgyDiJXvYoP9Ln0xR7au/PbHyMtKXjuaJp75upcOpRqZisP9tnYC70/8/3ZOo8t+dwx01681gjQ9hYcjve2d267XxpZ27GszYlS0Bzrtzvmj2YthjNW0yliPGezzgJKT2ZqIYvlgLmawCsF7JWOceFYZ64zmXe4ZX+ecqB44WSaLx6t4liCfDzZ3Yi7ZmsUvYhKPGFQmyjf60zwbLSi2smunMNrDxZY8iL6XElqG7arSWS2w2it+X+PrvCLX51uzsg3w48/p5+/fNVkIsFI2DZSCP7VZQP84AUFtNa866szfPCR3pr5R5Z8Xv6xo/zoJf28YCLNL31thv94/Qj/4frRxLYwoclwxmrT8b5wV5avn6w0X8vY7Tmu9Z5fvQbg0YzNeM5mphIihckCNZjMO0xu09VlLGszXguZqYRcHHuyXjGS2pQ3ecGGt16QQ7gZQqWbDgnrcdlwmrwjqYWaUGvumFoNrJ7uuyljS148mSVry7gVdu89cqTAESbjmmnpsNagkXFu/I6jaYvpuPGLcTyx6ZhfseKprmK4zTCasXAtScE1xaYTOZvJvMNLd2fZ37e9LGYr5w+4TfnHbDUk0pqC9nhkjYD5sXiVYjLnMBVLMvb2Gb/oV+zNUXAt/r7jWds45F5HnneNI8TINnTtrUhhJEv3zNVxLCP72WoIseJFzNQUS4FAVRUjORXb1q0fxDa8rU+W2bI+/47pGqfKISfjVukH+rd+n29n8SLvSvo3qXXvRRIw7yDfm67x7q9N862pjbuyNeh3JR+4aZI3XND3FO5ZwrlEY4D/21t2c+t5Bd755ak1J28ffKTYDKp/87vz3HakzN+/ek/T7zTh3CbS7XZqewsOP7AvjxCry8grHXKztRI/a41vL9+TZbYakXVEl2vF6dC5NL3V8TW7wTJ5J41ATmnaAuY929Au7zRSiA0lWC/ZlWZ4OeLSA1lyuRzfm67xRIuE5sIOWYwUgitGUtRD0wxFa9Ezr76dCYMlBVeMGEvBvKvIx6sTnYF32Vf806HV1G7BlbxuHR1rLxrZ3UrF3+CdJls/njWWdj9zxSDOOhOQBp0v7y04XD6S4oF5b833bIWCK9ucIbYyOfmRS/r59qkqj8zBZ5fifdniL5azJQfjgFfAmjKO9WhMxs5EvnBfn8OxNVx+NiIJmHeAe+fq/PZ35/jYGi031+JXrxniv75oLNGSJjxl/NBFfbx4MsM7bj+1bpfABg1f14QEMNm34cxqwNcIlNskF3p1oCt6EYNpCT2yslob26vZaqOj26pN2vhT0Lm0M5m8XWu3rXLBgMtk3mahHpG2JRVf44WaqUrQZrV1tnPZcIq52A1jT8FmMv6NWk+jRLTZe/UK1tYrHluPtCV4znCKWmgW7K8cTdPfYVPXmQXfbK3QVrGk0dzLuOmMYwlSG4zbjaPuug7pthk8nWdup9b8dKOJrf5cA2nT5GYr5Domo43HSafLynps93a+aiRNPVRESm15K0nAvE0ipfn80TL/574lPn148zplgIN9Dn990yQ37EkK+xKeeibzDp954z5++7tz/OZ357sesK2MZ2yOlnzO63e2PdAlPHvpdUVMtupJxdp6zFqoeMGuLPfOGZu0q0aeWm/5zn09Q/EyriX4icsG+OKxCndM1QgizXBaMlePeP15T0/AvJ1jzzqSVx/IEyraZFqdQWornZKM02UwZTEYT76e06I3Xt3HM/OMunYsw9FS0GwYspUVuFTHSXGlsTdsZGI13QWDW6GzsHKr0tzzB1zywmV2KkL3mc6DljTyk6eKH9iX51BxibGchdYw4FoIsbaHcsmPeHjRb/qf96cszu+wzdtsZjvvSl65L49SilJpa0nOJGDeBkv1kOf8zZNMVzevUQbTp/7/u26Ef/u8ka4ZVkLCU4kQgv/8gjF+5vJB3vbZk3ztZG83jUMrAa/82DHGsxbvumqIH7u0n8eXfV6+J4slk2v2XKOrzkt0Z4d25x32FRzqkebCARd7jYnWoWLA83dledHk1q3AtsPxcvuy6yOLXs9GH08Fu3ION+zOsuIpBlISIUTT8/bpYLvZdSEEHfWbTau+XuxkDU5r0xbTarp72wXXamsRfvVT5MHbqnneLI2Ew1DaYk/B5kQpJG0LLhpM0ZeyNqWN3wwH+l2qgUKZztdNW8fN8tCCx+FFn2okqNUVu/uM//dma1mU1lQD3cxMW6K7QK+TsazF88bTKMwqlisFr9ibW/Nz5UDx5HKrZCbgkqEU14yluXuuTtoSXLtBo5OdIAmYN4HWmiMrAX9y7yKfOVzm8aLftLfZLK/al+N9r9iVaEMTnlYm8g5f/aEDPLxQ519+/hR3ztZ7vm+mGvGfvj3Hf/r2HGC09nlH8svXDPELVw0lMqJzhM4lcAlkXBO8NCygHCl46e4slhRdvrxPJxf0u8xVV7XEF59hF5iRjL0tPedTwUYFXDvJ+QNuM7g5nfHueeMZ7pqtEynNNesEwjfuyXK8FJCyJBNPgbRnJ3jp7hyVQOFKseNF1afb2a4R6LoSDgy63LQ/vyWJVMlXfKZllX0wbXHLgfy6n+l3LV61P9+cyDmWWPde8cLe1+9GreR3mrPz6noauXeuxieeLPHVE1UeW/I5v9/hvnmP4jazAy+ZzPC7LxnnxWcoq5KQsBkuHU7zvbefx53TNX7+K1N8b6Z34Nyg6CuKvuLXvjHLb3xnjn949W5efbCQuLo8y/Ej3ZVhBJOBCrUmUppImyKts429BYdDKwEzlZCB/5+99w6X5Kju/r9VnSbPzfduuhu0UVpFJCGBQIBkIRAGLAswybLBJrxgI4ufDTY2xrw2WGCw/RqDDcYYXoxBLwiThZCMJJRXq7TSarU53hwmT6eq+v1RPT3dE27au+Hu9kfPPtrt6empqk6nTn3POQbF5q6T66xIahQrUiqOlU6s9/N0QKHAFQPyHXdZfwwDnixnVXrhJkbWUPCqORRtoYQsSuaME81SWVWe79RqIXpsTSHzSqV38qZ7MxMZzADGyg5+/55hPDFmNpWoHmqRy3YuXDuYxEcv7cY1gzPPtCIiTiWXDsTx2FvXYfeUiY88MIYfHShhtmJIJUfgdT88ipgiA34SGkVPTMHXX70CJhMYq7hI6xQX98YWbdkx4tRQdDiSAVukdmkMzpLP+HRAoQSvXJnwjP7ZsxmcCK5akcCxkgtjngbCYrM8qWFXoOJfY/7r46ErpuCVq5K+FIAQsiSujxPN6TeFbI1KCTQqq1zqyvzb3fi6WGixmpmYrbDLyeKMN5hdLnC4aOO+IxXsztkYrzLsztm496bV/gP0hh8cxuNjcytbPRMqBX5rYxYfvqT7hNYzj4hYbDZ2xfD91w8ibzH8245pfOGZaRycJfWOyQRMJjBlcRwtubjwP/eDoP4A/YMLO/Ge8ztxXrcRBRAuUYKGVcagS06KQwiB0Vjv+iRCCZlTIYcTTX9SxbKkiuGyC10hTYVYjgdDIfPWzZ6JbOrSQ2XI51qd8lTz0uUJXJQVeN5i2LI2ieQ8ZS1jlbBTMWctvlY/pVOc06FDCOFVgTw1z6HTzmBmXGDHhAUm5FJfbcmPe/++fXced+wtgnEBl8Vw48QkXreeIWcxjFcYjpYcHMg7OFCwZVRlm9/Zn3d8fdX6DuO4DOZVaRXvOrcD797aeVo8HCMiFkrWUPDhS3vw4Ut78MKUhX9+egr3Hyvj6YnZc5QCYW/DPz09jX96Wib3TKgEPXEFy5MalqdUnN9t4EX9cSQ1iqRKkNSkMUaJDBqhhIASwKoyTNnAhMmgxUT0Yj7JbOrQwUDBhMAFx5nV4nTVl54tXL0ygaLNEfMKmEQsLlu6DIyWXeQsjsGM5qfhizh+4irF5cdRDn2xOOFn9MEhqQVWqQwOke5/ApUCKpECeJXIZQGFAKYr8IrvHZrj0Sm+tquEr+2aX1o3ALjkW/uhUQIhxIxpctqhUeAN56Txe+d14trB5Gmp4YuIOB42dRn4P69cBgCYqLq472gF28eqeHLMxNMTFobnIVequAKHiy4Oe5Wd7phXzvIE8NhRXN4fw4qU5j8rGitpvXVTdt5FCyJmZmtPDEk9/JpgXKDqBeEICKiUID6L51lTyIyBWxEnHkJIJJE6gcRViuvXpMBPU01/xPFzwg3mr+/M4SvP5k70z8ybhSY4v3JZHDdtyOAdm7OnVJMWEXEy6Ymr+M0NGfzmhnpFysMFG3fsLeIH+4p4ZKQKczbx83Hy2KgJzBCceFFvLDKYF5lWhvC0xfCLQBGcvoTSNlYjpVNc2h9DVlfmXTkv4vSkJu2osTrSK/sQIifzEWcmJ9zic2aqkrAE6DQorl6ZxK8NJvHGc9JYnooeDhERADCY0XHLJd245ZJuAEDRYvjhgSJ+frCM7WNVHC66KDv8tIlwjlgcGp/pjaWxg2iULKnqdhGz0yjnUCNvasRZwgk3mOebrzjIlcvi0CmBQqWmUSEyhcmuactf2m1EJUBPXJl3UZGMTrEqrWFlSsWWLgMX9cZwcW8M53Ub0fJKRMQcSBsK3r65A2/f3OFvE0JgX87GfccqeGLMxP68jdEKw2BahUIJyg73/ghYTKBgs6ZMNRGnF405Uc3Avy/tj+HxwCrAxSepUEjEySN6G0YEWey80qczJ9xgvmZVEkmNwuUCDhdwuYDLpZfCFYG/83qQX+3v9/zm6pZLgl/fmcMvDuRBKzlsXtmLFdkEVqc1rO/QsTylggtgvOqCgoAQWSqSQC6XUCCwTQYW0TlUpomIiJg/hBCs7zSwvtPAu+ewf9nhOFJ0UHI4XC5QrFSx/8AhrBgchKbHvNy/gOulLgpmMNrafXILU5ytrEpr2DZqwvU8zecGxn1NRsdohWG86mIwraEvEWlmzzQaE96cgCxiEUuI1WkN2wPPg/PO4OfwCTeYf+e8DvzOeR2Lesybz+3ATas1PP/8OLZsySKZDCc3pwTRMmBExBIkqVFsDlRuKpcFOqc5tqyIN93nEacGhRK8clUCu6ZspDQamqhoCsFVK6IiTWcyjf7Ek1lFMOL0Q6EE1wwmsWvKQkqjkcEcERERERFRoyeu4qoV0evjbKQxp3rkYY7oiil4yVlQzTjSIURERERERETMiWOlcEGjKWt+8UIREUuVyEVwlqOqKt785jfj+eefh6pGl0NERERERHvO6zawPRDYuToq1hVxlhBZSBERERERERFzYm1Gx3iVYbLKsCajoTsemRERZwczXumihTiJ88WvE74QhBBQFEXWFj9N2rRUicZycYnGc/E4mWPZ6vitnoFnKqfz8/5kcTbfu3Ptu0KAKwfq6QLPlHGKzv3Z1feFPO+JmGEP13VRLpfbfRwRERFxRpNMJs8aqVL0vI+IiDibme15HwX9RURERERERERERMxAZDBHREREREREREREzEBkMEcsGp/+9KfR0dGxKMf68Y9/jC9+8YsL/v7OnTtxyy234BWveAX6+voWrV0RERERZzvRsz7ibGRGcR6ltKm6FiGkKXF5RAQAGIas8JNOp4/7WHfddRfuu+8+fOQjH1nQ93ft2oW7774bl1xyCZLJJB588MFFaVfEmYsQoinog9Kzx6cQPe8j5kr0rI9Y6izkeT+rwXw2vTAijo/ai3UxrpnjPdbNN9+M3/3d3wUAfOITn8CDDz4YXcsRETMQPe8j5kr0rI84G4muqohF59lnn8XLXvYyxONxDA4O4vOf/3zo8wMHDuBtb3sbent7EYvFcMkll+CHP/yh//nv/M7v4Otf/zoOHTrke7he8YpXAADGx8fxnve8B+vXr0c8HseaNWvw+7//+5iamgr9RvTAjIiIiDixRM/6iLOJsyNfUsRJ5Y1vfCPe+9734s///M/xve99Dx/+8IeRzWbx7ne/G0eOHMGLX/xiLF++HP/4j/+I7u5ufOtb38Ib3/hG/OhHP8INN9yAv/iLv8D4+DieeOIJfP/73wcAZDIZAMDU1BTi8Thuu+029Pb2YmhoCJ/73Odw/fXX47HHHjuV3Y6IiIg4q4ie9RFnFWKJ8Nd//dfiyiuvFPF4XGSz2Tl95+abbxYAQn9e/OIXn9iGLhEWMp6cc/GXf/mXYtmyZSIWi4mrr75aPPvss/7nf/mXfykAiM9//vOh791www1i1apVgjEm3vWud4n+/n4xPT0d2udVr3qVuOSSS/x/33zzzWL16tWztsl1XXH48GEBQGzfvr3lPrV2nSimpqbEO97xDpHJZEQmkxHveMc7mvrXSHRt1vnnf/5nsWbNGmEYhrjkkkvE/fffP+P+9957r7jkkkuEYRhi7dq14ktf+tJJaumZz3zPxVLgU5/6lLj00ktFKpUSvb294g1veIPYtWtXaJ/Znm1CCGGapvjgBz8ouru7RSKREL/+678ujhw5cjK7ctx86lOfEgDEhz70IX/bQvq+cePGJfGsP3r0qHj7298uurq6RDweFxdeeKF4/PHHj6vvS+W8O44jPvaxj4k1a9aIWCwm1q5dK/7qr/5KMMb8fc7k/p8Ilsxahm3beNOb3oT3v//98/re9ddfj+HhYf/PT3/60xPUwqXFQsbzM5/5DD7/+c/jC1/4ArZt24aBgQH82q/9GorFYmi/m266KfTvN7/5zThy5AiOHTuGO++8EzfccANSqRRc1/X/vOY1r8GTTz6JUqk0YxuEEPjCF76ACy64AKlUCqqqYnBwEACwe/fuOfdlMXnb296Gp556CnfeeSfuvPNOPPXUU3jnO9856/eiaxP4zne+g1tuuQUf+9jH8OSTT+JlL3sZXvOa1+Dw4cMt9z9w4ABe+9rX4mUvexmefPJJ/Nmf/Rn+8A//EN/73vdOcsvPPOZ7LpYK9913Hz7wgQ/gkUcewS9+8Qu4rovrrrsuVKRlLs+2W265Bd///vfx7W9/Gw888ABKpRJe97rXgTF2Kro1b7Zt24Yvf/nLuOCCC0LbF9J327YBAL/xG78ROtbp9Kyfnp7GS1/6Umiahp/97GfYuXMnPve5z4WyaJzJ5/22227Dv/zLv+ALX/gCnn/+eXzmM5/BZz/7WfzTP/2Tv8+Z3P8Twqm22OfL1772tXl5mN/whjec0PYsdeY6npxzMTAwIP72b//W32aapshms+Jf/uVfhBD12b1pmqHv3nnnnQKAeOSRR4Sqqk2e1eCfQ4cOCSHaex3+4R/+QQAQt956q/j5z38uHnvsMfHII48IAOJrX/tay7afSA/zzp07/b7VePjhhwWAJi9WkOjalFx++eXife97X2jb5s2bxUc/+tGW+//Jn/yJ2Lx5c2jbe9/7XnHFFVecsDaeLcz3XCxVxsbGBABx3333CSHm9mzL5XJC0zTx7W9/29/n2LFjglIq7rzzzpPbgQVQLBbFhg0bxC9+8Qtx9dVX+x7mhfb91ltvFQDED3/4w9DvnE7P+o985CPiqquuajsmZ/p5v+GGG8S73vWu0LYbb7xRvOMd7xBCnPn9PxEsGQ/zQrn33nvR19eHjRs34vd///cxNjZ2qpu0JDlw4ABGRkZw3XXX+dsMw8DVV1+Nhx56KLRv4xiPjo4CAJYvX47u7m685S1vwbZt21r+GRgYmLEdt99+O6655hp87nOfw3XXXYfLLrsMfX19i9TL+fPwww8jm83ixS9+sb/tiiuuQDabbRqXRs72a9O2bWzfvj10TQHAdddd13bsHn744ab9X/3qV+Pxxx+H4zgnrK1nOgs5F0uVfD4PAOjq6gIwt2fb9u3b4ThOaJ/ly5dj69atS2J8PvCBD+CGG27AtddeG9q+0L7X0rbdc889oeOdTs/6H/7wh7j00kvxpje9CX19fbj44ovxla985bj7vlTO+1VXXYV77rnH98Y//fTTeOCBB/Da174WwJnf/xPBGR3095rXvAZvetObsHr1ahw4cAB/8Rd/gVe96lXYvn27n0cyYm6MjIwAAPr7+0Pb+/v7cejQodC27373u/ijP/oj/9+33347Vq1ahRUrVuD666/Ho48+iq1btyIWi7X9PcMwYJpm0/ZKpeIHhdT4xje+Me/+LBYjIyMtH+J9fX3+mLUiujaBiYkJMMZaXlPtxm5kZKTl/q7rYmJiAsuWLTth7T2TWci5WIoIIXDrrbfiqquuwtatWwHM7dk2MjICXdfR2dnZtM/pPj7f/va38cQTT2Dbtm1Nnx1P3wHg0UcfDf37dHrW79+/H1/60pdw66234s/+7M/w2GOP4Q//8A9hGAZ++7d/+4w/7x/5yEeQz+exefNmKIoCxhj+5m/+Bm9961sBnPnX/YnglBrMn/jEJ/BXf/VXM+6zbds2XHrppQs6/lve8hb/71u3bsWll16K1atX4yc/+QluvPHGBR3zdOZEjyeApiIGQoimbV/84hfhui4uvPBCfO9738NPfvIT/Nu//RsopfjkJz+Jyy+/HFdffTU+8IEPYPXq1ZiensaOHTtw+PBh3wOwZcsWfPnLX8ZXv/pVXHDBBchkMti0aROuv/563HbbbfjUpz6Fyy+/HL/85S/x3e9+t6mdlUrF1wTv3LkTAPz9zj33XJx77rkz9nOuY9lqTNqNS5Cz7dqciblcU7Pt32p7xPyZ77lYanzwgx/EM888gwceeKDps4X0/XQfnyNHjuBDH/oQ7rrrrhmN1oWe9507d+Kzn/3safmsd10XW7duxac+9SkAwMUXX4znnnsOX/rSl/Dbv/3bx9X30/28AzIm4Zvf/Ca+9a1v4bzzzsNTTz2FW265BcuXL8fNN9/s73em9v9EcEoN5g9+8IP4rd/6rRn3WbNmzaL93rJly7B69Wrs2bNn0Y55OnEix7O2fDYyMhLy4o2NjTXNUP/7v/8b73vf+/Dxj38c3d3d+NznPod3v/vdAIDBwUE8/vjj+MQnPoE//dM/xfj4OLq7u3H++efjd37nd/xjvPvd78aDDz6IW2+9FYVCAVdffTXuvfdefPzjH0cul8Pf//3fwzRNXH311bjzzjuxbt26UBvGxsbwpje9KbSt9u+//Mu/xCc+8YkZ+zvXsXzmmWf8Zcgg4+PjTeMyE2f6tdmKnp4eKIrS5KlodU3VGBgYaLm/qqro7u4+YW0901nIuVhq/MEf/AF++MMf4v7778fKlSv97XN5tg0MDMC2bUxPT4e8bWNjY3jJS15yknowf7Zv346xsTG86EUv8rcxxnD//ffjC1/4Al544QUAC+s7IAOef/jDH562z/rGHM1btmzxA4TP5PMOAH/8x3+Mj370o/577Pzzz8ehQ4fw6U9/GjfffPMZ3/8TwilRTh8H8wn6a2RiYkIYhiG+/vWvL26jljDzDfq77bbb/G2WZYUCBM5GakF/jz76qL+tFpgyU9BfI2frtXn55ZeL97///aFtW7ZsmTHob8uWLaFt73vf+6Kgv0VgvudiqcA5Fx/4wAfE8uXLxe7du1t+PtuzrRb89J3vfMffZ2ho6LQPfioUCmLHjh2hP5deeql4xzveIXbs2HFG9/2tb31rU9DfLbfcIq688kohxJl93oUQoqurS3zxi18MbfvUpz4lNmzYIIQ48/t/IlgyBvOhQ4fEk08+Kf7qr/5KpFIp8eSTT4onn3xSFItFf59NmzaJO+64Qwgho4I//OEPi4ceekgcOHBA/PKXvxRXXnmlWLFihSgUCqeqG6cN8x1PIYT427/9W5HNZsUdd9whduzYId761reKZcuWnfXjef3114sLLrhAPPzww+Lhhx8W559/vnjd614X2ie6Nlvz7W9/W2iaJr761a+KnTt3iltuuUUkk0lx8OBBIYQQH/3oR8U73/lOf//9+/eLRCIh/uiP/kjs3LlTfPWrXxWaponvfve7p6oLZwyznYulyvvf/36RzWbFvffeK4aHh/0/lUrF32cuz7b3ve99YuXKleLuu+8WTzzxhHjVq14lLrzwQuG67qno1oIJZskQ4szt+2OPPSZUVRV/8zd/I/bs2SP+8z//UyQSCfHNb37T3+dM7bsQMvvIihUrxI9//GNx4MABcccdd4ienh7xJ3/yJ/4+Z3L/TwRLxmBuVegBgPjlL3/p74NAuplKpSKuu+460dvbKzRNE4ODg+Lmm28Whw8fPjUdOM2Y73gKUU9yPjAwIAzDEC9/+cvFjh07Tn7jTzMmJyfF29/+dpFOp0U6nRZvf/vbm5L1R9dme/75n/9ZrF69Wui6Li655BI/3ZcQ8jq9+uqrQ/vfe++94uKLLxa6ros1a9ZEhUsWkZnOxVKl1XNuIc+2arUqPvjBD/pFMF73utctyXu20WA+k/v+ox/9SGzdulUYhiE2b94svvzlL4c+P5P7XigUxIc+9CExODgoYrGYWLdunfjYxz4mLMvy9zmT+38iIEJ4ETMREREREREREREREU2c8XmYIyIiIiIiIiIiIo6HGbNkcM7BOQ9tI4SclelEIiIizmyElKiFtlFKmyLtz1Si531ERMTZwkKe97MazOVyeXFaFxEREbHESCaTZ5XBHD3vIyIizlZme96fHW+CiIiIiIiIiIiIiAUSGcwRERERERERERERMxAZzBEREREREREREREzMKOGuVWwx2Jp+iqVCvbs2YMNGzYgkUgc9/FOBkuxzcDSbHfU5pPHUmz3iWhzKw3v2RTwdiKf90uFpXgvLBYnq+9lh4MSIK6eXtdVdO7Prr4v5Hk/b4N5saLGCSFgjIEQsmQeyEuxzcDSbHfU5pPHUmz3yWrz2W4wn01ZQoCleS8sFiej7zsnLTw9boIQ4NL+ONZ36CfkdxZCdO7Pzr4Hme15f/aOTERERERERMRJgQuBp8dNAIAQwLaR6iluUUTE/JjRwxwRcSpwXRe33347hoaGsGHDhlPdnIiIiIiI48RmUVHhiKVN5GGOiIiIiIiIOKFE9nLEUifyMEe05LlJE3fsLeLJMRN7cjZKjqwANpBQsSKl4sLeGK4YiOOKZXGkdeUUtzYiIiIi4nTG5ZHFHLG0iQzmiBB3HSrh4w+P49E2+rKDBQcA8L29RQCArhC8YkUCt1zchdesTZ+0dkZERERELB0iD3PEUicymCMASH3Zb/zoCH56sDTv7911uIzf2pQ9QS2LiIg4VeQsjkdGymACeFFfDMtT2qluUsQSJfIwRyx1Ig1zBADpKV6TWdjLkAC4YW2q5WeTVRcFix1HyyIiIk4Vz0xUkbc4SjbHYyNVCBEZPRELg0XXTsQSJ/Iwn+VwIXD34TK+uSuPnx8szrr/J6/oQVdMxa5pC/cdq2DHhIUrlsXxlWen8a1dBQwkFQwkVAwkVWzuNPDwcAW37y7g/Rd24Y8u7oo8VBERS4jJKvf/XnUFbCZgqGdPbuqIxcPls+9zprBn2sLTExZqd8rmLh3ndcdOaZsijp/IYD6L+dH+Iv70wTE8N2nN+Tsff2QCALA6reHygThetzaFNWkN9w9VsXPKws6p1t/7u+2T+IcnJ/F753Xgk1f2oTcRXXoREac7KgVsHvz37MayywXKDkdap6BnUeGXiJnJ6BRbewy4XMDlQFfszA0WZwJwAqJtdhZNFs5kIqvlLCRnMvzBvSP45q78go9xqOjgUNGZ13dcDvzLjhy+8Xwef/OSPvzBRV1Q5vACjoiIODU0rqLPZv9WHI67DpVQdQU6YwquHUzOyciOOPPJGgrON85cIzlIJD45M4k0zGcZe6YtnP/NfW2NZUM58S+3iivwR/eP4qW3H8CBvH3Cfy8iImJhNL74Z3s6PDluourKb02bDMNld0G/y4XA46NV/ORAEU+NmZF2+izDZgJPjFXx2EgVZSdyz0acHkQe5rOIpydsvOHOI5ioNj+ANnbq+Pdrl2F9h47Bf98bqsq0/a1rsSdnY9toFY+OVLF9rP5SPB4eHTFx3v/di69ftxJv2pg57uNFREQsLs0e5plN5sOF8KrT/ryNVen5xy0cLbrYMy0n0wXLQn9SwbLk3I8zWnaxN2+j01CwpUuftd0RpxePj1ZxyLuWJqouXrvEUpYOpjVkdAoh5KQzrUW+yTOByGA+S9hXJnjfYyPI2a0N3d3TNo6WXLx0RRJv25TBf+yse6BXpjVc0h/HW7zUcS4XeG7Swp8/NIYfH5hfGrpGqi7w5p8exf832o3bruqLNI8REacpC7k1Uws0FB4aroT+/ehwFW9cPzeD2XQ5fnm0DCGAw3CgUWBDp7GgdkScfI4WHfzPkTI6DAXdMcXP0pLSl47ROVRy8Pio6f97U5eOS2LxU9iiiMUgMpjPAg4XXfzhc0ZbYxkA/tcFnbhpg/TyfuiibuzN2bhmVRLndOjINDyoVEpwYW8MTkNezb+6ogcX9cZxpOTgcMHB4aL8s3PSnPG3ARkUuH20irtuXL3AXkZERCw2wbtWCIBxMa+4A32BEq9Gz/Z8FuWfn7JC33981IwM5tMEy0uVUTs9MTX8bpmsuvjVsQpGyy5Gyy629hjI6Ar4EpPkNK5oLLHmR7QhMpjPcMoOx00/H8OY3X52/qGLuvD3V/f7N/lFfTH86s1rZz32rZd040V9cTw6UsW20SquHUzhJcsTTfvtmjKx5Rv7Zz3eL49WcN439uKxNw/Oum9ERMTJx2ICiXkYzAuN9+swKHJW3Uzu9jIqCCGwe9rGcNnFipTa0hDWoiDDE07RZjAUOq8JUcXh+MG+cOrSt24OF7wKemUBYF/ewcW9yqIG0QkhUHUFDIVEQecR82LprHFEzBshBN5z9xB2TrfPZvGWjRl8PmAsz4frVqfwNy/tw92/uRq592/CFctaLzkdK4UDf64bTOLd53W03Hd3zsGyr+zGntz8MnBERETMjU9/+tMghOCWW26Z93dnM1y2dIUN2IUar4165QEvDeVIxcUTYyaGyy4eHzUx2iKosDeuImNQZAyKrEGxqUtfUBsiWvPQUAU/3l/CD/cXMV6Ze1Dnk+Nm07bGYM5iQ4BfdZGTNzMu6w78YF8RPztYQuUEBRQmNYLlKRUrvD/ZMyA7iBBiyXn6F5vIw3wG84Wnp/GtFwoz7rM2qy2KbpgQ0jaC/prBFMof2IyDBQcHCjaSKsUrViVxeX8c7/2f4ab9q67Ap5/O42WdCt4flVONiFg0tm3bhi9/+cu44IILFvT9+b4vF/p+FQ2mee0R9ehwNbR922gVr1sXDgjrT6q4YYkFiS0VpkzmB+M5TODJcRPXrW5d5bWRuVwLJ9rhe6DgYKIqK88WbY7nJi1cNrD42uJlSW1eQaqnOxOeVMZiAhf0xHBu99kpcYo8zGcoT42Z+PD9I7Pud/vuwkkpXZ3QKM7tNnDD2jResSoJAHjPBZ340qsG2n7nV9MK7h1q9kpERETMn1KphLe//e34yle+gs7OzgUdo9GQbeREx+w2ZucxWfv2cCFgujJgLGcylOylmZ5MCIGjRQfHSid/1W2i6uKR4Qp2TVkQQmCooQ2T1cV9d5zbsEKxwjM6F8uxuWc6XKRrb+7EpDWdrLrYPW1hj/dn2jzx79gTybaRKkxXQAjg6XHT16KfbUQe5jMQy+V458+PYbbVpoGEil/cuBqZU7hc9L4LunCo4OBvH59s+fl39pbxG+eKKC1URMRx8oEPfAA33HADrr32Wvz1X//1nL5jWlao0l+5TEGd9n6WatWGadWNqnJVoLyAXMyVqgXTqn+vWhUo6y5MK2zwuBQol1s/vw4XXTwyWt9/TVrF5f0ze8aq1Wro/6cDj49Z2F+QY7GxQ8VFPSfGu9fYd5sJ/ORgBbVFPrOqw2QidH4BoFwuz+34pgmzwTlTKpdDK5zVqgPTsmG7sr+WLWBaHOUKhcaO379nWRZMK/xirLV/Mc/93ikbO6fq43R+t4YtnfOXBgkhsDfvouhwrMto6DBOjI9ztr6PFMLbD08By5NL23xcSG73pd3jiJZ8/OFxPDtDuesb16dRdjg+c1U/1mVPvb7vb17ah905G3fsLTZ9dvveMjY9Mo5PXtl3CloWEXFm8O1vfxtPPPEEtm3bNq/vjYwMwwx4k3ZVGVIzvDUOlAmGKvWXejrPwRNzfzHlHKDECHI2MGTVj5MtcDhxgeHxcACYQoDn26yQjVkEQ4X6MZgukJ6am2fs4MGDs+5TcIEXShQCwOYUR+YEvE2FAB6YqE8IhoYAo7d1f20OFF0gowLHk/a31ve9ZYKjgXM5NAQMxjmGquGDP+/MzXt6ME8xZocdH8/bLCTDOFQlGCpRTHvnTS0LqEWB3VWG9CKM75FpioLb0IaG9s/l3M9G432QzHNgHveB35YKwQtleZwHCfCKbgb1BPqO2vV9aDw8Kd1T4sgbAkerBKM2QbcmsDouTvgK02KiKArWrVs3r+9EBvMZxiPDFXx2e2tvLQD0xhX86zXL0BM/fU49JQT/cd1y7Jo6iJ1TzYb+/350AhWH4+9e3l6+ERER0ZojR47gQx/6EO666y7EYrF5fXdgYFnIw7xxMI7sDPlw2aQNMxBkvKxDxaqsBiYw4/cAYKjsYsewvP+Hyy4Guii4IFApMNitY0unjmf1cmh5XqXAlnXJlsfrqjCMBiRdAwkFW5bP3P9qtYqDBw9izZo1iMdn1rbefaSKWFwOTsGgePGqxdfCciHw7L5wTuot65v7W3Y47jlqwmQCOZXg2pVxxOZpWTX2vTJuoZoPrw4MZlS4hfC2Vu1pxdSwCbUcNk63nJMIeZi1vIPCuI3DirwOehMKlqdVbFgVQ+cirIQeO1LFVIOHudb+2c591eWw+ezXMSDvg2rgPjinW8fmzvlrmnfsLWN5IJFIolfHOdnF10bP1vcdWngVYeMyA7pCsOOoCR1AEUB2wMCKmWbTpxlCCDA2P6nM0uldxKy4XOB99wzPqDL851eeXsZyjbSu4AevX4XL/ms/cpXmzz/3xBSuGIjjpo3Z5g8jIiLasn37doyNjeFFL3qRv40xhvvvvx9f+MIXYFkWFKW1MRIzDNCAfZFMJJCcwXDZf8RFzKhLBg5XgcNeZdG3bErNGGC8Y7jgf/fYJMOhCodOCdI6haYTXLoyiZjhNhjMBMlk2GDbm7Px3KSFssOxM88xkFSxPKlBM5SmfdsRj8dn3FcIgQpcxLyuVgEkEolFl45xIRAzwi/1Vu3aMVwBVB0xVeasPmQpuCS7MAO+1vd4kSJmhjW+uqEhZkhDkHGB3TkbT+QAAgJDITMG0MViQMwNG9uJRDKU2i3p2IgZBLoq+6xpKmKGjkQiCUOnOFp0EFMpBhYoB4jHBWKYeTxr/XeYwM4pC1wIdMYoHhs1wTiwNqvgimXN6VODrBQaniuUMVJx0RVT0JdJIjmHIECHCUyYrl8h0IIVyrChGAaSyflNeudDu+t+eVZgKqDD7smksDtnI2bUb8YhW8XGOd5fpwOccxSLzavaM3H6WU4RC+YLT03h6Yn2UozXr0ngpg2nb/T4+g4d37p+BX73ziO4fmMSX98dntW+9WfH0BNX8KMDJXzmqv4oh2ZExBy45pprsGPHjtC23/3d38XmzZvxkY98pK2xDAA6RcjDfDzBVy4H9BmchFYgoC9nMdhMYCChomhzjLVJX9YqCHGi6qLicDwxZsJyBQ4XXSxPaqEX/vFygrKRNdFqvIUIx3QUbYafHypBJQSDGQ0qIThUcHBJ3+J7vN1AgziAaZPhaFGem7hKcBmO7zenqi6KdvN54kLg3qNljFfkZxf3xbC5a35abiEEIAS4d80QSEO/HQ8PV/yUqLtzFjZ2yN87kHdwbhebMfZnqMSwL29DACg5HM9OWliVmd1gLjsc9x6pe4wOFhxc2Fv/HfU00jw0FjTrii391HmzERnMZwjHSg7+4uHxtp+nFYG/f2nXaR8895q1abzwO+txx7cfxc7RAoKKS1cAr/reYQjIB8dtL+s/Vc2MiFgypNNpbN26NbQtmUyiu7u7aXsjjXbh8SQrYEIAMxgoQaZNhkRAiDs0j8DBimd4M6/xnIf/3Y7xiovSHLM+2A3ZOTIGPWnPVi6kdrvGfUcrmPAMSVcI37A7Ub9do9GYn63/ra6dKZPhUNGBQoCdkxYeHalCCLm9K6Zg0mRYl5WGZM1YBoAnx0xs6tTnNeYPDlWxe9rGC9PSa76hUwclwHf3FDCY1nBuqt5CIQS2j5ngQqA/oWKiwrA+K/wVkiMlB6u9vyc10tSOx0erKASkH4+PVvCatbOn35utO/Yip1l1ucAL0xbKFRt8hvujsV0Cx/csmA9cCBwruThcdCAEsCKlYu0cY68KFsPjYyZcLnBRbwx9ieMzeSOD+Qzh1vtGUZrB7fHOlQ76E0tjBpj0XpRvGGCwuzU8nQs8yLz/f2b7JF68LI4b12dOQQsjIs4Oyg4HIXXD9Xhekv+9t4ikRvH6c2Zf5eqJq6Hn2aq09M6RFm0YKbt4YqwKhRC8eCDuR7/Px359ZtzEc5MWTMuCViLYMsv+jYbLiaou2Gq8uQBqT/KCxVAMLAFMVBg2dpw8YybIbCPQyluetxj2TNtwucBjI1VMWgxdBsVEVRrMjjcxadWfsiOQ0uc+7oSEjfqJKgMhQHdMxb6cje7A63H7mIn9Xsq56YDhW/PuPzNu4ZlxuZr75o0ZNBY8dBquj8WodQAAz05YOL9n8SQZj45UsTdnY6RQhZKjOL/FPiWbo2xzlB3pm4972vjmCdOiNSvEA8cqoeJnR4oOMjpF9xykpU+M1YsbPTxcxevXpY5rYhsZzGcAvzhUwu17Zi5QcrKWEBebn76uH+fePoZ8ixyqX9+ZiwzmiIgFcO+9985pP4cJ6PN4S1zQa/iGRCvKc3wQ9cUVgAh0x6UVc1l/66V+IQQeGa74+ZmfGDexpcvAWKXiv9BTcwjSei6QVehQlc6acspp8DDrJ8pgbiXJCPx9MR2Oh4ou9pQJnEkbsTJFeZbsFyqVlR2vXpmAAKAswBBxvPYzr6OtzlTFlXm0UxpByQk6T+bXeYKwUX+46GB1ui6TeGbSwSrv73um69rtvMlw6UAcb92cxeOjJv7rhTw4l4bzOVltTq3Q5lhC3GrMK77Il1XOlFlJanKSA3kbT4+bKFsM03kFbyg72NAgQ350pIJto1V/YnZ+r+F5mMNtnW0FZyFUXR4yli3GwYQsof7qNbN77IfLLmwu80ebjGG0wtCfUBZsNEcG8xKHC4E//tXojPu8qFfHuwZbRNItAZKagp+9cRAvuf1g02cfvGBhxRciIiLmRlyjCL7DF5K7tJGhkoOURmfUgGYNBbpKsKlTygva5Xy1WLhc72jZxYsH4rhyWRw5i8HhAvocjZUgdx0xsb5HxdYeA5QQuFzgcNEBhfSkNWqqa79Rsjk0ChjqiasJxuchbZkPh0su9lcozGkHMYOiM6aAC4FDRQcVV6A/rqInXj9nNW9/zWhns5iOrT5lDRZ/WqOh/eIqxdPjJmwmMFZxsb5D9721870UFSKDRGuxL61WK9pBify92rXGhWyAaNOOc7sMP0sJAcGyOQYp5hZQRKxgMaiUhCRMraitogDAJX0xbOoyMFpxQ5O/x0ZtbGjI4NpoXEopuDRCbS6gUoCC4PkpC5u7dMTmeO0LIXCo4ICJ9hWHg20br7jYk7cBIfs8F4MZAJ4aN+F6x+Fc4ObzOhacmi8ymJc433mhMGOgX0Il+Oa1vSgdyZ28Ri0yA0kVfXEFYw36wht/cgzPvnMdVmdOfS7piIgzkZzJkQ4YtrMZGDMFUdW472gFlABXr0z62Q6mgpX4vEM0ek8tl2Oi6iKhUv+lzDhQdBgyOvUDon5+qISXLU+gL6E2aY3nSt4rmzxlMly9MgHTFaGy3HmbIRuIYEyoFI+NVLEvZ0OlBC9bkVhwJocgrVof3NbOUTbTWZisSj1oT1z1pS5As3dXQGCk4mLY8/DlTYZjZQcrkip64ioEgF1Tlh/spSkEN22YX8qzrKGgK6ag7AK6SgAmQud9vOoi5RnRVSYwWWXo9XSo8z2zL16WwJqMjrS34vDQUHsnkq4QqFROlID62HABxBUCh8ixz7QpJLI2q2M88L6aq8E8l0nASNn1r63to1KXTQlwxbJ423ehEAI7JkwcLjqougJ5i2FTl4Fiw4pPq8qZo2U3JPthQnp+Hx6p4pkxEzGV4LzuGAyF4NGRKpYnVaxMa4jPYjg/MWZit+fJP5C30RlT/Os5rlJs6TJCgf17AlUZj5akoT8Xz31wD47jW5WJDOYljM0E/vzhsbafXzuYxNevW4YscfD8SWzXYvOvO6abjGVARh//5o+P4uG3rJ3zkldERMTcqTKONOqGYbKNF+uhoQryFsOhgoP9BRtbumKIzXBPciH1k2/w9Mx3Hy7hmYl6zuS+hBryHDsc+OnBEl6YtkEAbO2JIaVRGAqRL1khsHPaQs7i6DQUaGRxdLzDZRfPTVpY7WU4mDaZt8QrQgbzgYKNos2heUbW9rEqblg794xEw2UHUybDqpQ2a+XV2gt/rOLi6XETx0oOBMScJisVh+MXh2u5rG28fGUCK1KePrzh61wAB/MNVf1sjt2OjbhKsS9v43DRwfk2Q1pX5u3xzRoUGpHG0lDZwXDJRUKjCHa/pgWuHbrgcPTW/m5L/fZAUoU6B0lMxeEo2QxVxkFAwCHaGk8bO3UcyNv+cj5I3ZseVwnihIIA6I+rLd89u6ctbBut+mckrhG8qI2saL48MFTBjevTcJjwDU4ugIeGqm0NZiaAoZKLIW/yU8tC03iTzOUccgEcK7nY5XmrTVfgWMlBZ0zBoyNVvHggjh2TFl6/Lj3jedkdkL0cLjoYrrhQCQEXAnGVYNMslRGrLoc2Q4afGizQKekZX/gKTWQwL2G++uw09jc80GqkNIp/u3YZlqd0lMut91kq/O8r+/DgUAUPDDWX7dw+ZuLPHhrFZ18WFTWJiFhshkoONCqX57tiCuIqhc2khyprKNg1ZeH5KQv/va+IDoOiYHFMWQybOw0ABENlB+NV6QFenQ4vu1acYDBV+HcFEJKC7MtZKNsc4xUXNgcUYuGygTgIkfl/90xbOFhw0KErmDYZ9uUdrEyHvZ0LjfXZMWFhVVrDcNnFgbx8ySd1ioFAXt1DRQdxhUDzjOiCNXdB51DJwX1HpbfzuUkLb1iX9iUdQgi4XGYJIERKU7gQMF2O/zlSRsXhOFRw/IlCjeBwOkyg6HCkNYr7jpZxpOCgJ64gplI8OFTBm73c9qtTKvJJjnN6dKQSMd+4akIA+/I2ijaD6QrszTu4uHd2w2VFSkVGr0suNnXqKFhc5jrm0ntcZQLLkkpzVgYR/n/eYvjFoTLiKkVap3jt2hRKNsekydCfUFvKEx4ZqeLR4YrfL8sVyKO1BOL8nhj25OxQqkNASjLGAtk6DhfD79aKw1GwOUwmQnICt41lLoTAtMWlEa7SJq9sqzmnwwSGSq4ffNd4vJLDEVNoyJAXItxWIYA9OatpUikAbBuphvJppxtiAJKanCwFGSm7mDSZb4ZarjTmz+2ePWMLFwJlhyMBCk7kPVB1OVK6gstnmGTM5sH2jx9Mizmnb7QnMpiXKGWH45OPTrT9/O9e1n/GSBU0heD2G1bikv88gMG0isdGzdDnf7d9Cq9cmcRr1hxfBGxEREQze/M2LovFQYn06tx1qIw90xaOllwYCsGGDs3PaOG/wohcAap5KMs2R0Kj6G8T2d5oT0xWXYybDAcK0qOcMxTkLIZpm0Nw4PkpCy/qj/kG+LGyi6or0OE98kYrru85bUQIgcdHTRwqOuiNK7hyliIUQG2JHuhLqhBCwFDCL2vBBUQLb5oQAs9NSm9jxRHY3KXjmsGw9vIhT+pRcjgOFmzkLI7f2pjxPc0vTNvIe9rWisMhhPTOBScZ4xXWshJe1eX4xaEyyo40Jn81VIHpcFACvPGcNPSAEGNFSkUhIbCxQ0MyabRdTVAp8eUzKZ0ioRGsSKmzasU3djYbTzunLAhR18nOpJEPao735W1f3160Of7juRwoAQzPUNzQoWOk7KInruCi3hgUSiCE8INDg+z38iWv904L4wKHCs3G8i8OlTDdoDEeDWjZp02Guw+X4XKBnZMWSq7MLJFQaVtL7WcHS5g0GWIKxdUrE025jNd3tH6Hc9GsLQak3Gm47CKmErxyZRId3vF4i3G1mGiaqOZtjqfGTGzs1P2CKY3fbAyerNEokZhL3nPu3R9jFRdxjaJDV1B15bU1WWXYl5eyKIUA5/cY4J5T3OXAvUfLUCnBi/pis67KBDme+ILIYF6i/J8npzDSJpn/K1Ym8J7zO05ug04wy5Iannj7WvTFFVzzvcO471hYf/a2nx3D8pSKe35zNZbNoaJSRETE3Kh5ysoOx31HKpiqujhUkIZwxfN+QgCTFReESIkEAcG0GX4+jZXdtgbzeNXFhPeCJQBMl6PChK+fHSq7oTc3FwjpWaWxRJDQCCqO9LKNVRwIkLp0o9aOCsNeTw85VHKxPy+Xll0uvYI2l4ZmLGDfGQpB2eHyZS4AuyFp7UzayNt3FzDt9W1f3sYrVoar29XG94VpCwfyDnZNWciZLj50cTe4EL6xDEgjZNpk2DZaxZ5pG/1JaSgoLWyAaZPhzoMl/9/78jZsJmB6huCxsou1AafKQyMmdk5RHD1SRSIu2i6J14wvAVmsYlVKw5YuHUmtbrTUUnn1z6Ld7Y2pGEiqIAQYN11ohPhV7oIUbY6SzX2Zj+kKjFeZb9TvLzhYllBhKHI8d3pygSlTroSs79AxYTIcLjqwmEBXjPoSlhGvrY7NsT4O3DdkoiTCxl47vbMQ9fO4fazqe5JNl2PIK+iSB0euxYrDTw8U8bMDJaiUYHOXjoeGKnjpsgSOlhxwIeUiUyZrKkBjMYGSw1qWPh/2+mK6Ak+Om3jlKpnyomXAJeNN2/fkXehlEyszKl6zRkqKVqakJtn09lcooLe64IAZZVj+73LhZ8sp2NzXR5cdDsZFKNvKzw6WMOqVU1+V1nzN/XNTFnqq8np7fMzEq1bNXGGQC4FxkyFbInh2woShkAWVGI8M5iXIlMlw2+Otvcu3XtKFD13UfUZ6WmuG8NdfvRxbvr4P1cCSV97myE/Z+MN7R/D/bljV7hARERHzQOYBIJg2GZ4rOyjYDFMmh8sFOgwFGiWYqDKkdQqVwjMqBcYrbuiFnrcZqq7AaLVuNK9Ka/7Ly2QC0xaDaKNkGK00G9tBB2BWV9CfVLGpw8DunAWXAbtzDjgXWJPVsDyp+ZXhCg2V5CZNBtOVlQFt18V0iWJk0sErkmFDZdrkvverscpo1pA5gy0mfDkB4wIKJcgHfs9hAhNVF/2BSf3arIY9ORsTVRn4uCyp4kjRxZ6cjbUN1eFUSvDUhIWKI42OPTkGKmTQYZCqw/Gzg6WQJ7ArpvgTHaC5alzRFigxgmmLowoG0dF6BsCEnNTYDDhccLwxEViZ0vHqNSk8MVbFC1NyQrKly8BFfe3zBic1inVeEYqRsgsuBFwuPc3MW6rvNBRw78IQkJOZo2UXh0sOuuMppDUFeZOhN0ZxIM9Qcjh64qofaLdtpIqVKRW7p+SEoeRwJFXSlJWh5AhMKsCEySFUeY17xQEBANMWQ9kVSKoEa7IaYorUMT/jpTIMFlbpiitYnlJRm1ocLTVowR2OB49VICD7W9ODj5Rd3H1IVrhdlVGbVg3KXtVAIcSs2ShGAsV+gpM5VwjkLY6DBaetR//ZCcs3mC/uiyNvcd8YB1pLRQD4XmmbCTw9UUXF5Tiv2/BXe2wmcM/hkj+ByFnM9zIndQqDEqxMaaAE+NWxChRS1xsfKTpYldbAuPDvQ6A+OQPkCsyzkxYoAc7vNiAgs+Q4XKBkc4xVOHZMWEjpNDKYzxZu2zbRMi8xAFwxkMDgHEpwLmVWZ3T84ysG8J57hps+++6eIn64rzin4ggREREzU3sv7cnZSOoUU2a94lrR4Vib0eBwoOoKlG2OEe+zXVOmvwRfdjlGywxpXWDftI24QpDRFRwpOjhSlMcvO3zGNF+zpW9O6xSOqHvClyc1PwPAwbyD5UkNpiuNoEZjVyUER1rodU2X+0bJnmkLQ2UHcZUippCmdr5iRQLf2V3ASFl6NmMqwcFCQqZma2h7YyICma4MvszBYhxpUOzPO+iOKZioujCZQEqjyOoUoxUZ5McgsG/awZqM5uerBqRhIQAcLUod96DnldNpOCywcZm+Ma9u1RWygiFIyMtdtBkygYDHcc9jn9YV/NeuHJ6ZkDKLmvZ1c5fuaawpDJXKSYPpIqvLtHVMCP/cO57xOFZmgLAxYTKsTGmYsuoG8/6Cg4o3VnumbZzjGdwTVe6nZSvaNjI6RVKjGK+4+O6eAggBBtMaDhUdlF2BlNZs9VlMbjuQd5ALSAoqbv26r9jAmqyG0bKLrriC5yYtFBrexy9M2/7qCAC4PKz5rTgcIxUXFVegJ6ag6ghoBkHw0nR4c6bpg0WnKRXfXAh+5WjJhe0K/PJoGTYniM8l00TjLrN85UjJgc0EugyGh4aq+I31MjBzT84Keds1SlCwOcqOQFKXz5spk8EVAhVHYFVaDWm1DxUcDJcdqIE2B2UgDw5VMOElCKhJbS7tj6PqcghBsLFTh8kE4gtMjxkZzEuMYyUH//jkZMvPliUVvGrV7Hq8M4Hf29qB7+7J467D4aUyjaBpNh8REbEwai/atE6R94KqarhMlqFWWrw9BeR9SEB8nWdQf3pxb9h7O1WVBRX8V2mD9dyu9ojL5dLzSNkFE1JuIIRMQTdecaFR4htu+/MOVErQ2aATbRcz//SEhcv7YyCEYPuo6WXA4FjdFcORhmAvSsNGtIA0MoQQvvZSSlWALqP594WQRnbRBqZMjk5DoD+h4J4jZZgMqDgCFUcG2R0puuiNK77hQRoyghwpOr5H72jR8Q1mzqW8o+JydMeUpmIrQRtipOLi0RGOrd3SO/zoSNU31MaqDGktrMt9YtRC1lBwrFg3EmsZEn56sATLlZkPXr4igXuPVvwleZsLPDVmgnGBfXkHnbHwiXa5lOtUXYGMTv3rQlcIbCZwpOjguUkLhvfvoI76UMHBsqSCx0aryBoKOBdgQl5rAKBRIKYooF6SZQJAp14aOe8wOZshb/GQnplxgZ8eKOGcrI6qy7G+Qw9dDy4XTdeTCHwmhDw+gZwk9XjXI0HY692yMuIcdME1uBB42rs3sgaFgIDNALvWF0JQsBniLWRStd8+VnLw+KiJ56dMdBoyE8runO0ZulJ7XcPhws93PFp2/ftssuri68/lYAuB4bKDVUndLybUSn4DAlRs7zhVF2vSdWmQaJHZpNZW0+F4ZLiKsiMzp/QnVDw/ZeO8LgMmEzCZzCluuTbSOsUlPQaycx5NSWQwLzE++cg42gVg/9drVs6pXOSZACEEX/21Fdj4H3tD0ozehIr3RwVNIiIWhZIjDR2FSIOrESYETC5apiQzXY4jReYv5RqWzPLQqjZDQiPI2xwVm4MQoMOQ3keHC2jeMm0wp3JHjOJQwQaEBsfLSFCwOXYxCytSKnblXN/TJKCg7HAYKsHuaRsX9RnIWcyrVgZ0WbRlUBTjAlOWNC4JIbhiWdxPaTfdyXyvIhMCnItQJgQhZACkAJD2vLHMWwr/2s4cuuNSV2soBJanDQ22YNrieHCoipROsCypYF+urvkcKslgxVoAG4HUWGc9g0YuQwMcMgPDvryNZUkVRYejM6ZAsWQS4caJw8uXx9BTYFi/KoafHHV9yYbNRMiryXl9QmDUyiRD6nxXB4ybmle6ZmwWbY4//tUY4qr0Xk9UGdZktFAZabdhIHSFYNrksBiH5cmA8hZDUpMG8oTJIARgKHLFw7WlBtbl0jh6bEQezOYCVUdgeVIF9exulcoxO8/L5BCHg0Rejt9UlaPicunpBpCNUX+CsS9ngwmZCYULmQucE+F//thIczanoi1ziKc0ioeHqzgnq0OhMrWdzWUWE0qAkj23nOdzUVxOWxw7vYwTT44zHC66TZUE2/2GocggyW0jVezP29g1JcuXdxoKhsouVCpjATZ0aKg4ws85nbM4urzVDsYFchbDfcfKGPJSBnYZCqarHC9ZnsCkpylnXCBlUGR1BSWbh+5FndYNctOV93jRYVAZRd6W5742ydqTt/2CQvtyNrI6Rd5kGCk72DVt43DBQX9CQdZQQAnBtlET1/bPPo5Bzg7r6gzhhSkLX3k217T9lSsTeO3aFK5eObPw/UyjPyErTwWXU4fKLt72s2P4v9evmFNuzoiIiPZUXAZbCPTEFSkD8JaJKQjKLkfRppiouuACSGnEL+SgUQImRJvyxeFtXNS9wjUJSNURWNehQaMEF/Ya2DllYW/e9jXOGiUYrzLsmraxJl0vT1x1BJ6bsNDpOQ44hJQIQHr11mR1TFTdUA5YjZC2AXsvTFkYSKpYl9WRUCm4kJKEqssxVHZwuOigaMvSzZOBXPEC0pN+QU8MjEvdbN5mKNpyErBz0kLJ5oirFF0x6uV1prBcgaLNZYCVEBgqsSZvJRMBT7zXr96E6ksTHC4zHTw1boELgS6DYrLKZKCidzDbFU2ThH0FFw9OKzhwxMSzk7KEsKFQVNxmD400UkmotPRYtXUQusWkXnbPtI0pkyGpU5QdDs6BvMVRcjm6jZqXtX51NHrsOZfX06QlsybkHQYKAl0B9uUY+hMCDpcadZfDl2Q4Qk6oKq40vM/JaCCEYF1Wbyouk1Bkie+8wzBaqp/PjFZP+RaUBxHIQjkqAV4byLutEKkVJqiXDB8uu9joZe/IWQzrszoOFx0cKjgySNUr48y9u6bkCHQEPNsOFxivumBCGo/tEkMcLToYq7rYl7PxilVJ7Mvb4NzTHXsrHYZCkFQpnDaJtBgEbCb8IicFi6HsSIO55EjpS9WpV/kLnql9eWlcPztl4cHhCqaqXKYT5DJQtzsmM968MCWzY4xWGeIKQW9cQadBoSoEcVVOIlemVFQcOSZHyw7KLsdklcEVUn+dUCmSOsV53SbuO1qBy4X/3i96sQOPj5ko2Bw5WwZKJjQKa4F1vCODeQnx/90/0vT6iasE/++Gs8ezHERTCB588xps/Pq+UIWib+8u4MGhCj77sn68ZdN8F10iIiJq2K5cRj5YkIU1xqsMTMDPuUyJi5wpvcJJVUXS8zgqlMB2RUiv2MLugoDAjkkLT41bsAL2lswlK49VcgUmTR7SAk9UWT0nr81ChnnVFSgVbXQbKkwmwDj8SPu8beGVKxOh3zlUlHpTJgR0IrNkVFwOx7POj5UcdMUU/PKozHvcHVfQH1dRcjn2Tcul+K3d4Sez5crl8F8NVaAAGMzo2J2zsLXbwHOTNpgQeGK8irUZHTkL2J93oXgZRkzGkbcFdk5ZMJloknDYTKBaS+NHAEpISEN7rOSi5AgUvbE/VmbQKENCDZs2jaej0TAvOxyjldYSgFpvD3t66VqfgwkdkhrBUMnBkaIsf1xypNeWceEHpU+asoBGn/f+mil/tckERspSulPzlGb0uvEjRN1YDrZxosKQtxgcJr2e5/fEsDqj+VIdLgSOllxMVyyscwgS/SJkLFMazvkbVwkcR/hjOFVlAAlr3kuOzMVctDmyOkVKV7F72vJT4ZmuTMHGuQwmNF0BUyHYMWGh6MjUiWVHYGXAoH94uIJpk/t/f/Xq5jSqJZv7+ZYJIXh4qIIeb2xre3bFFHTHFJzXHcP+go2hooOSxVFwCLjJ0BGjfhnwF6Yt7JyyUXYC13crGUUAy5UBvKYj76XaOAXZl7O8sQcUSE33lMkQ1yi2ZHXsL9igIOgwFFQcV3rxOeAwmaqv7HIMZuSxpk2GJ8ZM7M/bOFpyscaL4aJETtoLllwNMl0p2WIcfqaY+XL2WVlLlMdHq/jxwXLT9t/amDkrjeUaqzI6fvyGVbj2jsOh7UdKLn7v7iG8dHmiqYBBRETE3DEUgoojXziEAODwU0HVotWFQN1r48k3DuSd0Iup9nHN0B0uO3hwqIqyy0MR/Y0cKThIqES+yL3vT1Rdf7lWpaSpBHbNC8gFkA6UL67YHNMmg8Wkp3es4mKs4vpG1ookwZRDUCwz1Gyk1RkVLheoOBwOA0ZKDCNlhnWB4OojRRdC1A3BCdMFzdfb4wobDpNL1i6XhScUSjBSroIS+BlADEVKU2JegJ7FRCg7gTyWqKcU9aygqhM2mINe4aGSLOjCdHm+yt5nlsuRCqSCI954uV4lw9EKb5mLmRBgvCq9vBWHh3S3XAhQIpfzX5h28OSYNIxqy/QaJTC84je1vgDAaMWBLeQKAwGQ1Ik3HvXfzZkcgIu4QqURKBgyBsVkVfjtCk7KgleEE7D7+xJqKEf3SNnF0aID2+W4Z1pFB7NC/eVcSjpqx0zpNJQJpfbBoyNV9MYVmExgtOL6KQjzFkdfQhbyCBYzYVzAFjx0j3AOfxWlbHN/fABgf056VQs2x3hV5kB/5aqUfyyLiVBGlphKkLMYeuL1fh/IO/4x/3tfAQRSH+9yhrILOBYHCEfBYhgqu5isstAkJm8z5BzWlKO6huZ5d2uGfUIhgE6RNWTxo1osQldMxXDZhYC8fzO6gs6YgoRKsXvaxsGCAxBZ9j6Y83xPzvFXgxwmsCNvomDJlYOCLb3iNS173mbYm5OZRxRanzDUMv8spOLf2WtpLTH+8JfNGSHWZTR89deWn4LWnF5cM5jCRy/txt8+Hg6GLDkCb/rpEdz1G6t9HWFERMT8IACqTCCmygIVJZujygQqtgh5hafMQOW+bLPOMmCbgQlpUDueIdoK4f9HoBLieyEB6cHzPc4CTdpMAKgwmbtXV4j/puuKUVmxr2iDejl0Exrx9cdCNL9ChZCBZwWbQ6UEGpGGyIRZf5E/N2lCoQQU0ggUIOiLw/Nwy4wWCgiGyi4G0xpyliwnPeVJOYIFzRQiB33MdFG0BTSCUNlrwQXKdl2/DEhv8PNT0tDbPlYNBYzVl6il/rP23cb4saLD8WyRYlizsbcgUwW2MpgF6hOmaYuhO+blwqbAlcviAAiGKi725OqBcFNVBkMlUAhBSiMoQMoVapOoki2Qq3I/g0jaoFieVLHHqyhX645C5HVU63fFy64S9zTwjRgqgUKlJ15XCHSF4FjZQd5iWJnWEPd07YD03HNPnH1Oh+yTEDJYtFaApyeuIGcymWkhTkNG+e6cjQN52bIqE3AD46tQIjW6nuXuCpleMBe4Z2wmfINSofK8BVdOdYUgZ3JUPGN1X97BJX0McZVix6TZZMR2GQq6DAVZgyJvcT8VXE0iUrHl7KSxpHrR5nh0xERSLzSN50jZhekKtMto51dY9OQfCiFIa9KTXkuZGKTxrq2dK+rFlJquQNlhvt4+KJ2aNhmmqtxrF0NCo1KfTmQQ7pGii4RKMJAIv/snTQ6A40jJATC/4m6RwbwEuPtQEQ+PhKvbaRT41ZvWnJH5lhfCp17ah7sOlfHEeHicHhk2senre3HwXRtnrUQVERHRjCsCgVuONEI1RRojNY9iK49T4+1WM74E4HtALbc56r3GoaKLpEoxmFExbcmSw4ZC0BdXfIOQeMZlqzs7b9Z/A14RklqwT83TZ6gEvQkdFc+QaScI2J+3kTM5DBXoMuRrM2cxX79pMgHhSk1l3ua4emUCoxUv4FFIT9mL+uKwXBmUltFlqXHuGU6ANHqnTObJLKQ2VCf1dG+ukLrjwyWBQe+5X7NbXC784ihJjSKuEExRea4cJlBsleBa1PvmcoGSI3BBhmN5n4GcY2OyyjBCXGR0Gs7zHDhfDpeyCoUAaY1ipOxiRVrHaDmcRURV6uWhM7riywRsLnxjNChgVog0vmINBTIICBwGTHkBhZNmfQLQqoOWtwTP4JVgthlsZsFQKIoOh04JDhUdCAG4nPlZE2pGJSfNxyZongxajGPKlCWuOwwlNGFRKNAXV0EJsGtaTmoKNsfTE5Y/YdAUGeiqECCtt8mUQdA0Kdift5E1lJb3H/cyShwoOFAgMOYZl3VPa/3vNhMouQTUFUjr8nefnbARV4mX7aV+3IG4CgGBiiuDFRMqhdYwICpFaMIA1KUa/XEVMVUGnZYd6c0uORwTVYKkqmN5SvMneVXGcaTgQqUEcY34k26VEJQcOd5VT4+f0ghWJHVc0BNDSqd4aKiCqiuabKTaBH04MpjPPIQQ+L27m73L/3btciyPpAY+hBD89I2DWPvve0JZMwBguMzwv+4Zwr9dt+IUtS4iYmkS14i/hA9IfS1QW+Kuh2cpVFb5qmkdK25zGeKcyb3qbMCOCQsHiw5sHo6KV6hcSi7bwstQID2wMbXubTNdDiYEzsnq/hJw0eahbAtCwA9AFAB6EwpUCqxIab4nFpAv8ZGKG0qNZ3GgYjMo3su54lKkdOpLQvqTCqpMejY1RY5F3uLIaNSv0Hak5EjvlwAszlHyNMm6QpDWKBwusCdvY7Ii5R2E2P5wciGX5hkXMre0kJlCcp4ON6YRuQzNeciQrdVtU0AQVyk0W5bRNgOpJ5IBY+zB4TIuFwlwIZfzv7+7gJRDoXZKOYGAQMHiKFgca7IadEqagjiD+Z/jKoXtBd2NVhiYqFdtm6gwcNRT6nEIjJaZX1K9EerJPtI6xWBaQ95mGFZcEMhUY0D7Es01qq6AQurHD8Z5CQgcKzlwmDTK4m0q17VO11u37DVKQFSgllWu6gqk9eYv7Zm2MVx2cKCgI6FRdMfU0ETRYTKfdU9CQWN66Korvcq+oR74PG/zpvus6Mhrqtb2hEahUVn6vNbm2ngoVJ6LKYvD4oCwOVRFjkfJZlBp2PivGdkVV/hSjarL0B9XQDxfddXlsFhzuskq45jKMzAu782BhIotXQYOFW1sGzHRE1fgBKoABpmqMmRjFD0xFUxIw1ulNFTAJBjkz9sGHHv98DLWzJfIYD6NEULgX3dM41AxrGHrjSu4aUNUmKOR/qSK77x2BV7/o6NNn311Zx5v3ZzFNYOpU9CyiIilSVyhMBRgIEnAOEAo/JK/QZIqha4QlD1P7XC5dS708aqLAwWOC3oM2F6aKC3womMc0mMY8DZKKQQNvKC5V7XOhkIJEirFipSKfTkHy1IKcjYDRN2L3aErfsW0TkPB2qyG6cDLkkCWulYIwXCZoeISMAIonoGetznSuoK8LYOxRhWGnMkxkFSwIqXhYF4GvuVtDk9BAAqClC7HZLjk5RxmAiohmDBdHCq4fi5atZZ/LtCeiiswUmFIafUFc3+UvH3LjoBKpHdttMKgUOIXeSjaDC0UCiGmTYEDeRsKkXKaI2UGYlMUhizkbBm0V+Ng3oEaTr0MR8jqaUEv5XDZxc5JCyMVF+MmQ19c8SqzCegBa6Pk1Esi10jr1M9XrRKCos39apEE9WwMweDPmYrdMS8LRyM16VBtsmOoBL7d32BjtVD6+IVWAHnd2G74ixxSi171Jo+MSw+0sAl252yYrsCGDr2uQw9QsJl/P8RViimT4clxExDA6rTW0pNMSWNZdhIycimR41BxOWKKvCYF5GoRSFhWBciJbTwpJ4fHGu71lEYxWmUoBcZVcLnCEvfKzx8uuhjzYgA0RV6TBqVg3krVwbwNIWQhnp44cCDnyHzUcQUWF7BNhqo3uwn2q7ZiVHYFHNYs5+BCVgAsOgwmI7A5/AmsPzKkvnowMY+c1jUig/k05fbdeXznhQKenWyWYjz8lrVIaJEmtxW/fk4GH7usG3+zrbm4y+e2T0YGc0TEPJgyGXSNghCCobIDs1WqC49gpgZKCJrr4UmDwHKl/lIjBK4AWq6TNXw16Ims5Qd2mHyhOlT40oS0pnieprqGtOhwP91UrTR0cAl5yqoHMTEhPW1G4LcPF9xQ0FjNEG/vv5KFP+pZPDwDzWtkzQir9SndUJWl9oK3XBEyWmtNbvzd2vFHKy56PMuv6Ai/cmCNxldG0WE4VCA4VHCxsVMHhDT2pCe/5i+s03jqGRfhgDVImcpRb7KUVKnnLawZvQSOENibt5vKctf2Z0Kg6NRyWnMUHVnMI+6Nw0xew0bpwGzUzqnlClRVHvLsFmyGoiNCQXrwelJ1RdNYysDH+r6NKU1rco2kLgMed0yYTRUgAZmVxvaOU7YZcopsZE9cQbLF7KBgMxy1uX/NdBpKqDIeICeYjhBgrvQG18a+le4fkFUMV6c1TJrMlyrVKNo8ZCzXCMuvPAkRlzIcSgCD1s+N401kjpVcrMnoGKu4qDJPmuGKemEVyNWZqivv75RGYfJ6TvDGst7DXmYT6YknKDryu41VGBUqJzFJbf7mb2Qwn4b88kgZ7/z5UFPkNwB84ZXLcE7H/HQ3ZxuffEkfHh8z8fND4awiPztUxrd25fC2zR2npmEREUsQIYBnxs2mpd/QPoD/vIqrpKVBBMD3Zk1UmG/giBm8hDUCwf/+svCYJd1MXTHqGwyuaF7StVyBx0dNdMUUbOzUQ3l0bc7bRvwHCWaqCHotdUowkFKkgRzYf6zM0B2X7WrUcvoFTtp4L4OGadWR3tm41rqNeZuj7NoAZInjWvq8VsaQoUiDtIb00gnfMFYoUOvlbKExFuehgLRa22tlsWtoSj3ThcNlBoOCxTGQUCAgjSeNykmE6QWWlkMGjoChuMjbBCyQY7f+aeC3AtlSqBc81jj27ciZHL2x+vFsjoZ2SGorJzU7UiUENkSopLWceDSPf9WVE4GCzaEpxDfcZsJlQNHLApFqFYAppCZ32iui4nCBtZmwfcC4vN+SOkVMgX9yVdo8CYorUhLFhMBkVeaFDhYPmW5VdQj1wMH9eccPSqxRtmUh90qD1nxvzsbKlIpJkyFncaiUNWmhAdQ99YLBDDi8Zxo6IQQ0CgxmVIxVGMZN5mcfYf7/5zG78ogM5tOMp8dNvPFHR1oay+/d2oH3nN95Clq1tKCE4I7XrcIl/7kfL+Ts0Gfv/PkQrlmZRH8q0n9HRMyFSa80cZC0QZFU617IXMCinWsccs2wamVcZGPUX4JthFJpXFFIQ6uu0wV2TdotfZA1A3LKDKfEWkg6VqPBg9foiQ3SsFKOhOetr+HwsM+00divjXvVqVdTVCmwLKX6nnHL+yWiU2iezrjVJIAJ4Rsf/u+5AimvPwmVwvH+rfDmPrlCBhYKtDb0WhmnDqt7AkWgf66Q56LRZuEtxnKyWv+xjEF9KUUjtXfmQFLx/z01jwIVtaHhM+hbJwPXZNnl6E1oqDgyUFOlgKHSFhKJOtyTJcxlklb/TZkbeazi+EYiAWTwK8IZY8YrDBuywPpODXun67IohcoJANEpVCInYalAMZaRso1gMr3DRQdVL496Rwy+xrtx3JWaTCdw2lr1LGgsZ4x6esjnp6yWd0/Ry7UcjFU1XYSkWsEc7wVbTigUQjBhyuIo0uiXUquYQlBtOCmlGVbL2hEZzKcRx0oOXvvfh5uWEAAgoRJ84sq+U9CqpUlCo/jVm9dg49f3hm4sLoCN39iH0fds9JPMR0REtKfVC7BocaTU+uvjeJL1WK70RCY1qVLVFQIm6oYpF8IPlqsZp0WXB4y2+o+bjCPhBQ0GqbVvynLlkrBn4LeJOWuLQtFUSGTSbJ1DuspEQxU0AptzDJeZ32YmZPU0Kb0gbavlAd4Eg0hDcCygf9UV6VktOxxpjYaed6H2ONKDG8qNHXAHZ3SCvAnoKkHVJqgymYqMeGlIZPnv+jjM1Rb1C4lw+Gk9Sg5vaVQ2eiEbCeYENhSZpaXRdh4pz1+bCkhj3nUJCgUXKm2WPNZySddWG1wuMO4VdnG5vMYynhfYbaMNmY9kJAiB9KjWDMjaYYbLbkiDLISsMJltSKNauwpLNkcp4EOqXStB/1zZlvmwq0zqznMmh54goC3uceppgmt684RCkVAJYoqCvM08fXeYgsXRE1NgM4Hnp2z/euRCCthdUZcTZWPyDvLT7wXaGTSmK46AxRj64rIyYFzhIFTq7LkIl62vMdfVhyCRwXyaUHU53vijIxhqk8D/lou7msp4RsxMb0LFE29di03f2Bd6MRZsjnO+thcPvWUNVmcieUtERDsEczFdcSFarHiNlFzf4RPUYwpKwJmAYKzl91pBCKnrhpms/tURetx5x/P34RCexVa1GTTPQHAc1lKOIAAwV2CoTeU6fz/BIZgr/5DmCbXLgIrCfWnA/pwL2wUEd9GYua1cDf9bEQK2TUIFTioWgw4O05ZZDFynvcEcUwkolQUbHAcQjMEggAECSgRMLsAZUG2M4gpAFeqPW62NhDEcLXAczVuwLAatbGOiOvN5y+jUz4E7HxRC4DKB6sJs2jBegRSxGMeCV4SHuzCYG8rcwjwZxWhZoDemQjBvfCkBYzI7i8sEbFtgyLMQ+5IKEopoknUwhvr354HlEAhXhAxuy+Z+gZAgwwUXY1Tqnv3VD+7640QooJKwt7h23buui6mqVzrbs7lLjGO06CKpkab7OSiVUgQBh+JnV5nJi86YlJEE+zNZAi7qN2AxgPr2MZOrMC1mZ3GNIKPXPN8CUybHkOPCUIEY0fxy2O0MY8HmP8uPLLDTACEE3nP3MB4fNVt+rhLgw5d0n+RWnRms7TDw6FvW4ZL/2h/aPlR2sfbf9+Kb1y+PNM0REW04+PDPMV60Zt0vG6ymR4AqZHBco4dpJqnFGCBzKnvvsbRWX96uvVdrqbWCxyhSoKJJvXBj5oUgpoqmjACNcAg4jg1L0xu8w3WKVHp1NUrgMAFD9YpmzGI/coNC8fpT89ZVXI48CGwucFSjfjlro0Vb897/KQVcnaLkFe2o5be2ucAoJW3HFwDKijT6Ka1PcioKcMzLKOA4NmjCQKl1kpN6X3SC4ize4FY0ergXSkoncBQClRLk5+lRVhUZlOd4ZdNrcAiY3IYZM6AEdLtlr8IjALAY9c91VQVGOFoG71UUKTtpnERZC+x/q2s333pXn5ICv/ZAyRGtl4o85nLdz3b/mCpQUaXcYqZrcKZj3QdAV8KGeDvyAEbafFbV5YpN2eUtzw8A0KQBvO5ts/9Q8Dvz2jvihPC5JybxzV3tL/8/flE3us7i8tfHy8X9MXzxVf1N2wWAt985hE8/Nn7yGxURsQSYs8zPM3QJmTk3rtNG3Jmo5Qf2jIyQdpFxFC35R5bBDR+D87rmcSZmM5bnCuf1Pprz0KPannUW9KoRSD025/CNZaC9BrYRh0lpBONSztBKzhek5m3jXBrNtWMEjalKu1UBKrXraYOiN94+S5PR5lUV14hcttfn79lrxOYCplcGea4JoxQqpT+6QqB7KfgolR7XdggR9sSWbA6dAroqM1q0M8Yc1mwsA1jwZMFyZ25nK2wmKyiW7JmN5fm0YbbPTVe0vcfnylyM5dmoTUpbZRY5HiIr7BRz58ESPvLAWNvPu2JKpF1eBN5/QTd+cqCMnxwoNX32Zw+NY2/ewRdfOQAj0jVHRPhkLr4GfI6exOBeXTEKWBzxFl/Ntvl+4/behAKFEORtFgpWyxg0pGU9XoLpyJjgsCwThhGDMoOFIoPPCKgn8YjR1gZSkIQmSzNzXi8hPF51EVuAjro3rkL3gv4IAXoSCmgbT2tCJ7Nqg4HZ+04o0O85blZnNTiTVsvz0O73uhMKNEKgKzIHrhD1ILj5ktQJKq6A4EBiDvsndAKdkJC+uzcu+6hRgrEKg8s54qyKTDoFAlqvhNdGPxJvuXV+1AqR6JQgqco0iwJSp76QcWlFUm/W9Dcy1+seADpiNOR1b0W7e7zGYEbF4cIizWBb0BmjflYY2kaGlVnAxC0ymE8hu6ct/NZPj87oTfiXVw1EJZ0XiR/8+kq89r+P4K7D5abP/v25HJ6dMPHd163CqqiCYkQEACBmaCDzyDTgo1CAtsp5MHdKTKa3crgCEvAiEoWCKItnMCd0qfQs2wJEcBBFlX9mMBxcECiEgHjPZkIxs2sdABSCgqdDTRgKKCEQdP4BkxwAFAVEISBCyMIQGvXb0kiVITR+Tc3yAvhm67umABNewBgvchRdCtKiQl6r39NVwFBVDGY0QADDZtVfvZipbe2oeHGTc/0uA0GFi1B7p20pz1A4ASgBIRwcKsqMwgzoW4ly4sykmjTGFlIDHFcJDIX6Za3HZ9HcB0kbNLRKAXiV/AgBUWY2mOd63QPy/otRwJ3DpDVjSKO10WBnVMGKrAqVAmMVBouLWSecMVWuUORmkXsAQM4BlukqOBdt74t222cicqedIqoux00/OdpUGz7IRb0GbtqQOYmtOrNRKMX/vX4F+tosJz42auKSb+3H/UebDeqIiLORnriCbGz+r4lWwUhBYippSs9Wo7bEbnoVvRopO3xBy/opvXU/KrZomcazlva21W9VXRF6cc/2sge8cr3ezxRsWRI826ZNszFarqfHi2tEalQXyFxX0F1el38EIbRehhxoXX3PdmVJ68MFBxbniyIRmA+yVHIzLgt7uIsuQXmRPLtzalcw57YrYHOZYaPiiqY817NBIK+FYOESxj0ZyCL63HJm6+Ilrai6rYP/Kra8F7rjCvoSypzuH7PhnjsVRAbzKeKWe0ewY2LmYJqvXLs8lLMz4vjpS6j4j+uWt/18ospw3fcP43t7CiexVRERpydTVbaY71qfmErQabR+/cxmr9T0uvOlsfJdkFaGeS2zTtUVWLYIGYpqGtCETlB1ZTnrxrzL84VQwKAURqucX3MgpVM/aHA2gtrroOdT8HCVx9qKaMagoUkR57JQzHiVzRYf2URCl1XyFsp8U+52xCi647TtJOtEUbI5xivM1+y3alfGkH9Suvx/XJO6cFfIwi4dhuKXFPeZx+3SbiK7EBzWeuxzNseUyXA472JyUVKmtKZdNcOFEhnMp4Bvv5DHl5/NzbjP756bxaX9i6GSimjkNWvT+MOLugDIwIDNneHUchYTuOknR/EPTzSX146IOJuoumLOHsj5kDN523y5xxOoE/SuKRRNZYLnS0eMQvCFFTkA0NKzFwz8Ot6hVYjMpWxxMe+gMKCWl3cRvHbBoEHP220x0XL88xZrGyzXDkqkxvd4mMspJACyOoXDZcGURRmbRaAnrmAgqSCmECS94iguFyg5HFVHoGLLPzUj+3hG6niD9uaEkNfHpMlQPo7VkZnIWWzWINj5EmmYTzJ7czbec8/wjPukNYrPvqw5q0PE4nHbVX04ULBx4zkZfL6NYfxH949iynLxySujcxFx9tLKealQL92b8DymjlhwUYZGjsdIqVXG0xTZbrvh5R9XyYwlvhuJed7SVt6+OdHip4LG4kLscNWrrsa4lBSMzUPnutgEAyYbaVXRToi5FzwJUrI4msO1Twx5WxZsOZ1whUDRkkU4mGjOzpHSCRwuYLmQZdrbnJPZCs4YysKuydOR+dzncyUymE8ilsvxlp8enTFXKAB87uX96I7SyJ1QYirF/7l6AJu+sS+kX6QIp1P9349OwmXAp66KjOaIs5NWukEOoDcus1gAgODshLygFkq7CP4OQ0GHIb1oE3NYCiYgISPDUGXFs8YS0yeDlE6R0mTOZi5an5fjJakALapityWtzy9jydQp1qDOxkxntStOoRLSdoKiKjNPCOZTHbGRgt0+n3ANQ6GwXD7jxHW231/oPMFQZRGRaat9oZBTheoF1s6U1WOuRJKMk8gfPzCGJ8ZaFyepcWlfDO/e2nFyGnSWsyar43fP7Qhta/Ws+fTjk/jyjqmT0qaIiKVATaYwYboYq7rzDlBqpCtOsSypNskKumIUPQkFvQllzvl2Z2LKYhitunPWDpdDJbjrZboXi4Gkgq4YRUwlLYPlgihUljCeqvIFGcs17Ws7DAUos7kHAQKyzPHZEmYzbXJ/gtgKl81skDIuJz1N+uI5MJux7HCxKKkWzRZGZVeMoitO0SIhio/lyjLhcVX2L66RBd2vcZX4+uyOmHwmzHTNtiOmEiR1qe0WWBxjGYgM5pPGD/YV8U9PzWx0qRT4xvUrQM+WJ9BpwN+9rB/nZOtp5ARkWdBG/tf/jODOgydrUTAi4vSnYgu/cMbxyjHyFkeV8VC0vCw0QaARApUQJDSK1AJenkEsVxYJmas3vNEIcdjsVczmSlwjIJC5mVM6ndXwdPn80owF6UsoSKrUl5i0wlrAoTM6lTm3j5fjeOXNZMidSloZxroCdBoKehLKogbXLVZRniCEyD5UvDiGuXjHixaH6QpUndYZbmb/URnDkNYo4vM4sZSGs7OYrkxlV7HFgr36LX9n8Q4V0Y6jRQfv+sXQrPv99ZV92NJlnIQWRdRI6RT/99UrQjpNi4kmbw8TwJt/ehTPjM+8QhARcbaQ1Ob/wu+K0ZZp6hhvlhiolITK9CYUirQqvV1nAnFV5pkeKTNMVNisBkb5OLTdBZtjuOwuut5ZpXOzdWdNTTjPCVdSJxhIKliWVNE3T/liQicL8lwKAVQZn3PmjFZV/WpjpRHS0jFzOiGE7EOrdG4qPTETlaojMFx2kbMZLC4n0c4cVq9iKjkpKx1nxpPnNIZxgXfceQxTgbWOVoN+ca+BD7+o++Q1LMLnyuUJ/OllPaFtLgc2dGih4I+izXHDDw5jqOSc5BZGRJx6Gl+QHcb811wLNkfF4XPySlquQK6F2/N0WIFbDHlI3uKoBIyqmCorlLX6re7jmCR0xCisE5T5gJC52brtqtYtJLMHACQ1ioorMFJ1MVxp714lRP5G2ktxF/cmeWWXQ1cIsl6atrlmU8mZi5c5gxJ5bk9XD/lM9MZVZL0Jx2LcC00EpEdzWQ2qrXbNB30BHv4leKqWFp/aNoH7jlVC2wbT4RmxoRB849UroC4wl2bE8fPxF/c2FTTZk3PwG+ekQ9uOlly88UdHUHEYxGKlBYiIWAI0LjFPtRI8zoLLpayBtnjWtcp1XHWlx2mk6mLC9AyjhttuPgbHXDPWpWbwnhMK9MTUtkvq+hwdnozLwg49CYr+hIKURlsWssroFDpd+Ku66opFyaXdyjByvfRrc6GxCyqVJaFnQ1XktbEsqaLDW6EwXYGy40l4ZngMCyH19kVLFtCopWCruhwaIUgoMkXbQgNW52osElrzghKMmy7GTdc3vPviatOqiaES9CZmP3inp/NdllQXVMxnoXAhsDKlYVlSRU9s/gkKjFk8wq2uKErltbBYLGS0IoP5BPLAsQo+8ch4aNv6Dg0Hi+EZ8d+/vB9be2Ins2kRDegKwd2/OdiUQuvRkSpuubgztG3bqImL//MA/vyhsZPYwoiIkw/xll4NlSBxHPmRG1EJmgpRWDOIDYVnaOcdhoq3n+K1TaNkztrmhDZz4FuNlEZD3s9aNbukV0Z7uOy29JpqCkBBWqbia0VGp9AI9b3mrYK7pi2O0erxiVTnm/e4RtAgbOXB80dglv4SAH1e1ciaPMPl7T3Pod8I7JIzOfImR8Hix6VNdRj81YvjcXtQIoPbNGVmb3nNpMx5WSRcr6BHrfuNqyZcCKhk9sC5mELRm1CwJqs15S9PnkADWqEEnYaClE4x2UZATagcl1bt4EKgPyElNT0tAnpbDWVPTEFXrHlAFtrPuVx7jUQG8wlEIcCKVH32FVcJ9ubCy/k3bUjjfRd0Nn414hRwfk8cf9eQPu5IycWBgoMbz0mFtu/O2fjUtkl89vGJk9nEiIiTSm9MakS7DKXJJqK0hZFAWntx41qjQdCsyRWQBvBMnrJagQaFAh0GRVdMQaehzBjMpnoGjaEQrMtobav21fpSa2rwiANxFUmVQmBm41N435uLAqLWpnoDWu/Hef034ypBf1Ianl3xuUkJuBALDjCrGcntdOO1icHKpDpj9oeqK1BwOFQCxAN9nkv1vrlUIlQVOaHpiMkMC4qXSmwmaqsXrYI4kzqZ1RBL6xSWK/Mi98RUdLcw5moIACYTTSnXBAcszjHdsFrjMDkpm01mkDEoOg0FOiXY1KnjvB4DPQkZVLiYE9xGSg7HwaIDncqy542oSn2SW25RldNhQNXTJpdt3tRPrcX9XMv5HXzmKBTIaErLVSa6CIWLmo65qEeLCHHZQBz337QGb/SW9RuXfdZkNHzlmqj89enEH72oG7+7JRva9oN9JazLGriot3kV4E8eGMO/Pzd9spoXEXFKqBUC8fGi2QfiKuIa8cvzZnSK7rh8YQfhDfKlVgYlpQQqJdiQ1ZHUCQy1vYeN1SqxeenhNELaSyQUEjJmkipBSiNNL1PBgaxOsCHJ0R9vniB43Z4Rl4Wf8zXvfJCETrA6Iw0sEjjiXApGqIoMhEwoFAalc9IAO6x5/BeDuEr8GA+Hzy77qNgCDkdIdjKXuLe52H2ad3EKIQt/xdW5e/mDGBTojVNkNAUZTUHHDFr7lGfIz3loA/v1xhUMePfIVHV2b7mm1K8jQyXojst0i2szMsPT2qwGhRD0xBRoRGaWmZiDZKrV/dUXuHc7Yq2vMS4A2xVggWPUxptQzCkXc9W74FkLHz8XUtpkqPK+jqnyus9ZDATyviLepNkRouUqQUqtT56CGMdR4iIymE8gf/bgGK694xB6Ys13rkKA77x2JTpmmJVGnBr+5drluHJZuCz53z0xifef39kyWOlEzuQjIk4lJuMouRwdhoKuuOovPWd06uekPadDR4euYDCtYUOH3lKS0GQgk7CnyFAJ1mZULEsq6DCksdJlKOiOKbNKKKqMo+jwtqWwK7bAWKBISVylSOve7xgU/UklEGxHACKDwoKMVFzvZd18/ISXc1ZXCAYSCnpiChJeO3rjSsiLrBAAHFiW1JqONVtuZULRVB6azlGJqc9R6J1VBRqHsN1Xa9kiKAX6k3N7jzEh5l/0JbB7uwmUoch8u7Uc2QmNhjzTc9EapzQClcjMEEVHZmhoReN4CAEIyAlDvMVErJFaW+bjKOuKyfthWVKu9uiUYnlSxa+fI1eo4ypF3mEoOAxpg4IJEUrR2I6EQtETiN1JKEBaVzCQVNGbUBBXKPriChINK0Qlm4NDIGcy/94mXt90Kg16xZMx+cduGJfa9zKtTg4Bug3Z1y5DriLVvsO5l8qSAwqRFQ4bV30IlfelgMAlfbHQ6od2HPEAUTm5E8R3Xsjjs9tlyeV9+easCrdd1YfLB+JN2yNOPbpC8L3XrcTl/3UAR0tyven161J42+YsBpIKbvzxUQQz3fzpg2O4djCJ6GxGnGkULI6iLfz8vxf1xpBQKVSFYGVKxcG8Aw6ByQqTy6Xe9yhkKrGCzdu+uAfiKhwhMGkymTGAUihU3ljZGEXe5CAgiKtAwQp/l1D5wiRkbhXvOAcsyN8ymYDpcqzt0FGwGMqOAAu4CR0O2I4IBR4JIT1vVSZgsbpUoS+hoOJyZDQFGpVGkKYAWUWBAMN4laEjYDT0JVQZ/Eakocm5NCLdOWg4Gj3SAgIxhcCI0xkD7zIGxXldBl7IWZjy9ktoBJUWhmsrU6Kd1EIlBN0JFZS4iCsU6zo0PDdhz9iHxuX5mEJQaLOvSqXXnRDpweYQSOsU+YB+uTtOMe3lyk5r1Pf2ciFXQDiX8oGkRkG1mSsNci5QZoDrCChE7tdo/JI21fpsJlB1xYzBgxmDQqcEbuBam6m0eA1dlalOcw0e4+vXJLE6rcN0OZ4YrWK8wsA54AiBiTmkDzRUORfRGma3hgL0JTSMVRgqDgcFgUIIsjESlq8I+Vv+eBCZOadgcxBCoKvSmK1VOGQiXO2w5oXWKPHv59lomncHxi+pExiUwOVegCUIyg5HQqVYkVIxWWXIHWdxl8hgPgHsmrJw813t8y6/aUMat14SpZA7nVmW1PCj1w/iqv93AP/rgi58+qV9uOtQCW/92TG8c0sW/7Ez7+97sODgph8fxfdf3TPDESMilj41p5ihEGR1BSvS0kie9l6ktmf4ERAkFIK85/oJGgUZg/oeaI0QDMRVJHSKy/rjeNrLc95pKCjZ0jCikHlzLc59g48CYJBGW+NrNKaSljlwAZltQaNEGl8a9Sr+CRAiX+YCwvd8KQBq8syERkAJwdqMhpji4EjRRTrgZW9V/Y/A84Q1bO+KSbnHGm85vexw7M05bUsnx1WCqit8Y7bgMFhMzFiGuZHlaRUm51CJ7JHpCoCIpsaZgoCi7kVlXPZDncEbWrAYDhcXJitUCEEmRlFxODRK/Ny/gJwgjFYYlDblqDUK6JRiME2hUQJdoZjyVhKmTIa4KovCwJEGdEwlWJVWccQLulepTI8GACvSKvZMNefYDxrAcY2gQ1cwXG4W7c5W8ptQ4gflMSb88a1dxzOR0ZWmCVVcI0jqFA8OVeAKgcPF+QeFapRKj23g5qyw2r1McFGfAZUQ7M/bqDKB0YZ+N956tWslo1OsSKuwXIHnJ210xSimTA6LCXkt154RBOiJK5ioMvTGFATs7rq8w/u/PxFq9CRD3h9Fu+6pDmW79K6n7pgCnUrd9PEIYKO15EWCC4H/9T/DeGbcxMZOHe9vE8h3XpeOb7x6RaRbXgJc1BfD87+9Hp95WT9u313A6390BBVX4D925nHNqkRo3/uOVfDhh6aOu+JZRMTpTM3DWXUFKAVWpzUMpjVcNhDHtMWaDLCa7KKmcIhr0nBopRAOPhJ1SrA6o2Fzt46NXbr32/Vj1uQXSU+nGKSdsQwABwoO9ucdjJQZDhYdFAN62t64ApfLl2JCJdjUpaMnQdEdp8jqCkxXenTXZnSc2200ZSVopNaKRudxLRBc9SoYqoQgrVGc291ctIoSaYAkdeIb55xL71y6TcCcShFeZte8EsOagpVpDT0JmRKv1RuoymTmBuYtexsqgcVE29zX41W3KShvLoF8QH25PqlQ9MZUDKY1ZLx8yZoC9HrBa+3yfde+H1NllpHgMHOv+mQwVSulBGuymh/AWNu/dk1mAkF+SU0WFgnq32vX1VwLnlBaLyudCUgaYgrF2oyOrEH9lcqaSiDbcOzasDem3+uJK4h711/wGymdtkzVV/OU10pOJ3TpfS05zXmOh8sudk5aeG7SwrqsjoRKQSGv11nLehO5X8kWcLmn4Q9oWIK/lTXqgiLF013XqnrWPlmX1bAuq/kTjKDH3/C8yBQEg2m15XWnUuBwUU5wp00OQyFYm9VwbreBS/rnn5ks8jAvEv/70Ql86Zlp/MfOHP7x6gH85EBzGeVOg+Lu31yDWKR5XTKsSmsYr7j4/XuGQkE59xypYGOHht2BrCdf21VCzzoVH98iULAYMgso7BARcboR1BrXHl2cCzw9buKKgQTylvTmrUlrcDmQMYRfUrrRg5aaIePB46NV7A/cT/1JBRf2JJA1FMDTiQLS+B4uu74BI3MmUwiIlhH5rUiqBLYr/AIWBEBPXEW3QfBinWEyrmPSITCZDE4iAPoDwVBZve7NrA8UQhOGmqFcdji64zLDxoq0inVZHU+N1b2ZNftGVwiuXB7HgbyNiivHMKtTdMQUrDE0VB0BSoBJy0VHTJYOzjVkrO1JKOhPqJiqMhgqQVdMQW9cgeVKrbdC5GSgOy4zKwxV3BmXwpMqgapQnNdjwA54+OseaBEyiGROaQ3DZQcmkxlN/H56EpR6msK68UOoXJ2IqwQK5DgvS6qIKRSTpuxLMA3Y5i4dnZ6nnhJgvMqatNe657Gu0ZdQQEFgc2k4qlS2KWcy2EzAcgViVKA7RhGbIeFvQiXggmJdVgutegDSoHOE8D3ahkJ9aUmQgwUHRae+UafSC725S8eBggOXC9hMHuOCHgPPT9pIGxRFW57Drd3S+6srckLTEaOwmcDypIqywzFUCnuDOwwFCY37+bzjKlDx0lsE21EjmMFlXVbDE2PyWlcVgjhBWx16d0xBSqU45l1X3d7ErbZK4rcnRusTPgLYjIdKs+sKYND6Ck5fXMVw2UXWoFAUuTKS1GThGYUQZHU5+Uiq9dUI6klEamNPCEHJYRivMDhCIKnO3/yNDOZF4PbdeT/fctUVeM89w037KAS4+zdXY6BNSqOI05fehIrv3bAKb/jREVgB8fLunIOESkLVuv5uv4bHzFEUHIJ7b1odGc0RS56emIKYJj0/jcqDlyyP4ys7ckioBGWXY7zKsC6j+QazoRKpH+byJWcxAYdIT23Qx7kmrYbuI0Cm4QpCQJDUKDoMiorLYXoiSAKCtEa8QD0BXSGw25TTrW1VCAHzlmtrbR1MS+/o1jTHL2xAgdRp1750fq+BHeNSTK3QugHYdPCGfzoMvqGS0qS3OugzaVxtJJ7lHVelDnQgId8ZMQW4sDeG8SprmWVEV6XERfceOSvTGlYmVfQlFEyaLkYCacoqDp9T/lpCpOF5XpeBgwUHCpE5DVYkVSQ0ij05K7Tvlg4dKV0FIDBV5TivS5VxIF57u+MKyg5vWgUQHDC9TvXEFWzu0rEyreNXR8sApDfd5QzMM3b7E+H3aFKjSGgE4xWGjEFRdaWntTNG4QqBrpiCwbSOpEowaWmoOhyKdz37QWSQ+cFbFRCjRE6Api2GhEbQl1Dw1s1Z3HWojJ8dLMJh0ujf0KFBVygOFhzYTKDicgwkVcRVgtFya/FFTCXojSugVGrcRyoubJeg5mzui6s4ojsApJTJdAVsDjw9YeLX16UwWSW4dlUK20ZlgbSYQqCr9XRvKoVX1VCVmnBIY3PYM6pjCkEpcB0rVBq+Y2WG/mRNsuIibzNwEHBB4HDm3Y/E189rivQuJzQKlRA4EFBA0BmXwbW7p205xlT+Zn9CyjZWp1XkbY6JitT8A1KP3BdIZVgrJERAkPZmRpQC/Z6kZrjs+qtFKgX6Eyp0hWCs4obkLB26AgFZuGa+sadAZDAvCkdLs+uHbr9hJS7pi8LCliqvXpPCz944iF//4WGUA3daxdMW1p43AgT3DcmXyI0/PoqfvnGwpb4xImIpkNAJOgwF3XGlRUYG+dI7r9vAoYIDQpo1hllPV5izGapO3Ti9YlkMw2UGh0tjJq7VA/5qnJPVfY/0uqyO/oQCQgiGyw42dOjQqePrUQEZPEcI0B2jGK8y37OkURnIV2NFWkXWUEABPDNuouRK/azFBDZmFHRowEVpDUerFFmDYseEvJ/TgWh+Cqn1bQy4z8Zk4FnB4khqBJpCfI1ojsgwewABAABJREFUf1LBxk4DGYPi1atTuOuQNAZ9vab3984YheESxALfrRFXCbriih/U1RGjyJkcmiK1roA0ytdlKbIxBed2GaAE2Je30RVTfI/d4YKDvD17JgWFEMRVmXFiXUbDkZL0sNbGostQfUNFoQAlFGsyGgQEVOIiphCsSqlwhcDajAZB5AQqZzEMZjTsmDAxUpJ9yRoUGZ36WUxMl2NlWsOEyaARgj7POErqFFt7DOyasuFygVUZDRTAoYKDvqSCrEb960xXCOIKxaqUivO6pORFCIHnp23YrmiqrNho0vYk5FinvSqMpisnZKtSGjZ1yknEE2MqxqouUp40JOkVx9EIQWdMkYFvgVvH4tyf8MRVgjVZFWszOqYteR1e0BPDSNmFgMzosjqjYdxk2DlhIe+da4txuIygZHO8/pw0ChYDg8DhgoOcxWTe9BjBtMVguUJOIAmwJq3hot4YdkxaMFQbDhNIaRSMc9SmPjGV+pPZC3sM3FV2QUFgufWiPFevSGBlWkPO4ijYZe9aabOCJAQyuszGwYWQQX6QMpiVKQpDoXh+ykJvUgUTAiVHyFWQQAax2SSsQWlV7RzI79X3KXuZdAjkpHUhb+XIYD5OTJfjyoE4vvPaFXjrz461nPl/8ZUDuHF95uQ3LmJReeWqJO6+cTV+7Y7Dfv5XoHUZTwC450gZ/9+vRvF/XjFwchoYEbHIrExqiGtK05I4IGUZNW9cKPI/sI8jBDgXiCtSg2szjphKccWyBBRC8PhoVR5LAGuzOp6btMA5kNApumMKFO/4K9OqbwCrlKA3rmKswvwAv7VZDZNViimTIalRcAB5SxrNGV1B2fW8mkL4esmL+2KIqxTjXhW9pEbxxrUx2ENSN7xSlUF5aZ2GXsgVVxo8Un5CoSsEVSakcZPWMJBU8chwFXEujQtKZMGUjZ0GkhrFK1YmAQjcvrsA1wu6KrkcLhdIqhS9cZlJ44qBOB44Vgmfj5Qakh7EFYreTgUlu+6dpSCgFF7mEbkz49LgrxnnKiUoByQlGgUYk0ZPRqOoMHg5bIm/rN4dV7C128D9xyrYPW1DU4CBhBoaGwB4YdrGkaIDmwtc3BcDID2o7zy3A4+NVPA/RyryHHFgQ4eBKbMK2xUhr/G+nI3+pEyjdqhAMeKtJixLqUhrFBldwUV9MdhM4Mb1afzySAVlh+OCXgOHii5SFVeOCZFSFA741RJHKwwZnWKkoVoGAbDcEFiVVqF6y/UaJeBCvudr1P72P0fK2DFhYnlSxeq0hv2BbFgEwEBSasU3dOh4atzEQErBSImFsnyoVE46OmMqyo4DSoBzuwx0GQpcIWUplBAMpjTszckxX5vR/RiA2iQpYyi4tD+OrK5guOxgX85Bp0FhMR66H8/p0JHQKC7ujSFnuqi4AnmLNyyOyH/pat1QrTZoSpIaxfKkBkNhiCt141RXCFwu0BWTkwdCpJQpb3KohCBl1FMuntOhYyCpYsp00Z9QsG3MRN7i6DTkPdDo6V+WVEMBl+d3G3Moad4cFAxIyUdkMJ9kSjbHG350GPcfreDC3lhLY/nPL+vB+y/sOvmNizghXLEsgT95URc+/sjcKvxt6tBPcIsiIk4clBKsSmvIWQxWw4tnVVqDSmUQzd6cXG7tiishb1DR5rBcgU2dGoqOgBCysIni6S+7YgqmTOkFS2nSO+lyGekelD+ty+g4WnZRcTgu7o3hULFunGiKNGzShkxjl9YpVqSkJ+3ZKQsuE9g5ZcF0GVRKQlUBB9Mq+jyD8OUrE+jTXBwFsKlDwzSX925SI8joim80HA1mJCDSaKy93FO6NCRet07FUa+Nq9JaKHVXzftV01oKCBQtjuC7f3lSxevPyaDoCOzwMof0xBUsT8kgS4Fa/l/phUzp3Nc8A0DWUEKTGI72le8MT/ZBBIOocizv0XFhXwJ5i2N/3oHhtX1jhw5XyOweCpHL6jW/wcZOHYYqNcM2k+nfpkyGnrjMSnKhV/Sp6gocC5y7zpiCNWkt5IAIDC0AOZEaKcuJFSHAmqyGvMVkaj4COEzgpSvicLwX8LldFGNlF6vSBDsmLLhc5uGmkCnv+hKKP9mKKwomLZmOTVcoUqos+xw8X1aDvMcKGI+mKzyZAfEbrCkEA0kN6zp0dMcUaJRgS5eBPdM2lBSByTmSirxWG9PWaVRmY1me0kLbt3Tp6E8qyFkMz0/a/lhs7Ky/X7piCgYzGjg4kjr1r8l6YB38SUlNokEtacADaMqdDtQDCp2AcZM1aFP2iuDfZeaKevBhWlNQtgW64nJVp8NQsDaroT+h4rL+OIbKDjQqteq7uYPOmIKkRtGbUDFecUGIfK4IIdCbkMG3KY3i2tUp/M/hspfppk7t9u6JK7C4QF9CgcUEqg5HlQn0xGRBGqtRWD4HIoN5gUybDK/978N4ZETeyNvHmlPS/N55HfjkS3pPdtMiTjB/cUUf1nXouPnnQ2gjlfT5w/tGYHOBWy7uijKjRCw5VqYUaTDbzfrLWjzGiwfiXpCRaJn+C5CZGnq9wLc12fpLvnZLpHQKjcoiQK30x2mD4tf7U9iXc7A7Z2HnpIWjJRcVl9cLg/gv7LrG4eYtHdg5ZWLaYnCFwPraBNbbJa0ruLhPx7KkipVpDeWyXF5ellSxPlmT0Mn/75luSAbtHYYQeJ4yVk8zRwnevjmLh4erLcejFTXjJK5SrMnoSGoUv7+1A4+PmjhYsHFet4H1HTouG4g35ZNdldJhMuF7e/viSqj4ytqMhuddHlolMLyAMQppRKogmLaB1SkFnTEVnTHZr4NFB6vSGi7slR49lwtkDQVFh+NooW789sal/jahyUnRQEIFgTTk1mW14LDXx48AgxkNw2UXGzvkCkONsSrHyjRFygtSLDocK5Ia4goN7bcmrePSQE0DAoKeuILze2KYrDKMVVjod5clVTw3aeFo2YVOCeKKLLQRVwjyuebz0h1TMOkZbkIAqzNhJ0jwaqVEymiWeQVsqGfUL0uqGPK8ozFKveIi8jv78w6YkPs1elWJZ4fXZAydhoot3QRFm6M7Vs+UAUjZ0rqsjoGEAsYJnh43UXIEKpShIxYuUALICVBN9wwAcSqgKHKCV9u3dn9u7Y5hz7Q81zFFSkEcLvzVGX8sPFlUcFBqf+2JKeiLK1iR1kLtFkJKOV6+IomJahGdhoKsoeAly+LYNW37cUKTFSYLmBj19j9wrIKyI1c/Jk2Zpac3rkBTCNK6AsNbRVFVAgVA2WVI61KqlDPnn4ovMpjnScGVpT0PVC08Od5sJNe48Zw0/vXaZZGRdIby9s0duKgnhl/7/iEMtwnmAORS8633j2L7mIkvX7MMiRmyBEREnAg+/elP44477sCuXbsQj8fxkpe8BLfddhs2bdo063ezXh3ZVk+xmheOeIE+a7I6xqpVQHhePO9lldWlJrImr3hRfwxrMzoeG6n6x12eVEFAsK5Dx4G8DcsVoQqaQkhj5PkpC5NVF5TIwDWHCcS8JeGULlPMZQ2KrKFAJQSdBkWHVyksrtaj7mvEVILL5llAKqVTlAIyhDUZHa9dm4IA/GqAq9IqVmd0KJQ0ySpqXL0qgfuOVEBAoKkyoG8goUophVIf28sG4qE2XrksgQN5B3tz9SIhAsB53QamLQ6DykCr87sNPOE5clamNKzJ1FcCdk/bSOsU3GKwuTSCa8N9qMRQ4iYMhWBjp4HNnQauWiHTaKZ1YEuXgd3TNjKMY7wijY6ERjCQlF71C7oNPxPUb65PQ1OI/x5snAoRr22bOg1s6gobzBs6dJzfY+BBi6EnruC8bgPrsnqTkTZlMVDUKw8KUR8/o0X8yIsHElApwRNjJsyAfp22EdfFFILVWQ162cWypIq0Xjc+N3TqOFhw0J9QMWG6MBSCsitgMReCAJYrcGl/HISE07+JBm16wWY4LzBuazIarlweTl36o/1FlGyOzkDluxolm8PmAlwIpDQFN23I4DVrUrh9dx7PTVohL3+NrpiCq1YkcPehMohQMGp7E0DAl3zUmhnXKFZlVBwpyLGnhHh5swUszr2Jo5SvBJztWJOVqyujZW8sNRoyloPEVYprBxPQFYqNHTpesjyBtVn5nIipAppC4HizjMGMFrqWumMykHa0zBBTKRwmmjzP5/fGcCEBbtqQhelyJBQCsNb3Zjsig3kejFRcvPeZGPoPjuEXN63F/76yF3/ywFjTfjetT+Pbr13ZNndlxJnBeT0x7Prt9XjvPcP49u529aok/7krj8eGK/jWa1fi0v4o+DPi5HHffffhAx/4AC677DK4rouPfexjuO6667Bz504kk8kZv1t1OCZMd1a9X0KVWTTWZXXsz9noMBSszmgYq7iwOTxvm8w6cUFPDH0JFSmN4kDBxqYuw8+122Uo6OqLY+eUib5AGrexqotNMFB2pN5y2stBFQy6W5HSIASwsdNAlxcw9OyU5QeAxRQS0mKvzmjzCsj1PWWescSEwMYOHavSGrb2zD+n67vP68CUyTBlMqzr0ELSjka9eJDehIoOQ8HmLsM/L7oil/K7YwouH4hjIKn6y9plh2N5UsVPDpRkEQzPCtIoQU9c9ccySNHmcL3J/XkN+aE3dxnI6BSEELx8RRIqqa8anJPVccSTXPQmFOiNxlGbTCJlh+PBYxWUXI6izbEqraIrRrEiqeHlKxLo8q4fQDohtnQZ4JCGJ+MCHTEFv74u7R+3ZizFVZk9gwIAATZ1GlieUvHsJMU5HTr2Ttt+YKXDOIaqBFOKA13hUCnB6rT0jGd1BRf3xkCIzLywytte09IPpjUMlRxwAVQYh0opNnbIcetPqhituFiZ1vD8pIXehILRsovepIqyF0hYtDnSuoI3b0gjocmgx0ZePBDHPYfLLcfvibEqjgWSD7xyVRIDSRW/NpiCxaSMImioZgwKQyG4oT+JI0UXe6c4eFXAbnDo1K4VIaRePhuTcoyaI7zKRL2YEJXt6Utq6PR+T8YTCBwtkbap6F6+sv4Mkh7q+j15ToeOuEpQcjhWpjQ8OFRFTAFeszblF52p0epOTnrFidZlNfTGZYBybWWs4rhw5mcvRwbzXNmXs3HtD0dxsEKxt2Lh5bcfxI6JZg/zu8/rwL9es8z3pkSc2WQMBf/12pW4+dwS3nnnUUzMUPFpT97BZf91AFcui+Hu31iNRIuHYkTEYnPnnXeG/v21r30NfX192L59O17+8pfP+N1zOnS4gmBNWsejI1WpHQWa0mNu6TJwrORiIKFif87Gud3SCDYUGxW3Xu0vpVM/1qM/KbMDNHJpfwybujS8MFVf7q+lVtvYKQOoanTHVN+4rD1xg0/exqfwed0xxBSCrEHxkgYP3lzRKcFyr/99XpaQdgwk1FAFv8FMXZvan9TwmZf1w2YCdx0qhbLvzNoGhaBLaf38OKcjrGutTR5qRmRjcaWsTqHRcMbsi/piyNsyiG5LV7h/SY1iQ2frPr94II6MTsGEwLldzfs8Oxl+Z05VGdAp/+4KgZRKkVIpXCalFYQA53QYeH7a9tt94/o0Hhmp+mN66QwFKAyVYEVSRTamYl1Ww8ZO3Z8k9cQVHCwQuEyWiWYgWB4TyKZV6JraVD3v0oE4XrYiif/alQ9trxXzqF3XVUcgochgTkoIDhcc6ApBh0HRk1Aw5eUtz+oUaY1irMqgkJpOOaxdDtKXaG+uNfrmapOAjpgMnAwykFTxylV1I3VzpwHuOohVBY4FDrQyrSGhUmzu0vGUl06REoKUVs+iEZzYcQ64XKBoMzkZ9WwgCpn5I29zVOyZdcOtVuRreu6NnUbIuAYQTnPZ8NVzuw0MJFQ8O2kho8uCOJsD12QU9HeCuPtwCW/56TFMBWq5P95Cs/z+CzrxhVcORJ7ls5Dr16Sw860r8Gd378PXj+oz5nh8eNhEx5dewJdeNYB3be2MZDsRJ5V8Xr7wu7pmD0a2bRs2B0YKDGsSFGNUZpnoNThMy/I1vwkA52cFhisMK+JADC5My0WuYqMUeKlW4wKVioKyV5zCtMK6YEMhWK6rsBSBo8TBpMnRG6foUxWUyy42JATyCY59KkdvDHA5h8s5bJfAcgRsl6FoAnHilckVFKbNYbvS+Nk3WYahEvTFFJT7mpeGq9Vq6P9BShUHWZVj3JsUaxTYmtUxGHP9cWjFlrTAs5MO4irBujhtua9r2zAbjImZjtk4bnP5Xu07Dhdwef1dFlOAS7sppisupqcB5rqgzMGqGMGaGEO10t4NV3ZksGJtKT+hEpyT8DKnWFW4Dc20bNs/F43tspz6eYopRF5fFYJkXMEV3QRDZYaeGEW34uAlPQT7Cy6yOsUyzUG53PqY61MEBwocfTrHK/sV9CeAUqXif74pDRRt4LI+HfumOX4+DQjG4BKCii3zIdfa41gWyuXmsV+XlNtqbT+/U8HBoouyaUKjFOd3a9gxKSd/g3GgW1OxOmXgriMmbA70GcCWDg0bkmLGcx7sVw3HVFC2CfZOhL+37aiDa1fFUTVZ03cs1UXwZ0zLQtF0UGQEA0mgMw70xQUu7gSEXcW6uMCYwbBb4cimgHGTwSEcpkVguy5SmkDey9Jiuy5sh8OyAN6QczEBYNpxYQb095WqQFlzsFBe2qfgu/tkOHKvTnDpah2rUipWplQsSxB0GAJrvFWUgQSFY1ZQ+zXL5ZivyyoymGeAC4GPPTiG2x6fbJGYJMynXtKHj17WHRk/ZzEJleK9q1388VVr8Q/PlvGVZ3Not7LqCOD37hnBJx6dwL9eswyvXZtuvWNExCIihMCtt96Kq666Clu3bp11/5GRYZguR68uMGp7hhCAWmmm5xtSc3UCYDkFQzn578kygU6AnGd4jDsck4JhyntMDo2HX1k6AZ73HBPLBNArANUG9gQUT9NVgkSJwi5RWAyYsglSjkCiKjBtEgxVBFxD3njjRN5r0wX58p72jjFgCDzP2wf9HDx4sGnb/0woUBnATQJXAJ2GgDrO8ISnyhMCSCpAK4nmBu//x0rAsRa/N12gGLHq746MKvB8i8wRNRrHLUjjOfGPWSXYVZIe/qRD0anJDA/disDkWB5cAOsTAFgOg5VJKAR4/vm2PwMA+HlDO85NcayKt39bjo8qmK6EB2jIcxXbHJguyWzf6xIcQ0PAnjLDpOcw1wEUvD+ANMAcALuGmn8nOD4ZAP2EY+qQwBQAkwFDU15QG4CEIpBSOTYx4EldxVhR/kK5SjDtuY3PSXAcPCiQmeJNY9+jC/TrAryoIKMCnAkYFkFulKPPACyLY2gy/J1exnAxpXiiSKEQoEPlmDzI/fuiHckKwZ6yHL8VMYG93jXS2KYpCqwoMZRdYGg6/JmjCzyfC5SEtwjyeQUAgVspootyrFc5OqcFns/JfQwXSFUUMAEQk6AKIKkyZEwF3KKwbIKUAlSLFfAyMGRytArXGbMIiFG/PvYVOKwZrpfZyArgYkowbFEsowJbOYdRAlCSz6hgCbmDDd9lRMHm9evm9XuRwdyGQ3kbr/nBYTw/Zc+4X0wh+Np1y/Fbm7InqWURpzsrUiq+eM1y/PmLe/G32ybwLzumoVMZDNLI0ZKLG35wBJs7dfzDKwZw3WAymnRFnDA++MEP4plnnsEDDzwwp/0HBpZhWVLFuZ0afna42eu6ZX2zBnqqw8QxLxDWLbpYkVRwtOzC5gI3bUhhbUCWsEMLe8YSKsGWNTNLJWjOQWHCwpFR+Wwu5R10ZjWc26vDnqjn7z23SwMB8NyUg3HdRjWQfeOcTg1btjRPUqvVKg4ePIg1a9YgHg/HGrxwoAybAasD20oJBaOBzCCvWBHz09TNBzZpg07XPW3nd2vY0tk+JWXjuNVYl1Gxpa+1XGKTEPj/2XvvMEuO6u7/W9Xppsl5c9TuapWzQAFJSAgFwGSZIAwvRhjJCGxjDLzGUQT7tV8y5mfQS5YASSAsBBJCESQUV3Fz3p2cbu5UVb8/qrtv6jtzZ3Zndna3P88zWs2dvt3V1aFOnTrne5aMO5iwORJjdhCq4ePYNkZGR9DV2YXzTmxrqN3V7VjeqWNda/2wglVKDuMjlWPqoh4DJ7ZraDcI7thZRFKTy/7NOsGrlsZn9T6sbtfqdg0b2mV/CiFwkpDL+eWrwcViEWcV9uGxYhtUVYWalBKFFMAJPQaWNSnY0BOr2LfLBXoTCl7VG8PQzrwst00IFgF4w4pEEK6xbUdlezauTuBkQvCOGZ7XBgCjpqx42B2nQd9Un2+TRrBheQIWE9i1u3KFYGlKwYbeUhjLBgDDsQm8dGAUq3s7kIgZOHdJrKJwCACc7fWdV7QTKiV4tcXRuiWDhw6aWNqkQqMUK5tUtBqkomS6TwfjFZ9fWNZHs+VEyFLjIyaHAzmhX96komua59B2OepXUQgnMpirsJnAl54bw98/PlJTmrWaJSkV//PGZTXZpxERgIy9+vIlffiH87uhEeD/PDuGf35yNFSve8uEjSvv2ofFSRWfPrcT7z+xFUadbOKIiNlw00034e6778YjjzyCJUuWNPSdq9a0o81LrrnaqE06CksafM3KBF4aM8EFcNVaFTot1Qhs8oph+Jy1WMFLo6Ul4zN6Ykgm68cEA0DCshAzCHRVDnZJXUBXVcRjBk7v1bG8WUVfUsOJHQaeGzYRMyg2dKnBcSgB1nUmp0x4jMfjNX+PGQy0akzQDQUxVjKYk4kEklPEmtbjBCWGrhYGzdMAbtbplIo6FyxT8fRQZVhgQqM4a0ky0HkO49yU/HfCrYzDXdasYcdoDgTABUubpk0G9Tmxm1QU7NjQ01RXBQEAlrRyvDBRaaTEDAPnLGlG2uJY3la6N1IxBalUqqF2VBMzKlcPWlPT31cAcFITh9USh27E8KxXVKfFUBAzDMRjGpLJBNZ3Eezx5PSeGirCIhS5AYaXJjlO6Ywh4al1NKeSgVpHeXsIAZpmeV4AEHZpqs83blAkk0kkhEDMqFxxWN5e2xevXcGwyBrECatakEzIJEutgYTYZBL4q3OSsJ4cxYQpy5IvaTFAKEIqgwJrvTLYeYfj5M4YOlqmvyaN8ML+NFAWYKEZKlZ0Tj3x1lyGYj43o+NEBrOHEAK/3pPDxx4ZwtaJqb3KAHDViiR+9PolaDGixK2IqfE9Of9wfjc+eno7rrxrH54ftWoE8QHgYN7FX/xuEB97aBBvXtuEf7+wt0bEPiJiJgghcNNNN+Guu+7CQw89hJUrVzb83RZj5pM2XSE4o7sxJZi1rToO5lxMmAxdCQUrW2Ze6Kc7rkClMg3plK4YzimTYPMnp826gpM6DWQ9Wa5GjIFqTu2K4anBkpf9tO4YNlXlsowW2ZTJWfVoiyloizU+lqxu1VF0BcZNhhXNGtpiChIqbfi81rfr2OKtnsZUglf1xbGxiWOLxbC8qfH2b+yIYdLiyDkcGzuMKY1lQPbhr3aXjJQVLVogO1ftSJ79Qr1M+HrFk6hTqJT+a5TLlsTx1Hjp9xXNle/fkzoNjJoMOZsjqVG0x+Q59yZV5F2OlE6xOKVWXItXLYrjj4NFCIEZyxjOBv8qEEIq5NgAqSZTTUdMQacuq/IlkzO7f2MqxQ2ntOHhAwUQCJzdm8DLY1aNrBsgVUq6ZvF8zJRG7p0o6W8WcCHwy105/OuTI3hqqL6uso9BBL58UQc+eFpPtHQeMWPaYir+eN0qOEzge5snccMDA6FxzhYHfrw1ix9vzWJls4p/PK8L71jXOiMZrIgIAPjIRz6CH/3oR/jFL36BpqYmDA4OAgBaWlpqwg6morqowuEgplJcsTwJhwnoZXq9U7GuTceaVh3Ms4YJkYbxG1c3IVWlPHN2bxxn9cgSyvftyyNnS7mwk2chA7eiWcP+rIPBvIvuhILVLTpsJgLDjBAEBTrmGkoITjmElc1TOmPQqCwIsb5Nl4YVJZjp6nhKp3jdisa9pX1JFb1JWeCkWZclkH1jrvrQ1VrFM+HEdgMOl3JtG9qNGU2Q+pIqztE1bJuw0BFXA4+9/2+TruCqFSnYXEDsEIFixMoWDRvaDTTpSkXYESCLnfiG6lw8R9WUP0drWnRsHpf36NImbU5qASxKaXjnuubg2F1xBffsrvTebuwwaoqnHC7O6Y3jybLJ7Cmd03uuI4N5BuxJ2/je5jS+9dJEhX7hVFzcZ+CTSydw4QlNkbEccUhoCsEHTmrD5ctS+PvHh/GjrWnUy/HZnXHx3vsG8MEHBnHVihT++swOnN8nDZ3oPoyYjm984xsAgNe85jUVn99666143/ve1/B+qsfZ1ll4n8OgROojNwoh0rArD+2gBHUNAeLt/5qVKUxaXJZEnkW4k0oJLlmaBBciKNSwod1AweHI2Bzr2/Wg8MRCR6FkVtrRhwohBK9dlqwI4/Cl9g6nh1lTyJR693vSNiwmgmNWe6BXt+p474mteMKr1EgJsL4splyhBHFKcOHiJH7fX4AQQF9Sw+lTrKzMpaG8skXD7rI+LZ+4ndYdQ1dCAePAkhmsHsyU8rGo2VDwthOasScj1WHCvNqHk5UtGooux5i34hKmY13DLC7HMW8wO0wgbTMczLnYPG7hj4NF/GZvbtpkvnJ6Egq+c/kiXNxNsXnzxPRfiIhokGXNGv7f6xbja5f24T+fHcM/PjFSV1nDYgJ37czirp1ZNGsERSbQGVOwod3Axs4YvnRxtOoRUcuheOqm3O+c7HXuIITMKOyhHuWJYrpCaiqyRUzNKV0x7M+5cJiAQoGTPB1rUmXBzOX9tWXCxkSZTGxvQq2RGFvZossiOSbDyhY9NKdkaZOGq1akUHRFRaGd+eakjhgG8y6KrkBbTK5+lDPXBmsYKiWlUvRzDCXzMwE8pg3mvMOR+tqWWX/fUIBPnd2FT57dCV0h02okRkTMlqRG8bdndeL5ERN37shOm7ub8YSeBwoMA4UCfneggPv25nBqp4ElTRrWtcklwMVJDROmC4USnNMbP2o8YBELj/k0aGZDNFU8OoirFFetSGGo4KIzrgTewIRGsLZNR87mIARQCEHO5kHJ68NJubEMSG3qMHN3VYsOTCOA1WwoaD48uWuzJqVTXL2yCXmHo9mgUS2IBohCMiC9KUVXYLTIkLVlBZ1pxC5q0Chw46nt+Ox5XVFSX8S8oSkEP71mKYYLLn65K4v/fmkCGZtjb8YJlaSrZuuEPW3CakwhWNOqYUN7DD0JBd0JFS0Kgz2mID1kYkW7FpTVjYgoJ6ERXLUy5YUjSIMmImI2JDRak+BJCcGkxTBSJtO3oV1HCnP/LjoW7mVNIWitU/0xopbZrMYe1QbzIwfy2JNxYHMBhwlMWi4+/YfRwPOh0ZkZyzGF4M82tuBfX9VzWJbuIiJmQ3dCxQdOasMHTpJaqFwI/HZvHl/aNI6HDuRRdMWsvXsmE3hpzMZLY9WGtQFsHgIwBABIagQ9CRU9CRWfPbcLq1t1JDWCpEqR1KQ02CMH8nh62ITDBBwucOWKVGjc4LYJCyfUKaUbcfRACVnQDoQoHCmiUU7tiuF5r8R6e0xBa0xBtIAcMR1zbjC/OGriQNaBpnhZuJ7OpGvZ2JcnIBMOmm0LKiVQCIFKZewLJUBnPLx59+zO4qVRC995eRLbJut71KYolFTBmd0G/vK0Drz9hOZoyTpiwUEJwRUrUrhiRQpCCOyYtLFlwsYXnx7F4wPFGa+gNELeEdiVdrAr7eDau/c19Cz94xMj0BWZCHVqVwxJjeLvzurAnoyDVp3CFVLTNKlR/KG/gO1lz67LBbZNWHCFrJimENSNSXvHCc2hnz85WMTWCZkNLoT0gr5nQ+uMzz3i6GBVixYkjy1rjqQXj0XmahJ0YoeBJp3CdPmMJOcijm/m3GD+yqZx/H8vTdb5axx4LqSupYfzlxtCM0u/8/Ik7tyRPaR2LUmpeOe6FrzzhGacOUU2bUTEQoIQgrVtBta2Gbh2VRMYF3h2uIifbc/i13tzeKGsCMThotGJpysA1xV4ZtjEM54+rc04/uvFyWCb/35tHz5wUhtufWUS/133vTA1V69MIeyJ/cGWNL6yqSSgqiskMpiPYc7pjaMzrkJg/uTcIuaWOcpPDWVpU3TPRMyMOTeYnbCyZg1iugIpvdZgbpvFsqChECxNqXj7Cc24bl0LNnYY0RJexFGPQgnO7k3g7N4EvnBhDwoOxzNDRfx2fx6PHCzgpTELo0U2/Y7mCLXqGTuU98FM4fM5+kbMO4QQrJ6nLPyIiIiIOTeY3ZmV6q7AZDw04L99ivjihErQHVexvFnDCW06zu2N442rU+iMR7PJiGOfhEZx4ZIkLlxSqp9quRy7Mg62T9jYk7FxMOdgd8bF/qyDMdNFzubI2w4KjNaVtJst1RFOjXqrDwfzaJtHREQcBqon99GkN2IhMecG8xndMWRtDoeL4MflApbLkM2b0AwDglC4XMAVAOMCrhBgvL436rKlSegKQXtMQVdcwdKUhqVNGpak1FCtxIiI4xlDpdjQbmBDe3jiXT6fx+bNm7FhwwbE4gnszzrYPmnjzO4YEhoFC55L+e/BnIu33rMfpsthMoE2Q8HXL+1D3uEoeDNkLoCiK7Av66ArrkD3chiSmvQ4X7Q4UZGZLoTAvXtyoARB1bFLloTr28YUijDdvfN74yie1ApASgZF0koREUc3bB4n2BER0zHnBvPHzujAx87oqPm8NEivRDKZDPlmfV63IjWjcpwRERGNoVCCFS06VrTUX+ruSqjY8WdrG97nP7+qu+az92xoPaT44rxT+9l161tw3fppRFMjIiKOGljkYY5YQETu2IiIiIiIiIgFRxRWFbGQiAzmiIiIiIiIiAWHElkoEQuIo7pwScSxiaqqePvb347NmzdDVaNbNCIiIuJ4YF27jq3jUp/dUGXxpIiIhUJ0N0ZEREREREQccU7pjIGCoOBybGg3osTdiAXFlAazCAm45/zwpK0KIaAoCoQQh22fc83R2Gbg6Gx31Ob542hs91y0OWw/Ye/AY5W5fN8fLRyNz8LhYiGcOwVwSmcp4Xg+27EQzv9IcTye+2ze90RMsYXrushHBdYjIiKOU5LJ5HETFhS97yMiIo5npnvfRyH1EREREREREREREVMQGcwRERERERERERERUxAZzBFzytVXX42rr756xt/bu3cvWltb8cMf/vCwtcU0TXzmM5/BunXr0Nvbi8svvxx/+MMfDtv+IyIiIo5novd9xLHMlMF5lNKaKnyEEJAoczWiQfx4oKamphl9L5WSlRxjsdiMv1uPD3/4w7j33nvxhS98AStXrsTXv/51vPnNb8bjjz+OU0455bAcI+LoRQhRk/RB6fHjU4je9xGHSvS+jzhamM37flqD+XgaMCLmjpneR/72h+sefP755/HjH/8Y3/ve9/Ce97wHAHDJJZdg48aN+Kd/+ifceeedh3yMiIijmeh9H3G4iN73Ecci0dsxYlbs2LED7373u7FixQrE43GsXbsWn/jEJ1AoFKb83kMPPQRCCH7+85/j3e9+N5qbm9HW1oYPfehDod91HAd/93d/h56eHnR0dOCd73wnxsfHK7b5yle+gvPOOw9tbW1ob2/HxRdfjMcee6xim7vvvhu6ruNtb3tb8JmqqnjnO9+Je++9F47jHEJvRERERBy7RO/7iIiocEnELOnv78fSpUvxpS99CW1tbdi1axduueUW7NmzBz/5yU+m/f6NN96Ia665Bj/96U/x/PPP43//7/+NQqGA73//+xXb/cu//AsuvfRSfO9738POnTvxN3/zN9A0rWK7vXv34oMf/CBWrFgB13Xxy1/+EpdeeimeeuopnHrqqQCAl19+GWvWrEEsFqvY/8aNG2GaJnbt2oV169Ydhp6JiIiIOLaI3vcREQDEHPDwww+La665RvT19QkA4q677ppy+wcffFAAqPnZvHnzXDQvlFtuuUWcddZZIpVKia6uLvHGN75RbNmyZdrvPfTQQ+KMM84QhmGIlStXim984xvz0FrJbNo8V33tOI549NFHBSFEjI6OBp9ffPHF4uKLL645flNTU/Bz3nnniQ984AOCUhq0f/fu3QKAuPzyy4UQpX5WFEUAEF//+tdD28EYE47jiMsvv1zcdNNNweeXX365ePWrX12z/X333ScAiD/84Q9Tnt/Xv/51cfLJJ1e0+Ve/+lXd7RfCPV3NLbfcIgCIj370o1NudyTv6TAaafdC7O+jka997WtixYoVwjAMccYZZ4hHHnnkSDfpkGnkPck5F5/97GdFX1+fiMVi4uKLLxYvvfRSxTamaYobb7xRdHR0iEQiIa699lqxf//++TyVQybsWZrNuV999dXijjvuaPh9/4Y3vKFif//2b/825fve56abbhK6rgvOeej5TPe+P3DggHjXu94l2tvbRTweF6tWrap43x/L191xHPHpT39arFixQsRiMbFy5Urxj//4j4IxFmxzLJ//XDAnIRn5fB6nnnoqvvrVr87oe1u3bsXAwEDws3bt2rloXigPP/wwPvKRj+CJJ57A/fffD9d1ccUVV0wp5L97925cddVVuPDCC/Hcc8/hU5/6FP7yL/8Sd9xxx4Jts8+h9rVlWfiXf/kXrFu3DvF4HJqm4cILL4QQAtu3b5/2+3/+53+Op59+Gk8//TQuvfRSfPe73wXnHE8++WTFdq9//esr+vkzn/kMAFT085NPPomrrroKPT09UFUVmqbh/vvvx7Zt24L9CCFCk5caTWhasmQJPv/5z1e0+Y1vfCNefvnlKb93JO/pcp566il861vfmjbZ5Ujf09U02m6fhdLfRyO33347br75Znz605/Gc889hwsvvBCvf/3rsW/fviPdtEOikffkF7/4RfzHf/wHvvrVr+Kpp54KVBWy2Wywzc0334y77roLt912Gx577DHkcjlcc801YIwdidOaMfWepUbO/aabbsJ3v/tdxONxMMZwzz334C1veUvD7/u3vvWtFb+//e1vr/u+L2fjxo2wbRtDQ0PBZ42+7xljePWrXw1N03DvvffilVdewYc+9KEZn/vRet2/8IUv4Jvf/Ca++tWvYvPmzfjiF7+If/u3f8NXvvKVYJtj+fznhLm2yDEDD/PExMRcN6dhhoeHBQDx8MMP193mE5/4hFi/fn3FZx/60IfEeeedN9fNC6WRNh+uvr755puFqqrin//5n8UDDzwgnnzySXHnnXcKAOLBBx8Mtqvncfj1r39dsb/W1lYBQHz+858XQpQ8DrfeemtFP996660CgPjTP/1Tcd5554m9e/eK5uZmcf7554vbb79dPP744+Kpp54SV155ZcVx3/72t4sTTzyx5jx+8pOfCAANrSZU09bWJv77v/879G8L6Z7OZrNi7dq14v777xcXX3zxlJ7ahXRPz6TdC6m/j1bOOeccccMNN1R8tn79evHJT37yCLVobqh+T3LORW9vb/DuEUJ61VpaWsQ3v/lNIYQQk5OTQtM0cdtttwXbHDx4UFBKa95lC5F6z1Kj504pFYqiBO/7e+65RxBCZv2+N02z7vu+HP99v3v3biGEmNH7vqOjQ1xwwQUV+yt/3x/r1/3qq68W73//+ys+e/Ob3yze/e53CyGOj/v+cLOgkv5OP/109PX14bLLLsODDz54RNuSTqcBAO3t7XW3efzxx3HFFVdUfPa6170OTz/99BFJKmikzT6H2tc/+clPcP311+Mzn/kMLr30Upx99tlobW1t+PvDw8MAAMYYbrvttsDbs2jRopptw/r5oosuwtNPP41f/epXyGQy+MlPfoK3v/3tOO+883DWWWfVJJRs3LgRO3fuhGVZFZ+//PLLiMViWLVqVcNtL2/z+eefP+W2C+Ge/shHPoKrr74ar33ta6fddiHd0zNpt89C6O+jEdu28cwzz9Rc+yuuuOKY066tfk/u3r0bg4ODFeduGAYuvvji4NyfeeYZOI5Tsc2iRYtw0kknHRX9U+9ZavTcOee47rrrgvf9VVddhZUrVzZ8fP997+N7jMPe91Pxm9/8puH3/fj4OE477TS87W1vQ3d3N04//XR873vfC973x/p1v+CCC/DAAw8Envfnn38ejz32GK666ioAx8d9f7hZEAZzX18fvvWtb+GOO+7AnXfeiXXr1uGyyy7DI488ckTaI4TAxz/+cVxwwQU46aST6m43ODiInp6eis96enrgui5GR0fnupkVNNrmw9XXhUIBmqZVfPa9732v4e9/5zvfQSqVgmEYuOGGG/Ce97wHlFKce+65NduG9XNnZydc18XIyAgAVLRlx44dNQ/zG97wBliWhZ/97GfBZ67r4vbbb8eVV15Zcy5hvPjiixVtvuuuu3DiiSeGbrtQ7unbbrsNzz77LD73uc81tP1Cuadn2u6F0t9HK6Ojo2CMhV77wcHBI9Sqw0/Ye9I/v6nOfXBwELquo62tre42C5WpnqVGzx0oaSX7mKbZcBvK37uAdLjUe99PhW8YN/K+F0Lgm9/8JtauXYvf/OY3+OAHP4h77rkHJ554IjRNO+av+9/+7d/iuuuuw/r166FpGk4//XTcfPPNuO666wAc+/f9XLAgVDLWrVtXkbF6/vnnY//+/fj3f/93XHTRRfPenhtvvBEvvPBCjVRNGNUxsMITwp5vsf9G23y4+vrKK6/Ed7/7XZx88slYs2YNfv7zn+PRRx9t+Ps7duzAtddei1e/+tW444478J3vfAfXXnstTjjhhNDt6/XzRRddBFVV8d73vhd/9Vd/hcHBQfzDP/wDli5dWrH9aaedhne84x246aabYJomVqxYgW984xvYvXs3fvzjHzfU5nXr1mHTpk2YnJzEHXfcgeuvvx4PP/xwqNG8EO7p/fv346Mf/Sjuu+++mmzxqTjS9/Rs2r0Q+vtYIOzaH0uFS6Z6T87m3Bd6/zT6LE137pTSmvf9xMREw+149tlnccMNN+DNb34zNm3ahL//+7/Hn/7pn9Z939fjta99bcPve0IIhBBYvXo1xsfH8bvf/Q6U0pqVsmPxugMyJ+EHP/gBfvSjH2Hjxo3YtGkTbr75ZixatAjXX399sN2xev5zwYLwMIdx3nnnNZRMcLi56aabcPfdd+PBBx/EkiVLpty2t7e3ZpY1PDwMVVXR0dExl82sYCZtDmM2ff2Vr3wFb3jDG/DpT38a73jHO5BOpxs2PP3vU0rxqU99Cs899xwWLVqEzs7O0G3D+nlsbAyqquJVr3oVfvjDH2Lv3r14wxvegC984Qu45ZZbQo2kW2+9Fe973/vw6U9/GldffTUOHDiA3/zmNzjttNMaarOu61izZg3OOussfO5zn8Opp56KL33pSw2f83zf08888wyGh4dx5plnQlVVqKqKhx9+GF/+8pehqmpo0sZCuKdn0+4wjtQ75Giks7MTiqKEXvtqD9TRSr33ZG9vLwBMee69vb2wbbvGSFzo/TPds+S3fbpz55zjyiuvrHjfL168uOF2fOUrX0E2m8Vb3/pW3HLLLXjve9+Lb37zmzM+n40bNzb8vl+8eDHWr19f8b6/6aabMDY2FpxXI+d+NF53APibv/kbfPKTn8Q73/lOnHzyyXjPe96Dj33sY8FKw7F+/nPCXAdJo4GkvzDe8pa3iEsuueTwN6gOnHPxkY98RCxatEhs27atoe984hOfEBs2bKj47IYbbpi3BKnZtDmM+exrPwmkPFFECCEuvfRScf3114d+50j3cz2manMY831PZzIZ8eKLL1b8nHXWWeLd7363ePHFF0O/sxD6ejbtDmO++/to55xzzhEf/vCHKz7bsGHDUZ/0N9170k9++sIXvhB8ZllWaPLT7bffHmzT39+/4JOfpnuW5vrc673v54PrrruuJunv5ptvFueff74Q4ti+7kII0d7eXiO/esstt4i1a9cKIY79858L5sRgzmaz4rnnnhPPPfecACD+4z/+Qzz33HNi7969QgghPvnJT4r3vOc9wfb/+Z//Ke666y6xbds28dJLL4lPfvKTAoC444475qJ5oXz4wx8WLS0t4qGHHhIDAwPBT6FQCLapbveuXbtEIpEQH/vYx8Qrr7wivv3tbwtN08TPfvazBdvmI93X/gv0S1/6kti9e7d44YUXxKc+9SlBKRX33XdfaJuPdD8LIcTf/d3fiUceeaThNh/pfq5HtdrEQuzrMKZr90Lt76OJ2267TWiaJr797W+LV155Rdx8880imUyKPXv2HOmmHRKNvCc///nPi5aWFnHnnXeKF198UVx33XWir69PZDKZYJsbbrhBLFmyRPz2t78Vzz77rLj00kvFqaeeKlzXPRKnNWuqn6W5PPcjaTA/+eSTQlVV8a//+q9i+/bt4oc//KFIJBLiBz/4QbDNsXzdr7/+erF48WLxP//zP2L37t3izjvvFJ2dneITn/hEsM2xfP5zwZwYzPWKCPjeuOuvv75CAuYLX/iCWL16tYjFYqKtrU1ccMEF4p577pmLptUlrL2okrmpbrcQssjD6aefLnRdFytWrJjXIg+zafOR7mv/3ujp6RG6rouuri5x2WWXBYZnWJuFOLL9LIQQ73//+8Xy5csbbvOR7ud6VA+WC7Gvw5iu3Qu1v482vva1rwX3+RlnnDGlROXRQiPvSb+AQ29vrzAMQ1x00UU1qxnFYlHceOONQRGMa665Ruzbt2+ez+bQqX6W5vLcj6TBLIQQv/zlL8VJJ50kDMMQ69evF9/61rcq/n4sX/dMJiM++tGPimXLlolYLCZWrVolPv3pTwvLsoJtjuXznwuIEF5GT0REREREREREREREDQs26S8iIiIiIiIiIiJiITClrBznHJzzis8IIcelnEhERMSxjZAhahWfUUpB6fHhV4je9xEREccLs3nfT2sw+xXYIiIiIo43ksnkcWUwR+/7iIiI45Xp3vfHx0gQERERERERERERMUsigzkiIiIiIiIiIiJiCiKDOSIiIiIiIiIiImIKpoxhDkv2mK+YvkKhgO3bt2Pt2rVIJBJzfryFTtQflUT9UUnUH5XMpj/CYniPp4S3I/m+Xygcz8/R8XzuwPF9/gvh3DkXOJh3oSsEPYkpTdPDdLyZv+9nbDDPV9Y4IQSMMRBCjqsXdj2i/qgk6o9Kov6o5HD1x/FuMB9PKiHA8f0cHc/nDhzf578Qzv2hg3kM5V0AwKldMZzYYcx7G6Z73x9fd0VERERERERERMSCIWfzwFgGgOdHzCPYmvrMvd874pBxXRc/+clP0N/fj7Vr1x7p5kREREREREREHBbYUVJwOvIwRxx2hBDIOxyMHx0PQURERERERMThRwgBh01tC8SUylCIuLowQ+EiD3PEYWEw7+L2bWncuyeH50ZMDBcYNr93Nda3149DGsg7+MXOLB46UMCWcQvjFoNGCZakNJzZHcNVK1N4zZIkVLowH56IiIiIiIiIcIoux0P785i0OPqSKi5akgANy5Wo+ozXbLEwiAzmiEPiYM7BLU+O4r9fnoRdNYu06swqTZfjk48N4+svjMMJeTJ2pR08crCA/3xuHMubNPzVmR344EmtiKnRgkhERERERMTRwJZxC5OWHOQH8i72Zx0sb9ZrtlOqhvaFujgdWSARs4ILgW88P471392Jr78wUWMsA9IwDuNn2zP40qZwY7mavVkHf/nQIE774S48ejAq2xsRERFxuBnKuxgsuNNvGLHgcJjAg/vz+PGWNB49mF9QoZBbxu0pf/ep9jnzBRrTHBnMETMmbTFc84v9+IsHB5GrY/WmNIp6t/w3X5yY8TG3Tti46Kd78fePDy+oF0JERMTckbM5XhgxsSsdPtAe7WQshqcGi3h5zDxiRsILIyZ+tz+PR/otvJyNTIKjjV1pG4OewsSBrIsDOecIt6g+9e5wQgjKozIW6hAfhWREzIjtExbecPd+bJmoHcBOaNNx06ntuGplCiubtVBNwxdHTfy+vzjlMU5s13F2Txw/25FB3ql8cv75j6N4arCIW1/TfmgnEhERsaDhQuC+fTlYrnwHuFzghLb512adK7gQ+O3+fHB+jAOndMXmvR0vj1nB/x8wyYL17kWE8+xwpQTbphErNOxhISDq3FtcCJT/SQi57ULTwY8M5oiG+X1/Adf+Yh8mrEqvcqtB8e8X9uD6E1trEvRufXkSv+/Pgwng1isW44RWHT9+/WJ884UJPHywEHqcV8Zt7Mu6+Mgpbcg6At98YaJiZvrrvXlc+ysHn1t1uM8wIiJiobAn4wTGJAA8M2QeUwbzwZxbcX4vj1lHxGCuZhpBg4gFztGYI5+xalequQCUBXYukcEc0RAPH8jj6l/sq/H4Xrw4gR9ftRh9Sa3icy4E3vmrA/jp9mzw2YdPacc5vXG8c10L3rmuBa+MWfjKpnHc+spkTYJgzuH44jPj6Ior+Ojp7fjZ9gwO5Eoxdk+P2PjzfAz3r3GxOjkHJxwREXFEOZLLskIIDOZdxBSC1pgyJ8eolxR9pFlgNkrEDFkI16/gcAwXXBRcjkRZsn69Oz7scyaAuXnyZk8UsBQxLY8ezOOqn9cayx86uQ33vXl5jbEMALdtzVQYywBw80ODGMy7+PWeHL783Bh+uCUNhQLvXd+CCxbFkdJqH/WRIsP/fW4cFECiSptxd4HimnuGMRIlq0REHHM0aZXDU19y/vw7jw1aeHB/HvfuyWHPHMVP11uePtIszFZFNMqRjmIwXY579+Tw+EARL4yYdfOcygm758bMhTeuRx7miGlZ3aJjbauO50dLsW7/56IefOz09roxRptCSlv+cbCIvv9v26zacCDvhnqctkw6uOKuffjdW5ajbY48QREREfNPV0LBn6xpAiAH1Pny7uRcYCDPEDPk8Pj4QBErWhZmTOhcsEDt+IgQwtSpjrSH+eUxK2gXF8DutI2TO2WoUb1bK+zZfnrIxLWrap1xR5LIwxwxLYtSGh552wpcvkzGPnz1kl58/IwOEELAuAh9aD97bifevLqp4rNDESM/pyeOb7+2D4mQKd6mERPX3r2vroxdRETE0QclBDGVIqZSxFUKY5502HNsfkyOhWCXhnm5j7SHMmJ6uBB45EAed2zP1PxtNpfPZgI78gRbJ51DTvocyFd6hrP29ONyWNhTroHvzTeRhzmiIZoNBfe8cRkeOpDH5ctTYFzgJ9sy+Kc/juADJ7Xir8/sBCA1Ib/98gT+4YkRDBXYYTv+E4NFvDBq4s2rmxFXCQ5kbdy7r6S28fv+It77m37cdtXi0EpCEREREYA0EgVqq4stBLgQ89quatNIJYB2NGaNHWcczLk4mAsPWZjq/slYDI/1F5B3BDZ2GDixQybR/n7QxM4CRXHUhkOLOLcvMSftPtpXLyKDOaJhNIXg8uUpvDRq4u2/OoDNngj5F58ew4dPaccjB/L42CND2BoiOedjKATn9MZxSqeBpSkNbTEFjAuMmgy70w5eGDXx3IgZGn5RcAV+sDWDZp3iL09uwkg6h6fTpZnpT7dnsPRRFf/not7Dfu4REccjaYujLT5/C5FCCOQdgZhKahR3DhcZm+NXu3MgRCoKdMQUXLYsNSfHmopy48FiHKNFhkcPFHDx0vnLYvbbMFJ0YdsMk440xk6YwyYM5l1kbIZlTVpUvXWWbJ+sP8ZONd/aPG4h7SlSPD9iYkWzBkqAkWLJm7sr7eDcvtm3bbbzPUJqDWqXi1m9B4QQMJmAoZDDOgGNDOaIGmwmoE+h57K0SatYdhkpMlxw+25sKotxLkelwFvXNONd61tw6dIkEtrUL8mMxfC7/Xncti2Du3dlUXQrn6KMzfEvz6TRoRLEFMAsc2T/x7PjOKFVx4dOiXSaIyIOlRfHTFy0ZH6MSS5kxbLhAkNCo7h0aQJNemN5CRmL4Q8DRRBII7jVUHB2bzx0Wz+CTAj5/8PVK2Hz7AXjQuD5EQsuF9ibcbCyRcey5vmJ3eQCEBDYPmHDdl1kixRPDFk4oWdujrcnbePxAbkyuHncxrWrUgvS018P0+VwOZDSZ2/oCyHgcECjmBOd4an2uCtdWdRkX9bB4tTszcCczZHUSMV5rGsz8NRgafW3tyxZd6aPls1mbjC7XL5HRosMLQbFpUuTh21iFhnMERU8sC+HD/52ALdftaTugNNiKPjrMzvwmT+MBJ+FGctNOsXHTm/HDae0hSpp1KPZUPCmNc1405pmTJoMt74yia9sGsfuTOXDPubWPgQalfqtERERh87IYQyrmo59GScwXgsOx5ODJkzGZREDAM06xcVLwl2frgAmymbOU9khC6VSqN+KoYILt6xNv+8vYFlzy7y1QaUElMiEJoK5jWH2jWVAXuODORdLm+YvsStjMQx5qkoCcmLVHZYYE8KBrIM/DBTAOLCuXccZ3eHjYzlCCOxKO7CYwJpWHQoBHj5YwFDeRVtMwWuWJGZlzE11iWZiXzpcgM0iVJgLgdu3ZvDMcBFxleKGU9qwOKVN27ZGcYXMjWKziOHYm3EwWpTvgrTFsXXCxqkh+uazUamJ1kMiAobyLq679yB2Zxxc8NM9+M9nx+reVO9Z34JYHS+0SoGbTmvHzvetwT+e3z0jY7ma1piCj53Rga3Xr8HXLulFV3xqj5PDgc8/PYY7d9QmQ0RERMyMtMWQtubHaN5VNdHdNmEhY3FkbY6czdFfJ2YTQJCo5AqBl8ctPHKggCcHi6HvrwViLwdUr6AdbiZNhsf7C3h+xKyZLOgKwVvWNOO8vgTO6TFwYhOfV5WFRiTHDicjRYanh0w8PWTimSET+7ONO1d+318IjMut43ZDSebPj1h4crCI50dMPLg/j71ZB0Pe6uyEybBjitCK2eJ7ex0mUHDks5OxGKyQ9rp8dkZpf87Bk0NFWK7ApMlw29Z0Q99r5FBFxrFp2MSmYRMP7MvPeIK7ebzSeffKWPjK94t1Pp+KyGCOACBnWx98oB8j3szMZgIff2QI9+3N12z7xEABF/10L8wQdYwWneLZ61biy6/pRVeDM/dG0BSCvzi1HTv/bA3+97mdiE9TAuhd9x6o+6BEREQ0xp6MjV/vySFrMwgh8MxQEXfuyOA3e3LIHGZDutq4LUxjkLhc4OUxEy+NmkFc5mDeRdpkmDAZdk7aoYnHfEHoU8xd5AfjAv05J7hmvzuQx56Mg1fGLLwwWiv3SQiwqkXDiiYVfYbA0kNYop8p+jwnGFZ7z2dyDarttnRIdbpqyo23cZPhuaFixd9fnuUYNVWv+X/bNmnhFzuz+OWuLO7ZnQuNe3b57Co77ko7cMu+uCfd2MSj3qHGTRYY0/uzTqC8NW6ympXl6Wi0OuDOyZmvREcGcwQA4NsvT+KXu3I1n39p03gwkHEh8MWnR3HhT/dgb8jMnAD47usW4eSu6ZeqZkuTruCfzu/GE2/pw5kt9QfsRSkNq1sWloZjRMSR4HOf+xzOPvtsNDU1obu7G29605uwdevWhr6rEIJXxix8/5VJHMw52DZhw3IFxk2GZ4dNHMw52JW2sTdj40DZQDcbElVL0zFl6uHpiYEiXhix8OKoFRge+6oG1xdDDMSplqAZF3g+Oz/D4lwoBggh8Ju9OTx8oIBf7c7h+RGzovz2lvFao+nxgSJ2Zxzszjg4aAIdsUMzYnM2x692Z/HjLWk8XWUgVjNVrsxcUG30zvdqQ1+qckzy9YkPJyT4t7Jvw07V5WJWfZCsykNiXGCk4MJhYlaTkvL3RrkMnUDj8nITJsPTQ0UMFtw5KwoUGcwR2Dlp4+aHB0P/du+eHO7eJT1Mb/rlfvztY8Oo5/j5l1d14Y2rm+ewpSVWt2j4+kkW/vPV7QirV7Ir7eD99/cDWJh6jhER88XDDz+Mj3zkI3jiiSdw//33w3VdXHHFFcjna1ePqim4HOMmw7ZJB8PFygnqQN7Fy2MW/jhQxB/6i3j0YAH5Q1hibzEqH+S1rZXFQnqrKv2VL6dXH9dQ6xti1QbCirIEuwP5w+M1d7nAz3dk8OMtafx4SxpjxfmpWjZcYIHnkws0tOSfthgeHyjgj8M2XswqeKTfOiSD46Wxksd/+4SNcS+2vHqfYybDi6Mmtk1YGMq7hzTZapRtE5Ue3Z1zEBJRTrNRaWIJIeOG/VWO2eaiTRVn7jvtq533XKAmmdRQyaxCMpp1ilctSuBVixI4ozuGmEbx2315/Hpvbsrr2Mh9Vf0eaDGm7ySHCdy/L4ftEzZ2TNoYnKPciyjp7zhHCIEP/ra/ouy1QkrLNCubNeQchvNv3zPl8tHb1jbj787unOvmVkAJ8MENTbh8VSve8ssD2Fr18vvR1gxstg9PDln4+3M78YGT2ua1fRERC4Ff//rXFb/feuut6O7uxjPPPIOLLrqooX0IiNAl6LEqI3q44M6q4qYQAjurSlCndBoUNBBCoCM+u+FqIO/ghRELcZXgrJ54jYGglFkWL4d4YGfD5nELRVdguODCZDJr/60nVCbyiTkIyqg+t0aOQMr+ezjYXbU8vyttoz0Wr5moZG2G3ZlSaMPp3TGsbzcOWzvCEABsLmC6HAmNQp0mw5ELEUixZWyG5jLVloQ2fZ9tbDdgc/ns7Ji0cd/eHHanbTQZFGf3JPDMkAmdkqCSpM0ENo2YMF2OjR1GzT0vEyUdHMg6GMy7MFSKjqrnjdQxmF8Zs2q2pSAzSvpzmMCExTBccDFuMihEJq06nsGQszn+MFBAvGx1aNxkWD1NDmv5s0ABUConF34i6nRsn7SD8yCEYHfaRl/y8Ju3kcF8nHPb1gwePFCo+Oyrl/TiP58bx3m9cbzzhGa85zf9GDPrz9hO6TRw6xWL5kQipxE2dsTwzLtW4bpfHcAvd1eGlfxsh/z9Lx4cxMmdMZxTR/kjIuJ4IZ2WCTrt7Y1LL3IBOFyOSC4XyNocCY3AqAqbmK1/+ekhs2YlqD2mTGlAUVLpLb52VRNMl4OQ0nI0h8Dv+4vBgK5Rs0YVodywqPcG25uxsby58fLYL41a6M85gWLPUMHFm9c2NyShJmXeLJhMYG2rPqWKAuMClJQSvap3L/tharOZo/K8HS6/Ud3SrM2wedyGoRBs7DCgUoK8w/H0UBEaJdAVgmad1lyXVs9jWN0KISrDUuYqOiNrM2iU4PkRE48dLAT32cYOo8abWc22CRsvjEhH0fZJG6d2xgKZMz/+WggBJmT7q8dA3xDem7GxZVxgy4SFSZNjqMjQols4uTOGp4ZMLG3SoFCCTSMmto1bOJh38cRgER85pQ3JMiM9Y3M8PWRi26SN8SJDW0ypNZi9K+eExFq8PGaiSaeIaxQUBBwzS/rL2AwP7MujP+9gT9pBk05rKvntzzg4oa303Jb3SL0jlTdhVYuOVV6/vX5FKrQKYDXFsmXvuQybiAzm45iMxfBXjw5VfHbtqhQ+dHIb/nRdCx7Yn8eb/ufAlEss3QkFP792aU1M03yT1Ch+8YaluPnhQXx500TN320m8Jb/2Y9N71o1a09VRMTRjhACH//4x3HBBRfgpJNOmnb7gZyFuCoHrFzBhGlLY/nlCWkIntdTadA6pkA+P/Pwg5eGasNDzGL9faVtjrUpjvYyY4w4RYA5EN7vpgsM2gJclIbsLSMWkl06TKvkSTaLDPm8HHBtW37u2JWe5t/ttnBml95wXoRpWdhWlvBlu8D24QyWlCXU5Qs2TMuB47iw3ZJDYudYAZbXvq3DBK9fXqq6NmExbJlwEFMITCawP8fQrBNc0BdDSqMoFhhMq3RcRqUBXE55KE7B5RjOFJGgLhTKwFSBsbyFsUwOr4w7sLmAxQTiKsGYyYOVyHRexZldBkZNhl1jpTjxrjjFuR06Nk+UvMyZfAF5zYHDRUXbLMeFBRcmkdewWBTI6y64EHhhzMZwkWNRUsHGNm1KZ8yujIOtE/K6T1gcpiuwOKXg9E4DB/IudqZdKATQKVC0SnH2RQswoAT9USwWK/4FgMf3l/rKsh3sm+RY5HkuCwUFJgEeGzAxXOTojFFc0BcLjcuezDnYPVGAaZckBF8ZLWBtk9x2z5hAb0LFy0N57Ew7GDHlRbvtFYZ3ri3poBe862vbNmxXwLIZzKqFX0dnyOcFnuvP11z7dNHBHw7aaI+pWJJUsDTGoDEa3O/+v3/cNwFKgVadoi+hBP1/IO3AtGyYlrxnx8oeT9MyARAkqWxjf15usyiulq67S0JDwfIFt+LeCPZZpMiz6W2LQtGCacnGOI7sm/L9VR9TCIGiaQKYWQx5ZDkcx3z2iZGKAiRxleDLF/eCEIK7dmbx/vv7p0wIiCkEd1+7FCtbGve8zCWEEHzpNX1IqBSff3qs5u9pi2GK0MaIiGOeG2+8ES+88AIee+yxhrYfnsyhWZHG3B7BUOQEeReYKMhBrL9qPbcry2HH6r80si5w0CRIKcDiWClBqH+k1ovUmuFw46V9jdrA1hzFsE1AgSB3oUsXOLWZY4zU7ies1HRLhqM/VxqEtQmO2Kg8zvC4XAQeGR1BNf39wOu6GouNzIxTTExUtuX5LcMYMAQSivRq78oT9BcoRkyCCbvUxokJAE0cjgA0ArSkGdo06YV7cEyBI4CcC6RdgsUxgX4A+UGBE5s4CgyIm6V0rzwjGLAqz3+zUzqHrAv0TyhIAkgCaE8AKI5j65YRPDZear8Qld7r/n4gMcowagP9ZdVW+wFs3S1g8tLGOgF4J4PDgf6x0rYFB2gyBLh3bvtyArYhMGASvOAlXr4CYLKFoWOKIWZ3gWBbXm6/NUfQogLZmMD2vZUKEPuLBAVG4NmiGDAFCpqo6A8A2LNnT+l8yu6nsSyFyArAu79fKDLsKVIcMElw7sUhjlWJ2vt/Z55gaFxBOkuD48c1gf7+Sa8tDBOKPN62TOne3GoKPGW68OdZ415/jxYI0i6BSQUy4/JvAvJ+0dsYNo8LHBxVUK1YuCtHoHJAZcCONMFy20WBEewrEiRVBPf9M7sIRmyCE5Icq5MCChGwGMGQd58OVd2zAHDA5aAESDuApQHPZyi4ANS87GcAiCvA5pDV6mGLoD9DkXYIXAG0qgIxBTjgNPa8jRRJ8Ez7fVP+bqq+xlwAu8c0YPmKhvbvExnMxym3vjSBLz03XvHZp8/pxIoWHV9+bgwffXiozjdL/ODKxXNWc/5Q+NwFPZiwGP7rxcmKz7OOwLm378Er71kFSqN814jji5tuugl33303HnnkESxZsqSh78QTSbQlVBAAXb0G8q5AigmMeR7ERVUe5tU9BpY1hQ8rDhf45Z4C3DgwAWBll441nsf2Ra3W67S8U8e6Vvl3IQR+uacIV3ExmnbRqhOsaitZUcuXxZB3BZK8CA4hDWqVgnEOTSHQyp731V060iMlD3JPSsH6HgOEEGzfMYld/cPo6uzCMxOV1sZpHTo2rG+s6uGevQW0oeR5dTnHQMJAXqdoNyguWRwDm3RQHHfgZF24VUlK4ypBwRVIqAQjTTrOWZ7AqMnQpUuv2Y60DWaKoP8ZgA1rZFGXM719CCFw794iDkw6UCnQE1dACQm2A6THevveAjZPOHBdF+l0Gt1trdhixLFoUckgytgczVXV7TasSWJ/zsXAYKVnMK4C5TmOcZVgw4oELCawZXcp/K/N5Si6AsRL6hqlBK9ZlcBgv4lFTSVjx2hWsaG7fmiOMukgN2qDC4F9wzY6kwoWhUjjZScdxFxZMpkAWNSmokVXgv4oFovYs2cPVqxYgRFXw3CRoUdx0F9gIABaVYaehNx3zuHYq1PwOLCo7Bj5susAyGRUQoBEkWPPngK2uEX4f13comJRj/Rwrl8RR1yleFHLY5/i3zcEvS0q1qxNos1bSRkpMhw8aCKftkFNgbhCUCybFagqwcqVSWzo1PHKzjxs5ofsyP3tG7LQ5lXHNYTAA6bAspSCYdtBuzOJNX1doKqGfSM2WpOArQMHvZCQlEaxyLsHeN6Flau8Z/u6dSiEIG4xtBkK9ikWRosMYwqB633vhBYVGzY0Y6jAYDEBhQAKBQxXYNOeAtJZuU+iErxjYyrw5pdPeosuxxNDFtI2x5pmDSd16GgvMKT7Zb/lJm10CIJFbaXVoPJr8vyojbzDMZKuDEVthMhgPg7ZNWnhz383UBFPdEKbjr8+o6NhY/mLF3TjLWvnRxFjNnzj0j7sTju4b1/lQLx1wsYld+zDg29dflSVZI2ImC1CCNx0002466678NBDD2HlypUNf1ehCnRVDhObswKDeReLkmrwWcyoNGTi8Tigq9iVttGk0SCGE5CKBKrGgkHnlQxw6qKkt5/a0IvNWYGuZh1CyNhJqDr2FVzoqooCrzx2gcTw5FgRO3MCOUcWO+lLqnCFQHdCwfqymMp4PIaYUXr2Rx1AjcmKa4Yul+M1XYeuVhoEhqEjmQyvNAjIssmUEBRdjgHbRJbJY8QUgowLQNEQM1QUAAwzHfGYiphBYVgEepW3zgWgq/LfA0WKvaaKnkQMMe80VA3QXVbRB9VtG8q7eGosj6LrxTcrFCuaK8/BUhiy3EZ/0UGMUJicwCUKBiyKNWXXbtJ1MGwLNOlKYLwlk0msMDg0I44ny0ohGwqBUEqjS1yjSCaTUFyOmFHqU6FwFATD1ixDweFojyl4fExgcWsSWV6a0PS1xJBM1jeY85N5bM3K0uKgCnRNQ8yodElzIZBnDDGNgBFZbrkjFZPJa1xHd1zBpkETL41TPKO4GLYFFidVFKDC0BUcyDoYsYHTe+KI6RTbsybakzq0kDGkvH/v2ZYOktF0TYdKS+fVmTSC68e1GLhK4VALB/ICrpDhLbphIJlIIuktpxSpi5ghoOvy+hMqPfg+mkoRi8eQTMaR5laFKsiKFg2mINifYVjdoqPgcnAiMGJRKKqKiSKBpuuwiQpd9XTNTYYCJzBUgkFL4KQOFc26As2m0FU5ad42Ic/p1G4NbYYChbuIGSpAXeRcjh5dge6Fdem6vP/+uL+60AlBhimI6RRZm2HIEnhiFGAjDpY2aTipUwcFQUqnOFiwkeUcVAV2FYD1PTEkEwIxQ95zhi6lI+s9G3v3u3AFgc1n7jSLDObjDMvluOSOfTXScLe8qhs/2pppyFj+y9Pa8ddndsxRCw8PhBDccc1S3PC7fty2NVOxNPfIwQI+/MAAvnFZX2Q0RxzzfOQjH8GPfvQj/OIXv0BTUxMGB6WEZEtLizRwp6T04Oz3SlcfzLpojyvoDEnG4ZA6wJYr4HKBvMOxqlUavdWhnb7EVT2pKSGAxw5WeoH8EDFfocDhHDYTmLSkLBkXoqK8c3ViGYCaJKXys5zqdeBygaLLwYU0wBIqDRQ2nh8x8cqYBU0hWN6kYnfagenImN9mgyJr8YrY1t1pGz1e8mFnXJ2yiuHBnIOXxyysbdPRk5Te/sGCCy2k6Ie/LQHwyEGZnOUbuP05Fyuadfy+v4AJk4EQYG/awYujJiZNDsYZmkVtH1hMYLTIkLYZCFyc2GEEiXwxldb2WdXvBYdDiFpNEAJgpOii4JSKVGydcFCtwOdwgcf7C8g5HOvbjYpS2lwI3LEjC5cJ5F2OtMXRFZKj4t8DOYcj73K06gpeHDVRdAQeO1iApgCDaRtZh2DHmANCZQW7jpiCgjd4ZG0eyMBlbQ5XVBqrYZSHNJafP4dUwxACaIspuHd3HrpC8OKIFYzNYWXpx6vCGYqu9NL641h5L69u0Sri6Amkqo1vwPthNgWXI15mO5bbBnlHYGWLIisyCuBgzkVzuxJU1izH4hyuoBgpMHTH1Ypy7z79eRe/2ZPD7oyN5U1axfjrcgHOZUls04slGSsyDBdZUOr65E6jpiDZk4NFnFKmZz1dCuOEybA7Y2PMmnmuRWQwH2eMWwxOVdzhKZ0GBID/9dv+ab9/46lt+L8X9xwxRYyZkNIpfnDlEvzZiTlcfue+igfpWy9Nggngvy7rhRKFZ0Qcw3zjG98AALzmNa+p+PzWW2/F+973vim/6ysCMCGQKTM0x4ss1GD+n105ZG2ONoNi87iNJweL2NhpoCOm4pzeygQb336cqvpXmPEjv0uwZcLCzkkbIwWG50ZMUBCMmQxJlQYllyklNfv3PWLlNFK84YH9eTSVKRa8bkUK7TEFlsuDQdxhAo/3F5G1eW0Bh6pj5GwGmwnEppGH8L8WVykuXSo9ZUyIUIOq6IpA6m+kyGCHnFjB88ADwLPDxaBdAkCREQCkorH7szaSOg2u/660jTO6SxOtPw5UFie5YFECv61a2ds0YmJdmZefcYEtEzYGCy6atbIY6JyDjpiCVV6ojgAwVJAyZoAsT/3mNc3B5GPCZBUV53I2rzDUDmQdDBbc4PruzzpwGDCmMKxo1kBB8PKYiWeHTeRtF8W8guYmB11Jgs6Ygp1pOyioI3jJMG3SKUyXQ9drn4FyKq956Zdxk0GnBM8MmehLKehZKkN9qhMGmeBgXGDTsImcw+F6O/S34kJOAvxJ0VQJ+pSQIKb5QM5BzuFoNWiNCouhEPQlVan8QYETWnVkHA4mgKUpFad3x1FwOQaqJnlcyEqbNfMn74OiKycr4ybDQM5FQqPoKXu+/QI7WYdDIwQqlaXuy0vHh1UXHi2yCgOeC9nT3FMv0SiBEAKEEHAhsG3ShsX4rErSRwbzcUZfUsOeD5yA//Xbfnx/cxoqAf72rHb86b0Hph00bjy1DV9+Te9RYSyXc9myFL5/5WL8zSODGCgbZL798iT+OFjEC+9eddSdU0REoxxKEQq/AIg/CE1H0ZVhG4N5wGIchkLx8qiFZc0cly1LYm2bDuZVF+v0Bst6xU7CnsnTumMYLTLkHY4XR03k7cpWTVocSY2CeQNrQiXYl7WhUwCEQCXAkiatRn9XtklMqfla7THzB+l0lcdaAOjPOzA84ycTol/tcoEHDxQxmHcRn0ZhKKzf62nnll9rl4nQ7bZOWNiZdmBQqSqhEKAjoYBwIEE51rWqaEkYeOc6GXJ3z+4sDmZdDORcCAgMFRj68w4cJqAp/v0hsCNtI2Nx/O5ADq9ZkgQt680t43aF1NioyTBpstBzq9Zj/vGW0vK9ENJg9/+ulF1H//KUO95dIWqMSIUCLpNe4phK8dKoBcaloZZxCWJcYLzI0KrTijExrpFg3ye06bhocQJ/HKytJFkP4Wn1OVzAYVK1A5ATmMG8C5WixnNLIDW9D3jG6cGcg76kiknvnjKZVAWBUeoDfxfV+xosuBDcP6ZUPxkuMixrosGxAKk4tbJFh8U4dmdsPF42ITq9K4alTVrodduXdVFwOLrilTKCwwWpX22ykpY6IcDOCTswmAtlbm3TEdB1AkoIJi2OvMOwO0OkBnbIZFceu6TKIiBDuJ4eMuFygcUpDUwAKgGeHTJxIOuAARUOgEaJDObjEF0h+N7rFuO9G1owVmT40AMDNfIz1fzz+V349DmdR61h+a71LTi/L45Lfra34uF6aczCn/xyP37+hmVHsHUREQuTlGfMEYKKEsuNVCjL2hyGt9a7L+OgSaM4vStWoRucsRh+tScX+v1ze+M1ldiWN2vQKcEfh4oVxnLO4WjWFAghi6cMFxnopI3OmIKRIgOBjSbPk9mfd7GqWUNvUsPzoyYmTIY/DhahUQQyX77hKWXVOOIqrZCny9gMd2zPYlFKxdnd0nOetqRHN64SuLyqcASpNHz78y4mLTl5L3ovX6kfHeLtDrFO6mnn+p8eyDnYk7FlIqRG0FLmCT2QdeEygQmTYazoQiEEKiVwOUOzS7Az4+KMhLxGeZthzOQwmYDFBSYthrTJsUd38NCBPC5fLj2joybDqOeMGCvK0AiNEuzPOuAANArcu5tg87iF1S069mYcFFwOUnYfndoVQ3tMweppVJfKzz0xxWSDC4EXRy2kLQZKpNeUAFjdosPiAgN5FzG35MH0LocnowfkXY7FKQ0thgICwPQ0rwHAUCiadAUJjaIwxeDZm1Qx6ClRMSEvkN/+givQGpMJeC+Pm2jWFAxXxaOMFzkePJDHymYdKiVgQl5bl8lqgUN5hoRGQtVgqu+biapwDo0StBgKTu00YDsO0lblF2ThkJKmsysEnhosgEDAcgTWtekwmcC4xTBuMpgux2iRoeBynNwZg04JuhLyvlverIGlS/tfktIqKnVmbA4BgVGTI+tyZByOp4YKKLgctgsM5FwMVIUDlZMtuwZcCAiBwBt/MOfgd/tzWN1iYMJi6EuqsLmAMgtHQmQwH8ec2R3HebfvrvGQlKNS4L8u68P7N7bNY8vmhlUtOh5+63Kc8N0dFROEX+zK4a8eGcT/uaj3yDUuImIB4nDAUFBrtIXMm7kQOFA2CFYvL/9se6bi93esa8bmcasmVMGnOlJhbZuOtMUwVHBrvOYOA+CFto6ZPPC0jXhGwlCBoanFG2gFsCvtoDupwmGyzYtTmucR9mNB5fLv/qwj46+pjPkdKrpIqQSvjFvgHHhpFNiTseEwGZcMAAmVeoZVlQFS9v+TVqV3Na4SLG3S0BlX8eRgMTT+s5zqHBSflEYhhMBBzyNpugITJg8MZhnnLbcNO0SBEWRsEVzLO3ZksGPChuly7Mu6yDgsCKEYLTLkbCkltqPM80cgvcBCVIYI7NdsTJgMBxUHPQkVOZsh4zD029K4thjHOb2pwGtdzUjRxYTJ0B5TsNFLodEVgpWtGnZPlu47P4xowuIYLLgwXQFDAfZmBFQKWUyGyMIj69sNPFkVUuKb4JQAi1NaUBgk6/CaIhzTuY9UCmiKlPnL2BzNBoVVlBdPCLkKYDMZDw+t1us5XHQhAMQUiqVNGigR2DpuI67SYGWm4AiMFBl0hWB32kFXIosdkzaeHTbh32WKF65U0TYiC830JTXYtoCosgY5Sic4aTFMmDIM4/ZtGaxp1fGqvgQWx1VolOBXu3PgAtAUwHYFbC6gUSJDRbx+HC0yFKcoK5h3BSaKMsTGdAUmLI5Ji6GtbLK3Y9LGWT3xwHvuTxLKw5PC3ic7JhyMFTkKbmMydfWIDObjFJcLvPNXB0Lj+XxWNmu4/aolOPsYqo73P7tzoQ/Ufzw7jsVJFR8/c37Le0dELGTGiwx9qVovnsPkEu/+rIPFTWoQP1zOsiYtWDouxxUybGPLuF2xpA4Ae7MORgouUjrFOT0xnNkTw2BeHmckL5dSp3IMxVSCSUsEoSRCCBAQ8JBxmnkV7aTxKc2f8tYMF9zgWJYnR9YTVzGQd5D1Qj8AYNu4DSYEFCKLiWyflEvBJhPQKS15jcsa3mJQ9OekAZ53pEFbdAVaDQXLm7UKz3qXlxz41GARp3fLSnNFVy7jx1WCuEqDyYmhyJht5lnDCi152jgE7t+XAwHQbFCwKcJsBIDHB4oV8cGSUg9tHreQ0gkIkbGrHLKvKZF9W270u0Jgl1cyeyDvYom3rL8nI0uHQwDtcQV/skYaRU8NFjFYcFB0pfG0JKViuzdWvTBq4tSuGOKqrN43nJeefT/hrT/voD2mYHfahsMEcjZHDkBnHEjbIkgybNYpTu8ysKZVg8kEHOaiwAUSGg325XAOk5VCFgqex95ncUoNxtCRooslKQ2DeRe9nhzahYuTwbXbNGyiRacY8exzTZF5NtLwDb8SBMBAnkElUi3iQM4JtjTLnNFpS3poszbH7rSDta0G9mZsTJgcILJqplXHWK1Xop17YUrSAS/P2eVAzpaTqQfcPNa06oH0m8U4DEpQ5CL0GTUUgiKTIVgZhyHvSoM4rlLEVBJ44mMqCe7xVp1WTKBsJjBScLHTm5yubtHRlVArqktyLwxnX84BvHt8wmI4uTOGzWMWYodQjCEymI9xcjZHzuHojCvBzBsAPvHoUI3kWjlvX9uMb722b9rSoUcbhKBGzN3nrx4dRmtMOSa86RERh4OMw9Bd5XoSEBgzGQhU7MvaEJDG8faqybdVZWxxIeBwqSGcNhl+tHkSDgQMSsEh4439RKLxIsNDBwtIWwwPHiigzaAYLTIohIBSAYsBquIl9Hj7j2sErhBQKNDkxYzEVFoRSlIOISXD0h/gnx6x0cfl4GuWfS/r8GAp3eWy1PWqstAB3+4fLTJP+YAipVOMed7EgzkH2yZsnO/FbApvIC84HC6TIR4aBQbzDqojqf3fNg2buH9fDqtb9Br1kDbPCyoTm0ptKjdchvIsSPbLuwLtBkWTTjFhcSRDVvNzNquZBFkuD3STJ0yG50dMuV+HQwigI049j2zpwEXGkbEYehJyCcBmUid70mYoOqXtXhm1kLM5ii7H00MFbJtwkLaZbKNZ2dfPDBdBAQwWGPZmbIxbHD1J2Qd7MjK+erTIKhIvMzYPxkBC5AoC8RQmiozLMCFve4cL7M04IIQgpTpoNmggGehPlPxrCEiDbOuElLazmMDVK1NoLhs7Tc9YLZ+SKYSg4IhSGXch0JNQkPCSVn39ZELkZAcATKd0TD8axeHC89wS79pyTFgMO9OOrIQp5PMkIL228vrK+PbhogvGBNp0gWKBoJsL9OccbJ+0oVGCoYILlyEwMn3jmgkZcpWzOZCUoTEZW7afBK2tpCOuYMJi2JG2sT8jq0i+MmahJaagL6kG921coYAi99/epNVMqreXTSa3T9roSqjYnbaRdTj6cw4mLYHuBK2YJKctHqx+DRVZEGc9UyKD+RjnM38YxrdfnkBPQsX3XrcYr1qUwB3bM/jPqqIlPjGF4Euv6cUHT2o9auOVp+IvTmnDb/bm8Mtd4XGTH7h/AEmV4h3rWua5ZRERCw/TERguMHTGywf/0rK+ALA3a1f8/ZzeOGwm8NxwseId8vyoKQdugcAryYRMygGAoSrFCApg04gF2xUYchlGLYYOnSLvLfme0S3jJAFgcZOKRUkNGYtDJQR5L2ah6HIolEBwaXQkVRl72ptUoBBgY6eB7ZM2iq5AzBsNs640gFI6lQYBpLfU5cDTw0WMFV24HEHmPVAyav14VodJo9s31h0OjFsuBjyZtwmLYVfaxpgpPWxgBIN5BkN1KpQDgFLylsOFjOUMkaAjBJg0XYQFCfiflMvp5W1Zdjprc2QtjhwBWnRgZZzjpHYNCgE2T9rBd3Jlhm1FWIKo/HfCkkZqm0FBQDBmMYwVGOJqKRZ2X9aBrhDY1avjBPjbx4axokV6kx0mDR2bC6wqk/zPWBxbxiykdIot47IPB/MMlHiTuSIDF8CYydCmU3QnlEDlxfcuCyENzphCATDwqvgUQgjSFoPDBEwi4JoMLYYSGIKjRRePHMhj06iJlKZgx6SJfm9CIgSwzQsdKO8nKotIggBoj1MYVepMExbHnoyNgnfPpDSK1a0aiHftBgsuxiyGhEIAtTRJc4Tw4vlL57Bz0g5WFnxMJkInj64QsLg0gl+ZcOBCeqk74gpyNkdMoTBdGc7i30IFV0rsDRVcaAqwKKWAgmBli4Zxk+HF0VIyJCn7V6CU5Os3L2tz9IbUP3NZKTxmOnZlbDAO9HvFVISo/GZ5CFfB5hAhCj+NEBnMxzBbxi18ZdM4OIBc2sGrf7IHb1yVwu/2V3qW+5IKBvMM69t13H7VEpzcObP66kcThBB85/JFOOX7uzBQCNdhfNevDyKpUVyzqmmeWxcRsfCweOWirVKWwDZhclhMyl4BQNZh2Je1YTFRM+EuOtLrl3WkpJPDKzVzhS8j4EEJwXDBRdbTzhUc4Jo0IGWIBIdOSxrDB7MudqcdTFoMmkKQVAkcJoLEpeG8i4ROUXRlAttGT2lBowQjRZk85XKBp9IKNMsEAwnehbpCcCDnYNJk2J91oBKKjCOwukWFAAIpt+BcXYFczgUF0J1QYTGOLRM2WnSZmGgJGbJhMwGXM7Qbsh/2ph2kLVlZriOuYCDPMG4xdMTVKY0HCuAfnxhFb1LF4pQKTSFY3WKAwgHjcuIQht/bwvMYOtRfwi5Jz6W9pC5AxuOWt6N6r5xLQ5VxgY64gjEvtpRDFnYxmUDKk4foTagYyruwuQCH9ABTMPxuv41WncrwEgEUbIFdGRm3q1GCbRM2BGSBmrWtepCIx7lULGHeZAYCmLS59NiG5Ok06RSGtxIRpsfvMOCVMQtdcbnk35MUWJRUkXcYHtifR3tMQVKlmPS8uYYXfrMrbUOnBCN5F096sn19KTWY3AWWYxkTJkdCk6ocvmeUo2RUHsy5aNFtTBQZdloM69r0IEmxOvxBAGg1KJalNBRdHniw68W9DxUZhCAgDNBcgbQjx8WxIoPFRVCC3vVCmAgBbAZkbQGVcOQdG7pCpCeYyHstrsh+zTtyhXvbhIWBgou0xdEZUypOn3MRGrPfatCad4jwQkT8xSsh5HdNRwT5CkCtXrVTFSJULwxlOiKD+Rjmrx8drHmh/WZvvkLLMKYQ/PpNy7EzbeOK5alguelYpjOu4ntXLsIVVdrMPkwAb//VATzw5uU4f9HCK/0dETGfsJDBzB/Ux4qsQqmg4Ao8ckAqTrgCQTwrIGM2czbHhMWDQb48Kaz6KE8OFWFzUWHsmEwg73kJ92UcJDWZ1pRQKXoTpeGs4HIYCkHeS/gCZHx0T1IF49IDOGG60vD2vG5DBRc5y8W4TZBQOExGMFR00RNXA2/dgayLkQJHswEkvUS9uErxh2JtmV0CafS0GQpeGbPAIcMFXA7ENDnr6IgpICDQFRkCQgkB49IbucMrP05AsHXcwopmDROWNEZbDAUaJWjSKQSkRvO4yXAg62Co4ATln332l3ml8y5HwZuElHe6zYGXswqcQRMpgyFjySS1yWpjs2zHKilJD9pcGkcUBBoFmhiF7d0ojk2Qd6T3vyXmG6mo0MJ1IUNaQKQB2ZMoeQFdBmSZbHPSM3KZ50V2uLy3AFSEeAAIjV0P+sHhKDocOYchbZUnjZW1iQNpm6FZpxgtukioFI/15wMN4wmLYaQoPctGjEIIeZ9ZTOCHW9NY364jplA8O1yERinSFkOboeJgzkER8pi6QpBQZF9VCavAFdIALV8dYF5IkO55qKufm7zN8fhAEWOmbPd068Q5m4MIoJXXGtb+d10hC8N0xZSK54wQWdwkbXEkNIKYSTBSdLG2RQe8sBcm5ETZckSg3z7mTVBtLqBTIsMsROlYGiVQKMFgwUVMAVqN0jF1hWDCZF5IjMCmkSKGqjTJ0xYHByrkI3dlHLToFAN5F3nGQRsRX68iMpiPURwmb85qqoW/v3JJL07piuGUrmPXqxzGa5el8DdnduCLz4yF/r3oClxz93489vYV2NBevyxrRMQxDamSR4P0xqV0KhPX8i4szuFwGR/qV5/LOByTJkdMJUFRBdMRGDdLo7GqyAF3oOCiL6HWDPy70nYgVebjqwgkdXksx/tzmlcu6yqksmCJgIDpSp1m05MSK1/EXtGiYSDnYtziMDkgXJnE15910azRGsPL11aWCgxCxvB6e8uXGThxlaA9rqA1JqXJyo2R8smA6QpoCtAZUz194FKn+/bbk0NF7JqUSV+U2Dil08CrvAm9EFLRgwuBfRkGXSUwCIJkxPLOcLzwkozFUe0fsQSQdQQswTFpMkx61e2YEGgvC7sRkEZSs0GxtlXHpmEziNcGBAbzDDGFln2G4PwAGd9+IOcgFK+t1YUlBGTlyKQqwz1adQV9CQ37sk6w39I5Iuj/anSFQKcyWfG54SL2ZB3kbIGuuNx2zKyMwS04AnFVhnoUXY5daUdWRhSA7hlkuudRHTMZXC77q+AI7E47WJTSYLnAhCsN7tGiW2HYFx2BNDja47RiMuIyEXjom4zSJKGsiwDIZLps2e+sTNLQ4QI5h4GA1I3ll55Xgup5UVwlGDeBDDjy3h+zVGqr+wghJR1tJsBB0Smkwbsz7ci4akHQ4ZcR9Nq0L+tg0mEYKEhpQxAgqVJwyNUgx6sQ6le/TOgEp3YoKHhFT1oMBTolaDIoMiFx9n67HCGgliuuCARSjhmTo3W6Mo0hRAbzMcpw0cWLY/UVMADg3etb8IGNrfPToAXIP7+qGw/sz+OZ4XDx+XGT4XV37cXj71gZxFlGRBxXCECrirVUCUGTRtAeU5C2ymN2RWAwl8ct+uTLrEVdBdoN6V3NWhxthoyLtrg0TCipH0YASKPd9sroSg8rqdBibdJpYFUwL1VeU4CEIhUQfIN3wuKgVMp/OYEiQMXpB7KbSU0m8mne68LmApvHLWiU1MQV+0auSgleGrXAfcO1DC5ERWKhwzxPnsMxkBfoS/qFXbwYZiaQ1P3+Jcg7Ao8PFORpervJ2NJYJ65cGTCZQLLKaCxPxnQ4kNRIcIzy8x41mQy/IECLTpEum+xkHI7hAkNMpdg+adfIoVmuwI7J2vFHQMi4YMZDS5SXU71PJkre7PL9hf1/xpKVFnWV1OgkxxQSrKTuzjjBuQ8WGCybwDBqJQ1zjkwKHC66wTXjXAQb+hMP/5ZVCIFC5YqL77hiXKDgFxoJwWWVz4uvUS0goBFpOBIioCqoiH/WCEFnXPHit2UDyqUDbVfuxafZM779Z8C/LUccAifjQJDSxIgQBMYyAIybHElNyCIiHo53sJzNMVaU8nMJL8a6YDO0xyoLo5iuQNHTUPcn0FlFhmdl/ETCMgq2wM60VNQpMo68w1FwBVY2q5i0BHqTCjoTSsXk2hXTS/7NhshgPkb5+MNDQXnYMNa16fjGpX3HZGJfo+gKwfevXIzTf7irJqPfZ3/WxZV37cOjb1uB1lkmCkREHK2oiszw35P21RU4TCbjIvWc/FtMKRU3adJpRWU7k4nA8M07crBvNxSpAFA2pBVdGQs9ZgrYnkExXqz//iKQcZTB4CoqjdZJi6FFlwoZw64ffyuND/9JHykwFDw1Ap/qgDSblSrFuUJ6hH2v9oGsiyVNKpgQgXesBiHbaDF55Bat5G0PiwezXOmZrDYMGZcx4eWGEvVUMCzGkXcEVrVoGPEKXwguPY95Ry6lU4IgrjSkiaX/F1IRIaaSslAbAaVqmWEox4Ivp00eWpK5WsVMlk5mSOkcBaf+u1RTEBSnKLg8KFAT9oauriHQHqeBeoWAvH5OlQPSN/CEEKFa1GFwIScsjx4shhq8avUyDOBJnVWGHJVPOqopuryiPoD/VQIZy/3iqA2dUnTFaq+jnKgSdCXkJAEAeB1/WVwlwb2aKQuPKjLAMmWCZkojEJAx/YYqV3P8Zy3vCDicIaVRuFUScgLy94zFkdQJHE9xhguBCavWE1zOkpSKJ+tUThy3ZCjHpFUKIxozORRCMFiQbeHe9fQnO/4VGSy6jYlmN0BkMB+D3L0zi59UFQkohxLgh1cuDpIvjmc2tBv411d1468fHaq7zUtjFt74y/34zZ8sQ6yREmcREccIrbqCfJnzlAkReKxyDke5neSyUrxzeTJZuRYzAWo8rT4Wk8aygKjwFpfTmVCgERJUOfPJuwICrCIuVqME3QkFw57nyWHSYPMpOBxpW1aBS1tSzqzFoHAsWayFeOEoeUcu0XOOCuWBvMuxfdJGTJGGhZ/UJSB1b8s95I2O1fXK9e7NOqi2SQcKLiwGZG2G4SLDqClLHxMi28MEKrx1rg40aVJKrtxwK1R5l01XoDNB0VwVryEgQzlcIY1OnRIICBAaXgQFkB5hJoR3b/ifhveGocoplK6QIPY0rlBwjSNlUNiMw2beMcsMvtJeCYwqw9VmtUaaQqWnuF4NDZVKhQqbicCR4u/Vr5ZnqNJLHVMpEp6GMBMimAgqRP6dCVEj71eP6artsjpOnXLWtBpwucCkxZFQiNRhroKLsjArL1rHYQJ5RmAwgHEOlVKsa9MqJgdNZfdDxmGYsFlQarti/96/eVveGyYX2Jt1asrRl6NSUtdpBcAziFE1u4M3WQUmmYwl92kxKBQi2xm00TOaO+JSgzxk3jEtkcF8jDFpMtzwu4Ept/mn87twZs+xU4zkULn59HbctTOD3/cX627zyMEC3vXrg/jJVUtqvC0REccy5YaAU2YZlS//+oOpE5SUrt2Ppshwjqzn1U2qMtHJYoCVkwlhNhcyhtGuHTwTXjjCqOnWeA0hag0/QBrn9QyW/rxvNAlsn7Bhc4FWHWhWBZKGgpGigAOBYll1MIuX/t831noSlVn/KZVi3GKwXIEiZPiBL6GWd6S3q9mgiCkE7TGZtGdxgULIOQMymS7vylCC8slGxubQiJRdYxwYLjA0e0ZamOGdszkMBXVDAnwKrghCC9IOA/NicquLpBEKLGlSsSihoj9bmlW1xygMP/TF4bCqPJ05m6NJozUeY98B7nABi3MYVKpiMCGQszhswZGxODJWSZatWu2AgIDB02BGpZfbvw9UKnWrfe9v9UqGRgk64goOllWt9Lvdv4+kBxY4mHUqYq27EgpUIldm2gxFVnT0Q4Ma9GbHNXnPajMcZwRkTHDO4cjYvCasxCdnc7QaUuHDUAjGTVYzQU2XhWsIb4KUdTh6kyr6cw50haLXi2lnEBjOs6Bojm+g+vkLaz29ctMVcIRMRq2esDTpVK5UhSiIAMCk6cV4lzFmsbqTnrTF0ZdUMW5VhkoRADqlyDocZpT0d3wjhMBfPTqEgXy4XBoAvKovjr89K6pmV45CCf7fFYtx6g92BhqYYdy5I4ubHhrE1y7pPa5DWSKOH0aLDJO2QGuMIq4Qz7tcSc6WYRqK5/mViTuVIxmh0sPrlKXaCQDZMg9hR1yWlNYoQWvM8ziVPY66tyxcYyzPkvLBtujKSUHWluoYyZDtFVobZgBI7eHmqgJPdtlgXKE37McaWxxZb/zXPA9tXCVIaATLmzQc9N7hFuOlOE8K9MSVwLvabihQKTDq/Z1xedzqeORyck64Fm/QPOH/W4qbrtffgsuwllUtuiz5zAR0hcARQLroQqEIvV/qUT5hsJmAV+dE7sOonBAVHIECWIXx3xaTkxAhSuoYrTEKCtntftXAyTKva2dCQZNBYbocCgdsb7sJkwUx0006RbZKNFr2Ma9JTLR5KdFMeL+bLgtCTOqhKbLIDvPyAOIKqao7OT2+9vSoGe759Sm6AkldQCMkCF+pth2FqBQI4JD3je0KL9aeB+oz/iROhgDJA7fHStUnfWIqQQwEBZeHhusICCik/sSCQE6a/ftkiirboNSbgFXvy2tSzo6S/o57XnfXXty/r1beyCelUXz/ysWh8VbHO2tadXzxwh7c+ODglNt944UJLGvS8Mmzo0lHxPGDy4HcFKOwywAXAtARuuIe9tVyYxmQxlx7mWFRXYzL4QLmFImA5XSVSZKBSPkzhUy97At4cZzcz6av9GjplMAltQakzaRWsUIImg0KBQRxldT1GPv4feI7g10vVjilUaxq1jFuudg6ziu2n7A4kipB0YutVsuUQoDpl/WnMpbDmM4JdzDvYtyShUIUKpf602UGfD0mLBcqrb8mznjl/WFxXqEsEeYll+EHlQfVackrb/Nay59ArgikVAqXM1BHwETJwyr7i0OnFFbZvglBhQIIIA3GtMmRoVIxZbjISprKglVMoqpJarJEtryWAmnIVYiEOr3h7HvZkxrFcNGd0lj2KboCnAoUmaioWFiOX7Wy/JyDY/KSZ77Te9b8vAJAhkj5ZcTLC/wICBzI1s6iRgsywbQnoQbHHDFddBhKIBlHCYFOgUJVa1UFNasfnKNiNcynyVuBmS2RwXyMcP/e3JTGMgB86TU9FeVcIyr58Clt2DZho82g+Mc/jtbd7lO/H8aGdgNvXB0VNok4Pggr/BCGPxQ16wosRVTEmE5HnZw0ALVllOtCZMGD8njJXq84SsZhAKkvr1WOyWqVEkwvbldTQhLJPO95Spffa9IoCjWl7KYnZ0ut4ZSmgISEk1hupYe4WiM7LPluOmIqqTBAE17SGBNiSqMXkIZJZfhMY8c3XSChNd7W6RQ1poWER05PZYr66SqWK5CKV24Zdi/6kzHBpVRb+SLLdBM1mVRY+VnG4uCCokkjcERJEUYIQKeyiEzaKqluGCqrW5ykmrzN4ZcvIyRkYuSp1MgCMTJxV3gxw2G0x2WMcdKLa/fvUcvTYfafR9MVFTrXFW1yOFrKJsydMQUrW3QkNFm8SCFyBaCaamPZx2YCcY1UXKuMxaHGaaBAMlMig/kYgHOOt92zf8pt3rS6CX92Yuv8NOgohRJZFlwIgSeHirh3Tz50OwHgXb8+gD+8feVxp18dETEVI55XqnqgagR9ipWvRg3BuEpC1SB8L9xU3qV6DkCFAikvUc6axuWatblUAUH92Onp2JNx0B7jyNliVt+fKeXGsiVkkmaBORgpHJ7YF0MNn6SUh1goVCb7aVQu2dd4DGfZD8MFWeLc4RxWSHhIoFPtt4MAzRoBVSi64goO5lxZSW6Gnvnp5kp+MqSneFijde6T8zS5q/XIEzqBplROdMLOrxFC7zEhi6NQUmnID4fcEy73Co14kxKpSc3BhVwpyDkcuiI9vg4TdVdB8o4AITK/gQNIKHKfGpVe6qEin9HzwET4xKboCjTrU2SqTkFkMB8DPD1kIjvF4NSTUPCty45vCbmZQIiMab5zewY3PjQYGlOVdwTecPd+PHXdSnQlosco4tiF0qkrppUTSFTN0FgGZGJPmnK06LTG6J3K06kppdLG5YlSrhDBsqzpqW90xuQS74wGXh4uB6bS2nLDlivg6tKjpiuNebOrGStyjBV5jcc9bOnZp/oaGSpBq1FKrBvON278jlscsUO0lTsTCkwmk/X8PvD1f7MOrwkbYBwocgGmhscHTOfpnoqpVkcyNkdnmVwCJUBCo1ApRdER0LxrOFVceL39huEnviZ1WhHwM1WUJA8x7ISYG53hchgHprsNNAUVMeFhDOYZiq5bt0/KKb9WOQBNmgvqSarPdPJYrb/tU3RkOe2kNvMejDSyjnIO5hy8896DU06W/t8ViyOjboZ0J1TccGo7/v7crrrb7M06ePP/7J/VMmhExFEBkWWnDwetMYqwOXt7nAbL34LXausCU4drdMQUtBvyJ1nW1qKX4DVpymVrAoLBfH1j2TfoGqXe8rfFBHJeae6g7bOwbhivjBsNM5ZTOkVbjCJVJQFnuVLVIOfwWXlnq8MDNEVWV0zqNChDPRWjBYZcVTiOwwWSKkVnTEF7nAYFLcqxXcyrCpHDgIG8i4G8G6hmFF2BtMPgCNGQlJtPSqeYIiwbCpWVDhOa9BgPez/j5tTxzeNhBukCGXIaScANe96mu4cIARIawYTFsGPSmVKTfSqMOmaPw1Ad7t4QkcF8FDNadHH5nXuxO1OnxCiAm05rx5UrUvPYqmOLT53TiXN66oddPNZfxMcenjpRMCLiaCWlUYQl+HclFPQmFfR4P31JVVamm8LWmTTDl1QpIRXGp+BSzizrcPhFiluMSmO7NUaDH8AvSFK582rP4lTL6poiZe5CKinPCF2VK71Ziwfx23GNoi+hhk4WqttQDiFAb0L2a0sd0VgmBBwRXhWxYEupujAPZTWt04jStugKmjUFzRoNquTNFH/VQfUKsBjK1AbmfFNwBSxvwlawpRfSDblhO6uzUT0UOrUUnB9yVJ3E5/Kpi/SEoSkkUPGIa2TWMbmHG4WWYr/Lqe7F6QxtIWTIznidd0Y51UV+fFwOxBRa15Mcm8XDHrkdj1LSFsOVd+3D5vH65a83dhj4wgXd89iqY4/79+YCiad6fP2FCZzTG8f1UYx4xDGGoRCMh3h8XS4gKIFKKg2AmcbtKlSW9iW0UklDKkwI5GwZikAgVQAMRRolBAQFxmtCJVo8GbGpqpyG0XGYqniGKRDJsISpB35CpPcxY7Ng+b9CoaDOd6QRKjfU1XAZt1APZdV+4gpFu0Ex6QIxFQgryiYgi6FU2+aGStBmUGQcPq0yyHDRBYdfwCb8vGYTyqIpjXk7p0JK7pFgglgv1GC0GH6gqar4AdJzzeDOSGqvHmHJtIZKwEV9GUAANc9Z6DazjL2vR71dVSebNkKYtGPYvRJXSSD5l9Ip8iGdEukwHycUHI5rfrEPzwyHl5EE5EvnR1cuRjyqTDdrGBf4298P42Cu8g2nUZmlXG5H3/DAAE7uNHBGd1QQJuLYgYnwghp+BbH2OIVOS0Zzb1ytW0UtbICUdQrElIO4H4pgqLLoACB1YcNko6YzWqoxVFKxzHqor0vGZTnnavzxOq7J0mrV+r2B/nGd/YYZb9VGTUqjmGAzS4zy95NzOWyv3RQELZ7utr8rv6x1NZSiIekzH/++kGJtQHdCARNlawMCMBQKh80s0a4zVmnKZBwWWvxmJoTFqB8qh8NYnoomjSKL+lrljUjOxVUCm8kKheX7SelS5q7girpx4QS1k6CiK0KfCYUS9CQp8k79/YXtX/MmzWGFiqrhQk7uw//Y0CEriKypowyLCVx6xx48NkVVOgD43Ku7IwWHQ0ShBP91aV/NC8Dh0lg2ynSnTCbw5l8ewFhxjt+IERHzyHRm0HiRYzDPgjjQEbN+yWHTFTVL8C4PN8TC4EJg0pLHGsqzab2ZjWC5AkVXYKTIMG4xL+EQswrNaDLotJ7RoiNqjGUAdUMTHCEa1o0db1BFICw0JGtxFJjUoc65AgWHg4CAej/1wkla/AptAOIKmfEyt80FdEqhU+JJ9slKfU06nTZMpB6OqA7OKZHSG9/ndOHLa9t0aIqcdFXH2GuKTHzsTCgNxXwfLhiAuDp72TSgNBGrLjwCSC3zlCZLzncmFKR0WhH77/JaLfCcwxHXCBI6gUrlipGmyBUG6mmWN0p7TEFnTJ32O/4zlrV5jV72oRAZzEcRTADveWAEfxys71kGgKtWpHDz6e3z1Kpjm/MXJXDDKW2hf+NVo9PerIPr7j1Yo40aEXG0Ul0ydzrCEtO6EkpQ1tpXcmgyKLqTM7MkbBZubB4OGJfGMyEESUWEJqRNhxFiYDQCIdLwBKR3r9w49ZPDDieNGNXVm9QLcSn/XKd0xkWx0pbU+rW5wGCeYSjPMJxnyNi8Qkt7OrIOh+3dXKZbv8x4o55MYPp+ytgMDpP3TfUk0WHy2o0W2LShIocafxzXpNFpuUIWTrFqVUjqxcCHISANX4XICVBcI0jqBLr3uBLIYjAaIWjSqCyxPY0Bm1ApWjQFnXEFnZ7R6yfozqSQyJHW+YoM5qMEAeCuQRX37Zvas7yiWcP3r1wcyCxFHDqfe3W3TGgqgwD40sU9+MSZlROT+/fl8dknRuaxdRERc0c9GbPpiKkk+MnYtbGtOYc3tKQKSM3ZzoSC2GEIL5vOA2p7OrEznCcAQFAWeMZtUmQFQktIVYv50F6eDpfJeOMR04XNa0tA+zgMGDVdjFkuCoyHJh9OhSyGUWsIWa6oGyscRs7mGLdk0Zd8VRsIkZ7l5DSlkFt0ipRSe56GStARr7336hUjmYkXuyshDchGCBvSUzpFq65MW3qb8cYNcz/UiQmpHNPqJXwatCQLOG7JFZkxyw0Njaom53AUGZfKLa4IrlPGYUF413SkDAoQBOovR4LIYD4KEELgR9vy2JSZ+nLpCsHPrl6C9sOUwBIhaTEUfPk1vRWfCQAJTcG/vroHr12WrPjbvz45irt3ZuexhRERCwtdIWgzFLTVGcgFr/T21ZN/AqRRpRECZ5byjYSUDGVzmn2MWxxpl2CiLKmq0TCD2WhPA1I+q+AIL6Zy6mPNxfJ+vI6KAOPScLbY1LGvDpOxuWmTz0ov2WKi4RjoqRAcod5VIbwKinW8zgm9FC4QhuuFjahVfV8vKbDai13P+0qpVAxpxOAEaj3exJMrnLAYJsKyNIMNZZy5X/FyOvxrmLc5RotSrSbryKI9jqdt7lectF250qp4oRb1KDpS4jFvS23ujPeTb6A4T0wlaDIomlQKQyFIaGRepQfLiQzmo4DPPj6CBw5O7VkGgK+8phdn9kRJZ3PBW9Y04dpVlfJ8H39kCOMmw5tCSmS/5zcHsX3Cmq/mRUQsKDIWx4jpYtJmsyreUY5viM6m0AAAdMWVUqjALJpSrXF8OCEEQYylANA8jZbvXER7rWrWsb5dR1Od/vW7biot7ENB8ww/QuXPfErN9SYVtGgKOgylRhIuphJotHTLzHa1xXJFTd91JRR0e/J0spDJzO/tFp0i70iN8SkTJAUCj/BM4d7ENmdzUMgwk7DnuUVT0BVT0RGnofcJITO7rioFOuIUXQk56daoTEydtDgsVj/kplFmGwYTGcwLnC88NYrPPTU67XZ/eVo7/vzk8FjbiEOHEIKvXdJXMXiOmwyv+dke3PhgrQ5zxuZ4yz0HZr1MGxFxNNOk07qlaQFUJO1oyvRlfV0hoFNS1xs6bXs0it6kTFRaSNFqbQYNzmnC5BgpsLpVFZM6OeyqR5TKGNImndatFhsmYTYbCAk3VDI2R9piUAnQbtBZx4LPhqK34uBHcQgQ+Au0LhdQaUkpJKXXGoOE1o8P9i9VWIiGVJMoVadsnsXSwaRZ602vh8vC5dd8posiaY/TumsA4ybHhCU90QIIvUeFaLxaaEqnSHnvj6BSJxOBtvlMlXDKafESFLvraGlPR2QwL2C+8fw4Pvn74Wm3u3ZVCv9xUc88tOj4ZmmThlteXalrPZUO9oujFm54YABiIQQlRkTMIwqtVJqIe5n1vqGcUEuFSKrH8ZM69ZqEQJvLZftqb69C5WDeEadoD4kzBYDhglTWGMwzTJgzK4s912hU9stUy9k+9eS8kjpBSq80NAkFur2iMl2J+jvnXCbeHcjOrbpPe5yiN6GiNy4LsXQmlCCx0mGln3GT15cB82hUPcNXeai2RctDbNImx0DeRdblGCww5Blgeo5Yl8sCIe2ekdWkUXRUGVoEQEKh3mSmtiAJID20ukLQlZDXojOhhJbCbvS8YursPNLlVNu0001SlDqzTL9vTe/erBfXXU2zQdFUp7JmzuZBhc5JzxifqV5zPcqfn+4pnot6RAbzAuUHmyfxkRDPZTVn9cTwoyuXHLGYnuONvzilDef2hoe96LR2pv6DLWl8/YWJeWhZRMT84CdQJXUCTZGDb5jn1yhzxxUdgeECQ9GV+q6+uyqhE4iqEtCEECio9CanTQ6L80CHVVX8qmKyapxOad1BvZx6MbYpnaIvqaI3ZBAdNw9NpSKpk1AvpK5Kw6HoiGmX+1M6retNbNYU6FUV1gQHhj3ViamgFDAZr6mSWE1nXFZzrNfF08l8GVXr8Rohwf1R4bWd5hKqSv3Y4WoIZMhFZ0yFrsp7tCcZHldfXca7Yj+EBBUnKWTymf8M+GXjmzUl8F6GUXQEVEKgeuoSBATjVkmOcSDvwmICnQ1MniwuDklj2lCBLi+eWffCmuutLvgQAoyEJGFWK4AQ1KlGU4XNZOzydOoagDTGG/VOT4dvz5Oy/86EyGBegNyzO4v33dc/bbjdGd0x3Pcny2eUlRtxaCiU4FuX9dXM0DUK2Fz+VM/Wb354EH/oL8xjKyMiDg/dCTXQXO1MKOiIUyk1pVLEVYqlKQ19SRWtuoL2uPQaJXWCmEKwKKmGxi0KIYsCCYFSIl+5wez921T2XkuoJJC66oyp6Iqp6I6raC8zfgSk8dWZUGY8Fup1jJRmg4bGDYclAmoK0JdUAwk9H87DY48dBvQmVTjTuLzb4+HlydvjJTkvOkV1tqm8fpyXitBMZbsQIicb9Y6R0ML1dNtjFG1lkwVHCNhcTn4szwpSCAkqLQo+dcW+mcSTL21SEVcpiowHBUPqBxaEk7E4RgssSBYlIGhSKZo0imZNQVNZeyanCV0pMC6VRLxZW3WIRNGROuPNU5yjOkWlvphKpjVACZVlzjviCnqTKjoMdcrv+PVgwrYov97+RCpn82DyEQvRp/YxXQGL8RlpMPv4z7jRwPlOhT0LKzyytBYYv+8v4G33HJhWNP30rhjuf/NytEWKGPPOKV0x/M2ZHRWflYcqW0xUDD4uB952zwEMTlNiOyJioSEgPcO+puy4xZG1efD7rrQTyHgZlCKlUggAYyZDf96ta574xoVvSJYbAf53KhyPpL7h5wiZvZ+3pUrDhMmmTe4rH8ibdIqVzbo05pioHBRJePZ/WFEHQA7C1XHbRa8QSNgiIAWpSTarJufwUJm7vCPgclkMRlcIVEWGd1QbKY3EIHMhjdsOTaA7LuOq5cRIevqDJpLwaogCqChg4WMopcImQKmQxLj3Y6gykldXSEMKIHW7ishrqngxxS0xihZDmseBR3oGtlW1J70RQ2k6PeG0KeNvs3blzV4+MXTZ1O1srTPet8YoWg0FbYaMsw6bABICQEhDVwBBeIlGZdy2QQFDkUa14q0aJT3jnUKWr/cLkCT1SmNVD7kwrQZFR0ypmUCWE1coemaox96kU2iEoN07Xz/muR6GWmpz0lNEYQKV16FBIoN5AfHiqIlrfrFvWnH+S5Yk8MBblkfycUeQ/31uF05o05FQCb55aS/ef2JL8LfehIqvXtJb8d7rz7v4k1/un7FOaUTEkSRsqKv+LGtzZN2S58xhAg6T3qZ6IRC+1FuLTtFeHRdK/OOQwENNvQpjPhwCE17mv2+8++/NRqTNfEPdUAlaYxQxhSBtyQlB8HUiq9ddsDhRY8yFPceaQjBp1eooN+sU3XEVK1o0nNypB5q+vudVmcaSs11p9CZUUmEw+gUz+hIqljXrSKoyLIWSOooWUxxmuMAwXOQYcwiGixwKkbKArYaCVl0JPLMaBRJlHtCWGEVnQoFOZUXAao/fUNFFxrP2/XCIZllLHboqlU/aYwpOaNODMIEwplXoEPK6My6vWUKhWNGsz6gPVFLapNqT3UjEY3Xtg1bPcK8XWw8ArV656er91PO8jk5RxMZQCVp0BcuaVDR7hmR5v/UmVPQmVNhMFl0BgLwrtbNdLnMJYgpFb1yu3iQ1edX99qsEaNWlqkizpiCuUOhq/XLylMrVgxZNhvNUx9L7j0n1fRNTpVFcLTXpV1UsvxYEsnhKaopk4FZDFk0BCPK2VNgYKbBZSSA2JswXMefsSdt43V37pl3WedvqBH5w7fK6Ho6I+SGuUtz++iVIaAQntBn4wEkCeVfgsf4CfveW5TihzcBQgVUUMXlisIj339ePH71+MchCStWPiGiUkLl8k0YxbkpvYVyp9c4ptGTEEi90YFWLBk0hMCjBQN5FSqdBQk7515t1iklTemc74yooZPnfHZP2YYlrlFqyAtWvUwJgdauGRUkVJ7Qa2DJmBSW86716WzQFA3btKpJvEBUdmbjov7uXNWkApGGRMuiUcbQ+hkJqHCp5l6NVKRkjvkpBtWc57n232aDTep2rE++YkNVFuJB/I1QWXOlLqhWqBe2GgrzCg/1zLvV8OfdDGmT4RtaV94HNpGcTCJ+EAJVyZBMmR3ucYnyKcsetMQUGJeiMKzXv2YRG0aQR5BlHpioWulmnMJhAMk4R0yhsLsCFABfSiHWFkAUzhHwMVCqNNeZ94HuYiRc2oRBZ/bDa85xUpZbw8mYVCpHXJK6SUnw/ICcqhpSDa0SWkQCwXQEbpUmjwwXaYwoUIkMJy58Xl8mJrX+dmFdAprp6rUIIVjRrcJjAqFl7b3cYKhIawVCB1awA+c9qaV9yghWsIJVdmiaNwvIC+XVFVg+0OWC5HJoCdMQUEEit7Jn6nBgHDA3I5w/9hREZzAuEW1+ZxEADS/Yb2vTIWF4gnNYdC/5fpQTff91iDBVcLPEGwg+f0or/+9xYRRGE27ZlsL7dwGfP65r39kZEzJThAgu0gSct7nkvCTRFBLGm/hhr1wmZSGgUWe8Z8LftTqjBMu72Sbtu1b+Y965TCUFvQg5XDhfYRYDy4a9Jp3C41KNtj0kDfipqikBUGVadMRmjfW5vAgWXY2WLDkNxUHAFNApkHAGnykVVZLzC8K/GUAlO6jTQn3Pg8lJS5AltOjaPWYgrCjI2r2sg1TObMjavWG2sZwybQQW3xozm0nFFRXnuAhdB5dMWXcF4kQXXQmvAEeByIWOVASS08uOE06pTWEwgz6XRTokM3wiLdSYU6Ak81SQoGw0ASZWiYHN0xzVsbDfwxECx4j4gkEac7ylurwocd7ioCLfRFIEmjWI4z6ApXqgAJVjdonvGo8BAjsl90tIxUrqCxSk1kJUzXQEQgWKOQfXUZQQEXFE7cfFRqTQiVzRrYJDPyc5Jp2IbyxVQDYKETgEhUKjaV9bmaNYpMmX3a/nVU4mc1J3UGYPLBQYK4fZJd1ytKZxiqAQK8SYTwb4JEiG2y6TFKiaBfjMXpzQADjQqkyQNlaA3qeJgzqlJkl3erIFSgqLNK8Zb/6QKIc/UbPS+o5CMBcBA3sHOyfryZD6tqsCHNtYWyYhYGGgKCYzlPWkbF/50b+3DC+AfnhjBDzZPznPrIiJmTndCLs3GFYp2g4J5HkOHlTLcg3vcGwtbdAUpz8g2VBJqSFfHJ2OK34FKFQ5CEKgT+MQUEhjgeW9w1CjQk1CQ8uItDa9Ud7UWMCFStq7dUErL4wQoOBwalUvAOiXoS2nQKIFCSKhu7aTJkdJKcnlxr0KZz9ImFUwIJDUKlZBAbjKlUek9I/WrzQVtDfmMc2noTZf87ceJq5ChMCpFTThAcJyyj8Nstq6EgmaDYl/GqYhxFxCgpH5FQl0lsLj8e1IniJcZUPXU5AiRky6/TZqX+BkaM02BgbyL/ryLgivjhR0uYHNRUVFP9zzQlQcqO+eQuUR1WAb3VB50FWiLKYhRCgUytKc7XgpjIZAxw9wLGbFdqe+8vFmOFTGVYFWLLpNqPU8qIGPx64UNtOgKdFWuVhiU1CShVl/VFoNiebMWSNv5JLXKQjmuEEg7DJM2Q9riKLgcMZVApSQ0dGhZk4qkTmsmoDPR0q6+7JQCq1o1tBgU69oMrGnVsaZNw/ImDQYlWNWsI6VTcIggkbLgCnTHldBJF0W4oTubhMHIYD6C2Ezg354exQn/byd+uDVT8/dmjeDPTmwNZvPX9LhBEH7EwoULgS9tGkf/FCsG77uvH7/cFZXPjph7HnnkEVx77bVYtGgRCCH4+c9/flj2W+0JbfUMNgIZv8w5ghK6NZSNVdWlgcuHMYXKZX//Hbih3ZDL+l4ymkKlwUEAZB1eId/mekvp3QmpptHuleo2qiyfoiuwddzCUIFVeKLcIIxAbm9QEiRKpTSKZJmBqlCZ9EYgjRO/bHHWkoM5iMBQnsHhsupbk0HRGlPQnZBxwr4xqIYYP+UU6mWDC5k41xFXkNQIuuNS57hZp7X7I8CKFg3n9sZxWncMXQkFcU1OAjQipTGTasmQCjtim6GE5tAQIo2oVy9K1NV/TqlUGryaNA4T3mQopRF0JmSf9CZl3GtvslQNryehVChIhJWUbtIVZL3KdBaT4TaUePHNQhpj/i4U4lXc836qu6l6AlJtMAshr/k5vXFsaDMCXelWQ8Fly5Kh597mxTSf2hnzPKgIzs+Xm/MpN+gpkUVuEhpBm0GhqwTdcRVDBYZJm2Nfpnas8SdfQkhPeFtMCcrVK8G1rZRsMxSKgi096UVXYLTI8PRgEU8PFaVetcPRm1SwKKViUUpFT0L+xL1cAP8npsgQltrrUzXR9ZQ0ylfNOw0FKiFBQqzi/X85NhdgQiZSZiyOhDdxqA5pTWjy+a+ZlFOgqRHx8yqikIwjxKMH8/jQAwN1C198YGMrbnl1N7oTKsbzHfirsRTWYHyeWxkxU7I2w/W/6cddO7NYnFLhchGaxMmEVM64903LcMnS8JdrRMThIJ/P49RTT8Wf/dmf4S1vecth379CZWKN7llY+WliLlVaaXzoCoVdFphYocns/4+QBoTiJRLFVQriJbj5xgSoTKAzvfVa34CptviqW5e2eE0RBVK2XflYO27KSnxalXJdd1nCWlyhiMVJEPOcsTkYpDF08eIECq7AyR0chkJqEsXiCvG8irV9WHQFDJUgqRJoCqk4vqESXNgRh65Q/JoJZG0OBQSGAjCbBbHjANBiSO+nbxCrhKBVV+CqQJ4JnLsohsXNMezNyiV+lwMTVjFYBvdDHFQqE9smLRK0oSumos1QcHpXDFwAnXEXDhPQFCk1mFApdqVLoQMCAgolYEKAEAKViAqjkYCgSSewXBmCkNIV7MvI7+sKqQiRMFR5LuXXCvDi4C0OlQAndRhBEYzTe2LYNGQF26d0ikKZ+qeANLB5sIBSGQrCASiQnnsXAh0xBZ1xFa9alMDerIMDeQckxNBrNZTQsMrya+SbzuXhRTGVIgapbtHklcXO2hxLm1TkqiedBMGETgigJabiqhUp/HxnBkVHoFlXGiri4+sqCwDNMRrEfa9rMwIPeX/OgRCVqwX+ccvRVTn57U4IjJsMLgdO6zLw/KiFrM0xmHfBhYzb7vLkLP2woR1p22uPV2bbu87dcQUKJYiptCaGOqYSLG3SsKpFw2jRRUdcvjOIt5+wFYrpiAzmeWbSZPjbx4bwrZcmQ/9+dk8MX72kD+eUFcdoNhRctCiG/v55amTErBg3GS766R68PCZfwgdzLlY1a9ibdUJlAi0m8Ia79+O3b16Gc/sS89zaiOOF17/+9Xj9618/q+8K5oJ7N69BAQi3Rgc2rlKoAuCMozOhQCMcuWKlx8tXxQC82ERWinvtNoBsWXxks6LDaJbL2EIAuyYdMAYsiutIFx1w5qJgSaMpaIqqgHj7bFYF4jGZOT9WdAFKwJlAV0J6154dtmFWrXVzRiEYhxDcO2cXSYWCuS444+BMts915L9cADEioOvSK+z/vbrvSv9PYBCCwayFM3vieHnUhWClpCjBXblkD4FskUnPuUKQr4rtbo2r4JwDrFSyeXGTijM6fEUIjrO7NOxKlxwxGZsgYwvkHCFjX3Ve2V7uglKgRRFo1hwsTxB0xylcl+JgzgHnAi2qQF7IGPakSsGZi1O7YsjaCiYKNggB+hIKkpoA5y6Gshb2TBaD1YWOuILWmAqXs4pjDxZcZIoE6YKCkaILnRK0VMUOdyVUGHGCNa06EhrFQMaExQQMCGiEwXblpI1yAl4W25qiGtp1gdEiR4cOtMaAU9s1pC05gUhpFDvGijBdgZROMVl0g2vPhUC2CPSlFAzmS4ZlnHLYZTG/eyY5+lIacjbH4qQKlRIw1wVzGbjrllYnwAACxAiFEAzMdSFI5X3DmVsbWy8EBGPgBOBMrno4GoMpFPTnHEAArZq8J8a8Z0hVgMUJBUN5Bs44GCFgjoONbSp+zuRnSSowxlwIyP379738Kbs1mLx3GeOIgcFVOCzHAWcqmCvPTT4j0+dfdSVUcCalJv3bNUYEzujQ8PRwEa2aDJvp0AmY62BZQsMLBVvGczul/RMi3zmcSflIIgg4oyBcBM+cQoGEogDMBeUKBGPS2C3r39kkDUcG8zzyx4EC3v6rg9iXdWr+llAJvvyaXvzZxtYar0PE0UGbQXFebzwwmAFgV8ZBX0KtmzCRcziuuGsffvWmZXj1oshojlhYDD75G0wW5P0cUwhcJldMynWBaYwg7f2e1SgoAfIFBi5kXL/D5LK47cUDj8UoMuVVAF2OdK5kQG/dqcEuG7QdzjGiUTydUqW3VgD9VStzSkrFQM6FgHyXdjVrcLiAsDnGmEDGEchrBEMKxWjWgV+0jEJ6ES1KwLgA4xyu64K1GBhUKB59WYfDBPbm5BcGC6UB2WaAyQRSKgkNW0iXVWOjMYKsoWD7bgNo1fDCmF3hEbMnHUxaHBYXyNhSC7o9RqWKBxfQCEFfgiJuUIyaHEUv1KBJpzhICR5trhzKd6RDlui9fw9Ufe5ymchIiYDu5PHK7iZsVVUUXYGDeQYhBIa9DmvSKAYdDiRUuC1yj8xLNttfNm6NxCj2TthBXHJGo1BbVGiUYGzMljHnCgErMGQIkIV0INgMyFfp9joxipXNGrZ6Hsyid26TliwoIiAnTjaAPKRNFFcJ+jt0xAXQ4crl+xad4NFXSvsuOBxDWXnPTADIWgxFy0ZW00G8JRDuJZrajCPnyGtRdAV8m9nRCFQvXKiYlNfGTamwmMDAcGkc8CkSINWk4vdJFS4HtpdNbMbKElULrsCylIJxk2PClteHqQR5V6Cfylj3vCNgKATLeg3sz8nr5PfFHi6C3AKdEhRjCu7clUR+2EJ/nmHMYsHxGJcro5S7mIjpyLmliSjXCawYRd4RQfEWq0XFH0Gw1wu5yTsc/aNT52A1axR7Q8JJWauKToOCpl04eRcUwDgh4AbBswnZjzvTDsarQi06YrKvAekt5gkVQghMpl006QQcwAiAgkZRjFH0h5V+b44Br7puynZXExnM84AQAl/ZNI6/fnQoVIAekA/I/qwTGctHMYQQfPOyPoybDHftLMUnDxRctBu05qH3ydgcV9y5F794w1K8dllqvpobETEtGrOguyYEANcFbEagCLkUDchwCG4JWFy+txQXSCgCSRDYAmCOXMJ2hTRmACAhKgv7cAF0UGDC8bzKRRMagElHbqRTgSUaR25SJh1yAJZVOfiaKCLGCBxB0Ew5eAEwCNAugAIHHCa9j3kAxCVIALC4VALg3j4JkQOirgBdpIhhk2BiIg+FCNhFCkcAlk1gUMAUsliRyQDqlkJMkgrgV6PmTJ5bSgHiTKCTC8TzGQwVgUKeYK8pCyhQADmXIOsQr/qh/IwoAkkhPXztmkAXBKgF6A7Aua+wAFgAJnzj25M8y5vh4wgl4cl1KU0gqQKgQCYr311xRSCf9wpXMNk2IgQ6iEA+TzDh7ShfrD1WOxMommUx3i4QNxiWxwW2uAq4C5gAbLvsu0KGN1hV7ctxgUnPqC8/niIAyyl9P6kI9BjSmG2nQCZdiq8gAGxTYHFcGpUWB16aVDBklb7vt9Z2SsbfoC2NXkrl/g3IpElfoYw4QD5vAgBGbGmYTziAxQDTpKEJrFkUMWED+4oEFgPiCpB15f3vh327DLCKAgkBOCBgLgAu7ymHAIwARVf+m54oIm8TdOsCw15/Skk2+f8OgBZw9Penkc5SaAIgFpDkZQmXhIAr8tx1AdhCxng3CYFiQa70WC4BBZDNFpFQgAl/PiAA16IVEnKAfLZc7/4WQiAfYlMrKgMsgBQoFgEoePc+mNy/y4FckQTnAsh72IVAjAEGEchzgrz3ciGMwC6bpyiuwKQjKt4XBgGaNIGmelmmUxAZzHNM2mL4X7/tx8+2T53g1aRT/NeLk0jbHF+4oCeSjjtKUSnBj16/GG+4ez/u35cPPh+3uPQS8Eo5LJ+CK3DVz/fhO5cvwrs3tM5beyMipuK0y98UxCSbLseBfGlYbNIIDIV4lf3kXd1mSJ1bP+LhYIHBdKWerT88ndSuQwmpBDFccEEIcNniOOIqxZjJ4HAp8XbdCXIiuTvj4OlhGy1DJnZ5iU46JTi3W8eAp817RqcGjcqCEAXv2JvG7MBZUXRkSd5JWxZsiCsIvssZA7eKeO36Niiajtcti0OlwP/sKWIgz/DyhI1Wg2JP1oXpSsO/M04xbgkkvRhNJqQM20DeRZNOcW63gZ6EimtWxNHjeSx/uiOP05lsFwUwZjGkTF+7WMDlwKmdOhIqkHWAs7uNir56YUyGQVBIb/cJzSqeGi2tXLZCev7KVXpSGsEli+IwmcBggWGiLLRgTbOKZsrgpIeRbO/GyT0pjBUZ7txdMjqbNIJVTSp0hcDmJcm/P1Z5UnUKXLEkjtEXS4ns7QbFDWe2YE2LjoOPj8MPc9+RrlxtFUIgY3MIAF0xmQy5LKXilA490JfelXYw4p1X2uIYNWWIxcntOq5bm8KLYyXLbGfaCRIlmzWCD21sRpNOkbY5vvjsJF6eKB2fcIZ8sYhYLA6qVIaFaBRY7ikgpS2OcUuuoDTpBEuTKkCAZpVCpQTLmlRkHQ4cLCITIpd4ZpeO9a06/jhsoV0nWNuq46VxCw8dMNHmJfoRIbC6VQ/6pFknuGpZArfvzGNn2oEAkV5hQvDaE1LYNO7g9E4Nz3n3QN7hGPCkAOMqwYltGm48qy14fgoOx8uTDnpjFMMmx0DOwkC6UHPua1o0rG1W/3/23jvOkqO+Fj9V1fHGuZPDZu1qlYUCSAIFJBASksjBwAOL9xwAA7bAP2PZ+BlssEwytgGb4OeHwSbpkUyWCJKQEMqrsNJqgzbv7OSZm2+nqt8f1d339g0TdmdzHz7Dau50963qru4+9a3zPV8ICBysSEeKm1YmMF71IlaMqbEaCg6HRqVWfVfRxYW9Gh6bsqEShHrnZvze2WmsSiv4/s5K5POqy2EqFDWX44FxC2NVD72GTGjNahRn5epFaaZrHtZnVGzN2/j5/lrkON06xWCCQW24zimFyJLtsYb5+MLjEzW84Sf7sWMBy7iEQnBWTsOD4zX806YZPHCwim/dsAKrOgyyGMc3DIXih69cid+9YxS3b6+/NIIXdmNCUSMcDrztjlH8fG8Z//Gy4bi4SYxjDl3X4fnvT00XWK8jzHYbSSnYMWdDCEDz/MphAiBCukQULQ5NVaA1PcYuWZnG5qkoyVqZVpGpurBdge6MCUoIkg25sNlsVv4LG8lyFS9YYSA7K5+rK1IKugwGs+yiYHN0paU/epdOUSg6mK156Esr8pgqRcXhmKx6GPRt1C2PQ3N9jTLz4HgOEskE1vemsHYgiarL0TXN0JUGZrl8sStVGyrh0BnFcFZD2k/AMvzyZGsNgKmOTHRL6hjq0tGXSyFrMAghkEwCSQBGqQJKAcpdaE266nMG08gZDLsKNpJJSRDO6Nbw7IyNy5KticJGMUo6LOKBN5gVC0ZRJjpW5FR0pTk2TdTJRU/GQJ/qYUSdwpln9iGZTOK+AxUYer1N2QRDT1ZHr8lw+XAC3/dX0Qw9+pxSGUEqnYKm1q+xpjMMd3chm1Jx5oCL7f61O71Xl77MfmR8tCR1tDojSCdk4ZjTchqyOgtLiJ+dEDhYdrGn4MDQgb60AAHQl1LRlUkjWav3S1RqCGo3OgpFKp2R16Dmgag1aA0yAU94ILYDRdOh0FYHBUPX/H+BAf8zQgWMgGASIKExJJMqPNuDaQrU2oRHBrNJJJMqDJ3ATDAkkzq6bAW6IaD5fnwjaYa+pNRG9yUUnJZVcfX6LPpyNfzFfRPwOJBTBTRK0JVJ4XTmIpdS62OACWiOJIl9KQbD0JDNZnFOWmAGFYyXXVzXQzCSVLB5poa79xRwsGSDqBo0VqeFhq6ht8uAyShW9cjPXnpaGv/9XDQAmDQJkn7aVW+CAaqDi1ekMOuVcKDkwtA1KJS0+Ep3ZTPozWo4b0QPrXVtzpH0M1KZJ6DnCfoVmVQJAAldnrPwu5PA687I4hvP5rG6zHCwVJdfrO2W40tTKaqenCBDAMRjqES59aIQE+YjACEE/v3pObznrrGWzM1mmArBq9al8Y1tdWL1wFgVX90yh7+6JC5ucaJCVyi+ccMIRn6t4B83Rd1NFloI+uqWPO7eX8Gv37Aaq9uVd40R4yhCoQRdOsVU1YPR8MYIVsEMRrAyrcjCD5Qgo1FUXQ6FEsxWowu1K9IKMlorGVmZVrHPz+1olqWd3hBNChIHpXRB3km78g66LI71WQ1DSflZzmBhlbZR/wV6fp+BpEpheTIqGSRY9SUU7Gmw5dKpwOq0iosH6onXzaBULs2vzao4u0cHBzBWdlDzosUtGrsSfFpq0uVx3t7fNQjCr89quHwkIRPrkiqe7eCs1AxpQVo//4wAo2VZWOmSIRMlh2P7rI2swWQZZBK9Vs2LAMNJFW88PQNKpPzsvD4dT0626nQFgPGKiz6TYdK//rYnMOhrfV8waGJX3pFEhtb9swHptOFxIGsyDCdlFbkBU8GVKxL49f6K3y6CkZSKPYXoeAkcFBorSzaCi3olvcCOsBEpRlCa94zKoi8HSi6qrgj18i9fK8dVt0Ex669UyJLQ9d8bYTSsHpOGf/sTDDqTOQCrMxpO74quKhAC9JkKVqZVlB2OpEKhMYLXb8jguzskgdWY9D03FALNt1tMqxSr0vLcK5Tg6hUJOFxGzR8er4ERAseXJ3mB9YePXIMvdGM7OkFhBGsyWpismlQpNnZreF6fAZUS7C06GPPtVikBVvrWes8fMLA2o4ILYKzi4JmGVQKNEZzWpckS7QRQKcGGnIbtszYoQWiQQIjsK0srmLE4eg2GvoSC2VrdrYZzABqB6nIkjDjCfMxRdjje9auD+M8t+QW3zWgUP3rVSqzJaHhq2sJmP1nsmpUJ/MXze490U2McYVBC8OmrBnHpkIk/+MXBSEWlhbC36OB3frIf375xZVgMJUaMQ0GpVMKOHTvC33ft2oXHH38c3d3dWLVq1YL7r0yr6NJl4k9j+WIh4JOnejlmhcoobs0V6DEoujUqk5AKDky/cMhjE9XI8cuuhwfHqpixPBQsD6fn9IgHbSOxCar9NYNAvlhfNJxAzieAv2yQRAF1wqozinN7DczUPKRVCpUCT05ZqDgcNU/g4iRHvykj0aZCI0UYVqVV7C06GEzIimNDKSU87uqMhoRCcf9oVDs73zpRUHGvpdKgyZBQKaaqLvaXXJiqLA6xNuvh0iETDxysdjhiHcU2z5sufxl6TUbDK9ZR/HR3CVm97p/diOYyyTWPR6Q0nYpqQMhqgzmDhVHFjEbDPp7TY2DXoAMupObX4wKP+dHufpNhf0NS/LqshrRGI57FnUAIMJCQHtD3Hqi0OE5wIVBx5ESOgOD8PgNbZ2Xy5Rk5HcOmwMNWAZ5GUfPkCWl2UhiveDhQcsKk1OG0AkqknV2voaDHkM99SoBtcxbQxh85aNYLhxNYm1Vx6VACj0/WMNUwuVSaxgMXIiwv/kLfUUlARuYVKsf9b0Yr2NCl4ZkZC+uyGjRFTjY0RZa3rp8ngmDOKoSAzqRswS0L9HarKHkUXABDSdaxDHwzLh028eRkDaszGjRK8OrT0thTcEAJsCGnh8+HFSkVNpfe7CvSKlT/CwghGKu42DZrRwodEf9c5HTpVw4AA0kFFw+YOLtHerIbTdV+EoosRx5MTEyFIGdQjFVkfsJAUkHFFh2fJfMhJszLiC0zFl7/o/14ZqZ11t2MXpPhjteswoX9cnZ07xvW4LU/2oe9RQfffPmKthq/GCcm3nh6FhcPmHj7naO490Bl4R18PDhWwwVf34n/um4E162JkwFjHBoeeeQRXH311eHv73//+wEAN998M/7jP/5jwf1NRfoeX9BvQAiBRyZqYXTXVGjkpdpjSpIEeKAg8Ih0eNjQVY8S15p8mgcScul5Q5cGSqTv7vMHTazLqn7UsCEiRwguH0ngRx2K/iiUIN0QwY688Bu+NqVSpPzl+JLNsSqtYLTkgsJDwSN4eMLGmbDRY0opxwsGTTw0VsVgUsGMXwZ4Q5eGQVMJAx2DSQXrshpWZVTsLTh+YQ2KFT7Z85oKoQDAmrSG/dTBzoKNwSQLo605QzpKOFwGVoJy2yWb4+weHSVfVrI6rSKtUewvORGiDsj9JiseehMMU76edWXD5HtlWkaMAzmEVa1gvOG07i5E9cXbmiLb3nwmvn4XmwtVANLv+BXr0rhjTwm2J6BSgo3dckk+ozOs79KQMxhOz+ltVyMCrM2qyNs8vK4X9JvI6gxZneFlq1O4Y3cJAwkFrpDEsuR4+NW+chiR3tCl4UOX9uLJKQsGo6hZFs5KcyR7dJQ5CyOT22br/Q4miAFyOguJ+VBSjcgoD5YcPNHGKaPxrAWHOq9Hwwdf0IsHxqooOhwU0pFld8EOSfvZPQYGko0ezgHZlFUkL+w3MFn1cMWKBIQANk3W6sS7Q1g4aEtWpxglUrM8kDbh+UR6y4wFGUtvjYo34i0bs3hen4Gpqoc1GRWrMxqe1yerLKoNXEZjBGfkZOQ8WHEI8My01ZKQ2jxpA4AXr5ATBrMNUdb8yXUXZfB43Qu6z1TgdMn7hxEpiryw32h7TuZDTJiXCV97No93/HK0xTezHVanVfz0NatwZnd9yaXLYPjpq1dhsipNu2OcXFiX1XDP61fjq1vy+P/uHQ+jCc8fMLCv6IZ2Vc2Yqnp4+ff34tbn9+DDl/ZDoTJzOE4KjbFYvPjFLw7LMC8VpkLDyA4gCev6rIZtczYE/AigSqWHLpNeueOVhqX96Ls2UpwBkEu4ur8UviajQmcUlwyZWJftLEVK+NXBZEle3yOa1clDgKmqh7QmX6K2K5C3vbblo6seBwUBFzKaVfUz8nflHVw6JLc5rUvDaNnB/qKLc3t0eEIWXXhorFUI+Y5zc3h4vIofPFfEUEoJX+xTNflsb4yFpDSK56dMzDRJVwKi6QkRWWHaXXBw2XAC5/ZGX/b9CQWP99fwXN6Gy2XpZYcDKd1DVqeYqnjIGSxsC4GcXPQ2FFwpC4bxhmOuzqh4sCGS3ZxT08lkoOmSt0VKo3j5mlSohe0xGHr8pXXHl28EqwydvmcoqeLa1To25nR4QkQIVFCFcG/RCXWzedvDxlz9nTtV83Dj2jQuGkhgouLix9st6FRGuccsgtk231lxOVRK0Z9h4EImJwbNa+akK9NapPCJrhBcMWyit6FCYhBMpZRiMEXx6vUqZmseVErw091F7PR1vT0mgyeET/aiCCaVG7t1bPQ/m6i4kSj1dLXZwyL6/SNJhm1E9kWhBIp/BV0uot7FEK0VSfzvP7snOiYTKkWvyZBvSDx90XACT0zVoFKCC/oWJqxBBLrxKzs5ib3ytDTKDXKnvUUnInXqMRlsT6BLk6tGyU712+dBzMwOExWH4713j+H/Pj23qO1fOGTie69Yif42pFhXKFak49LXJysIIbj5rC685rQ0PvvEDP7h0Wl8+spBrEwrePUP9+PxyfZZCALA3z88jR/vKuHtZ3bhHx+fxvsu6MHbz+pCrk152hgxlgs3rUth+1x0MpczGC7qN7A2q4W642AcvnxNGk9O1pBUCAgBHhmLSgeGUyrGy25IYtZ3aThQdDCUVKCzesR3PggAaZXh/N42pZmb9ZYgOLtHx2jJxdps1N85QPBCz+oUkw0etOuyUYJ4fq+Bkl2Rlfp6dRk5a6NRLtgcp+d0XLtaYMov4EIIsM4nnIZCcOPaVF1PSwju2B1Vz/YnFLxkVRJbZ+2IRGE+mArFOQ2kpc9keGpavmdKNo+8cxYz3zYYxYachrGyi4RKWyKC4cp5UxazQGv0rx0SbXx5g+M1RpY7bWYopGHisHCHXN+hKDhcoYHI9ZkMG7IKJg8CgwmGsYa/DSQYXMhkNk3RMV52kdMZDpZd6Axh30u2h0fHpdMLATBWdtGtM0zVPKhElgxfkY5OBNtNBoJ7aW1Wxf6i3L4/wfBc3sb5fQZ0hYQFYfoTrG1y+GId0zbmNBwoOVApxfMyHl62wsAzDasMOYPhqSkrvF8zOsV1qxe/2vn8ARO/Ga3A4cBFAwZWZdQlmRkohKDfH3ekQa/cCef06mFC8eqMilesTeG3Y1XcuacEOMDFgwY2dGmYrS1eHhlpzyHtFQMAsGmiirf+bHRREgwA+B8bMyi7HNM1ry1hjnFqIKMzfPAFfbjlgh4/MQf4zRvX4Pd/MYpvbC103O/JKQvvv1fGgN7/63F88P4JfOuGFXjFuvRRaXeMUw8X9pu4oJ/gl/vKkSiVQklbIpPSKEyVhLrC5pe5QoG3n92FX+wtIalQZHUpQ3hm2oLKHGiM4FVd849ne55E6qTaSh5MRnFaVsPpOb3FnaMRaY2ibBHAlcdpjphldIaXr623bbzsYjChyMQoAiT95eBAdmV5clk7qVKc3aND988JJQQZff6JrsokabY9ESHM/YnFT5AJqWu+N3Rp4WSh12RhWxZCn6mgz2z/rvI6sLKRFMPzBw083DBZOq1r8cnLF/cbYWQRAC7yky+TKo1EEFOdmHQDGodft+nLJ/zPLhsyG7YjuKBPhzHl4cxhA9tL9eBFVmc4PafhBYMm0hrFl5+eRc2V6ZQplWLG8pDVGaaqHvY3ODTUPI7TujR011rlMAHaSQ4CDCVVDCbr1351WoVCCa5dlQxlImf36J12XxR6TQU3rEljbA6YsjjWd2noSqt44GAVXABXr0ji61tlPhalBOsyGiYq7aPV7dCXUPDq9ZlFbXten4HHJ1qDRuuzGs7p1bE6rS5435yR01F1BQr+akJSY0gqFM/rM/ycC3nxuw8x0BSztkOA7Qn83UOTuO3hKbiLmKgYjODtZ3XhW9vymLU4Nk/beOhNa+Po4CmOZMMDP6FSfO36EVw1ksT7fz2GirtwiMD2BJ4/TyZ/jBjLAUbJopN/gNboVlKlOD2ngUC+FM/q1rFjrp7ck1IpzunVUXY4sjprcWdoxlyHAkDBd3XCQl2gIFiZUpBROK5babaVbzRiIKngipFEx2RenRFcNmRizTzykgCGQiLa7pxPDDRGkNJkwh8XYlEkMcD6Lg0TFUla12U1DCZV9CdYRJYwH7oW8KkNNM5BgHkgqWBFSkFCpegzGIZTCqb9xMreJbzr3nJGFltmbIxXXIykVPT50oxLBk38al89kXMxGtQeg8HlAoQQqIzgmpVJHCg56DGVea9Lr8GwL7gmBHj5mhTW++ftef0mXA6IsQoISOiE1fzIpkS6wsxasoTzIucoIYaTCkZSCg6UXKQ0irN8cpzWWDiJ6ARjCTdsSqMYSiqY89u3OqOhP6HA4/Jvz8xYKNocSZVAZ3TBcXGoOLNbx5ndOh48WMHOJn/ugs0XJMuAlHA0R6EHkgp2F5xw8hQk2h4KYsK8RNyxu4Q/vXc8Uv64EzRGcP3qJD50SR9e8t094YN+x5yN3/nJfvzk1av8BJkYMWSU4x3n5XDZkIkLv74TCzgSwhPAzXccwCeuGMD6pqraT0/X0GsoGEjGt3iMw8f5fQZ+vqdOVi4aMPzx2SoXWJFSkVRlpvreooPuBu1sSqVQGcElgybuG607GSQUikSDvnY+9HQgX2sy6ryVUjv9qfFzQgCd4qg/l0/r0rBlxgLngKmSUA7Sn1AWvYJ0do8evpcGkgpWZzTUXIH9JQcDCUW6CizB231dVsPmaSsk8pePRB8ygQNGoEvPaBQ6k+SeUoo1GQ1rFgguXtBvRPygX7oqCZVRnNdG3zqQVHDZkImxiovhpIqeDpHvAIQgooVnFFiRVhflOkQIwXm9BmYtD6ZCsarB3pOCQKOt8p9mw9AVKTV8hjMi7WM3T1s4WK5HoVfN0xZCCK5ckUTN5dAYWVIV4C6Docdk4arQeX1Li0Q3SmquHEngt76WvddkJ9zq+JqMii0zFgoWB6PACwZM7JizMVpanNSpESdWz48hHpuo4i9/M4E79pQX3hjyof69V6zAFSPSYP4vn9+LD9w3EdkmiKjEiNGI8/oM7P+9DXjXr8bwg13FefVod+4t486v7cQ53Qr+eITiTP/z9941hnsOVPDSlUn8jzOyeN2GzLzRtxgx5kOPwXBWj47dBQe9JsNan0A8PW2FjhkvGpaEaiBZn6g1+/QGPHRFWsUNa1KouAJ37Ys+UxeaKA4kFfQlGCb9peFBPxJ32gKR3IRCWyK5QJSg68uQTLsqo4Z2cou9565dlYLOpH91Tmc4s3vpGfzn9Rno0hlsLrDW14lu7NaxsfvQlu0ZJbh+TQr7ig6yGmuZfF82ZOLeAxUkVQrbqxeWWMpy97qshomKi6madFdotBNshzVZbVERe0COtcaxtJS812DyEfgea0zKhp7L23h0otr2WM0JeRojuHw4AdtLQGMEhBCsc0VImHMG61gBrxHNtmmLxTUrk9iVt2EotK0cZLFY41v7VVyB4aSCJZzGQ8JyF+yihOC61SlMVl2kVYaUJhOZn1aAdhP++RAT5nngeAI/21PCP22axq/2Ld4O7Iychh+8ciU2NCx9ved53fjM4zOYtTx88vIBvOO83JJmjDFOLQymVHzvlSsxW3Xx7rvH8MRkDVtm7I4Pq80zLv5wxsCfPrsXf3R+N+7eX4GAT6j3lnHpkInTF7kUGyNGMwiRvrXnN0X+rl+dwp6ijZzOMLwIr9zGyFVGZ8joCJedAbkE3DEZrAHXrExisuIhoUZt5BpxYb8R+vvqCsHqjApTobh7f7kjeVqVUjC7+Ec90n57K762diSlhBOHpeC0Lg0CwJzlSQ/dQyTuy10d1lRox+fGirSKl61O4sxuDU9NWeH7bCG5QCM0JqOoRwLn9Uaj181jd959+wz0mgwul5ZtAPCEn5RtdZDL5XQKVygYL0vHmLN7DBBCoDf4iK/KqEhpKZRsjpGUckSruSqURDjI4aDHVOAX+mup1rfcOBJnRKEEQ8n6vaEyuYJQXGRCbXic5W7YiY6SzXHfaAX//VwB39xWmFcv1w4vGDDwy9etadG/mQrF118+gtVpLS55HWPRyJkKvv7yFQCAnXkb//rEDL741FxLtbAARUfg449MRz67dDAmyzGODFIabUmOa8Tz+uuJPAmVYiTV+sq5eMAEJTW4XCya1FBCFpQbbezWoVCCksNxWlZ6PA8mFVy3OoWC7WEoqeL+0Qp25mUC1UBSgamItnZinUAIwQuHTDw+6VtlHYK3a4D1S0iMO17QYyroMRUMp1Qc9JMgu426h/GxxGlZDftLjvSiNtm8VoXt0DwBDCQOgc92gKBaHSGyil7B5jAY6ZhY2W2wQ046Ox6gUBJZqckus6a53RwioRwfwcVTljDvKzq4e38ZBZtjquph66yFxydq2DK7uLKjnWAqpGOySCDPiBHjULAuq+FTVw7iry/pw//ZPIuPPzq9qIzlxyaruOH7e7EqpeCy4QR+5/RMuMz3qUen0G0wvGFDpmOkLkaMQ8WZ3Tpsj2PHnIMVKQVbZ21kdRqJ9iRU2qKPXS60c2fIGSxMuL5qRQJdOsVjEzWYiixekWJLi6D1JRRcuwSrrZMR/QllybZ1RxoqI3jJymRYBvpwo7kXD5i4c48shlK0OYoOx0BCichuCCGnhMzyEr+QDyVk3hLyS8FU1cVMzcP2NhzszEOUFS03TlnCvGmiht+9Y3TZj/vrA1XsLzpxOeMYRwwZneH9F/Xijy/owWc2TeOjD01hdp6VENsDfur7vH5x8xzefuco+k3ppfurfWVYXGqeX78hg8+8ePCUeODHOHqghMD2RGiF1WuyCGE+lgikJlM1D5MVDwoFzkofWgZ9jDqk3KbuRrA2e2yud2MZ6MNFt8Fw7aokJqoeXrkuHT5TT0UMp1S8ev3yXtNfH6h0lLscqo57uXHKEubMApZBi8GajIpPXTGAf9o0jftGq+g2GN64IbNo0/AYMQ4HCiV4/0W9eN+FPfjZjmn88pl9KJg9+PGeGkbL7SsHBpioevhpQwJrxRX4xtY8GAQuGkzgvF4d5/YY6DqBlw5jHB94bi4aMZrqUHXsWIEQaTc2U/MAm2L39mPdopMDV40k8cyMBZUiUlDlREYgQYmx/HAWyvY9DnDKXvnMYehuBhIM/99FPXjP+d0wFOlLWHUFXrY6FZcsjnHUQQjBlcMm+vIezjyzB1+4NoEHD1bxnR0FfHdHEbsKi0tscDjw5S0FfHlLvXhKn8FwVq+GM7t1vHgkgfP7TGzo0sBiO8QYi8SJEK+lRJaJLvPjI5J1MiCl0QUrs51MiJ+JRwYbu48fbf8pRZgtl4dC/KGkgt87u0t6R1Lg44/OLGiXsjqt4v+7qAe/d05XJNv7JatObf1ajOMLlBBcNpzAZcMJfPKKAbzmh/vw3zsPbflwsubhnv1V3LO/ii88OQdAZjEPJRWs79LwvD4dF/dL94R1WX3BYg8xTj30GAyjDRXQllKtLkaM4xVn9eh4xve9JgQL2hrGWDr6EgxnHEcJ6yc1YZ6quviL30xgZ97GM9M2Ts9puOcNa1C0PTw352B9l4bzenXcsDaNH+0uY/MCxUg+fWU/Xrshe5RaHyPG4YMQgleflsFYxcODDaVqDwcCwGjZxWjZxa8PRD24kgwYSqkYTqnoNRhWpOQSpqkQ6EyWTNaZ/G/uVy/zBOAJAY/7/wpZLc7j9b9xAfzphT2RkrkxTgysyciSvqTh9xgxTnSc2a2j4sjkvzNyery6vMxgFHjpcRaMPOKE+We7S3hisgaVESgEUCnx/1v+q1L/s+DH/8yxLOzKU3z2nimo6hwAAkJklZY5y8Os5WG2xjFd83DFSALXr07iubwDhcqX7ZndOrIaxf/ZPBe2xeECP9pZwCt+sD/87E2nZ3DD2jQuHTJDwjyQYHj5mhQsV+A3Byu4+awuvO2M7LJ5GsaIcTTx9rO78Pazu7BlxsJXnpnDN7YWsHeJ/pOLRdkDduQd7Mgv//FfviYFlRJYHofliQ4/HJYrUKxaGB9X8LdnLnzcGEcWqzMaVmfi6FuMkwsakyt5MU4dHHHC/J0dhQhpXRoMANEqUO2OxYXAZNXFRx6cCj/70kuG8Afn5pDVKPK2VNFN17wW/8NfH6hACIG3bMzirG4dV4wkcGG/AUrIIZWkjBHjeMWZ3To+dvkA/v5F/dg8beHHu0r40a4iHhqrop2ts0KBK4YTeHi8ipJz7BMynve1nUva3qAq/vYItSVGjBgxYpxaOOKE+UhXhQGAvUUHp+eiEYyKKxnA6oyKJ6dk5DipSvJrKgRV375ktOxiZ97B1SuTuHpl1Cf5eLEyiRFjOUEIwbm9Bs7tNXDr83thuRxPTFl4aKyKRyeq2DFn47m8gz6T4VevXwMAqLkcP9lVwp17SliZVvG5J2YwtggP6GOJDrVdYsSIESNGjCXjiBPmo/HS2lN04DZ9T8WPiH3i8gEQApzeJSvsUUJwYb+BsiNwYb+BK0cS6Imts2KcwtAVmc3enNHeaPNjKBSv3ZDBazdkAABll2PztIWyw1GyOQo2R97yMFXz4HLMm0CrUuD0nI6npy1cMmjivF4djBAwCvx8Txnb5g6veFAADwRe7PEYI0aMGDGWAUecML/mtDTWZVU4noAr4P8r4HgCDpe64siPv13NcVEqV1CADkaJrMlOCS4fSSCnM3TpVFZs0hlGUgqenbHxqnVpuEKAAFjnG6Vft6ZVNH7vG9Yc0RruMWKcDJgvwe62Fw3Mu29jwp7LBcoOx/6ii8mqC4USXDpkomhzZDQWcda49b5x3LGnHK4QkeCHAAQEmp8w2P5HJhRqjIByF4XZ6QWdb2LEiBEjRozF4IgT5tdtyOB1flRqKSiXy9iyZQvOPHMdksmFS0pfOpTA28/uWtSxY7IcI8aRBaMEct1G3mtpjWGwqbpbu1LcH7t8AB+7/PC/Xz4/xqDE3qgxYsSIEWMZEIt0Y8SIESNGjBgxYsSYBzFhjhEjRowYMWLEiHHM0J+ICh76j8MS5DFhPgGgKAre+MY34rrrroOiHH+DKEaMGDFixIgR41Ah7Xzlf1MCXNBvHNsGtcG87EuI1pQZzo+OV5MQAowxCCGO2ncez4jPRxTx+YgiPh9RHMr5aLddu2fgyYpj+bw/XnAq30enct+BU7v/x0Pf0yrBy1YlMVl10W8qSKvkiLblUJ73RMyzheu6KJfLnf4cI0aMGCc1ksnkKbOqEz/vY8SIcSpjoed9LMmIESNGjBgxYsSIEWMexIQ5RowYMWLEiBEjRox5EBPmGEcUN954I2688cYl77dnzx50dXXha1/72rK15SMf+Qhe+9rXYt26dct+7BgxYsQ41RE/72OczJhXnEcpbSkaQgiJC3/EWDQCPVA6nV7SfqmUrNBoGMaS9+2EL37xi3je856Hm266CV/96leX9dgxTnwIIVqSPig9dWIK8fM+xuEift7HOFFwKM/7BQnzqfTCiHHksNRxFGy/nGMwn8+DUordu3fjq1/9ajy+Y8RoQHw/xFguxM/7GCcj4tET45CwY8cOvPWtb8WaNWtgmiY2bNiAD3zgA6hUKvPud/fdd4MQgu9///t461vfikwmg1wuh3e84x1t93UcB3/xF3+BgYEB9PT04E1vehNmZmYi23z2s5/FpZdeilwuh+7ublx11VW47777Wo4VPyxjxIgRY+mIn/cxYiwQYY4RoxNGR0excuVK/PM//zNyuRx27tyJ2267Dbt378btt9++4P7vec97cNNNN+H//b//hyeeeAL/+3//b1QqFfznf/5nZLuPfvSjuOaaa/DVr34Vzz33HP7sz/4MqqpGttuzZw/+4A/+AGvWrIHruvjhD3+Ia665Bg8//DDOP//8Ze97jBgxYpxKiJ/3MWIAEMcQ//Iv/yLWrFkjdF0XF154ofj1r3/dcdu77rpLAGj52bJly1Fs8ZHBPffcI2666SYxNDQkAIjvfe97C+5z9913iwsvvFDoui7Wrl0rPv/5zx/5hs4Dx3HEvffeKwghYmpqKvz8qquuEldddVX4e3AdX/nKV0b2/+QnPykopeLZZ58VQgjxzW9+UwAQmqZFzsl73/teoWma4JxH9l/K+Ni1a5cAIL785S8vT+ePMG677TZx8cUXi1QqJfr6+sSrXvWq8DzNh+NtjCwXDuV8nMzPj+XEUp7JJwoWM1445+JDH/qQGBoaEoZhiKuuukps3rw5sk2tVhPvec97RE9Pj0gkEuIVr3iF2Ldv39HsymHjtttuEwDEn/zJn4SfHUrfb7zxRvGd73xn2Z73wTP52muvjWzX6XkfwPM84TiOuPbaa8V73/velr/v379fvOpVrxIAhKqq4vzzzxePPPLIYfX9RLnujuOID37wg2LNmjXCMAyxdu1a8Td/8zfC87xwm5O5/0cCx2zN4lvf+hZuueUWfPCDH8SmTZtwxRVX4OUvfzn27t07735bt27FwYMHw58NGzYcpRYfOZTLZZx//vn43Oc+t6jtd+3ahRtuuAFXXHEFNm3ahL/8y7/EH//xH+M73/nOEW5pHZZl4aMf/Sg2btwI0zShqiquuOIKCCGwffv2Bfd//etfH/n9jW98IzjneOihhwAA1WoVAPCWt7wlst3ZZ58N27YxPj4efvbQQw/h1ltvBYBIgtKLX/zik2J83HPPPXj3u9+NBx54AD//+c/hui5e9rKXzVtk4ngYI0cKh3I+ApyMz4/lwqE+k493LGa8fOITn8CnP/1pfO5zn8PDDz+MwcFBXHvttSgWi+E2t9xyC773ve/hm9/8Ju677z6USiXcdNNN8DzvWHRryXj44YfxpS99Ceedd17k88X0/b3vfS++8pWvwDRNeJ6HH//4x3jd6163bM/7AC9/+csjv3d63t9www0YGBiAoihQVRU///nPsW3btsi+s7OzeNGLXhQmIt522234h3/4B3R1dS2p7yfqdf/4xz+OL3zhC/jc5z6HLVu24BOf+AQ++clP4rOf/Wy4zcnc/yOCY8XUX/CCF4h3vvOdkc/OOOMMceutt7bdPpipzs7OHoXWHTtgERHmD3zgA+KMM86IfPaOd7xDXHrppUewZVHccsstQlEU8ZGPfET88pe/FA899JD47ne/KwCIu+66K9yuU8ThZz/7WeR4tVpNABAf+9jHhBDRKHDjOQl+37VrlxBCiD179ohMJiPOPvtsAUDceeed4uGHHxbXX3995HsbcaJFmJsxMTEhAIh77rmn4zbHwxg5WljM+ThVnh+Hg6U+k09UNI8XzrkYHBwMnz1CyOdRNpsVX/jCF4QQQszNzQlVVcU3v/nNcJsDBw4ISmnLs+x4RLFYFBs2bBA///nPxVVXXRVGmBfbd0qpYIyFz/sf//jHghByRJ73jej0vL/sssvEt771LfHb3/624/P+z//8z8Xll1/e8dgn+3W/8cYbxf/6X/8r8tlrX/ta8da3vlUIcfL3/0jgmESYbdvGo48+ipe97GWRz1/2spfh/vvvn3ffCy64AENDQ3jJS16Cu+6660g287jFb3/725Zzd9111+GRRx6B4zhHpQ233347br75ZvzVX/0VrrnmGjz/+c+PzNwXwsTEROT3IIIwPDy8pHbccccdKBQK+NCHPgQA+MM//EO84hWvwAMPPIC5ubklHetEQT6fBwB0d3d33OZ4GCNHC4s5HwHi50d7HM4z+URD83jZtWsXxsbGIn3XdR1XXXVV2PdHH30UjuNEthkeHsY555xzQpyfd7/73bjxxhvx0pe+NPL5YvvOOceb3/zm8Hl/ww03YO3atYv+/uV+3t9+++144xvfiEsvvRQXX3xx2wTCH/zgB7j44ovxR3/0RwCAv/7rv8a//du/hX8/2a/75Zdfjl/+8pdh5P2JJ57AfffdhxtuuAHAyd//I4FjQpinpqbgeR4GBgYinw8MDGBsbKztPkNDQ/jSl76E73znO/jud7+LjRs34iUveQl+/etfH40mH1cYGxtre+5c18XU1NRRaUOlUoGqqpHPvvrVry56/29/+9uR32+//XZQSnHJJZcsuR2AvImD8fGZz3wGhUIBTzzxxEk3PoQQeP/734/LL78c55xzTsftjocxcjSw2PMRPz/mx6E8k09EtBsvQf/m6/vY2Bg0TUMul+u4zfGKb37zm3jsscfw93//9y1/W2zfgbpXcoBarbboNiz3877x3bNjx4625G3nzp34/Oc/jzVr1gAArr76avzxH/9x+J462a/7n//5n+PNb34zzjjjDKiqigsuuAC33HIL3vzmNwM4+ft/JHBMXTKaDfGFEB1N8jdu3IiNGzeGv1922WXYt28fPvWpT+HKK688ou08HtHu3LX7/Ejh+uuvx1e+8hWce+65WL9+Pb7//e/j3nvvXfT+jz32GN75znfita99LR5//HH89V//Nd7ylrfg9NNPX1I7XvrSl0JRFPzt3/4t/vRP/xSbN2/Ghz/8YaxevRr5fD4yPu655x5MTk5icnISAPDII48glUohmUy2aOeOV7znPe/Bk08+2dZGqRnHeowcDSz2fMTPj8VhKc/kExHzjZdD6fvxfn727duHP/mTP8Gdd94JwzA6brdQ3ymlLc/72dnZRbdjuZ/3v/u7v4s//dM/xdjYGD784Q9j5cqVLdu6rovTTjsN5557LgBZTOXqq6/Gbbfdht/93d9ddN/b4Xi/7oDMSfiv//ovfP3rX8fZZ5+Nxx9/HLfccguGh4dx8803h9udrP0/EjgmEebe3l4wxlpmKBMTEy2znflw6aWXLirh4GTD4OBg23OnKAp6enqOShs++9nP4pWvfCU++MEP4nd+53eQz+fxjW98Y0n7F4tFvP71rw8fYF/4wheW3I6zzz4bX/va17Bnzx688pWvxMc//nHcdtttuPLKK5HJZCLj40Mf+hDe8IY3hEt0//Iv/4I3vOENeNe73rXk7z0WeO9734sf/OAHuOuuu7BixYp5tz0exsiRxlLORzucqs+PdliuZ/LxjE7jZXBwEADm7fvg4CBs224hicf7+Xn00UcxMTGBiy66CIqiQFEU3HPPPfjMZz4DRVHCti/Ud845rr/++sjzfmRkZNHtOBrP+2YoioJt27ZFnvc//elPsWPHjrBfi+n7iXjdAeDP/uzPcOutt+JNb3oTzj33XLztbW/D+973vnCl4WTv/xHBsRBOCyETTN71rndFPjvzzDOXlGDyute9Tlx99dXL3bRjCiwy6e/MM8+MfPbOd77zhEjoCpJAGhNFFsJizkk7nCzjg3Mu3v3ud4vh4WGxbdu2Re1zIo+RhXAo56MdTpbxsVxYjmfy8YiFxkuQ/PTxj388/MyyrLbJT9/61rfCbUZHR4/75KdCoSCeeuqpyM/FF18s3vrWt4qnnnrqiPf9UJ73y4U3v/nN4vLLL498dsstt4jLLrtMCHFyX3chhOju7hb/+q//GvnstttuExs2bBBCnPz9PxI4ZoT5m9/8plBVVfz7v/+7eOaZZ8Qtt9wiksmk2L17txBCiFtvvVW87W1vC7f/x3/8R/G9731PbNu2TWzevFnceuutAoD4zne+c6y6sGwoFoti06ZNYtOmTQKA+PSnPy02bdok9uzZI4RoPRc7d+4UiURCvO997xPPPPOM+Pd//3ehqqr49re/fay6sGgs9gG61HNyMo+Pd73rXSKbzYq7775bHDx4MPypVCrhNifTGFkIh3I+TubxsVxY6Jl8omIx4+VjH/uYyGaz4rvf/a546qmnxJvf/GYxNDQkCoVCuM073/lOsWLFCvGLX/xCPPbYY+Kaa64R559/vnBd91h065DR6JIhxJHt+7EkzA899JBQFEX83d/9ndi+fbv42te+JhKJhPiv//qvcJuT+brffPPNYmRkRPzoRz8Su3btEt/97ndFb2+v+MAHPhBuczL3/0jgmBcuWb16tdA0TVx44YURW6ibb745YhPz8Y9/XJx22mnCMAyRy+XE5ZdfLn784x8fg1YvPzoVVbj55puFEK3nQghZlOKCCy4QmqaJNWvWnDBFKRb7AF3qOTmZx0e784Amm6STaYwshEM5Hyfz+FhOzPdMPlGxmPESFHAYHBwUuq6LK6+8Ujz11FOR41SrVfGe97xHdHd3C9M0xU033ST27t17lHtz+GgmzEey78eSMAshxA9/+ENxzjnnCF3XxRlnnCG+9KUvRf5+Ml/3QqEg/uRP/kSsWrVKGIYh1q1bJz74wQ8Ky7LCbU7m/h8JECH8TKAYMWLEiBEjRowYMWK04JhV+osRI0aMGDFixIgR40TAvLZynHNwziOfEUJOSTuRGDFinNwQUqIW+YxSCkpPjbhC/LyPESPGqYJDed4vSJjL5fLytC5GjBgxTjAkk8lTijDHz/sYMWKcqljoeX9qvAlixIgRI0aMGDFixDhExIQ5RowYMWLEiBEjRox5EBPmGDFixIgRI0aMGDHmwbwa5nbJHsul6atUKti+fTs2bNiARCJx2Mc7mjhR236iths4cdset/vo41Db3k7De6InvO3evRsf+chH8Ktf/QpjY2MYHh7GW9/6Vnzwgx+EpmmRbY/k8/5EwYk87g8Xp3LfgVO7/6di3w/leb9kwrxcWeOEEHieB0LICfdAPlHbfqK2Gzhx2x63++hjOdt+ohPmZ599FpxzfPGLX8T69euxefNm/MEf/AHK5TI+9alPRbY9ks/7o4WqyzFb89BjMOjK0tt9Io/7w8Wp3Hfg5On/RMXF7oKDHoPhtC5t4R1w8vT9cHFYhDlGjBgxYpy4uP7663H99deHv69btw5bt27F5z//+RbCfKKjaHu4c08ZtidgKgTXr0nBOATSHCPGiYqKw/GrfWUIATwHQKHA6sziSPOxhscFHhir4mDZxVBSwaWDJhg9vgIWMWGOEeMIwXVd3H777RgdHcWGDRuOdXNixAAA5PN5dHd3L2rbSqVywkTZHxyroVDxAAA1C3j0gIvn9epLOka1Wo38ezzC4QKPTlqYrnGsSik4p1tdlmt0IvT9SOJk6P9jkxaqNTf8/Ve7LLxxfXLB/Y6Hvu8uONg2aQMAihWgi9hYk1GP2PcdSpHrmDDHiBEjximC5557Dp/97GfxD//wD4vafvv27fA87wi3annw4CSL/D4zBug9h9b23bt3L0OLjgz2Vgm2lGTkfDuAUpeHrmXkFcdz348Gjvf+2xzYWSHgguC0JIfesIiyJU8xaUcnT1ucxd8Dx7Lvd04yNFLY/x4FXtZ35J49jDGsW7duSfvEhDlGjCVACHHCRNxinLz48Ic/jL/5m7+Zd5uHH34YF198cfj76Ogorr/+erzhDW/A7//+7y/qezZs2HDCjPen1GgCj6kQnLlmaQlM1WoVu3fvxpo1a2Ca5mG3ac7iqLgcgwkGukzn8akdZQxn6r/XTIozRw6/rcvd9xMNR6v/e4ouSg7H2oyCxCFIhu49WIOjSyI5a1Jc3XDt+2se9hRlhFkA6DUYVqcXpnmNfd9VZXh61kFKIXjhkIGsdnRkTU9rZfAGxkwJcOZpC0fHDxVCiCUHA2LCHCPGApiouPiXJ2Zw+7YCPnv1IF66KtV2ux/tLGLbnI0rhhO4oN84yq2McSrhPe95D970pjfNu82aNWvC/x4dHcXVV1+Nyy67DF/60pcW/T2JROKESQIydDfyu64QJJOH9sI1TfOQ9w2wv+jgvskKhAB6q8C1q5fn5R/00+UCCiUwDXbYbW3EcvT9RMaR7P+2WQtPzLkAKEYtgVeelljyRGrWdWHokroVOWAm6sdIJoFVPYfRQFXHjikOXdPhANhRprgyd3jnoupycAEk1fmfIxcMMTw9bYW/n9urI5k8cu9RzjmKxeKS9okJc4wYHSCEwN8/PIWPPjSFqiunvj/dXepImP/v03P43nPyBsxoFJ+5oveotTXGqYXe3l709i5ufB04cABXX301LrroInz5y18+6gS47HD89qAkjgJAWqW4bHj5ratWplXsKzrh733msX293Tcq+wwAU1UP42UXA8nDb5PDBbbMWCjZHIZC8OIVy0fuSi5wx94qPObh3F4dZ3QvTQN+vOFg2cFoycVgUsFI6sjpYReLR8dr4X9XXYEDJRcr0/O3a6zsouZyCMj7x+YC2hFKhjtQ9gDUj32g5HbeeBHYU7DxwMEquADO7tFxXl9nAtw8b1iuFZnlREyYY8ToAEII9pfckCwDwE92lfAPV7ZuK4TAfaOV8PeCzTFWcbH1oIW5OYo791bxwrWJ4+KhHePUwejoKF784hdj1apV+NSnPoXJycnwb4ODg0elDWXbw6PjNagUGEwo8Ay28E6HgD6TRQhzusNS8l37yhivSCJAAFyzMom+xPK/CptzimYtb1kI83jZRcnmAICaK7Cv5Cywx+KxvUyhmByGLrBpooa1GfWQ7PmOB8zUPNy9Tz6Tt83auHZ1Er0LTKKmbOCX+6vIJoGLB8wFo6KHi6rLF9zm6ekaJip16YDlcWi0fg8JgUaOe1iQ8ov6wNXY4R34/tF6EuHT0xbO7NahHuYxjyViwhwjxjz42Iv68cOdRez3Z9q7Ck7bSNG2WRuT1age6tb7JoBniwAUfO/OCYDN4MJ+A79/dhf+19ldJ+yLKMaJgzvvvBM7duzAjh07sGLFisjfDiVLvHnfxeibfz1awX6fyFqeQNcRIszNTeEd+idQJ7PBFvuLDrbN2dA8C8qhn5Z5sVyHnaxFnzMHy4cXBWzEhE0w3PD7aNnF2uyJYUvWjEfGo44Pj47XcN2a9quDgBwvTxYY+pIcZeHicVrDi5pWQhxP4OHxKvYVHeR0hhevSEA7jOf4YqKozffYYdy2LeBCYPOUhdF8Da5FYDgclgd4QkAIoD+xvAGeqsuhsoXv/+mahwfGKqi4HM/rM6AcJ/Zy8Rs7Rox5kNEZvvCSIQwnFXzhmiFMv3NjC1nembfx17+dwGLu6ccmaviju8bQ9fmt+LenZo9Qq2PEkHj7298OIUTbn0PFZMXF954r4lvbCtg6Y8277WzNQ9Guf9doyV0UcVxM+8bKLn65t4Rf7Svjrn1l7M5HI628wyHKTjSqV3I47hutYLzsYnvexa4ywSMTFr6/o4CHxqodifd8bd9bcDBZdSEaertsRKfpOPM9dvYXHWydsWAtIpLZDscJTzkkzFnevL8342DZw5RNYHnyXO0ttEbudxdsbJ6q4f7RCn64s4jbtxfgdRpoi8B85zcYR+NlN3I/NH9bp28XQuDZGQvf21HAAwfLbaPZO+ZsPD1t4WDZwxMFirzNoTOChEKRVOlRlTUFXXS4wNZZCzNVD9tnbTy7wDPmaCKOMMeIsQBuXJvGjv+ZhNkUSZiouPjIg5P44lOzcJb4Pqp5An/4y4P4zo4CvnvTSiSO8NJfjBjLhUcnarB8mdJjEzWsy2odl1ltT8DyOCgFKAgIAXraRJgtl6PocGQ1igfHathXdNCXYLhiONFxJabq8shStak0ReI6tD+QMwR4bKIWIbOPFShqBReGzvDcnI2BBFtS8YdHJ2rYPmtj+6yNWYvh9K5l1gE3nepOQcrtsxYe8TWz2+ds3Lg2tWTHE7Jca/3HAM08lhBgruZhb9FBt8GwokE7bHsCd4/WsLtKUJp2cKGqt5VjPDJew868Ex77mWkLO/M2NuTmv8YFy4Pe5h6Z76n/yHgNO+ZsjJYd5AyGc3vkdzw1XYts1zh2qy7HzrwNIYCtsxbuO1CFywVMhWKmxnHD2nRk32dn7MjvU1UOoH5/ussZzl4kxspu5OZ9asrCOb3HRxJ9TJhjxIDMOGek8xJzI1keK7v43BMz+KfHplF22z9QVApcOmCCTBqolCvQh3Q8OOWheZJ/x54yzv2v53D/G9cui74xRozlRrP8YrZJEjBreehv0gCXbI4npmqYrbrYMWthtOSiL8EwnFThNdwy22Yt/Ha0iq2zFtZlNTAKqJSAEYLJiofdBQcbOySeWV703puqehGS0ynw15wcmFYJxn2i4ToOnilSZMou1uvye+8frWIoqbbVc1oux2jZRU5nodRk+2ydhExVPGzoEiAgkWjzcoELgbLDUXV5y4T+kYYEs6LNcbDsYnieHIpdBQdPFylGJyyoKsdgQsEVI8ve5BA78zaenKwhoVJcNmQirR2eVMflApTUZQ7NXM/xgDv2lMJxccVIIiTN22atMFfFE8DOgo1ze9qTtMYVCgEpW+FCJvFt7NZarsNvRivYW3CgMoK85SGr1/s53/xlx5wcR8NJ2cbnD5qRzwHI+8Ufl1wIPD5Zxa45B4QQ3D9awbZZG6fnNFRdjv1Fp2Wc9Jos0p+USlBpCKwH7yshBCqurKC5nMl4BcvDroKDrEZDaZHnX7iZmoehlLKklZkgObL5GiwX4jd0jBgA/vI3E9hXdPDv1w53jPY+NVXDPzw6ja9tzbcQ32Y4HLh3tIr+WQc9FLhm0MD7np/D/75/HFtmo0t9O/MOXnj7LtzxmtVY33Vi6gVjnJz43o4ibD5/hnu7F9oDYxVMVjxsm7NQcwUGfUK9Mq2GMUvbE3h0vIaxiouiw7G/7KDqCKzKqOj2ScVjE7WOhLnQFCmerkUJc2WRyz4cchK8fcaGyTiqnGCq6mF9wzZPTdWgM4KizbGxW0e3weBygZ/sLqHmChAikwebJw6AJFWkw3lqh6Lt4YGDVVRdgfN6daxp0hAH549DYE/RgalS/HhXCZcMmtiZt2F7Auc3XKvZmoc5y8OajDovYeYCcP0fygU8IToSugMlBy4XWJU+tCqDjifw8FjVJ5oeNk9ZS3ZOEULgwbEqdhccFH0pQVqjuGIk0Ta5b7TsYEVD/38zWsHvbMwCkDrwxstTtOpjp+pyPDFRw76Sg32FaESWANg5J504AGBfycEr1tWjuDM1L5R2WC7HzoKDC/rqhPmpKQsPj9eQVinO7zOwebqGsiNwjh9NnrU8bPGt1kZSrK1v82zNw3TNw7MzFh6frKHmCpzZrcFrmqB5onUMNg/JYJEmsIJ7bs7GBX0GfrmvjNmah6xOcc3K5GGVnK84HDVPIK0S3Lm3jP1FB2WHI6lSDCQUBCN8puZhk7+adXpOxwsG5/fG3jFnh7r1C/sNnN4m6u9ygWk/14hAYKlrPzFhjnHK4659ZXzq0WkIAM/O2vjeTSvCl5QQAnfuKePTj03jzr3l+Q/UBhNVDxOg2LIpDzxZhkKAfpNhoilBcGfewdXf3o173rAG607QJJsYJy/seXSa7f4y6Uslpioeii5HRpUkYabqopBg2DFnh9pPh0u/uemKB5UR+fsi0EzTxsouVjUss3dKhmvmd1tmbOyYk0m7GuVQiWjRlm5riBrvL7l4zfo0Rksuan5UUghgy4yF6aboe80TKFgcjEqttMcF2ALC4KemLEz5z4eHfAJACcHKtBIhp3mLw/GAhCoJ6Le35zHoJ2ndP1qBEAIlh2OLrwF9YKwakn1AEjiHAymN4pHxKn61r4qxGkGaCwRPoHZNfXKyFvrl7ku7uHxk6RaBB0pOZAVgd8HBZcOdt2+Hg2UXu/IObC7w5GQNqzIqVKri8claW+vP5nHV+Ot8nP/X+yv41b4SKo7AwbKLoaaVwN1FG2f45Kxkc3zj2Tw0RnB+nwHb4yjYHhRKoDGCatMkruhP+mY8Dz/dXYTBJBF9ZLyKqstDsgwA+4ousnqUqE5WPPxsd6neDwB5y8NU1UNOZ0iqLrp0WUGvncQko1H0JRiqhKKsCDgC2DZnhSR/JKVitOyg4soxnLc49nRY9ZmpudhXdNCly5WkdjKtsYqLTRM1TNU8HCg5EFzAFQjvo4GEEl6LqZoH1R+A//nMHJ6crOH3z821HDPAw2P1JM9Hx2vY0KVh87SFiYoLU6HY0KVBYwS/2iff4xoFXjrQ8XBtERPmGKc0ZmsefveOA+FL//HJGt700wO463Wr8LWteXzikRlsn7PnPcZS4AqEZJkgSjb2l1xc/e09uOf1q1uiSjFiHAtMVlxkDQUHSi4ubni5FG0pL+rSaVupASV1QpJQKM7plS/Y/oSCA0UXszUeVs1sjHp5XER0nQsFLzkEJiue3I4cmuTB8uo9qLgCRBBUXHlsivpy90TFBaEE/SbDnoITvswDzNY8PDxWxVhFLtF7kPrtbXMWViRVbJ+1cWa3HlmSb8bOvI2f7C4ho1KMpBWAE/z2oCQC67IqLhlKhBOFsuNLZYJ955yQMJcdjn1FB+MNGm8C6YvbbZgYK7u490AFLhfoMxkmqx72FFyM2wTOrIMeUyCr07D/gCSDlCBSXGJf0UHN5XhmxgrJ5PP6DFBC4HGBzdMWyg73+0397cW8E7DF4qkp2Y68n8y3tyAjyJOV9sl9/aaClFbvUaP/cd7ysKvQOsGquRwHSg4q/rmesWSUVQi5MpFQCYaNVhpleyJcQdk8ZQEEWJtpje47XGDbrI2yw+FygRf6UXYuEEl2q7gcT03V0G0qePW6NOYsuZpy/8GoE4hCgWnLw9xEFc/rM7Amq+Gsnjq5bT7r5/YaOBdAuUywpcixqeDg/gP1752zOMbLbpizAAB5m+PHu4oo2x425nSc32+CC4G/e3AKti+TOj2n4b0XtFZQecgntdtmLUxWPExXPWQNioGGFYGUT+yLFofBPJiMwFAoJqut1+fHu4oo+uPywTHZZ43Kfj43Z2PTRA3Ml+nsKTgYTh2e7CcmzDFOaTw7Y0UiDxRSf5z9/NZFJfIZjODykQT6TQZCCEo2x1TNxXjZxY6Z+e2e2r0y9hYdvOS7e/Db31nbdnk3RoyjiZ0FG6wkCeBISsHFA6Yf2ZOTyG6DIWswJBQbG7q0UBNKiFz+BACFEKRVCkIIaq5AQpUvv115J9QsB2jmUY12UlNV6T88kpLRK0JkMtu0T5CmLQ9PTNXgcIE1aQ295uJejhqVRCNAwA32F+sR662zdqjdLtsKLuwXLZrmJ6dq0BkNyQUDgUYJXA/YW3IwkFTmTaGbrXl48GAV+ZqHfM2DrhD0NxCJnXkHLxisn6BEk9400xB93D5nY7TktiSa6X4E88mpWnh97j9YxeqMClMh6FIETAaszqhYmapH+56YrOGZaQuUyElU4Fs9WXVx176yH8UmmK568Diwv+RgV96BwQhyBvP100roZDJV82BQAl0hUlrjO0IYCln0cy9wL2l8TD84VsUFHaRDvSaLyCUCVF2OTZMWal7rE5mLaGR6qiKvjaFQJBWCnE4xmFLgtOHogVQCkBOYTRM1VF0RThQB6amd7+DekVApqq4HAYHRsguDEUzXLHz6MTs8R/0JBY0qG0YIsipFQqXoNZTI+AEWlgU1TwJLNg+jvwF+tLOALTOS5J+e03BrUkHJ5iFZBhAJMjV6nluegKEQcA4EQzNf4xjooLaYrHjoMRkMBR2vjxBSbuJxAZdz3LWvghnLw/0HK6AgOKdXR1KheHSihorrQaNSh312j4alUuD4jRzjlMZlwwlsvXk9br1vHF94ag4cwH2j1QX3C3DViImfvXZ127+t+uIz2HcIbdqZd3Dj9/fgnjesjd0zYhxT5C2OiuuhS2f4ytNzeHS8GpJlQOoMn56yMJBQMF5x8erT0jAUCkoIDvgFNQQEduVt9JiKnCQKgd0FqYF1OTBhu1idlk4b67IqKg0JAo4n8NhEFT0GC4sgpDULN65NAUKEZBkAag5H2V/ifi5voy/R/i3cTFozGgNr+NQTcqm85M+YXSEiiY5jZReUtB6naHPsrNpwuSwAQQmBwwETAOeSaM6HbbNR+6wdszb2FR0QyAkIJQSv35CGw2U0MqNSZLsoGJHa3cACjAup0yREkj2PA34l5ZAAN05Myq6HrTMc0zW5DyHEdzSRG7tc4Bk/qsyFJEMBYZa2X3ZIfgym47s7Cjiv18CuvA1dIbhQN1BzZSGUnB9dd7nAhOXBVGg4nn4zWkGvyXDt6rqcouJwFG2OXpO1SFnmfJ1x8wrFvpKDiTbnulHOsjtvY79fAbBgtyeshiInZc00zeGAY3OoVEodzu81kFYp9hQd7PQnBC4X0lZQCEzXvFA3m9Yo5ixZodHhAnuL7YvOiAb9eM0V4BzQFIJqTeDJ6RrWdWnoNihma14YlQaAPQUHCiVQfQnIeMWFy+UKiu0KPD1Vw5qsBkMhsDyZ6M5IfTmonYxCNKzc5C0Po2UXBf/c7yk4eGCsipEmmUrjuQ5cO7gQGK96GPLHTnPB0WBy3EzqgybNVD08cLCCS4fq/W2eYB+suCjYHHbD5RdC3sOufy9wKlcjdCaA1TFhjhFjSbhvtIKvbMkf0r4HOyz/AcDbz+rCw/tMGFYRv3/jAFLJJH6xr4yvPVvArjYen414ZMLCW356AN+5acWCmscYMY4Upqseio5Al0909hYdmAqNeLruKzoYq7i4sM/A45M1zFkeNk1UMV52oVKCyZqLR8ZqWJlRoRCCHpNFyGZWY5izPGzIaegxGJgVHe9bZ2zMWh4qjiR1aZXi3F69hbHmGqQOHhdtbbwA+ZJvRLPswxHAeNUD5myc2a3h3B4dDzUtfVO/H5cOmnh0ooZZy8POvBNa1k1WPazNqPCEQFZnEBDI6CwkBZMVF09M1aBSgov6TTwxWcU/PT6DuZqMgK3LahAQsCK8T2B/0cFzc06k+ugF/QaKfmXRnMFCSUvZ4ZiqehACSGoU6xvkn4HziSsEduVlEY79ZQ9VjwD+eS45HEIgEjkEpJ59suqG0hKPCzD/XFddjpLNcbAsz3HF4Xh6xpIEiwhcMpiQBM0nMAeaqhRONeR27C/a+KdNM3A8gRcOm7hhbRoJhYJREvXpbmJY42UXDxxsDXoEl3mq6uKeA5WQ/M9ZUZ/jyBmfJyJbdeWk6sGDVTBKcHqXhpvWpQAB/GBnEft9Aj3d0CdCgGdmapJQNhx7oiIryppqDRBSfmMyit4EsLcgx5TliLDy7I45CxmNIaszuEJA6aBdOlCSEhjPv87dJsPOggNCBISo71OzLExNMRi9rcuqOiUYSiqgREqxig3JthVHSIlV1UPR8ZDT5aoEI/K7Z2oensvbIJDR6qon0J+ITlADSGIvWiRejddgV97BSMrpWE58T74unwkqIAZRaED+d8WVUfNDEQXFhDnGKY3tsxbe9auDkRdQI1IqwZndOnI6g8YIBORNx4WASgnO6JDBDwB/fVk//nNHEqOjeVw+ZCCbTeGqlSl85IUD+OXeEt780wMt1QEb8d87i/jTX4/jn158dEoYx4ixEIQAVAa4QkbfZmoe8pZ0KHiS1vCLfWXM1DwMJJRQ01qwOCquCKNS3Wa71yVw34FKuPw9mFAi9lXPTNdCT+CSzbF5ysJwKvr6ao5YBS9Jy+XYXXBkhb823xt8i8LqMhJAThbyllyubwYl0roqq1PsyNugaPJ3FvLlftWKBEZSKlwh8IJBEwmVQgiB34xWwmcOJVU8cLCK3XknlHOsTKtgVGpm52q+Xtyg+JcnZ7Gv5MDxAF0hoATIBVIMIYlX4EgyWZHL+TVPYEhluHTQwAbfhYdDarTHyi643+yaK1D2ANeREepnpmUSY2OiWdnh2FN0oBBAYxSuaE88dvmR1rzNJUEGULSlq0VOZ9hdsH2HjdZ952oeGAU+/dg0npqyQEGwv+RivOJhXVbDNSuT0HyLNssTmKi6mLM8OA0Jlb85UMa5vXqokZfPbSmHeGS8hs1TNezIO6g4MinvhW0cOp6asnDd6hQICDhEuOIQQKEEBAQl3+HhF3tLmKwZMBjF9rwN2xXYkW9y1SCyeM9Ik1uJwwUsT4RJgZ6QEpuVmoqyzbG/KFc1gsFqucCk60EhREZZO8RUiJ8pM1F2MVl18cx0Def0GvjtWBXPHzBDN5qSwzFhAzmbo+xwGIzIc0lkVLbXZKi60iHm8aka5mrRc7G/5KJkCzDigVH5rfmaB08IuP4rbsbf52DJjbiVNIILeQ+OV13M2R5SKsVk1UOuwbv9vgMVvPkM6W5yXq+OOcuDJ+Qz4pkZq2Xy0Dw+LVeg6vGWPiwGMWGOccriqakarvnOnkhUoxnvOb8bt72o/5Csk+bDS1alsPXm9XjVD/bi3nkkIP/8+Aye12fg7Wd3Lev3x4hxKOAAzu0xQCE1hd/amocQQBkyQSqI7kz7JK8xOjlnebC4gMU5NuZ0aD65KTge5moc+0sOap5ARqNYn9OwsaHgx0zNQ09DctXmaQsjKRXn9OoYLcmy1lVHoOq6yBgU/SYLta01T+CxiWixh0ZoTEbQDpRd7MlzqB5BEkBap3C4QDtFLIUk4nfuKeFAh2V1QOZI7ClKR4iay5HTmYzIl+sR2v1FN+ILDUhimtFpKDmZtVwUHQrBEeplA3Jt8YB4SxuywQYN8KzFYbkCo9TFtlkbLxhMYLrqhnZogNRCU9LqhDJRcfHd7Xm8eGUSB8sOkirFaNmF4HViM13zkFUpAsamUl/G4B9qquKFpKzmcuQtjscnayjbHFVXYEWqlYL81Hd9uP9AFVUuMGgqKNoepiouHE9gruZhXZeKZ2dtVFwZRS/bHDbn0PxZ01TFQ68/adtfdOAJgZGUgpesSqLscBRsjootE0/LtpzMUQCWBxwoufAIR9728MRk1dcQeyjbHlI6DRlYVqcwGAmTIEfLLp6esXDpYAKTFalNDiYjjn9CHE9eNwERKQoj3y/18//UVA19CQYlLc8npXKblErRbdCG/dpHwQWAvUUbuws2hBDY5l/roiNw174yZmscT7MaBKTGeK7qQLEZ1hkuDIUgoRD0mQoKjocf7i6i7FfrHE4pyGoMKU0OwqQqdfoplaLs8kj0+Ts7Cmgnzy7aXK4YtQlScQHsKTrI1ziqjoDDPfQZCjwuMGdzjFdc9JsMewsOHh6X/u05nUEhBI9OSF2+4q927Cu5WJmSMjBGiZzgEDmJK9m8xU9+MYgJc4xTDsLXUF73vb0tZJkRRAorfOLRabzh9Awu7J/fA/JQkDMY7nztarz8+3tx9/4KVIq2iYbv/NVBnN2jh8b1MWIcKwghcN+BMoo2h0KjL+uALAMItcTEv58IAWpcgEC+MHfnHWT9F3/R5qg40iKt5nAkFJ8odvnfCQGdURQdDwdLvl1dzUNCIUhpBLuLDmaq9RunUONIKhS2y0PXilnLi0g2yoG8Q6PI6dIlwmAEGgNKFtANSaQ1Rtou1xdsjiemSnhkXPrzEhDUPB7agjlCgAuBJ6ZdUCGTs/YWHAgQJFWKp6ct6Ir874xGsWPOjjgRMNoaNCzbHCLZek0C+kQIULA4Cj6jLjo8PKaMwFn4ya4S9pccbJ2xcVaPjoRCMFZpT/inax7uOVDBoxO1MCJYDiOgAgoIMirFirQa+vKmtGiYv8dPvLQ8SXY0SlGwOQSXUg6PI5Ss9CcYug0WVp8jBBC8bg+4q+DA5TZmahxp36miuWpjI3YXbBRsSdIBYG/RxQMHK9g2Z2PrjIW8zSPPegiBSZvAVAUUCoyXPXz84WkMJBVUbA4CgnRDNcms31cuBBAoLIgkoIHuuPFcCi6voSeAtV3Sj9zlAkWHo2h7KLkCBUdGjfcXXdhcIG/xMLnU5QJVl8PjQEYn0BjDeNXFzryNNVkVBiMhEa+5HLvynp+DUG/I/pIDBgJG5RjeNmuj5gl4nEPYBKmqB0YoLE9GjSkFts44UrNPgFlbSqLSGkFaZai4HE9O1bCn4OBAyUVGo0j6Y+Fg2QUjRK7cNHHTTk4mT07JKpnBuoXrybG2r+TC9gSem7NRcRQUnSI2T0lnFhCBwYTaoje3XRHa12W7dTx/wMCTU5bMnfCi7/nFIibMMU4pPDJexbt+eRDTNa/Fp/WPzsvh5rOyuOH7+8Ls5v977fARIcsBDIXi+69YiT++ewwfurQPn318Bl98ahYer0d8LE/gNT/ah0ffvC6uBhjjmMDlAmN+Qk2w5Dljeejyl+vlarFvwQaBqivABcCqDS9Hf2k84RONvL8kWnZ56Mk8Z3N0m9E32U5/6bxBcgkhBO4drSChUNhtIlXjFRc/3VPCnqIDnRHsLji4aMCE48ml7yemaiCQ7hg5naHm8bAMsICM1lL/l3YOaF/ZMgePy2XnqiPFHrM1joQWRBJFGAn2ECRxETw5VUOCATMWB7VlBD4g743YU3TR1abyXTsBRLD6FZz/zZMWig5HuYEoTVY9PD5pIaszP/ovsNsvwrFjzoFCEUon6t8lrevmLBcDCQUqJdiet1GyOQiB76whi01kNBoSEFkgQ4AROQHJ6RSPTNhhywWvH19AFsUB5KrbvfvLOFh2kdJIaJsn+whMVDwoVF6bssNDYhaQxOZ5zb5i1A7NEwI78w7GSg4AAi84PX63q54IVyUsLolt1ZPJf57fn0YE4+K5vBN+d0pjmKl5IXluRtWNuqtIyQ2H60lXxLGSBy14xPva27wlS8ZXXIGUSn03CIIZy0HZFnjwYBVbZixkNIL9xVYimm8owhLoegeTDHlLRBwwbIGWqO9AgmG05IZjy+MEu/MOEgpBf0JOdlelqZSrCCntSCoUnhCYqnoy2tt0XSarXjgh7NIZUirFrC+ryGgULgdKje0g9ZWqguNh235ZvRCQ42J/0YVC2ifH52scJVMmyZb9ey3oy2L93hsRv31jnDLgQuAPfj6Kx6daNYl/fnEP/t6XXtz9+tV46Xf34G8u7cfNZ3Ud8XZldYavXCdr0H7ksn7cckE3Hpuo4bU/2h9uc6Dk4sb/3ovf/s7atpnMMWIcScikO4GZmhdaVXlCYLwsX9B9CRZWCRMCoV5ZaTNU26mbgldXzRWhjnhPwcFk1cXWWQuOJx0GABk5nbU82FxI/9Y2x+NcFhIRXNpEUkZwsOxioiLlDwWLI6lRKBTYMeug4snlZMfzST0jYUS63WvVdqXOMl/jqHl1662Kv3TdrhCaxwW2TFuwuEDNJ4O9CYaEf5JYXYqMmaoHreFEBf9Vsltb89ycjdGSgx1zDk7Pab71m9fScItzbJ624HFp7TVelYRyrubB9GUZjZDuAhyWK0kwhQijw5Yrdc5DSQVPz1g4M6eHBVJ2ztlQGcHqtIq0ynBGt47n8k4k0snanJ/n5mw8OlHDrryNsWpr1FtAEkXuF2Q56LlgRMocTJWEBDrsbxP5C0g1o9G+MgJoDPDc+ocVp04mKXHlfqx1QlF1eYSoT1c9pBSKboNhqtKekNmegOAiUhUmb3uyiIzvilT1OPI2h6kSzNQ86YFNue8jTWBzHsokAOlEwQiJrKK0w6wlJQ6mQrEyrWBP0Y44SrQDh5wAeULAELJ9Y2UZvc4ZFK6/cgTI+26sIs/XnO2hR1fgCI60yqApgO3646qB11ddHlYWHC1JUtvBuARj/gpTMClXKEFCpZitufBEqy1esC0lBDv9CeLhvD1jwhzjlME/b5puS5bfflY2JMsAcE6vga03r5+3wMCRQkqjSGkaVmc0/NULevHRh6bCvz06UcMf/WoU/3btyFFvV4xTG7NtEmQ6vXiCd1Y7S652+1meCMkNIZLQ1DyBoiOdGmzPjyBCvrRbluEbvsTxE7ssT76cJ6seqi5H3pHVBscrLmqegCuk00PRltE7jyOyhD5e9bAiK8JksXbgPLCcaz03bhulQLDEHkBXCFRCwAhpiSbPWB5mLBc6C5a46xrXtE4jpZtdLiJa0UBHSpvkZZzLiYztCegKgeUKeFwSj3aTcFcI6JTCAocnBBxHRt09AAVbJtmlNYqUSjHTECFvDtwJtE6SpE6WhpMgQBa16NIpLI9jdz7K4ggBSo6AwSTpC6PD/u4mIxGyNOsnBAZ6bi4EpmocmyZroXYeRJ7VpCoLtDRyrWbv4Zonv7sRDud4dtbxx40cn6ZCsavgINtQIEVAyIljw/6cyF/b3UMuB+ZqHAQuXH+8lxwZ1Q/caRolSDbncDnAFvG6cj3ZHiFaJxTtULbl5KTiyOWhvMXDKHzVFXArHvoT0cEe3C62C+Sph6zKsCqtwuayUmLjfUaASEJwgIRGwpWsdrFjlwMHiq7fRg6bC/SZCjgEyq48VwTyHshbsrCJRgmqwZei/bNpIcSEOcYpAS4EPv3YTMvnF/Yb+Pw1Qy1JfceCLDfjw5f24r+ezWN3gw3W/3k6jwv7Tbzr/O5j2LIYpxIEZPlahUhngPlsrACg3FAuupEg+tWxWyOZDa8uIWSUbq7GMVfzkLc9uAJQiawYOGvJ5VxPADqTlnXB4RghobczICPSjq8RFVyW/LU9GZWa8jWl8vtbpQ5VVxJhgQZLKshjUyJ/UhptSdbrfA5bEUTSeTuZBYCaKxMLFVoncAIi1BEHeHzSQo8hXXwKthdW9/OaSHvJkclOvaYSkqXgWlRdDt60g0ZpSHT3FJww+gnIXAvh65JTWS0kckHbGt1GuJDnXGcUOpPFShRKYHOBc3sN7JizMVF1Meon6OmURMgcpYCpyKJQ9SLQ0fNKmqinEFF9fdkVvid1PbKb0yWpnbGkljeltKdQXgdZTt4WKFih0MTvvzz3B4DQxYWLeqImiLyPiCCwRH2i03h4lQJrswocLjXajXpfT2m9f9yGvhYdD0nfB122vSnKTuvnKrBoFIokoBVPoGALWNwFIZLw1jy/EE8gt+kwV+3xq0U2o2ILGIyH7eBN+3d6jHAOJPX2MovG7w3+2+VS2qUxEiHfKpP3zljZQdXlmKp5cHnn1a+FEBPmGKcEPvnINPaXmqIWAD5/zWCYsHK8YbIiE5Ga8e67xrAmo+Lla1urVsWIsZyYqLhQGcVU1QOhkC9YSOIWkIjm5fVm395uk0Jv9nxrQOM7PafTMKIUEMngDpioeFAJhcYEqo6MSgcFIDwOOE3Ek8BPtvI/LjQkFzbC4aLFpcAVUvoBSPcDQBLOYEm44nEULN6im50PzUlGuv/Gbj6GgAidL0ibROBm0gHIJf0+U8FYud45UyERu0wpsYjuR4hMbLQ9EWmfIwRMRdpo9icYKJH9byRMwl8Cr7oc+8oOuCdXC4AosXpiykJCIZisuugzZLVDj8so8HRQ7lgAriej3bpWf+apTJLNchspSrCf7Ef7Pwco+fKGHXk7PCe6H5Xu8xMT5xpWLgita63zNbkvIKUSUp8v2q4iRJsmBcMi+iFcIWULjUmyAJA1KNbnVOyYdVD1T2BbiYGQk4hgHBDIezClURRtjvGKh5xBoTPasnIQ3FtCyDETuIowIlAS8h6hDXpgj7eS5EYE56C56mUjbE9OuFoKktDGdZMoaq6ArfpPAF9G0zhJD3IRPCHC61l0OLrbPGdkgRgXU1UPMzUPhEit+qHUN4gJc4yTHo+OV/GX90+0fC4AvP3OUfzydasxlGzvC3kswIXAP2+awYcfmJRlY5sgALziB/tw1+tX44qRNmnzMWIsE/pMhmCeKbh8+SQUAg6p0yV+5He+iHMjqr5WOFgybZysUiJ/d7mA5XKAELhCkt5BXzftCuEn2UkQSBI/Z7XeJ0HhDUB6LDMiK5k5bdLj0wpFqekYe4sOiraHWT9JLiDLNY+HCcA9rUUAAGghSURBVItLQXO0r2ILeFxGzIVAvZhCw2aCIyK/mG3j08Wo9Oxdk1XD0tMR+KzEkzMd1DwOgnqZbErkMbyGQ3MuoDbNhBgR6DUZap6UCXhc+i3vyjswVYKMJp0rGCHoS0gSWnQ4PE5QdmVim4dAGyzgcYJHJqqYqXFwIZfrg8TQsOkLDKucIUli82ZhRUPIAjYpjSKhEjAQ6Nr8xwSApEIj0h/mr6wsxbvX5VLz3TyBBOQ1bSRsLofvmuG3W0j5THMsp52MYkVaxXTFQ9mtu6LM1jiGkhS2J6CyoHZAa3S6ETZvjtOj7funGeNlD9NWqxDaVAgURlC0eFs3E49LOUaxzb0LANMNspOMTqE0hITnLFkopXHyK3h0nwBTNQ8ZjeJgxUHJrk929PTSV5FjwhzjpIblcrztjgNtl9QAYMuMjRd/ew8eetPa40KGAcgqYr/YW573YeUJ4KXf2Ys7XrMKL14Zk+YYRwYE9aghgFBPPJiQVetEmyHaLG8gkERPoPWFrzKBnM5CZ4qRtIKnpixYroCpMmn76JM8g9EWr+Dg+O3QuKnBZPZ9pU2kMjHPCtOzMzZSKsVEw3Lz3CJIRDPGO9hoWa4Ao1JfuZhodZAsldQoMmq03RXXw5ztQWcEJqNIaRQq85fCRSApkRG5miuQVGUylMulZCKSaNUuWRMEIG005ID0wPbZS0ojMGyZgFa0OUZtL5SUzDS5gQQ2geF3kPmr6zVjpsqhsmhzyw6X/tmKdNoYSiqwPYGJqheOzKRKkZ7nupsKgcbqDjCMELS76gmNIOtrjVw/GTEg64HncjMYlQRcYwQ6IxHv4pofzg3G7kJRbFMhGC+7yNteS/Ke5XGUXRFZVchostjM7oKDslsv/25Q6ZLRuQzX/Gi36lF1W5c0ghWDAM3a5U5oPosaJcgZDBPl9vdVAMcDFCKwp+gcUqGSZsSEOcZJjb95cBJbZux5t3nbGVlktONLlvGPVw3g53tLbX2ZA9hc4Prv78UPX7kS165OHb3GxThlMGvzsEIXUI9QMYqOWTMplYL7bxYBSQ5sziMv1cDvPCAjgCRLFYeH+tdqg3ay5EjC3LzyW3VFxyqdjaAdEhCBzp6wgtfJf9H2UHLkb+0mCYcDP/C7KITWbVyEEeug6MgTE5IxVR0BGHLbYpOeU5KVQG/rfzMBUgpBxanrOmVSmdx3qMHKsmxzqBRI+/Zp7VCyBRIKh+UJzFpyGTy49s1+vM3IahQaJShAJjcKAPYCrFFnFJpCoFDfh9onoB4XIFQ6OOgKiYy/qsOlPtxf6kiq9SQ9lwuUXA9cSB28yRr8lhtA25D7ZMMkZqppXPWY0hqu6srE1IGEAoUSeALQfG1ySLCDqP8Cg0NXSMfxG9wXzeOVCwHHTywMkF+ImQf90yhqLu+oZQbqchZGo2WpAaDHYMjbPLJKtBhoVE6mg0S/ouNFSo63g8qkbR1FXV51uIgJc4yTFg8drODjj0x3/DsjwL+9dBj/8zisond6Tsf7LujBvz89B5cL5DtEtSxP4BU/2Ifv3rQCN8Sa5hjLjKxOIYh0EAgKWk9UXVQ9SUwIZNlc2hDjm0+vHCAgfmVbgHMPjhAwGInoZBujT44nCyGkdYqhpII5PxwavHgDPbDekPRDST1SJ23klq5ZnKx4EKaUosxXJKMZpkqi0hHamWgLtLoyLISaK1Dz2WfOoC1OHUuJphEAJqMwGVqS6mT76lXpXJ9sdSLLAYoOx96i00IaG9FyPYSswtbr21F4QoQrArpPiE1Fygwax0bJ5kiqBGMVr4WQMxJ1YRj0pSKzliT0ge7dVERoYlFyOBzue4XbHIYpS2C3m5ilVYqqJy3Rqm6rPr4Rni9XgRCYsThWCoE9RRemIq3iuJBSirzjtV0JaYd21zmlSXGwSlp9lR0ODCSUJY3lRqgUcCkJfdMDKIQgiN8H47zHkCtHjYR+bIGIcCd4ou6KIcu9z6+tBmQUOpCKtVu1WGx0uxHHV1gtRoxlQs3leNn39nSUYvQnZJW945EsB/jfl/Rh+9vX49NXDQAA3rghg/+4dqhlO8sTePUP9+H7OwpHu4kxTnJQSOszCumrerDswuNyqZj7CUGLoaHN2wRJVIBvT+XJyORCL7GiJUsgSwlA/eZOKCRiUQa0OhsEWlZ1CcqrfUUXmyasttrIvgRDb6L9wZojaBSyWEQ7HG7Eup2tXTs0kzlC/D74SW+dAnYVV8DyOIptEiY7oeoIjJZcKPOc66LNMdegyRZN/zaSRsuVJaynKl7LGAkkEO2i164HbJ2xQ9nDeNXDRNWLyIwiXwqgcS4guNwHkKsfjehPMHi+rrldEYykRpDQCFI6BaFSKhIQVduTpJlzGbW33HoSYUAETYWgx2Do0um82uNGpHUqK/EpUr7ULAkpWByztQWMl+dB0FdC5CSwy6Ao2l5b33FATljaeW4DMgLcXBmyE8ymg8w3EQvQeEkWswq1GMQR5hgnHbgQuPL/7Ua+wyz9qpEEvnHDyHGV6NcOwcPk5jO7cH6vgYsGZMXBZ2dtfKwpcu5w4A0/2Y/vv2IlbowjzTGWGaUmgrpQhMoR0sarU3LcUpdkG9GOvAakkVHp8dtWL01l9ltpfoXWohFEr1IaBYeYNyqo0LrsRGOASukhR/mWA4RK+QUjUpscVAVs14MWj1yFRCKXlErpguWKlki5QgjcDpoCmScmsK5L+kwHlVenFlhqD9CbkMvtlKCtvrgRARkVon0fBfykTIGW2V0gzWmMUgb2bIGNnu2JloinqVCo/hiptplsjJVdZBr8t0t2NDmu6go43GvRMRuKtORr1g0zKt0jZjxZ1KfdRIwFlt6HgfB7hZyEBgmK7UAgLQSbI9KGQpBUSUsEvB2MJv+3xXLf5iTb5UBMmGOcVHh2xsIbf7wfT023FighAD58aR/+8gW94QvsRACjJCTLAPC3l/Xj7v0VPDBWjWzncuCtPzuA3f9rw3GTwBjjxIbwXFguR3EeElOoeaEOWfGJhMMXjhYfCWiMIOOHNceaMvcrlrSVmy+iKwSH8Fz506HcbgDunxKTigWTjxSFYrIki1x4wk/aUwLJAMdhBP0OCcIDbAAW42CEwHMFhOfCdV0wNn/fOSEQDRFazwNcQlFtMwFgjIKBt43+9iakyCfDBCaq8pwvBbYv3wkIs1hojX4ezFYB1+OwLA+63tp/y+GR4xMhr3/VH2Pt+ic8AR5IAjy3ZdxVOaBBegd36nszyR5IynNWdnmLLtf1gIXOoOsBkw4wmFSQVXko6eg07hldQPrgASYBasRrmaCO+xIKXYmOFwAwNQpFUGjgqCxw3WwBjDuOdOYhredEfkcwAWsAqd+j7c6v8GJbuRinKLgQ+MymGfzFbyZQa2PjA0jP5Xecd+IX/FAZwZevHcILvrU7kmHNCHD7DStishxj2TD5yM8xVaq1LckcIN/ms7YvsKOAPICqIZPF8jaPhB7btROQrg4VV0bsOAQcx4atai3FMJrhasQvj9w+mtfcrkZMNfy3wtoTrpQmJx5H8jw2tivou6VqEU36fPsEqHW43rYqnSCqLo+4OCQ0gmk/aFFTKfI272gvtpR2HAqCZMj5+t/8XRoDDjDS8b5INvQPkPZsjRFhSiXRL0DKdTqVgm6GpUjvaAEx7z25EPJNBsiLvfbNKDOEPt6dEjrbkW5Pk5Fnh4vOHtuLBZXJooHVYQCFAbafhJmv8Zb7jCZ14Ka3LPWrYsQ4sbG/6OAl39mD9/16vCNZfvGIiT88N3eUW7b8mKq6eM9dB/Hy/96HT17eH/mbJ4DHJ2vHqGUxTkbYXLS1clsIi9lDUyRxmA+USuK4QLA3gjlbej0vuE7vo2S3Lm8nFlEGrGxLGcbhaJApla4i6TZVzaquOKTiCgCWvOy+lPO7lC9cTPNLDgcjUg/bZRx9SrJYXXYASoGESue1fCvbdc/muSayDEhZQ+CN3JYsdzgNNVegaPPDIssA5r9BfdnQYuB6Uo6iENIinQgQkFhdkfdyWqfhuFYpgXa4YdtAbtOmbXL1Rm5Awv87dMQR5hgnNO7ZX8Ybf7IfEwskAXzqysGW8tcnGr7wpIygB0Uats5a2NClYftcXZT5V7+dxHWrUzivzzhWzYxxEkE95xqYtoC58KYtmHcfAgwmFFQXKALCqHThmKh6oFg4Mz7yFUv09AUAT3BYVg1MN5A9fBa5IFQG9BgKXCFAm55hlErf3GYnhOYKfu2Q0MiinRYCBH3XdQNsiX0nFDCark2PyVCwZXEJE/JHYbI4jclIKONphul4cDyxZCJ7uFhK/3MmRcHm0BdoowCQXUIbdEX6MxtM6pQPpUDOfDAUEtGZEyqLtOQtt6XvS7nnVSZ17LNWVPLUOFazhrToq/kOO4wAGZ3BdPmSx2ozCAXS85yqdv7SmUNwzYkJc4wTFp9/YgbvvXuspeRso50UAPyPM7IRDfCJioNlN1LR7B83zQIAUipByU+isj2Bt95xAA+/aS3047Tkd4wTB4QpIKx+M3WbFBWnNblrPgwkpe3cjBXVORY5gcMpSKc0esjgUYXLkoIcAFmi2mgpr8S0TjFXc/0+KyBHmDATAoACDqHwAJAmk2kBIO+g5fzUxMLnoeodwrkSPOx7T0KBRgkmq17HSQohkoBRIiOqzd836wACSuRzD0DZlT8gwECChZUXg3y7LkOBw8Wik/8WC10hyGr16oZVL0pIG/u/0LWftQGALvkcz4dek0GhdV/uBICy1/n8Hwo4jY4LlQFMoSAuDmvcu5DnpMtUYHO58pJQCbIaQ81P5iw4QNkDPE4BQuECmLEAgC3LeVzyeJ+nnHcnxG/UGCcchBD42wcm8Ud3tZLls7q1CFnWGcHfvbDv6DbwCOEDF/diJNU6x22OnD81ZeGvfzsJQJ6rJ2OZRoxlgK4Q6JQiqRK/Wtnil/Jd0WpxVbFbo4jtLN+OhpuEygDzEF6ghwMh5LLxbI23WJYtBimdhtchcQjRskY0n3dKpPdwl06R6yCTEEK6nXTSoC4Y3ReyeMpMTRbHmfX/zTseZtqUAD8cdBsUOiOoeSL8Uam0GTzcedFSbAoBOelshxnLw1jZw3jZw0RZOl3oC0iDVCaP1+P/zIekSiIOE4QA3QZbtGXdYqBRgoQ/was4AuOVqLB9uci/rpAln/flQEyYY5xQEELg/b8ex4cemIx8TgnwoUt6sb/oRD7/k+d1Y3VGO5pNPGJIqhQfe1F/y+dFm+OSwagE45OPTuO/nyvgxv/ehxd8cxeemopJc4zDg0IBi3MIvwJaVqcYNBUMJhmGkkrblzuVNRTmLfPeCFOhHcPCh0psBpOsI0mR30mgM3pEbKgWAyGWrqPVFYK0QtFvKug3ldDibrmhUQqDyWIxjd7Zy4V2k6FKG0354aLmu7YEP2WHg0Jq1Xt0OT6yHTyBdYWgL8E6+m47ntTjpzQ678SFUekMotP2XuDNe5YdWcxnvoVCx5NVGaerHNO1+U3Ry05Uby+ELECzHCWjA0xUvUgbDiH9YVEQEGHp8qOJmDDHOKHwN4/M4Z82zUQ+Y0TqsB4er6LQ4O/aazL85Qt6j3YTjyj+xxlZXDWSiHyW0yluv2EEazN1X2kB4LU/2o+f7i7B8gTe8tMDqC6y/GmMGM2gVNoWzlRlFHCuxkOHFgKCisdbfY+ZHJsLuU1EvocAPZ0imv7wTek0/FkIg0kGgs56WUAm15Vs3tbfGVhcJP2wE5eWCMsVcJeR4PcaCgYTDCkmoBBgzvIwVXMjll/H2okz8Ng+lIqNzRpZxwMmKh4mKh6mqh5mqrxjNVVKpJ+0QmQRn3ZkV6XSi3u+BFCPSzen5gIwhtI+Yc7yE/wS6iJp2gJltNthqRO1BVV+Aigs1vLjMGC7suDL0UZMmGOcMPjJBMOnHo9Ws6NEukMUHY6f7C5H/vbhS/tOOos1Qgi++JIhaA1vr1mL4+8emsZXrxuJUJPG2f3maQt/cd/E0WtojJMC/QkFQ0mlbSGQxpdtO02z40mdKLB4LfFcrTNxDZDyo6tphaLXlNHBZhJjqiQkywDCLP6FyG9Opwj4GCG+DEUhMNqwxcDVocuQUdilQmFRl5CUSsOy0Ish4EF1OUeIBSfDKgMyhly2byY9jeeDQy7bu55vtdZwWU2FotukLZXbFlOtTWWdK74tFh6vlxE/mvnbVUdgrOriYFn+CIGW7yd+uxZKxrS81hLrwb7t5ArN12AxyBq0o2PFUtCXYC2TE0YXlkIE9oF9CdbW9URnrVU55bFlkutCzjmLwZGSa8SEOcYJg2t6PLx2XT26mlRIx4fJxpyGPzznxLeRa4eN3Tr+6pJo5PxLm+cwVXXx5xf3dNzvM4/P4IGDlSPdvBgnI0R7q7VZy8NkzW0h0wEqtkDZ5bD8G3UxL7KFpMRFx4/SCalFLdqtVdRMJjW4AgKekNHYlEbRYzAMmJ0boTMSEjsh5CTB9toTIU/IqLvOCMwlEhQpAyGh9CCjSY0tIPXIPboChQU6U4pMG4IxV+MYr7qYqngLRgp1RmFQGWnvMhi6DRpqXwFgsuZiuuqh2WyIQBLzqZqLos1RdqSu1lTlBCSlU/QY80sRQKScbDn0q5ZPLIXAvGW3lxuNkWPX/35dqY8Vt03lvXYI9N6Nk4eAfJv+5KwZ7SL78+UQUEi98uFCIaQlYG25Mu9gMYRcQNrG9ZjtdeIZnUYIOfPHSZ8/aVQowvtqqQR4KataS0HskhHjhIHBgK9c04vz+qv4x8emYc3zBP7kFQNQj3ISz9HEn1/ci9u3FbC5oaLh7/3iIB5581r8dHcJT0y1VjoUAP7wlwfx6JvXndTnJsbyIigL3K6M7WLcMhor/qVVipkFmFMHK/UQwVIsJRQOb28/xiFL4xZtHiG7hkLmjYgKIVqS1TrdKUGhDZ0x5HQKlpIJfKZCUXY4rHk6ktYoSm69nDcjsr2WJwmpKwSSKkW+xmEoNGxT1eVQGUFCIZiqtvr7tkMgCw+sN3tNBt1nbMKXEbhof96j1RrlBoZCkPNX7gQEVqRVcAFU7NbKJSqTJKidVrnXZH65cnmcsQWqJbbsbzCMV73D8sE+HDROEtvdB0mNzisb0BQZjQ1Go/TdbrMdJeg1GUDktoQgUlxkquZG7oFZv0hHJygU83pIA3USn1AoKm0kFsHqhqkQMEpQc3nkmD0GQ6/JwH1ZNaMeuJCV+kyFghGZ5FhtkMrYnsyRcHm9jS4X6DYpKCGYmsc6djFWi8uBOMIc44QCIQR/+fxenJZVUetw/7xsVRI3rU0d3YYdZWiM4OsvHwmjUoBM4Pifd47iyy8bhtaBED81ZeHTj00frWbGOFnQNJzaFdpohN5h/M00JRh1WlpfTASrYLXqpgNojKDaJjJcc0X9xUtaEwLHqxzNOVDzRW8pkYQmo1MMJ1Wc1qW1jTg3RtJMVUZ6g7Z5XCZFzlmS3Af68ASTOu05y0PB9qAyoNdUkNUYlEUKirsMGVVvhPD/V3Y5CofgztEITwA755y2BaMUJrXRJqMtkcqUFpV2EMw/kWmHSZ8sB9FLSg8t6tyXYOhPMgyYFD2qwGBCJrEOJZUFx/l8qLocvf6x2kVIs351kMbiNMk2emVKZKRWJXLcLKYSX6eqe8D8ZDmlylUDEClzrLkCCkNL5LtejERO3pqbpPmrJwMJhoQq471yM4KKyzFWlrrx5nvL5fK8NbaRgsw78cjoFF16XT4iJ8WtyZLLIVOJCXOMEw5/8+AkHptsjaAC8kX9r9cMnfBFShaDc3sNfOqKgchn9xyo4Nb7JvC3l3aWZnz4gUnsLTgd/x4jRh0CBARndGsgRJKTLoMipbRqYQP0mwwr0wsvXuoKQc6QDgwDSRYhUIuJXHe6xVUGsHkWZSmV2xDIF3izrItBJh520lKm2sgPNk/ZeHSihqenLYxV3BZSojOC3gRDj0nR1aaMmic6JFQJac9VbdK4EpBFaT3nahzjFS9CWIn/v6JzaAUjGntv+BMjCkmCUxoJ5QCNzh3N35JUW4nffISmnTtFcD76DElKcwY9JFeGOSuwcQNcIRPzHCEwa3lLKtetK1HpAOdA2eEou63EEJDnJ2tQ5Ju+IyDrwc98CauA7yyzTEj6+QGCSweTks3hen7ybpuvmavJ/jVe4KC5RZtjx5yD3XkH42UPRYfD5tI+EJDjJcgrCHIBNEaQVOVEMdDxT1W9RUWPM6qUGp2W1TCQUFvuwUOpWNqMmDDHOG5ht4la/GxvBX/30FTHff7qBb04revksJFbDN59fq4lmn7n3jIeGbdwZZObRoCaJ/AXvxk/Gs2LcYJjouLJgjk1joGE1P+aPrPN+JGooaTSopksBpHLBeatVVdgzvZAAJzdoyOlEXT5JLotgqVijYTazpyvxw3geIDNeUfy1K0zdBssXIVpttXyIJOzOskd2kXPA9mKQqW+tZkTz1Q5yjaH1sByu5oiqo28KPhPRusJc7oio2bBxCLXJvqpsDr5yBo0jNpGWuz/opCoBnaxKq1kQ7uDc0wJQVqlSKsMGb/zXQ3t6zNZ6HlMWgOSCzp+qIR0LJs9XZO+zYIjcs0Wq3sNyOysxZF3CSquABetxXl0pX7cduTRcltXI6pOPSGTyhlaBIsN6zhCwOIcFpeks3EKZMxz4RZKtGxO6qu4on7vNmCs3Fn6UrYFEioNo9DNix8qq19wV8j2VjyOhFqPCJtMVgFUifzvtEKRUOiikjsrriTtNufQGUXe9lCyvZao+HJYFcaEOcZxiT0FGxv+Ywe+9mweAHDvwRo+vVPBH9zdWU5wTo+OP7uoc2T1ZAQhBF+9bgR9TYlM395RxNUrEh0fpl/fWogTAGMsGg7nICAwFYr+BJMOEpRidVpBzmAtb/4gepjTKQaTCvoSDIpP/IKXoOVKUlJ1BHRGkdMZ0lqdkLeFzxMSjIaETJbZjUZcp6u8ow3aVFUWh7BcSULaoeR0JnBtXTz8zQPCRIkkt70JhoEkQ8agMFSZLBe0k/gEKiBfjlcvyBAs0ycYRa+hYHVaxbk9Bl59WganZTUwKj2Sg2sRHM/15ARgriaXtQPLsoQqE8qSGkHAIwYTCtJqlNQGf1MYOiYyqg0sRqGdfYIF6lFjChJqqYWQJLfUEAKcqXlwG2Y47SLK7cgTIVJna7kidGQJMJ+UZr6CIO3+EpBvSuRxm8ljRqcdvb4jFVdF5J9wjOoKwUCStU2sBSQpnKny0HO5UR/fKXKa0VsJZ/MkYkVKxVBSCa0ci06ri0dkf4qwcFEjAnlUoMdXGu5zRgi6dQrTr7SYUAgyGgXzz3TZFjIXgQvwhokAF/NPBgK4nvz+OUsS54LNwYVoe/8frpd4TJhjHHfwuMDb7hjF3qKDt/7sAK7+9m68+sfj+MaohtkOS2QaI/ja9SOnZDnonMHw/VesbHnQ/+2DU3jDhkzH/d53zzjEMSrWEOPEgamSUO87kFCQVGn4MuoxGfpMhj5TCYlgMKJ6DIakSrE2q2J1WkWfKUN07UhBVm/aGfOTGgEBjVIkNem+oBCCAVOJRCENNr9sQVfIggU/2tlfRY7hv9DbaU8BSb4oCJKMYm1GxUhKgUoJPMiJgsEINErQ72tdu3XWQmQBYCSl4vScFkbyg+g4I7IaX0anLcmKjidQcTkMhWAgIY+dUetilazBMJBgGEy2ktOcztCls7bXQDRcJJ0RrMtqkagzIKvPGQrBqnTdG56LepTP8RApFOPxaKS/kewk20TJGZWkLIy4k1ZC3XgKDYXgrB4NK9NKeJ7nu/QplaIvwWD6E44gyTGt0ZbVlJxBkVRoxOozgobrojL5UyfM/sRIIegzGcq+hd1Y1Y1EepslCZGS3m2+klE5/hvnEIxKm8jgmhIKJFQCr02iaye4wk+obfMa1hWCflPBYFLBuqyG/oZJj84oVELheDLIE0yKB5IMvQmKqssxVfXCZEJAViFsnAzobbTSwe8qkzrvoDCNzREmlEbOy2FKNWOXjBjHHT7xyDTuPVCPft69f+FI6N+/qB/n9RkLbney4oXDCdxyQQ7/uGk2/EwA+Ma2PE7Lqngu36pZfmCsim9sLeAtZ2SPYktjnEgwVAINkjBIFwMPhkJaXpgJRXoITzRksmuM4Iychg05XUYA3WrIFMpO1FFhT9FpIYkqJaCqjEg2L48Hv+V0GolMmowiT2Q1QkqkntrmAgqpFy/xhACHjJTOV92PEqnpdLiM0Lo86hpBiCRWOYNiRFFaqow2I6tL14ChpIKqy7HLdVB2pOuF2mAx1wmEIMzNGEoooEQSLpsLbJ2N5nTIv/kFO7Qoz0hoFBWbw/EEcrqc7ExWq9Hv8v89I6fh6Wkr1IMSGrXsKtocRduev+FolQZojMD1gEnXRTt5as0VUJncLt0mKshFMBmRFoJlCmTUuqMDITIxNW9xeAJY36Uib3NMVj1YXIBCRtgZFSjYUjahCEAQgFI5kRpIMCgkGqYO2sooACHbF7ROrsDI32q8tYiJzgi6fOIdrGwE85FeU8GcxcFIPXpdsnl4T3RpFC5HWCyoEe04YFaTKzCTVdn+lE6RUSlOy6pIaxRzNVmw5UDRxUTFg9fQWJUCDpeyomaDDEqALo2h5rotJNtyBebghX3MagwuB6ar0YOktLprStX3rg4PJeT9WfMETJVFxm1jgq+pSneOpCIlNAKA2zA76NIpSrZocnk5fJx64bgYxz2GU1FN5EKrMq9al8YtF3Qf4VYd//jUlYN46cpk5Hy5HHgu73Rcnv7g/ROw4gqAMTogo8qIZ1dTAaB6gpYfrSJtojdERka7dAaB9klt4UtXIBJdAoCyy1F16lrSpCbdKLoMimFf49xjyChgozsCIXWCRkCgUxppG/MrtgFSztGlS5/jQFurELns3GMwECKXj3UqreIa0WtIt4qEStFnMgymFBBCGn6iz64gwq5RgpQqNZp9poIuLUoMGs/CnO1houpiZ8HGk5M1FGwPWV1+bxCd1CipF04h9ah4cD4Fj5KqFUkFhiKjcTVPksWkStFtUJhMIKXKtq9MK0hrFAMJJfT9VUi0qEqwhL4irUhNO2QksDmhr89kaDx7jMhr12cqMNs84DOalKKsTKtt0zeFkKTZ5TJhsrchGqlSYF1GgU5l6fBzujUYCoXrV20WXEZIZy0PFVfq7U1FFp/RmYDJpINJVqdyBcWX1aQ1hoxK4Qm5vyck4WvsV5cfmR8wWcskobGIlqHIVQFGpUzGVGSym4B0qgjOY9hfNGncG45NCcFpXWpI1pOqLPU+U+OR+0sIeT8E/7ocEUmQRuQEVKEyGbHbd1chtH7v5QwW+oO3Q9UVqHlSMsIjaxF1dDe4thQsjrItwomrgNSzFyyO2arMbWgXQq86Ah4XmKl5qLkcyYZqicz3bw5WrRrbGqxILKZ6ZzvEEeYYxx1uPqsLLxpO4K0/O4CdeRsFy+vozXpOj47/vH44fHmcyqCE4OevW41vby/gd36yP5L01CkBan2Xhumah+FUPHeO0YqhFAMgE3r6Ewx5m4NAeugeKLsty9ODSYacztCfkK+W4OUoyVZ9W12RSVIBGJEEr/HFKbgkWowAeZuj4gr0GhQKIX7yHIfGCNZ3aSg5HIZJMF7ywLlMfFtM8QKHi3C53VQI+g2GcrGCTFaFpsg+7PEjx41R9bRGQ2s3UyEoOxxpjaLqShlEWqVQmQoneHD5/VvXpWHnnN1CODwO1DzZn5Qio2OAvG897lfe48DmKQuEAGMVFzmdYWVaxUTFRb/JUPM4KCFgAGyPwFAJ8jXe1mEgZzAcLLlwuABlBEmFolunSFJAVyk25jQ/Si/Pb4/BYHFZ/EUhJHTZqbgCXZo8057/kFmZUjBZ8yKrAnKVoH4CuajrXOV5i0YhczrFnB9hDZ5dPZSBERcVV5L8ICJPiZQJBNFQ0lBwoy/BULQ5pmtuuG0A2T4OwWXbqi6H5zXoswmJWPJNUA8ZncIRAglVoNdQMGN5bQM6BFIqE+rdidQha4xg2vdDFf4EyvYEJioeGJXnJaVSlF0vnPgkVArP1+QWbXkuuxsSKlVCMJxUUHEETJVjMKFi1vI6jv7myPBQgmGszJHSBE7LKJixgbN7DRRtDyp1oFASXsvhlAIuBA6UWj23AxRtjj0FB6syaluph8HkJLTQEC0nIGBU9jGyDwEMSuSzoSmuwyhB1c81UKgkzC4H0pqcQOZ0ihnLCycLJUcmAjM/MfdQShHEhDnGcYn1XRp+/frVuODrO8NlpWYMJBj++5UrkW5j03Qq4/UbMvj2jSvw5p8emLd4wmBCap876S9jxACkhnRDlwbLlYSp6goMJlVc1G/gmRm5HD9n+UvhIOGY0xiB4YeVGSERsqJQAqsp/kQIIpIOQBIcQghqXl1nGVTvo34kiQPggiKtUoyj/bNCYa3etCvTCsarrp8gJCO2pkpwwCEolr0wIu5yAY3KRCeP16UYgCxDrFCKfpNhrOxi0J8oeFxAoRyTDf0RQj6zCDRUXI6JihdZMpaFKwgGkyp2tJE5kIbjDCZksqXHBfK2h6IjoPuCbS4E0hoFY0AeiOi6g6hnoL22PSBveXAER8UBHAEMaHKy4QkBEOkWkjHqSVqA/L1Q41ApsCKthmRZXh+gR2eYIzyMktY8EV1e9wSGkwoMhfqEsRp2skunkcJKgbVbUJyibHOoDBjwVxkYlZFmSkR4nhKKXImouXxei8JgTC023qIEqxOEYG1WRXWat/U17kswTFQEkhrFirSCrTM2XF6XCBAq7Q05l3rumsdhgiKpUt+7WN5nWYNiwFTwXJ5jbUbFUIJj06TV4sMd/KZTiuGUlOvsmGsdQ7OWF8o6kv5Ep+wKdOsUvULAUCie361jbVaDQggoKig4HsZKchw7nnTESKuyMmXJn8i2awuErFLZpcvI+d6iE05ymq+IxuQEg4DgYLlOxrs0BuInElquiPi4l/xzuTqj4Lo1aeiUQGECW2ed0Le522DhNe5JMOwtOtCobL8nllYoB4glGTGOY2gKxf992XDbJKEeg+EXr12NddlTx0JuKXjN+gzufM0qZOdJWhqrePjgbyaOYqtinGhIKBRnduvhS1AhBIMJBWf36Ogy6vGWRi/YICGtP6Hg/tEK7h+t4NkZGRldkVawMq2g1y/PHBCVtM6gtlkjJZDEImfIQheTFQ9jZQ/PzTnIGTKBTZZKbq9xdoW0rXM8gTnLiyRSmYoUDkSkFotIfgoy9weSDBtzOs7v03Funx7ZpuB4GG148RP/0AQyAW9tRmspLpTWZOKYQoBLhkwAUd1mM6mbrXnYVbAxXvZQaehDkFiY1SRpNFldIHFaVsPpOQ3n9ug4r18HiEDJlTrf0YqHsicnNpYnMGtx7Jh1MFp24bjRBfZBU8H6nIqzenScntMwmFTQbUrpAiFyQrQ6o+KifhOJDhNyAWBVRkV/UkFWk8v9p+dUnNujI61KohNcVk0h6DZZmMin+DKE9V2qn2hWD5pkNIILB8wWTXx4IRp/9X8PJDuNl3+s4mJfycH+kov9JTmxWplWkdUp0hrF6TkNk1Vpuxj82D4jTKrSD/jcHh0X9Zty4tfU+bVZtSUpteJydOksnJAF+yRVit6EglUZFetzWjgRDTBrcygUqHocLhfY2KWFfSGkLqFqnMClVYrBhBKWXR/UBc7rUZFtkAitaJLE1DwO3Y8QM0IWTIplhEClMrF1IKGgx5SrIsHETfHvAcMvZQ/UJ3WzluxLl8awMqViMKm2jQpfOZLApUOm9HVmLOLnPWAqGEzInwFTiVyD1CEYBMSEOcZxASEE/nPLXCSB57k5G+/85VjLDLbbYLjjNatwTu+pm+S3GFy5Iol737gGI6nOC0mfeXwGvxmN7eVidIbW9LZvJJgrfReEXrPBdYAA5/cZGK9El22DyJ+pyKiszqT2dyStoN9kEY9hQCb2rM2qWJtVoVNJqAyVwNQIVmaUetEMghYiFLwzBWRUzBUyuchqWtc1FYoug2E4pWBNVr6QBaJL3sGLPCio0Jtg6E1IUjOUlJXszuqOEuZKU/5fl6+HbUQzN1cIwaq0ghcMmTitS0NCpeHLP3ABCDCckue9cZ6Q0ijWd6nYmNPRbTKc3W0gEXi5BedDCPSaCnpMBRf0Gbiwz8SVwwmsSsm+E+JXHbQ9TFRcpHSKVWkVhBBsn3UifskUBF0aw3BSJtSZjCKrsRb9MhcCWZ9gUT+yKiuxyQjlRMVFQpVtH0qoEXldY3InICPjCpVEjBGC4ZSCvC3lCzIyDfQllJDIWU1BRJ3VLdEIkVrpoHhOv0kRBOP7EgyWJ51MKg5HxZFqbdUnfsF1b64V4ApJTjUq+9fJRUVAkleVSBvBZiKW1eUEMTj6FSMJnJHTsSGnYySpYE1ajTiTFCwZ6R5JSlLa4690mApBj8kiVoABCKQuOa1SEEKQUwUSCo2Q+17fBSfjO7EE7hahlWHLUeuJqeWm0HtalVUnuw0WSiyTihwTg0klPGZj9FxA2uOZSpAn0Lqa7IqoN/p8iwVptT46GZ1vy/aIJRkxjim4EPj7h6awfc7GV7bk8Yu9ZXz5ZcP44c4Sbr7jAPJNWcGrUgx3vHYNzmh6QcVoj405Ha9Ym8IXnpoDAJzbo8PyOLbNyTe6APB7Px/F4/9jXUvUIkaMAJ30wFeMJLBpooayy7E+qyFveegzpfUcgbStqvg6w3bVNxkhWJNW4XAhk418Z4NA26o1MGiVEOSCcsJEJs4JIaOhjfrongSDRghWpBUIALUE84mzC0ZkdcGgLWlVJvwFET2bE/RqAmvSDDuKUv6RYxSzNY6kX0hhdVr1yR8Jo3ONZLbs8sjEH5DEY8Bc+HWrMpkwx4hAf4JhzvIg4GEooWAgqaDmSfI5klLgco45S5bMNhS5RD6QVLEmKyOQlic1of0J1jaHoewIlHwy2KUzzFYJTCrCZEFCZDKdBAn/fyCpYLzsQmUEaxexwqcrBHO2lBTMWVLHO5xUcF6fjrGy1O42R1o35qRsZSChQKEEGpPtJSDoNRkSCsV5vQY8wVFxpNyjx2RwPHmugxWPlEoik5e0SnFWj45Hx2rh6gkg3VT2FwnGvRJyKYakTjHVJAUkiFrVCbSSM5PRtgGKnMEw3rDiACFJruEnpylEJq4mEEwCpSWfwQhOz+nIGQzXrExisuri4fFqqMsFZN7AdM2TRNlg0JkkplwImCptS5ajHZM+ycFWtifw24MVUD9x1eUilBoFsqRmJDTpLNIoMap5wpdTyoZmdQaNErhChNp+CjlJ7NL9yaGQspHAJrB50YmCoNukmGnQhg8nFWzM6Xh62grPyeqMij1tqtmuzsjJX5dGMdTG63shxIQ5xjHFZzbN4K9+Oxn+/tUteTw1VcOmNqWvT09y/PiVI1gfk+VF48/uHQ/JMgA8M2PhX68Zwjt/eTCMXmydtfHRh6bw0Rf2w/WJS4wYAPxMd9ExanNur4HdBQc5yJfPygbf3WAY9SYYpioe2iirACAkOxVHYF1Ww4GiJBbN87fml3VaoyhYHNNVTxZP8TGSVDBZ8cIlY1uhGEmpMJmFOcvDcFKBxUWoky05HL2GCEl0VhE4q1tHzdc45gyGqZqH6YqLSwZNZHSGp6YtrMuq4VJ+JCoqICUSDf0lIKh6PFKUpUenqPmaXEBGNQPawoiMUvaakmQYitSmNu6/vksHCMFE2UXNE1BpPbHuogEDa7MaRksOBOoacwHA8qQEg5EgsVBquF0hr9nKlILBtBpqt4NrBEhiflpWk8vqpMEZpQ0hDzyYBxIKdhdsrElr6DMFUhrD2qyK12/I4M49ZVieBo9X4HGZzBc4QlyzMokzunVsnbVQ8wT2FpxQenNOj+7bk9XPbwAugCcmaxhJqfCEQMmPEgPSNm5FUgUG63KXWctD1eUYL7soOQRpLo/RmrBGMJxSMVXzwv6u61KxfVYSM5UBq9LtS1kPJxUULK9FT72+S8NExQ118c/N1UmeSgnWZDXkDIbLhkwMJGXk/JJBE3sKDrbN2lAokNHk+AzIfPDtqQ5SmGiPpB6+YnHA5/OBfWEgiam6AgQCOYNhRUrFc3M2BkwF+0suCJGl0E2lNdrueP7YJlJCWfM4NMpk8ZeGGRwlxJdHyc/O7dVRcQVSKsW6rIrRhgRDARmttz1J5BMqgcZkSe1rViaxfc4GFyKSUNgIg1GszqhybBxC6b+YMMc4Znh8ooZb22ho25Hlt2xI4l29k51L5sZoiz+/uAff2JoPEyc1JpcU3/u8bnzm8Zlwu79/aAp37i7hzB4dX7lu5Fg1N8Zxhl1zLs7obj+JIkTKAK5ckcDugoMeg2HTRC38e58pk48uHDDxmwMVDCYVPDkp/96sRRxJqdg+a0eLDTRt0/g6TmsUZ+R0jFdcbJu1I5tOVjx4QpJ8nRGkNJlIdaBEcX6fAU8Au/I2Lug3sL/kIF/zs+f9gwzo0Rf/md06yg7Hw+NVZA0GCuCcbh1vPTMDRiXFnao16JWJXO41WL3aXM0V2Dxl4fkDZrhdzlBkJ5Ot5z3cRmdIqzISenG/KT2bqXTleGbawqqUipmqh9VJqc/s9yOyG3M6BpIKhC+eDni25QnszDuY9Z0a0hqFqcpKecEKv8PlcRqdEAIOOOfvp1HpnpHRZELXL/aWMNcQXRxOsrAS48UDJs7tMdDvyxyGkgoUWq9QRwgwXnFDxwN5zjm6DQVbZiw8eFAmBBYcDyVH4LSshpFUfWJm+E4twSqDb5GM1RkVB0oOVqQU7MjbfoKdwMGyi15DFtfYXbQxXnWlVp4QVLlc4m8/txNIKLLfu/L/f3tnHiZHVe/976m9t+mZ6Z59zz7ZSEgICUYgCAkQWUQRuKDEBY2CAvKqePU+cH1ZfRR5RYULL5cbBYEXQQNi2DchLCEJIUAgkMxk9n3vvarO+0f1Vr1Nz2SWnsn5PE+eJNWnqn/n1Omq3/md3xKCV9Xx+XIrQroHQ0EjS4olxS6d28Kj22dUsgxoNLrjUukwdmLyZR5z8kX0+XVT0KvAGVk6Pl9hRWV48WHMU2IEySlc9Fo8MTKdxGPkkUmPwBmLroBGcTgYU9SdEo+TykQENYp3O8NjH9RREHanaCBG5g6OGPLIPIfhsA81z8WNXfgfxRYj1V5/XGBw/MKXUrN/vlXgYBWMd1ViGXopXPQn4p/uiHOdKrYKxvwnwItNnpR99qo6WkZCCGgUxr7T2IxvTPtgTAsjQR0X7WjJmMUBMH40d51ain+rE/Hxx90Z2zKSKbeL+POmCpz59yZUO0RsP6cKK4oVnF5tw5OHh9EY3rbSAezq8mNXlx/XHe/CXOv0ys3IDSyi8XJOtaUfeZVV2MWoAhOvMFc5ROTLHE6rsmFtqQXbDw1HPyuxGWnKLDzBihIFDpHHp/3mqH6Z5zCvQAIB0OlToemIFjygMBQKu2gEYDklzpR6UuAIChSjUt1X5+ehUDFcAA72h9DuUVHpECEQAqtAMBj3nW6FgyVFxiyrSNAf0PBeuH/5Co+BoI6yuGCzSoeIluEQCMJKHAVCYTU/dUZaQ1HIl/moAhtPvsJjwK+hOBzslViYqcwm4NBAEMcVybAIBEeGVXAw3DraPSpKbALm50v4tD8Ih8jDUWBU9ov49Yp82ILNE3zSF4RL4dE1aBSsKLEKuHFtEf7PXmNR7VKMbf/43MDL3HLUjcspcWgZjg1cfbUtavWVOAKbhcNISI8qvwBwzlyH4RNNabitYTGUw4sCwFCeIlAY/vPL3HJsN0DmsKRQNlyAwpbLiN/6imIFO8PxGbUOERSILuBk3lDcFubL0HWgZTiEEgsH3RtJm5FwnzgAupFJpdgiwCnx8Ks6SmwizqnLQ0DTcSBF8ZhiqwCrYFhAiyw8QjqFqhsp3yrC2SwAo3BJqZWgx6vCbeER0oFKuwCLyEWVZQDR9Hg8AQplHqquwSJyWFGkGApp3DQTeZKyKAxguOnky0blzqZw2sRIl5tHQlhYEFMkCYx5Sqlh9a5zSujwqnHKOEVAo5iXL0PkDJcOkTO+u0/Vopb6obBSPRTUTK5a6XY0KYzS3Q1xRbcWuYxCOhGq4hZOUXnDriSpCGgUAa8GTQcsPFOYGTOEK19ux8H+zBWiavNE/HVzJVaVWODxpF4xMkZnU60dD5xRjrPr7NH8uDaRwz2nleHMvzcltT/nySbsvbB8qsVk5CBiuOpZKEVC1SwSSkSxioZ1971uPwgBFhRIWF1C8LkyC+qcIv766XDSOfmyUXgCMKx0lBov46BGodOYX+Rp1TaAAt0+Fc3DJKpM1xfKKLMLqHcZimaJTcCCAg2fDQTxcTgdXqI1rNYhoCv8qKmwGxZK42OCmrDiwnMk6sccwROiqHIIsAgEQY0iX+bxSX8A74d3y1L5u1IYMQa9KZTlOqcIv0bRzIWwpFDC58qTV7ARixoADAU07OmKKRKdnIoVxclB0S6LEVzlVIyiKapO4QvpYT9hgiKJotImYG2pBfMKZFyxrCCqoJTbRbR6QlH5IsqyTinqnDI+6guCUuPaiZZBTU9eMnR4VOzq8EGlMZeJIosAmSfQqHF+faGhIHV5VZRYBBQrQlTBKrLyWFVsMS2UKuwiun0qrIJRinyOU8LhwSAEjmA4pKHSbvjZUxhKIwcStdqDElj4WIxkid3wifaGjPzWhba4QNC47tklDnZw4OLeZ24rj5PKrFjuVtDhVVFu57G3KwDAD5tkFK2pdhj5w+Mr4eXJPBYUpFfiRsLjxIWDCsttRvAmYLjjeFUala3IwqPHl9rGHP9zTvwNRMZX5IA5ThF9fhUSJ0IKu100DYcMdykauxYhRsVFm8hBpUZMgV+j6PNp0fvuEDm0j2hwiCG4FR4dugZCYu41TaFkn+NqhwCLaORblniChQUK+vw62kZCsAkclruTx8q4f+mV8Gi7cdRuYAozY8r500cD+NOBwYxtFuaL2HnxHFNVIMb42bIkP+nYplo7LpjnwBOfmZWVpmEVr7b6UTVFsjFyl5q8sJKY4t2Soap0Sha7ZKwts5j8OOucUsoqlBaBoMJmVJg7qcyKwYCGA/0BVOeJGAhoJl/eApnH8iIZ73cHooFaEk9MZYsjOCQ+IW97bBsfML9Qqx1C1PeXI8CaUosp52683EZmARKt5gcYFr6IRVbmCSwJPqUrixQENKNaWSJry6xYXCibcllnoifhGqkCsyLUOMSo+4TEkbDLQggiBxyXp2OuU4wG85XbhajCXKjwqHMKWFdmM/mM69RY0NTkidBpLO1ePDpNLp4UWfSYKtiF/45UaZtfIOO4IqOEOM8ZVvWWYRUWgeD4YkvS/ZU4ggqbMWc5YljQ+wM8BsP+w72+EAbD99RtMdK9VdgF5IscNC2EjoCOHsHwqV1XZg3n+AZcCpfyPsUjcMQU7Jkv8yixGcGaqm7kEO7za1D8GupdhlXcIhC0jhj+whwH1OUlW0zjifzmUs0IReCgCMDyIhlLXEaWmh0NI6mvE/dvQgCFB4RI4ZfocYI+v44gBQZ8GgAt6hedeC85xO5jxJ8/PlMNYOxCDIaryygCh/UVMiodhrW+P6BFi+EMhjQj5oEj2Nnuh0sRUJhvpIsTCMHp1Ta0DqsoVPik1IyR/mTShSmM3a+gNvY8zExhZkwpB/oC+P7L7RnbrC5W8OZFtRASa4syJpz/+kIZnm/yRJPZR7h5zwDuXjBNQjFyhsiW50RV0lxQICGo0XB5YWpKI1fhENEwGDQCzxwi5uTLGAnqCOkU9YUygjqF28JD5o3KY8NBDUEdAKVoHDIHHaXyJY2QqKA75VjWDFDAEk07RnD+PAdq8yTYRIKn0ygfQGoFJqhRqGHLqlelcCd8HlEY9TQrjzw5e2NBusVL4m1TeIITSi14tcWLoEZRmydiXbkVn6+wIujz4pNPOlE/xxpNueUQOawrsyA8zFAEw9VlOKhFA+Mi8ktxA5tKkRkOmhWUVO54hBhKesQVQ+AIzqy1o9unwinxsEsc9LB/OiEk6lOdsu+I7TQAwK5OH1pHQigO32AaNvu3jYRQYRextyOEPh8Ht5VgZbECAgIOwOk1NhRZBTz+6VB0jhEkzzF3uHhN5NrxiwqBIzir1g5fSMeIqsMhGp9xhOA4t4IlLhlz86WkOZZqVyJy3mhU2AVIAhBUjUwlFBTBsNdMROEttQkYCWmQ8kSM9BjH4ndO2j0hFFp4+FSjwudn4SqV8QozhaEMV9pFDAU1dPk0BLWYn3GvX4NL4cFzRuaPJS4ZFoHDhiorLIKRLvKxg0PR6+nh0uUUxu9Hp9QUwJgv8xkXhJlGxioYFQAvmJ9nlBJFcrxUJpjCzJgyXm0ZwXlPtsATSvN0B7C21II3Lqplpa6niAKZx/pyK3Y0mh/Uu7uDeMnFY/E0ycWYeAKBAE488UTs27cPe/fuxYoVK0Y9xynziKyl8mTOlDYqGxK3PZe4kt0EIpkPymwC+v0qWjmjoIXMA+91B6KKCU+A49xG9oe3O3x4pcWDvrBFuT+ooVAWon6/kfzFFSl8HOPVHIknGInbCaYAVjk1IE+A0yZjqVuOblGvLbXgzbAPbpGVhzvOmlzpEMF3+kwvcptglM+OuJEkKuqRpmIaH86xYBVTX6MwQekmxPCXPWeOAyGdRvMDW0UeNMgZmS84Ar+q4+mGEUhhdwWHxEXdQiil+Mfh2POC55IXIal2JISEuRDpd+RogcLhpHJr0nhIvJFz+dOBmHJTZhNNuXtTkShTOuXTrxrZE+Y5RRwZoVhQKKbc0l9XZsEbbT6oOkV9oYTPBswuBLV5IqrDvtLldgG1CdZiRTD8kRPLSvMcQaldyGqBVGYTcHgwBJ+mQ+RTFw6JyD4Y0FHtEKHqRoCf4WNu9qnfUGVDsZXHOy0qIne0yGqksLMIBG+0edHr09AybGTyWFhgBC+2xf1oNIqoK4ZOgflOCe1eFSGNotwuoNohwKUIGA4ZObedYct7dZ45JaFDMoIHPeEdKGs4VWJvXHU/Rcim4H36AiMKbyz45uVL0HUdw8NMYWbkIIf6A9j4RHM0MjoVJ1dY8fwFNUxZniJ8qo7LnmnFjsYRCOHysvHc1SDiu58b4747I2f5yU9+gvLycuzbt29c56dKl5WJoEbROhLCoYEgeGJY5EpSZLmJKNUSR1BfqMCnUlTaRQR1arKcFipGXuXDg0E0DAaNXMBcLEOCN6SjNk/EB0Edmm6U8Z6fn5wnOL5gwdoyC5qHQ0ZxDhCsLKAItgH1xTJsNrNyX+uUkCcb1rbEbD0CR7C21Ir9vUZ+X03X8fxIKJa6zK8lVbyjYRNniVUwlQOuH0faTAvPGZZyGOMZcWVzynw4/Zrx4z6+2MjSEXFZSUdIp1FFHzBbwRMXQZpu+LoeHBDAwRjfVD7b7QmFbDhiWDjbwn7RlXYx7eLhjTavaQfh474gLlnkTCt/5Prx8ISYrNq+kA5J5hE29sIhciiUaFprfbldxHlzBWjUSLeXqDAb5dWNf4thH+NsiaiBS1yyKagtcS64LQLOm+tAl1c1uSWZrhX+2j6/Bg4EKXRqI4CPjyxYzHJaw79TSo3sOPHDoYarHcYH93pVHRyHsDXY8G8X+HBAbdDIzy5wBEUwrN4ba2wpXS0jCrM3GCuFTmFUF4yN0+hlzCPBnWk/P4pXGlOYGZNOn1/DmkcbMirLGyqt2HF+dcaHOGPi0HSKM//WhNdajSjyRGUZAFoDHB74eATXnmDHx30B/GJnF+4/oxzOMWwVM3KDHTt24LnnnsPjjz+OHTt2jOsakRdVKqttKg4NBhHUqSnTwWhKTvzLMKglB4rt7wlEr63HqWUR5cQqcFhRrMApcTizxh5VCuKJf5kqPIf15VZU2EXYJQ75JIgDbenlM170qed/dZ6I6rBV8Xd7ew0FjxjWrlKbkKRAhj+GVTRyw7Z7jGC1RYWjFwNJJF/hcXadI+l4JKiq169B4gnmpCk0EtSMTAd+zVhID4TMrg6DAcP/Vg4HdiWyptSCw4PJQVsRHFJi/T8jAPHzFVbs7fbDpQSwsCB9v9MpSZneFomKvVM2UrhF8GsUTiRbvlM9CyMY7yej/aJCKRo8mkiGV11GFhZIRqrDgB61hKYik0IY+chtMRZLEYkj2TAAQ6msdYziL53wPQTG4ui9Lr85xzgxykzHLakw1ylhQ5UNLzebg/VlnoMrTQGfSOBpgYXHSFCLfnd53POGkNEtzJRmruIXcZWh49CcmcLMmHTu3teHPn/6p9AXqmx4+rwqyKzS3JTBcwRfmuuIKswAUKhwSffphnf68YVaJ774ZDOah1UcGjyCZ86vTmkpZOQmnZ2duOKKK/D3v/8dVuvY8gX6A4GoS0YoFIRMgAKBgz/smpGYvcYfiFnHPP5g+Fjs5ZUu203kPEop6mzAIrsOHQRev/maEQLBECotHFrDllkbIdhYbYFV4CByhoUv6PcilToT8Gmma4YEHpWyIaPP5zP9PR50SqGrIdh4Cost7M5RIsApUQzHfe9gQIsuPl0i4Mo3nn9awAfP2HaK0zLPqiOkAAQ8CCFQA154QokWYorHD3sRCgbR3cfj00ODOLPOmTTu2z8JYE6egNXFMpy8is5wzmCXwsHr9aa8TxFOdCn4tEeDbIllcRC0AEpFDpvKeAx7gWAw+W5F5ovfH0jyefZ4PPAE9ZTfu7hQhMfjMX0WCIbA6yqCariqY5DCz2tYlEdwUgkHPUjQFtSwl6qm84J+Dh6a/G5aYAPcPAHPETzbZJ4vHi6EVFPd7/fDn1ivG0DAT+AJL8Q+X0Tg1zgjj7ffi1TLEJ9fSzveXh+Fx6OCaCFY4o6rqhYNSrTxBH3egDGG3iBC4bGP/O3xeKBTCjUUAjQdqq6Fr0FAOQJNi12rUAIo1aCGVARVCq8/AF0gONyjQ1dDSHBdT3oG6JTCHwjALQLufA4WQtDtN9JwBANB8Bzg58LBtxqBoJHo8yfVNb2+ANRQEEHVvKNRY+fBEQJeC8Hj8bDCJYzc4/8dHMR/vJk+f/IZ1TY8eS5TlqeDa453oWUkhN/s6cPKIgVPnVuFc59qxp647bbhEMUpjx3BQFhreq/bj/WPNeKVr9RkbWlkTB+UUmzZsgVbt27F6tWr0djYOKbzOzrao9Xj3ByFTyfoiU0PHEiwRLZ1xyx4vR5jS7UtQNO2T3UeAAzqGuwCYBvi0BYkEIlRUCJypREfgcuio5ICXo2gzq9h4AgwkEWf+oJA22Ds+wIihWvA/PIc6zjFo1Ogt5NHvyf2TOvQdXQktOsMACUpvC/SjdFE8FmKYzoF2npi49HZ1Y1WvQP8MIdmv1m55voobL068jSgy2uUJi+w6TgwDHT18Ka8v1aeoliiKBCBviMU3l7zPe70URwYNMZ9sI+DR0u2CkbGwj/EoT1u4eUWKQ6EdHg1oK3PfF2FAxDUcKDTPK+6fQScDvSHv6fTR1Hh1FBjp2g9ZLQhBBjo6UZ8mE1zQEP7KK+nxPnrFSkODCUrZEcGOXQFzf1UOKA7pGEsVQaGVaCtP/VOR/6QDt1Ck2QK+nj4NcDOU3h0IKjqOHCgDZ+OEHT7jA529xhSRMZdHRYwEiTQAgQ8Afo1iiqFgvgIPEECAUbebkWgcPg0aCEOXSEKgQMcgzqaveb86ATJ8zvxHgZCBFK4iYNoqLZQ2GgsNePHIxx6ExZ98ddsGObQNUzQ7zPftGpNhwagG8ABXQPH8Zg7d076QU4BU5gZk8YLTSO47JnWtPlaz5tjx6NnVzJleRr51edLUGIVsHV5ARwSjz9tKsfyBw+bthQHEjJofDYQxOmPH8GrF9ZG88AyppYbb7wR//mf/5mxza5du7Bz504MDQ3hZz/72bi+p7S0LGphzpe4pLlQP89cpm6/GGfpGQzBr1KUu4xtZYUnqK9LbeGOPw8AltQa1uJ6GLlnJY5gX28QHV7DslVJgTOqLHCmctAchS6fhtZWv+lYpB8+nw+NjY2ora2FxWJJdXp23+H0YbjZD59GUSATrK6woM1jVhTqeCCFsTFpTCcbSik+PBS2MPd0o9BVhPr6AtQDODgQwns9MctvpYNHfYnh270y4TpOj4rX2w2rJyHA2dUWk/tG0j0uFFEfdj9x+TS8knBPgNhY1GeQP9jhR/NIbCCXuUTUh9074r/THtQx18mj3aOjx6+hyi5gU7UlGlQaufclxcXQ+ZgxYGGddVRXwcS+uRQO9ZXJ82ekKwBhyGz5PKvakjJ4LxNDQR2NTal3QRaVyKh2CEkynV6gomHIyIsscsDlyxyotIvwdgcw0u1Fd083itxFECUpOu7n5/vxTlcAhAICb2TDKLEKsHT58VFfCJQCc50iFuWLqC8QcSiub3PcEjy9QdO7hCNA/Vzz/NYpxaFDsZ3O+AoAJ5fLKE14x/ja/JC95h9O/G9muDOAnu4ABgfN41wetzqtn2cDpRTaGFPLsbcdY1J4udmDc59sRijNrsc3Fztx7+nlGX2NGJMPRwh+vDqW8GqJS8G/n+DCTe/0mto5RILhOLPLx/1BnP7EEbzylVqWK3sauOqqq3DxxRdnbFNbW4ubbroJb731FmTZbMpcvXo1Lr30Umzbti3jNRRZBhf+DdsUDn5i/kHbbLaE9rGX1CK3BIfE4cxaOzhCIHDp02HFnwcATrs9qqREvqGGBtEe8IHngS6PisNeHvAaEf+fr8heyXRwKpSE8teJ/bBYLEnHxsIZcyzIs3pxaDCEeU4Jq0stODIUMgVLnVVrw7tdfnTHvfyLrfxRfe94UeSYDKIkRWXICwWhDMfumc6nl2++DZCVEHr8KqrsIooSFJ1Ta0W8Fc4ywnPAivK8qI95nQ3QBQXvdJiVwGzGojukQpFj39XkJ1gdPi9+XikysKLMis12wahGx5OU83FTXR6sVmvUdcQuckn+0Ikkzl9JTj1O1YUiBMloSwgw1ymhdBzubVTUocipX64OmxU2m4jiPN2U1eb4fCvOnsfjYH8Qy90K5ocLpAiDBGJYJlGSoMhyVPbP1VjQpw1HA+U2VNlQahPgtHmwrCiEkB5LIZhnk5GvGYsrCiDPpkAO55iOIHAk5bjkWXVTUGcEm9UGW8L4zC8SMRA3T0psgumakkJgVwApwSVGCT8DnTIHm80WzpKRXDApE0xhZkw4zx8ZwXlPNcOXpi7nj1e5cPv64nFV2mFMPr9YU4T/+WgALXFWm+EQxRnVNjzfFHsK7e8J4JztTXj+gpqkDACMycXtdsPtTszsm8zvfvc73HTTTdH/t7W1YdOmTXj00Udx4oknjuk7s1ncri5R8G6noRQKHMFJ5dasCm+4LXy06IhD4lJa9OqcEuwiB6+q4402H5qHU3l3js5UPHVkgcPJlXacXBk7Ntcpod+voT+gYY5TQr4i4PRqO95q96JhMARFIFhZPH6r9tFwQqkFrzcFQACsLo4FmtU5RXzSH8BgwEhjtroks3zxgY+J1DmNMucDAR11TjEpIHO88d7FVh5dcYuOIkv6BbxR1IKYSnwn4pA42MYY2CwLJJoRBQDy0liM5ziltIGXY8EmkqTvjBB5rZ5QYsGLcc/rVSUWFCo8lrnN9zBT6JtN5LCxxo6W4RCKrDxKbQJea/Fgb5cf/X6jxHd9oQRCCJa45KTqkrsSFkDpXvl1ThGfpAmgTGqbJ+JgeE5KPIlWOoyg6RR54WdIRAlfGN7J4DkjQHW8MIWZMaH8x85O3PxOb8of4dpSBd9YXIDvLC+YcrkY2SMLHB7Y4MYZT3WajpdaeXxprgN/OxRble9s9+HiHS144otVY0qjxJgaqqurTf+32+0AgLlz56KysjLVKWlJzCaQito8CV0+Dd1eFdV5YkblJZ7VJRa82+mDToHjU5R0jhCxWr7d4ctYvCAT05W2UuQJ1qUocb22zIrjivQxpyKbSOblSyggVnzi1zAnTuHlCMGmGjv6/BrsEpexIEw21GZQFsd7X1YVW/DMkRGjWh4BlrtTz58DfQEENB0FijGHTquyJlR9HD/ryix4pTnmVrAsRcnmiYQQgjXh30yiYSoyhYqtAtZXWNHhUVFuF9LuBC4qkHAwzoF6ZcLvr1DhTed2eo1F32CCP1E2ty/d9I7PoTIc0owS3wCah4OwicR0n3gu85xUqTE+S10y2jwqZJ7gu8sK0OvXYRc52MfhxhWBKcyMCePnb3Till29KT/7XLkFO86vnrAHFGNyWVQgokDQ0a/GHi5//ngI/7OxHF5Vx7NHYpaLpw6PYOuL7bjv9DK2azCLySblo8iTaIGLsVCg8Dijxj4esQAYfrjZzr1cnKFHq4hOBIqQOmcvz5Ek94rJYLxrhXyFx8YaOzo8quEukMY6HNQofCqFHPYTHG/qt1SU2UR8rtyKbp+KKoc4Je+5SoeISoeIhz8eNB1X4vIzVzlEVI2SPs5lEVBfIKKvA6i085g7igU8OdmjQVDVESAxFwwlhRU/3S2Ov/c9Pg3t4eIuMkeQLyeWs888J7XwjVUELmrNF3kOpbaj/40xhZkxIfz89U7c8m5qZXldmQX/PI8pyzOJn73Vb1KWI1z5cjtevKAGfX4Nuzpj/pj3fziAOqeIn68pmkoxGWOktrZ2XPlHAaPoQONQzA2iJs3W+1RgZGON9SNVoYx0sFzvuUmaOhxZkWgFzeY70v0MnmnyIkiMynYEwOY6R1ZWyUzuKJNJfLGTfJkzleTOlmUuCYJLQ32pkjJ3eSLxLSIL1V1dfrTFVTE8pTJ54ZxuFyFeYU68L2M1wqRwhZ4wpn9Zy5jx3PhmV1pleU2Jgh3nV2dV9pORO9x8YgEW2ZL3vD0hiot3tGLbxvKkKmq/2NmdZO1gzB5KrAJOLLOg1CZgXr40qj/rZKImmAf9aeIlUmEVObjiXEXSFYdgTC3JpU2OnvlxxVAUkcAhxu77aDOGUsMKPZGbZkGNotOjoturotenRiswHg3L3DLWlFpwXJGCL1SPf5cmWzZU2nB8kYIlLhlLXDHXk0RlMtX4pttF8MdpuR1xVS8JAdo9Y4tV0I6mlN8oMAsz46jY3enDnXv7Un62okjGs1+qYZXhZiAuhcfdy/z494YCdAd0UxnYxqEQLn2mDb8/tRRfe67VFHDzjefbUJ0njmtbnpHbyOEtzokIWppoxrq9fmqlDQf7AxA5YlKqGNPHZLhvryxSIPNGSex8mcNAXNaIwYCW0ho7FKRQ4lyQQxoFJshw3OtXTb7OFXYBJ1ceXVYUQgjmTuGir8gqIERp0ns9G0twuhbdcWXT5xVI+Kw/FgCYzic9HdUOER/EFXUZzSVlLDALM2NcaDrFze9048RHGjCYYpW8pFDCq1+pRT5LOTZjsQvA9rOLseviObju+ELTZ3u7/fjy0y144PRyKHFbeAGN4vynmnFoILuIZwZjIhirFVDiCZa6FSwslKctCJBhplDhTe4AZRNQTZTnCJa5FawusZjKYgPp3QMSPRIsGTJqjJWI60SE1hE1TcvcJnF3EQB6fOa+DKfQC9ItiuKV2iILj+o8EfkKjxNKLGM2uM3Pl6JZmySe4LiiiQvAZAozY8wcGgjg5Mca8Yud3Sn9hU6vsuLdf5vD3DBmAVaBQ77C4/b1JTir1rzdNxLS8W/PtOLu00pNx3t8GjZvb0Kff/IqljGmlgU5boVlKu/Mh+cITihRYBEInDKH44rGZlmcKNaVylFf5yUueUILa403s0uu4bYkL2bKEyq/yil8odP9TuPdoggIKu0iFhfK4/IJVwQOZ9XasaHKhs119gmNnWIuGYys0XSKW3Z148a3etJugV5e78T/PaOcpRibZfAcwcNnVaDuvz9Ff/y2ZlDHd15sxw+OK8Bd+/qjxz/pD+LL/2jGs1+qYUFWM5gyGw9Z4LFsjNuiDMZ4qMmTUJM3OYszniNRC3YkBV0qym0Cqgst0CgmPL/8JMajTSnZPNIJkOQGk64yLJ/G2j/eN4fEk3EVhBkNpjAzsuLdDh/O+NsR0+SPhwD4zckluGZlIUstNktxyjz2XTYHi7YdgjcuyCqkA3ft68dJZRbsbI8lqn+lxYvvvNCGBzaWszkxQ1lbZgXH5d5GZJ7MRauYETKx2+aM2cmaUkvWRSsm0qocz3gz1OQaqdxZEvtGiFEQ54UmDyg1Uk7GBwkmtk1FLqRbjIcpzIxR+cazrfifA+mzH7gUDn85qxIbjyKPKmNmUOWQsOviOix76HDSLsO7nT6sKVHwTly6uW0HBjEvX8IvTmTp5hgTxwklFuxs80KlwAklCluQMWYEs0NdTm2dr82TUKjwoDAs+AUyjzzZyJHd61NRZhPTVv1M9etVBJJzFWSZwsxIi04p/n5oGA8fHErb5swaK7Ztqky71cKYfSx2K3j1yzU45fEjJqU5qAO7Ov0otfLoiMuc8R9vdmOuU8Ili5zTIC1jNlJsFXD+vLzpFoMxg+jyqvCpOig1FNdSmzDlFszZojCnUnBLbAJKUrhBZJMjO9V6155jyjLAFGZGCnp9KrYdGMS9+/vxSX/qbAciAf54Wim+tbSAWXeOQdZX2vDJ1+di2YOHTTk0KYAOrwaJMxToCN94vm1CUigxGAzGeDjQFzAV1thQZZtyhdkuxlyJgJlbRGeiX/kcIdhUa8ezjSMTe+EJhinMDPT4VPz10yG80eZDt1fFiy0eqBmiec+dY8f9Z5SnjJRlHDvMK5DR/K35WP7QIbR7zBkxEjMKBTSKs7c34cULanBiGcvRzGAwppZEHS9diefJ5Di3graRmFK4tmz6iv8cDZORinEmLB2YxnMM4lcp3mwawY7GEfzts2E0DGVXSefMGht+c3IpFqdx3Gcce7itAhq/uQAbnziCV1u9Gds6JX5ayykzGIxjl1zYCM1XeJxcaUXzcAjFVgEV9pn5PJwKu3wuuq8whfkYQKcUe7v8ePqzQTz2sYyP32hCtpVkFZ7gsnonfrrazUrIMlIi8QSvXFiL//12N254szvtg67No2L7oRF8d3nBlMrHYDAYiZBpsmlW2MUZqyhHmIqssZ6QDp3SnCosxBTmWYqqU7zS7MF/fzSAfzaMxFXjyy6Jt8QR3L6+CJcvLkhZPpTBSOQ/TizCOXV2nL29KclFAzC23BqGAuj3py5Jy2AwGJNFbZ6ElmHDh1kRCIos7Bk0XtJluzga9IS0dH6VYjioj7nS32TCFOZZxFBAw/7eAO7b34+HPxlCMF11kVGozRPxq/XF+PL8vJxa3TFynxXFFjR9awGufbUDf9jXb7I2UwC3v9uHe94fwFUrCvBupx9XLM3HwnwZS6epqheDwTg2qHKI2FBlw2BAQ3WeCJ4V1xo3Ek+woEDCwXBSgBOyzG+diZdbkl36cu0OMYV5BqJTisODIezr9uP9Hj/e7wlgX7c/a1/kVMzPl7AgX8R3lhXgnDkOlvmCMW4EjuCuDWX4/nGF+P5L7Xgl4UE4GNRx8zu9AIBnj3gAABV2AbecVIQL5jlhl3IvnRCDwZj5lNqESakAdyyyqsSCOU4JPAHyJsAKHNKSDXy5poawmZPjDAY07O8JYF+PH+93G8rx/l4/PKGjd4kvVDh8bVE+tizOx4piZuFjTCz1hTJe+nINHvp4EP++swvNw2ratq0jKi5/rh1bnmvH58qt2Fhjw8oiBSuKFFTYBbaAYzAYjBxjsl3rcm2HmynMOYKmUxwaDEatxRHLceNRWI1TQ/GVOTZcurgQZ9c5ZmweSMbMgBCCy+rz8ZX5ebh3fz9u3tWDLm+yf3MECuD1Ni9eb4tZpfMkDlUOEdUOEefPdeA7y1jQIIPBYMx2bDlWvIQpzNNAn1/DBxFXirDl+IPeALzZpq4YIwRAmU3AOTUKzrF14dQVtbDZWAEJxtShCBx+uNKFby4pwH/t78cde3rR5klvcY5nKKjjw94APuwN4OXmEdy5txcFMo/FhRIq7CIcEge7yKG+UAYhwIjHh0/6OJT4VVitlFmnGQwGY4ZxcmXu5etnCnMCmk6h6hQiT9JuB1A6umLrCeloHg7hyJBhNd7T5UfDYBCNwyF0ZrCwTRQuhceZtXZ8eZ4Dp1fb4JB4eDweHDjQNenfzWCkwy5xuG6VC9esLMQ/GkZwx+5uvNbmz/p8vwYc6DMCTXa2+zK0VHDtR63Qrs7LucARBoPBYGSGz0FDx6QrzL/d04sdjSMQOAKRMwKCBEJAqIaRIQnu7l5I4iB4QsARgOcIOAA8Z/iv8CQWKUlILHciiTte7RBxRZpt2rv39WEoqBvfywHfWJyf0kH92lc78HqbF8NB3VQOutTKY12ZBTwhEDhDxicPDWFEtQCvH4m2I4jJk6FI3qSyukTBpho7NtfasabUwqKAGTkLzxGcN9eB8+Y60ONT8aePBnHfB/1oGAwiMEE/IILc84FjMBgMxuhMRyXG0Zh0hfnD3gCeb/Kk//quo68dvrbUklZh/vWeXhwejPkBf3leXkqF+Z8Nwzg4kOwv3OHV8LdDqWQ0v4gpprYyjV3k4A3pqHYI+EK1Dd9cko+TypmbBWPm4bYI+NEqF360yoWgRvFWuxf/avNiX3cAe7v9+GwgOPpFUpBj7m8MBoPBmMFMusKsjjMX8FjIZERSE6xVYhqra2K7yYQnwPHFCpa5FRznlrHcreCsvzfBnyKtSgSHxOELVTacP8ewyuUrPFSdQmBWZMYsQuIJTq604eTK2OKvdTiE11s9ePjgEA4NBtHr1+FXdeRJPII6RUijsIoECwpkUAp0eYL4uD+IMivzOGMwGIyZSBaer1POpL9RQlOgMGciUWFPV6Amg6464awptWDnRXWmYwsKJLzfEwBg2K4LFA7znBI2VNmwuc6OtaVWiAkZLZiyzDgWqHCIuGhRPi5alJ9Ve8NX/wDq6+snVzAGg8FgTAo5qC9PvsJ8w9oifHdZAVRqJKZWqWERGvH5caSlFSVl5RAlGToFNEqNv3UKHcbfEUU2Mng0+n8a/Xd5hrrs/2uVCwMBDapuKM/WNPu0WxY78VKzB6puKPkqpdFzVEqh6TH5eEIBNQRBlCDyHCSewCYQzMuXUGIVUG4XUGIRkCfzsIscun0q3urwIV/msbJIQXEKy9cfNpRBoxTVDhGVdjFJOWYwGAwGg8E4FjgmLcwLCmQsKJCTjns8PA4ENNTPt09qirNrj3dl1e7GdcW4cV1214xZsOqylv3CBc6Mn6+vyL0UKgwGg8FgMBhTTQ7qy2BhMQwGg8FgMBgMRgZYVAyDMUkIgoCvfvWrOHDgAASB/dQYDAaDwcgGp5R79tzck4jBYDAYDAaDccywvsKKSB6D6jwxZfrf6Saj2StVRTtdn5j8a5RS8DwPSumEXXOqmKmyz1S5gZkrO5N76hmv7KnaZlPVc7Ywmc/7mcJMnvdHy7Hcd+DY7n8u9L3CxuPsWhuCGkW+zE26HON53hOaoYWqqvB40hUdYTAYjNmNzWY7Ztxp2POewWAcy4z2vGcuGQwGg8FgMBgMRgaYwsxgMBgMBoPBYGSAKcwMBoPBYDAYDEYGMvow67qe5BhNCAEhrAodg8GYXVBKk4I+OI4Dxx0bdgX2vGcwGMcK43neZ1SYGQwGg8FgMBiMY51jw3TCYDAYDAaDwWCME6YwMxgMBoPBYDAYGZhShbm2tjbqExf5c/3112c8h1KKG2+8EeXl5bBYLDj11FPx4YcfTpHEZgKBAFasWAFCCN57772Mbbds2ZLU17Vr106NoCkYi+y5MObnnnsuqquroSgKysrK8LWvfQ1tbW0Zz8mVMR+P7NM95o2NjfjWt76Furo6WCwWzJ07FzfccAOCwWDG86Z7zMcr93SPd67yxz/+EXV1dVAUBatWrcK//vWv6RbpqLn11ltxwgknwOFwoLi4GOeffz4++eQTU5ts5kMgEMAPfvADuN1u2Gw2nHvuuWhpaZnKrhw1t956KwghuOaaa6LHZnPfW1tbcdlll8HlcsFqtWLFihXYvXt39PPZ3HdVVfGLX/wi+mycM2cOfvnLX5riFGZz/ycFOoXU1NTQX/7yl7S9vT36Z3h4OOM5t912G3U4HPTxxx+n+/fvpxdddBEtKyujQ0NDUyR1jB/+8If0rLPOogDo3r17M7a9/PLL6Zlnnmnqa29v79QImoKxyJ4LY37HHXfQN998kzY2NtI33niDrlu3jq5bty7jObky5uORfbrHfMeOHXTLli302WefpYcOHaLbt2+nxcXF9Lrrrst43nSP+Xjlnu7xzkUeeeQRKooive++++hHH31Er776amqz2eiRI0emW7SjYtOmTfSBBx6gH3zwAX3vvffo5s2baXV1NR0ZGYm2yWY+bN26lVZUVNDnn3+e7tmzh27YsIEed9xxVFXV6ejWmHnnnXdobW0tXb58Ob366qujx2dr3/v6+mhNTQ3dsmULffvtt2lDQwN94YUX6GeffRZtM1v7TimlN910E3W5XPQf//gHbWhooI899hi12+30zjvvjLaZzf2fDKZcYf7tb3+bdXtd12lpaSm97bbbosf8fj91Op30nnvumQQJ0/PPf/6TLlq0iH744YdZK8znnXfelMg2GmORPZfGPJ7t27dTQggNBoNp2+TSmMczmuy5Oua/+tWvaF1dXcY2uTjmo8mdq+M93axZs4Zu3brVdGzRokX0+uuvnyaJJoeuri4KgL766quU0uzmw8DAABVFkT7yyCPRNq2trZTjOPrMM89MbQfGwfDwMJ0/fz59/vnn6SmnnBJVmGdz33/605/S9evXp/18NvedUko3b95Mv/nNb5qOXXDBBfSyyy6jlM7+/k8GU+7DfPvtt8PlcmHFihW4+eabM26dNjQ0oKOjAxs3bowek2UZp5xyCnbu3DkV4gIAOjs7ccUVV+DPf/4zrFZr1ue98sorKC4uxoIFC3DFFVegq6trEqVMzVhlz5Uxj6evrw8PPfQQTjrpJIiimLFtLox5PNnInotjDgCDg4MoLCwctV2ujflocufqeE8nwWAQu3fvNo0JAGzcuHHWjcng4CAAROdINvNh9+7dCIVCpjbl5eVYunTpjBifK6+8Eps3b8bpp59uOj6b+/7kk09i9erVuPDCC1FcXIyVK1fivvvui34+m/sOAOvXr8eLL76IgwcPAgD27duH119/HWeffTaA2d//yWBKFearr74ajzzyCF5++WVcddVVuPPOO/H9738/bfuOjg4AQElJiel4SUlJ9LPJhlKKLVu2YOvWrVi9enXW55111ll46KGH8NJLL+E3v/kNdu3ahdNOOw2BQGASpTUzHtlzYcwj/PSnP4XNZoPL5UJTUxO2b9+esX0ujHmEscieS2Me4dChQ7jrrruwdevWjO1yacyB7OTOxfGebnp6eqBp2qwfE0opfvSjH2H9+vVYunQpgOzmQ0dHByRJQkFBQdo2ucojjzyCPXv24NZbb036bDb3/fDhw7j77rsxf/58PPvss9i6dSt++MMf4k9/+hOA2d13wHgHXXLJJVi0aBFEUcTKlStxzTXX4JJLLgEw+/s/GRy1wnzjjTcmBf0k/nn33XcBANdeey1OOeUULF++HN/+9rdxzz334P7770dvb2/G70hMnE8pPepk+tnKfdddd2FoaAg/+9nPxnT9iy66CJs3b8bSpUtxzjnnYMeOHTh48CCefvrpo5J7KmQHpnfMI/z4xz/G3r178dxzz4HneXz9619PSjQeTy6M+XhlB3JjzAGgra0NZ555Ji688EJ8+9vfznj9yRrzyZYbmJzxnunM9jG56qqr8P777+Phhx9O+mw8fc/18WlubsbVV1+NBx98EIqipG03G/uu6zqOP/543HLLLVi5ciW++93v4oorrsDdd99tajcb+w4Ajz76KB588EH85S9/wZ49e7Bt2zb8+te/xrZt20ztZmv/JwPhaC9w1VVX4eKLL87Ypra2NuXxSDT9Z599BpfLlfR5aWkpAGOVU1ZWFj3e1dWVtCoaK9nKfdNNN+Gtt96CLMumz1avXo1LL700afKlo6ysDDU1Nfj000/HLXOEyZQ9F8Y8gtvthtvtxoIFC1BfX4+qqiq89dZbWLduXVbfNx1jHmEssufSmLe1tWHDhg1Yt24d7r333jF/30SN+WTKPZnjPVNxu93geT7JajSbxuQHP/gBnnzySbz22muorKyMHs9mPpSWliIYDKK/v99kbevq6sJJJ500RT0YO7t370ZXVxdWrVoVPaZpGl577TX8/ve/j2YLmY19Lysrw+LFi03H6uvr8fjjjwOY3fcdMIw2119/ffQ5umzZMhw5cgS33norLr/88lnf/0lhqp2m43nqqacogLRR2BGn9Ntvvz16LBAITGlwzpEjR+j+/fujf5599lkKgP71r3+lzc3NWV+np6eHyrJMt23bNonSmhmP7Lkw5qloamqiAOjLL7+c9TnTMeapGE32XBnzlpYWOn/+fHrxxRePOwJ6OsZ8rHLnynjnGmvWrKHf+973TMfq6+tnfNCfruv0yiuvpOXl5fTgwYMpPx9tPkSCnx599NFom7a2tpwPfhoaGjK9A/bv309Xr15NL7vsMrp///5Z3fdLLrkkKejvmmuuiWYsms19p5TSwsJC+sc//tF07JZbbqHz58+nlM7+/k8GU6Yw79y5k95xxx1079699PDhw/TRRx+l5eXl9NxzzzW1W7hwIX3iiSei/7/tttuo0+mkTzzxBN2/fz+95JJLpjX9U0NDQ8pME/FyDw8P0+uuu47u3LmTNjQ00JdffpmuW7eOVlRUTGvaqmxkp3T6x/ztt9+md911F927dy9tbGykL730El2/fj2dO3cu9fv9KeXOlTEfj+yUTv+Yt7a20nnz5tHTTjuNtrS0mNLExZNrYz4euSmd/vHORSJp5e6//3760Ucf0WuuuYbabDba2Ng43aIdFd/73veo0+mkr7zyiml+eL3eaJts5sPWrVtpZWUlfeGFF+iePXvoaaedNiPTa8VnyaB09vb9nXfeoYIg0Jtvvpl++umn9KGHHqJWq5U++OCD0Tazte+UGhmMKioqomnlnnjiCep2u+lPfvKTaJvZ3P/JYMoU5t27d9MTTzyROp1OqigKXbhwIb3hhhuox+MxCwTQBx54IPp/XdfpDTfcQEtLS6ksy/Tkk0+m+/fvnyqxk0indMbL7fV66caNG2lRUREVRZFWV1fTyy+/nDY1NU29wHFkIzul0z/m77//Pt2wYQMtLCyksizT2tpaunXrVtrS0pJW7lwZ8/HITun0j/kDDzxAAaT8k07uXBjz8chN6fSPd67yhz/8gdbU1FBJkujxxx8fTb02k0k3P8Y6H3w+H73qqqtoYWEhtVgs9Itf/OK0P9PHQ6LCPJv7/tRTT9GlS5dSWZbpokWL6L333mv6fDb3fWhoiF599dW0urqaKopC58yZQ3/+85/TQCAQbTOb+z8ZEEpHiURiMBgMBoPBYDCOYaY8DzODwWAwGAwGgzGTYAozg8FgMBgMBoORAaYwMxgMBoPBYDAYGWAKM4PBYDAYDAaDkQGmMDMYDAaDwWAwGBlgCjODwWAwGAwGg5EBpjAzGAwGg8FgMBgZYAozg8FgMBgMBoORAaYwMxgMBoPBYDAYGWAKM4PBYDAYDAaDkQGmMDMYDAaDwWAwGBlgCjODwWAwGAwGg5GB/w/TMxGlESJfxgAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 8 Axes>"
       ]
@@ -1207,6 +1236,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "7927af5d",
    "metadata": {},
    "outputs": [
     {
@@ -1244,50 +1274,50 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>beta0</th>\n",
-       "      <td>-0.749</td>\n",
-       "      <td>0.240</td>\n",
-       "      <td>-1.191</td>\n",
-       "      <td>-0.306</td>\n",
+       "      <td>-0.740</td>\n",
+       "      <td>0.249</td>\n",
+       "      <td>-1.241</td>\n",
+       "      <td>-0.298</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>3711.0</td>\n",
-       "      <td>3245.0</td>\n",
+       "      <td>3222.0</td>\n",
+       "      <td>2907.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta1</th>\n",
-       "      <td>2.796</td>\n",
-       "      <td>0.399</td>\n",
-       "      <td>2.049</td>\n",
-       "      <td>3.519</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3487.0</td>\n",
-       "      <td>2971.0</td>\n",
+       "      <td>2.791</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>2.072</td>\n",
+       "      <td>3.524</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>2745.0</td>\n",
+       "      <td>2711.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha0</th>\n",
-       "      <td>1.397</td>\n",
-       "      <td>0.277</td>\n",
-       "      <td>0.879</td>\n",
-       "      <td>1.923</td>\n",
+       "      <td>1.396</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.870</td>\n",
+       "      <td>1.910</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2673.0</td>\n",
-       "      <td>2528.0</td>\n",
+       "      <td>2726.0</td>\n",
+       "      <td>2507.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>alpha1</th>\n",
-       "      <td>-3.097</td>\n",
-       "      <td>0.389</td>\n",
-       "      <td>-3.787</td>\n",
-       "      <td>-2.336</td>\n",
+       "      <td>-3.088</td>\n",
+       "      <td>0.397</td>\n",
+       "      <td>-3.881</td>\n",
+       "      <td>-2.402</td>\n",
        "      <td>0.007</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>2819.0</td>\n",
-       "      <td>2217.0</td>\n",
+       "      <td>3011.0</td>\n",
+       "      <td>2806.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1296,10 +1326,10 @@
       ],
       "text/plain": [
        "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "beta0  -0.749  0.240  -1.191   -0.306      0.004    0.003    3711.0    3245.0   \n",
-       "beta1   2.796  0.399   2.049    3.519      0.007    0.005    3487.0    2971.0   \n",
-       "alpha0  1.397  0.277   0.879    1.923      0.005    0.004    2673.0    2528.0   \n",
-       "alpha1 -3.097  0.389  -3.787   -2.336      0.007    0.005    2819.0    2217.0   \n",
+       "beta0  -0.740  0.249  -1.241   -0.298      0.004    0.003    3222.0    2907.0   \n",
+       "beta1   2.791  0.393   2.072    3.524      0.008    0.006    2745.0    2711.0   \n",
+       "alpha0  1.396  0.280   0.870    1.910      0.005    0.004    2726.0    2507.0   \n",
+       "alpha1 -3.088  0.397  -3.881   -2.402      0.007    0.005    3011.0    2806.0   \n",
        "\n",
        "        r_hat  \n",
        "beta0     1.0  \n",
@@ -1319,16 +1349,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1027ebca",
    "metadata": {},
    "source": [
     "# Real data example\n",
     "\n",
-    "Finally, I demonstrate the model using a real data example. These data come from @henden2013, and were used as a demonstration in @hooten2019, Chapter 23. They represent detection/non-detection data of Willow Warblers from Finnmark, Norway. The $J=27$ sites were sampled $K=3$ times. Replicating the analysis in Box 23.7 in @hooten2019, I use two covariates for site: site area and willow tree height. Further, I use two covariates for visit: an indicator for the visit and willow tree height. "
+    "Finally, I demonstrate the model using a real data example. These data come from @henden2013, and were used as a demonstration in @hooten2019, Chapter 23. They represent detection/non-detection data of Willow Warblers from Finnmark, Norway. The $J=27$ sites were sampled $K=3$ times. Replicating the analysis in Box 23.7 in @hooten2019, I use two covariates for site: site area and willow tree height. Further, I use two covariates for visit: an indicator for the visit and willow tree height. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "05574bd9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1360,14 +1392,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "46648100",
    "metadata": {},
    "source": [
-    "This example uses an extremely handy feature of PyMC: coordinates. This allows us to specify a prior for each $\\alpha$ and $\\beta$ value in one line of code, using the `dims` argument in our prior distribution. The length of vector is implied by length of the list in `coords`."
+    "This example uses an extremely handy feature of PyMC: coordinates. This allows us to specify a prior for each $\\alpha$ and $\\beta$ value in one line of code, using the `dims` argument in our prior distribution. The length of vector is implied by length of the list in `coords`.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
+   "id": "fig-warbler",
    "metadata": {},
    "outputs": [
     {
@@ -1426,7 +1460,7 @@
        "<polygon fill=\"black\" stroke=\"black\" points=\"64.5,-231.48 61,-221.48 57.5,-231.48 64.5,-231.48\"/>\n",
        "</g>\n",
        "<!-- y -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node4\" class=\"node\">\n",
        "<title>y</title>\n",
        "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"197\" cy=\"-81.16\" rx=\"67\" ry=\"40.66\"/>\n",
        "<text text-anchor=\"middle\" x=\"197\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">y</text>\n",
@@ -1448,7 +1482,7 @@
        "<text text-anchor=\"middle\" x=\"197\" y=\"-287.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- p -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
+       "<g id=\"node5\" class=\"node\">\n",
        "<title>p</title>\n",
        "<polygon fill=\"none\" stroke=\"black\" points=\"242.12,-219.82 151.88,-219.82 151.88,-162.32 242.12,-162.32 242.12,-219.82\"/>\n",
        "<text text-anchor=\"middle\" x=\"197\" y=\"-202.52\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
@@ -1471,7 +1505,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x15e9e1550>"
+       "<graphviz.graphs.Digraph at 0x1426c0c10>"
       ]
      },
      "execution_count": 19,
@@ -1480,7 +1514,7 @@
     }
    ],
    "source": [
-    "#| fig-cap: Visual representation of the willow warbler occupancy model. \n",
+    "#| fig-cap: Visual representation of the willow warbler occupancy model.\n",
     "#| label: fig-warbler\n",
     "coords = {\"beta_coefs\": [\"Intercept\", \"Pland\", 'Wheight'],\n",
     "          \"alpha_coefs\": [\"Intercept\", \"Visit\", 'Wheight']}\n",
@@ -1516,14 +1550,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2d5cc21f",
    "metadata": {},
    "source": [
-    "Now the dimensionality of the prior distributions is clear, with (3) different priors specified for each random variable in the vectors $\\alpha$ and $\\beta$."
+    "Now the dimensionality of the prior distributions is clear, with (3) different priors specified for each random variable in the vectors $\\alpha$ and $\\beta$.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
+   "id": "ecdd7543",
    "metadata": {},
    "outputs": [
     {
@@ -1595,14 +1631,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3a7da4f0",
    "metadata": {},
    "source": [
-    "I upped the number of draw iterations to 4,000 per chain, 16,000 total, since this dataset includes real-world messiness. Nevertheless, sampling the posterior took only 6 seconds!  "
+    "I upped the number of draw iterations to 4,000 per chain, 16,000 total, since this dataset includes real-world messiness. Nevertheless, sampling the posterior took only 6 seconds!  \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
+   "id": "8daeb506",
    "metadata": {},
    "outputs": [
     {
@@ -1640,74 +1678,74 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Alpha[Intercept]</th>\n",
-       "      <td>-0.016</td>\n",
-       "      <td>0.376</td>\n",
-       "      <td>-0.709</td>\n",
-       "      <td>0.701</td>\n",
+       "      <td>-0.012</td>\n",
+       "      <td>0.374</td>\n",
+       "      <td>-0.712</td>\n",
+       "      <td>0.684</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>9984.0</td>\n",
-       "      <td>10970.0</td>\n",
+       "      <td>10561.0</td>\n",
+       "      <td>12234.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alpha[Visit]</th>\n",
-       "      <td>0.466</td>\n",
-       "      <td>0.286</td>\n",
-       "      <td>-0.068</td>\n",
-       "      <td>1.003</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>0.471</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>-0.060</td>\n",
+       "      <td>1.028</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>14198.0</td>\n",
-       "      <td>10877.0</td>\n",
+       "      <td>13515.0</td>\n",
+       "      <td>10762.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alpha[Wheight]</th>\n",
-       "      <td>0.738</td>\n",
-       "      <td>0.432</td>\n",
-       "      <td>-0.100</td>\n",
-       "      <td>1.526</td>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.437</td>\n",
+       "      <td>-0.113</td>\n",
+       "      <td>1.528</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>10616.0</td>\n",
-       "      <td>11420.0</td>\n",
+       "      <td>10589.0</td>\n",
+       "      <td>12235.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta[Intercept]</th>\n",
-       "      <td>0.818</td>\n",
-       "      <td>0.817</td>\n",
-       "      <td>-0.550</td>\n",
-       "      <td>2.427</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>-0.600</td>\n",
+       "      <td>2.360</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.007</td>\n",
-       "      <td>8510.0</td>\n",
-       "      <td>8343.0</td>\n",
+       "      <td>9092.0</td>\n",
+       "      <td>8314.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta[Pland]</th>\n",
-       "      <td>2.274</td>\n",
-       "      <td>0.874</td>\n",
-       "      <td>0.655</td>\n",
-       "      <td>3.855</td>\n",
+       "      <td>2.251</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>3.879</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.007</td>\n",
-       "      <td>9741.0</td>\n",
-       "      <td>7506.0</td>\n",
+       "      <td>10612.0</td>\n",
+       "      <td>8474.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta[Wheight]</th>\n",
-       "      <td>0.964</td>\n",
-       "      <td>0.689</td>\n",
-       "      <td>-0.361</td>\n",
-       "      <td>2.282</td>\n",
+       "      <td>0.965</td>\n",
+       "      <td>0.691</td>\n",
+       "      <td>-0.392</td>\n",
+       "      <td>2.233</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>13009.0</td>\n",
-       "      <td>11306.0</td>\n",
+       "      <td>12334.0</td>\n",
+       "      <td>10500.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1716,20 +1754,20 @@
       ],
       "text/plain": [
        "                   mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "Alpha[Intercept] -0.016  0.376  -0.709    0.701      0.004    0.003    9984.0   \n",
-       "Alpha[Visit]      0.466  0.286  -0.068    1.003      0.002    0.002   14198.0   \n",
-       "Alpha[Wheight]    0.738  0.432  -0.100    1.526      0.004    0.003   10616.0   \n",
-       "Beta[Intercept]   0.818  0.817  -0.550    2.427      0.009    0.007    8510.0   \n",
-       "Beta[Pland]       2.274  0.874   0.655    3.855      0.009    0.007    9741.0   \n",
-       "Beta[Wheight]     0.964  0.689  -0.361    2.282      0.006    0.004   13009.0   \n",
+       "Alpha[Intercept] -0.012  0.374  -0.712    0.684      0.004    0.003   10561.0   \n",
+       "Alpha[Visit]      0.471  0.290  -0.060    1.028      0.003    0.002   13515.0   \n",
+       "Alpha[Wheight]    0.739  0.437  -0.113    1.528      0.004    0.003   10589.0   \n",
+       "Beta[Intercept]   0.798  0.809  -0.600    2.360      0.009    0.007    9092.0   \n",
+       "Beta[Pland]       2.251  0.858   0.758    3.879      0.009    0.007   10612.0   \n",
+       "Beta[Wheight]     0.965  0.691  -0.392    2.233      0.006    0.004   12334.0   \n",
        "\n",
        "                  ess_tail  r_hat  \n",
-       "Alpha[Intercept]   10970.0    1.0  \n",
-       "Alpha[Visit]       10877.0    1.0  \n",
-       "Alpha[Wheight]     11420.0    1.0  \n",
-       "Beta[Intercept]     8343.0    1.0  \n",
-       "Beta[Pland]         7506.0    1.0  \n",
-       "Beta[Wheight]      11306.0    1.0  "
+       "Alpha[Intercept]   12234.0    1.0  \n",
+       "Alpha[Visit]       10762.0    1.0  \n",
+       "Alpha[Wheight]     12235.0    1.0  \n",
+       "Beta[Intercept]     8314.0    1.0  \n",
+       "Beta[Pland]         8474.0    1.0  \n",
+       "Beta[Wheight]      10500.0    1.0  "
       ]
      },
      "execution_count": 21,
@@ -1743,19 +1781,21 @@
   },
   {
    "cell_type": "markdown",
+   "id": "aa82f33b",
    "metadata": {},
    "source": [
-    "I compare the parameter estimates to the ones estimated by @hooten2019, Chapter 23. "
+    "I compare the parameter estimates to the ones estimated by @hooten2019, Chapter 23. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
+   "id": "fig-warbler_trace",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x400 with 4 Axes>"
       ]
@@ -1782,21 +1822,23 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e29e1bf2",
    "metadata": {},
    "source": [
     "There is a high level of agreement between the two methods. While their algorithm was designed for teaching and interpretability, it is noteworthy that the PyMC model is 10x faster. \n",
     "\n",
-    "Arviz also produces forest plots for looking at effect sizes. "
+    "Arviz also produces forest plots for looking at effect sizes. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
+   "id": "fig-warbler_forest",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 600x400 with 2 Axes>"
       ]
@@ -1806,7 +1848,7 @@
     }
    ],
    "source": [
-    "#| fig-cap: Forest plots from willow warbler occupancy model. ESS refers to the effective sample size. \n",
+    "#| fig-cap: Forest plots from willow warbler occupancy model. ESS refers to the effective sample size.\n",
     "#| label: fig-warbler_forest\n",
     "az.plot_forest(warbler_idata, var_names=['Alpha', \"Beta\"], \n",
     "               figsize=(6,4),\n",
@@ -1815,16 +1857,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8a7efa01",
    "metadata": {},
    "source": [
     "## Model comparison\n",
     "\n",
-    "PyMC also has handy tools for model comparison. I demonstrate these by fitting a model to the warbler data with a constant probability of detection. "
+    "PyMC also has handy tools for model comparison. I demonstrate these by fitting a model to the warbler data with a constant probability of detection. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
+   "id": "fig-warbler_pdot",
    "metadata": {},
    "outputs": [
     {
@@ -1904,7 +1948,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x15f588e90>"
+       "<graphviz.graphs.Digraph at 0x1435ac790>"
       ]
      },
      "execution_count": 24,
@@ -1938,6 +1982,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
+   "id": "8c662fc5",
    "metadata": {},
    "outputs": [
     {
@@ -2009,14 +2054,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0c16dbfc",
    "metadata": {},
    "source": [
-    "Next, I caclculate the leave-one-out (loo) cross-validation score for each model [@vehtari2017]. This involves first computing the log likelihood for each model. "
+    "Next, I caclculate the leave-one-out (loo) cross-validation score for each model [@vehtari2017]. This involves first computing the log likelihood for each model. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
+   "id": "dfb42109",
    "metadata": {},
    "outputs": [
     {
@@ -2072,6 +2119,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
+   "id": "bacf3a52",
    "metadata": {},
    "outputs": [
     {
@@ -2080,14 +2128,14 @@
        "Computed from 16000 posterior samples and 37 observations log-likelihood matrix.\n",
        "\n",
        "         Estimate       SE\n",
-       "elpd_loo   -54.32     7.35\n",
-       "p_loo        6.12        -\n",
+       "elpd_loo   -54.39     7.39\n",
+       "p_loo        6.19        -\n",
        "------\n",
        "\n",
        "Pareto k diagnostic values:\n",
        "                         Count   Pct.\n",
-       "(-Inf, 0.5]   (good)       37  100.0%\n",
-       " (0.5, 0.7]   (ok)          0    0.0%\n",
+       "(-Inf, 0.5]   (good)       36   97.3%\n",
+       " (0.5, 0.7]   (ok)          1    2.7%\n",
        "   (0.7, 1]   (bad)         0    0.0%\n",
        "   (1, Inf)   (very bad)    0    0.0%"
       ]
@@ -2106,6 +2154,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
+   "id": "9f63e530",
    "metadata": {},
    "outputs": [
     {
@@ -2161,6 +2210,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
+   "id": "890af995",
    "metadata": {},
    "outputs": [
     {
@@ -2169,8 +2219,8 @@
        "Computed from 16000 posterior samples and 37 observations log-likelihood matrix.\n",
        "\n",
        "         Estimate       SE\n",
-       "elpd_loo   -39.20     5.10\n",
-       "p_loo        3.67        -\n",
+       "elpd_loo   -39.34     5.12\n",
+       "p_loo        3.81        -\n",
        "------\n",
        "\n",
        "Pareto k diagnostic values:\n",
@@ -2194,26 +2244,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b64f6abf",
    "metadata": {},
    "source": [
-    "Arviz has handy tools for comparing the results. First, I generate a tabular summary. "
+    "Arviz has handy tools for comparing the results. First, I generate a tabular summary. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
+   "id": "fc5d7d13",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'False' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
-      "  df_comp.loc[val] = (\n",
-      "/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
-      "  df_comp.loc[val] = (\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -2250,11 +2292,11 @@
        "    <tr>\n",
        "      <th>$p(\\cdot)$</th>\n",
        "      <td>0</td>\n",
-       "      <td>-39.201375</td>\n",
-       "      <td>3.666810</td>\n",
+       "      <td>-39.342053</td>\n",
+       "      <td>3.814302</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>5.101775</td>\n",
+       "      <td>5.123410</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
@@ -2262,12 +2304,12 @@
        "    <tr>\n",
        "      <th>$p(visit,wheight)$</th>\n",
        "      <td>1</td>\n",
-       "      <td>-54.318527</td>\n",
-       "      <td>6.115271</td>\n",
-       "      <td>15.117151</td>\n",
+       "      <td>-54.388511</td>\n",
+       "      <td>6.193775</td>\n",
+       "      <td>15.046458</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7.353916</td>\n",
-       "      <td>4.177674</td>\n",
+       "      <td>7.394264</td>\n",
+       "      <td>4.221815</td>\n",
        "      <td>False</td>\n",
        "      <td>log</td>\n",
        "    </tr>\n",
@@ -2277,12 +2319,12 @@
       ],
       "text/plain": [
        "                    rank   elpd_loo     p_loo  elpd_diff  weight        se  \\\n",
-       "$p(\\cdot)$             0 -39.201375  3.666810   0.000000     1.0  5.101775   \n",
-       "$p(visit,wheight)$     1 -54.318527  6.115271  15.117151     0.0  7.353916   \n",
+       "$p(\\cdot)$             0 -39.342053  3.814302   0.000000     1.0  5.123410   \n",
+       "$p(visit,wheight)$     1 -54.388511  6.193775  15.046458     0.0  7.394264   \n",
        "\n",
        "                         dse  warning scale  \n",
        "$p(\\cdot)$          0.000000    False   log  \n",
-       "$p(visit,wheight)$  4.177674    False   log  "
+       "$p(visit,wheight)$  4.221815    False   log  "
       ]
      },
      "execution_count": 30,
@@ -2298,29 +2340,23 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d08ca93a",
    "metadata": {},
    "source": [
     "This indicates that the $p(\\cdot)$ model is favored over the $p(visit,wheight)$ model. \n",
     "\n",
-    "Arviz also generates plots for these comparisons. "
+    "Arviz also generates plots for these comparisons. \n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
+   "id": "fig-warbler_comp",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/plots/backends/matplotlib/compareplot.py:87: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
-      "  scale = comp_df[\"scale\"][0]\n"
-     ]
-    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAD3CAYAAADR2vF+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABDd0lEQVR4nO3deVxUVf8H8M8AggPDvrmxuoELiqnZowmKO5pLpeGKj0impWimj1JpIFZqKZVLLrlr5v64lSFBRqJhKooWGqu5gCwOM4Ah3N8f/ryPI9sMMA6Mn/frNa+895x77/ceT8J3zrnnSgRBEEBERERERKQHDHQdABERERERUV1hgkNERERERHqDCQ4REREREekNJjhERERERKQ3mOAQEREREZHeYIJDRERERER6gwkOERERERHpDSY4RERERESkN5jgEBERERGR3mCCQ0SkAV9fX0gkEuzevVtl/9mzZyGRSODq6lqr8xsZGSEmJkatujExMTAyMqrV9RoCmUyGM2fO6DoMIiJqIJjgEBFpyNPTExs2bFDZt2HDBnh6euooIv1UUlICAFAoFHjppZd0HA0RETUUTHCIiDQ0atQoJCYm4saNGwCAgoIC7N+/H5MnT1apV1hYiFmzZsHJyQl2dnYYMWIEMjIyxPKCggJMmjQJNjY2cHFxwdatW8td69ChQ3jhhRdgZWUFT09P7Ny5U6NYDxw4gK5du8LS0hJNmjRBaGioWLZ//3506tQJlpaW6NSpEw4ePCiWbdmyBa1atcKqVavQokULWFhY4D//+Q/y8vLw+uuvw8LCAm3btsUvv/wiHhMYGIhx48ZhwoQJsLCwQMuWLbFlyxax/ObNmxg0aBDs7e1haWmJ3r1748KFC2L54sWL4efnh/feew+Ojo4YNmwYAEAikYjXSU9Px8CBA2FlZQVLS0t07twZf/75JwDg4cOHCAsLg7u7O2xsbODn54crV66oxDdhwgRMnToVVlZWaN68OdasWaNRexIRUf3HBIeISEONGzfGuHHjsGnTJgDA7t274ePjg6ZNm6rUmz17NuLj4xEfH4/09HTY2dlh2LBhKC0tBQCEhITg+vXruHr1KhITE3H48GGxDAB+/PFHTJkyBatWrUJubi42b96MGTNmIDY2Vq04T5w4gUmTJmHRokXIyclBcnIyBg8eDAA4c+YMxo0bh08++QQ5OTlYunQpAgICcPbsWfH49PR05OXlISUlBadPn8bKlSsxcOBAvPvuu8jLy8PIkSPLJXXfffcdBg4ciNzcXKxduxbTpk3Dr7/+CgAoKyvD9OnTkZ6ejjt37sDb2xujRo0SR2oAIDY2Fg4ODsjMzMSBAwfK3dPChQvh4uKCu3fvIjc3F9u2bYOVlRUAYPny5di2bRuOHz+O27dv4+WXX0b//v0hl8vF4/ft24ehQ4ciNzcXa9aswcyZM5GSkqJWexIRUQMhEBGR2nx8fITw8HDh8uXLQpMmTYSSkhLhhRdeEI4ePSps375dcHFxEQRBEEpLS4XGjRsLJ0+eFI8tKCgQGjVqJPz6669CaWmpYGJiIkRFRYnlycnJAgDhp59+EgRBEPz9/YWPPvpI5fpvv/228O9//1sQBEH46aefBENDw0pjHTx4sDB37twKy6ZOnSqMHTtWZd8bb7whBAcHC4IgCJs3bxbMzc2F0tJSsbxbt27C9OnTxe2kpCQBgJCfny8IgiBMmjRJ6NWrl8o5x44dK0ydOrXCGORyuQBASEpKEgRBEBYtWiS4ubmVqwdAOH36tHiNoUOHCklJSUJZWZlKvdatWwvr168Xt0tLS4XmzZsLu3btEo8dMmSIyjGOjo7Cvn37KoyPiIgaJo7gEBHVQIcOHeDq6orw8HDcvXsXgwYNUinPzs5GcXEx3N3dxX0ymUwcncjOzsaDBw9UFiVwc3NTOUdqaio+/fRTWFlZiZ8tW7bg9u3basWYlpaGNm3aVFiWmZmpEhsAtGzZEpmZmeK2g4MDDAz+92PC1NRUZZTK1NQUwKOpdo89vciCm5sbbt68CQC4d+8eJk6cCGdnZ1hYWMDJyQnAo7aq7PinLV++HG5ubnjllVfg6OiIGTNmiNd/+p4MDAzg6uqqck9Pj7LJZDKV+ImIqOFjgkNEVEPBwcEIDw/HlClTYGhoqFJmb28PExMTpKamivsUCgWysrLg5OQEe3t7GBsbIy0tTSx/si4AuLi4YNGiRcjPzxc/BQUFOH78uFrxubq64vr16xWWOTk5lbteSkqKmHTU1JP3Azy6pxYtWgAAFixYgNu3b+Ps2bOQy+Vi4iEIglj/yYSqIvb29vjiiy9w48YNnDlzBrGxsVi+fHmF91RWVoa0tLRa3xMRETUsTHCIiGooICAAJ0+exKxZs8qVGRgYYOLEifjggw9w69YtFBYW4t1334WHhwe6d+8OAwMDjB07FosWLcLdu3chl8uxYMEClXOEhIRg5cqViI2NRWlpKf755x+cP38eCQkJasU3Y8YMrFu3DsePH8fDhw8hl8sRFxcH4NED9/v378cPP/yA0tJSnDhxAgcOHCj3TI2m4uPjsXv3bjx8+BBRUVHYv38/Jk6cCACQy+UwNTWFtbU1FApFuftVx549e5CWlgZBEGBjY4PGjRuLS2UHBgZi2bJlSE5Oxj///IOIiAg8fPgQ/v7+tbonIiJqWJjgEBHVUOPGjdGvXz9YW1tXWL5y5Up07doV3bp1g7OzM27fvo3//ve/4mhPZGQk3Nzc4OHhgY4dO2LYsGEqI0EDBgzAxo0bMX/+fNjZ2aFp06aYPXs2FAqFWvH5+/tj48aNCA0NhY2NDdq2bYvvv/8eAPCvf/0LW7duxdy5c2FtbY158+Zhx44d6NGjR63aZPTo0Th+/DhsbGwwdepUrFmzBr169QIAfPTRR8jKyoKtrS28vLzQo0ePciNf1blw4QJ69+4NmUwGT09PeHt7Y+7cuQCA9957DwEBARgwYAAcHR0RHR2NkydPwsLColb3REREDYtEeHJuABERUQ0FBgbCyMgIGzdu1HUoRET0HOMIDhERERER6Q0mOEREREREpDc4RY2IiIiIiPQGR3CIiIiIiEhvMMEhIqpjrq6u2LFjR6XlS5cuxbBhw9Q+X2BgIIKCguoitCpNmzYNb7/9tsbHpaWlQSKRiC/0rImK7nHLli1o1apVjc9JRETPJyNdB0BE9LxZuHChTq9fUlKCRo0aldu/bt06HUSjXaWlpZBIJNW+QJSIiPQH/8UnItKCjIwM+Pn5QSaToX379jh9+rRYtnjxYvTr10/cvnPnDoYNGwZLS0t4eHhgy5YtkEgkSEtLE+s8ePAAU6dOhZWVFZo3b441a9aoXO/06dPo1asXbGxs0LJlS3z22Wd4/IhlTEwMjIyMsGPHDri7u8PKyqrCmJ8cRREEAaGhoWjWrBlkMhmcnJzw5ZdfVnnP33//Pdq0aQNLS0sMHz4cWVlZYllhYSHmzp0LNzc32NjYYNCgQbhx4wYAYNmyZdi5cye2bt0KmUwGmUyGuLg4TJs2DSkpKeK+mJgYAMCVK1cwcOBA2NnZwdnZGQsWLEBJSQmA/40mffPNN/D09ISpqSnu3r1bZdxERKRfmOAQEWnBN998g8jISNy/fx+DBw/GpEmTKq07btw4GBsbIzMzEz///DO2bt1ars6+ffswdOhQ5ObmYs2aNZg5cyZSUlIAAElJSRgyZAjee+89ZGdn4+jRo/jyyy+xfft28fjS0lIcOXIEv//+O7Kzs6uNPyoqClu3bsXZs2ehUCiQkJCAnj17VnnMtm3b8PPPPyMjIwOCIGD8+PFiWVBQEP744w/Ex8fjzp076NatG4YOHYqSkhLMmzcP48aNw6RJk6BQKKBQKNCzZ0+sW7cO7u7u4j5fX19kZWXBx8cHo0aNwq1bt/Drr7/i5MmT+Pjjj1Vi2b59O6Kjo1FQUAB7e/tq75eIiPQHExwiIi1488030aFDBxgaGiIoKAipqanIyckpV+/mzZuIjo7G8uXLYWFhAQcHB7z//vvl6vXt2xfDhw+HgYEBhg8fDjs7O1y4cAEAsHbtWrz++usYPnw4DA0N4enpiXfeeadcorRs2TJYWVnB1NS02vgbNWqE4uJiJCUloaioCI6OjujSpUuVxyxatAhNmjSBpaUlPvvsM/z444+4desW7t27h927d2PNmjVwdHSEsbExPvroI9y+fRvx8fHVxvKkbdu2oVOnTnjzzTdhbGyMFi1aYOHCheXudfHixWjatCmMjY1hZMTZ2EREzxP+q09EpAVNmzYV/yyTyQAABQUFsLW1Van3999/AwCcnZ3FfS4uLlWe7/E5CwoKAACpqamIjo7GgQMHxPKysjI4OTmJ2wYGBirXqI6vry+WLl2KJUuW4LXXXkOPHj2wdOlSdO/evdJjXF1dxT+7ubkBeJTASSQSAICXl5dK/ZKSEo0XJkhNTUVcXJzKNDtBEFBaWqpS7/H1iYjo+cMRHCIiHWrevDkAID09Xdz35J/V4eLigsmTJyM/P1/8yOVyJCUliXUkEomYaKgrODgYv/zyC+7evYvOnTvjtddeq7L+k88MpaamAgBatGghJmzJyckqMRYWFiIgIAAAKlwEoKJ9Li4u8PPzUznP/fv3oVAoqj2WiIieD/wJQESkQy1atICvry/mz58PuVyOrKwsLFmyRKNzvPXWW/j2229x6NAhlJSU4OHDh7h69SpiY2NrHNdvv/2GX375BQ8ePEDjxo1hbW1d7VSv8PBw3L17F3K5HPPmzYOfnx+aNWsGBwcHBAQEYNq0aeKITX5+Pg4ePCgmJk2aNMGNGzdQVlYmnq9Jkya4e/cu7t+/L+6bMGECEhISsGHDBhQVFaGsrAwpKSn4/vvva3yvRESkX5jgEBHp2K5du1BcXAwnJyf06tULo0ePBgCYmJiodXzHjh1x7NgxfPnll2jatCkcHBwQGBio1mIClSkoKMDMmTNhZ2cHW1tbfP/99/j222+rPGb8+PF4+eWX4eTkhIcPH6q8C2jjxo1o164d+vTpA3Nzc3Ts2BF79+4VR5WCgoJQVFQEW1tbWFlZobS0FH379sWAAQPEld9iY2PRtGlTxMTE4MiRI3Bzc4O1tTVGjhwpLrhAREQkER6vI0pERPXCDz/8gOHDh6OoqEjjaWVERETPOy4yQESkY5cuXYJEIkHHjh2RmpqK999/H2PGjGFyQ0REVAOcokZEpGO5ubkYNWoUZDIZevXqBS8vL0RGRuo6LCIiogaJU9SIiIiIiEhvcASHiIiIiIj0Bp/Bec6VlZWpLMsK1Ox9GURERKR/BEHA05N9DAwM+K4pqteY4DznysrKoFQqdR0GERERNRBmZmZMcKheY+8kIiIiIiK9wQSHiIiIiIj0BhMcIiIiIiLSG3wG5zlX0WIC+jS3trCwENevX0fr1q1hamqq63D0BttVO9iu2sF21Q5N2jU/Px/nz58Xt1944QVYWVlpOcKGqb7114qe1eVCRFTfMcF5zlX0j5Q+rY4ikUhQWloKiUSiN/dUH7BdtYPtqh1sV+3QpF0VCgV+/fVXcbtt27awsbHRdogNUkPor0xwqL6rn//nEBERERER1QATHCIiIiIi0hucokZEREREda64uBjZ2dm6DoP0hImJCRwcHNSqywSHiIiIiOpUcXExsrKy0Lx5cxgaGuo6HNID2dnZkMvlsLCwqLYup6gRERERUZ3Kzs5mckN1ys7ODvfv31erLhMcIiIiIqpzTG6oLmmyeh+nqBERERFRvSKXy3Hq1Cnk5OTA1tYWfn5+ak1NIgKY4BARERFRPaFUKhEaGoqNGzeqvGDUzMwMQUFBiIiIgJmZmQ4jpIaAU9SIiIiISOeUSiX69euHyMhIleTmcVlkZCT69etXrqy2fH19YWJiAplMpvK5cOECAgMDERQUVOFxixcvhpGRkVi/ZcuWCAsLgyAIAIDAwEA0atQIMpkMFhYWcHJywquvvopTp07VafxUHhMcIiIiItK50NBQxMfHV1knPj4eoaGhdX7tDz74AAqFQuXj7e1d7XG+vr5QKBQoKCjAhg0b8PHHH2Pz5s1i+aRJk6BQKCCXy5GQkICePXvC398fX3zxRZ3fA/0PExwiIiIi0qmkpCSsX79e3La1tcWOHTuQkZGB7du3w9bWVixbv349fvjhB2RkZOgi1ApJJBL07dsX7du3x4ULFyqs4+joiDlz5iA0NBQLFixAfn7+sw3yOcIEh4iIiLTK3t4e48aNEz/29va6DonqmbCwMBQVFYnbq1atwrhx4+Dk5ITx48dj5cqVYllRUREGDRqEb775RhehVqisrAynTp3ClStX0LVr1yrrvvHGGygsLKx2tIpqjosMEBERkVZJpVK0atVK12FQPfZkcgMAPj4+Ktu+vr5avX5ERARWrFihsk+dEZbY2FhYWVnBwMAATZs2RVhYGCZNmlTlMS1atAAA5OTk1DheqhpHcIiIiIhIp6RSqcp2bGysynZMTIxWrx8aGor8/HyVjzp8fHyQn5+P3NxcJCUlYd68edUec/PmTQBQmXZHdYsjOERERESkUx9++CGOHDkijuSEhIQAeJRAxMTEYPbs2WJdqVSKgwcPwtPTUxeh1tqePXsglUrRo0cPXYeit5jgEBEREZFOtW/fHsHBwYiMjATwaPrWhAkTKqwbHByMgQMHPrPYSktLUVxcrLLPxMRE4/NkZWVh9+7diIiIwNKlS2FlZVVHEdLTOEWNiIiItOqff/5BVlaW+Pnnn390HRLVQxEREdWOavTo0QMRERF1fu3w8PBy78E5evQoAGDLli2QSqUqn7i4OLXOu3XrVvE9OF26dEFMTAwOHz6sMiJFdY8jOERERKRVd+7cUXk3yOTJk+Hs7KzDiKg+MjMzQ1RUFEJDQ7Fp0yYoFAqxTCaTYcqUKYiIiICZmVmdXreq53uGDh2KLVu2VFjWq1evKs+7ZcuWSo8l7WKCQ0RERET1gpmZGVatWoWwsDCcOnUKOTk5sLW1hZ+fHywsLHQdHjUQTHCIiIiIqF6xsLDAyJEjdR0GNVB8BoeIiIiIiPQGExwiIiIiItIbTHCIiIiIqF4qLCzUdQjUADHBISIiIqJ6p6SkBNu2bUNJSYmuQ6EGhgkOEREREdU7CQkJuHv3LhISEnQdCjUwTHCIiIiIqF4pKSkRX6YZFxfHURzSCBMcIiIiIqpXEhISoFQqAQBKpZKjOKQRJjhEREREVG88OXrzmDZHcXx9fWFiYgKZTKbyuXDhAgAgMDAQQUFBFR67ePFiGBkZice0bNkSYWFhEARBPLZRo0aQyWSwsLCAk5MTXn31VZw6dUrjOFu1aoUtW7YAAE6fPg0rKyuxTC6XY9CgQbCysoJMJkNWVhZ+/PFHtGnTBubm5s/dO4WY4BARERGRTt2/fx8ZGRnIyMhAVFSUOHrzmFKpRFRUlFgnIyMD9+/fr7Prf/DBB1AoFCofb29vtY719fWFQqFAQUEBNmzYgI8//hibN28WyydNmgSFQgG5XI6EhAT07NkT/v7++OKLL2oc78svv4z8/Hxx+/vvv0dCQgJu374NhUIBBwcHrFy5Ev369UNBQQEOHjyo8TUa8rRAJjhERESkVVKpFB4eHuJHKpXqOiSqZy5cuIDNmzdj8+bNOHfuXIV1zp07J9bZvHmzOMJSX0gkEvTt2xft27evNDZHR0fMmTMHoaGhWLBggUqS8qSSkhLMmTMHDg4OaNKkCT799FOV8piYGBgZGQEAli5diokTJyI3Nxf29vZiDD/88AM2bdoEmUyGTZs2AXg08tOrVy/Y2NigZcuW+Oyzz8TRpsfn3LFjB9zd3cURooyMDLz22mto2rQpmjZtiuDgYBQUFKjc95o1a9CtWzfIZDJ0794dV69eVbmXpUuXom3btjA3N0fLli2xf/9+sXzDhg3o0KEDLC0t4e3tjZMnT2rW8BXQKMFZvXo1Ll68CAD47bff4OLiglatWnFeJBEREVXKxMQExsbGKCgogLGxMUxMTHQdElGdKysrw6lTp3DlyhV07dq1yrpvvPEGCgsLER8fX2H5J598gqNHj+LXX39Famoq0tLSkJ6eXmHdhQsXYt26dXB3d4dCoUB0dDSSkpLw8ssviyNTU6ZMQVJSEoYMGYL33nsP2dnZOHr0KL788kts375dPFdpaSmOHDmC33//HdnZ2SguLkbfvn3Rrl07pKSkICkpCRkZGZg1a5ZKDFu2bMH+/fuRm5sLd3d3vP3222LZ+++/jx07dmDv3r2Qy+WIjY1F69atAQDr16/Hp59+ip07dyIvLw/h4eEYOXIkrl+/rlabV0ajBGfZsmVo3rw5ACA0NBSjR4/Gv//9b7z77ru1CoKIiIj0j1KpREhICJo1a4ZRo0Zh6tSpGDVqFJo1a4aQkJBy05CIdCUiIgJWVlYqH3XFxsbCysoKdnZ2mDlzJsLCwjBp0qQqj2nRogUAICcnp8Lybdu2Yf78+WjVqhWkUilWrFgBiUSidkwVWbt2LV5//XUMHz4choaG8PT0xDvvvIOtW7eq1Fu2bBmsrKxgamqKo0ePQhAEhIWFQSqVwsbGBhEREdi5cydKS0vFY9577z04OzvD2NgYgYGB+O233wAAgiBg9erVWL58Oby8vCCRSNCiRQt4eXkBAL744gt8+OGH6NSpEwwMDDB06FD07dsX3377ba3u1UiTyo+HvoqLi3HmzBkcPnwYxsbGWLFiRa2CICIiIv2iVCrRr1+/Cr+hViqViIyMxNmzZxEVFQUzMzMdREj1ibe3N4qKiiqdnvak7t27o3379rC0tKyz64eGhuL999+v0bE+Pj6IiorS6JibN28CAGxtbSstd3V1FbfNzMzg4OBQo/geS01NRXR0NA4cOCDuKysrg5OTk7htYGAAZ2dnlWMyMjLKJXwSiQR37twRBz6aNm0qlslkMigUCgBAdnY2lEol2rRpU2lMM2bMwMyZM8V9Dx8+FBPAmtJoBMfCwgK3bt1CbGwsOnfuDKlUitLSUpUMjnTjgw8+KDc/8zFBEPDCCy8gKSnpGUdFRETPq9DQ0Eqn3zwWHx+P0NDQZxQR1WempqZq/56SlJSEpk2b1mmC86zt2bMHUqkUPXr0qLC8efPmSEtLE7eVSiWysrJqdU0XFxdMnjwZ+fn54kcul6u0u0QiURkpcnFxQevWrVWOyc/PR3FxsZjcVMXe3h5mZmaVTjlzcXHBpk2bVM6tUCiwdu3aWt2rRiM4gYGBePHFF/HgwQMsXboUwKNncVq1alWrIKh2cnJysGnTpko7j0Qiwdy5c/Hhhx+qPNRFRET/83hlJlJPUVER0tPTkZeXV27RAKVSifXr14vbtra2iIyMRO/evREbG4uQkBBxas769esxePBgjuL8v6ratTrOzs4q3743JE++96Y6j9+L89JLL2k5qv8pLS1FcXGxyr6aPEuWlZWF3bt3IyIiAkuXLq10KtyECROwfPly+Pr6olmzZpg3b564GEBNvfXWW/Dx8cGAAQPg7+8PiUSC5ORkZGdnw8fHp8Jj/P39ERoaivDwcISEhEAmk+HWrVs4d+6cWktPSyQSvPXWW5g3bx6cnZ3Rvn173Lp1C7m5uejYsSNCQkKwePFitGrVCt7e3iguLsb58+dhZ2cHDw+PGt+rRglOREQE+vTpg0aNGokNYWJiwilqz8CePXuwfPlytG7dGjExMRAEAZ9//jnGjh2Lbdu2YcCAAVX+cBgxYgRmzJiB27dvqwwjVqSwsLDW8zzri6KiIpX/Ut1gu2oH21U71G3XdevW4eOPP34WIT13Vq1ahXHjxgEAxo8fD0EQMHHiRACP/l4GDRqky/D0xoIFC+p8RKy2v1Sro6L33lQnLi4OXbt2RaNGjeokhvDwcHzyyScq+7799lsMHToUwKOH6B+/g+ax06dPq3XurVu34ttvv4WBgQEsLCzQrVs3HD58GAMGDKj0mAULFiA3Nxc9evSAoaEhZs+eDRcXF81u6ikdO3bEsWPH8P777yMoKAhlZWVo1aoV5s2bV+kxZmZmiI6OxoIFC+Dh4YGCggI0a9YMY8aMUfvdOhERETA3N8eIESNw584dNGnSBMuWLUPHjh0RHBwMExMTTJkyBampqWjUqBG6dOlS69xCIjyLnku1FhoaihUrVmDjxo0YM2YMtm3bhjlz5uDevXsYMmQIxo8fj8DAwCrP0b9/f4wdOxaTJ08W95WVlaks9QcAKSkpnHZIRM+d9evXY8OGDboOQy9lZGSozPPPzMxssCMN9dnUqVMRHBxcp+c0NDSEu7u7yj5zc3MYGFT9lENmZqbK33lViouLazT9ysHBAY0bN9b4OGq41O1Xao3gqPM/y5ND0VT3Ll++jOnTp2PChAkAHk0XDA4Oxq1bt5CYmIi2bduq1Pf19YWvry8WL14s7vP09BSX+a5K69at9WoEJy0tDa6urnzvQh1iu2oH21U71G1XOzu7ZxjV8yU2Nhbjx48Xt2NiYnQXjB6zs7ODp6dnnZ5TEAStf+nZuHFjJrxUp9RKcBrym0z1RWJiIhYsWCBuZ2VlQSKRwNbWFnl5eTA3N1epHxgYqLL6BvDoG5fMzMxqr2VqalrtNzMNjVQq5fxuLWC7agfbVTuqa9dp06ZhyJAhzzCihu3xsyIuLi4VPoMzcuRIcVpgSEgIgEerTcXExGD27NliXalUioMHD7LP/7+q2rU6zs7Odd6OFc30IKrv1EpwNm/erO04qApyuRzp6elo0qSJuG/fvn3w8fGBubk5rK2ty/3jU9F0tYKCAlhbW2s7XCKiBqkhP6CtC0qlEtbW1vD09Kzwl+rg4GBERkYCeLQYzuMZCBXVGzhwoFZjbUiqa1ciqp7GX9OXlpYiLi4Oe/bsAfDogXQ+EKtdly9fhqGhIXbt2oXS0lJER0cjIiICERERAAAvLy8kJydXe55r166hc+fOWo6WiIjo0YPFlS2B+1iPHj3En2VERHVFowTnr7/+QocOHeDv748pU6YAAE6ePImpU6dqJTh6JDExEQEBAbh48SIsLS0xZ84c7Ny5U1wecciQIYiNja3yHI+X3eO3ZERE9CyYmZkhKioKs2bNKjcSYWZmhlmzZvEln0SkFRolOO+88w5Gjx6N3NxccVk+X19ftZfJo5q5fPkyvLy8sHfvXigUCly8eBH9+vUTyydOnIgTJ05UuX784cOHxbXUiYiIngUzMzOsWrUKZ8+exZgxYzBs2DCMGTMGZ8+exapVq5jc6DETExNkZ2c/k2Wm6fmgUChgZKTeG240eg/OuXPn8N///hcGBgbiKltWVlbIz8/XOEhSX2JiYpUPvtrZ2SEoKAhfffUV5s+fX65cEAQsX7683PrtREREz4K5ubnK6l5PL4xD+sfBwQFyuRw3b97UdSikJ4yMjODo6KheXU1ObGFhgfz8fJWlNP/++2+1L0Y1c+XKlWrf5hoeHl5pmUQiQUJCQl2HRURERFQpCwsLWFhY6DoMeg5pNEVt1KhR+Pe//y1m4/fu3cPMmTPxxhtvaCU4eiQ/Px+tWrXSdRhERERERPWeRglOeHg4ZDIZnJ2dkZ+fD0dHR0ilUixcuFBb8REREREREalNoylqUqkUu3btwhdffIG0tDS4uLjA3t5eW7ERERERERFpRKME5zE7OzuV53CIiIiIKtO8eXPMnTtX3G7cuLEOoyEifadWguPm5iaumlaZlJSUOgmIiIiI9IuhoSGXhCaiZ0atBGfJkiUAHiUxa9aswZQpU+Dm5oaUlBRs3rwZ06dP12qQRERERERE6lArwRk3bhwAoHfv3jhy5Ai6du0qlr366qsICQnB+++/r50IiYiIiIiI1KTRKmoXL15E586dVfZ5eXnh4sWLdRgSERERERFRzWiU4LRt2xYrV65U2bdq1Sq0adOmToMiIiIi/aFUKpGYmCh+lEqlrkMiIj2m0Spqq1evxpAhQ7B69Wq4uLggPT0dCoUCx44d01Z8RERE1MDl5OTg4MGD4vbkyZO56AARaY1GCU737t2RkpKCI0eO4O+//0bz5s0xdOhQWFpaais+IiIiIiIitWn8HhwLCwtx0QEiIiIiIqL6RKNncARBwGeffQZPT0/IZDJ4enris88+Q1lZmbbiIyIiIiIiUptGIzhLly7FN998g/nz56Nly5b466+/sGzZMhQVFXGZaCIiIiIi0jmNEpzNmzfj2LFj8PDwAAD4+fnBx8cHgwcPZoJDREREREQ6p9EUtdzcXLRs2VJln7u7O/Lz8+syJiIiIiIiohrRKMHx9vbG8uXLVfatWLGi3Ms/iYiIiIiIdEGjKWorV67EgAED8PXXX8PV1RXp6ekoLi7GyZMntRUfERERERGR2jRKcLy8vJCcnIyjR4/i5s2bcHJygr+/PywsLLQVHxERERERkdpq9B6csWPHaiMWIiIiIiKiWtEowSkrK8N3332H3377DQUFBSpl69evr9PAiIiIiIiINKVRghMcHIz//ve/6NOnD0xNTbUVExEREekRS0tL9OnTR2WbiEhbNEpw9u/fj0uXLsHZ2Vlb8RAREZGesbS0RO/evXUdBhE9JzRaJtre3h4ODg7aioWIiIiIiKhWNEpwIiIiEBISgtzcXG3FQ0REREREVGMaJTjt2rVDVFQU7O3tYWxsrPIhIiIiIiLSNY2ewRk/fjxeeuklfPnll1xkgIiIiIiI6h2NEpwbN24gISEBhoaG2oqHiIiI9ExmZia2b98ubk+YMAFOTk46jIiI9JlGU9S6d++Ov/76S1uxEBERkR4SBAElJSXiRxAEXYdERHpMoxEcPz8/DBs2DG+++SaaNGmiUjZ27Ng6DYyIiIiIiEhTGiU4GzZsAAB8+eWXKvslEgkTHCIiIiIi0jmNEpzU1FRtxUFERERERFRrGj2DQ0REREREVJ8xwSEiIiIiIr3BBIeIiIiIiPQGExwiIiIiItIbTHCIiIiIiEhvqLWKWp8+fSCRSKqsEx0dXScBERERERER1ZRaIzj9+vWDn58fOnXqhHPnzqFZs2bo2bMnmjVrht9++w2dO3fWcphERERERETVU2sEJzQ0FADwyiuv4ODBg+jfv79YFhUVhcjISO1ER0RERA2eoaEhzM3NVbaJiLRFoxd9xsTE4NChQyr7+vTpg1GjRtVlTERERKRHmjdvjjlz5ug6DCJ6Tmi0yICTkxP27Nmjsm/fvn1o0aJFnQZFRERERERUExqN4Cxbtgyvvvoq1q1bB1dXV6SlpeHs2bPYt2+ftuIjIiIiIiJSm0YjOP7+/rh69SoGDhwIMzMzDBgwAFevXsXQoUO1FR8REREREZHaNBrBAQB3d3csXLhQG7EQkZ4qLCyEqamprsMgIiKi54DGL/rcsWMH+vfvDy8vLwDAzz//jAMHDtR5YESkH0pKSrBt2zaUlJToOhQi0pG8vDz88MMP4icvL0/XIRGRHtMowfn888/x4YcfYvDgwcjIyAAA2NvbY9myZVoJjogavoSEBNy9excJCQm6DoWIdKSgoADx8fHip6CgQNchEZEe0yjBWbt2LU6cOIE5c+ZAIpEAANq0aYMbN25oJTgiathKSkoQFxcHAIiLi+MoDhEREWmdRglObm4u2rZtCwBigiMIgvhnIqInJSQkQKlUAgCUSiVHcYiIiEjrNEpw2rVrh6NHj6rsO3bsGDp16lSnQRFRw/fk6M1jHMUhIiIibdNoFbWlS5fC398fo0ePxoMHD/D222/j22+/xbFjx7QVH1G9d//+fdy/f1/XYTxTRUVFyM3Nxc2bNyGVSiusk5SUJI7ePKZUKhEVFYX27ds/izAbDEtLS1haWuo6DCIiIr2gUYLz8ssv48yZM1i3bh369OkDQRAQExODDh06aCs+0oHCwkK9mXZYVFSk8l9tOHv2LM6cOaO189dnNbnvc+fO4dy5c1qIpuF66aWX0LNnz2fSX59HbFft0KRdn65TVFRU7gsQeqS+9VdBEHQdApHGJIIGPffo0aMVvtRz2bJlmDdvXp0GRs9GWVlZudVsUlJSUFpaqqOIGp7k5GRcv35d12FQA9a6dWu0adNG12EQaU1ubq7KFyIvvfQSbGxsdBgRqcvQ0BDu7u4q+8zNzWFgoPGbRoieGY1GcKZOnYrjx4/D29tb3Ld27VqsXr2aCY4ead26tV6N4KSlpcHV1bXSqVS1lZubywSHasXOzg6enp7PpL8+j9iu2qFJu968eVMlwXFxcUGLFi20HWKDVN/6qyAI/NKTGhyNEpzNmzdj+PDhiIuLg5OTE3bs2IGwsDDExsZqKz7SAVNTU737ZkYqlcLMzEwr537xxRfh4eGhlXPXV0VFRUhPT4eLi0u5H8BJSUlqTUHr3r07n8X5f5aWlir9U5v99XnGdtUOddr16X8n+HdRvfrSRhXN9CCq7zRKcAYNGoTQ0FAMGTIEc+fOxdy5c/Hjjz9yagU9157HB8SVSiUKCgrQokULlR/AJSUl+O6779Q6R1JSEvr164dGjRppK0wiIiJ6Dmn8Nf2bb74Jf39/vPPOOzhy5Ag6d+6shbCIqCF68r031eF7cYiIiEgb1BrBefqZjLKyMgDAxIkTxX3Jycl1HBoRNSQVvfemOnFxcejatStHcYiIiKjOqJXgvP/++9qOg4gauNLSUowePbpGxzHBIdJv9vb2CAgIUNkmItIWtRKcSZMmaTsOImrgGjduDGdnZ12HQUT1kFQq5fO6RPTMaLTIAACkpaXhwoUL5VbUeHK6GhERERERkS5olOCsXbsWM2fOhJ2dHWQymbhfIpEwwSEiIiIiIp3TKMFZunQp9u/fj1deeUVb8RAREREREdWYRgmOQqFgckNEREQaKSkpwf3798VtS0tLLi5CRFqjUYLz+uuv4+jRoxg6dKi24iEiIiI9c/v2bWzevFncnjx5MhclISKt0SjBKS4uxpgxY9C3b180bdpUpWz9+vV1GhgREREREZGmNEpwDA0NxfdclJSUaCUgItIfcrkcp06dQk5ODmxtbeHn5wcLCwtdh0VERER6TKME58nhZSKiyiiVSoSGhmLjxo1QKpXifjMzMwQFBSEiIgJmZmY6jJCIiIj0lcbvwQEeTVXLzs6GIAjiPs6lJSLgUXLTr18/xMfHV1gWGRmJs2fPIioqikkOERER1TkDTSqnpaWhV69eMDMzg6urK9zc3MRPXfvggw/w6aefqlVXEAS4u7sjKSlJo3qurq4V/hJWG87Ozjh//rzGx6l7D+pe58l7EwQBL7zwgtrnJqqN0NDQav+/io+PR2ho6DOKiIiIiJ4nGo3gvP3222jevDkuXbqEXr16IS4uDosXL4a/v3+dBpWTk4NNmzbh+vXratWXSCRISUnRqJ5cLkdmZiY6dOhQq1iflJ2djXv37sHLy0vjY9W9B3Wu8/S9SSQSzJ07Fx9++CH279+vcWx1ISMjAxkZGc/8ukVFRUhPT0deXh6kUukzv76+qqxdlUqlyoIjtra2iIyMRO/evREbG4uQkBDk5OQAeLQwyeDBgzmK8wR97K/Ozs4c4SciomdKowQnPj4eqampMDc3BwC0b98e69atQ+/evREYGKjRhffs2YPly5ejdevWiImJgSAI+PzzzzF27Fhs27YNAwYMEH/xUSqVMDc3R0ZGBlq0aAEAyMzMRKdOnXD58mVER0dj165dOHHiBAAgPT0ds2bNQlxcHB48eID27dvj9OnT2L17N3bt2oW1a9fC09MTANCkSRNIJBLcunVLvK+nWVlZ4cKFC3Bzc8Pu3bsxduxYXLlyBe3bt8euXbuwefNm/Pjjjzh//jy8vb0RERGBdevWoaysDOvWrcOoUaPEc23duhWff/450tLS4O3tja1bt8LFxQXbt29XuYeysjIsW7YMa9euRXFxMZYuXYq5c+fizJkz4rEVXSctLa3CexsxYgRmzJiB27dvl1sB72mFhYWQSCQa/X1WZ926dfj444/r9JxU/61atQrjxo0DAIwfPx6CIGDixIkAHv0yP2jQIF2GR8/AggULdD5aV1RUpPJfqhuatOvTdYqKilSez6P/qW/99cnHEYgaCo0SHIlEIn6rKJPJkJ+fD2tr6xp9M5+YmIjLly9j1qxZ2Lp1K7Zt24Zp06bhtddew7FjxzB+/HixrpmZGdzc3HD16lUxwVm0aBGCg4PRvHlzXLx4EZ06dRLrBwYGYvjw4di3bx9KS0tx9uxZGBkZifVcXV2xatUqHD9+HIcPH642VmtraxQUFAB49Atb27ZtkZeXBwBYvXo1FixYAAA4f/48rl27hoULF+KDDz7AmjVrEBYWJiY4y5Ytw44dO/Ddd9+hVatWmDdvHmbNmoVDhw6Vu4clS5bgxIkT+Pnnn2Fra4sRI0aguLgYrVu3xv79+yu9TlX39sILL+D777/H5MmTq7zf69evo7S0tNp20cS9e/fq9HzUMPj4+Khs+/r66iYQ0pl79+7h2rVrug4DwKNp1lT31GnX3Nxcle309HTx5ypVrL70V0NDQ7i7u+s6DCKNaJTgtG/fHnFxcfDx8cGLL76I2bNni8mHpi5fvozp06djwoQJAB4lJcHBwbh16xYSExPRtm1blfodOnTAtWvXMGDAAFy9ehVHjx5FcnIyAODixYsICgoS6964cQNlZWUoLS2FiYkJevfuXa7epUuX0LlzZ7VitbKyQkFBAeLi4iCTydChQwfk5+fj4sWLyMrKwpAhQwA8SnDmzp0rTtnz8PAQz5GTk4OPPvoIZ8+eFfdPnToVfn5+5WLLzc3FihUrcO7cObi4uAAAXnnlFeTl5cHQ0LDK61R1b56enrh48WK199u6des6H8Gxs7Or0/NRwxAbG6vyZUVMTIzugiGdsLOzE0eVdaWoqAhpaWlwdXXVm6l/9YEm7Xrz5k2cOXNG3HZxcRG/sCRV9a2/CoJQ5196EmmbRgnOF198If7iu3z5ckybNg1yuRxff/21xhdOTEwURz4AICsrCxKJBLa2tsjLyys3Xaxjx464evUqAGDhwoWYP38+rKysAJT/hX7Hjh1YsmQJIiIi4O/vj+XLl8PR0VGl3sWLFzF37ly1Yn08grNx40aEhIQgOjoaeXl5OHToEGbOnAkDg0drNZw/fx6LFy8Wj7ty5QratWsHAIiOjkZxcTF69eollguCACcnp3L3EB0dDRcXF5XEJTc3V3zmpqrrVHVv5ubmyMzMrPZ+TU1NxXuqK9OmTRMTwWfp8TMNLi4u9eIHhb6orF2VSiVGjhwpTq0ICQkB8GgkJyYmBrNnzxbrSqVSHDx4kM/gPEEf+6uzs3O9+TuWSqX1JhZ9ok67Pt2f+XdRvfrSRmVlZRxtowZH7QTn4cOHuHbtGkaMGAEAcHd3x8mTJ2t0UblcjvT0dDRp0kTct2/fPvj4+MDc3FxlSthjHTp0wOrVq3HmzBlcuHABe/bsAfDoWZzCwkK0adNGrOvj4wMfHx/k5uZi5MiRWLNmDYKCgsR6giDg8uXLGo3gXL58GUlJSfD398f58+eRmpqKY8eOYeXKlQAeTcPIyspSSTQuXLggXiM3NxdDhw6tcErc0/dw7949NGvWTKXOkSNHMH78+GqvU9W9FRQUwNraWq17rmu6etBYqVTC2toanp6e9eIHhb6oql2Dg4MRGRkJ4NHI5eNR2qcFBwdj4MCBWo+1IWF/JSIiqj21v6Y3MjJCUFAQTExMan3Ry5cvw9DQELt27UJpaSmio6MRERGBiIgIAICXl5c4/eyxjh074tq1a5g/fz7CwsLEOC5evIgOHTrA0NAQAHDgwAEkJydDEAQoFArk5OTA29tbpV5hYSEUCkW5aViBgYEVLpZgbW2Nzz//HDNmzICBgQEsLCywdu1aBAQEiCNN58+fR/v27WFk9L+c8cKFC+JzNV26dMHp06fx22+/AQAUCgWOHz+OoqKicvfQrl07xMfH488//4RCocC8efNw4cIFeHl5VXudyu4NAK5du6Z2UkdUUxEREejRo0eVdXr06CH+/05E+k8qlaJNmzbiR19GKImoftJoHlK3bt3UeoajOomJiQgICMDFixdhaWmJOXPmYOfOnXjppZcAAEOGDEFsbKzKMW3btkVBQQHy8/NVvhG+dOmSysP5v/zyC/r06QNzc3P07dsXwcHBGDFihEo9MzMzvPXWW+jUqRNsbW3x8OFDAI9GUnr27FkuXmtraxQVFYnJj6WlJbKysvD222+Ldc6fP6+SPDx48AB//PGHuK9bt25YsmQJxowZA5lMhjZt2mDHjh2QSqXl7qF3796YPHkyunXrBi8vL7Ru3RrGxsbw9vau9jqV3VtxcTHOnz/Pb8xJ68zMzBAVFYVZs2ZBJpOplMlkMsyaNYsv+SR6ztjb2yMgIED82Nvb6zokItJjEkGD9f+WLFmC9evXIzg4GK6urirPaYwdO1bti06fPh1ubm547733Kiy/d+8eOnbsiBs3bjyzX4L++ecfdOrUCYmJiWjUqNEzuaa6du/ejdWrV+OXX36p8Tn27NmDPXv24MCBAyr7K5pba25uXufP4OiKUqnEtWvXOOWnjqnbrnK5HKdOnUJOTg5sbW3h5+cHCwuLZxhpw8L+qh1sV+1gu2pHfWtXff89gfSTRosMbNq0CYaGhti0aZPKfolEolGCk5iYWOUD53Z2dggKCsJXX32F+fPnaxJijRkbG9ebpUzPnDmDJk2awMXFBadPn8a7776Lbdu21fh8giBg+fLl2LJlS90FSaQGCwsLjBw5UtdhEBER0XNEowQnNTW1Ti565cqVcksbPy08PLxOrtUQXbp0CcOGDcODBw/QunVrrF69Gv369avx+SQSCRISEuowQiIiIiKi+kmjBKeu5Ofn6+KyDca0adMwbdo0XYdBRERERNTg6CTBISIioufH3bt3ceLECXF78ODBcHR01GFERKTPmOAQERGRVj148ADp6ekq20RE2sIlMIiIiIiISG8wwSEiIiIiIr3BBIeIiIiIiPQGExwiIiIiItIbTHCIiIiIiEhvMMEhIiIiIiK9wQSHiIiIiIj0BhMcIiIiIiLSG0xwiIiIiIhIbzDBISIiIiIivcEEh4iIiIiI9IaRrgMgIiIi/da8eXPMmTNH3DY1NdVhNESk75jgEBERkVYZGhrC3Nxc12EQ0XOCU9SIiIiIiEhvMMEhIiIiIiK9wQSHiIiIiIj0Bp/BISIiIq1SKpVISUkRt93d3WFmZqbDiIhInzHBISIiIq3KycnBgQMHxO3JkyczwSEireEUNSIiIiIi0htMcIiIiIiISG8wwSEiIiIiIr3BBIeIiIiIiPQGExwiIiIiItIbTHCIiIiIiEhvMMEhIiIiIiK9wQSHiIiIiIj0Bl/0+ZwTBKHcvrKyMh1Eoh2CIMDQ0BCCIOjVfeka21U72K7awXbVDk3aVSKRQCqVqmzz76Ji9a2/VhRDRb87ENUnEoG99Ln28OFDKJVKXYdBREREDYSZmRmMjPgdOdVfnKJGRERERER6gwkOERERERHpDSY4RERERESkN/gMznOurKys3AOEEokEEolERxERERFRfSEIQrlFBQwMDGBgwO/Iqf5igkNERERERHqD6TcREREREekNJjikt3766Sd0794dMpkMTZs2xVdffSWWZWZmYsCAATAzM0PLli1x4MABHUbacAQGBsLY2BgymUz8ZGRkiOWurq6QSqViWfv27XUYbcNSXduyz9bOgwcP4OHhgSZNmqjsZ5+tncralf21Zj777DO4u7vDwsICzZo1w+zZs1FSUiKWs78SqYcJDumlP/74A6NHj8aHH36IvLw8/Pnnn/Dz8xPLAwIC0LZtW9y7dw/r1q3DpEmTkJycrMOIG445c+ZAoVCIH2dnZ5XygwcPimVJSUk6irJhqqpt2Wdr5+OPP0bTpk0rLGOfrbnK2pX9tWZGjBiBCxcuQC6X4/Lly7h06RJWrlypUof9lah6THBIL4WFhSE4OBhDhw5Fo0aNYGFhAU9PTwDA9evXce7cOYSHh0MqlaJ///4YMGAAtm/fruOoiSrGPls7ycnJ2Lt3L+bPn6/rUPRKZe3K/lpzLVu2hKWlJYBHD/IbGRnhxo0bOo6KqOFhgkN6KT4+HgYGBujYsSMcHR0xcuRI3Lx5EwBw5coVuLi4wMrKSqzfuXNnXLlyRUfRNizr16+HjY0NOnXqhG+++aZc+aRJk2Bvbw9fX1/ExcXpIMKGq7K2ZZ+tnWnTpmHFihVo3LhxheXsszVTWbuyv9bOrl27YGFhARsbG/z+++946623VMrZX4mqxwSH9NLNmzexdetWfPfdd0hNTYWNjQ3GjRsHAFAoFOI3ZI9ZWVmhoKBAF6E2KDNnzkRycjKys7MRGRmJefPmYf/+/WL59u3bkZqaioyMDIwZMwaDBw9Genq6DiNuOKpqW/bZmtuyZQusra0xePDgCsvZZ2umqnZlf62dsWPHQi6X448//kBwcLDKFED2VyL1MMGhBqdfv37iu3qe/ri6ugIATE1NMWnSJHh6esLU1BRhYWH4+eefoVAoIJPJIJfLVc55//59mJub6+Bu6g912rVLly6ws7ODoaEhfH19MWPGDOzdu1c8x8svvwxTU1NIpVK89dZb8Pb2xokTJ3R0R/VHbduWfbZi1bVrbm4uFi9ejFWrVlV6DvbZ8mrbruyvFVPn34EntW3bFu3atcP06dPFfeyvROox0nUARJqKioqqto6Xl1e5l5U+3u7QoQPS0tJw//598VvGixcvokOHDnUfbAOiTrs+zcDAoNwL4DQpf17Utm3ZZytWXbvGxMTg1q1b8Pb2BgCUlJSgoKAAdnZ2+OWXX+Dh4VHuGPbZ2rcr+2vFavLvwMOHD/HXX39VWs7+SlQJgUgPbd26VXB2dhauX78uFBcXC8HBwULfvn3F8n/961/CzJkzhcLCQiEqKkqQyWTCn3/+qcOIG4Y9e/YIcrlcKC0tFU6fPi3Y2dkJu3fvFgRBENLT04Wff/5ZePDggfDgwQNhw4YNgpmZmXDjxg0dR90wVNW2gsA+WxPFxcVCZmam+Pnuu+8Ee3t7ITMzUygpKWGfraHq2lUQ2F9rav369cLdu3cFQRCEK1euCB4eHsKcOXMEQeC/sUSaYIJDeis8PFxwcHAQrK2thREjRgh///23WJaRkSH069dPkEqlgpubm7Bv3z4dRtpwvPzyy4KlpaUgk8mEdu3aCWvXrhXLkpKShE6dOglmZmaCtbW10LNnT+Gnn37SXbANTFVtKwjss3Xhp59+EhwdHcVt9tm68XS7CgL7a02NHz9esLe3F0xNTQVXV1dh/vz5QnFxsSAI7K9EmpAIAsc2iYiIiIhIP3CRASIiIiIi0htMcIiIiIiISG8wwSEiIiIiIr3BBIeIiIiIiPQGExwiIiIiItIbTHCIiIiIiEhvMMEhIiIiIiK9wQSHiIiIiIj0BhMcIqJ6IjAwEEFBQbU6h5GREWJiYqqt5+rqih07dtTqWtX5z3/+gw8//FBr1/zXv/6FU6dO1dn5iIhIPxjpOgAiItI/GRkZ2LhxI1JSUrR2jcWLF2P27NlITEzU2jWIiKjh4QgOEdEzUlhYiLlz58LNzQ02NjYYNGgQbty4UWl9iUSCVatWoXPnzjA3N0efPn1U6hcUFGDSpEmwsbGBi4sLtm7dWuPYYmNj8eKLL8LS0hIeHh74+uuvNSp/2po1azBixAhYWFjU+JrHjh1Du3btYG5ujuHDh2POnDnw9fUVy/v374/8/HyO4hARkQomOEREz0hQUBD++OMPxMfH486dO+jWrRuGDh2KkpKSSo9Zv3499u3bh6ysLHh4eOCVV15BaWkpACAkJATXr1/H1atXkZiYiMOHD4tlmkhNTcWgQYMwbdo05OTkYMuWLViwYAH27t2rVnlFDh06hP79+9f4mn/99RdGjRqFDz74AHl5eZg5cyY2btyocg6JRAI/Pz8cOnRI43smIiL9xQSHiOgZuHfvHnbv3o01a9bA0dERxsbG+Oijj3D79m3Ex8dXety7776LVq1aQSqVYsWKFbhx4wbOnj2LsrIy7Ny5E+Hh4WjSpAksLS3x6aef1ii23bt3o0uXLpg8eTKMjIzQo0cPvPnmm2JCUV3504qKivDnn3/C09OzVtd88cUXERAQACMjI/j5+WH48OHlztOhQwf8/vvvNbpvIiLST0xwiIiegdTUVACAl5cXrKysYGVlBRsbG5SUlODmzZuVHufq6ir+2czMDA4ODrh58yays7Px4MEDlXI3N7caxZaZmQl3d3eVfS1btkRmZqZa5U/Ly8sDgCqnp1V3zr///hsuLi4q5U9vP77G4+sREREBTHCIiJ6Jx7+cJycnIz8/X/wUFhYiICCg0uPS0tLEPyuVSmRlZaFFixawt7eHsbGxSvnjJEpTTk5O5Y5NSUmBk5OTWuVPs7a2BgDI5fIaX7N58+Yq9wYA6enp5c4jl8vF6xEREQFMcIiIngkHBwcEBARg2rRp4ohNfn4+Dh48CIVCUelxK1euxF9//YWioiLMmzcPLVu2xIsvvggDAwOMHTsWixYtwt27dyGXy7FgwYIaxRYQEIDz589j27ZtePjwIc6dO4evv/4aU6ZMUav8aVKpFG3atMHVq1drdc2zZ89i9+7dKC0tRXR0NA4fPlzuPFeuXIG3t3eN7puIiPQTExwiomdk48aNaNeuHfr06QNzc3N07NgRe/fuhUQiqfSYoKAgjBo1Cg4ODrhy5QoOHz4MQ0NDAEBkZCTc3Nzg4eGBjh07YtiwYWKZJtzc3HD8+HF89dVXsLW1xYQJExAWFobRo0erVV6RESNGICoqqsbXbNmyJfbt24ewsDBYWlri888/x4QJE2BiYiKeQxAEnDp1qsJnc4iI6PklEQRB0HUQRERUnkQiwenTp9GrVy9dh6KxjIwMdOnSBSkpKVU+i6OJgIAAmJubY/369QCAkydPYs6cObh8+XKVSSIRET1fOIJDRER1ztnZGUFBQVi+fHmNz3HkyBHk5eXh4cOHOHz4MPbv36/yvNLixYuxatUqJjdERKTCSNcBEBFR3Rs8eDBOnz5dYdn9+/drNJVNU5988kmtjo+NjcXkyZNRXFwMZ2dnrFu3Dn369BHLf/3119qGSEREeohT1IiIiIiISG9wihoREREREekNJjhERERERKQ3mOAQEREREZHeYIJDRERERER6gwkOERERERHpDSY4RERERESkN5jgEBERERGR3mCCQ0REREREeuP/AJ+mx1MTeP4lAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 600x200 with 1 Axes>"
       ]
@@ -2330,7 +2366,7 @@
     }
    ],
    "source": [
-    "#| fig-cap: Comparison between the $p(visit,wheight)$ and the $p(\\cdot)$ models in terms of loo.\n",
+    "#| fig-cap: 'Comparison between the $p(visit,wheight)$ and the $p(\\cdot)$ models in terms of loo.'\n",
     "#| label: fig-warbler_comp\n",
     "az.plot_compare(df_comp_loo, insample_dev=False);"
    ]
@@ -2352,9 +2388,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_quarto.yml b/_quarto.yml
index b394553..deb96d2 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -35,7 +35,7 @@ website:
               text: Occupancy
             - href: closed_cmr.qmd
               text: Closed capture-recapture
-            - href: dist.qmd
+            - href: distance.qmd
               text: Distance sampling
             - href: scr.qmd
               text: Spatial capture-recapture
diff --git a/about.qmd b/about.qmd
deleted file mode 100644
index 3b6ff74..0000000
--- a/about.qmd
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: "Philip T. Patton"
-about:
-  template: solana
-  image: biopic.png
-  links:
-    - icon: twitter
-      text: twitter
-      href: https://twitter.com/philip_patton_
-    - icon: github
-      text: Github
-      href: https://github.com/philpatton
-    - icon: book
-      text: Google Scholar
-      href: https://scholar.google.com/citations?user=4TUtf04AAAAJ&hl=en&oi=ao
-
----
-
-Phil is a PhD candidate in the [Marine Mammal Research Program](https://www.mmrphawaii.org) at the [Hawaiʻi Institute of Marine Biology](https://www.himb.hawaii.edu), and a [NOAA QUEST Fellow](https://www.fisheries.noaa.gov/content/quantitative-ecology-and-socioeconomics-training-quest-program) with the [Cetacean Research Program](https://www.fisheries.noaa.gov/pacific-islands/science-data/whale-and-dolphin-surveys-pacific-islands) at the Pacific Islands Fisheries Science Center.
-
-For his dissertation, he's researching ways to improve stock assessments of non-migratory dolphins in Hawaiʻi. This includes [automating photo-identification](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14167) of these animals, understanding how these automated tools [interact with capture-recapture models](https://github.com/philpatton/autocapture), and estimating demographic parameters using cutting edge methods in capture recapture.
-
-He did his master's with [Krishna Pacifici](https://cnr.ncsu.edu/directory/krishna-pacifici/) at North Carolina State University, where he studied ways to improve estimates of species distribution, particularly when [species interact](https://link.springer.com/article/10.1007/s10530-022-02825-3) and when the data contains sampling errors.
\ No newline at end of file
diff --git a/closed_cmr.qmd b/closed_cmr.qmd
index fdcc546..b521a45 100644
--- a/closed_cmr.qmd
+++ b/closed_cmr.qmd
@@ -37,25 +37,22 @@ import pandas as pd
 import pymc as pm
 import arviz as az
 import matplotlib.pyplot as plt 
-import seaborn as sns
 from pymc.distributions.dist_math import binomln, logpow
 
+# plotting parameters
 plt.style.use('fivethirtyeight')
 plt.rcParams['axes.facecolor'] = 'white'
 plt.rcParams['figure.facecolor'] = 'white'
-# pal = sns.color_palette("Set2")
-# sns.set_palette(pal)
 
 # hyperparameters 
 SEED = 808
 RNG = np.random.default_rng(SEED)
-M = 1500
 ```
 
 I explore fitting the simplest closed capture-recapture model, Model $M_0,$ through parameter-expanded data-augmentation [PX-DA, @royle2008]. The idea with PX-DA is to augment the capture histories with $M-n$ all zero capture-histories, where $M$ is a hyperparameter that should be much greater than the true population size $N,$ and $n$ is the total number of individuals that were captured during the study. This allows us to treat the data as a zero-inflated binomial distribution (see below).
 
 ```{python}
-def augment_history(history):
+def augment_history(history, M):
     '''Augment a capture history with all-zero histories.'''
     
     animals_captured, T = history.shape
@@ -70,7 +67,7 @@ def augment_history(history):
     return augmented 
 ```
 
-To demonstrate this approach, I use the salamander dataset from @bailey2004, as demonstrated in @hooten2019, Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (*Plethodon jordani*) and the pygmy salamander (*Desmognathus wrighti*), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting $M=1500$ (see above). There were $n=92$ individual red-cheeked and $n=132$ pygmy salamanders captured during the course of the survey.
+To demonstrate this approach, I use the salamander dataset from @bailey2004, as demonstrated in @hooten2019, Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (*Plethodon jordani*) and the pygmy salamander (*Desmognathus wrighti*), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting $M=1500$. There were $n=92$ individual red-cheeked and $n=132$ pygmy salamanders captured during the course of the survey.
 
 ```{python}
 def get_histories():
@@ -89,19 +86,12 @@ def get_histories():
     pyg = sal_data.loc[is_pyg, col_labs].to_numpy()
     red = sal_data.loc[is_red, col_labs].to_numpy()
 
-    # # augment each set separately since they differ in length
-    # pyg_augmented = augment_history(pyg)
-    # red_augmented = augment_history(red)
-
-    # # recombine into one history 
-    # history = np.concatenate((pyg_augmented, red_augmented))
-
     return {'pyg': pyg, 'red': red}
 
-def augment_histories(histories):
+def augment_histories(histories, M):
 
-    pyg_augmented = augment_history(histories['pyg'])
-    red_augmented = augment_history(histories['red'])
+    pyg_augmented = augment_history(histories['pyg'], M=M)
+    red_augmented = augment_history(histories['red'], M=M)
 
     # recombine into one history 
     history = np.concatenate((pyg_augmented, red_augmented))
@@ -114,11 +104,12 @@ n_red, T = histories['red'].shape
 n_pyg, T = histories['pyg'].shape
 
 # # summarize into binomial data
-history_augmented = augment_histories(histories)
+M = 1500
+history_augmented = augment_histories(histories, M=M)
 history_summarized = history_augmented.sum(axis=1)
 ```
 
-For this model, I use the `pm.ZeroInflatedBinomial` class, just as I did in the [occupancy notebook](https://philpatton.github.io/occ.html). That said, the parameters here are different. First, $p$ represents the probability of capturing a given individual during the survey. Second, $\psi$ represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation $M$ is included in the population of interest $N.$ Then, we can estimate the population size as $\hat{N}=M\hat{\psi},$ or generate posterior draws of $N,$ e.g., $N^{(s)} \sim \text{Bin}(M,\psi^{(s)})$
+For this model, I use the `pm.ZeroInflatedBinomial` class, just as I did in the [occupancy notebook](https://philpatton.github.io/occ.html). That said, the parameters here are different. First, $p$ represents the probability of capturing a given individual during the survey. Second, $\psi$ represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation $M$ is included in the population of interest $N.$ Then, we can simulate the posterior distribution for $N$ using $M$ and the posterior distributions of $\psi.$
 
 In this example, I combine the two species into one `pm.Model` object, making use of `coords`. That said, the parameters for each species are treated as independent. In other words, this is a "no-pooling" model. 
 
@@ -164,7 +155,7 @@ az.plot_trace(M0_idata, figsize=(8,4));
 
 For faster sampling, it's better to separate the two species into two separate models. On my machine, the individual species models finish sampling in 2-3 seconds, compared to 15-20 seconds for the two species model. That said, the two species model is somewhat more convenient.
 
-Of course, the trace plots lack our true parameter of interest: the population size $N.$ We can simulate the posterior of $N$ as a *derived quantity*, using the posterior distribution of $\psi$ and $p.$ 
+Of course, the trace plots lack our true parameter of interest: the population size $N.$ We can simulate the posterior of $N$ as a *derived quantity*, using $M$ and the posterior distribution of $\psi$.
 
 ```{python}
 # az.extract flattens the chains
@@ -203,7 +194,7 @@ ax.legend()
 plt.show()
 ```
 
-We might expect estimates of capture probability $p$ and the abundance $N$ to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws. For a more custom look to the plots, I plot the draws using matplotlib.
+We might expect estimates of capture probability $p$ and the abundance $N$ to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws. 
 
 ```{python}
 #| fig-cap: Posterior draws of $N$ and $p$ for both species of salamander.
@@ -250,7 +241,7 @@ n, T = micro_hist.shape
 
 # augment with all zero histories
 M = 100
-micro_augmented = augment_history(micro_hist)
+micro_augmented = augment_history(micro_hist, M=M)
 
 # note the occasion when each individual was first seen
 first_seen = (micro_hist != 0).argmax(axis=1)
diff --git a/cv.qmd b/cv.qmd
index 996c940..184f24e 100644
--- a/cv.qmd
+++ b/cv.qmd
@@ -46,7 +46,7 @@ description: Graduate Research Assistant & NOAA QUEST Fellow
 
 1. **Patton, P.T.** Some hierarchical and machine learning models for wildlife science. Invited talk at University of Natural Resources and Life Sciences, Vienna (BOKU). July 2023.
 2. **Patton, P.T.** et al. The effect of fully automated photo--identification on mark-recapture estimates. Paper presented at the EURING Analytical Meeting. Montpellier, France. April 2023
-3. **Patton, P.T.** Assessing populations of resident cetaceans. HIMB Scholarship Symposium. K\=ane`ohe, Hawai`i. April 2022.
+3. **Patton, P.T.** Assessing populations of resident cetaceans. HIMB Scholarship Symposium. Kāneʻohe, Hawaiʻi. April 2022.
 4. **Patton, P. T.** & Gardner, B. Misspecifying movement models in spatial capture recapture studies. Paper presented at The Ecological Society of America Conference. Portland, OR, USA. August 2017
 5. **Patton, P. T.** et al. Modeling and estimating co--occurrence between generalist brood parasites and host communities. Paper presented at the EURING Analytical Meeting. Barcelona, Spain. June 2017
 6. **Patton, P. T.** et al. Multi--species occupancy models that incorporate false positive and false negative sampling errors. Paper presented at The Wildlife Society Conference. Raleigh, NC, USA. October 2016
@@ -70,7 +70,7 @@ description: Graduate Research Assistant & NOAA QUEST Fellow
 
 - Referee: *Wildlife Society Bulletin*, *Marine Mammal Science*
 - Member: British Ecological Society, The Wildlife Society (biometrics working group), The Ecological Society of America (statistical ecology section)
-- Representative to the Faculty, Marine Biology Graduate Program, University of Hawai`i at Mānoa
-- Representative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawai`i at Mānoa
+- Representative to the Faculty, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa
+- Representative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa
 
 ---
diff --git a/dist.qmd b/distance.qmd
similarity index 96%
rename from dist.qmd
rename to distance.qmd
index 037efe9..b298ddb 100644
--- a/dist.qmd
+++ b/distance.qmd
@@ -116,16 +116,6 @@ az.plot_trace(
 This model samples slower than the models in the other notebooks, presumably because of the unobserved $x.$ As in the closed capture-recapture notebook, we will have to simulate the posterior for $N$ using the posterior distribution of $\psi$ and $M.$ 
 
 ```{python}
-# RNG = np.random.default_rng()
-
-# this does not make a copy of the posterior
-# post = distance_idata.posterior
-
-# # simulate draws of N using the posterior of psi 
-# N_samples = RNG.binomial(M, post.psi)
-# N_samples = N_samples.flatten()
-# N_hat = N_samples.mean()
-
 RNG = np.random.default_rng()
 
 posterior = az.extract(distance_idata)
diff --git a/docs/.DS_Store b/docs/.DS_Store
new file mode 100644
index 0000000..9914d3d
Binary files /dev/null and b/docs/.DS_Store differ
diff --git a/docs/cjs.html b/docs/cjs.html
index 20d1d3d..8ab863d 100644
--- a/docs/cjs.html
+++ b/docs/cjs.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -728,6 +728,24 @@ <h1>Cormack-Jolly-Seber</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -762,7 +780,11 @@ <h1>Cormack-Jolly-Seber</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1038,7 +1060,7 @@ <h1>Cormack-Jolly-Seber</h1>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./msom.html" class="pagination-link  aria-label=" community="" occupancy"="">
+      <a href="./msom.html" class="pagination-link" aria-label="Community occupancy">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Community occupancy</span>
       </a>          
   </div>
diff --git a/docs/closed_cmr.html b/docs/closed_cmr.html
index d6a07cb..4f0aa60 100644
--- a/docs/closed_cmr.html
+++ b/docs/closed_cmr.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -87,7 +87,7 @@
 <script src="site_libs/quarto-search/fuse.min.js"></script>
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
-<link href="./dist.html" rel="next">
+<link href="./distance.html" rel="next">
 <link href="./occupancy.html" rel="prev">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -394,30 +394,27 @@ <h1 class="title">Closed capture-recapture</h1>
 <p>This produces a capture history for each individual, which allows us to estimate the probability of capture and the number of individuals in the population <span class="math inline">\(N\)</span>.</p>
 <section id="model-m_0" class="level1">
 <h1>Model <span class="math inline">\(M_0\)</span></h1>
-<div id="9522f8bc" class="cell" data-execution_count="1">
+<div id="6c03f573" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># libraries </span></span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pymc <span class="im">as</span> pm</span>
 <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> arviz <span class="im">as</span> az</span>
 <span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt </span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pymc.distributions.dist_math <span class="im">import</span> binomln, logpow</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pymc.distributions.dist_math <span class="im">import</span> binomln, logpow</span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># plotting parameters</span></span>
 <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>plt.style.use(<span class="st">'fivethirtyeight'</span>)</span>
 <span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'axes.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
 <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'figure.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="co"># pal = sns.color_palette("Set2")</span></span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="co"># sns.set_palette(pal)</span></span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="co"># hyperparameters </span></span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>SEED <span class="op">=</span> <span class="dv">808</span></span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>RNG <span class="op">=</span> np.random.default_rng(SEED)</span>
-<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>M <span class="op">=</span> <span class="dv">1500</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="co"># hyperparameters </span></span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>SEED <span class="op">=</span> <span class="dv">808</span></span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>RNG <span class="op">=</span> np.random.default_rng(SEED)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>I explore fitting the simplest closed capture-recapture model, Model <span class="math inline">\(M_0,\)</span> through parameter-expanded data-augmentation <span class="citation" data-cites="royle2008">(PX-DA, <a href="#ref-royle2008" role="doc-biblioref">Royle and Dorazio 2008</a>)</span>. The idea with PX-DA is to augment the capture histories with <span class="math inline">\(M-n\)</span> all zero capture-histories, where <span class="math inline">\(M\)</span> is a hyperparameter that should be much greater than the true population size <span class="math inline">\(N,\)</span> and <span class="math inline">\(n\)</span> is the total number of individuals that were captured during the study. This allows us to treat the data as a zero-inflated binomial distribution (see below).</p>
-<div id="cb34d84c" class="cell" data-execution_count="2">
-<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> augment_history(history):</span>
+<div id="ae74bc57" class="cell" data-execution_count="2">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> augment_history(history, M):</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Augment a capture history with all-zero histories.'''</span></span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>    </span>
 <span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>    animals_captured, T <span class="op">=</span> history.shape</span>
@@ -431,8 +428,8 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 <span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> augmented </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<p>To demonstrate this approach, I use the salamander dataset from <span class="citation" data-cites="bailey2004">Bailey, Simons, and Pollock (<a href="#ref-bailey2004" role="doc-biblioref">2004</a>)</span>, as demonstrated in <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (<em>Plethodon jordani</em>) and the pygmy salamander (<em>Desmognathus wrighti</em>), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting <span class="math inline">\(M=1500\)</span> (see above). There were <span class="math inline">\(n=92\)</span> individual red-cheeked and <span class="math inline">\(n=132\)</span> pygmy salamanders captured during the course of the survey.</p>
-<div id="c8417415" class="cell" data-execution_count="3">
+<p>To demonstrate this approach, I use the salamander dataset from <span class="citation" data-cites="bailey2004">Bailey, Simons, and Pollock (<a href="#ref-bailey2004" role="doc-biblioref">2004</a>)</span>, as demonstrated in <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (<em>Plethodon jordani</em>) and the pygmy salamander (<em>Desmognathus wrighti</em>), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting <span class="math inline">\(M=1500\)</span>. There were <span class="math inline">\(n=92\)</span> individual red-cheeked and <span class="math inline">\(n=132\)</span> pygmy salamanders captured during the course of the survey.</p>
+<div id="8dab4ba5" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> get_histories():</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Read, augment, and recombine the salamander histories.'''</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    </span>
@@ -449,35 +446,29 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 <span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a>    pyg <span class="op">=</span> sal_data.loc[is_pyg, col_labs].to_numpy()</span>
 <span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>    red <span class="op">=</span> sal_data.loc[is_red, col_labs].to_numpy()</span>
 <span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>    <span class="co"># # augment each set separately since they differ in length</span></span>
-<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>    <span class="co"># pyg_augmented = augment_history(pyg)</span></span>
-<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>    <span class="co"># red_augmented = augment_history(red)</span></span>
+<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> {<span class="st">'pyg'</span>: pyg, <span class="st">'red'</span>: red}</span>
+<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> augment_histories(histories, M):</span>
 <span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a>    <span class="co"># # recombine into one history </span></span>
-<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>    <span class="co"># history = np.concatenate((pyg_augmented, red_augmented))</span></span>
+<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a>    pyg_augmented <span class="op">=</span> augment_history(histories[<span class="st">'pyg'</span>], M<span class="op">=</span>M)</span>
+<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>    red_augmented <span class="op">=</span> augment_history(histories[<span class="st">'red'</span>], M<span class="op">=</span>M)</span>
 <span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> {<span class="st">'pyg'</span>: pyg, <span class="st">'red'</span>: red}</span>
-<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> augment_histories(histories):</span>
-<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a>    pyg_augmented <span class="op">=</span> augment_history(histories[<span class="st">'pyg'</span>])</span>
-<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a>    red_augmented <span class="op">=</span> augment_history(histories[<span class="st">'red'</span>])</span>
+<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a>    <span class="co"># recombine into one history </span></span>
+<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a>    history <span class="op">=</span> np.concatenate((pyg_augmented, red_augmented))</span>
+<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> history</span>
+<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a>histories <span class="op">=</span> get_histories()</span>
 <span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a>    <span class="co"># recombine into one history </span></span>
-<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a>    history <span class="op">=</span> np.concatenate((pyg_augmented, red_augmented))</span>
+<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a>n_red, T <span class="op">=</span> histories[<span class="st">'red'</span>].shape</span>
+<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a>n_pyg, T <span class="op">=</span> histories[<span class="st">'pyg'</span>].shape</span>
 <span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> history</span>
-<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a>histories <span class="op">=</span> get_histories()</span>
-<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a>n_red, T <span class="op">=</span> histories[<span class="st">'red'</span>].shape</span>
-<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a>n_pyg, T <span class="op">=</span> histories[<span class="st">'pyg'</span>].shape</span>
-<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a><span class="co"># # summarize into binomial data</span></span>
-<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a>history_augmented <span class="op">=</span> augment_histories(histories)</span>
-<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a>history_summarized <span class="op">=</span> history_augmented.<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a><span class="co"># # summarize into binomial data</span></span>
+<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a>M <span class="op">=</span> <span class="dv">1500</span></span>
+<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a>history_augmented <span class="op">=</span> augment_histories(histories, M<span class="op">=</span>M)</span>
+<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a>history_summarized <span class="op">=</span> history_augmented.<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<p>For this model, I use the <code>pm.ZeroInflatedBinomial</code> class, just as I did in the <a href="https://philpatton.github.io/occ.html">occupancy notebook</a>. That said, the parameters here are different. First, <span class="math inline">\(p\)</span> represents the probability of capturing a given individual during the survey. Second, <span class="math inline">\(\psi\)</span> represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation <span class="math inline">\(M\)</span> is included in the population of interest <span class="math inline">\(N.\)</span> Then, we can estimate the population size as <span class="math inline">\(\hat{N}=M\hat{\psi},\)</span> or generate posterior draws of <span class="math inline">\(N,\)</span> e.g., <span class="math inline">\(N^{(s)} \sim \text{Bin}(M,\psi^{(s)})\)</span></p>
+<p>For this model, I use the <code>pm.ZeroInflatedBinomial</code> class, just as I did in the <a href="https://philpatton.github.io/occ.html">occupancy notebook</a>. That said, the parameters here are different. First, <span class="math inline">\(p\)</span> represents the probability of capturing a given individual during the survey. Second, <span class="math inline">\(\psi\)</span> represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation <span class="math inline">\(M\)</span> is included in the population of interest <span class="math inline">\(N.\)</span> Then, we can simulate the posterior distribution for <span class="math inline">\(N\)</span> using <span class="math inline">\(M\)</span> and the posterior distributions of <span class="math inline">\(\psi.\)</span></p>
 <p>In this example, I combine the two species into one <code>pm.Model</code> object, making use of <code>coords</code>. That said, the parameters for each species are treated as independent. In other words, this is a “no-pooling” model.</p>
 <div id="cell-fig-m0" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># index for each species</span></span>
@@ -502,7 +493,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 <span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a>    )</span>
 <span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a>    </span>
 <span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(M0)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
+<div class="cell-output cell-output-display" data-execution_count="21">
 <div id="fig-m0" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-m0-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -515,7 +506,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 </div>
 </div>
 </div>
-<div id="e32df492" class="cell" data-execution_count="5">
+<div id="c2ab43f7" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> M0:</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    M0_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -523,7 +514,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 Initializing NUTS using jitter+adapt_diag...
 Multiprocess sampling (4 chains in 4 jobs)
 NUTS: [psi, p]
-Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 17 seconds.</code></pre>
+Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 19 seconds.</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 
@@ -547,7 +538,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 
     <div>
       <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [8000/8000 00:16&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [8000/8000 00:18&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
@@ -568,8 +559,8 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 </div>
 </div>
 <p>For faster sampling, it’s better to separate the two species into two separate models. On my machine, the individual species models finish sampling in 2-3 seconds, compared to 15-20 seconds for the two species model. That said, the two species model is somewhat more convenient.</p>
-<p>Of course, the trace plots lack our true parameter of interest: the population size <span class="math inline">\(N.\)</span> We can simulate the posterior of <span class="math inline">\(N\)</span> as a <em>derived quantity</em>, using the posterior distribution of <span class="math inline">\(\psi\)</span> and <span class="math inline">\(p.\)</span></p>
-<div id="9b036652" class="cell" data-execution_count="7">
+<p>Of course, the trace plots lack our true parameter of interest: the population size <span class="math inline">\(N.\)</span> We can simulate the posterior of <span class="math inline">\(N\)</span> as a <em>derived quantity</em>, using <span class="math inline">\(M\)</span> and the posterior distribution of <span class="math inline">\(\psi\)</span>.</p>
+<div id="79bd2788" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># az.extract flattens the chains</span></span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>posterior <span class="op">=</span> az.extract(M0_idata)</span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>psi_samps <span class="op">=</span> posterior.psi.values</span>
@@ -603,7 +594,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 <div id="fig-n_trace" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-n_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="closed_cmr_files/figure-html/fig-n_trace-output-1.png" width="600" height="381" class="figure-img">
+<img src="closed_cmr_files/figure-html/fig-n_trace-output-1.png" width="587" height="381" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-n_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;3: Posterior distributions of <span class="math inline">\(N\)</span> from the <span class="math inline">\(M_0\)</span> model. Estimates from <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span> are shown by the vertical lines’
@@ -612,7 +603,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 </div>
 </div>
 </div>
-<p>We might expect estimates of capture probability <span class="math inline">\(p\)</span> and the abundance <span class="math inline">\(N\)</span> to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws. For a more custom look to the plots, I plot the draws using matplotlib.</p>
+<p>We might expect estimates of capture probability <span class="math inline">\(p\)</span> and the abundance <span class="math inline">\(N\)</span> to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws.</p>
 <div id="cell-fig-posterior" class="cell" data-fig-lab="post" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># create the plot</span></span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(<span class="dv">1</span>, <span class="dv">1</span>, figsize<span class="op">=</span>(<span class="dv">4</span>, <span class="dv">4</span>))</span>
@@ -654,7 +645,7 @@ <h1>Model <span class="math inline">\(M_0\)</span></h1>
 <h1>Model <span class="math inline">\(M_b\)</span></h1>
 <p>Next, I fit model <span class="math inline">\(M_b,\)</span> which accounts for the possibility that the capture probability changes after the animal is first caught. This could be from trap happiness, whereby animals are more likely to be trapped after their first time. Conversely, this could be from subsequent trap avoidance.</p>
 <p>Mirroring <span class="citation" data-cites="royle2008">(<a href="#ref-royle2008" role="doc-biblioref">Royle and Dorazio 2008, chap. 5</a>)</span>, I fit this model to the <em>Microtus</em> dataset reported in <span class="citation" data-cites="williams2002">(<a href="#ref-williams2002" role="doc-biblioref">Williams, Nichols, and Conroy 2002, 525</a>)</span>. This version of the <a href="https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/chapters.htm">dataset</a> includes encounter histories of <span class="math inline">\(n=56\)</span> adult males that were captured on <span class="math inline">\(T=5\)</span> consecutive days.</p>
-<div id="68041eba" class="cell" data-execution_count="10">
+<div id="68a42337" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># read in the microtus data</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>microtus <span class="op">=</span> np.loadtxt(<span class="st">'microtus.data.txt'</span>).astype(<span class="bu">int</span>)</span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -664,7 +655,7 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 <span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="co"># augment with all zero histories</span></span>
 <span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>M <span class="op">=</span> <span class="dv">100</span></span>
-<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>micro_augmented <span class="op">=</span> augment_history(micro_hist)</span>
+<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>micro_augmented <span class="op">=</span> augment_history(micro_hist, M<span class="op">=</span>M)</span>
 <span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a><span class="co"># note the occasion when each individual was first seen</span></span>
 <span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>first_seen <span class="op">=</span> (micro_hist <span class="op">!=</span> <span class="dv">0</span>).argmax(axis<span class="op">=</span><span class="dv">1</span>)</span>
@@ -715,7 +706,7 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 <span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a>    )</span>
 <span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a>    </span>
 <span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(mb)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="11">
+<div class="cell-output cell-output-display" data-execution_count="28">
 <div id="fig-mb" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-mb-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -728,7 +719,7 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 </div>
 </div>
 </div>
-<div id="7fb464cb" class="cell" data-execution_count="12">
+<div id="78ec8d9f" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> mb:</span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>    mb_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -765,9 +756,10 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
     
 </div>
 </div>
-<div id="ea6b5470" class="cell" data-execution_count="13">
+<div id="943a495a" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>az.summary(mb_idata, var_names<span class="op">=</span>[<span class="st">'Alpha'</span>, <span class="st">'psi'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="13">
+<div class="cell-output cell-output-display" data-execution_count="30">
+<div>
 <div>
 
 
@@ -789,39 +781,39 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 <tbody>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">Alpha[Intercept]</td>
-<td>0.103</td>
-<td>0.249</td>
-<td>-0.347</td>
-<td>0.567</td>
+<td>0.095</td>
+<td>0.257</td>
+<td>-0.388</td>
+<td>0.561</td>
 <td>0.006</td>
 <td>0.004</td>
-<td>1954.0</td>
-<td>2046.0</td>
-<td>1.00</td>
+<td>1861.0</td>
+<td>1800.0</td>
+<td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">Alpha[B_Response]</td>
-<td>0.626</td>
-<td>0.296</td>
-<td>0.074</td>
-<td>1.186</td>
+<td>0.632</td>
+<td>0.300</td>
+<td>0.079</td>
+<td>1.217</td>
 <td>0.007</td>
 <td>0.005</td>
-<td>1895.0</td>
-<td>1883.0</td>
-<td>1.00</td>
+<td>1898.0</td>
+<td>1827.0</td>
+<td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">psi</td>
-<td>0.575</td>
+<td>0.576</td>
 <td>0.051</td>
-<td>0.470</td>
-<td>0.666</td>
+<td>0.486</td>
+<td>0.678</td>
 <td>0.001</td>
 <td>0.001</td>
-<td>2446.0</td>
-<td>2479.0</td>
-<td>1.01</td>
+<td>2292.0</td>
+<td>2250.0</td>
+<td>1.0</td>
 </tr>
 </tbody>
 </table>
@@ -829,6 +821,7 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-forest" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>az.plot_forest(mb_idata, var_names<span class="op">=</span>[<span class="st">'Alpha'</span>], combined<span class="op">=</span><span class="va">True</span>, ess<span class="op">=</span><span class="va">True</span>, figsize<span class="op">=</span>(<span class="dv">6</span>,<span class="dv">2</span>))<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -1054,6 +1047,24 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -1088,7 +1099,11 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1364,12 +1379,12 @@ <h1>Model <span class="math inline">\(M_b\)</span></h1>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./occupancy.html" class="pagination-link  aria-label=" occupancy"="">
+      <a href="./occupancy.html" class="pagination-link" aria-label="Occupancy">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Occupancy</span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./dist.html" class="pagination-link" aria-label="Distance sampling">
+      <a href="./distance.html" class="pagination-link" aria-label="Distance sampling">
         <span class="nav-page-text">Distance sampling</span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
diff --git a/docs/closed_cmr_files/figure-html/cell-10-output-1.png b/docs/closed_cmr_files/figure-html/cell-10-output-1.png
deleted file mode 100644
index 1b3b10f..0000000
Binary files a/docs/closed_cmr_files/figure-html/cell-10-output-1.png and /dev/null differ
diff --git a/docs/closed_cmr_files/figure-html/cell-18-output-1.png b/docs/closed_cmr_files/figure-html/cell-18-output-1.png
deleted file mode 100644
index 4a807a1..0000000
Binary files a/docs/closed_cmr_files/figure-html/cell-18-output-1.png and /dev/null differ
diff --git a/docs/closed_cmr_files/figure-html/cell-9-output-1.png b/docs/closed_cmr_files/figure-html/cell-9-output-1.png
deleted file mode 100644
index 06d3b28..0000000
Binary files a/docs/closed_cmr_files/figure-html/cell-9-output-1.png and /dev/null differ
diff --git a/docs/closed_cmr_files/figure-html/fig-b_effect-output-1.png b/docs/closed_cmr_files/figure-html/fig-b_effect-output-1.png
index e8cd718..5eae717 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-b_effect-output-1.png and b/docs/closed_cmr_files/figure-html/fig-b_effect-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-discovery-output-1.png b/docs/closed_cmr_files/figure-html/fig-discovery-output-1.png
index 59c0d6c..79133ae 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-discovery-output-1.png and b/docs/closed_cmr_files/figure-html/fig-discovery-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-forest-output-1.png b/docs/closed_cmr_files/figure-html/fig-forest-output-1.png
index d2a7202..098f781 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-forest-output-1.png and b/docs/closed_cmr_files/figure-html/fig-forest-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-latent-output-1.svg b/docs/closed_cmr_files/figure-html/fig-latent-output-1.svg
deleted file mode 100644
index ac6b8f5..0000000
--- a/docs/closed_cmr_files/figure-html/fig-latent-output-1.svg
+++ /dev/null
@@ -1,77 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
- "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
-<!-- Generated by graphviz version 9.0.0 (20230911.1827)
- -->
-<!-- Pages: 1 -->
-<svg width="246pt" height="377pt"
- viewBox="0.00 0.00 246.00 376.95" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
-<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 372.95)">
-<polygon fill="white" stroke="none" points="-4,4 -4,-372.95 242,-372.95 242,4 -4,4"/>
-<g id="clust1" class="cluster">
-<title>clusteralpha_coeffs (2)</title>
-<path fill="none" stroke="black" d="M138,-137.82C138,-137.82 218,-137.82 218,-137.82 224,-137.82 230,-143.82 230,-149.82 230,-149.82 230,-247.63 230,-247.63 230,-253.63 224,-259.63 218,-259.63 218,-259.63 138,-259.63 138,-259.63 132,-259.63 126,-253.63 126,-247.63 126,-247.63 126,-149.82 126,-149.82 126,-143.82 132,-137.82 138,-137.82"/>
-<text text-anchor="middle" x="177.75" y="-145.02" font-family="Times,serif" font-size="14.00">alpha_coeffs (2)</text>
-</g>
-<g id="clust2" class="cluster">
-<title>cluster100</title>
-<path fill="none" stroke="black" d="M20,-137.82C20,-137.82 106,-137.82 106,-137.82 112,-137.82 118,-143.82 118,-149.82 118,-149.82 118,-247.63 118,-247.63 118,-253.63 112,-259.63 106,-259.63 106,-259.63 20,-259.63 20,-259.63 14,-259.63 8,-253.63 8,-247.63 8,-247.63 8,-149.82 8,-149.82 8,-143.82 14,-137.82 20,-137.82"/>
-<text text-anchor="middle" x="99.88" y="-145.02" font-family="Times,serif" font-size="14.00">100</text>
-</g>
-<g id="clust3" class="cluster">
-<title>cluster500</title>
-<path fill="none" stroke="black" d="M76,-8C76,-8 162,-8 162,-8 168,-8 174,-14 174,-20 174,-20 174,-117.82 174,-117.82 174,-123.82 168,-129.82 162,-129.82 162,-129.82 76,-129.82 76,-129.82 70,-129.82 64,-123.82 64,-117.82 64,-117.82 64,-20 64,-20 64,-14 70,-8 76,-8"/>
-<text text-anchor="middle" x="155.88" y="-15.2" font-family="Times,serif" font-size="14.00">500</text>
-</g>
-<!-- psi -->
-<g id="node1" class="node">
-<title>psi</title>
-<ellipse fill="none" stroke="black" cx="63" cy="-328.29" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="63" y="-339.74" font-family="Times,serif" font-size="14.00">psi</text>
-<text text-anchor="middle" x="63" y="-323.24" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="63" y="-306.74" font-family="Times,serif" font-size="14.00">Uniform</text>
-</g>
-<!-- z -->
-<g id="node3" class="node">
-<title>z</title>
-<ellipse fill="none" stroke="black" cx="63" cy="-210.98" rx="47.38" ry="40.66"/>
-<text text-anchor="middle" x="63" y="-222.43" font-family="Times,serif" font-size="14.00">z</text>
-<text text-anchor="middle" x="63" y="-205.93" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="63" y="-189.43" font-family="Times,serif" font-size="14.00">Bernoulli</text>
-</g>
-<!-- psi&#45;&gt;z -->
-<g id="edge1" class="edge">
-<title>psi&#45;&gt;z</title>
-<path fill="none" stroke="black" d="M63,-287.28C63,-279.5 63,-271.25 63,-263.16"/>
-<polygon fill="black" stroke="black" points="66.5,-263.43 63,-253.43 59.5,-263.43 66.5,-263.43"/>
-</g>
-<!-- Alpha -->
-<g id="node2" class="node">
-<title>Alpha</title>
-<ellipse fill="none" stroke="black" cx="175" cy="-210.98" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="175" y="-222.43" font-family="Times,serif" font-size="14.00">Alpha</text>
-<text text-anchor="middle" x="175" y="-205.93" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="175" y="-189.43" font-family="Times,serif" font-size="14.00">Normal</text>
-</g>
-<!-- y -->
-<g id="node4" class="node">
-<title>y</title>
-<ellipse fill="lightgrey" stroke="black" cx="119" cy="-81.16" rx="47.38" ry="40.66"/>
-<text text-anchor="middle" x="119" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
-<text text-anchor="middle" x="119" y="-76.11" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="119" y="-59.61" font-family="Times,serif" font-size="14.00">Bernoulli</text>
-</g>
-<!-- Alpha&#45;&gt;y -->
-<g id="edge3" class="edge">
-<title>Alpha&#45;&gt;y</title>
-<path fill="none" stroke="black" d="M158.95,-173.35C153.02,-159.81 146.21,-144.27 139.91,-129.89"/>
-<polygon fill="black" stroke="black" points="143.27,-128.82 136.05,-121.07 136.85,-131.63 143.27,-128.82"/>
-</g>
-<!-- z&#45;&gt;y -->
-<g id="edge2" class="edge">
-<title>z&#45;&gt;y</title>
-<path fill="none" stroke="black" d="M79.35,-172.66C85.16,-159.39 91.79,-144.26 97.94,-130.22"/>
-<polygon fill="black" stroke="black" points="101.12,-131.69 101.93,-121.13 94.71,-128.88 101.12,-131.69"/>
-</g>
-</g>
-</svg>
diff --git a/docs/closed_cmr_files/figure-html/fig-latent_n-output-1.png b/docs/closed_cmr_files/figure-html/fig-latent_n-output-1.png
deleted file mode 100644
index b64c2d0..0000000
Binary files a/docs/closed_cmr_files/figure-html/fig-latent_n-output-1.png and /dev/null differ
diff --git a/docs/closed_cmr_files/figure-html/fig-m0-output-1.svg b/docs/closed_cmr_files/figure-html/fig-m0-output-1.svg
index 2cc5ad8..a2e67ae 100644
--- a/docs/closed_cmr_files/figure-html/fig-m0-output-1.svg
+++ b/docs/closed_cmr_files/figure-html/fig-m0-output-1.svg
@@ -18,11 +18,11 @@
 <path fill="none" stroke="black" d="M32,-8C32,-8 180,-8 180,-8 186,-8 192,-14 192,-20 192,-20 192,-117.82 192,-117.82 192,-123.82 186,-129.82 180,-129.82 180,-129.82 32,-129.82 32,-129.82 26,-129.82 20,-123.82 20,-117.82 20,-117.82 20,-20 20,-20 20,-14 26,-8 32,-8"/>
 <text text-anchor="middle" x="170.5" y="-15.2" font-family="Times,serif" font-size="14.00">3000</text>
 </g>
-<!-- p -->
+<!-- psi -->
 <g id="node1" class="node">
-<title>p</title>
+<title>psi</title>
 <ellipse fill="none" stroke="black" cx="168" cy="-210.98" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="168" y="-222.43" font-family="Times,serif" font-size="14.00">p</text>
+<text text-anchor="middle" x="168" y="-222.43" font-family="Times,serif" font-size="14.00">psi</text>
 <text text-anchor="middle" x="168" y="-205.93" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="168" y="-189.43" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
@@ -34,23 +34,23 @@
 <text text-anchor="middle" x="106" y="-76.11" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="106" y="-59.61" font-family="Times,serif" font-size="14.00">MarginalMixture</text>
 </g>
-<!-- p&#45;&gt;history -->
+<!-- psi&#45;&gt;history -->
 <g id="edge1" class="edge">
-<title>p&#45;&gt;history</title>
+<title>psi&#45;&gt;history</title>
 <path fill="none" stroke="black" d="M150.23,-173.35C143.84,-160.17 136.52,-145.09 129.71,-131.03"/>
 <polygon fill="black" stroke="black" points="133.03,-129.85 125.51,-122.38 126.73,-132.91 133.03,-129.85"/>
 </g>
-<!-- psi -->
+<!-- p -->
 <g id="node2" class="node">
-<title>psi</title>
+<title>p</title>
 <ellipse fill="none" stroke="black" cx="61" cy="-210.98" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="61" y="-222.43" font-family="Times,serif" font-size="14.00">psi</text>
+<text text-anchor="middle" x="61" y="-222.43" font-family="Times,serif" font-size="14.00">p</text>
 <text text-anchor="middle" x="61" y="-205.93" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="61" y="-189.43" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
-<!-- psi&#45;&gt;history -->
+<!-- p&#45;&gt;history -->
 <g id="edge2" class="edge">
-<title>psi&#45;&gt;history</title>
+<title>p&#45;&gt;history</title>
 <path fill="none" stroke="black" d="M74.38,-171.97C78.75,-159.55 83.68,-145.57 88.32,-132.39"/>
 <polygon fill="black" stroke="black" points="91.6,-133.59 91.62,-122.99 85,-131.27 91.6,-133.59"/>
 </g>
diff --git a/docs/closed_cmr_files/figure-html/fig-mb-output-1.svg b/docs/closed_cmr_files/figure-html/fig-mb-output-1.svg
index 83dadd4..67d45da 100644
--- a/docs/closed_cmr_files/figure-html/fig-mb-output-1.svg
+++ b/docs/closed_cmr_files/figure-html/fig-mb-output-1.svg
@@ -27,7 +27,7 @@
 <text text-anchor="middle" x="44.72" y="-176.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- y -->
-<g id="node4" class="node">
+<g id="node3" class="node">
 <title>y</title>
 <ellipse fill="lightgrey" stroke="black" cx="172.72" cy="-81.16" rx="67" ry="40.66"/>
 <text text-anchor="middle" x="172.72" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
@@ -35,7 +35,7 @@
 <text text-anchor="middle" x="172.72" y="-59.61" font-family="Times,serif" font-size="14.00">CustomDist_y</text>
 </g>
 <!-- psi&#45;&gt;y -->
-<g id="edge3" class="edge">
+<g id="edge2" class="edge">
 <title>psi&#45;&gt;y</title>
 <path fill="none" stroke="black" d="M76.04,-169.27C91.56,-155.28 110.58,-138.14 127.64,-122.78"/>
 <polygon fill="black" stroke="black" points="129.62,-125.71 134.7,-116.41 124.93,-120.51 129.62,-125.71"/>
@@ -49,7 +49,7 @@
 <text text-anchor="middle" x="172.72" y="-298.74" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
 <!-- p -->
-<g id="node3" class="node">
+<g id="node4" class="node">
 <title>p</title>
 <polygon fill="none" stroke="black" points="217.85,-227.23 127.6,-227.23 127.6,-169.73 217.85,-169.73 217.85,-227.23"/>
 <text text-anchor="middle" x="172.72" y="-209.93" font-family="Times,serif" font-size="14.00">p</text>
@@ -63,7 +63,7 @@
 <polygon fill="black" stroke="black" points="176.22,-238.98 172.72,-228.98 169.22,-238.98 176.22,-238.98"/>
 </g>
 <!-- p&#45;&gt;y -->
-<g id="edge2" class="edge">
+<g id="edge3" class="edge">
 <title>p&#45;&gt;y</title>
 <path fill="none" stroke="black" d="M172.72,-169.27C172.72,-158.44 172.72,-145.73 172.72,-133.41"/>
 <polygon fill="black" stroke="black" points="176.22,-133.78 172.72,-123.78 169.22,-133.78 176.22,-133.78"/>
diff --git a/docs/closed_cmr_files/figure-html/fig-mb_n-output-1.png b/docs/closed_cmr_files/figure-html/fig-mb_n-output-1.png
index e985c85..1a20f19 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-mb_n-output-1.png and b/docs/closed_cmr_files/figure-html/fig-mb_n-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-n_trace-output-1.png b/docs/closed_cmr_files/figure-html/fig-n_trace-output-1.png
index bd9233f..7a4fd48 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-n_trace-output-1.png and b/docs/closed_cmr_files/figure-html/fig-n_trace-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-posterior-output-1.png b/docs/closed_cmr_files/figure-html/fig-posterior-output-1.png
index a2d93fe..9938559 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-posterior-output-1.png and b/docs/closed_cmr_files/figure-html/fig-posterior-output-1.png differ
diff --git a/docs/closed_cmr_files/figure-html/fig-trace-output-1.png b/docs/closed_cmr_files/figure-html/fig-trace-output-1.png
index 358a942..b386669 100644
Binary files a/docs/closed_cmr_files/figure-html/fig-trace-output-1.png and b/docs/closed_cmr_files/figure-html/fig-trace-output-1.png differ
diff --git a/docs/comparison.html b/docs/comparison.html
index b3714cc..9e1dc86 100644
--- a/docs/comparison.html
+++ b/docs/comparison.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -245,7 +245,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -901,6 +901,7 @@ <h1>Training each model</h1>
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>az.summary(mh_idata, var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'sigma_gamma'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -961,6 +962,7 @@ <h1>Training each model</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="d0e2061d" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(mh_idata, figsize<span class="op">=</span>(<span class="dv">8</span>, <span class="dv">6</span>), var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'sigma_gamma'</span>])<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -1061,6 +1063,7 @@ <h1>Training each model</h1>
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>az.summary(mth_idata, var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'sigma_alpha'</span>, <span class="st">'sigma_gamma'</span>, <span class="st">'alpha'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="18">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -1337,6 +1340,7 @@ <h1>Training each model</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-mth_trace" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(mth_idata, figsize<span class="op">=</span>(<span class="dv">8</span>, <span class="dv">10</span>),</span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>              var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'sigma_alpha'</span>, <span class="st">'sigma_gamma'</span>, <span class="st">'alpha'</span>])<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1442,6 +1446,7 @@ <h1>Training each model</h1>
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>az.summary(mbh_idata, var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'beta'</span>, <span class="st">'sigma_gamma'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="22">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -1514,6 +1519,7 @@ <h1>Training each model</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="84d2a0a1" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(mbh_idata, figsize<span class="op">=</span>(<span class="dv">8</span>, <span class="dv">10</span>),</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>              var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'beta'</span>, <span class="st">'sigma_beta'</span>, <span class="st">'sigma_gamma'</span>])<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -1619,6 +1625,7 @@ <h1>Training each model</h1>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>           var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'alpha'</span>, <span class="st">'beta'</span>, <span class="st">'sigma_alpha'</span>, <span class="st">'sigma_beta'</span>, <span class="st">'sigma_gamma'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -1919,6 +1926,7 @@ <h1>Training each model</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-mtbh_trace" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(mtbh_idata, figsize<span class="op">=</span>(<span class="dv">8</span>,<span class="dv">14</span>),</span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>           var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'mu'</span>, <span class="st">'alpha'</span>, <span class="st">'beta'</span>, <span class="st">'sigma_alpha'</span>, <span class="st">'sigma_beta'</span>, <span class="st">'sigma_gamma'</span>])<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -2202,6 +2210,7 @@ <h1>Model comparison</h1>
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>comparison.<span class="bu">round</span>(<span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -2322,6 +2331,7 @@ <h1>Model comparison</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="67238ba1" class="cell" data-execution_count="32">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>good_dict <span class="op">=</span> {<span class="st">"m0"</span>: m0_idata, <span class="st">"mt"</span>: mt_idata, <span class="st">"mb"</span>: mb_idata, <span class="st">"mtb"</span>: mtb_idata}</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>good_comparison <span class="op">=</span> az.compare(good_dict)</span>
@@ -2336,6 +2346,7 @@ <h1>Model comparison</h1>
 </div>
 <div class="cell-output cell-output-display" data-execution_count="31">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -2408,6 +2419,7 @@ <h1>Model comparison</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-comp" class="cell" data-execution_count="33">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>az.plot_compare(good_comparison, figsize<span class="op">=</span>(<span class="dv">5</span>, <span class="dv">4</span>))<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -2610,6 +2622,24 @@ <h1>Model averaged predictions</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -2644,7 +2674,11 @@ <h1>Model averaged predictions</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -2920,7 +2954,7 @@ <h1>Model averaged predictions</h1>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./jssa.html" class="pagination-link  aria-label=" jolly-seber"="">
+      <a href="./jssa.html" class="pagination-link" aria-label="Jolly-Seber">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Jolly-Seber</span>
       </a>          
   </div>
diff --git a/docs/cv.html b/docs/cv.html
index 9b2d8ed..64d1e9c 100644
--- a/docs/cv.html
+++ b/docs/cv.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -213,7 +213,7 @@ <h2 class="anchored" data-anchor-id="presentations">Presentations</h2>
 <ol type="1">
 <li><strong>Patton, P.T.</strong> Some hierarchical and machine learning models for wildlife science. Invited talk at University of Natural Resources and Life Sciences, Vienna (BOKU). July 2023.</li>
 <li><strong>Patton, P.T.</strong> et al.&nbsp;The effect of fully automated photo–identification on mark-recapture estimates. Paper presented at the EURING Analytical Meeting. Montpellier, France. April 2023</li>
-<li><strong>Patton, P.T.</strong> Assessing populations of resident cetaceans. HIMB Scholarship Symposium. K=ane<code>ohe, Hawai</code>i. April 2022.</li>
+<li><strong>Patton, P.T.</strong> Assessing populations of resident cetaceans. HIMB Scholarship Symposium. Kāneʻohe, Hawaiʻi. April 2022.</li>
 <li><strong>Patton, P. T.</strong> &amp; Gardner, B. Misspecifying movement models in spatial capture recapture studies. Paper presented at The Ecological Society of America Conference. Portland, OR, USA. August 2017</li>
 <li><strong>Patton, P. T.</strong> et al.&nbsp;Modeling and estimating co–occurrence between generalist brood parasites and host communities. Paper presented at the EURING Analytical Meeting. Barcelona, Spain. June 2017</li>
 <li><strong>Patton, P. T.</strong> et al.&nbsp;Multi–species occupancy models that incorporate false positive and false negative sampling errors. Paper presented at The Wildlife Society Conference. Raleigh, NC, USA. October 2016</li>
@@ -243,8 +243,8 @@ <h2 class="anchored" data-anchor-id="professional-service">Professional Service<
 <ul>
 <li>Referee: <em>Wildlife Society Bulletin</em>, <em>Marine Mammal Science</em></li>
 <li>Member: British Ecological Society, The Wildlife Society (biometrics working group), The Ecological Society of America (statistical ecology section)</li>
-<li>Representative to the Faculty, Marine Biology Graduate Program, University of Hawai`i at Mānoa</li>
-<li>Representative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawai`i at Mānoa</li>
+<li>Representative to the Faculty, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa</li>
+<li>Representative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa</li>
 </ul>
 <hr>
 
@@ -331,6 +331,24 @@ <h2 class="anchored" data-anchor-id="professional-service">Professional Service<
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -365,7 +383,11 @@ <h2 class="anchored" data-anchor-id="professional-service">Professional Service<
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/distance.html b/docs/distance.html
new file mode 100644
index 0000000..1dd3408
--- /dev/null
+++ b/docs/distance.html
@@ -0,0 +1,1030 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+
+<meta charset="utf-8">
+<meta name="generator" content="quarto-1.4.554">
+
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+
+<meta name="author" content="Philip T. Patton">
+<meta name="dcterms.date" content="2024-02-02">
+<meta name="description" content="Distance sampling models using data augmentation">
+
+<title>Philip T. Patton - Distance sampling</title>
+<style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+ul.task-list li input[type="checkbox"] {
+  width: 0.8em;
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
+  vertical-align: middle;
+}
+/* CSS for syntax highlighting */
+pre > code.sourceCode { white-space: pre; position: relative; }
+pre > code.sourceCode > span { line-height: 1.25; }
+pre > code.sourceCode > span:empty { height: 1.2em; }
+.sourceCode { overflow: visible; }
+code.sourceCode > span { color: inherit; text-decoration: inherit; }
+div.sourceCode { margin: 1em 0; }
+pre.sourceCode { margin: 0; }
+@media screen {
+div.sourceCode { overflow: auto; }
+}
+@media print {
+pre > code.sourceCode { white-space: pre-wrap; }
+pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+}
+pre.numberSource code
+  { counter-reset: source-line 0; }
+pre.numberSource code > span
+  { position: relative; left: -4em; counter-increment: source-line; }
+pre.numberSource code > span > a:first-child::before
+  { content: counter(source-line);
+    position: relative; left: -1em; text-align: right; vertical-align: baseline;
+    border: none; display: inline-block;
+    -webkit-touch-callout: none; -webkit-user-select: none;
+    -khtml-user-select: none; -moz-user-select: none;
+    -ms-user-select: none; user-select: none;
+    padding: 0 4px; width: 4em;
+  }
+pre.numberSource { margin-left: 3em;  padding-left: 4px; }
+div.sourceCode
+  {   }
+@media screen {
+pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
+}
+/* CSS for citations */
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
+  margin-bottom: 0em;
+}
+.hanging-indent div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
+}
+div.csl-left-margin {
+  min-width:2em;
+  float:left;
+}
+div.csl-right-inline {
+  margin-left:2em;
+  padding-left:1em;
+}
+div.csl-indent {
+  margin-left: 2em;
+}</style>
+
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js" integrity="sha512-bLT0Qm9VnAYZDflyKcBaQ2gg0hSYNQrJ8RilYldYQ1FxQYoCLtUjuuRuZo+fjqhx/qtq/1itJ0C2ejDxltZVFg==" crossorigin="anonymous"></script><script src="site_libs/quarto-nav/quarto-nav.js"></script>
+<script src="site_libs/quarto-nav/headroom.min.js"></script>
+<script src="site_libs/clipboard/clipboard.min.js"></script>
+<script src="site_libs/quarto-search/autocomplete.umd.js"></script>
+<script src="site_libs/quarto-search/fuse.min.js"></script>
+<script src="site_libs/quarto-search/quarto-search.js"></script>
+<meta name="quarto:offset" content="./">
+<link href="./scr.html" rel="next">
+<link href="./closed_cmr.html" rel="prev">
+<script src="site_libs/quarto-html/quarto.js"></script>
+<script src="site_libs/quarto-html/popper.min.js"></script>
+<script src="site_libs/quarto-html/tippy.umd.min.js"></script>
+<script src="site_libs/quarto-html/anchor.min.js"></script>
+<link href="site_libs/quarto-html/tippy.css" rel="stylesheet">
+<link href="site_libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles">
+<script src="site_libs/bootstrap/bootstrap.min.js"></script>
+<link href="site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
+<link href="site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+<script id="quarto-search-options" type="application/json">{
+  "location": "navbar",
+  "copy-button": false,
+  "collapse-after": 3,
+  "panel-placement": "end",
+  "type": "overlay",
+  "limit": 50,
+  "keyboard-shortcut": [
+    "f",
+    "/",
+    "s"
+  ],
+  "show-item-context": false,
+  "language": {
+    "search-no-results-text": "No results",
+    "search-matching-documents-text": "matching documents",
+    "search-copy-link-title": "Copy link to search",
+    "search-hide-matches-text": "Hide additional matches",
+    "search-more-match-text": "more match in this document",
+    "search-more-matches-text": "more matches in this document",
+    "search-clear-button-title": "Clear",
+    "search-text-placeholder": "",
+    "search-detached-cancel-button-title": "Cancel",
+    "search-submit-button-title": "Submit",
+    "search-label": "Search"
+  }
+}</script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" integrity="sha512-c3Nl8+7g4LMSTdrm621y7kf9v3SDPnhxLNhcjFJbKECVnmZHTdo+IRO05sNLTH/D3vA6u1X32ehoLC7WFVdheg==" crossorigin="anonymous"></script>
+
+<script type="application/javascript">define('jquery', [],function() {return window.jQuery;})</script>
+
+  <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
+  <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml-full.js" type="text/javascript"></script>
+
+<script type="text/javascript">
+const typesetMath = (el) => {
+  if (window.MathJax) {
+    // MathJax Typeset
+    window.MathJax.typeset([el]);
+  } else if (window.katex) {
+    // KaTeX Render
+    var mathElements = el.getElementsByClassName("math");
+    var macros = [];
+    for (var i = 0; i < mathElements.length; i++) {
+      var texText = mathElements[i].firstChild;
+      if (mathElements[i].tagName == "SPAN") {
+        window.katex.render(texText.data, mathElements[i], {
+          displayMode: mathElements[i].classList.contains('display'),
+          throwOnError: false,
+          macros: macros,
+          fleqn: false
+        });
+      }
+    }
+  }
+}
+window.Quarto = {
+  typesetMath
+};
+</script>
+
+<link rel="stylesheet" href="styles.css">
+</head>
+
+<body class="nav-sidebar docked nav-fixed">
+
+<div id="quarto-search-results"></div>
+  <header id="quarto-header" class="headroom fixed-top">
+    <nav class="navbar navbar-expand-lg " data-bs-theme="dark">
+      <div class="navbar-container container-fluid">
+      <div class="navbar-brand-container mx-auto">
+    <a class="navbar-brand" href="./index.html">
+    <span class="navbar-title">Philip T. Patton</span>
+    </a>
+  </div>
+            <div id="quarto-search" class="" title="Search"></div>
+          <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarCollapse" aria-controls="navbarCollapse" aria-expanded="false" aria-label="Toggle navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+  <span class="navbar-toggler-icon"></span>
+</button>
+          <div class="collapse navbar-collapse" id="navbarCollapse">
+            <ul class="navbar-nav navbar-nav-scroll me-auto">
+  <li class="nav-item">
+    <a class="nav-link" href="./index.html"> 
+<span class="menu-text">Home</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="./cv.html"> 
+<span class="menu-text">CV</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link active" href="./notebooks.html" aria-current="page"> 
+<span class="menu-text">Notebooks</span></a>
+  </li>  
+</ul>
+          </div> <!-- /navcollapse -->
+          <div class="quarto-navbar-tools tools-wide">
+    <a href="https://twitter.com/philip_patton_" title="" class="quarto-navigation-tool px-1" aria-label=""><i class="bi bi-twitter"></i></a>
+    <a href="https://github.com/philpatton" title="" class="quarto-navigation-tool px-1" aria-label=""><i class="bi bi-github"></i></a>
+    <a href="https://scholar.google.com/citations?user=4TUtf04AAAAJ&amp;hl=en&amp;oi=ao" title="" class="quarto-navigation-tool px-1" aria-label=""><i class="bi bi-book"></i></a>
+</div>
+      </div> <!-- /container-fluid -->
+    </nav>
+  <nav class="quarto-secondary-nav">
+    <div class="container-fluid d-flex">
+      <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
+        <i class="bi bi-layout-text-sidebar-reverse"></i>
+      </button>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./occupancy.html">PyMC</a></li><li class="breadcrumb-item"><a href="./distance.html">Distance sampling</a></li></ol></nav>
+        <a class="flex-grow-1" role="button" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
+        </a>
+    </div>
+  </nav>
+</header>
+<!-- content -->
+<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-article page-navbar">
+<!-- sidebar -->
+  <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal quarto-sidebar-collapse-item sidebar-navigation docked overflow-auto">
+    <div class="sidebar-menu-container"> 
+    <ul class="list-unstyled mt-1">
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./notebooks.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Notebooks</span></a>
+  </div>
+</li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true">
+ <span class="menu-text">PyMC</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-1" class="collapse list-unstyled sidebar-section depth1 show">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./occupancy.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Occupancy</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./closed_cmr.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Closed capture-recapture</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./distance.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">Distance sampling</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./scr.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Spatial capture-recapture</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./msom.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Community occupancy</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./cjs.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Cormack-Jolly-Seber</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./jssa.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Jolly-Seber</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./comparison.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Model comparison</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+    </ul>
+    </div>
+</nav>
+<div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>
+<!-- margin-sidebar -->
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        
+    </div>
+<!-- main -->
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./occupancy.html">PyMC</a></li><li class="breadcrumb-item"><a href="./distance.html">Distance sampling</a></li></ol></nav>
+<div class="quarto-title">
+<h1 class="title">Distance sampling</h1>
+</div>
+
+<div>
+  <div class="description">
+    Distance sampling models using data augmentation
+  </div>
+</div>
+
+<div class="quarto-title-meta-author">
+  <div class="quarto-title-meta-heading">Author</div>
+  <div class="quarto-title-meta-heading">Affiliations</div>
+  
+    <div class="quarto-title-meta-contents">
+    <p class="author">Philip T. Patton </p>
+  </div>
+  <div class="quarto-title-meta-contents">
+        <p class="affiliation">
+            Marine Mammal Research Program
+          </p>
+        <p class="affiliation">
+            Hawaiʻi Institute of Marine Biology
+          </p>
+      </div>
+  </div>
+
+<div class="quarto-title-meta">
+
+      
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">February 2, 2024</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+
+</header>
+
+
+<p>In this notebook, I explore how to fit distance sampling models for estimating the size of a closed population. Similar to the occupancy and closed capture-recapture notebooks, I use parameter-expanded data-augmentation (PX-DA) and the zero-inflated binomial model in this notebook.</p>
+<p>The idea with distance sampling, also known as line-transect sampling, is that a surveyer traverses a transect, typically in a boat or a plane. As they survey, they note when they detect an individual, or a group, from the species of interest, and further note the distance from the transect to the animal. Further, they note the angle to the animal(s), such that they can calculate the perpendicular distance from the animal to the transect. We assume that probability of detecting an animal <span class="math inline">\(p\)</span> decreases monotonically as the distance from the transect grows, e.g., <span class="math inline">\(p=\exp(-x^2/\sigma^2),\)</span> where <span class="math inline">\(x\)</span> is the distance and <span class="math inline">\(\sigma\)</span> is a scale parameter to be estimated. These simple assumptions permit the estimation of the population size <span class="math inline">\(N\)</span> as well as density <span class="math inline">\(D.\)</span></p>
+<p>Following <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, Chapter 24 and <span class="citation" data-cites="royle2008">Royle and Dorazio (<a href="#ref-royle2008" role="doc-biblioref">2008</a>)</span>, Chapter 7, I use the impala data from <span class="citation" data-cites="burnham1980">Burnham, Anderson, and Laake (<a href="#ref-burnham1980" role="doc-biblioref">1980</a>)</span>, who credits P. Hemingway with the dataset. In this dataset, 73 impalas were observed along a 60km transect. The distance values below are the perpendicular distances, in meters, from the transect.</p>
+<div id="cell-fig-hist" class="cell" data-execution_count="1">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pymc <span class="im">as</span> pm</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pytensor.tensor <span class="im">as</span> pt</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> arviz <span class="im">as</span> az</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="co"># plotting defaults</span></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>plt.style.use(<span class="st">'fivethirtyeight'</span>)</span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'axes.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'figure.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="co"># hyper parameters</span></span>
+<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>M <span class="op">=</span> <span class="dv">500</span></span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>U_X <span class="op">=</span> <span class="dv">400</span></span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>U_SIGMA <span class="op">=</span> <span class="dv">400</span></span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="co"># burnham impala dataset with distances in m</span></span>
+<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>x_observed <span class="op">=</span> np.array(</span>
+<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>    [<span class="fl">71.933980</span>, <span class="fl">26.047227</span>, <span class="fl">58.474341</span>, <span class="fl">92.349221</span>, <span class="fl">163.830409</span>, <span class="fl">84.523652</span></span>
+<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">163.830409</span>, <span class="fl">157.330098</span>, <span class="fl">22.267696</span>, <span class="fl">72.105330</span>, <span class="fl">86.986979</span>, <span class="fl">50.795047</span></span>
+<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">0.000000</span>, <span class="fl">73.135370</span>,  <span class="fl">0.000000</span>, <span class="fl">128.557522</span>, <span class="fl">163.830409</span>,  <span class="fl">71.845104</span></span>
+<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">30.467336</span>, <span class="fl">71.073909</span>, <span class="fl">150.960702</span>, <span class="fl">68.829172</span>, <span class="fl">90.000000</span>, <span class="fl">64.983827</span></span>
+<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">165.690874</span>, <span class="fl">38.008322</span>, <span class="fl">378.207430</span>, <span class="fl">78.146226</span>, <span class="fl">42.127052</span>, <span class="fl">0.000000</span></span>
+<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">400.000000</span>, <span class="fl">175.386612</span>, <span class="fl">30.467336</span>, <span class="fl">35.069692</span>, <span class="fl">86.036465</span>, <span class="fl">31.686029</span></span>
+<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">200.000000</span>, <span class="fl">271.892336</span>, <span class="fl">26.047227</span>, <span class="fl">76.604444</span>, <span class="fl">41.042417</span>, <span class="fl">200.000000</span></span>
+<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">86.036465</span>, <span class="fl">0.000000</span>, <span class="fl">93.969262</span>, <span class="fl">55.127471</span>, <span class="fl">10.458689</span>, <span class="fl">84.523652</span></span>
+<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">0.000000</span>, <span class="fl">77.645714</span>, <span class="fl">0.000000</span>, <span class="fl">96.418141</span>, <span class="fl">0.000000</span>, <span class="fl">64.278761</span></span>
+<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">187.938524</span>, <span class="fl">0.000000</span>, <span class="fl">160.696902</span>, <span class="fl">150.453756</span>, <span class="fl">63.603607</span>, <span class="fl">193.185165</span></span>
+<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">106.066017</span>, <span class="fl">114.906666</span>, <span class="fl">143.394109</span>, <span class="fl">128.557522</span>, <span class="fl">245.745613</span>, <span class="fl">123.127252</span></span>
+<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">123.127252</span>, <span class="fl">153.208889</span>, <span class="fl">143.394109</span>, <span class="fl">34.202014</span>, <span class="fl">96.418141</span>, <span class="fl">259.807621</span></span>
+<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a>    ,<span class="fl">8.715574</span>]</span>
+<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a>)</span>
+<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a><span class="co"># plot the distances </span></span>
+<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">4</span>,<span class="dv">4</span>))</span>
+<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a>ax.hist(x_observed, edgecolor<span class="op">=</span><span class="st">'white'</span>)</span>
+<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">'Hemingway Impala Data'</span>)</span>
+<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a>ax.set_ylabel(<span class="st">'Number of detections'</span>)</span>
+<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">'Distance (m)'</span>)</span>
+<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display">
+<div id="fig-hist" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-hist-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="distance_files/figure-html/fig-hist-output-1.png" width="429" height="408" class="figure-img">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-hist-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;1: Histogram of the number of detected impalas at varying distances.
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+<p>Again, we treat this as a zero-inflated binomial model using PX-DA. The trick for doing so is to create a binary vector of length <span class="math inline">\(M\)</span>, <span class="math inline">\(y,\)</span> that represents whether the individual was detected during the study. Then, combine the indicator with the distance vector <span class="math inline">\(x\)</span> to create a the full dataset <span class="math inline">\((x,y).\)</span></p>
+<div id="77eebeaa" class="cell" data-execution_count="2">
+<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>n <span class="op">=</span> <span class="bu">len</span>(x_observed)</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>unobserved_count <span class="op">=</span> M <span class="op">-</span> n</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>zeros <span class="op">=</span> np.zeros(unobserved_count)</span>
+<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> np.ones(n)</span>
+<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>y_augmented <span class="op">=</span> np.concatenate((y, zeros))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>The issue is that <span class="math inline">\(x\)</span> is unobserved for the undetected individuals. To work around this, we put a uniform prior on the unobserved <span class="math inline">\(x,\)</span> i.e., <span class="math inline">\(x \sim \text{Uniform}(0, U_x).\)</span> With this “complete” <span class="math inline">\(x,\)</span> we can construct the detection function <span class="math inline">\(p\)</span> for the unobserved individuals.</p>
+<div id="cell-fig-dist" class="cell" data-execution_count="3">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> pm.Model() <span class="im">as</span> distance:</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    psi <span class="op">=</span> pm.Uniform(<span class="st">'psi'</span>, <span class="dv">0</span>, <span class="dv">1</span>)</span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>    sigma <span class="op">=</span> pm.Uniform(<span class="st">'sigma'</span>, <span class="dv">0</span>, U_SIGMA)</span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    x_unobserved <span class="op">=</span> pm.Uniform(<span class="st">'x_unobserved'</span>, <span class="dv">0</span>, U_X, shape<span class="op">=</span>unobserved_count)</span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>    x_complete <span class="op">=</span> pt.concatenate((x_observed, x_unobserved))</span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>    p <span class="op">=</span> pm.Deterministic(<span class="st">'p'</span>, pm.math.exp(<span class="op">-</span> x_complete <span class="op">**</span> <span class="dv">2</span> <span class="op">/</span> sigma <span class="op">**</span> <span class="dv">2</span>))</span>
+<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>    pm.ZeroInflatedBinomial(<span class="st">'y'</span>, p<span class="op">=</span>p, psi<span class="op">=</span>psi, n<span class="op">=</span><span class="dv">1</span>, observed<span class="op">=</span>y_augmented)</span>
+<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(distance)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display" data-execution_count="3">
+<div id="fig-dist" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-dist-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="distance_files/figure-html/fig-dist-output-1.svg" class="img-fluid figure-img">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-dist-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;2: Visual representation of the distance sampling model.
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+<div id="0c5a63c8" class="cell" data-execution_count="4">
+<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> distance:</span>
+<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    distance_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>Auto-assigning NUTS sampler...
+Initializing NUTS using jitter+adapt_diag...
+Multiprocess sampling (4 chains in 4 jobs)
+NUTS: [psi, sigma, x_unobserved]
+Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 12 seconds.</code></pre>
+</div>
+<div class="cell-output cell-output-display">
+
+<style>
+    /* Turns off some styling */
+    progress {
+        /* gets rid of default border in Firefox and Opera. */
+        border: none;
+        /* Needs to be in here for Safari polyfill so background images work as expected. */
+        background-size: auto;
+    }
+    progress:not([value]), progress:not([value])::-webkit-progress-bar {
+        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);
+    }
+    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {
+        background: #F44336;
+    }
+</style>
+</div>
+<div class="cell-output cell-output-display">
+
+    <div>
+      <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
+      100.00% [8000/8000 00:12&lt;00:00 Sampling 4 chains, 0 divergences]
+    </div>
+    
+</div>
+</div>
+<div id="cell-fig-trace" class="cell" data-execution_count="5">
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(</span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>    distance_idata, </span>
+<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>    figsize<span class="op">=</span>(<span class="dv">10</span>,<span class="dv">4</span>),</span>
+<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>    var_names<span class="op">=</span>[<span class="st">'psi'</span>, <span class="st">'sigma'</span>]</span>
+<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display">
+<div id="fig-trace" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="distance_files/figure-html/fig-trace-output-1.png" width="866" height="367" class="figure-img">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;3: Traceplots for the distance sampling model.
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+<p>This model samples slower than the models in the other notebooks, presumably because of the unobserved <span class="math inline">\(x.\)</span> As in the closed capture-recapture notebook, we will have to simulate the posterior for <span class="math inline">\(N\)</span> using the posterior distribution of <span class="math inline">\(\psi\)</span> and <span class="math inline">\(M.\)</span></p>
+<div id="2653efe1" class="cell" data-execution_count="6">
+<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>RNG <span class="op">=</span> np.random.default_rng()</span>
+<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>posterior <span class="op">=</span> az.extract(distance_idata)</span>
+<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>psi_samples <span class="op">=</span> posterior.psi.to_numpy()</span>
+<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>p_samples <span class="op">=</span> posterior.p.to_numpy()</span>
+<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>not_p <span class="op">=</span> (<span class="dv">1</span> <span class="op">-</span> p_samples[n:])</span>
+<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>p_included <span class="op">=</span> (not_p <span class="op">*</span> psi_samples) <span class="op">/</span> (not_p <span class="op">*</span> psi_samples <span class="op">+</span> (<span class="dv">1</span> <span class="op">-</span> psi_samples))</span>
+<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>n_undetected <span class="op">=</span> RNG.binomial(<span class="dv">1</span>, p_included).<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>N_samples <span class="op">=</span> n <span class="op">+</span> n_undetected</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div id="cell-fig-posteriors" class="cell" data-execution_count="7">
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>sigma_samples <span class="op">=</span> posterior.sigma.to_numpy()</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co"># plot the results</span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>fig, (ax0, ax1) <span class="op">=</span> plt.subplots(<span class="dv">1</span>, <span class="dv">2</span>, sharey<span class="op">=</span><span class="va">True</span>, figsize<span class="op">=</span>(<span class="dv">8</span>,<span class="dv">4</span>))</span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co"># histograms of the posteriors</span></span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>ax0.hist(N_samples, edgecolor<span class="op">=</span><span class="st">'white'</span>, bins<span class="op">=</span><span class="dv">30</span>)</span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>ax1.hist(sigma_samples, edgecolor<span class="op">=</span><span class="st">'white'</span>, bins<span class="op">=</span><span class="dv">30</span>)</span>
+<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co"># show the abundance dist in terms of M</span></span>
+<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a><span class="co"># ax0.set_xlim((100, M))</span></span>
+<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a><span class="co"># axes labels </span></span>
+<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>ax0.set_xlabel(<span class="st">'Abundance $N$'</span>)</span>
+<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>ax0.set_ylabel(<span class="st">'Number of samples'</span>)</span>
+<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a>ax1.set_xlabel(<span class="st">'Detection range $\sigma$'</span>)</span>
+<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a><span class="co"># add the point estimates</span></span>
+<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a>N_hat <span class="op">=</span> N_samples.mean()</span>
+<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>sigma_hat <span class="op">=</span> sigma_samples.mean()</span>
+<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a>ax0.text(<span class="dv">200</span>, <span class="dv">350</span>, <span class="ss">f'$\hat</span><span class="ch">{{</span><span class="ss">N</span><span class="ch">}}</span><span class="ss">$=</span><span class="sc">{</span>N_hat<span class="sc">:.1f}</span><span class="ss">'</span>, ha<span class="op">=</span><span class="st">'left'</span>, va<span class="op">=</span><span class="st">'center'</span>)</span>
+<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a>ax1.text(<span class="dv">205</span>, <span class="dv">350</span>, <span class="ss">f'$\hat</span><span class="ch">{{</span><span class="ss">\sigma</span><span class="ch">}}</span><span class="ss">$=</span><span class="sc">{</span>sigma_hat<span class="sc">:.1f}</span><span class="ss">'</span>, ha<span class="op">=</span><span class="st">'left'</span>, va<span class="op">=</span><span class="st">'center'</span>)</span>
+<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a><span class="co"># the results from royle and dorazio (2008) for comparison</span></span>
+<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a>N_hat_royle <span class="op">=</span> <span class="fl">179.9</span></span>
+<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>sigma_hat_royle <span class="op">=</span> <span class="dv">187</span></span>
+<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a>ax0.axvline(N_hat_royle, linestyle<span class="op">=</span><span class="st">'--'</span>, linewidth<span class="op">=</span><span class="dv">3</span>, color<span class="op">=</span><span class="st">'C1'</span>)</span>
+<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a>ax1.axvline(sigma_hat_royle, linestyle<span class="op">=</span><span class="st">'--'</span>, linewidth<span class="op">=</span><span class="dv">3</span>, color<span class="op">=</span><span class="st">'C1'</span>)</span>
+<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display">
+<div id="fig-posteriors" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-posteriors-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="distance_files/figure-html/fig-posteriors-output-1.png" width="754" height="382" class="figure-img">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-posteriors-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;4: Posterior distributions for <span class="math inline">\(N\)</span> and <span class="math inline">\(\sigma.\)</span> Estimates from <span class="citation" data-cites="royle2008">Royle and Dorazio (<a href="#ref-royle2008" role="doc-biblioref">2008</a>)</span> are shown with vertical lines.
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+<p>The model shows a high level of agreement with <span class="citation" data-cites="royle2008">Royle and Dorazio (<a href="#ref-royle2008" role="doc-biblioref">2008</a>)</span>, Chapter 7, although note that they reported <span class="math inline">\(\sigma\)</span> in terms of 100m units. It is also possible to plot the posterior distribution of the detection function.</p>
+<div id="cell-fig-func" class="cell" data-execution_count="8">
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>xx <span class="op">=</span> np.arange(<span class="dv">400</span>)</span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> det_func(x, s):</span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> np.exp(<span class="op">-</span> (x <span class="op">**</span> <span class="dv">2</span>) <span class="op">/</span> (s <span class="op">**</span> <span class="dv">2</span>))</span>
+<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>p_samps <span class="op">=</span> np.array([det_func(xx, s) <span class="cf">for</span> s <span class="kw">in</span> sigma_samples])</span>
+<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>p_mean <span class="op">=</span> p_samps.mean(axis<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>p_low <span class="op">=</span> np.quantile(p_samps, <span class="fl">0.02</span>, axis<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>p_high <span class="op">=</span> np.quantile(p_samps, <span class="fl">0.98</span>, axis<span class="op">=</span><span class="dv">0</span>)</span>
+<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">5</span>,<span class="dv">4</span>))</span>
+<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>ax.plot(xx, p_mean, <span class="st">'-'</span>)</span>
+<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>ax.fill_between(xx, p_low, p_high, alpha<span class="op">=</span><span class="fl">0.2</span>)</span>
+<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">'Detection function'</span>)</span>
+<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a>ax.set_ylabel(<span class="vs">r'$p$'</span>)</span>
+<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="vs">r'Distance (m)'</span>)</span>
+<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display">
+<div id="fig-func" class="quarto-figure quarto-figure-center quarto-float anchored">
+<figure class="quarto-float quarto-float-fig figure">
+<div aria-describedby="fig-func-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+<img src="distance_files/figure-html/fig-func-output-1.png" width="498" height="408" class="figure-img">
+</div>
+<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-func-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
+Figure&nbsp;5: Posterior distribution for the detection function. The line represents the posterior mean while the shaded area is the 96% interval.
+</figcaption>
+</figure>
+</div>
+</div>
+</div>
+
+
+
+
+<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-burnham1980" class="csl-entry" role="listitem">
+Burnham, Kenneth P, David R Anderson, and Jeffrey L Laake. 1980. <span>“Estimation of Density from Line Transect Sampling of Biological Populations.”</span> <em>Wildlife Monographs</em>, no. 72: 3–202.
+</div>
+<div id="ref-hooten2019" class="csl-entry" role="listitem">
+Hooten, Mevin B, and Trevor Hefley. 2019. <em>Bringing Bayesian Models to Life</em>. CRC Press.
+</div>
+<div id="ref-royle2008" class="csl-entry" role="listitem">
+Royle, J Andrew, and Robert M Dorazio. 2008. <em>Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities</em>. Elsevier.
+</div>
+</div></section></div></main> <!-- /main -->
+<script id="quarto-html-after-body" type="application/javascript">
+window.document.addEventListener("DOMContentLoaded", function (event) {
+  const toggleBodyColorMode = (bsSheetEl) => {
+    const mode = bsSheetEl.getAttribute("data-mode");
+    const bodyEl = window.document.querySelector("body");
+    if (mode === "dark") {
+      bodyEl.classList.add("quarto-dark");
+      bodyEl.classList.remove("quarto-light");
+    } else {
+      bodyEl.classList.add("quarto-light");
+      bodyEl.classList.remove("quarto-dark");
+    }
+  }
+  const toggleBodyColorPrimary = () => {
+    const bsSheetEl = window.document.querySelector("link#quarto-bootstrap");
+    if (bsSheetEl) {
+      toggleBodyColorMode(bsSheetEl);
+    }
+  }
+  toggleBodyColorPrimary();  
+  const icon = "";
+  const anchorJS = new window.AnchorJS();
+  anchorJS.options = {
+    placement: 'right',
+    icon: icon
+  };
+  anchorJS.add('.anchored');
+  const isCodeAnnotation = (el) => {
+    for (const clz of el.classList) {
+      if (clz.startsWith('code-annotation-')) {                     
+        return true;
+      }
+    }
+    return false;
+  }
+  const clipboard = new window.ClipboardJS('.code-copy-button', {
+    text: function(trigger) {
+      const codeEl = trigger.previousElementSibling.cloneNode(true);
+      for (const childEl of codeEl.children) {
+        if (isCodeAnnotation(childEl)) {
+          childEl.remove();
+        }
+      }
+      return codeEl.innerText;
+    }
+  });
+  clipboard.on('success', function(e) {
+    // button target
+    const button = e.trigger;
+    // don't keep focus
+    button.blur();
+    // flash "checked"
+    button.classList.add('code-copy-button-checked');
+    var currentTitle = button.getAttribute("title");
+    button.setAttribute("title", "Copied!");
+    let tooltip;
+    if (window.bootstrap) {
+      button.setAttribute("data-bs-toggle", "tooltip");
+      button.setAttribute("data-bs-placement", "left");
+      button.setAttribute("data-bs-title", "Copied!");
+      tooltip = new bootstrap.Tooltip(button, 
+        { trigger: "manual", 
+          customClass: "code-copy-button-tooltip",
+          offset: [0, -8]});
+      tooltip.show();    
+    }
+    setTimeout(function() {
+      if (tooltip) {
+        tooltip.hide();
+        button.removeAttribute("data-bs-title");
+        button.removeAttribute("data-bs-toggle");
+        button.removeAttribute("data-bs-placement");
+      }
+      button.setAttribute("title", currentTitle);
+      button.classList.remove('code-copy-button-checked');
+    }, 1000);
+    // clear code selection
+    e.clearSelection();
+  });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
+  function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
+    const config = {
+      allowHTML: true,
+      maxWidth: 500,
+      delay: 100,
+      arrow: false,
+      appendTo: function(el) {
+          return el.parentElement;
+      },
+      interactive: true,
+      interactiveBorder: 10,
+      theme: 'quarto',
+      placement: 'bottom-start',
+    };
+    if (contentFn) {
+      config.content = contentFn;
+    }
+    if (onTriggerFn) {
+      config.onTrigger = onTriggerFn;
+    }
+    if (onUntriggerFn) {
+      config.onUntrigger = onUntriggerFn;
+    }
+    window.tippy(el, config); 
+  }
+  const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
+  for (var i=0; i<noterefs.length; i++) {
+    const ref = noterefs[i];
+    tippyHover(ref, function() {
+      // use id or data attribute instead here
+      let href = ref.getAttribute('data-footnote-href') || ref.getAttribute('href');
+      try { href = new URL(href).hash; } catch {}
+      const id = href.replace(/^#\/?/, "");
+      const note = window.document.getElementById(id);
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
+    });
+  }
+  const xrefs = window.document.querySelectorAll('a.quarto-xref');
+  const processXRef = (id, note) => {
+    // Strip column container classes
+    const stripColumnClz = (el) => {
+      el.classList.remove("page-full", "page-columns");
+      if (el.children) {
+        for (const child of el.children) {
+          stripColumnClz(child);
+        }
+      }
+    }
+    stripColumnClz(note)
+    if (id === null || id.startsWith('sec-')) {
+      // Special case sections, only their first couple elements
+      const container = document.createElement("div");
+      if (note.children && note.children.length > 2) {
+        container.appendChild(note.children[0].cloneNode(true));
+        for (let i = 1; i < note.children.length; i++) {
+          const child = note.children[i];
+          if (child.tagName === "P" && child.innerText === "") {
+            continue;
+          } else {
+            container.appendChild(child.cloneNode(true));
+            break;
+          }
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(container);
+        }
+        return container.innerHTML
+      } else {
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        return note.innerHTML;
+      }
+    } else {
+      // Remove any anchor links if they are present
+      const anchorLink = note.querySelector('a.anchorjs-link');
+      if (anchorLink) {
+        anchorLink.remove();
+      }
+      if (window.Quarto?.typesetMath) {
+        window.Quarto.typesetMath(note);
+      }
+      // TODO in 1.5, we should make sure this works without a callout special case
+      if (note.classList.contains("callout")) {
+        return note.outerHTML;
+      } else {
+        return note.innerHTML;
+      }
+    }
+  }
+  for (var i=0; i<xrefs.length; i++) {
+    const xref = xrefs[i];
+    tippyHover(xref, undefined, function(instance) {
+      instance.disable();
+      let url = xref.getAttribute('href');
+      let hash = undefined; 
+      if (url.startsWith('#')) {
+        hash = url;
+      } else {
+        try { hash = new URL(url).hash; } catch {}
+      }
+      if (hash) {
+        const id = hash.replace(/^#\/?/, "");
+        const note = window.document.getElementById(id);
+        if (note !== null) {
+          try {
+            const html = processXRef(id, note.cloneNode(true));
+            instance.setContent(html);
+          } finally {
+            instance.enable();
+            instance.show();
+          }
+        } else {
+          // See if we can fetch this
+          fetch(url.split('#')[0])
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.getElementById(id);
+            if (note !== null) {
+              const html = processXRef(id, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      } else {
+        // See if we can fetch a full url (with no hash to target)
+        // This is a special case and we should probably do some content thinning / targeting
+        fetch(url)
+        .then(res => res.text())
+        .then(html => {
+          const parser = new DOMParser();
+          const htmlDoc = parser.parseFromString(html, "text/html");
+          const note = htmlDoc.querySelector('main.content');
+          if (note !== null) {
+            // This should only happen for chapter cross references
+            // (since there is no id in the URL)
+            // remove the first header
+            if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+              note.children[0].remove();
+            }
+            const html = processXRef(null, note);
+            instance.setContent(html);
+          } 
+        }).finally(() => {
+          instance.enable();
+          instance.show();
+        });
+      }
+    }, function(instance) {
+    });
+  }
+      let selectedAnnoteEl;
+      const selectorForAnnotation = ( cell, annotation) => {
+        let cellAttr = 'data-code-cell="' + cell + '"';
+        let lineAttr = 'data-code-annotation="' +  annotation + '"';
+        const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+        return selector;
+      }
+      const selectCodeLines = (annoteEl) => {
+        const doc = window.document;
+        const targetCell = annoteEl.getAttribute("data-target-cell");
+        const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+        const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+        const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+        const lineIds = lines.map((line) => {
+          return targetCell + "-" + line;
+        })
+        let top = null;
+        let height = null;
+        let parent = null;
+        if (lineIds.length > 0) {
+            //compute the position of the single el (top and bottom and make a div)
+            const el = window.document.getElementById(lineIds[0]);
+            top = el.offsetTop;
+            height = el.offsetHeight;
+            parent = el.parentElement.parentElement;
+          if (lineIds.length > 1) {
+            const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
+            const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+            height = bottom - top;
+          }
+          if (top !== null && height !== null && parent !== null) {
+            // cook up a div (if necessary) and position it 
+            let div = window.document.getElementById("code-annotation-line-highlight");
+            if (div === null) {
+              div = window.document.createElement("div");
+              div.setAttribute("id", "code-annotation-line-highlight");
+              div.style.position = 'absolute';
+              parent.appendChild(div);
+            }
+            div.style.top = top - 2 + "px";
+            div.style.height = height + 4 + "px";
+            div.style.left = 0;
+            let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+            if (gutterDiv === null) {
+              gutterDiv = window.document.createElement("div");
+              gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+              gutterDiv.style.position = 'absolute';
+              const codeCell = window.document.getElementById(targetCell);
+              const gutter = codeCell.querySelector('.code-annotation-gutter');
+              gutter.appendChild(gutterDiv);
+            }
+            gutterDiv.style.top = top - 2 + "px";
+            gutterDiv.style.height = height + 4 + "px";
+          }
+          selectedAnnoteEl = annoteEl;
+        }
+      };
+      const unselectCodeLines = () => {
+        const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+        elementsIds.forEach((elId) => {
+          const div = window.document.getElementById(elId);
+          if (div) {
+            div.remove();
+          }
+        });
+        selectedAnnoteEl = undefined;
+      };
+        // Handle positioning of the toggle
+    window.addEventListener(
+      "resize",
+      throttle(() => {
+        elRect = undefined;
+        if (selectedAnnoteEl) {
+          selectCodeLines(selectedAnnoteEl);
+        }
+      }, 10)
+    );
+    function throttle(fn, ms) {
+    let throttle = false;
+    let timer;
+      return (...args) => {
+        if(!throttle) { // first call gets through
+            fn.apply(this, args);
+            throttle = true;
+        } else { // all the others get throttled
+            if(timer) clearTimeout(timer); // cancel #2
+            timer = setTimeout(() => {
+              fn.apply(this, args);
+              timer = throttle = false;
+            }, ms);
+        }
+      };
+    }
+      // Attach click handler to the DT
+      const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+      for (const annoteDlNode of annoteDls) {
+        annoteDlNode.addEventListener('click', (event) => {
+          const clickedEl = event.target;
+          if (clickedEl !== selectedAnnoteEl) {
+            unselectCodeLines();
+            const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+            if (activeEl) {
+              activeEl.classList.remove('code-annotation-active');
+            }
+            selectCodeLines(clickedEl);
+            clickedEl.classList.add('code-annotation-active');
+          } else {
+            // Unselect the line
+            unselectCodeLines();
+            clickedEl.classList.remove('code-annotation-active');
+          }
+        });
+      }
+  const findCites = (el) => {
+    const parentEl = el.parentElement;
+    if (parentEl) {
+      const cites = parentEl.dataset.cites;
+      if (cites) {
+        return {
+          el,
+          cites: cites.split(' ')
+        };
+      } else {
+        return findCites(el.parentElement)
+      }
+    } else {
+      return undefined;
+    }
+  };
+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+  for (var i=0; i<bibliorefs.length; i++) {
+    const ref = bibliorefs[i];
+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
+        var popup = window.document.createElement('div');
+        citeInfo.cites.forEach(function(cite) {
+          var citeDiv = window.document.createElement('div');
+          citeDiv.classList.add('hanging-indent');
+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>
+<nav class="page-navigation">
+  <div class="nav-page nav-page-previous">
+      <a href="./closed_cmr.html" class="pagination-link" aria-label="Closed capture-recapture">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Closed capture-recapture</span>
+      </a>          
+  </div>
+  <div class="nav-page nav-page-next">
+      <a href="./scr.html" class="pagination-link" aria-label="Spatial capture-recapture">
+        <span class="nav-page-text">Spatial capture-recapture</span> <i class="bi bi-arrow-right-short"></i>
+      </a>
+  </div>
+</nav>
+</div> <!-- /content -->
+
+
+
+
+</body></html>
\ No newline at end of file
diff --git a/docs/distance_files/figure-html/fig-dist-output-1.svg b/docs/distance_files/figure-html/fig-dist-output-1.svg
new file mode 100644
index 0000000..5e83292
--- /dev/null
+++ b/docs/distance_files/figure-html/fig-dist-output-1.svg
@@ -0,0 +1,86 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 9.0.0 (20230911.1827)
+ -->
+<!-- Pages: 1 -->
+<svg width="297pt" height="385pt"
+ viewBox="0.00 0.00 297.45 384.95" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 380.95)">
+<polygon fill="white" stroke="none" points="-4,4 -4,-380.95 293.45,-380.95 293.45,4 -4,4"/>
+<g id="clust1" class="cluster">
+<title>cluster427</title>
+<path fill="none" stroke="black" d="M111.72,-247.13C111.72,-247.13 233.72,-247.13 233.72,-247.13 239.72,-247.13 245.72,-253.13 245.72,-259.13 245.72,-259.13 245.72,-356.95 245.72,-356.95 245.72,-362.95 239.72,-368.95 233.72,-368.95 233.72,-368.95 111.72,-368.95 111.72,-368.95 105.72,-368.95 99.72,-362.95 99.72,-356.95 99.72,-356.95 99.72,-259.13 99.72,-259.13 99.72,-253.13 105.72,-247.13 111.72,-247.13"/>
+<text text-anchor="middle" x="227.6" y="-254.33" font-family="Times,serif" font-size="14.00">427</text>
+</g>
+<g id="clust2" class="cluster">
+<title>cluster500</title>
+<path fill="none" stroke="black" d="M31.72,-8C31.72,-8 179.72,-8 179.72,-8 185.72,-8 191.72,-14 191.72,-20 191.72,-20 191.72,-223.23 191.72,-223.23 191.72,-229.23 185.72,-235.23 179.72,-235.23 179.72,-235.23 31.72,-235.23 31.72,-235.23 25.72,-235.23 19.72,-229.23 19.72,-223.23 19.72,-223.23 19.72,-20 19.72,-20 19.72,-14 25.72,-8 31.72,-8"/>
+<text text-anchor="middle" x="173.6" y="-15.2" font-family="Times,serif" font-size="14.00">500</text>
+</g>
+<!-- sigma -->
+<g id="node1" class="node">
+<title>sigma</title>
+<ellipse fill="none" stroke="black" cx="44.72" cy="-320.29" rx="44.72" ry="40.66"/>
+<text text-anchor="middle" x="44.72" y="-331.74" font-family="Times,serif" font-size="14.00">sigma</text>
+<text text-anchor="middle" x="44.72" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="44.72" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
+</g>
+<!-- p -->
+<g id="node4" class="node">
+<title>p</title>
+<polygon fill="none" stroke="black" points="150.85,-227.23 60.6,-227.23 60.6,-169.73 150.85,-169.73 150.85,-227.23"/>
+<text text-anchor="middle" x="105.72" y="-209.93" font-family="Times,serif" font-size="14.00">p</text>
+<text text-anchor="middle" x="105.72" y="-193.43" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="105.72" y="-176.93" font-family="Times,serif" font-size="14.00">Deterministic</text>
+</g>
+<!-- sigma&#45;&gt;p -->
+<g id="edge2" class="edge">
+<title>sigma&#45;&gt;p</title>
+<path fill="none" stroke="black" d="M63.19,-283.02C70.47,-268.72 78.84,-252.28 86.22,-237.79"/>
+<polygon fill="black" stroke="black" points="89.28,-239.49 90.7,-228.99 83.04,-236.31 89.28,-239.49"/>
+</g>
+<!-- psi -->
+<g id="node2" class="node">
+<title>psi</title>
+<ellipse fill="none" stroke="black" cx="244.72" cy="-198.48" rx="44.72" ry="40.66"/>
+<text text-anchor="middle" x="244.72" y="-209.93" font-family="Times,serif" font-size="14.00">psi</text>
+<text text-anchor="middle" x="244.72" y="-193.43" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="244.72" y="-176.93" font-family="Times,serif" font-size="14.00">Uniform</text>
+</g>
+<!-- y -->
+<g id="node5" class="node">
+<title>y</title>
+<ellipse fill="lightgrey" stroke="black" cx="105.72" cy="-81.16" rx="78.14" ry="40.66"/>
+<text text-anchor="middle" x="105.72" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
+<text text-anchor="middle" x="105.72" y="-76.11" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="105.72" y="-59.61" font-family="Times,serif" font-size="14.00">MarginalMixture</text>
+</g>
+<!-- psi&#45;&gt;y -->
+<g id="edge4" class="edge">
+<title>psi&#45;&gt;y</title>
+<path fill="none" stroke="black" d="M212.14,-170.44C195.23,-156.42 174.23,-138.99 155.36,-123.33"/>
+<polygon fill="black" stroke="black" points="157.67,-120.71 147.74,-117.02 153.2,-126.09 157.67,-120.71"/>
+</g>
+<!-- x_unobserved -->
+<g id="node3" class="node">
+<title>x_unobserved</title>
+<ellipse fill="none" stroke="black" cx="172.72" cy="-320.29" rx="64.88" ry="40.66"/>
+<text text-anchor="middle" x="172.72" y="-331.74" font-family="Times,serif" font-size="14.00">x_unobserved</text>
+<text text-anchor="middle" x="172.72" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="172.72" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
+</g>
+<!-- x_unobserved&#45;&gt;p -->
+<g id="edge1" class="edge">
+<title>x_unobserved&#45;&gt;p</title>
+<path fill="none" stroke="black" d="M151.53,-281.38C143.69,-267.37 134.81,-251.49 126.97,-237.47"/>
+<polygon fill="black" stroke="black" points="130.15,-235.98 122.21,-228.96 124.04,-239.4 130.15,-235.98"/>
+</g>
+<!-- p&#45;&gt;y -->
+<g id="edge3" class="edge">
+<title>p&#45;&gt;y</title>
+<path fill="none" stroke="black" d="M105.72,-169.27C105.72,-158.44 105.72,-145.73 105.72,-133.41"/>
+<polygon fill="black" stroke="black" points="109.22,-133.78 105.72,-123.78 102.22,-133.78 109.22,-133.78"/>
+</g>
+</g>
+</svg>
diff --git a/docs/distance_files/figure-html/fig-func-output-1.png b/docs/distance_files/figure-html/fig-func-output-1.png
new file mode 100644
index 0000000..60399ba
Binary files /dev/null and b/docs/distance_files/figure-html/fig-func-output-1.png differ
diff --git a/docs/distance_files/figure-html/fig-hist-output-1.png b/docs/distance_files/figure-html/fig-hist-output-1.png
new file mode 100644
index 0000000..7bbc40c
Binary files /dev/null and b/docs/distance_files/figure-html/fig-hist-output-1.png differ
diff --git a/docs/distance_files/figure-html/fig-posteriors-output-1.png b/docs/distance_files/figure-html/fig-posteriors-output-1.png
new file mode 100644
index 0000000..3d35bef
Binary files /dev/null and b/docs/distance_files/figure-html/fig-posteriors-output-1.png differ
diff --git a/docs/distance_files/figure-html/fig-trace-output-1.png b/docs/distance_files/figure-html/fig-trace-output-1.png
new file mode 100644
index 0000000..f06e12c
Binary files /dev/null and b/docs/distance_files/figure-html/fig-trace-output-1.png differ
diff --git a/docs/index.html b/docs/index.html
index 8340f6f..f0f9f96 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -223,6 +223,24 @@ <h1 class="title">Philip T. Patton</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -257,7 +275,11 @@ <h1 class="title">Philip T. Patton</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/jssa.html b/docs/jssa.html
index 6a677e9..58823a7 100644
--- a/docs/jssa.html
+++ b/docs/jssa.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -813,6 +813,24 @@ <h1 class="title">Jolly-Seber-Schwarz-Arnason</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -847,7 +865,11 @@ <h1 class="title">Jolly-Seber-Schwarz-Arnason</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1123,7 +1145,7 @@ <h1 class="title">Jolly-Seber-Schwarz-Arnason</h1>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./cjs.html" class="pagination-link  aria-label=" cormack-jolly-seber"="">
+      <a href="./cjs.html" class="pagination-link" aria-label="Cormack-Jolly-Seber">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Cormack-Jolly-Seber</span>
       </a>          
   </div>
diff --git a/docs/msom-pr.html b/docs/msom-pr.html
index 46768a5..d092110 100644
--- a/docs/msom-pr.html
+++ b/docs/msom-pr.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -809,6 +809,7 @@ <h1>Puerto Rican Birds</h1>
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>az.summary(pr_idata, var_names<span class="op">=</span>[<span class="st">'mu_a'</span>, <span class="st">'sigma_a'</span>, <span class="st">'mu_b'</span>, <span class="st">'sigma_b'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -953,6 +954,7 @@ <h1>Puerto Rican Birds</h1>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-pr_trace" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> az.plot_trace(pr_idata, combined<span class="op">=</span><span class="va">False</span>, legend<span class="op">=</span><span class="va">True</span>, figsize<span class="op">=</span>(<span class="dv">10</span>,<span class="dv">8</span>),</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>                   var_names<span class="op">=</span>[<span class="st">'mu_a'</span>, <span class="st">'sigma_a'</span>, <span class="st">'mu_b'</span>, <span class="st">'sigma_b'</span>])</span>
@@ -1119,6 +1121,24 @@ <h1>Puerto Rican Birds</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -1153,7 +1173,11 @@ <h1>Puerto Rican Birds</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/msom.html b/docs/msom.html
index 4765e87..29a6a16 100644
--- a/docs/msom.html
+++ b/docs/msom.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -811,6 +811,24 @@ <h2 class="anchored" data-anchor-id="unknown-n">Unknown <span class="math inline
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -845,7 +863,11 @@ <h2 class="anchored" data-anchor-id="unknown-n">Unknown <span class="math inline
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1121,7 +1143,7 @@ <h2 class="anchored" data-anchor-id="unknown-n">Unknown <span class="math inline
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./scr.html" class="pagination-link  aria-label=" spatial="" capture-recapture"="">
+      <a href="./scr.html" class="pagination-link" aria-label="Spatial capture-recapture">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Spatial capture-recapture</span>
       </a>          
   </div>
diff --git a/docs/notebooks.html b/docs/notebooks.html
index 614b253..37c7c23 100644
--- a/docs/notebooks.html
+++ b/docs/notebooks.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -176,7 +176,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -248,11 +248,11 @@ <h1 class="title">Notebooks</h1>
 </header>
 
 
-<p>Please find here any notebooks that I may have thought would be of general interest. For now, these mainly consist of my attempts to port standard ecological models to <a href="https://www.pymc.io/welcome.html">PyMC</a>, a Python library for doing Bayesian analysis.</p>
+<p>Please find here any jupyter notebooks that I may have thought would be of general interest. For now, these are all about <a href="https://www.pymc.io/welcome.html">PyMC</a>, a Python library for doing Bayesian analysis.</p>
 <section id="pymc" class="level1">
 <h1>PyMC</h1>
-<p>There are many valuable tools for fitting hierarchical models in ecology. These tools are typically R libraries, such as <a href="https://rbchan.github.io/unmarked/">unmarked</a>, or programs called from R, such as <a href="https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/">JAGS</a> or <a href="https://fukamilab.github.io/BIO202/09-C-occupancy-models.html">Stan</a>. There are relatively fewer examples of how to fit these models in Python. While most ecologists, and arguably statisticians, use R, there are some benefits to using Python generally. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Besides, Python is widely used for machine learning, which is increasingly applied in ecology.</p>
-<p>In the PyMC notebooks, I try to demonstrate how to use PyMC to train the most common hierarchcial models in ecology. For this, I have drawn considerable inspiration from <span class="citation" data-cites="royle2008">Royle and Dorazio (<a href="#ref-royle2008" role="doc-biblioref">2008</a>)</span>, <span class="citation" data-cites="kery2011">Kéry and Schaub (<a href="#ref-kery2011" role="doc-biblioref">2011</a>)</span>, <span class="citation" data-cites="mccrea2014">McCrea and Morgan (<a href="#ref-mccrea2014" role="doc-biblioref">2014</a>)</span>, and <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC’s core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.</p>
+<p>There are many valuable tools for fitting hierarchical models in ecology, including <a href="https://rbchan.github.io/unmarked/">unmarked</a>, <a href="https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/">JAGS</a>, <a href="https://r-nimble.org">NIMBLE</a> and <a href="https://fukamilab.github.io/BIO202/09-C-occupancy-models.html">Stan</a>. These are, for the most part, R libraries or programs called from R. There are relatively fewer examples of how to fit these models in Python. While most ecologists use R, they may find some benefit from using Python. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Also, Python is widely used for machine learning, which is increasingly applied in ecology.</p>
+<p>In the PyMC jupyter notebooks, I try to demonstrate how to use PyMC to train the most common hierarchical models in ecology. For this, I have drawn considerable inspiration from <span class="citation" data-cites="royle2008">Royle and Dorazio (<a href="#ref-royle2008" role="doc-biblioref">2008</a>)</span>, <span class="citation" data-cites="kery2011">Kéry and Schaub (<a href="#ref-kery2011" role="doc-biblioref">2011</a>)</span>, <span class="citation" data-cites="mccrea2014">McCrea and Morgan (<a href="#ref-mccrea2014" role="doc-biblioref">2014</a>)</span>, and <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC’s core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.</p>
 
 
 
@@ -351,6 +351,24 @@ <h1>PyMC</h1>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -385,7 +403,11 @@ <h1>PyMC</h1>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
diff --git a/docs/occupancy.html b/docs/occupancy.html
index 0396b88..bfecc2f 100644
--- a/docs/occupancy.html
+++ b/docs/occupancy.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -367,66 +367,68 @@ <h1 class="title">Occupancy models</h1>
 <section id="simulated-examples" class="level1">
 <h1>Simulated examples</h1>
 <p>To start, I demonstrate how to simulate this zero-inflated model using numpy. Throughout this section, I use hyperparameter values similar to those of <span class="citation" data-cites="kery2011">Kéry and Schaub (<a href="#ref-kery2011" role="doc-biblioref">2011</a>)</span>.</p>
-<div id="401300a8" class="cell" data-execution_count="1">
-<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pymc <span class="im">as</span> pm</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> arviz <span class="im">as</span> az</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>plt.style.use(<span class="st">'fivethirtyeight'</span>)</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'axes.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'figure.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="co"># pal = sns.color_palette('Set2')</span></span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="co"># sns.set_palette(pal)ß</span></span>
+<div id="9ecb8ee7" class="cell" data-execution_count="1">
+<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># relevant libraries</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pymc <span class="im">as</span> pm</span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> arviz <span class="im">as</span> az</span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="co"># plotting styles</span></span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>plt.style.use(<span class="st">'fivethirtyeight'</span>)</span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'axes.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>plt.rcParams[<span class="st">'figure.facecolor'</span>] <span class="op">=</span> <span class="st">'white'</span></span>
 <span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> scale(x):</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (x <span class="op">-</span> np.nanmean(x)) <span class="op">/</span> np.nanstd(x)</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> invlogit(x):</span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> <span class="dv">1</span> <span class="op">/</span> (<span class="dv">1</span> <span class="op">+</span> np.exp(<span class="op">-</span>x))</span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> sim_y(p, z, site_count, visit_count):</span>
-<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>    ones <span class="op">=</span> np.ones((site_count, visit_count))</span>
-<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>    p_array <span class="op">=</span> p <span class="op">*</span> ones </span>
-<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>    flips <span class="op">=</span> rng.binomial(<span class="dv">1</span>, p_array)</span>
-<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> (flips.T <span class="op">*</span> z_true).T</span>
-<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> y </span>
-<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a><span class="co">## simulation</span></span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Scale x: 0 is the mean and 1 is one standard deviation from the mean.'''</span></span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> (x <span class="op">-</span> np.nanmean(x)) <span class="op">/</span> np.nanstd(x)</span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> invlogit(x):</span>
+<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Compute inverse logit of x.'''</span></span>
+<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> <span class="dv">1</span> <span class="op">/</span> (<span class="dv">1</span> <span class="op">+</span> np.exp(<span class="op">-</span>x))</span>
+<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> sim_y(p, z, site_count, visit_count):</span>
+<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Simulate detections given detection probability p and occurrence state z.'''</span></span>
+<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>    ones <span class="op">=</span> np.ones((site_count, visit_count))</span>
+<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>    p_array <span class="op">=</span> p <span class="op">*</span> ones </span>
+<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>    flips <span class="op">=</span> rng.binomial(<span class="dv">1</span>, p_array)</span>
+<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> (flips.T <span class="op">*</span> z_true).T</span>
+<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> y </span>
 <span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a>SEED <span class="op">=</span> <span class="dv">808</span></span>
-<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a>rng <span class="op">=</span> np.random.default_rng(seed<span class="op">=</span>SEED)</span>
-<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a><span class="co"># sampling characteristics</span></span>
-<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a>site_count <span class="op">=</span> <span class="dv">200</span></span>
-<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a>visit_count <span class="op">=</span> <span class="dv">3</span></span>
-<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a><span class="co">## ecological model</span></span>
+<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a><span class="co">## simulation</span></span>
+<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a>SEED <span class="op">=</span> <span class="dv">808</span></span>
+<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a>rng <span class="op">=</span> np.random.default_rng(seed<span class="op">=</span>SEED)</span>
+<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a><span class="co"># sampling characteristics</span></span>
+<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a>site_count <span class="op">=</span> <span class="dv">200</span></span>
+<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a>visit_count <span class="op">=</span> <span class="dv">3</span></span>
 <span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
-<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a>psi_true <span class="op">=</span> <span class="fl">0.8</span></span>
-<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a><span class="co"># simulate occurrence state</span></span>
-<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a>z_true <span class="op">=</span> rng.binomial(<span class="dv">1</span>, psi_true, size<span class="op">=</span>site_count)</span>
-<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a><span class="co">## detection model</span></span>
+<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a><span class="co">## ecological model</span></span>
+<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
+<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a>psi_true <span class="op">=</span> <span class="fl">0.8</span></span>
+<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a><span class="co"># simulate occurrence state</span></span>
+<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a>z_true <span class="op">=</span> rng.binomial(<span class="dv">1</span>, psi_true, size<span class="op">=</span>site_count)</span>
 <span id="cb1-47"><a href="#cb1-47" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-48"><a href="#cb1-48" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
-<span id="cb1-49"><a href="#cb1-49" aria-hidden="true" tabindex="-1"></a>p_true <span class="op">=</span> <span class="fl">0.5</span></span>
-<span id="cb1-50"><a href="#cb1-50" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-51"><a href="#cb1-51" aria-hidden="true" tabindex="-1"></a><span class="co"># simulate detection</span></span>
-<span id="cb1-52"><a href="#cb1-52" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> sim_y(p_true, z_true, site_count, visit_count)</span>
-<span id="cb1-53"><a href="#cb1-53" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-54"><a href="#cb1-54" aria-hidden="true" tabindex="-1"></a><span class="co"># number of detections at each site </span></span>
-<span id="cb1-55"><a href="#cb1-55" aria-hidden="true" tabindex="-1"></a>y_summarized <span class="op">=</span> y.<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">1</span>)</span>
-<span id="cb1-56"><a href="#cb1-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-57"><a href="#cb1-57" aria-hidden="true" tabindex="-1"></a><span class="co"># detection data at the first five sites </span></span>
-<span id="cb1-58"><a href="#cb1-58" aria-hidden="true" tabindex="-1"></a>y[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="1">
+<span id="cb1-48"><a href="#cb1-48" aria-hidden="true" tabindex="-1"></a><span class="co">## detection model</span></span>
+<span id="cb1-49"><a href="#cb1-49" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-50"><a href="#cb1-50" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
+<span id="cb1-51"><a href="#cb1-51" aria-hidden="true" tabindex="-1"></a>p_true <span class="op">=</span> <span class="fl">0.5</span></span>
+<span id="cb1-52"><a href="#cb1-52" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-53"><a href="#cb1-53" aria-hidden="true" tabindex="-1"></a><span class="co"># simulate detection</span></span>
+<span id="cb1-54"><a href="#cb1-54" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> sim_y(p_true, z_true, site_count, visit_count)</span>
+<span id="cb1-55"><a href="#cb1-55" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-56"><a href="#cb1-56" aria-hidden="true" tabindex="-1"></a><span class="co"># number of detections at each site </span></span>
+<span id="cb1-57"><a href="#cb1-57" aria-hidden="true" tabindex="-1"></a>y_summarized <span class="op">=</span> y.<span class="bu">sum</span>(axis<span class="op">=</span><span class="dv">1</span>)</span>
+<span id="cb1-58"><a href="#cb1-58" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-59"><a href="#cb1-59" aria-hidden="true" tabindex="-1"></a><span class="co"># detection data at the first five sites </span></span>
+<span id="cb1-60"><a href="#cb1-60" aria-hidden="true" tabindex="-1"></a>y[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display" data-execution_count="32">
 <pre><code>array([[0, 1, 1],
        [1, 0, 1],
        [0, 0, 0],
@@ -437,7 +439,7 @@ <h1>Simulated examples</h1>
 <section id="estimating-parameters-with-pymc" class="level2">
 <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating parameters with PyMC</h2>
 <p>Next, I use PyMC to train the occupancy model with the simulated data. First, similar to JAGS and Stan, the model must be specified using the PyMC syntax. This is done using a <a href="https://peps.python.org/pep-0343/">context manager</a> in Python, essentially, a <code>with</code> statement. This creates a <code>Model</code> object.</p>
-<div id="ca7c5eb6" class="cell" data-execution_count="2">
+<div id="524c3b46" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> pm.Model() <span class="im">as</span> constant:</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># priors for the detetion and occurrence probabilities\</span></span>
@@ -451,7 +453,7 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 <p>In JAGS, the prior for <span class="math inline">\(p\)</span> would be specified as <code>p ~ dunif(0, 1).</code> The PyMC equivalent is <code>p = pm.Uniform('p', 0, 1)</code>. This could, alternatively, be specified as <code>p = pm.Uniform('detection probability', 0, 1)</code>. For the likelihood, I use PyMC’s built-in <code>ZeroInflatedBinomial</code> distribution. We tell PyMC that this is an observed random variable by supplying data to the <code>observed</code> argument. PyMC also has handy tools for visualizing the model.</p>
 <div id="cell-fig-pdot" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(constant)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="3">
+<div class="cell-output cell-output-display" data-execution_count="34">
 <div id="fig-pdot" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-pdot-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -465,7 +467,7 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 </div>
 </div>
 <p>Now I can sample from the posterior. Again, I use the context manager, this time referring to the model by name. It’s typical to name the output with <code>idata</code> because, by default, PyMC returns an object of class <code>InferenceData</code> from the Arviz package. Arviz is similar to the coda package for R.</p>
-<div id="01233629" class="cell" data-execution_count="4">
+<div id="c1d6b0a6" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> constant:</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    constant_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -497,7 +499,7 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 
     <div>
       <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
@@ -514,7 +516,7 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 <div id="fig-pdot_trace" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-pdot_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="occupancy_files/figure-html/fig-pdot_trace-output-1.png" width="695" height="367" class="figure-img">
+<img src="occupancy_files/figure-html/fig-pdot_trace-output-1.png" width="687" height="367" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-pdot_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;2: Traceplots for the <span class="math inline">\(p(\cdot)\psi(\cdot)\)</span> model. The true parameter values are shown by vertical and horizontal lines.
@@ -523,9 +525,10 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 </div>
 </div>
 </div>
-<div id="b9f9cab1" class="cell" data-execution_count="6">
+<div id="d86fc2c3" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>az.summary(constant_idata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
+<div class="cell-output cell-output-display" data-execution_count="37">
+<div>
 <div>
 
 
@@ -546,27 +549,27 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 </thead>
 <tbody>
 <tr class="odd">
-<td data-quarto-table-cell-role="th">psi</td>
-<td>0.8</td>
-<td>0.042</td>
-<td>0.716</td>
-<td>0.876</td>
-<td>0.001</td>
+<td data-quarto-table-cell-role="th">p</td>
+<td>0.499</td>
+<td>0.030</td>
+<td>0.443</td>
+<td>0.554</td>
 <td>0.001</td>
-<td>2063.0</td>
-<td>1627.0</td>
+<td>0.000</td>
+<td>2381.0</td>
+<td>2276.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
-<td data-quarto-table-cell-role="th">p</td>
-<td>0.5</td>
-<td>0.029</td>
-<td>0.445</td>
-<td>0.553</td>
+<td data-quarto-table-cell-role="th">psi</td>
+<td>0.801</td>
+<td>0.042</td>
+<td>0.723</td>
+<td>0.883</td>
 <td>0.001</td>
-<td>0.000</td>
-<td>2282.0</td>
-<td>2373.0</td>
+<td>0.001</td>
+<td>2277.0</td>
+<td>1896.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -575,11 +578,12 @@ <h2 class="anchored" data-anchor-id="estimating-parameters-with-pymc">Estimating
 </div>
 </div>
 </div>
+</div>
 </section>
 <section id="adding-site-covariates" class="level2">
 <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covariates</h2>
 <p>Next, I add in some realism by simulating a site-level covariate <span class="math inline">\(x\)</span> that affects the occurrence probability. I model this effect with a logit-linear model, i.e., <span class="math inline">\(\psi_j=\text{logit}^{-1}(\beta_0 + \beta_1 x_j).\)</span></p>
-<div id="b79e5cc2" class="cell" data-execution_count="7">
+<div id="0659b87d" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co">## ecological model</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
@@ -609,7 +613,7 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 <span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a><span class="co"># detection data at the first five sites </span></span>
 <span id="cb9-29"><a href="#cb9-29" aria-hidden="true" tabindex="-1"></a>y[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
+<div class="cell-output cell-output-display" data-execution_count="38">
 <pre><code>array([[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
@@ -639,7 +643,7 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 <span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a>                            observed<span class="op">=</span>y_summarized)</span>
 <span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-20"><a href="#cb11-20" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(psix)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="8">
+<div class="cell-output cell-output-display" data-execution_count="39">
 <div id="fig-psix" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-psix-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -652,7 +656,7 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 </div>
 </div>
 </div>
-<div id="32382029" class="cell" data-execution_count="9">
+<div id="77266f95" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> psix:</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>    psix_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -700,15 +704,16 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 <div class="cell-output cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="occupancy_files/figure-html/psix_trace-output-1.png" width="689" height="523" class="figure-img"></p>
+<p><img src="occupancy_files/figure-html/psix_trace-output-1.png" width="698" height="523" class="figure-img"></p>
 <figcaption>Traceplots for the <span class="math inline">\(p(\cdot)\psi(x)\)</span> model. The true parameter values are shown by vertical and horizontal lines</figcaption>
 </figure>
 </div>
 </div>
 </div>
-<div id="b74fcde7" class="cell" data-execution_count="11">
+<div id="c83b437c" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>az.summary(psix_idata, var_names<span class="op">=</span>[<span class="st">'beta0'</span>, <span class="st">'beta1'</span>, <span class="st">'p'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="11">
+<div class="cell-output cell-output-display" data-execution_count="42">
+<div>
 <div>
 
 
@@ -730,38 +735,38 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 <tbody>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">beta0</td>
-<td>-1.234</td>
-<td>0.263</td>
-<td>-1.707</td>
-<td>-0.749</td>
+<td>-1.248</td>
+<td>0.261</td>
+<td>-1.746</td>
+<td>-0.778</td>
 <td>0.005</td>
 <td>0.004</td>
-<td>2842.0</td>
-<td>2754.0</td>
+<td>2472.0</td>
+<td>2902.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">beta1</td>
-<td>2.855</td>
-<td>0.415</td>
-<td>2.083</td>
-<td>3.626</td>
+<td>2.863</td>
+<td>0.406</td>
+<td>2.146</td>
+<td>3.663</td>
 <td>0.008</td>
 <td>0.006</td>
-<td>2667.0</td>
-<td>2796.0</td>
+<td>2814.0</td>
+<td>2832.0</td>
 <td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">p</td>
-<td>0.744</td>
-<td>0.032</td>
+<td>0.745</td>
+<td>0.031</td>
 <td>0.686</td>
-<td>0.803</td>
+<td>0.800</td>
 <td>0.000</td>
 <td>0.000</td>
-<td>4504.0</td>
-<td>2983.0</td>
+<td>4368.0</td>
+<td>3055.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -770,11 +775,12 @@ <h2 class="anchored" data-anchor-id="adding-site-covariates">Adding site covaria
 </div>
 </div>
 </div>
+</div>
 </section>
 <section id="adding-visit-covariates" class="level2">
 <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covariates</h2>
 <p>Finally, I add in visit-level covariate <span class="math inline">\(w_{j,k}\)</span> that affects detection.</p>
-<div id="685bd926" class="cell" data-execution_count="12">
+<div id="5816a397" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co">## ecological model</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="co"># true parameter values</span></span>
@@ -807,7 +813,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 <span id="cb16-30"><a href="#cb16-30" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> sim_y(p_true, z_true, site_count, visit_count)</span>
 <span id="cb16-31"><a href="#cb16-31" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb16-32"><a href="#cb16-32" aria-hidden="true" tabindex="-1"></a>y[:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="12">
+<div class="cell-output cell-output-display" data-execution_count="43">
 <pre><code>array([[0, 0, 0],
        [0, 0, 0],
        [0, 1, 1],
@@ -815,15 +821,15 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
        [0, 0, 0]])</code></pre>
 </div>
 </div>
-<p>Our PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state <span class="math inline">\(z_j.\)</span> The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit painfully slowly.</p>
+<p>Our PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state <span class="math inline">\(z_j.\)</span> The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit slowly.</p>
 <p>Since I am impatient, I instead use the <a href="https://mbjoseph.github.io/posts/2020-04-28-a-step-by-step-guide-to-marginalizing-over-discrete-parameters-for-ecologists-using-stan/">marginalized version of the model</a>, that is, a model that does not include the discrete latent states. To do this in PyMC, I use the <code>CustomDist</code> class. This requires, first, defining the log probability of the distribution, <code>logp</code>, given the data and it’s parameters. We can write <code>logp</code> using the likelihood of the occupancy model, <span class="math display">\[
 P(\mathbf{y}_j)=
 \begin{cases}
     P(\mathbf{y}_j | z_j = 1)\; \psi_j \; + \; (1 - \psi_j),   &amp; \text{if } \mathbf{y}_j = \mathbf{0}\\
     P(\mathbf{y}_j | z_j = 1)\; \psi_j,  &amp; \text{otherwise}
 \end{cases}
-\]</span> where <span class="math inline">\(P(\mathbf{y}_j | z_j = 1) = \prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}\)</span> <span class="citation" data-cites="royle2008">(<a href="#ref-royle2008" role="doc-biblioref">Royle and Dorazio 2008</a>)</span>. To do this in PyMC, I rely on the <code>pm.math.switch</code> function, which is similar to <code>ifelse()</code> in R or <code>np.where()</code>.</p>
-<div id="5f771a69" class="cell" data-execution_count="13">
+\]</span> where <span class="math inline">\(P(\mathbf{y}_j | z_j = 1) = \prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}\)</span> <span class="citation" data-cites="royle2008">(<a href="#ref-royle2008" role="doc-biblioref">Royle and Dorazio 2008</a>)</span>. To do this in PyMC, I rely on the <code>pm.math.switch</code> function, which is like the <code>ifelse()</code> function in R or <code>np.where()</code>.</p>
+<div id="aacf8a7c" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># likelihood for y data</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> logp(x, p, psi):</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Computes the log-likelihood for an occupancy model</span></span>
@@ -878,7 +884,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 <span id="cb19-29"><a href="#cb19-29" aria-hidden="true" tabindex="-1"></a>    )</span>
 <span id="cb19-30"><a href="#cb19-30" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb19-31"><a href="#cb19-31" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(marginal)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="14">
+<div class="cell-output cell-output-display" data-execution_count="45">
 <div id="fig-pw" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-pw-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -891,7 +897,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 </div>
 </div>
 </div>
-<div id="28c73d56" class="cell" data-execution_count="15">
+<div id="7a71fba4" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> marginal:</span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>    marginal_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -899,7 +905,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 Initializing NUTS using jitter+adapt_diag...
 Multiprocess sampling (4 chains in 4 jobs)
 NUTS: [beta0, beta1, alpha0, alpha1]
-Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.</code></pre>
+Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 
@@ -923,7 +929,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 
     <div>
       <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
@@ -940,7 +946,7 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 <div id="fig-pw_trace" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-pw_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="occupancy_files/figure-html/fig-pw_trace-output-1.png" width="701" height="523" class="figure-img">
+<img src="occupancy_files/figure-html/fig-pw_trace-output-1.png" width="695" height="523" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-pw_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;5: Tracepots for the <span class="math inline">\(p(w)\psi(x)\)</span> model. The true parameter values are shown by vertical and horizontal lines
@@ -949,9 +955,10 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 </div>
 </div>
 </div>
-<div id="bff8ece8" class="cell" data-execution_count="17">
+<div id="50e809e3" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>az.summary(marginal_idata, var_names<span class="op">=</span>[<span class="st">'beta0'</span>, <span class="st">'beta1'</span>, <span class="st">'alpha0'</span>, <span class="st">'alpha1'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="17">
+<div class="cell-output cell-output-display" data-execution_count="48">
+<div>
 <div>
 
 
@@ -973,50 +980,50 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 <tbody>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">beta0</td>
-<td>-0.738</td>
-<td>0.236</td>
-<td>-1.154</td>
-<td>-0.261</td>
+<td>-0.741</td>
+<td>0.244</td>
+<td>-1.219</td>
+<td>-0.308</td>
 <td>0.004</td>
 <td>0.003</td>
-<td>3489.0</td>
-<td>3085.0</td>
+<td>3323.0</td>
+<td>3051.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">beta1</td>
-<td>2.783</td>
-<td>0.396</td>
-<td>2.057</td>
-<td>3.547</td>
+<td>2.786</td>
+<td>0.391</td>
+<td>2.080</td>
+<td>3.537</td>
 <td>0.007</td>
 <td>0.005</td>
-<td>2960.0</td>
-<td>2419.0</td>
+<td>3061.0</td>
+<td>2753.0</td>
 <td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">alpha0</td>
-<td>1.401</td>
+<td>1.407</td>
 <td>0.272</td>
-<td>0.922</td>
-<td>1.943</td>
+<td>0.906</td>
+<td>1.915</td>
 <td>0.005</td>
 <td>0.004</td>
-<td>2759.0</td>
-<td>2290.0</td>
+<td>2892.0</td>
+<td>2681.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">alpha1</td>
 <td>-3.096</td>
-<td>0.381</td>
-<td>-3.803</td>
-<td>-2.410</td>
+<td>0.387</td>
+<td>-3.837</td>
+<td>-2.408</td>
 <td>0.007</td>
 <td>0.005</td>
-<td>2803.0</td>
-<td>2517.0</td>
+<td>3066.0</td>
+<td>2881.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -1025,12 +1032,13 @@ <h2 class="anchored" data-anchor-id="adding-visit-covariates">Adding visit covar
 </div>
 </div>
 </div>
+</div>
 </section>
 </section>
 <section id="real-data-example" class="level1">
 <h1>Real data example</h1>
 <p>Finally, I demonstrate the model using a real data example. These data come from <span class="citation" data-cites="henden2013">Henden et al. (<a href="#ref-henden2013" role="doc-biblioref">2013</a>)</span>, and were used as a demonstration in <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, Chapter 23. They represent detection/non-detection data of Willow Warblers from Finnmark, Norway. The <span class="math inline">\(J=27\)</span> sites were sampled <span class="math inline">\(K=3\)</span> times. Replicating the analysis in Box 23.7 in <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, I use two covariates for site: site area and willow tree height. Further, I use two covariates for visit: an indicator for the visit and willow tree height.</p>
-<div id="c6a90c41" class="cell" data-execution_count="18">
+<div id="f664b952" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># read in the data</span></span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> pd.read_csv(<span class="st">'PlosOne-DataFinnmark.csv'</span>)</span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1088,7 +1096,7 @@ <h1>Real data example</h1>
 <span id="cb25-28"><a href="#cb25-28" aria-hidden="true" tabindex="-1"></a>    )</span>
 <span id="cb25-29"><a href="#cb25-29" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb25-30"><a href="#cb25-30" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(warbler)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="19">
+<div class="cell-output cell-output-display" data-execution_count="50">
 <div id="fig-warbler" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-warbler-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -1102,7 +1110,7 @@ <h1>Real data example</h1>
 </div>
 </div>
 <p>Now the dimensionality of the prior distributions is clear, with (3) different priors specified for each random variable in the vectors <span class="math inline">\(\alpha\)</span> and <span class="math inline">\(\beta\)</span>.</p>
-<div id="8ecec578" class="cell" data-execution_count="20">
+<div id="1b0f2ac7" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> warbler:</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>    warbler_idata <span class="op">=</span> pm.sample(<span class="dv">4000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -1110,7 +1118,7 @@ <h1>Real data example</h1>
 Initializing NUTS using jitter+adapt_diag...
 Multiprocess sampling (4 chains in 4 jobs)
 NUTS: [Beta, Alpha]
-Sampling 4 chains for 1_000 tune and 4_000 draw iterations (4_000 + 16_000 draws total) took 5 seconds.</code></pre>
+Sampling 4 chains for 1_000 tune and 4_000 draw iterations (4_000 + 16_000 draws total) took 6 seconds.</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 
@@ -1134,15 +1142,16 @@ <h1>Real data example</h1>
 
     <div>
       <progress value="20000" class="" max="20000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [20000/20000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [20000/20000 00:05&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
 </div>
 <p>I upped the number of draw iterations to 4,000 per chain, 16,000 total, since this dataset includes real-world messiness. Nevertheless, sampling the posterior took only 6 seconds!</p>
-<div id="72657111" class="cell" data-execution_count="21">
+<div id="6c5727d0" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>az.summary(warbler_idata, var_names<span class="op">=</span>[<span class="st">'Alpha'</span>, <span class="st">'Beta'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="21">
+<div class="cell-output cell-output-display" data-execution_count="52">
+<div>
 <div>
 
 
@@ -1164,74 +1173,74 @@ <h1>Real data example</h1>
 <tbody>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">Alpha[Intercept]</td>
-<td>-0.018</td>
-<td>0.371</td>
-<td>-0.689</td>
-<td>0.700</td>
+<td>-0.016</td>
+<td>0.375</td>
+<td>-0.699</td>
+<td>0.705</td>
 <td>0.004</td>
 <td>0.003</td>
-<td>11112.0</td>
-<td>11667.0</td>
+<td>11041.0</td>
+<td>11366.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">Alpha[Visit]</td>
-<td>0.465</td>
-<td>0.285</td>
-<td>-0.069</td>
-<td>1.002</td>
+<td>0.466</td>
+<td>0.287</td>
+<td>-0.064</td>
+<td>1.013</td>
 <td>0.002</td>
 <td>0.002</td>
-<td>14029.0</td>
-<td>10295.0</td>
+<td>13445.0</td>
+<td>10918.0</td>
 <td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">Alpha[Wheight]</td>
-<td>0.750</td>
-<td>0.432</td>
-<td>-0.058</td>
-<td>1.554</td>
+<td>0.746</td>
+<td>0.431</td>
+<td>-0.075</td>
+<td>1.547</td>
 <td>0.004</td>
 <td>0.003</td>
-<td>10531.0</td>
-<td>11794.0</td>
+<td>9996.0</td>
+<td>11686.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">Beta[Intercept]</td>
-<td>0.818</td>
-<td>0.819</td>
-<td>-0.594</td>
-<td>2.398</td>
+<td>0.816</td>
+<td>0.822</td>
+<td>-0.573</td>
+<td>2.419</td>
 <td>0.009</td>
 <td>0.007</td>
-<td>8720.0</td>
-<td>8233.0</td>
+<td>9248.0</td>
+<td>8510.0</td>
 <td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">Beta[Pland]</td>
-<td>2.267</td>
-<td>0.865</td>
-<td>0.787</td>
-<td>3.946</td>
+<td>2.264</td>
+<td>0.868</td>
+<td>0.789</td>
+<td>3.951</td>
 <td>0.009</td>
 <td>0.007</td>
-<td>9698.0</td>
-<td>8419.0</td>
+<td>10343.0</td>
+<td>8090.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">Beta[Wheight]</td>
-<td>0.950</td>
-<td>0.693</td>
-<td>-0.357</td>
-<td>2.274</td>
+<td>0.957</td>
+<td>0.694</td>
+<td>-0.370</td>
+<td>2.261</td>
 <td>0.006</td>
-<td>0.005</td>
-<td>11806.0</td>
-<td>10295.0</td>
+<td>0.004</td>
+<td>12848.0</td>
+<td>10855.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -1240,6 +1249,7 @@ <h1>Real data example</h1>
 </div>
 </div>
 </div>
+</div>
 <p>I compare the parameter estimates to the ones estimated by <span class="citation" data-cites="hooten2019">Hooten and Hefley (<a href="#ref-hooten2019" role="doc-biblioref">2019</a>)</span>, Chapter 23.</p>
 <div id="cell-fig-warbler_trace" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>alpha_hat_hooten <span class="op">=</span>  [<span class="fl">0.04</span>, <span class="fl">0.47</span>, <span class="fl">0.68</span>]</span>
@@ -1275,7 +1285,7 @@ <h1>Real data example</h1>
 <div id="fig-warbler_forest" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-warbler_forest-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="occupancy_files/figure-html/fig-warbler_forest-output-1.png" width="678" height="377" class="figure-img">
+<img src="occupancy_files/figure-html/fig-warbler_forest-output-1.png" width="679" height="377" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-warbler_forest-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;8: Forest plots from willow warbler occupancy model. ESS refers to the effective sample size.
@@ -1306,7 +1316,7 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 <span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a>    pm.ZeroInflatedBinomial(<span class="st">'y'</span>, p<span class="op">=</span>p, psi<span class="op">=</span>psi, n<span class="op">=</span>J, observed<span class="op">=</span>Y_sum)</span>
 <span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a>pm.model_to_graphviz(warbler_constantp)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="24">
+<div class="cell-output cell-output-display" data-execution_count="55">
 <div id="fig-warbler_pdot" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-warbler_pdot-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
@@ -1319,7 +1329,7 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 </div>
 </div>
 </div>
-<div id="33b37b9c" class="cell" data-execution_count="25">
+<div id="22e1c155" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> warbler_constantp:</span>
 <span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>    warbler_constantp_idata <span class="op">=</span> pm.sample(<span class="dv">4000</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -1351,13 +1361,13 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 
     <div>
       <progress value="20000" class="" max="20000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [20000/20000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [20000/20000 00:05&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
 </div>
 <p>Next, I caclculate the leave-one-out (loo) cross-validation score for each model <span class="citation" data-cites="vehtari2017">(<a href="#ref-vehtari2017" role="doc-biblioref">Vehtari, Gelman, and Gabry 2017</a>)</span>. This involves first computing the log likelihood for each model.</p>
-<div id="0723e471" class="cell" data-execution_count="26">
+<div id="abd0b5d2" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> warbler:</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>    pm.compute_log_likelihood(warbler_idata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -1387,16 +1397,16 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
     
 </div>
 </div>
-<div id="af4255fb" class="cell" data-execution_count="27">
+<div id="7d8a7d19" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>warbler_loo <span class="op">=</span> az.loo(warbler_idata)</span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>warbler_loo</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="27">
+<div class="cell-output cell-output-display" data-execution_count="58">
 <pre><code>Computed from 16000 posterior samples and 37 observations log-likelihood matrix.
 
          Estimate       SE
-elpd_loo   -54.39     7.38
-p_loo        6.19        -
+elpd_loo   -54.43     7.38
+p_loo        6.24        -
 ------
 
 Pareto k diagnostic values:
@@ -1407,7 +1417,7 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
    (1, Inf)   (very bad)    0    0.0%</code></pre>
 </div>
 </div>
-<div id="ffd0a195" class="cell" data-execution_count="28">
+<div id="4c28cf4c" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> warbler_constantp:</span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>    pm.compute_log_likelihood(warbler_constantp_idata)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -1437,16 +1447,16 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
     
 </div>
 </div>
-<div id="116fbef9" class="cell" data-execution_count="29">
+<div id="a28fbf1e" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a>warbler_constantp_loo <span class="op">=</span> az.loo(warbler_constantp_idata)</span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a>warbler_constantp_loo</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="29">
+<div class="cell-output cell-output-display" data-execution_count="60">
 <pre><code>Computed from 16000 posterior samples and 37 observations log-likelihood matrix.
 
          Estimate       SE
-elpd_loo   -39.33     5.12
-p_loo        3.78        -
+elpd_loo   -39.32     5.11
+p_loo        3.79        -
 ------
 
 Pareto k diagnostic values:
@@ -1458,17 +1468,12 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 </div>
 </div>
 <p>Arviz has handy tools for comparing the results. First, I generate a tabular summary.</p>
-<div id="76f22eaf" class="cell" data-execution_count="30">
+<div id="dd4858bf" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>df_comp_loo <span class="op">=</span> az.compare({<span class="vs">r"$p(visit,wheight)$"</span>: warbler_idata, </span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>                          <span class="vs">r"$p(\cdot)$"</span>: warbler_constantp_idata})</span>
 <span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>df_comp_loo</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'False' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
-  df_comp.loc[val] = (
-/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
-  df_comp.loc[val] = (</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="30">
+<div class="cell-output cell-output-display" data-execution_count="61">
+<div>
 <div>
 
 
@@ -1491,11 +1496,11 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">$p(\cdot)$</td>
 <td>0</td>
-<td>-39.325247</td>
-<td>3.779850</td>
+<td>-39.322333</td>
+<td>3.791042</td>
 <td>0.000000</td>
 <td>1.000000e+00</td>
-<td>5.119967</td>
+<td>5.111831</td>
 <td>0.000000</td>
 <td>False</td>
 <td>log</td>
@@ -1503,12 +1508,12 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 <tr class="even">
 <td data-quarto-table-cell-role="th">$p(visit,wheight)$</td>
 <td>1</td>
-<td>-54.392997</td>
-<td>6.186874</td>
-<td>15.067751</td>
-<td>4.654055e-13</td>
-<td>7.378487</td>
-<td>4.204041</td>
+<td>-54.426099</td>
+<td>6.235365</td>
+<td>15.103765</td>
+<td>4.405365e-13</td>
+<td>7.382243</td>
+<td>4.201577</td>
 <td>False</td>
 <td>log</td>
 </tr>
@@ -1518,19 +1523,16 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 </div>
 </div>
 </div>
+</div>
 <p>This indicates that the <span class="math inline">\(p(\cdot)\)</span> model is favored over the <span class="math inline">\(p(visit,wheight)\)</span> model.</p>
 <p>Arviz also generates plots for these comparisons.</p>
 <div id="cell-fig-warbler_comp" class="cell" data-execution_count="31">
-<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>az.plot_compare(df_comp_loo, insample_dev<span class="op">=</span><span class="va">False</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/plots/backends/matplotlib/compareplot.py:87: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
-  scale = comp_df["scale"][0]</code></pre>
-</div>
+<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>az.plot_compare(df_comp_loo, insample_dev<span class="op">=</span><span class="va">False</span>)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div id="fig-warbler_comp" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-warbler_comp-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="occupancy_files/figure-html/fig-warbler_comp-output-2.png" width="775" height="237" class="figure-img">
+<img src="occupancy_files/figure-html/fig-warbler_comp-output-1.png" width="775" height="237" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-warbler_comp-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;10: Comparison between the <span class="math inline">\(p(visit,wheight)\)</span> and the <span class="math inline">\(p(\cdot)\)</span> models in terms of loo.
@@ -1641,6 +1643,24 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -1675,7 +1695,11 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1951,7 +1975,7 @@ <h2 class="anchored" data-anchor-id="model-comparison">Model comparison</h2>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./notebooks.html" class="pagination-link  aria-label=" notebooks"="">
+      <a href="./notebooks.html" class="pagination-link" aria-label="Notebooks">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Notebooks</span>
       </a>          
   </div>
diff --git a/docs/occupancy_files/figure-html/fig-pdot_trace-output-1.png b/docs/occupancy_files/figure-html/fig-pdot_trace-output-1.png
index bb16009..0bdc7cb 100644
Binary files a/docs/occupancy_files/figure-html/fig-pdot_trace-output-1.png and b/docs/occupancy_files/figure-html/fig-pdot_trace-output-1.png differ
diff --git a/docs/occupancy_files/figure-html/fig-psix-output-1.svg b/docs/occupancy_files/figure-html/fig-psix-output-1.svg
index 73aa234..7facb57 100644
--- a/docs/occupancy_files/figure-html/fig-psix-output-1.svg
+++ b/docs/occupancy_files/figure-html/fig-psix-output-1.svg
@@ -35,11 +35,11 @@
 <path fill="none" stroke="black" d="M77.31,-170.44C94.22,-156.42 115.22,-138.99 134.09,-123.33"/>
 <polygon fill="black" stroke="black" points="136.25,-126.09 141.71,-117.02 131.78,-120.71 136.25,-126.09"/>
 </g>
-<!-- beta1 -->
+<!-- beta0 -->
 <g id="node2" class="node">
-<title>beta1</title>
+<title>beta0</title>
 <ellipse fill="none" stroke="black" cx="133.72" cy="-315.79" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="133.72" y="-327.24" font-family="Times,serif" font-size="14.00">beta1</text>
+<text text-anchor="middle" x="133.72" y="-327.24" font-family="Times,serif" font-size="14.00">beta0</text>
 <text text-anchor="middle" x="133.72" y="-310.74" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="133.72" y="-294.24" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
@@ -51,23 +51,23 @@
 <text text-anchor="middle" x="183.72" y="-193.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="183.72" y="-176.93" font-family="Times,serif" font-size="14.00">Deterministic</text>
 </g>
-<!-- beta1&#45;&gt;psi -->
+<!-- beta0&#45;&gt;psi -->
 <g id="edge1" class="edge">
-<title>beta1&#45;&gt;psi</title>
+<title>beta0&#45;&gt;psi</title>
 <path fill="none" stroke="black" d="M149.68,-277.99C155.22,-265.22 161.44,-250.87 167.04,-237.97"/>
 <polygon fill="black" stroke="black" points="170.19,-239.49 170.96,-228.92 163.77,-236.7 170.19,-239.49"/>
 </g>
-<!-- beta0 -->
+<!-- beta1 -->
 <g id="node3" class="node">
-<title>beta0</title>
+<title>beta1</title>
 <ellipse fill="none" stroke="black" cx="233.72" cy="-315.79" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="233.72" y="-327.24" font-family="Times,serif" font-size="14.00">beta0</text>
+<text text-anchor="middle" x="233.72" y="-327.24" font-family="Times,serif" font-size="14.00">beta1</text>
 <text text-anchor="middle" x="233.72" y="-310.74" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="233.72" y="-294.24" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
-<!-- beta0&#45;&gt;psi -->
+<!-- beta1&#45;&gt;psi -->
 <g id="edge2" class="edge">
-<title>beta0&#45;&gt;psi</title>
+<title>beta1&#45;&gt;psi</title>
 <path fill="none" stroke="black" d="M217.77,-277.99C212.23,-265.22 206.01,-250.87 200.41,-237.97"/>
 <polygon fill="black" stroke="black" points="203.68,-236.7 196.49,-228.92 197.26,-239.49 203.68,-236.7"/>
 </g>
diff --git a/docs/occupancy_files/figure-html/fig-pw-output-1.svg b/docs/occupancy_files/figure-html/fig-pw-output-1.svg
index f39715c..ffc31e2 100644
--- a/docs/occupancy_files/figure-html/fig-pw-output-1.svg
+++ b/docs/occupancy_files/figure-html/fig-pw-output-1.svg
@@ -10,105 +10,105 @@
 <polygon fill="white" stroke="none" points="-4,4 -4,-341.13 404.02,-341.13 404.02,4 -4,4"/>
 <g id="clust1" class="cluster">
 <title>cluster200</title>
-<path fill="none" stroke="black" d="M227.01,-129.82C227.01,-129.82 309.01,-129.82 309.01,-129.82 315.01,-129.82 321.01,-135.82 321.01,-141.82 321.01,-141.82 321.01,-215.82 321.01,-215.82 321.01,-221.82 315.01,-227.82 309.01,-227.82 309.01,-227.82 227.01,-227.82 227.01,-227.82 221.01,-227.82 215.01,-221.82 215.01,-215.82 215.01,-215.82 215.01,-141.82 215.01,-141.82 215.01,-135.82 221.01,-129.82 227.01,-129.82"/>
-<text text-anchor="middle" x="302.89" y="-137.02" font-family="Times,serif" font-size="14.00">200</text>
+<path fill="none" stroke="black" d="M91.01,-129.82C91.01,-129.82 173.01,-129.82 173.01,-129.82 179.01,-129.82 185.01,-135.82 185.01,-141.82 185.01,-141.82 185.01,-215.82 185.01,-215.82 185.01,-221.82 179.01,-227.82 173.01,-227.82 173.01,-227.82 91.01,-227.82 91.01,-227.82 85.01,-227.82 79.01,-221.82 79.01,-215.82 79.01,-215.82 79.01,-141.82 79.01,-141.82 79.01,-135.82 85.01,-129.82 91.01,-129.82"/>
+<text text-anchor="middle" x="166.89" y="-137.02" font-family="Times,serif" font-size="14.00">200</text>
 </g>
 <g id="clust2" class="cluster">
 <title>cluster200 x 3</title>
-<path fill="none" stroke="black" d="M69.01,-8C69.01,-8 195.01,-8 195.01,-8 201.01,-8 207.01,-14 207.01,-20 207.01,-20 207.01,-215.82 207.01,-215.82 207.01,-221.82 201.01,-227.82 195.01,-227.82 195.01,-227.82 69.01,-227.82 69.01,-227.82 63.01,-227.82 57.01,-221.82 57.01,-215.82 57.01,-215.82 57.01,-20 57.01,-20 57.01,-14 63.01,-8 69.01,-8"/>
-<text text-anchor="middle" x="178.39" y="-15.2" font-family="Times,serif" font-size="14.00">200 x 3</text>
+<path fill="none" stroke="black" d="M205.01,-8C205.01,-8 331.01,-8 331.01,-8 337.01,-8 343.01,-14 343.01,-20 343.01,-20 343.01,-215.82 343.01,-215.82 343.01,-221.82 337.01,-227.82 331.01,-227.82 331.01,-227.82 205.01,-227.82 205.01,-227.82 199.01,-227.82 193.01,-221.82 193.01,-215.82 193.01,-215.82 193.01,-20 193.01,-20 193.01,-14 199.01,-8 205.01,-8"/>
+<text text-anchor="middle" x="314.39" y="-15.2" font-family="Times,serif" font-size="14.00">200 x 3</text>
 </g>
-<!-- alpha1 -->
+<!-- beta0 -->
 <g id="node1" class="node">
-<title>alpha1</title>
+<title>beta0</title>
 <ellipse fill="none" stroke="black" cx="41.01" cy="-296.48" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="41.01" y="-307.93" font-family="Times,serif" font-size="14.00">alpha1</text>
+<text text-anchor="middle" x="41.01" y="-307.93" font-family="Times,serif" font-size="14.00">beta0</text>
 <text text-anchor="middle" x="41.01" y="-291.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="41.01" y="-274.93" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
-<!-- p -->
-<g id="node6" class="node">
-<title>p</title>
+<!-- psi -->
+<g id="node5" class="node">
+<title>psi</title>
 <polygon fill="none" stroke="black" points="177.14,-219.82 86.89,-219.82 86.89,-162.32 177.14,-162.32 177.14,-219.82"/>
-<text text-anchor="middle" x="132.01" y="-202.52" font-family="Times,serif" font-size="14.00">p</text>
+<text text-anchor="middle" x="132.01" y="-202.52" font-family="Times,serif" font-size="14.00">psi</text>
 <text text-anchor="middle" x="132.01" y="-186.02" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="132.01" y="-169.52" font-family="Times,serif" font-size="14.00">Deterministic</text>
 </g>
-<!-- alpha1&#45;&gt;p -->
-<g id="edge3" class="edge">
-<title>alpha1&#45;&gt;p</title>
+<!-- beta0&#45;&gt;psi -->
+<g id="edge1" class="edge">
+<title>beta0&#45;&gt;psi</title>
 <path fill="none" stroke="black" d="M67.58,-265.29C77.71,-253.77 89.37,-240.53 99.95,-228.5"/>
 <polygon fill="black" stroke="black" points="102.46,-230.95 106.44,-221.13 97.21,-226.32 102.46,-230.95"/>
 </g>
-<!-- beta1 -->
+<!-- alpha1 -->
 <g id="node2" class="node">
-<title>beta1</title>
+<title>alpha1</title>
 <ellipse fill="none" stroke="black" cx="259.01" cy="-296.48" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="259.01" y="-307.93" font-family="Times,serif" font-size="14.00">beta1</text>
+<text text-anchor="middle" x="259.01" y="-307.93" font-family="Times,serif" font-size="14.00">alpha1</text>
 <text text-anchor="middle" x="259.01" y="-291.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="259.01" y="-274.93" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
-<!-- psi -->
-<g id="node5" class="node">
-<title>psi</title>
+<!-- p -->
+<g id="node6" class="node">
+<title>p</title>
 <polygon fill="none" stroke="black" points="313.14,-219.82 222.89,-219.82 222.89,-162.32 313.14,-162.32 313.14,-219.82"/>
-<text text-anchor="middle" x="268.01" y="-202.52" font-family="Times,serif" font-size="14.00">psi</text>
+<text text-anchor="middle" x="268.01" y="-202.52" font-family="Times,serif" font-size="14.00">p</text>
 <text text-anchor="middle" x="268.01" y="-186.02" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="268.01" y="-169.52" font-family="Times,serif" font-size="14.00">Deterministic</text>
 </g>
-<!-- beta1&#45;&gt;psi -->
-<g id="edge1" class="edge">
-<title>beta1&#45;&gt;psi</title>
+<!-- alpha1&#45;&gt;p -->
+<g id="edge4" class="edge">
+<title>alpha1&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M262.49,-255.5C263.18,-247.55 263.91,-239.22 264.6,-231.3"/>
 <polygon fill="black" stroke="black" points="268.06,-231.87 265.44,-221.6 261.09,-231.26 268.06,-231.87"/>
 </g>
-<!-- beta0 -->
+<!-- alpha0 -->
 <g id="node3" class="node">
-<title>beta0</title>
+<title>alpha0</title>
 <ellipse fill="none" stroke="black" cx="359.01" cy="-296.48" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="359.01" y="-307.93" font-family="Times,serif" font-size="14.00">beta0</text>
+<text text-anchor="middle" x="359.01" y="-307.93" font-family="Times,serif" font-size="14.00">alpha0</text>
 <text text-anchor="middle" x="359.01" y="-291.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="359.01" y="-274.93" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
-<!-- beta0&#45;&gt;psi -->
-<g id="edge2" class="edge">
-<title>beta0&#45;&gt;psi</title>
+<!-- alpha0&#45;&gt;p -->
+<g id="edge3" class="edge">
+<title>alpha0&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M332.45,-265.29C322.32,-253.77 310.66,-240.53 300.07,-228.5"/>
 <polygon fill="black" stroke="black" points="302.82,-226.32 293.59,-221.13 297.56,-230.95 302.82,-226.32"/>
 </g>
-<!-- alpha0 -->
+<!-- beta1 -->
 <g id="node4" class="node">
-<title>alpha0</title>
+<title>beta1</title>
 <ellipse fill="none" stroke="black" cx="141.01" cy="-296.48" rx="41.01" ry="40.66"/>
-<text text-anchor="middle" x="141.01" y="-307.93" font-family="Times,serif" font-size="14.00">alpha0</text>
+<text text-anchor="middle" x="141.01" y="-307.93" font-family="Times,serif" font-size="14.00">beta1</text>
 <text text-anchor="middle" x="141.01" y="-291.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="141.01" y="-274.93" font-family="Times,serif" font-size="14.00">Normal</text>
 </g>
-<!-- alpha0&#45;&gt;p -->
-<g id="edge4" class="edge">
-<title>alpha0&#45;&gt;p</title>
+<!-- beta1&#45;&gt;psi -->
+<g id="edge2" class="edge">
+<title>beta1&#45;&gt;psi</title>
 <path fill="none" stroke="black" d="M137.53,-255.5C136.84,-247.55 136.12,-239.22 135.43,-231.3"/>
 <polygon fill="black" stroke="black" points="138.94,-231.26 134.58,-221.6 131.96,-231.87 138.94,-231.26"/>
 </g>
 <!-- y -->
 <g id="node7" class="node">
 <title>y</title>
-<ellipse fill="lightgrey" stroke="black" cx="132.01" cy="-81.16" rx="67" ry="40.66"/>
-<text text-anchor="middle" x="132.01" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
-<text text-anchor="middle" x="132.01" y="-76.11" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="132.01" y="-59.61" font-family="Times,serif" font-size="14.00">CustomDist_y</text>
+<ellipse fill="lightgrey" stroke="black" cx="268.01" cy="-81.16" rx="67" ry="40.66"/>
+<text text-anchor="middle" x="268.01" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
+<text text-anchor="middle" x="268.01" y="-76.11" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="268.01" y="-59.61" font-family="Times,serif" font-size="14.00">CustomDist_y</text>
 </g>
 <!-- psi&#45;&gt;y -->
 <g id="edge6" class="edge">
 <title>psi&#45;&gt;y</title>
-<path fill="none" stroke="black" d="M244.48,-162.11C234.72,-151.37 222.9,-139.39 211.01,-129.82 204.75,-124.78 197.91,-119.88 190.95,-115.26"/>
-<polygon fill="black" stroke="black" points="193.02,-112.43 182.72,-109.98 189.24,-118.32 193.02,-112.43"/>
+<path fill="none" stroke="black" d="M155.54,-162.11C165.31,-151.37 177.12,-139.39 189.01,-129.82 195.27,-124.78 202.12,-119.88 209.07,-115.26"/>
+<polygon fill="black" stroke="black" points="210.78,-118.32 217.31,-109.98 207,-112.43 210.78,-118.32"/>
 </g>
 <!-- p&#45;&gt;y -->
 <g id="edge5" class="edge">
 <title>p&#45;&gt;y</title>
-<path fill="none" stroke="black" d="M132.01,-162C132.01,-153.29 132.01,-143.4 132.01,-133.63"/>
-<polygon fill="black" stroke="black" points="135.51,-133.79 132.01,-123.79 128.51,-133.79 135.51,-133.79"/>
+<path fill="none" stroke="black" d="M268.01,-162C268.01,-153.29 268.01,-143.4 268.01,-133.63"/>
+<polygon fill="black" stroke="black" points="271.51,-133.79 268.01,-123.79 264.51,-133.79 271.51,-133.79"/>
 </g>
 </g>
 </svg>
diff --git a/docs/occupancy_files/figure-html/fig-pw_trace-output-1.png b/docs/occupancy_files/figure-html/fig-pw_trace-output-1.png
index 2eb099f..ec80f3d 100644
Binary files a/docs/occupancy_files/figure-html/fig-pw_trace-output-1.png and b/docs/occupancy_files/figure-html/fig-pw_trace-output-1.png differ
diff --git a/docs/occupancy_files/figure-html/fig-warbler_comp-output-1.png b/docs/occupancy_files/figure-html/fig-warbler_comp-output-1.png
new file mode 100644
index 0000000..6ec1b53
Binary files /dev/null and b/docs/occupancy_files/figure-html/fig-warbler_comp-output-1.png differ
diff --git a/docs/occupancy_files/figure-html/fig-warbler_comp-output-2.png b/docs/occupancy_files/figure-html/fig-warbler_comp-output-2.png
deleted file mode 100644
index 3a56624..0000000
Binary files a/docs/occupancy_files/figure-html/fig-warbler_comp-output-2.png and /dev/null differ
diff --git a/docs/occupancy_files/figure-html/fig-warbler_forest-output-1.png b/docs/occupancy_files/figure-html/fig-warbler_forest-output-1.png
index edb7657..a742ffa 100644
Binary files a/docs/occupancy_files/figure-html/fig-warbler_forest-output-1.png and b/docs/occupancy_files/figure-html/fig-warbler_forest-output-1.png differ
diff --git a/docs/occupancy_files/figure-html/fig-warbler_trace-output-1.png b/docs/occupancy_files/figure-html/fig-warbler_trace-output-1.png
index bd4bdd2..fd29c1a 100644
Binary files a/docs/occupancy_files/figure-html/fig-warbler_trace-output-1.png and b/docs/occupancy_files/figure-html/fig-warbler_trace-output-1.png differ
diff --git a/docs/occupancy_files/figure-html/psix_trace-output-1.png b/docs/occupancy_files/figure-html/psix_trace-output-1.png
index 95069c0..5a2e42a 100644
Binary files a/docs/occupancy_files/figure-html/psix_trace-output-1.png and b/docs/occupancy_files/figure-html/psix_trace-output-1.png differ
diff --git a/docs/scr.html b/docs/scr.html
index 8e54d3a..b136f51 100644
--- a/docs/scr.html
+++ b/docs/scr.html
@@ -2,7 +2,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
 
 <meta charset="utf-8">
-<meta name="generator" content="quarto-1.4.549">
+<meta name="generator" content="quarto-1.4.554">
 
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
@@ -88,7 +88,7 @@
 <script src="site_libs/quarto-search/quarto-search.js"></script>
 <meta name="quarto:offset" content="./">
 <link href="./msom.html" rel="next">
-<link href="./dist.html" rel="prev">
+<link href="./distance.html" rel="prev">
 <script src="site_libs/quarto-html/quarto.js"></script>
 <script src="site_libs/quarto-html/popper.min.js"></script>
 <script src="site_libs/quarto-html/tippy.umd.min.js"></script>
@@ -246,7 +246,7 @@
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./dist.html" class="sidebar-item-text sidebar-link">
+  <a href="./distance.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Distance sampling</span></a>
   </div>
 </li>
@@ -346,7 +346,7 @@ <h1 class="title">Spatial capture-recapture</h1>
 </header>
 
 
-<p>In this notebook, I present an approach to fitting spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs.</p>
+<p>In this notebook, I show how to train spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs.</p>
 <p>In this notebook, I train the simplest possible SCR model, SCR0 <span class="citation" data-cites="royle2013">(<a href="#ref-royle2013" role="doc-biblioref">Royle et al. 2013, chap. 5</a>)</span>, where the goal is estimating the true population size <span class="math inline">\(N\)</span>. Similar to the other closed population notebooks, I do so using parameter-expanded data-augmentation (PX-DA). I also borrow the concept of the <em>detection function</em> from the distance sampling notebook.</p>
 <p>As a motivating example, I use the <a href="https://www.otago.ac.nz/density/examples/index.html">ovenbird mist netting dataset</a> provided by Murray Efford via the secr package in R. The design of the study is outlined in <span class="citation" data-cites="efford2004">Efford, Dawson, and Robbins (<a href="#ref-efford2004" role="doc-biblioref">2004</a>)</span> and <span class="citation" data-cites="borchers2008">Borchers and Efford (<a href="#ref-borchers2008" role="doc-biblioref">2008</a>)</span>. In this dataset, ovenbirds were trapped in 44 mist nets over 8 to 10 consecutive days during the summers of 2005 to 2009.</p>
 <div id="cell-fig-nets" class="cell" data-execution_count="1">
@@ -448,7 +448,7 @@ <h1 class="title">Spatial capture-recapture</h1>
 </div>
 <p>One difference between spatial and traditional (non-spatial) capture is the addition of the trap identifier in the capture history. Whereas a traditional capture history is <code>[individual, occasion]</code>, a spatial capture history might be <code>[individual, occasion, trap]</code>.</p>
 <p>In the ovenbird example, I ignore the <code>year</code> dimension, pooling parameters across years, which allows for better estimation of the detection parameters. My hack for doing so is treating every band/year combination as a unique individual in a combined year capture history. This is easy to implement, creates an awkward interpretation of <span class="math inline">\(N\)</span> (see below).</p>
-<div id="011c12e7" class="cell" data-execution_count="2">
+<div id="26fdb388" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ovenbird capture history</span></span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>oven_ch <span class="op">=</span> pd.read_csv(<span class="st">'ovenbirdcapt.txt'</span>, delimiter<span class="op">=</span><span class="st">' '</span>)</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -467,6 +467,7 @@ <h1 class="title">Spatial capture-recapture</h1>
 <span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a>ovenbird.head(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="2">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -567,11 +568,12 @@ <h1 class="title">Spatial capture-recapture</h1>
 </div>
 </div>
 </div>
+</div>
 <section id="simulation" class="level2">
 <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
-<p>Before estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the <em>activity center</em>. The activity center <span class="math inline">\(\mathbf{s}_i\)</span> is the most likely place that you’d find an individual <span class="math inline">\(i\)</span> over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space. (The study takes place in homogenous habitat.)</p>
+<p>Before estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the <em>activity center</em>. The activity center <span class="math inline">\(\mathbf{s}_i\)</span> is the most likely place that you’d find an individual <span class="math inline">\(i\)</span> over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space.</p>
 <p>I compute the probability of detection for individual <span class="math inline">\(i\)</span> at trap <span class="math inline">\(j\)</span> as <span class="math inline">\(p_{i,j}=g_0 \exp(-d_{i,j}^2/2\sigma^2),\)</span> where <span class="math inline">\(g_0\)</span> is the probability of detecting an individual when it’s activity center is at the trap, <span class="math inline">\(d_{i,j}\)</span> is the euclidean distance between the trap and the activity center, and <span class="math inline">\(\sigma\)</span> is the detection range parameter.</p>
-<div id="7d52e091" class="cell" data-execution_count="3">
+<div id="bf76025f" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># true population size</span></span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>N <span class="op">=</span> <span class="dv">150</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -638,7 +640,7 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 </div>
 </div>
 </div>
-<div id="d01d91a9" class="cell" data-execution_count="5">
+<div id="44141e05" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> known:</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    known_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -670,15 +672,16 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 
     <div>
       <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [8000/8000 00:25&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [8000/8000 00:26&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
 </div>
-<div id="c66b88b0" class="cell" data-execution_count="6">
+<div id="3e032afd" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>az.summary(known_idata, var_names<span class="op">=</span>[<span class="st">'g0'</span>, <span class="st">'sigma'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="6">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -699,26 +702,26 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 <tbody>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">g0</td>
-<td>0.033</td>
+<td>0.034</td>
 <td>0.004</td>
-<td>0.026</td>
+<td>0.027</td>
 <td>0.041</td>
 <td>0.000</td>
 <td>0.000</td>
-<td>1735.0</td>
-<td>2548.0</td>
+<td>1589.0</td>
+<td>2642.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">sigma</td>
-<td>79.366</td>
-<td>5.218</td>
-<td>69.491</td>
-<td>88.931</td>
-<td>0.174</td>
-<td>0.123</td>
-<td>892.0</td>
-<td>1932.0</td>
+<td>78.940</td>
+<td>5.087</td>
+<td>69.510</td>
+<td>88.420</td>
+<td>0.173</td>
+<td>0.122</td>
+<td>866.0</td>
+<td>1694.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -727,6 +730,7 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-known_trace" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>az.plot_trace(</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    known_idata, </span>
@@ -738,7 +742,7 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 <div id="fig-known_trace" class="quarto-figure quarto-figure-center quarto-float anchored">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-known_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-<img src="scr_files/figure-html/fig-known_trace-output-1.png" width="687" height="367" class="figure-img">
+<img src="scr_files/figure-html/fig-known_trace-output-1.png" width="703" height="367" class="figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-known_trace-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 Figure&nbsp;3: Trace plots for model where <span class="math inline">\(N\)</span> is known. The true parameter values are shown by vertical and horizontal lines.
@@ -752,7 +756,7 @@ <h2 class="anchored" data-anchor-id="simulation">Simulation</h2>
 <section id="ovenbird-density" class="level2">
 <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 <p>Now, I estimate the density <span class="math inline">\(D\)</span> for the ovenbird population. Like distance sampling, SCR can robustly estimate the density of the population, regardless of the size of the state space. The difference between the model above and this one is that we use PX-DA to estimate the inclusion probability <span class="math inline">\(\psi,\)</span> and subsequently <span class="math inline">\(N.\)</span> First, I convert the <code>DataFrame</code> to a <code>(n_detected, n_traps)</code> array of binomial counts.</p>
-<div id="0efb6373" class="cell" data-execution_count="8">
+<div id="13264cc0" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> get_Y(ch):</span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">'''Get a (individual_count, trap_count) array of detections.'''</span></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -831,7 +835,7 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 </div>
 </div>
 </div>
-<div id="34cf5b5d" class="cell" data-execution_count="10">
+<div id="e3ee5f8b" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> oven:</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    oven_idata <span class="op">=</span> pm.sample()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stderr">
@@ -839,7 +843,7 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 Initializing NUTS using jitter+adapt_diag...
 Multiprocess sampling (4 chains in 4 jobs)
 NUTS: [sx, sy, g0, sigma, psi]
-Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 48 seconds.</code></pre>
+Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 50 seconds.</code></pre>
 </div>
 <div class="cell-output cell-output-display">
 
@@ -863,15 +867,16 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 
     <div>
       <progress value="8000" class="" max="8000" style="width:300px; height:20px; vertical-align: middle;"></progress>
-      100.00% [8000/8000 00:48&lt;00:00 Sampling 4 chains, 0 divergences]
+      100.00% [8000/8000 00:49&lt;00:00 Sampling 4 chains, 0 divergences]
     </div>
     
 </div>
 </div>
-<div id="c90de47a" class="cell" data-execution_count="11">
+<div id="fa93ecd4" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>az.summary(oven_idata, var_names<span class="op">=</span>[<span class="st">'g0'</span>, <span class="st">'sigma'</span>, <span class="st">'psi'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
 <div>
+<div>
 
 
 <table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
@@ -898,32 +903,32 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 <td>0.036</td>
 <td>0.000</td>
 <td>0.000</td>
-<td>1678.0</td>
-<td>2463.0</td>
+<td>1682.0</td>
+<td>2575.0</td>
 <td>1.0</td>
 </tr>
 <tr class="even">
 <td data-quarto-table-cell-role="th">sigma</td>
-<td>71.117</td>
-<td>4.668</td>
-<td>62.261</td>
-<td>79.867</td>
-<td>0.147</td>
-<td>0.104</td>
-<td>1011.0</td>
-<td>1764.0</td>
+<td>71.114</td>
+<td>4.651</td>
+<td>62.763</td>
+<td>80.019</td>
+<td>0.148</td>
+<td>0.105</td>
+<td>972.0</td>
+<td>1848.0</td>
 <td>1.0</td>
 </tr>
 <tr class="odd">
 <td data-quarto-table-cell-role="th">psi</td>
-<td>0.705</td>
-<td>0.056</td>
-<td>0.602</td>
-<td>0.814</td>
+<td>0.704</td>
+<td>0.055</td>
+<td>0.606</td>
+<td>0.812</td>
 <td>0.001</td>
 <td>0.001</td>
-<td>3321.0</td>
-<td>2507.0</td>
+<td>3082.0</td>
+<td>2321.0</td>
 <td>1.0</td>
 </tr>
 </tbody>
@@ -932,6 +937,7 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 </div>
 </div>
 </div>
+</div>
 <div id="cell-fig-oven_trace" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>g0_mle <span class="op">=</span> [<span class="fl">0.025</span>]</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>sigma_mle <span class="op">=</span> [<span class="dv">73</span>]</span>
@@ -957,7 +963,7 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 </div>
 <p>The estimates are quite close to the maximum likelihood estimates, which I estimated using the secr package in R.</p>
 <p>Finally, I estimate density <span class="math inline">\(D\)</span> using the results. As in the closed capture-recapture and distance sampling notebooks, I use the posterior samples of <span class="math inline">\(\psi\)</span> and <span class="math inline">\(M\)</span> to sample the posterior of <span class="math inline">\(N.\)</span> This <span class="math inline">\(N,\)</span> however, has an awkward interpretation because I pooled across the years by combining all the detection histories. To get around this, I compute the average annual abundance by dividing by the total number of years in the sample. Then, I divide by the area of the state space.</p>
-<div id="e95a3e5c" class="cell" data-execution_count="13">
+<div id="dd0427f6" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> sim_N(idata, n, K):</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>    psi_samps <span class="op">=</span> az.extract(idata).psi.to_numpy()</span>
@@ -1207,6 +1213,24 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
     // clear code selection
     e.clearSelection();
   });
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+    var mailtoRegex = new RegExp(/^mailto:/);
+      var filterRegex = new RegExp('/' + window.location.host + '/');
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href) || mailtoRegex.test(href);
+    }
+    // Inspect non-navigation links and adorn them if external
+ 	var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item):not(.quarto-navigation-tool)');
+    for (var i=0; i<links.length; i++) {
+      const link = links[i];
+      if (!isInternal(link.href)) {
+        // undo the damage that might have been done by quarto-nav.js in the case of
+        // links that we want to consider external
+        if (link.dataset.originalHref !== undefined) {
+          link.href = link.dataset.originalHref;
+        }
+      }
+    }
   function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) {
     const config = {
       allowHTML: true,
@@ -1241,7 +1265,11 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
       try { href = new URL(href).hash; } catch {}
       const id = href.replace(/^#\/?/, "");
       const note = window.document.getElementById(id);
-      return note.innerHTML;
+      if (note) {
+        return note.innerHTML;
+      } else {
+        return "";
+      }
     });
   }
   const xrefs = window.document.querySelectorAll('a.quarto-xref');
@@ -1517,7 +1545,7 @@ <h2 class="anchored" data-anchor-id="ovenbird-density">Ovenbird density</h2>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="./dist.html" class="pagination-link  aria-label=" distance="" sampling"="">
+      <a href="./distance.html" class="pagination-link" aria-label="Distance sampling">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Distance sampling</span>
       </a>          
   </div>
diff --git a/docs/scr_files/figure-html/cell-16-output-1.png b/docs/scr_files/figure-html/cell-16-output-1.png
deleted file mode 100644
index 555100f..0000000
Binary files a/docs/scr_files/figure-html/cell-16-output-1.png and /dev/null differ
diff --git a/docs/scr_files/figure-html/fig-activity-output-1.png b/docs/scr_files/figure-html/fig-activity-output-1.png
index 15afe80..1e80480 100644
Binary files a/docs/scr_files/figure-html/fig-activity-output-1.png and b/docs/scr_files/figure-html/fig-activity-output-1.png differ
diff --git a/docs/scr_files/figure-html/fig-density-output-1.png b/docs/scr_files/figure-html/fig-density-output-1.png
index b74037d..a09c6b4 100644
Binary files a/docs/scr_files/figure-html/fig-density-output-1.png and b/docs/scr_files/figure-html/fig-density-output-1.png differ
diff --git a/docs/scr_files/figure-html/fig-func-output-1.png b/docs/scr_files/figure-html/fig-func-output-1.png
index 02240bf..772c85c 100644
Binary files a/docs/scr_files/figure-html/fig-func-output-1.png and b/docs/scr_files/figure-html/fig-func-output-1.png differ
diff --git a/docs/scr_files/figure-html/fig-known-output-1.svg b/docs/scr_files/figure-html/fig-known-output-1.svg
index f08904b..dee7524 100644
--- a/docs/scr_files/figure-html/fig-known-output-1.svg
+++ b/docs/scr_files/figure-html/fig-known-output-1.svg
@@ -27,7 +27,7 @@
 <text text-anchor="middle" x="168" y="-282.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- p -->
-<g id="node5" class="node">
+<g id="node6" class="node">
 <title>p</title>
 <polygon fill="none" stroke="black" points="266.12,-215.32 175.88,-215.32 175.88,-157.82 266.12,-157.82 266.12,-215.32"/>
 <text text-anchor="middle" x="221" y="-198.02" font-family="Times,serif" font-size="14.00">p</text>
@@ -49,41 +49,41 @@
 <text text-anchor="middle" x="61" y="-282.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- sx&#45;&gt;p -->
-<g id="edge4" class="edge">
+<g id="edge1" class="edge">
 <title>sx&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M81.39,-267.89C90.12,-255.04 101.26,-241.26 114,-231.32 128.94,-219.66 147.41,-210.65 164.78,-203.92"/>
 <polygon fill="black" stroke="black" points="165.8,-207.27 173.98,-200.54 163.39,-200.7 165.8,-207.27"/>
 </g>
-<!-- g0 -->
+<!-- sigma -->
 <g id="node3" class="node">
-<title>g0</title>
+<title>sigma</title>
 <ellipse fill="none" stroke="black" cx="275" cy="-304.48" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="275" y="-315.93" font-family="Times,serif" font-size="14.00">g0</text>
+<text text-anchor="middle" x="275" y="-315.93" font-family="Times,serif" font-size="14.00">sigma</text>
 <text text-anchor="middle" x="275" y="-299.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="275" y="-282.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
-<!-- g0&#45;&gt;p -->
-<g id="edge1" class="edge">
-<title>g0&#45;&gt;p</title>
+<!-- sigma&#45;&gt;p -->
+<g id="edge3" class="edge">
+<title>sigma&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M257.77,-266.48C251.73,-253.52 244.93,-238.94 238.84,-225.87"/>
 <polygon fill="black" stroke="black" points="242.17,-224.73 234.78,-217.14 235.83,-227.68 242.17,-224.73"/>
 </g>
-<!-- sigma -->
+<!-- g0 -->
 <g id="node4" class="node">
-<title>sigma</title>
+<title>g0</title>
 <ellipse fill="none" stroke="black" cx="382" cy="-304.48" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="382" y="-315.93" font-family="Times,serif" font-size="14.00">sigma</text>
+<text text-anchor="middle" x="382" y="-315.93" font-family="Times,serif" font-size="14.00">g0</text>
 <text text-anchor="middle" x="382" y="-299.43" font-family="Times,serif" font-size="14.00">~</text>
 <text text-anchor="middle" x="382" y="-282.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
-<!-- sigma&#45;&gt;p -->
-<g id="edge3" class="edge">
-<title>sigma&#45;&gt;p</title>
+<!-- g0&#45;&gt;p -->
+<g id="edge4" class="edge">
+<title>g0&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M361.63,-267.87C352.9,-255.01 341.76,-241.24 329,-231.32 313.79,-219.49 294.95,-210.41 277.29,-203.66"/>
 <polygon fill="black" stroke="black" points="278.5,-200.38 267.91,-200.28 276.13,-206.96 278.5,-200.38"/>
 </g>
 <!-- y -->
-<g id="node6" class="node">
+<g id="node5" class="node">
 <title>y</title>
 <ellipse fill="lightgrey" stroke="black" cx="221" cy="-81.16" rx="47.38" ry="40.66"/>
 <text text-anchor="middle" x="221" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
diff --git a/docs/scr_files/figure-html/fig-known_trace-output-1.png b/docs/scr_files/figure-html/fig-known_trace-output-1.png
index 7f2a680..eb9dc4c 100644
Binary files a/docs/scr_files/figure-html/fig-known_trace-output-1.png and b/docs/scr_files/figure-html/fig-known_trace-output-1.png differ
diff --git a/docs/scr_files/figure-html/fig-nets-output-1.png b/docs/scr_files/figure-html/fig-nets-output-1.png
index 71a6725..0f9bd90 100644
Binary files a/docs/scr_files/figure-html/fig-nets-output-1.png and b/docs/scr_files/figure-html/fig-nets-output-1.png differ
diff --git a/docs/scr_files/figure-html/fig-oven-output-1.svg b/docs/scr_files/figure-html/fig-oven-output-1.svg
index 3b557f1..91e7f80 100644
--- a/docs/scr_files/figure-html/fig-oven-output-1.svg
+++ b/docs/scr_files/figure-html/fig-oven-output-1.svg
@@ -27,7 +27,7 @@
 <text text-anchor="middle" x="168" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- p -->
-<g id="node6" class="node">
+<g id="node7" class="node">
 <title>p</title>
 <polygon fill="none" stroke="black" points="266.12,-227.23 175.88,-227.23 175.88,-169.73 266.12,-169.73 266.12,-227.23"/>
 <text text-anchor="middle" x="221" y="-209.93" font-family="Times,serif" font-size="14.00">p</text>
@@ -49,41 +49,13 @@
 <text text-anchor="middle" x="61" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- sx&#45;&gt;p -->
-<g id="edge4" class="edge">
+<g id="edge1" class="edge">
 <title>sx&#45;&gt;p</title>
 <path fill="none" stroke="black" d="M81.55,-283.91C90.3,-271.09 101.41,-257.28 114,-247.13 128.95,-235.09 147.43,-225.45 164.79,-218.07"/>
 <polygon fill="black" stroke="black" points="166.06,-221.34 174.02,-214.34 163.44,-214.85 166.06,-221.34"/>
 </g>
-<!-- g0 -->
-<g id="node3" class="node">
-<title>g0</title>
-<ellipse fill="none" stroke="black" cx="275" cy="-320.29" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="275" y="-331.74" font-family="Times,serif" font-size="14.00">g0</text>
-<text text-anchor="middle" x="275" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="275" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
-</g>
-<!-- g0&#45;&gt;p -->
-<g id="edge1" class="edge">
-<title>g0&#45;&gt;p</title>
-<path fill="none" stroke="black" d="M258.36,-282.37C252.04,-268.34 244.82,-252.33 238.43,-238.15"/>
-<polygon fill="black" stroke="black" points="241.65,-236.78 234.35,-229.1 235.27,-239.65 241.65,-236.78"/>
-</g>
-<!-- sigma -->
-<g id="node4" class="node">
-<title>sigma</title>
-<ellipse fill="none" stroke="black" cx="382" cy="-320.29" rx="44.72" ry="40.66"/>
-<text text-anchor="middle" x="382" y="-331.74" font-family="Times,serif" font-size="14.00">sigma</text>
-<text text-anchor="middle" x="382" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
-<text text-anchor="middle" x="382" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
-</g>
-<!-- sigma&#45;&gt;p -->
-<g id="edge3" class="edge">
-<title>sigma&#45;&gt;p</title>
-<path fill="none" stroke="black" d="M362.89,-283.36C354.19,-270.15 342.74,-256.22 329,-247.13 316.05,-238.57 309.52,-244.63 295,-239.13 288.89,-236.82 282.64,-234.1 276.5,-231.19"/>
-<polygon fill="black" stroke="black" points="278.18,-228.12 267.67,-226.82 275.08,-234.39 278.18,-228.12"/>
-</g>
 <!-- psi -->
-<g id="node5" class="node">
+<g id="node3" class="node">
 <title>psi</title>
 <ellipse fill="none" stroke="black" cx="349" cy="-198.48" rx="44.72" ry="40.66"/>
 <text text-anchor="middle" x="349" y="-209.93" font-family="Times,serif" font-size="14.00">psi</text>
@@ -91,7 +63,7 @@
 <text text-anchor="middle" x="349" y="-176.93" font-family="Times,serif" font-size="14.00">Uniform</text>
 </g>
 <!-- y -->
-<g id="node7" class="node">
+<g id="node6" class="node">
 <title>y</title>
 <ellipse fill="lightgrey" stroke="black" cx="221" cy="-81.16" rx="67" ry="40.66"/>
 <text text-anchor="middle" x="221" y="-92.61" font-family="Times,serif" font-size="14.00">y</text>
@@ -104,6 +76,34 @@
 <path fill="none" stroke="black" d="M317.69,-169.27C302.17,-155.28 283.14,-138.14 266.09,-122.78"/>
 <polygon fill="black" stroke="black" points="268.79,-120.51 259.02,-116.41 264.11,-125.71 268.79,-120.51"/>
 </g>
+<!-- sigma -->
+<g id="node4" class="node">
+<title>sigma</title>
+<ellipse fill="none" stroke="black" cx="275" cy="-320.29" rx="44.72" ry="40.66"/>
+<text text-anchor="middle" x="275" y="-331.74" font-family="Times,serif" font-size="14.00">sigma</text>
+<text text-anchor="middle" x="275" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="275" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
+</g>
+<!-- sigma&#45;&gt;p -->
+<g id="edge3" class="edge">
+<title>sigma&#45;&gt;p</title>
+<path fill="none" stroke="black" d="M258.36,-282.37C252.04,-268.34 244.82,-252.33 238.43,-238.15"/>
+<polygon fill="black" stroke="black" points="241.65,-236.78 234.35,-229.1 235.27,-239.65 241.65,-236.78"/>
+</g>
+<!-- g0 -->
+<g id="node5" class="node">
+<title>g0</title>
+<ellipse fill="none" stroke="black" cx="382" cy="-320.29" rx="44.72" ry="40.66"/>
+<text text-anchor="middle" x="382" y="-331.74" font-family="Times,serif" font-size="14.00">g0</text>
+<text text-anchor="middle" x="382" y="-315.24" font-family="Times,serif" font-size="14.00">~</text>
+<text text-anchor="middle" x="382" y="-298.74" font-family="Times,serif" font-size="14.00">Uniform</text>
+</g>
+<!-- g0&#45;&gt;p -->
+<g id="edge4" class="edge">
+<title>g0&#45;&gt;p</title>
+<path fill="none" stroke="black" d="M362.89,-283.36C354.19,-270.15 342.74,-256.22 329,-247.13 316.05,-238.57 309.52,-244.63 295,-239.13 288.89,-236.82 282.64,-234.1 276.5,-231.19"/>
+<polygon fill="black" stroke="black" points="278.18,-228.12 267.67,-226.82 275.08,-234.39 278.18,-228.12"/>
+</g>
 <!-- p&#45;&gt;y -->
 <g id="edge5" class="edge">
 <title>p&#45;&gt;y</title>
diff --git a/docs/scr_files/figure-html/fig-oven_trace-output-1.png b/docs/scr_files/figure-html/fig-oven_trace-output-1.png
index 012c90f..5cc674e 100644
Binary files a/docs/scr_files/figure-html/fig-oven_trace-output-1.png and b/docs/scr_files/figure-html/fig-oven_trace-output-1.png differ
diff --git a/docs/search.json b/docs/search.json
index 3812642..31fc318 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -16,7 +16,7 @@
     "href": "occupancy.html#estimating-parameters-with-pymc",
     "title": "Occupancy models",
     "section": "Estimating parameters with PyMC",
-    "text": "Estimating parameters with PyMC\nNext, I use PyMC to train the occupancy model with the simulated data. First, similar to JAGS and Stan, the model must be specified using the PyMC syntax. This is done using a context manager in Python, essentially, a with statement. This creates a Model object.\n\nwith pm.Model() as constant:\n\n    # priors for the detetion and occurrence probabilities\\\n    psi = pm.Uniform('psi', 0, 1)\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=visit_count, \n                            observed=y_summarized)\n\nIn JAGS, the prior for \\(p\\) would be specified as p ~ dunif(0, 1). The PyMC equivalent is p = pm.Uniform('p', 0, 1). This could, alternatively, be specified as p = pm.Uniform('detection probability', 0, 1). For the likelihood, I use PyMC’s built-in ZeroInflatedBinomial distribution. We tell PyMC that this is an observed random variable by supplying data to the observed argument. PyMC also has handy tools for visualizing the model.\n\npm.model_to_graphviz(constant)\n\n\n\n\n\n\n\nFigure 1: Visual representation of model \\(p(\\cdot)\\psi(\\cdot).\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\nNow I can sample from the posterior. Again, I use the context manager, this time referring to the model by name. It’s typical to name the output with idata because, by default, PyMC returns an object of class InferenceData from the Arviz package. Arviz is similar to the coda package for R.\n\nwith constant:\n    constant_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\nPyMC will try to use the No-U-Turn Sampler (NUTS) whenever possible. As you can see, it samples the posterior quickly. I can plot the output using the az.plot_trace(), supplying the true values for \\(p\\) and \\(\\psi\\) for comparizon. I can also look at a tabular summary using az.summary().\n\naz.plot_trace(\n    constant_idata,\n    compact=True,\n    figsize=(8,4),\n    lines=[(\"psi\", {}, [psi_true]), (\"p\", {}, [p_true])] \n);\n\n\n\n\n\n\n\nFigure 2: Traceplots for the \\(p(\\cdot)\\psi(\\cdot)\\) model. The true parameter values are shown by vertical and horizontal lines.\n\n\n\n\n\n\naz.summary(constant_idata)\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.8\n0.042\n0.716\n0.876\n0.001\n0.001\n2063.0\n1627.0\n1.0\n\n\np\n0.5\n0.029\n0.445\n0.553\n0.001\n0.000\n2282.0\n2373.0\n1.0",
+    "text": "Estimating parameters with PyMC\nNext, I use PyMC to train the occupancy model with the simulated data. First, similar to JAGS and Stan, the model must be specified using the PyMC syntax. This is done using a context manager in Python, essentially, a with statement. This creates a Model object.\n\nwith pm.Model() as constant:\n\n    # priors for the detetion and occurrence probabilities\\\n    psi = pm.Uniform('psi', 0, 1)\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=visit_count, \n                            observed=y_summarized)\n\nIn JAGS, the prior for \\(p\\) would be specified as p ~ dunif(0, 1). The PyMC equivalent is p = pm.Uniform('p', 0, 1). This could, alternatively, be specified as p = pm.Uniform('detection probability', 0, 1). For the likelihood, I use PyMC’s built-in ZeroInflatedBinomial distribution. We tell PyMC that this is an observed random variable by supplying data to the observed argument. PyMC also has handy tools for visualizing the model.\n\npm.model_to_graphviz(constant)\n\n\n\n\n\n\n\nFigure 1: Visual representation of model \\(p(\\cdot)\\psi(\\cdot).\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\nNow I can sample from the posterior. Again, I use the context manager, this time referring to the model by name. It’s typical to name the output with idata because, by default, PyMC returns an object of class InferenceData from the Arviz package. Arviz is similar to the coda package for R.\n\nwith constant:\n    constant_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\nPyMC will try to use the No-U-Turn Sampler (NUTS) whenever possible. As you can see, it samples the posterior quickly. I can plot the output using the az.plot_trace(), supplying the true values for \\(p\\) and \\(\\psi\\) for comparizon. I can also look at a tabular summary using az.summary().\n\naz.plot_trace(\n    constant_idata,\n    compact=True,\n    figsize=(8,4),\n    lines=[(\"psi\", {}, [psi_true]), (\"p\", {}, [p_true])] \n);\n\n\n\n\n\n\n\nFigure 2: Traceplots for the \\(p(\\cdot)\\psi(\\cdot)\\) model. The true parameter values are shown by vertical and horizontal lines.\n\n\n\n\n\n\naz.summary(constant_idata)\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\np\n0.499\n0.030\n0.443\n0.554\n0.001\n0.000\n2381.0\n2276.0\n1.0\n\n\npsi\n0.801\n0.042\n0.723\n0.883\n0.001\n0.001\n2277.0\n1896.0\n1.0",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -28,7 +28,7 @@
     "href": "occupancy.html#adding-site-covariates",
     "title": "Occupancy models",
     "section": "Adding site covariates",
-    "text": "Adding site covariates\nNext, I add in some realism by simulating a site-level covariate \\(x\\) that affects the occurrence probability. I model this effect with a logit-linear model, i.e., \\(\\psi_j=\\text{logit}^{-1}(\\beta_0 + \\beta_1 x_j).\\)\n\n## ecological model\n\n# true parameter values\nbeta0_true = -1\nbeta1_true = 3\n\n# covariates \nx = scale(rng.uniform(size=site_count))\n\n# linear model\nmu_true = beta0_true + beta1_true * x\npsi_true = invlogit(mu_true)\n\n# simulate occurrence state\nz_true = rng.binomial(1, psi_true)\n\n## detection model\n\n# true parameter values\np_true = 0.75\n\n# simulate detection\ny = sim_y(p_true, z_true, site_count, visit_count)\n\n# vector with the number of detections at each site \ny_summarized = y.sum(axis=1)\n\n# detection data at the first five sites \ny[:5]\n\narray([[0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0]])\n\n\nAgain, I specify the model with PyMC. Like JAGS, the random variables can be manipulated, as in a linear model with \\(x_j.\\) These behave like numpy arrays, meaning that vectorized operations and broadcasting are available. To monitor the output of these manipulations, use the pm.Deterministic class. In this case, I am monitoring the site level occurrence probability \\(\\psi_j.\\)\n\nwith pm.Model() as psix:\n\n    # occurrence process \n    # priors \n    beta0 = pm.Normal(\"beta0\", mu=0, sigma=2)\n    beta1 = pm.Normal(\"beta1\", mu=0, sigma=2)\n    \n    # linear model\n    mu = beta0 + beta1 * x\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process\n    # prior\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=visit_count, \n                            observed=y_summarized)\n\npm.model_to_graphviz(psix)\n\n\n\n\n\n\n\nFigure 3: Visual representation of model \\(p(\\cdot)\\psi(x).\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\n\nwith psix:\n    psix_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [beta0, beta1, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(\n    psix_idata,\n    figsize=(8,6),\n    var_names=['beta0', 'beta1', 'p'],\n    lines=[(\"beta0\", {}, [beta0_true]), (\"beta1\", {}, [beta1_true]), \n           ('p', {}, [p_true])]\n);\n\n\n\n\nTraceplots for the \\(p(\\cdot)\\psi(x)\\) model. The true parameter values are shown by vertical and horizontal lines\n\n\n\n\n\naz.summary(psix_idata, var_names=['beta0', 'beta1', 'p'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nbeta0\n-1.234\n0.263\n-1.707\n-0.749\n0.005\n0.004\n2842.0\n2754.0\n1.0\n\n\nbeta1\n2.855\n0.415\n2.083\n3.626\n0.008\n0.006\n2667.0\n2796.0\n1.0\n\n\np\n0.744\n0.032\n0.686\n0.803\n0.000\n0.000\n4504.0\n2983.0\n1.0",
+    "text": "Adding site covariates\nNext, I add in some realism by simulating a site-level covariate \\(x\\) that affects the occurrence probability. I model this effect with a logit-linear model, i.e., \\(\\psi_j=\\text{logit}^{-1}(\\beta_0 + \\beta_1 x_j).\\)\n\n## ecological model\n\n# true parameter values\nbeta0_true = -1\nbeta1_true = 3\n\n# covariates \nx = scale(rng.uniform(size=site_count))\n\n# linear model\nmu_true = beta0_true + beta1_true * x\npsi_true = invlogit(mu_true)\n\n# simulate occurrence state\nz_true = rng.binomial(1, psi_true)\n\n## detection model\n\n# true parameter values\np_true = 0.75\n\n# simulate detection\ny = sim_y(p_true, z_true, site_count, visit_count)\n\n# vector with the number of detections at each site \ny_summarized = y.sum(axis=1)\n\n# detection data at the first five sites \ny[:5]\n\narray([[0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0],\n       [0, 0, 0]])\n\n\nAgain, I specify the model with PyMC. Like JAGS, the random variables can be manipulated, as in a linear model with \\(x_j.\\) These behave like numpy arrays, meaning that vectorized operations and broadcasting are available. To monitor the output of these manipulations, use the pm.Deterministic class. In this case, I am monitoring the site level occurrence probability \\(\\psi_j.\\)\n\nwith pm.Model() as psix:\n\n    # occurrence process \n    # priors \n    beta0 = pm.Normal(\"beta0\", mu=0, sigma=2)\n    beta1 = pm.Normal(\"beta1\", mu=0, sigma=2)\n    \n    # linear model\n    mu = beta0 + beta1 * x\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process\n    # prior\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=visit_count, \n                            observed=y_summarized)\n\npm.model_to_graphviz(psix)\n\n\n\n\n\n\n\nFigure 3: Visual representation of model \\(p(\\cdot)\\psi(x).\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\n\nwith psix:\n    psix_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [beta0, beta1, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(\n    psix_idata,\n    figsize=(8,6),\n    var_names=['beta0', 'beta1', 'p'],\n    lines=[(\"beta0\", {}, [beta0_true]), (\"beta1\", {}, [beta1_true]), \n           ('p', {}, [p_true])]\n);\n\n\n\n\nTraceplots for the \\(p(\\cdot)\\psi(x)\\) model. The true parameter values are shown by vertical and horizontal lines\n\n\n\n\n\naz.summary(psix_idata, var_names=['beta0', 'beta1', 'p'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nbeta0\n-1.248\n0.261\n-1.746\n-0.778\n0.005\n0.004\n2472.0\n2902.0\n1.0\n\n\nbeta1\n2.863\n0.406\n2.146\n3.663\n0.008\n0.006\n2814.0\n2832.0\n1.0\n\n\np\n0.745\n0.031\n0.686\n0.800\n0.000\n0.000\n4368.0\n3055.0\n1.0",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -40,7 +40,7 @@
     "href": "occupancy.html#adding-visit-covariates",
     "title": "Occupancy models",
     "section": "Adding visit covariates",
-    "text": "Adding visit covariates\nFinally, I add in visit-level covariate \\(w_{j,k}\\) that affects detection.\n\n## ecological model\n\n# true parameter values\nbeta0_true = -1\nbeta1_true = 3\n\n# covariates \nx = scale(rng.uniform(size=site_count))\n\n# linear model\nmu_true = beta0_true + beta1_true * x\npsi_true = invlogit(mu_true)\n\n# simulate occurrence state\nz_true = rng.binomial(1, psi_true)\n\n# true parameter values\nalpha0_true = 1\nalpha1_true = -3\n\n# covariates\nw = rng.uniform(size=site_count * visit_count).reshape(site_count, visit_count)\nw = scale(w)\n\n# linear model\nnu_true = alpha0_true + alpha1_true * w\np_true = invlogit(nu_true)\n\n# simulate detection\ny = sim_y(p_true, z_true, site_count, visit_count)\n\ny[:5]\n\narray([[0, 0, 0],\n       [0, 0, 0],\n       [0, 1, 1],\n       [1, 1, 0],\n       [0, 0, 0]])\n\n\nOur PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state \\(z_j.\\) The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit painfully slowly.\nSince I am impatient, I instead use the marginalized version of the model, that is, a model that does not include the discrete latent states. To do this in PyMC, I use the CustomDist class. This requires, first, defining the log probability of the distribution, logp, given the data and it’s parameters. We can write logp using the likelihood of the occupancy model, \\[\nP(\\mathbf{y}_j)=\n\\begin{cases}\n    P(\\mathbf{y}_j | z_j = 1)\\; \\psi_j \\; + \\; (1 - \\psi_j),   & \\text{if } \\mathbf{y}_j = \\mathbf{0}\\\\\n    P(\\mathbf{y}_j | z_j = 1)\\; \\psi_j,  & \\text{otherwise}\n\\end{cases}\n\\] where \\(P(\\mathbf{y}_j | z_j = 1) = \\prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}\\) (Royle and Dorazio 2008). To do this in PyMC, I rely on the pm.math.switch function, which is similar to ifelse() in R or np.where().\n\n# likelihood for y data\ndef logp(x, p, psi):\n    '''Computes the log-likelihood for an occupancy model\n\n    Args: \n        x: (site_count x visit_count) array with binary detection data\n        p: (site_count x visit_count) array of probabilities\n        p: site_count vector of probabilities\n    '''\n    \n    bern = (p ** x) * ((1 - p) ** (1 - x))\n    bern_prod = pm.math.prod(bern, axis=1)\n    \n    res = pm.math.switch(\n        x.sum(axis=1) &gt; 0,\n        bern_prod * psi,\n        bern_prod * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\nThen, I simply provide this function as an argument to the CustomDist class in our PyMC model. Note that we have to tell CustomDist how many dimensions each parameter has via the ndims_params argument. In this case, p has the same number of dimensions of Y (2), while psi has one dimension since it is vector of length site_count.\n\nwith pm.Model() as marginal:\n\n    # occurrence process \n    # priors \n    beta0 = pm.Normal(\"beta0\", mu=0, sigma=2)\n    beta1 = pm.Normal(\"beta1\", mu=0, sigma=2)\n    \n    # linear model\n    mu = beta0 + beta1 * x\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process\n    # priors\n    alpha0 = pm.Normal('alpha0', mu=0, sigma=2)\n    alpha1 = pm.Normal('alpha1', mu=0, sigma=2)\n\n    # linear model\n    nu = alpha0 + alpha1 * w\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # likelihood\n    pm.CustomDist(\n        'y',\n        p,\n        psi,\n        logp=logp,\n        observed=y,\n        ndims_params=[2, 1]\n    )\n\npm.model_to_graphviz(marginal)\n\n\n\n\n\n\n\nFigure 4: Visual representation of the \\(p(w)\\psi(w)\\) model.\n\n\n\n\n\n\nwith marginal:\n    marginal_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [beta0, beta1, alpha0, alpha1]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(\n    marginal_idata,\n    figsize=(8,6), \n    var_names=['beta0', 'beta1', 'alpha0', 'alpha1'],\n    lines=[(\"beta0\", {}, [beta0_true]), (\"beta1\", {}, [beta1_true]), \n           ('alpha0', {}, [alpha0_true]), ('alpha1', {}, [alpha1_true])]\n);\n\n\n\n\n\n\n\nFigure 5: Tracepots for the \\(p(w)\\psi(x)\\) model. The true parameter values are shown by vertical and horizontal lines\n\n\n\n\n\n\naz.summary(marginal_idata, var_names=['beta0', 'beta1', 'alpha0', 'alpha1'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nbeta0\n-0.738\n0.236\n-1.154\n-0.261\n0.004\n0.003\n3489.0\n3085.0\n1.0\n\n\nbeta1\n2.783\n0.396\n2.057\n3.547\n0.007\n0.005\n2960.0\n2419.0\n1.0\n\n\nalpha0\n1.401\n0.272\n0.922\n1.943\n0.005\n0.004\n2759.0\n2290.0\n1.0\n\n\nalpha1\n-3.096\n0.381\n-3.803\n-2.410\n0.007\n0.005\n2803.0\n2517.0\n1.0",
+    "text": "Adding visit covariates\nFinally, I add in visit-level covariate \\(w_{j,k}\\) that affects detection.\n\n## ecological model\n\n# true parameter values\nbeta0_true = -1\nbeta1_true = 3\n\n# covariates \nx = scale(rng.uniform(size=site_count))\n\n# linear model\nmu_true = beta0_true + beta1_true * x\npsi_true = invlogit(mu_true)\n\n# simulate occurrence state\nz_true = rng.binomial(1, psi_true)\n\n# true parameter values\nalpha0_true = 1\nalpha1_true = -3\n\n# covariates\nw = rng.uniform(size=site_count * visit_count).reshape(site_count, visit_count)\nw = scale(w)\n\n# linear model\nnu_true = alpha0_true + alpha1_true * w\np_true = invlogit(nu_true)\n\n# simulate detection\ny = sim_y(p_true, z_true, site_count, visit_count)\n\ny[:5]\n\narray([[0, 0, 0],\n       [0, 0, 0],\n       [0, 1, 1],\n       [1, 1, 0],\n       [0, 0, 0]])\n\n\nOur PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state \\(z_j.\\) The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit slowly.\nSince I am impatient, I instead use the marginalized version of the model, that is, a model that does not include the discrete latent states. To do this in PyMC, I use the CustomDist class. This requires, first, defining the log probability of the distribution, logp, given the data and it’s parameters. We can write logp using the likelihood of the occupancy model, \\[\nP(\\mathbf{y}_j)=\n\\begin{cases}\n    P(\\mathbf{y}_j | z_j = 1)\\; \\psi_j \\; + \\; (1 - \\psi_j),   & \\text{if } \\mathbf{y}_j = \\mathbf{0}\\\\\n    P(\\mathbf{y}_j | z_j = 1)\\; \\psi_j,  & \\text{otherwise}\n\\end{cases}\n\\] where \\(P(\\mathbf{y}_j | z_j = 1) = \\prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}\\) (Royle and Dorazio 2008). To do this in PyMC, I rely on the pm.math.switch function, which is like the ifelse() function in R or np.where().\n\n# likelihood for y data\ndef logp(x, p, psi):\n    '''Computes the log-likelihood for an occupancy model\n\n    Args: \n        x: (site_count x visit_count) array with binary detection data\n        p: (site_count x visit_count) array of probabilities\n        p: site_count vector of probabilities\n    '''\n    \n    bern = (p ** x) * ((1 - p) ** (1 - x))\n    bern_prod = pm.math.prod(bern, axis=1)\n    \n    res = pm.math.switch(\n        x.sum(axis=1) &gt; 0,\n        bern_prod * psi,\n        bern_prod * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\nThen, I simply provide this function as an argument to the CustomDist class in our PyMC model. Note that we have to tell CustomDist how many dimensions each parameter has via the ndims_params argument. In this case, p has the same number of dimensions of Y (2), while psi has one dimension since it is vector of length site_count.\n\nwith pm.Model() as marginal:\n\n    # occurrence process \n    # priors \n    beta0 = pm.Normal(\"beta0\", mu=0, sigma=2)\n    beta1 = pm.Normal(\"beta1\", mu=0, sigma=2)\n    \n    # linear model\n    mu = beta0 + beta1 * x\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process\n    # priors\n    alpha0 = pm.Normal('alpha0', mu=0, sigma=2)\n    alpha1 = pm.Normal('alpha1', mu=0, sigma=2)\n\n    # linear model\n    nu = alpha0 + alpha1 * w\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # likelihood\n    pm.CustomDist(\n        'y',\n        p,\n        psi,\n        logp=logp,\n        observed=y,\n        ndims_params=[2, 1]\n    )\n\npm.model_to_graphviz(marginal)\n\n\n\n\n\n\n\nFigure 4: Visual representation of the \\(p(w)\\psi(w)\\) model.\n\n\n\n\n\n\nwith marginal:\n    marginal_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [beta0, beta1, alpha0, alpha1]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(\n    marginal_idata,\n    figsize=(8,6), \n    var_names=['beta0', 'beta1', 'alpha0', 'alpha1'],\n    lines=[(\"beta0\", {}, [beta0_true]), (\"beta1\", {}, [beta1_true]), \n           ('alpha0', {}, [alpha0_true]), ('alpha1', {}, [alpha1_true])]\n);\n\n\n\n\n\n\n\nFigure 5: Tracepots for the \\(p(w)\\psi(x)\\) model. The true parameter values are shown by vertical and horizontal lines\n\n\n\n\n\n\naz.summary(marginal_idata, var_names=['beta0', 'beta1', 'alpha0', 'alpha1'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nbeta0\n-0.741\n0.244\n-1.219\n-0.308\n0.004\n0.003\n3323.0\n3051.0\n1.0\n\n\nbeta1\n2.786\n0.391\n2.080\n3.537\n0.007\n0.005\n3061.0\n2753.0\n1.0\n\n\nalpha0\n1.407\n0.272\n0.906\n1.915\n0.005\n0.004\n2892.0\n2681.0\n1.0\n\n\nalpha1\n-3.096\n0.387\n-3.837\n-2.408\n0.007\n0.005\n3066.0\n2881.0\n1.0",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -52,7 +52,7 @@
     "href": "occupancy.html#model-comparison",
     "title": "Occupancy models",
     "section": "Model comparison",
-    "text": "Model comparison\nPyMC also has handy tools for model comparison. I demonstrate these by fitting a model to the warbler data with a constant probability of detection.\n\nY_sum = Y.sum(axis=1)\n\nwith pm.Model(coords=coords) as warbler_constantp:\n\n    # occurrence process priors \n    Beta = pm.Normal(\"Beta\", mu=0, sigma=2, dims=\"beta_coefs\")\n    \n    # linear model\n    mu = pm.math.dot(X, Beta)\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process priors\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=J, observed=Y_sum)\n\npm.model_to_graphviz(warbler_constantp)\n\n\n\n\n\n\n\nFigure 9: Visual representaion of the warbler occupancy model with constant \\(p.\\)\n\n\n\n\n\n\nwith warbler_constantp:\n    warbler_constantp_idata = pm.sample(4000)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [Beta, p]\nSampling 4 chains for 1_000 tune and 4_000 draw iterations (4_000 + 16_000 draws total) took 5 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [20000/20000 00:04&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\nNext, I caclculate the leave-one-out (loo) cross-validation score for each model (Vehtari, Gelman, and Gabry 2017). This involves first computing the log likelihood for each model.\n\nwith warbler:\n    pm.compute_log_likelihood(warbler_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 00:00&lt;00:00]\n    \n    \n\n\n\nwarbler_loo = az.loo(warbler_idata)\n\nwarbler_loo\n\nComputed from 16000 posterior samples and 37 observations log-likelihood matrix.\n\n         Estimate       SE\nelpd_loo   -54.39     7.38\np_loo        6.19        -\n------\n\nPareto k diagnostic values:\n                         Count   Pct.\n(-Inf, 0.5]   (good)       37  100.0%\n (0.5, 0.7]   (ok)          0    0.0%\n   (0.7, 1]   (bad)         0    0.0%\n   (1, Inf)   (very bad)    0    0.0%\n\n\n\nwith warbler_constantp:\n    pm.compute_log_likelihood(warbler_constantp_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 00:00&lt;00:00]\n    \n    \n\n\n\nwarbler_constantp_loo = az.loo(warbler_constantp_idata)\n\nwarbler_constantp_loo\n\nComputed from 16000 posterior samples and 37 observations log-likelihood matrix.\n\n         Estimate       SE\nelpd_loo   -39.33     5.12\np_loo        3.78        -\n------\n\nPareto k diagnostic values:\n                         Count   Pct.\n(-Inf, 0.5]   (good)       37  100.0%\n (0.5, 0.7]   (ok)          0    0.0%\n   (0.7, 1]   (bad)         0    0.0%\n   (1, Inf)   (very bad)    0    0.0%\n\n\nArviz has handy tools for comparing the results. First, I generate a tabular summary.\n\ndf_comp_loo = az.compare({r\"$p(visit,wheight)$\": warbler_idata, \n                          r\"$p(\\cdot)$\": warbler_constantp_idata})\ndf_comp_loo\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'False' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\n$p(\\cdot)$\n0\n-39.325247\n3.779850\n0.000000\n1.000000e+00\n5.119967\n0.000000\nFalse\nlog\n\n\n$p(visit,wheight)$\n1\n-54.392997\n6.186874\n15.067751\n4.654055e-13\n7.378487\n4.204041\nFalse\nlog\n\n\n\n\n\n\n\nThis indicates that the \\(p(\\cdot)\\) model is favored over the \\(p(visit,wheight)\\) model.\nArviz also generates plots for these comparisons.\n\naz.plot_compare(df_comp_loo, insample_dev=False);\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/plots/backends/matplotlib/compareplot.py:87: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n  scale = comp_df[\"scale\"][0]\n\n\n\n\n\n\n\n\nFigure 10: Comparison between the \\(p(visit,wheight)\\) and the \\(p(\\cdot)\\) models in terms of loo.",
+    "text": "Model comparison\nPyMC also has handy tools for model comparison. I demonstrate these by fitting a model to the warbler data with a constant probability of detection.\n\nY_sum = Y.sum(axis=1)\n\nwith pm.Model(coords=coords) as warbler_constantp:\n\n    # occurrence process priors \n    Beta = pm.Normal(\"Beta\", mu=0, sigma=2, dims=\"beta_coefs\")\n    \n    # linear model\n    mu = pm.math.dot(X, Beta)\n    psi = pm.Deterministic(\"psi\", pm.math.invlogit(mu))\n\n    # detection process priors\n    p = pm.Uniform('p', 0, 1)\n\n    # likelihood\n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=J, observed=Y_sum)\n\npm.model_to_graphviz(warbler_constantp)\n\n\n\n\n\n\n\nFigure 9: Visual representaion of the warbler occupancy model with constant \\(p.\\)\n\n\n\n\n\n\nwith warbler_constantp:\n    warbler_constantp_idata = pm.sample(4000)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [Beta, p]\nSampling 4 chains for 1_000 tune and 4_000 draw iterations (4_000 + 16_000 draws total) took 5 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [20000/20000 00:05&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\nNext, I caclculate the leave-one-out (loo) cross-validation score for each model (Vehtari, Gelman, and Gabry 2017). This involves first computing the log likelihood for each model.\n\nwith warbler:\n    pm.compute_log_likelihood(warbler_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 00:00&lt;00:00]\n    \n    \n\n\n\nwarbler_loo = az.loo(warbler_idata)\n\nwarbler_loo\n\nComputed from 16000 posterior samples and 37 observations log-likelihood matrix.\n\n         Estimate       SE\nelpd_loo   -54.43     7.38\np_loo        6.24        -\n------\n\nPareto k diagnostic values:\n                         Count   Pct.\n(-Inf, 0.5]   (good)       37  100.0%\n (0.5, 0.7]   (ok)          0    0.0%\n   (0.7, 1]   (bad)         0    0.0%\n   (1, Inf)   (very bad)    0    0.0%\n\n\n\nwith warbler_constantp:\n    pm.compute_log_likelihood(warbler_constantp_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 00:00&lt;00:00]\n    \n    \n\n\n\nwarbler_constantp_loo = az.loo(warbler_constantp_idata)\n\nwarbler_constantp_loo\n\nComputed from 16000 posterior samples and 37 observations log-likelihood matrix.\n\n         Estimate       SE\nelpd_loo   -39.32     5.11\np_loo        3.79        -\n------\n\nPareto k diagnostic values:\n                         Count   Pct.\n(-Inf, 0.5]   (good)       37  100.0%\n (0.5, 0.7]   (ok)          0    0.0%\n   (0.7, 1]   (bad)         0    0.0%\n   (1, Inf)   (very bad)    0    0.0%\n\n\nArviz has handy tools for comparing the results. First, I generate a tabular summary.\n\ndf_comp_loo = az.compare({r\"$p(visit,wheight)$\": warbler_idata, \n                          r\"$p(\\cdot)$\": warbler_constantp_idata})\ndf_comp_loo\n\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\n$p(\\cdot)$\n0\n-39.322333\n3.791042\n0.000000\n1.000000e+00\n5.111831\n0.000000\nFalse\nlog\n\n\n$p(visit,wheight)$\n1\n-54.426099\n6.235365\n15.103765\n4.405365e-13\n7.382243\n4.201577\nFalse\nlog\n\n\n\n\n\n\n\n\nThis indicates that the \\(p(\\cdot)\\) model is favored over the \\(p(visit,wheight)\\) model.\nArviz also generates plots for these comparisons.\n\naz.plot_compare(df_comp_loo, insample_dev=False);\n\n\n\n\n\n\n\nFigure 10: Comparison between the \\(p(visit,wheight)\\) and the \\(p(\\cdot)\\) models in terms of loo.",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -140,7 +140,7 @@
     "href": "notebooks.html",
     "title": "Notebooks",
     "section": "",
-    "text": "Please find here any notebooks that I may have thought would be of general interest. For now, these mainly consist of my attempts to port standard ecological models to PyMC, a Python library for doing Bayesian analysis.\n\nPyMC\nThere are many valuable tools for fitting hierarchical models in ecology. These tools are typically R libraries, such as unmarked, or programs called from R, such as JAGS or Stan. There are relatively fewer examples of how to fit these models in Python. While most ecologists, and arguably statisticians, use R, there are some benefits to using Python generally. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Besides, Python is widely used for machine learning, which is increasingly applied in ecology.\nIn the PyMC notebooks, I try to demonstrate how to use PyMC to train the most common hierarchcial models in ecology. For this, I have drawn considerable inspiration from Royle and Dorazio (2008), Kéry and Schaub (2011), McCrea and Morgan (2014), and Hooten and Hefley (2019), oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC’s core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.\n\n\n\n\n\nReferences\n\nHooten, Mevin B, and Trevor Hefley. 2019. Bringing Bayesian Models to Life. CRC Press.\n\n\nKéry, Marc, and Michael Schaub. 2011. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective. Academic Press.\n\n\nMcCrea, Rachel S, and Byron JT Morgan. 2014. Analysis of Capture-Recapture Data. CRC Press.\n\n\nRoyle, J Andrew, and Robert M Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier.",
+    "text": "Please find here any jupyter notebooks that I may have thought would be of general interest. For now, these are all about PyMC, a Python library for doing Bayesian analysis.\n\nPyMC\nThere are many valuable tools for fitting hierarchical models in ecology, including unmarked, JAGS, NIMBLE and Stan. These are, for the most part, R libraries or programs called from R. There are relatively fewer examples of how to fit these models in Python. While most ecologists use R, they may find some benefit from using Python. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Also, Python is widely used for machine learning, which is increasingly applied in ecology.\nIn the PyMC jupyter notebooks, I try to demonstrate how to use PyMC to train the most common hierarchical models in ecology. For this, I have drawn considerable inspiration from Royle and Dorazio (2008), Kéry and Schaub (2011), McCrea and Morgan (2014), and Hooten and Hefley (2019), oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC’s core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.\n\n\n\n\n\nReferences\n\nHooten, Mevin B, and Trevor Hefley. 2019. Bringing Bayesian Models to Life. CRC Press.\n\n\nKéry, Marc, and Michael Schaub. 2011. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective. Academic Press.\n\n\nMcCrea, Rachel S, and Byron JT Morgan. 2014. Analysis of Capture-Recapture Data. CRC Press.\n\n\nRoyle, J Andrew, and Robert M Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier.",
     "crumbs": [
       "Notebooks"
     ]
@@ -162,7 +162,7 @@
     "href": "comparison.html",
     "title": "Model comparison",
     "section": "",
-    "text": "In this notebook, I demonstrate an approach to model selection in PyMC. To do so, follow the lead of King and Brooks (2008), although not nearly as elegantly. They demonstrate an approach to model selection for a typical suite of closed capture-recapture models. These include the effects of behavior \\(b\\), time \\(t,\\) and individual heterogeneity \\(h\\) on capture probabilities \\(p\\). The eight models considered here are combinations of the three: \\(M_{0},\\) \\(M_{t},\\) \\(M_{b},\\) \\(M_{tb},\\) \\(M_{h},\\) \\(M_{th},\\) \\(M_{bh}\\). The full model, \\(M_{tbh}\\), is\n\\[\n\\begin{equation}\n\\text{logit} \\; p_{it} = \\mu + \\alpha_t + \\beta x_{it} + \\gamma_i,\n\\end{equation}\n\\] where \\(\\mu\\) is the average catchability, \\(\\alpha_t\\) is the effect of each occasion on catchability, \\(\\beta\\) is the behavioral effect, \\(x_{it}\\) indicates whether the individual has been previously caught, and \\(\\gamma_i\\) is the individual random effect such that \\(\\gamma_i \\sim \\text{Normal}(0,\\sigma)\\). Formulating the model this way makes the other models nested subsets of the full model.\nLike King and Brooks (2008), I use the the Moray Firth bottlenose dolphin data as a motivating example. Wilson, Hammond, and Thompson (1999) detected \\(n=56\\) dolphins over the course of \\(T=17\\) boat surveys between May and September 1992. They generated the capture-recapture histories by way of photo-identification, which is near and dear to my heart (and my dissertation).\n\n# libraries \nimport numpy as np\nimport pandas as pd\nimport pymc as pm\nimport arviz as az\nimport matplotlib.pyplot as plt \nimport seaborn as sns\nfrom pymc.distributions.dist_math import binomln, logpow\n\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\npal = sns.color_palette(\"Set2\")\nsns.set_palette(pal)\n\n# hyperparameters \nSEED = 808\nRNG = np.random.default_rng(SEED)\n\ndef augment_history(history):\n    '''Augment a capture history with all-zero histories.'''\n    \n    animals_captured, T = history.shape\n\n    # create M - n all zero histories\n    zero_history_count = M - animals_captured\n    zero_history = np.zeros((zero_history_count, T))\n\n    # tack those on to the capture history\n    augmented = np.row_stack((history, zero_history))\n\n    return augmented \n\ndef get_behavior_covariate(history):\n    \n    # note the occasion when each individual was first seen\n    first_seen = (history != 0).argmax(axis=1)\n    \n    # create the covariate for the behavior effect\n    behavior_covariate = np.zeros_like(history)\n    for i, f in enumerate(first_seen):\n        behavior_covariate[i, (f + 1):] = 1\n\n    return behavior_covariate\n\ndef get_occasion_covariate(history):\n\n    _, T = history.shape\n    l = []\n    for t in range(T):\n        oc = np.zeros_like(history)\n        oc[:, t] = 1\n        l.append(oc)\n\n    return np.stack(l, axis=2)\n\ndef sim_N(idata):\n    \n    psi_samps = az.extract(idata).psi.values\n    p_samps = az.extract(idata).p.values\n    not_p = (1 - p_samps)\n    \n    if p_samps.ndim == 1:\n        p_included = psi_samps * (not_p) ** T \n        number_undetected = RNG.binomial(M - n, p_included)\n\n    elif p_samps.ndim == 3:\n        p_included = psi_samps * not_p.prod(axis=1)\n        number_undetected = RNG.binomial(1, p_included).sum(axis=0)\n\n    N = n + number_undetected\n    return N\n\n# convert the dolphin capture history from '1001001' to array\ndolphin = np.loadtxt('firth.txt', dtype=str)\ndolphin = np.array([list(map(int, d)) for d in dolphin])\n\n# augment the capture history with all zero histories\nn, T = dolphin.shape\nM = 500\ndolphin_augmented = augment_history(dolphin)\n\n# covariates for t and b\noccasion_covariate = get_occasion_covariate(dolphin_augmented)\nbehavior_covariate = get_behavior_covariate(dolphin_augmented)\n\nThe discovery curve, the number of unique dolphins encountered as a function of the total number of dolphins encountered, may be flattening. This suggests that, at this point in the study, Wilson, Hammond, and Thompson (1999) may have encountered many of the unique individuals in the population.\n\n# how many dolphins have been seen?\ntotal_seen = dolphin.sum(axis=0).cumsum()\n\n# how many new dolphins have been seen?\nfirst_seen = (dolphin != 0).argmax(axis=1)\nnewbies = [sum(first_seen == t) for t in range(T)]\ntotal_newbies = np.cumsum(newbies)\n\nfig, ax = plt.subplots(figsize=(5, 3.5))\nax.plot(total_seen, total_newbies)\nax.fill_between(total_seen, total_newbies, alpha=0.2)\nax.set_title('Discovery curve')\nax.set_xlabel('Total dolphins')\nax.set_ylabel('Unique dolphins')\nplt.show()\n\n\n\n\n\n\n\nFigure 1: Discovery curve for the Moray Firth bottlenose dolphin surveys (Wilson, Hammond, and Thompson 1999).\n\n\n\n\n\n\nTraining each model\nThis notebook looks messier than the others, in that I train several models with little commentary along the way. In practice, it would probably be better to wrap these up into a function or a class. To complete the model, I used the following priors, \\[\n\\begin{align}\n\\psi &\\sim \\text{Uniform}(0, 1)\\\\\n\\mu &\\sim \\text{Logistic}(0, 1) \\\\\n\\alpha_t &\\sim \\text{Normal}(0, \\sigma_{\\alpha}) \\\\\n\\beta &\\sim \\text{Normal}(0, \\sigma_{\\beta}) \\\\\n\\gamma_i &\\sim \\text{Normal}(0, \\sigma_{\\gamma}) \\\\\n\\sigma_{\\alpha} &\\sim \\text{InverseGamma}(4, 3) \\\\\n\\sigma_{\\beta} &\\sim \\text{InverseGamma}(4, 3) \\\\\n\\sigma_{\\gamma} &\\sim \\text{InverseGamma}(4, 3),\n\\end{align}\n\\] which were also used by King and Brooks (2008). Although note that I used an informative \\(\\text{Beta}(1, 5)\\) prior for \\(\\psi\\) in the full model (see below). I use the same logp seen in the occupancy and closed capture-recapture notebooks, which accounts for row-level zero-inflation. Unlike other notebooks, I did not look at the summaries or the trace plots unless the sampler indicated that it had issues during training.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\n\nwith pm.Model() as m0:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # Linear model\n    mu_matrix = (np.ones((T, M)) * mu).T\n    p = pm.Deterministic('p', pm.math.invlogit(mu_matrix))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(m0)\n\n\n\n\n\n\n\nFigure 2: Visual representation of model \\(M_{0}\\).\n\n\n\n\n\n\nwith m0:\n    m0_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mt:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # Linear model\n    nu = mu + pm.math.dot(occasion_covariate, alpha)\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mt)\n\n\n\n\n\n\n\nFigure 3: Visual representation of model \\(M_t\\).\n\n\n\n\n\n\nwith mt:\n    mt_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 28 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:27&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mb:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n    \n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # Linear model\n    nu = mu + behavior_covariate * beta \n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mb)\n\n\n\n\n\n\n\nFigure 4: Visual representation of model \\(M_b\\).\n\n\n\n\n\n\nwith mb:\n    mb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_beta, beta]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 13 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:12&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mtb:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # Linear model\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + behavior_covariate * beta\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mtb)\n\n\n\n\n\n\n\nFigure 5: Visual representation of model \\(M_{tb}\\).\n\n\n\n\n\n\nwith mtb:\n    mtb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_beta, beta]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 37 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:36&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mh:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mh)\n\n\n\n\n\n\n\nFigure 6: Visual representation of model \\(M_h\\).\n\n\n\n\n\n\nwith mh:\n    mh_idata = pm.sample(3000, target_accept=0.99)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 92 seconds.\nThe rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 01:32&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mh_idata, var_names=['psi', 'mu', 'sigma_gamma'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.184\n0.038\n0.122\n0.253\n0.002\n0.001\n608.0\n1078.0\n1.01\n\n\nmu\n-2.784\n0.323\n-3.377\n-2.216\n0.017\n0.012\n390.0\n706.0\n1.02\n\n\nsigma_gamma\n0.777\n0.336\n0.295\n1.396\n0.022\n0.016\n215.0\n468.0\n1.03\n\n\n\n\n\n\n\n\naz.plot_trace(mh_idata, figsize=(8, 6), var_names=['psi', 'mu', 'sigma_gamma']);\n\n\n\n\n\n\n\n\n\nwith pm.Model() as mth:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mth)\n\n\n\n\n\n\n\nFigure 7: Visual representation of model \\(M_{th}\\).\n\n\n\n\n\n\nwith mth:\n    mth_idata = pm.sample(draws=3000, target_accept=0.95)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 213 seconds.\nThe rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 03:32&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mth_idata, var_names=['psi', 'mu', 'sigma_alpha', 'sigma_gamma', 'alpha'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.177\n0.035\n0.118\n0.238\n0.001\n0.001\n700.0\n983.0\n1.00\n\n\nmu\n-3.006\n0.401\n-3.775\n-2.289\n0.017\n0.012\n575.0\n900.0\n1.01\n\n\nsigma_alpha\n0.923\n0.358\n0.375\n1.565\n0.004\n0.003\n9159.0\n7416.0\n1.00\n\n\nsigma_gamma\n0.822\n0.357\n0.312\n1.452\n0.023\n0.016\n241.0\n464.0\n1.01\n\n\nalpha[0]\n-1.107\n0.617\n-2.330\n-0.006\n0.006\n0.005\n11171.0\n7188.0\n1.00\n\n\nalpha[1]\n0.563\n0.414\n-0.206\n1.354\n0.005\n0.004\n6862.0\n6647.0\n1.00\n\n\nalpha[2]\n-0.807\n0.557\n-1.889\n0.177\n0.005\n0.005\n10878.0\n8036.0\n1.00\n\n\nalpha[3]\n0.563\n0.407\n-0.210\n1.331\n0.005\n0.003\n7144.0\n7485.0\n1.00\n\n\nalpha[4]\n0.449\n0.415\n-0.339\n1.221\n0.005\n0.004\n7572.0\n7528.0\n1.00\n\n\nalpha[5]\n0.780\n0.401\n0.054\n1.571\n0.005\n0.004\n6376.0\n7094.0\n1.00\n\n\nalpha[6]\n0.177\n0.444\n-0.662\n1.015\n0.005\n0.004\n7483.0\n7897.0\n1.00\n\n\nalpha[7]\n0.026\n0.451\n-0.830\n0.866\n0.005\n0.004\n8610.0\n7551.0\n1.00\n\n\nalpha[8]\n-0.803\n0.562\n-1.839\n0.254\n0.006\n0.005\n10471.0\n7051.0\n1.00\n\n\nalpha[9]\n0.027\n0.455\n-0.847\n0.858\n0.005\n0.004\n8828.0\n8326.0\n1.00\n\n\nalpha[10]\n1.151\n0.382\n0.441\n1.884\n0.005\n0.004\n5996.0\n6314.0\n1.00\n\n\nalpha[11]\n-0.335\n0.496\n-1.274\n0.593\n0.005\n0.005\n8981.0\n7564.0\n1.00\n\n\nalpha[12]\n-1.106\n0.598\n-2.217\n0.008\n0.005\n0.004\n12939.0\n8242.0\n1.00\n\n\nalpha[13]\n-0.151\n0.477\n-1.018\n0.763\n0.005\n0.004\n7860.0\n6848.0\n1.00\n\n\nalpha[14]\n-1.107\n0.612\n-2.290\n-0.007\n0.006\n0.005\n12375.0\n8210.0\n1.00\n\n\nalpha[15]\n1.604\n0.363\n0.920\n2.279\n0.005\n0.003\n5734.0\n7286.0\n1.00\n\n\nalpha[16]\n-0.805\n0.564\n-1.852\n0.234\n0.005\n0.004\n12198.0\n7592.0\n1.00\n\n\n\n\n\n\n\n\naz.plot_trace(mth_idata, figsize=(8, 10),\n              var_names=['psi', 'mu', 'sigma_alpha', 'sigma_gamma', 'alpha']);\n\n\n\n\n\n\n\nFigure 8: Trace plots for model \\(M_{th}\\).\n\n\n\n\n\n\nwith pm.Model() as mbh:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n    \n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + behavior_covariate * beta + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mbh)\n\n\n\n\n\n\n\nFigure 9: Visual representation of model \\(M_{bh}\\).\n\n\n\n\n\n\nwith mbh:\n    mbh_idata = pm.sample(draws=3000, target_accept=0.95)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_beta, beta, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 359 seconds.\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 05:59&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mbh_idata, var_names=['psi', 'mu', 'beta', 'sigma_gamma'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.551\n0.190\n0.251\n0.925\n0.010\n0.007\n385.0\n1566.0\n1.00\n\n\nmu\n-3.491\n0.586\n-4.582\n-2.452\n0.035\n0.025\n285.0\n694.0\n1.00\n\n\nbeta\n-1.538\n0.259\n-2.013\n-1.037\n0.006\n0.004\n2013.0\n4830.0\n1.00\n\n\nsigma_gamma\n2.117\n0.772\n0.843\n3.505\n0.050\n0.035\n233.0\n572.0\n1.01\n\n\n\n\n\n\n\n\naz.plot_trace(mbh_idata, figsize=(8, 10),\n              var_names=['psi', 'mu', 'beta', 'sigma_beta', 'sigma_gamma']);\n\n\n\n\n\n\n\n\n\nwith pm.Model() as mtbh:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 5)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + behavior_covariate * beta + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mtbh)\n\n\n\n\n\n\n\nFigure 10: Visual representation of model \\(M_{tbh}\\).\n\n\n\n\n\n\nwith mtbh:\n    mtbh_idata = pm.sample(draws=2000)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_beta, beta, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 90 seconds.\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 01:30&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mtbh_idata, \n           var_names=['psi', 'mu', 'alpha', 'beta', 'sigma_alpha', 'sigma_beta', 'sigma_gamma'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.713\n0.102\n0.522\n0.898\n0.002\n0.001\n3048.0\n4771.0\n1.0\n\n\nmu\n-3.100\n0.466\n-4.020\n-2.262\n0.015\n0.010\n997.0\n2002.0\n1.0\n\n\nalpha[0]\n-3.116\n0.699\n-4.435\n-1.844\n0.011\n0.008\n3818.0\n4897.0\n1.0\n\n\nalpha[1]\n-0.670\n0.509\n-1.608\n0.297\n0.010\n0.007\n2799.0\n3931.0\n1.0\n\n\nalpha[2]\n-1.697\n0.659\n-2.967\n-0.501\n0.010\n0.008\n4092.0\n4887.0\n1.0\n\n\nalpha[3]\n-0.053\n0.488\n-0.969\n0.871\n0.009\n0.006\n3164.0\n4502.0\n1.0\n\n\nalpha[4]\n0.214\n0.481\n-0.677\n1.126\n0.008\n0.006\n3506.0\n4991.0\n1.0\n\n\nalpha[5]\n0.835\n0.451\n-0.030\n1.653\n0.008\n0.006\n3053.0\n4420.0\n1.0\n\n\nalpha[6]\n0.381\n0.499\n-0.537\n1.307\n0.008\n0.005\n4143.0\n4921.0\n1.0\n\n\nalpha[7]\n0.380\n0.509\n-0.627\n1.289\n0.008\n0.006\n3847.0\n4482.0\n1.0\n\n\nalpha[8]\n-0.553\n0.661\n-1.814\n0.665\n0.009\n0.007\n5914.0\n4893.0\n1.0\n\n\nalpha[9]\n0.465\n0.528\n-0.536\n1.436\n0.008\n0.006\n4120.0\n4516.0\n1.0\n\n\nalpha[10]\n1.666\n0.432\n0.837\n2.459\n0.008\n0.006\n2646.0\n4381.0\n1.0\n\n\nalpha[11]\n0.138\n0.562\n-0.936\n1.181\n0.008\n0.006\n5079.0\n5170.0\n1.0\n\n\nalpha[12]\n-0.898\n0.752\n-2.312\n0.477\n0.010\n0.008\n6392.0\n5209.0\n1.0\n\n\nalpha[13]\n0.389\n0.542\n-0.638\n1.390\n0.008\n0.006\n4114.0\n5409.0\n1.0\n\n\nalpha[14]\n-0.868\n0.746\n-2.272\n0.493\n0.009\n0.008\n7017.0\n5038.0\n1.0\n\n\nalpha[15]\n2.242\n0.417\n1.457\n3.003\n0.008\n0.006\n2503.0\n4622.0\n1.0\n\n\nalpha[16]\n-0.257\n0.686\n-1.512\n1.030\n0.009\n0.008\n6241.0\n5109.0\n1.0\n\n\nbeta\n-3.283\n0.388\n-4.001\n-2.549\n0.007\n0.005\n2715.0\n4782.0\n1.0\n\n\nsigma_alpha\n1.555\n0.597\n0.676\n2.669\n0.009\n0.007\n4883.0\n5056.0\n1.0\n\n\nsigma_beta\n2.381\n1.502\n0.610\n4.921\n0.020\n0.015\n8249.0\n5157.0\n1.0\n\n\nsigma_gamma\n2.779\n0.650\n1.681\n4.051\n0.033\n0.023\n383.0\n839.0\n1.0\n\n\n\n\n\n\n\n\naz.plot_trace(mtbh_idata, figsize=(8,14),\n           var_names=['psi', 'mu', 'alpha', 'beta', 'sigma_alpha', 'sigma_beta', 'sigma_gamma']);\n\n\n\n\n\n\n\nFigure 11: Trace plots for the model with \\(M_{tbh}\\).\n\n\n\n\n\nThe trace plots and summary statistics show convergence issues for many of the individual heterogeneity models. The variance parameter, \\(\\sigma_{\\gamma},\\) seems to sample poorly. Further, models with both behavioral and individual effects lead to extremely large estimates of \\(\\psi\\). This appears to happen regardless of the size of the data augmentation \\(M.\\)\nNote that I upped the target_accept value for some models. This slows the sampler, but lowers the risk of divergence.\n\n\nModel comparison\nNext, I select a model for inference using an approximation of leave-one-out (loo) cross-validation (Vehtari, Gelman, and Gabry 2017). This approximation can be calculated using PyMC. To do so, I calculate the log-likelihood for each model, which is added to the InferenceData object. This makes it possible to compare the models using loo and az.compare.\n\nwith m0:\n    pm.compute_log_likelihood(m0_idata)\n\nwith mt:\n    pm.compute_log_likelihood(mt_idata)\n\nwith mb:\n    pm.compute_log_likelihood(mb_idata)\n\nwith mtb:\n    pm.compute_log_likelihood(mtb_idata)\n\nwith mh:\n    pm.compute_log_likelihood(mh_idata)\n\nwith mth:\n    pm.compute_log_likelihood(mth_idata)\n\nwith mbh:\n    pm.compute_log_likelihood(mbh_idata)\n\nwith mtbh:\n    pm.compute_log_likelihood(mtbh_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:00&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:00&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:04&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:03&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:03&lt;00:00]\n    \n    \n\n\n\nmodel_dict = {\"m0\": m0_idata, \"mt\": mt_idata, \"mb\": mb_idata, \n              \"mtb\": mtb_idata, \"mh\": mh_idata, \"mth\": mth_idata, \n              \"mbh\": mbh_idata, \"mtbh\": mtbh_idata}\n\ncomparison = az.compare(model_dict)\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n\n\nThe comparison tools notes issues with several of the models, suggesting a lack of robustness. Inspection of the comparison table shows that the struggling models all include the individual effect \\(h.\\) A more thorough analysis would consider reparameterizing the model, e.g., through the non-centered parameterization. In lieu of that, I simply discard the models that fail this test and re-do the comparison with the passing models.\n\ncomparison.round(2)\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\nmtbh\n0\n-430.62\n87.06\n0.00\n0.97\n57.18\n0.00\nTrue\nlog\n\n\nmth\n1\n-489.22\n38.02\n58.60\n0.00\n58.89\n11.21\nTrue\nlog\n\n\nmtb\n2\n-493.26\n21.47\n62.64\n0.00\n62.12\n11.43\nTrue\nlog\n\n\nmt\n3\n-493.92\n14.34\n63.30\n0.00\n59.54\n12.50\nFalse\nlog\n\n\nmbh\n4\n-495.19\n68.06\n64.57\n0.00\n60.82\n11.47\nTrue\nlog\n\n\nmh\n5\n-518.62\n25.59\n88.00\n0.03\n61.47\n15.31\nTrue\nlog\n\n\nmb\n6\n-521.53\n5.45\n90.91\n0.00\n63.17\n15.28\nFalse\nlog\n\n\nm0\n7\n-522.55\n2.50\n91.93\n0.00\n62.10\n16.12\nFalse\nlog\n\n\n\n\n\n\n\n\ngood_dict = {\"m0\": m0_idata, \"mt\": mt_idata, \"mb\": mb_idata, \"mtb\": mtb_idata}\ngood_comparison = az.compare(good_dict)\ngood_comparison.round(2)\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\nmtb\n0\n-493.26\n21.47\n0.00\n0.61\n62.12\n0.00\nTrue\nlog\n\n\nmt\n1\n-493.92\n14.34\n0.66\n0.39\n59.54\n6.77\nFalse\nlog\n\n\nmb\n2\n-521.53\n5.45\n28.28\n0.00\n63.17\n8.61\nFalse\nlog\n\n\nm0\n3\n-522.55\n2.50\n29.29\n0.00\n62.10\n10.41\nFalse\nlog\n\n\n\n\n\n\n\n\naz.plot_compare(good_comparison, figsize=(5, 4));\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/plots/backends/matplotlib/compareplot.py:87: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n  scale = comp_df[\"scale\"][0]\n\n\n\n\n\n\n\n\nFigure 12: Differences in the ELPD criteria, calculated using loo, for each model (Vehtari, Gelman, and Gabry 2017).\n\n\n\n\n\nThe comparison shows that all of the model weight belongs to two models: \\(M_t\\) and \\(M_{tb}.\\)\n\n\nModel averaged predictions\nFinally, we can use the model weights to simulate a weighted posterior of \\(N.\\) To do so, I take a weighted sample of each of the posteriors of \\(N,\\) with the weight dictated by the comparison tool.\n\nposteriors = [sim_N(good_dict[model]) for model in good_dict]\nweights = [good_comparison.loc[model].weight for model in good_dict]\nsample_count = len(posteriors[0])\n\nl = []\nfor w, p in zip(weights, posteriors):\n    weighted_sample = RNG.choice(p, size=int(w * sample_count))\n    l.append(weighted_sample)\n\nweighted_posterior = np.concatenate(l)\n\nfig, (ax0, ax1) = plt.subplots(2, 1, figsize=(7, 6), sharex=True, sharey=True, tight_layout=True)\n\npal = sns.color_palette(\"Set2\")\n\n# labs = [k for k in good_dict.keys()]\nlabs = [r'$M_{0}$', r'$M_{t}$', r'$M_{b}$', r'$M_{tb}$']\nfor i, p in enumerate(posteriors):\n    ax0.hist(p, color=pal[i], edgecolor='white', bins=60, alpha=0.6, label=labs[i])\n\nax0.set_title(r'Posteriors of $N$')\n# ax1.set_title(r'Weighted posterior')\n\nax0.set_xlim((53, 150))\nax0.legend()\n\nax0.set_ylabel('Number of samples')\nax1.set_ylabel('Number of samples')\n\nax1.hist(weighted_posterior, edgecolor='white', bins=60, alpha=0.9, color=pal[6], label='Weighted')\nax1.legend()\n\nplt.show()\n\n\n\n\n\n\n\nFigure 13: Posteriors of \\(N\\) from the four models under consideration (top panel), with the model averaged posterior (bottom panel).\n\n\n\n\n\nWe can also look at the posterior densities of \\(p\\) from Model \\(M_t,\\) the second most weighted model.\n\np_samps = az.extract(mt_idata).p.mean(axis=0)\n\nfig, ax = plt.subplots(figsize=(6, 4))\n\na = 0.4\n# ax[0].set_title(\"Poisson\")\npal = sns.color_palette('viridis', T)\nfor t in range(T):\n    label_idx = t % 2\n    if label_idx == 0:\n        az.plot_dist(p_samps[t], ax=ax, color=pal[t], label=f'$t_{{{t}}}$',\n                     plot_kwargs={'linewidth':3, 'alpha': a})\n    else:\n        az.plot_dist(p_samps[t], ax=ax, color=pal[t],\n                     plot_kwargs={'linewidth':3, 'alpha': a})\n\nax.set_title(r'Posterior densities of $p$ from $M_t$')\nax.set_xlabel(r'$p$')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 14: Posteriors of \\(p\\) from model \\(M_t\\)\n\n\n\n\n\nThis notebook demonstrates a simple way to compare models using leave one out cross-validation (loo) and a classic example from capture-recapture. This is just one way, however, to perform model comparison using PyMC. Perhaps a more effective solution for this problem would be placing a shrinkage prior on the \\(\\sigma\\) parameters.\n\n\n\n\n\nReferences\n\nKing, Ruth, and SP2526632 Brooks. 2008. “On the Bayesian Estimation of a Closed Population Size in the Presence of Heterogeneity and Model Uncertainty.” Biometrics 64 (3): 816–24.\n\n\nVehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. “Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC.” Statistics and Computing 27: 1413–32.\n\n\nWilson, Ben, Philip S Hammond, and Paul M Thompson. 1999. “Estimating Size and Assessing Trends in a Coastal Bottlenose Dolphin Population.” Ecological Applications 9 (1): 288–300.",
+    "text": "In this notebook, I demonstrate an approach to model selection in PyMC. To do so, follow the lead of King and Brooks (2008), although not nearly as elegantly. They demonstrate an approach to model selection for a typical suite of closed capture-recapture models. These include the effects of behavior \\(b\\), time \\(t,\\) and individual heterogeneity \\(h\\) on capture probabilities \\(p\\). The eight models considered here are combinations of the three: \\(M_{0},\\) \\(M_{t},\\) \\(M_{b},\\) \\(M_{tb},\\) \\(M_{h},\\) \\(M_{th},\\) \\(M_{bh}\\). The full model, \\(M_{tbh}\\), is\n\\[\n\\begin{equation}\n\\text{logit} \\; p_{it} = \\mu + \\alpha_t + \\beta x_{it} + \\gamma_i,\n\\end{equation}\n\\] where \\(\\mu\\) is the average catchability, \\(\\alpha_t\\) is the effect of each occasion on catchability, \\(\\beta\\) is the behavioral effect, \\(x_{it}\\) indicates whether the individual has been previously caught, and \\(\\gamma_i\\) is the individual random effect such that \\(\\gamma_i \\sim \\text{Normal}(0,\\sigma)\\). Formulating the model this way makes the other models nested subsets of the full model.\nLike King and Brooks (2008), I use the the Moray Firth bottlenose dolphin data as a motivating example. Wilson, Hammond, and Thompson (1999) detected \\(n=56\\) dolphins over the course of \\(T=17\\) boat surveys between May and September 1992. They generated the capture-recapture histories by way of photo-identification, which is near and dear to my heart (and my dissertation).\n\n# libraries \nimport numpy as np\nimport pandas as pd\nimport pymc as pm\nimport arviz as az\nimport matplotlib.pyplot as plt \nimport seaborn as sns\nfrom pymc.distributions.dist_math import binomln, logpow\n\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\npal = sns.color_palette(\"Set2\")\nsns.set_palette(pal)\n\n# hyperparameters \nSEED = 808\nRNG = np.random.default_rng(SEED)\n\ndef augment_history(history):\n    '''Augment a capture history with all-zero histories.'''\n    \n    animals_captured, T = history.shape\n\n    # create M - n all zero histories\n    zero_history_count = M - animals_captured\n    zero_history = np.zeros((zero_history_count, T))\n\n    # tack those on to the capture history\n    augmented = np.row_stack((history, zero_history))\n\n    return augmented \n\ndef get_behavior_covariate(history):\n    \n    # note the occasion when each individual was first seen\n    first_seen = (history != 0).argmax(axis=1)\n    \n    # create the covariate for the behavior effect\n    behavior_covariate = np.zeros_like(history)\n    for i, f in enumerate(first_seen):\n        behavior_covariate[i, (f + 1):] = 1\n\n    return behavior_covariate\n\ndef get_occasion_covariate(history):\n\n    _, T = history.shape\n    l = []\n    for t in range(T):\n        oc = np.zeros_like(history)\n        oc[:, t] = 1\n        l.append(oc)\n\n    return np.stack(l, axis=2)\n\ndef sim_N(idata):\n    \n    psi_samps = az.extract(idata).psi.values\n    p_samps = az.extract(idata).p.values\n    not_p = (1 - p_samps)\n    \n    if p_samps.ndim == 1:\n        p_included = psi_samps * (not_p) ** T \n        number_undetected = RNG.binomial(M - n, p_included)\n\n    elif p_samps.ndim == 3:\n        p_included = psi_samps * not_p.prod(axis=1)\n        number_undetected = RNG.binomial(1, p_included).sum(axis=0)\n\n    N = n + number_undetected\n    return N\n\n# convert the dolphin capture history from '1001001' to array\ndolphin = np.loadtxt('firth.txt', dtype=str)\ndolphin = np.array([list(map(int, d)) for d in dolphin])\n\n# augment the capture history with all zero histories\nn, T = dolphin.shape\nM = 500\ndolphin_augmented = augment_history(dolphin)\n\n# covariates for t and b\noccasion_covariate = get_occasion_covariate(dolphin_augmented)\nbehavior_covariate = get_behavior_covariate(dolphin_augmented)\n\nThe discovery curve, the number of unique dolphins encountered as a function of the total number of dolphins encountered, may be flattening. This suggests that, at this point in the study, Wilson, Hammond, and Thompson (1999) may have encountered many of the unique individuals in the population.\n\n# how many dolphins have been seen?\ntotal_seen = dolphin.sum(axis=0).cumsum()\n\n# how many new dolphins have been seen?\nfirst_seen = (dolphin != 0).argmax(axis=1)\nnewbies = [sum(first_seen == t) for t in range(T)]\ntotal_newbies = np.cumsum(newbies)\n\nfig, ax = plt.subplots(figsize=(5, 3.5))\nax.plot(total_seen, total_newbies)\nax.fill_between(total_seen, total_newbies, alpha=0.2)\nax.set_title('Discovery curve')\nax.set_xlabel('Total dolphins')\nax.set_ylabel('Unique dolphins')\nplt.show()\n\n\n\n\n\n\n\nFigure 1: Discovery curve for the Moray Firth bottlenose dolphin surveys (Wilson, Hammond, and Thompson 1999).\n\n\n\n\n\n\nTraining each model\nThis notebook looks messier than the others, in that I train several models with little commentary along the way. In practice, it would probably be better to wrap these up into a function or a class. To complete the model, I used the following priors, \\[\n\\begin{align}\n\\psi &\\sim \\text{Uniform}(0, 1)\\\\\n\\mu &\\sim \\text{Logistic}(0, 1) \\\\\n\\alpha_t &\\sim \\text{Normal}(0, \\sigma_{\\alpha}) \\\\\n\\beta &\\sim \\text{Normal}(0, \\sigma_{\\beta}) \\\\\n\\gamma_i &\\sim \\text{Normal}(0, \\sigma_{\\gamma}) \\\\\n\\sigma_{\\alpha} &\\sim \\text{InverseGamma}(4, 3) \\\\\n\\sigma_{\\beta} &\\sim \\text{InverseGamma}(4, 3) \\\\\n\\sigma_{\\gamma} &\\sim \\text{InverseGamma}(4, 3),\n\\end{align}\n\\] which were also used by King and Brooks (2008). Although note that I used an informative \\(\\text{Beta}(1, 5)\\) prior for \\(\\psi\\) in the full model (see below). I use the same logp seen in the occupancy and closed capture-recapture notebooks, which accounts for row-level zero-inflation. Unlike other notebooks, I did not look at the summaries or the trace plots unless the sampler indicated that it had issues during training.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\n\nwith pm.Model() as m0:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # Linear model\n    mu_matrix = (np.ones((T, M)) * mu).T\n    p = pm.Deterministic('p', pm.math.invlogit(mu_matrix))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(m0)\n\n\n\n\n\n\n\nFigure 2: Visual representation of model \\(M_{0}\\).\n\n\n\n\n\n\nwith m0:\n    m0_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mt:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # Linear model\n    nu = mu + pm.math.dot(occasion_covariate, alpha)\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mt)\n\n\n\n\n\n\n\nFigure 3: Visual representation of model \\(M_t\\).\n\n\n\n\n\n\nwith mt:\n    mt_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 28 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:27&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mb:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n    \n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # Linear model\n    nu = mu + behavior_covariate * beta \n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mb)\n\n\n\n\n\n\n\nFigure 4: Visual representation of model \\(M_b\\).\n\n\n\n\n\n\nwith mb:\n    mb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_beta, beta]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 13 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:12&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mtb:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # Linear model\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + behavior_covariate * beta\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mtb)\n\n\n\n\n\n\n\nFigure 5: Visual representation of model \\(M_{tb}\\).\n\n\n\n\n\n\nwith mtb:\n    mtb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_beta, beta]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 37 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:36&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\nwith pm.Model() as mh:\n\n    # Priors\n    # inclusion\n    psi = pm.Uniform('psi', 0, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mh)\n\n\n\n\n\n\n\nFigure 6: Visual representation of model \\(M_h\\).\n\n\n\n\n\n\nwith mh:\n    mh_idata = pm.sample(3000, target_accept=0.99)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 92 seconds.\nThe rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 01:32&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mh_idata, var_names=['psi', 'mu', 'sigma_gamma'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.184\n0.038\n0.122\n0.253\n0.002\n0.001\n608.0\n1078.0\n1.01\n\n\nmu\n-2.784\n0.323\n-3.377\n-2.216\n0.017\n0.012\n390.0\n706.0\n1.02\n\n\nsigma_gamma\n0.777\n0.336\n0.295\n1.396\n0.022\n0.016\n215.0\n468.0\n1.03\n\n\n\n\n\n\n\n\n\naz.plot_trace(mh_idata, figsize=(8, 6), var_names=['psi', 'mu', 'sigma_gamma']);\n\n\n\n\n\n\n\n\n\nwith pm.Model() as mth:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mth)\n\n\n\n\n\n\n\nFigure 7: Visual representation of model \\(M_{th}\\).\n\n\n\n\n\n\nwith mth:\n    mth_idata = pm.sample(draws=3000, target_accept=0.95)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 213 seconds.\nThe rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 03:32&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mth_idata, var_names=['psi', 'mu', 'sigma_alpha', 'sigma_gamma', 'alpha'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.177\n0.035\n0.118\n0.238\n0.001\n0.001\n700.0\n983.0\n1.00\n\n\nmu\n-3.006\n0.401\n-3.775\n-2.289\n0.017\n0.012\n575.0\n900.0\n1.01\n\n\nsigma_alpha\n0.923\n0.358\n0.375\n1.565\n0.004\n0.003\n9159.0\n7416.0\n1.00\n\n\nsigma_gamma\n0.822\n0.357\n0.312\n1.452\n0.023\n0.016\n241.0\n464.0\n1.01\n\n\nalpha[0]\n-1.107\n0.617\n-2.330\n-0.006\n0.006\n0.005\n11171.0\n7188.0\n1.00\n\n\nalpha[1]\n0.563\n0.414\n-0.206\n1.354\n0.005\n0.004\n6862.0\n6647.0\n1.00\n\n\nalpha[2]\n-0.807\n0.557\n-1.889\n0.177\n0.005\n0.005\n10878.0\n8036.0\n1.00\n\n\nalpha[3]\n0.563\n0.407\n-0.210\n1.331\n0.005\n0.003\n7144.0\n7485.0\n1.00\n\n\nalpha[4]\n0.449\n0.415\n-0.339\n1.221\n0.005\n0.004\n7572.0\n7528.0\n1.00\n\n\nalpha[5]\n0.780\n0.401\n0.054\n1.571\n0.005\n0.004\n6376.0\n7094.0\n1.00\n\n\nalpha[6]\n0.177\n0.444\n-0.662\n1.015\n0.005\n0.004\n7483.0\n7897.0\n1.00\n\n\nalpha[7]\n0.026\n0.451\n-0.830\n0.866\n0.005\n0.004\n8610.0\n7551.0\n1.00\n\n\nalpha[8]\n-0.803\n0.562\n-1.839\n0.254\n0.006\n0.005\n10471.0\n7051.0\n1.00\n\n\nalpha[9]\n0.027\n0.455\n-0.847\n0.858\n0.005\n0.004\n8828.0\n8326.0\n1.00\n\n\nalpha[10]\n1.151\n0.382\n0.441\n1.884\n0.005\n0.004\n5996.0\n6314.0\n1.00\n\n\nalpha[11]\n-0.335\n0.496\n-1.274\n0.593\n0.005\n0.005\n8981.0\n7564.0\n1.00\n\n\nalpha[12]\n-1.106\n0.598\n-2.217\n0.008\n0.005\n0.004\n12939.0\n8242.0\n1.00\n\n\nalpha[13]\n-0.151\n0.477\n-1.018\n0.763\n0.005\n0.004\n7860.0\n6848.0\n1.00\n\n\nalpha[14]\n-1.107\n0.612\n-2.290\n-0.007\n0.006\n0.005\n12375.0\n8210.0\n1.00\n\n\nalpha[15]\n1.604\n0.363\n0.920\n2.279\n0.005\n0.003\n5734.0\n7286.0\n1.00\n\n\nalpha[16]\n-0.805\n0.564\n-1.852\n0.234\n0.005\n0.004\n12198.0\n7592.0\n1.00\n\n\n\n\n\n\n\n\n\naz.plot_trace(mth_idata, figsize=(8, 10),\n              var_names=['psi', 'mu', 'sigma_alpha', 'sigma_gamma', 'alpha']);\n\n\n\n\n\n\n\nFigure 8: Trace plots for model \\(M_{th}\\).\n\n\n\n\n\n\nwith pm.Model() as mbh:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 1)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n    \n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + behavior_covariate * beta + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mbh)\n\n\n\n\n\n\n\nFigure 9: Visual representation of model \\(M_{bh}\\).\n\n\n\n\n\n\nwith mbh:\n    mbh_idata = pm.sample(draws=3000, target_accept=0.95)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_beta, beta, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 359 seconds.\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [16000/16000 05:59&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mbh_idata, var_names=['psi', 'mu', 'beta', 'sigma_gamma'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.551\n0.190\n0.251\n0.925\n0.010\n0.007\n385.0\n1566.0\n1.00\n\n\nmu\n-3.491\n0.586\n-4.582\n-2.452\n0.035\n0.025\n285.0\n694.0\n1.00\n\n\nbeta\n-1.538\n0.259\n-2.013\n-1.037\n0.006\n0.004\n2013.0\n4830.0\n1.00\n\n\nsigma_gamma\n2.117\n0.772\n0.843\n3.505\n0.050\n0.035\n233.0\n572.0\n1.01\n\n\n\n\n\n\n\n\n\naz.plot_trace(mbh_idata, figsize=(8, 10),\n              var_names=['psi', 'mu', 'beta', 'sigma_beta', 'sigma_gamma']);\n\n\n\n\n\n\n\n\n\nwith pm.Model() as mtbh:\n\n    # Priors\n    # inclusion\n    psi = pm.Beta('psi', 1, 5)  \n\n    # mean catchability \n    mu = pm.Logistic('mu', 0, 1)\n\n    # time effect\n    sigma_alpha = pm.InverseGamma('sigma_alpha', 4, 3)\n    alpha = pm.Normal('alpha', 0, tau=1/sigma_alpha, shape=T)\n\n    # behavior effect\n    sigma_beta = pm.InverseGamma('sigma_beta', 4, 3)\n    beta = pm.Normal('beta', 0, tau=1/sigma_beta)\n\n    # individual effect\n    sigma_gamma = pm.InverseGamma('sigma_gamma', 4, 3)\n    gamma = pm.Normal('gamma', 0, tau=1/sigma_gamma, shape=M)\n\n    # Linear model\n    individual_effect = (np.ones((T, M)) * gamma).T\n    nu = mu + pm.math.dot(occasion_covariate, alpha) + behavior_covariate * beta + individual_effect\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # Likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=dolphin_augmented\n    )\n    \npm.model_to_graphviz(mtbh)\n\n\n\n\n\n\n\nFigure 10: Visual representation of model \\(M_{tbh}\\).\n\n\n\n\n\n\nwith mtbh:\n    mtbh_idata = pm.sample(draws=2000)\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, mu, sigma_alpha, alpha, sigma_beta, beta, sigma_gamma, gamma]\nSampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 90 seconds.\nThe effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 01:30&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mtbh_idata, \n           var_names=['psi', 'mu', 'alpha', 'beta', 'sigma_alpha', 'sigma_beta', 'sigma_gamma'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\npsi\n0.713\n0.102\n0.522\n0.898\n0.002\n0.001\n3048.0\n4771.0\n1.0\n\n\nmu\n-3.100\n0.466\n-4.020\n-2.262\n0.015\n0.010\n997.0\n2002.0\n1.0\n\n\nalpha[0]\n-3.116\n0.699\n-4.435\n-1.844\n0.011\n0.008\n3818.0\n4897.0\n1.0\n\n\nalpha[1]\n-0.670\n0.509\n-1.608\n0.297\n0.010\n0.007\n2799.0\n3931.0\n1.0\n\n\nalpha[2]\n-1.697\n0.659\n-2.967\n-0.501\n0.010\n0.008\n4092.0\n4887.0\n1.0\n\n\nalpha[3]\n-0.053\n0.488\n-0.969\n0.871\n0.009\n0.006\n3164.0\n4502.0\n1.0\n\n\nalpha[4]\n0.214\n0.481\n-0.677\n1.126\n0.008\n0.006\n3506.0\n4991.0\n1.0\n\n\nalpha[5]\n0.835\n0.451\n-0.030\n1.653\n0.008\n0.006\n3053.0\n4420.0\n1.0\n\n\nalpha[6]\n0.381\n0.499\n-0.537\n1.307\n0.008\n0.005\n4143.0\n4921.0\n1.0\n\n\nalpha[7]\n0.380\n0.509\n-0.627\n1.289\n0.008\n0.006\n3847.0\n4482.0\n1.0\n\n\nalpha[8]\n-0.553\n0.661\n-1.814\n0.665\n0.009\n0.007\n5914.0\n4893.0\n1.0\n\n\nalpha[9]\n0.465\n0.528\n-0.536\n1.436\n0.008\n0.006\n4120.0\n4516.0\n1.0\n\n\nalpha[10]\n1.666\n0.432\n0.837\n2.459\n0.008\n0.006\n2646.0\n4381.0\n1.0\n\n\nalpha[11]\n0.138\n0.562\n-0.936\n1.181\n0.008\n0.006\n5079.0\n5170.0\n1.0\n\n\nalpha[12]\n-0.898\n0.752\n-2.312\n0.477\n0.010\n0.008\n6392.0\n5209.0\n1.0\n\n\nalpha[13]\n0.389\n0.542\n-0.638\n1.390\n0.008\n0.006\n4114.0\n5409.0\n1.0\n\n\nalpha[14]\n-0.868\n0.746\n-2.272\n0.493\n0.009\n0.008\n7017.0\n5038.0\n1.0\n\n\nalpha[15]\n2.242\n0.417\n1.457\n3.003\n0.008\n0.006\n2503.0\n4622.0\n1.0\n\n\nalpha[16]\n-0.257\n0.686\n-1.512\n1.030\n0.009\n0.008\n6241.0\n5109.0\n1.0\n\n\nbeta\n-3.283\n0.388\n-4.001\n-2.549\n0.007\n0.005\n2715.0\n4782.0\n1.0\n\n\nsigma_alpha\n1.555\n0.597\n0.676\n2.669\n0.009\n0.007\n4883.0\n5056.0\n1.0\n\n\nsigma_beta\n2.381\n1.502\n0.610\n4.921\n0.020\n0.015\n8249.0\n5157.0\n1.0\n\n\nsigma_gamma\n2.779\n0.650\n1.681\n4.051\n0.033\n0.023\n383.0\n839.0\n1.0\n\n\n\n\n\n\n\n\n\naz.plot_trace(mtbh_idata, figsize=(8,14),\n           var_names=['psi', 'mu', 'alpha', 'beta', 'sigma_alpha', 'sigma_beta', 'sigma_gamma']);\n\n\n\n\n\n\n\nFigure 11: Trace plots for the model with \\(M_{tbh}\\).\n\n\n\n\n\nThe trace plots and summary statistics show convergence issues for many of the individual heterogeneity models. The variance parameter, \\(\\sigma_{\\gamma},\\) seems to sample poorly. Further, models with both behavioral and individual effects lead to extremely large estimates of \\(\\psi\\). This appears to happen regardless of the size of the data augmentation \\(M.\\)\nNote that I upped the target_accept value for some models. This slows the sampler, but lowers the risk of divergence.\n\n\nModel comparison\nNext, I select a model for inference using an approximation of leave-one-out (loo) cross-validation (Vehtari, Gelman, and Gabry 2017). This approximation can be calculated using PyMC. To do so, I calculate the log-likelihood for each model, which is added to the InferenceData object. This makes it possible to compare the models using loo and az.compare.\n\nwith m0:\n    pm.compute_log_likelihood(m0_idata)\n\nwith mt:\n    pm.compute_log_likelihood(mt_idata)\n\nwith mb:\n    pm.compute_log_likelihood(mb_idata)\n\nwith mtb:\n    pm.compute_log_likelihood(mtb_idata)\n\nwith mh:\n    pm.compute_log_likelihood(mh_idata)\n\nwith mth:\n    pm.compute_log_likelihood(mth_idata)\n\nwith mbh:\n    pm.compute_log_likelihood(mbh_idata)\n\nwith mtbh:\n    pm.compute_log_likelihood(mtbh_idata)\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:00&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [4000/4000 00:01&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:00&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:04&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [12000/12000 00:03&lt;00:00]\n    \n    \n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:03&lt;00:00]\n    \n    \n\n\n\nmodel_dict = {\"m0\": m0_idata, \"mt\": mt_idata, \"mb\": mb_idata, \n              \"mtb\": mtb_idata, \"mh\": mh_idata, \"mth\": mth_idata, \n              \"mbh\": mbh_idata, \"mtbh\": mtbh_idata}\n\ncomparison = az.compare(model_dict)\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n\n\nThe comparison tools notes issues with several of the models, suggesting a lack of robustness. Inspection of the comparison table shows that the struggling models all include the individual effect \\(h.\\) A more thorough analysis would consider reparameterizing the model, e.g., through the non-centered parameterization. In lieu of that, I simply discard the models that fail this test and re-do the comparison with the passing models.\n\ncomparison.round(2)\n\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\nmtbh\n0\n-430.62\n87.06\n0.00\n0.97\n57.18\n0.00\nTrue\nlog\n\n\nmth\n1\n-489.22\n38.02\n58.60\n0.00\n58.89\n11.21\nTrue\nlog\n\n\nmtb\n2\n-493.26\n21.47\n62.64\n0.00\n62.12\n11.43\nTrue\nlog\n\n\nmt\n3\n-493.92\n14.34\n63.30\n0.00\n59.54\n12.50\nFalse\nlog\n\n\nmbh\n4\n-495.19\n68.06\n64.57\n0.00\n60.82\n11.47\nTrue\nlog\n\n\nmh\n5\n-518.62\n25.59\n88.00\n0.03\n61.47\n15.31\nTrue\nlog\n\n\nmb\n6\n-521.53\n5.45\n90.91\n0.00\n63.17\n15.28\nFalse\nlog\n\n\nm0\n7\n-522.55\n2.50\n91.93\n0.00\n62.10\n16.12\nFalse\nlog\n\n\n\n\n\n\n\n\n\ngood_dict = {\"m0\": m0_idata, \"mt\": mt_idata, \"mb\": mb_idata, \"mtb\": mtb_idata}\ngood_comparison = az.compare(good_dict)\ngood_comparison.round(2)\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:805: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n  warnings.warn(\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/stats/stats.py:309: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'log' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n  df_comp.loc[val] = (\n\n\n\n\n\n\n\n\n\n\nrank\nelpd_loo\np_loo\nelpd_diff\nweight\nse\ndse\nwarning\nscale\n\n\n\n\nmtb\n0\n-493.26\n21.47\n0.00\n0.61\n62.12\n0.00\nTrue\nlog\n\n\nmt\n1\n-493.92\n14.34\n0.66\n0.39\n59.54\n6.77\nFalse\nlog\n\n\nmb\n2\n-521.53\n5.45\n28.28\n0.00\n63.17\n8.61\nFalse\nlog\n\n\nm0\n3\n-522.55\n2.50\n29.29\n0.00\n62.10\n10.41\nFalse\nlog\n\n\n\n\n\n\n\n\n\naz.plot_compare(good_comparison, figsize=(5, 4));\n\n/Users/philtpatton/miniforge3/envs/pymc/lib/python3.11/site-packages/arviz/plots/backends/matplotlib/compareplot.py:87: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n  scale = comp_df[\"scale\"][0]\n\n\n\n\n\n\n\n\nFigure 12: Differences in the ELPD criteria, calculated using loo, for each model (Vehtari, Gelman, and Gabry 2017).\n\n\n\n\n\nThe comparison shows that all of the model weight belongs to two models: \\(M_t\\) and \\(M_{tb}.\\)\n\n\nModel averaged predictions\nFinally, we can use the model weights to simulate a weighted posterior of \\(N.\\) To do so, I take a weighted sample of each of the posteriors of \\(N,\\) with the weight dictated by the comparison tool.\n\nposteriors = [sim_N(good_dict[model]) for model in good_dict]\nweights = [good_comparison.loc[model].weight for model in good_dict]\nsample_count = len(posteriors[0])\n\nl = []\nfor w, p in zip(weights, posteriors):\n    weighted_sample = RNG.choice(p, size=int(w * sample_count))\n    l.append(weighted_sample)\n\nweighted_posterior = np.concatenate(l)\n\nfig, (ax0, ax1) = plt.subplots(2, 1, figsize=(7, 6), sharex=True, sharey=True, tight_layout=True)\n\npal = sns.color_palette(\"Set2\")\n\n# labs = [k for k in good_dict.keys()]\nlabs = [r'$M_{0}$', r'$M_{t}$', r'$M_{b}$', r'$M_{tb}$']\nfor i, p in enumerate(posteriors):\n    ax0.hist(p, color=pal[i], edgecolor='white', bins=60, alpha=0.6, label=labs[i])\n\nax0.set_title(r'Posteriors of $N$')\n# ax1.set_title(r'Weighted posterior')\n\nax0.set_xlim((53, 150))\nax0.legend()\n\nax0.set_ylabel('Number of samples')\nax1.set_ylabel('Number of samples')\n\nax1.hist(weighted_posterior, edgecolor='white', bins=60, alpha=0.9, color=pal[6], label='Weighted')\nax1.legend()\n\nplt.show()\n\n\n\n\n\n\n\nFigure 13: Posteriors of \\(N\\) from the four models under consideration (top panel), with the model averaged posterior (bottom panel).\n\n\n\n\n\nWe can also look at the posterior densities of \\(p\\) from Model \\(M_t,\\) the second most weighted model.\n\np_samps = az.extract(mt_idata).p.mean(axis=0)\n\nfig, ax = plt.subplots(figsize=(6, 4))\n\na = 0.4\n# ax[0].set_title(\"Poisson\")\npal = sns.color_palette('viridis', T)\nfor t in range(T):\n    label_idx = t % 2\n    if label_idx == 0:\n        az.plot_dist(p_samps[t], ax=ax, color=pal[t], label=f'$t_{{{t}}}$',\n                     plot_kwargs={'linewidth':3, 'alpha': a})\n    else:\n        az.plot_dist(p_samps[t], ax=ax, color=pal[t],\n                     plot_kwargs={'linewidth':3, 'alpha': a})\n\nax.set_title(r'Posterior densities of $p$ from $M_t$')\nax.set_xlabel(r'$p$')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 14: Posteriors of \\(p\\) from model \\(M_t\\)\n\n\n\n\n\nThis notebook demonstrates a simple way to compare models using leave one out cross-validation (loo) and a classic example from capture-recapture. This is just one way, however, to perform model comparison using PyMC. Perhaps a more effective solution for this problem would be placing a shrinkage prior on the \\(\\sigma\\) parameters.\n\n\n\n\n\nReferences\n\nKing, Ruth, and SP2526632 Brooks. 2008. “On the Bayesian Estimation of a Closed Population Size in the Presence of Heterogeneity and Model Uncertainty.” Biometrics 64 (3): 816–24.\n\n\nVehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. “Practical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC.” Statistics and Computing 27: 1413–32.\n\n\nWilson, Ben, Philip S Hammond, and Paul M Thompson. 1999. “Estimating Size and Assessing Trends in a Coastal Bottlenose Dolphin Population.” Ecological Applications 9 (1): 288–300.",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -193,7 +193,7 @@
     "href": "closed_cmr.html",
     "title": "Closed capture-recapture",
     "section": "",
-    "text": "In this notebook, I explore fitting closed population capture-recapture models in PyMC. Capture-recapture, at least the Lincoln-Peterson estimator, has been around for almost 100 years. Since then, countless varieties of capture-recapture models have been developed for closed populations (Otis et al. 1978).\nThe basic steps in capture-recapture are: capture several individuals–e.g., via trapping–from the population of interest, mark these animals, then release them. We repeat this process several times, each time noting when we recapture individuals.\n\n\n\nTable 1: Example capture history, where \\(t\\) is the sampling occasion and 1 indicates capture\n\n\n\n\n\nIndividual\n\\(t_1\\)\n\\(t_2\\)\n\\(t_3\\)\n\\(t_4\\)\n\n\n\n\n001\n1\n1\n0\n1\n\n\n002\n0\n1\n1\n1\n\n\n003\n0\n0\n1\n1\n\n\n\n\n\n\nThis produces a capture history for each individual, which allows us to estimate the probability of capture and the number of individuals in the population \\(N\\).\n\nModel \\(M_0\\)\n\n# libraries \nimport numpy as np\nimport pandas as pd\nimport pymc as pm\nimport arviz as az\nimport matplotlib.pyplot as plt \nimport seaborn as sns\nfrom pymc.distributions.dist_math import binomln, logpow\n\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\n# pal = sns.color_palette(\"Set2\")\n# sns.set_palette(pal)\n\n# hyperparameters \nSEED = 808\nRNG = np.random.default_rng(SEED)\nM = 1500\n\nI explore fitting the simplest closed capture-recapture model, Model \\(M_0,\\) through parameter-expanded data-augmentation (PX-DA, Royle and Dorazio 2008). The idea with PX-DA is to augment the capture histories with \\(M-n\\) all zero capture-histories, where \\(M\\) is a hyperparameter that should be much greater than the true population size \\(N,\\) and \\(n\\) is the total number of individuals that were captured during the study. This allows us to treat the data as a zero-inflated binomial distribution (see below).\n\ndef augment_history(history):\n    '''Augment a capture history with all-zero histories.'''\n    \n    animals_captured, T = history.shape\n\n    # create M - n all zero histories\n    zero_history_count = M - animals_captured\n    zero_history = np.zeros((zero_history_count, T))\n\n    # tack those on to the capture history\n    augmented = np.row_stack((history, zero_history))\n\n    return augmented \n\nTo demonstrate this approach, I use the salamander dataset from Bailey, Simons, and Pollock (2004), as demonstrated in Hooten and Hefley (2019), Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (Plethodon jordani) and the pygmy salamander (Desmognathus wrighti), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting \\(M=1500\\) (see above). There were \\(n=92\\) individual red-cheeked and \\(n=132\\) pygmy salamanders captured during the course of the survey.\n\ndef get_histories():\n    '''Read, augment, and recombine the salamander histories.'''\n    \n    # read in salamander data \n    sal_data = pd.read_csv('sal_data.csv')\n    \n    # labels for capture history columns \n    col_labs = [f'y{t}' for t in range(1, 5)]\n\n    # subset each dataset before augmenting \n    is_pyg = sal_data.spp == 1\n    is_red = sal_data.spp == 0\n\n    pyg = sal_data.loc[is_pyg, col_labs].to_numpy()\n    red = sal_data.loc[is_red, col_labs].to_numpy()\n\n    # # augment each set separately since they differ in length\n    # pyg_augmented = augment_history(pyg)\n    # red_augmented = augment_history(red)\n\n    # # recombine into one history \n    # history = np.concatenate((pyg_augmented, red_augmented))\n\n    return {'pyg': pyg, 'red': red}\n\ndef augment_histories(histories):\n\n    pyg_augmented = augment_history(histories['pyg'])\n    red_augmented = augment_history(histories['red'])\n\n    # recombine into one history \n    history = np.concatenate((pyg_augmented, red_augmented))\n\n    return history\n\nhistories = get_histories()\n\nn_red, T = histories['red'].shape\nn_pyg, T = histories['pyg'].shape\n\n# # summarize into binomial data\nhistory_augmented = augment_histories(histories)\nhistory_summarized = history_augmented.sum(axis=1)\n\nFor this model, I use the pm.ZeroInflatedBinomial class, just as I did in the occupancy notebook. That said, the parameters here are different. First, \\(p\\) represents the probability of capturing a given individual during the survey. Second, \\(\\psi\\) represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation \\(M\\) is included in the population of interest \\(N.\\) Then, we can estimate the population size as \\(\\hat{N}=M\\hat{\\psi},\\) or generate posterior draws of \\(N,\\) e.g., \\(N^{(s)} \\sim \\text{Bin}(M,\\psi^{(s)})\\)\nIn this example, I combine the two species into one pm.Model object, making use of coords. That said, the parameters for each species are treated as independent. In other words, this is a “no-pooling” model.\n\n# index for each species\nspecies_idx = np.repeat([0, 1], M)\n\n# coordinates identifying parameter each species  \ncoords = {'species': ['pygmy', 'red_cheeked']}\n\nwith pm.Model(coords=coords) as M0:\n\n    # priors for the capture and inclusion probabilities\n    psi = pm.Uniform('psi', 0, 1, dims='species')\n    p = pm.Uniform('p', 0, 1, dims='species')\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial(\n        'history', \n        p=p[species_idx], \n        psi=psi[species_idx], \n        n=T,\n        observed=history_summarized\n    )\n    \npm.model_to_graphviz(M0)\n\n\n\n\n\n\n\nFigure 1: Visual representation of model \\(M_0.\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\n\nwith M0:\n    M0_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 17 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:16&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(M0_idata, figsize=(8,4));\n\n\n\n\n\n\n\nFigure 2: Traceplots for the salamander \\(M_0\\) model. The red-cheeked salamander is in blue while the pygmy salamander is in red.\n\n\n\n\n\nFor faster sampling, it’s better to separate the two species into two separate models. On my machine, the individual species models finish sampling in 2-3 seconds, compared to 15-20 seconds for the two species model. That said, the two species model is somewhat more convenient.\nOf course, the trace plots lack our true parameter of interest: the population size \\(N.\\) We can simulate the posterior of \\(N\\) as a derived quantity, using the posterior distribution of \\(\\psi\\) and \\(p.\\)\n\n# az.extract flattens the chains\nposterior = az.extract(M0_idata)\npsi_samps = posterior.psi.values\np_samps = posterior.p.values\n\n# conditional probability that the animal is in the sample \nnot_p = (1 - p_samps)\np_included = (psi_samps * (not_p) ** T) / (psi_samps * (not_p) ** T + (1 - psi_samps))\n\n# simulate the number of undetected animals in each population\nnumber_undetected_pyg = RNG.binomial(M - n_pyg, p_included[0])\nnumber_undetected_red = RNG.binomial(M - n_red, p_included[1])\n\n# simulate N\nN_pyg = n_pyg + number_undetected_pyg\nN_red = n_red + number_undetected_red\n\nBelow I plotted the posterior distributions of \\(N\\) for both species, adding the estimates from Hooten and Hefley (2019), Chapter 24. Although note that they used a different prior for \\(\\psi.\\)\n\nN_hooten = [229.6, 450.9]\nfig, ax = plt.subplots(figsize=(6,4))\nax.hist(N_pyg, color='C0', edgecolor='white', alpha=0.9, bins=30, label='Pygmy')\nax.hist(N_red, color='C1', edgecolor='white', alpha=0.9, bins=30, label='Red-cheeked')\nax.axvline(N_hooten[0], linestyle='--', color='black', linewidth=2)\nax.axvline(N_hooten[1], linestyle='--', color='black', linewidth=2)\nax.set_title('Posterior distributions of $N$')\nax.set_ylabel('Number of samples')\nax.legend()\nplt.show()\n\n\n\n\n\n\n\nFigure 3: Posterior distributions of \\(N\\) from the \\(M_0\\) model. Estimates from Hooten and Hefley (2019) are shown by the vertical lines’\n\n\n\n\n\nWe might expect estimates of capture probability \\(p\\) and the abundance \\(N\\) to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws. For a more custom look to the plots, I plot the draws using matplotlib.\n\n# create the plot\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\n\n# add the scatter for each species\nlabs = ['Pygmy', 'Red-backed']\nax.scatter(p_samps[0], N_pyg, s=10, alpha=0.2, label=labs[0])\nax.scatter(p_samps[1], N_red, s=10, alpha=0.2, label=labs[1])\n\n# this removes the opacity for the dots in the legend\nleg = ax.legend()\nfor lh in leg.legend_handles: \n    lh.set(sizes=[25], alpha=[1])\n\n# update aesthetics \nax.spines.right.set_visible(False)\nax.spines.top.set_visible(False)\n\nax.set_ylabel(r'$N$')\nax.set_xlabel(r'$p$')\nax.set_title('Posterior draws')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 4: Posterior draws of \\(N\\) and \\(p\\) for both species of salamander.\n\n\n\n\n\n\n\nModel \\(M_b\\)\nNext, I fit model \\(M_b,\\) which accounts for the possibility that the capture probability changes after the animal is first caught. This could be from trap happiness, whereby animals are more likely to be trapped after their first time. Conversely, this could be from subsequent trap avoidance.\nMirroring (Royle and Dorazio 2008, chap. 5), I fit this model to the Microtus dataset reported in (Williams, Nichols, and Conroy 2002, 525). This version of the dataset includes encounter histories of \\(n=56\\) adult males that were captured on \\(T=5\\) consecutive days.\n\n# read in the microtus data\nmicrotus = np.loadtxt('microtus.data.txt').astype(int)\n\n# the last column is not relevant\nmicro_hist = microtus[:,:-1]\nn, T = micro_hist.shape\n\n# augment with all zero histories\nM = 100\nmicro_augmented = augment_history(micro_hist)\n\n# note the occasion when each individual was first seen\nfirst_seen = (micro_hist != 0).argmax(axis=1)\n\n# create the covariate for the behavior effect\nbehavior_effect = np.zeros((M, T))\nfor i, f in enumerate(first_seen):\n    behavior_effect[i, (f + 1):] = 1\n\n# covariate matrix\nx_int = np.ones((M, T))\nX = np.stack((x_int, behavior_effect), axis=2)\n\nI use the same custom distribution as the occupancy notebook, the zero-inflated model, except the zero-inflation happens at the row-level.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\ncoords = {'alpha_coeffs': ['Intercept', 'B_Response']}\nwith pm.Model(coords=coords) as mb:\n\n    # priors for the capture and inclusion probabilities\n    psi = pm.Uniform('psi', 0, 1)\n    Alpha = pm.Normal('Alpha', 0, 2, dims='alpha_coeffs')\n\n    nu = pm.math.dot(X, Alpha)\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=micro_augmented\n    )\n    \npm.model_to_graphviz(mb)\n\n\n\n\n\n\n\nFigure 5: Visual representation of model \\(M_b.\\)\n\n\n\n\n\n\nwith mb:\n    mb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, Alpha]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mb_idata, var_names=['Alpha', 'psi'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nAlpha[Intercept]\n0.103\n0.249\n-0.347\n0.567\n0.006\n0.004\n1954.0\n2046.0\n1.00\n\n\nAlpha[B_Response]\n0.626\n0.296\n0.074\n1.186\n0.007\n0.005\n1895.0\n1883.0\n1.00\n\n\npsi\n0.575\n0.051\n0.470\n0.666\n0.001\n0.001\n2446.0\n2479.0\n1.01\n\n\n\n\n\n\n\n\naz.plot_forest(mb_idata, var_names=['Alpha'], combined=True, ess=True, figsize=(6,2));\n\n\n\n\n\n\n\nFigure 6: Forest plot showing the catchability parameters from model \\(M_b.\\)\n\n\n\n\n\nThe forest plot indicates that there is some evidence of a weak, positive behavioral response. Although note that the 94% credible intervals between the baseline capture rate and the behavioral effect overlap considerably.\n\n# simulate draws of N \npsi_samps = az.extract(mb_idata).psi.values\np_samps = az.extract(mb_idata).p.values[:n]\n\nnot_p = (1 - p_samps)\nbin_prod = not_p.prod(axis=1)\np_included = (psi_samps * bin_prod) / (psi_samps * bin_prod + (1 - psi_samps))\n\nnumber_undetected = RNG.binomial(1, p_included).sum(axis=0)\nN_samples = n + number_undetected\n\n# use a bar chart since the posterior is bunched up \nN_values, N_counts = np.unique(N_samples, return_counts=True)\n\n# create the plot\nfig, ax = plt.subplots(figsize=(4, 4))\n\nax.bar(N_values, N_counts)\n\nax.annotate(\n    'Number\\ndetected $n$', \n    ha='left',\n    xy=(N_values[0], N_counts[0]), \n    color='black',\n    xytext=(n+1, 1600), \n    arrowprops=dict(arrowstyle=\"-&gt;\", color='black', linewidth=1,\n                    connectionstyle=\"angle3,angleA=90,angleB=0\")\n)\n\nax.set_ylabel('Number of samples')\nax.set_title('Posterior of $N$')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 7: Posterior distribution of \\(N\\) from model \\(M_b.\\) The number voles that were detected \\(n\\) is shown by the vertical red line.\n\n\n\n\n\nMost of the posterior density of \\(N\\) is at \\(n,\\) the number of animals detected. The discovery curve hints at why this may be the case. It seems that all the voles in the population may have been captured by the end of the study.\n\n# how many voles have been seen?\ntotal_seen = micro_hist.sum(axis=0).cumsum()\ntotal_seen = np.insert(total_seen, 0, 0)\n\n# how many new voles have been seen?\nfirst_seen = (micro_hist != 0).argmax(axis=1)\nnewbies = [sum(first_seen == t) for t in range(T)]\ntotal_newbies = np.cumsum(newbies)\ntotal_newbies = np.insert(total_newbies, 0, 0)\n\nfig, ax = plt.subplots(figsize=(5, 3.5))\nax.plot(total_seen, total_newbies)\nax.fill_between(total_seen, total_newbies, alpha=0.2)\nax.set_title('Discovery curve')\nax.set_xlabel('Total voles captured')\nax.set_ylabel('Unique voles captured')\nplt.show()\n\n\n\n\n\n\n\nFigure 8: Discovery curve for the Microtus study.\n\n\n\n\n\nWe can also look at the behavioral effect by visualizing the posterior distributions of \\(p.\\) As we can see, the voles who have been captured before are more likely to be captured again.\n\np_samples = az.extract(mb_idata).p.values.reshape(500, 4000)\nb_cov = X[:,:,1].flatten()\n\nzero_p = p_samples[b_cov==0,].flatten()\none_p = p_samples[b_cov==1,].flatten() \n\nfig, ax = plt.subplots(figsize=(5, 3.5))\naz.plot_dist(zero_p, ax=ax, label='First detection', color='C0')\naz.plot_dist(one_p, ax=ax, label='Seen before', color='C1')\nax.set_title('Posterior distributions of $p$')\nax.set_xlim((0,1))\nax.set_yticks([])\nax.legend()\nplt.show()\n\n\n\n\n\n\n\nFigure 9: Posterior distributions for the probability of detection given the behavioral effect.\n\n\n\n\n\n\n\n\n\n\nReferences\n\nBailey, Larissa L, Theodore R Simons, and Kenneth H Pollock. 2004. “Estimating Detection Probability Parameters for Plethodon Salamanders Using the Robust Capture-Recapture Design.” The Journal of Wildlife Management 68 (1): 1–13.\n\n\nHooten, Mevin B, and Trevor Hefley. 2019. Bringing Bayesian Models to Life. CRC Press.\n\n\nOtis, David L, Kenneth P Burnham, Gary C White, and David R Anderson. 1978. “Statistical Inference from Capture Data on Closed Animal Populations.” Wildlife Monographs, no. 62: 3–135.\n\n\nRoyle, J Andrew, and Robert M Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier.\n\n\nWilliams, Byron K, James D Nichols, and Michael J Conroy. 2002. Analysis and Management of Animal Populations. Academic press.",
+    "text": "In this notebook, I explore fitting closed population capture-recapture models in PyMC. Capture-recapture, at least the Lincoln-Peterson estimator, has been around for almost 100 years. Since then, countless varieties of capture-recapture models have been developed for closed populations (Otis et al. 1978).\nThe basic steps in capture-recapture are: capture several individuals–e.g., via trapping–from the population of interest, mark these animals, then release them. We repeat this process several times, each time noting when we recapture individuals.\n\n\n\nTable 1: Example capture history, where \\(t\\) is the sampling occasion and 1 indicates capture\n\n\n\n\n\nIndividual\n\\(t_1\\)\n\\(t_2\\)\n\\(t_3\\)\n\\(t_4\\)\n\n\n\n\n001\n1\n1\n0\n1\n\n\n002\n0\n1\n1\n1\n\n\n003\n0\n0\n1\n1\n\n\n\n\n\n\nThis produces a capture history for each individual, which allows us to estimate the probability of capture and the number of individuals in the population \\(N\\).\n\nModel \\(M_0\\)\n\n# libraries \nimport numpy as np\nimport pandas as pd\nimport pymc as pm\nimport arviz as az\nimport matplotlib.pyplot as plt \nfrom pymc.distributions.dist_math import binomln, logpow\n\n# plotting parameters\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\n\n# hyperparameters \nSEED = 808\nRNG = np.random.default_rng(SEED)\n\nI explore fitting the simplest closed capture-recapture model, Model \\(M_0,\\) through parameter-expanded data-augmentation (PX-DA, Royle and Dorazio 2008). The idea with PX-DA is to augment the capture histories with \\(M-n\\) all zero capture-histories, where \\(M\\) is a hyperparameter that should be much greater than the true population size \\(N,\\) and \\(n\\) is the total number of individuals that were captured during the study. This allows us to treat the data as a zero-inflated binomial distribution (see below).\n\ndef augment_history(history, M):\n    '''Augment a capture history with all-zero histories.'''\n    \n    animals_captured, T = history.shape\n\n    # create M - n all zero histories\n    zero_history_count = M - animals_captured\n    zero_history = np.zeros((zero_history_count, T))\n\n    # tack those on to the capture history\n    augmented = np.row_stack((history, zero_history))\n\n    return augmented \n\nTo demonstrate this approach, I use the salamander dataset from Bailey, Simons, and Pollock (2004), as demonstrated in Hooten and Hefley (2019), Chapter 24. These data were collected on two salamander species, the red-cheeked salamander (Plethodon jordani) and the pygmy salamander (Desmognathus wrighti), in Great Smoky Mountains National Park. The salamanders were counted in 15m by 15m square plots. In this case, we augment the history by setting \\(M=1500\\). There were \\(n=92\\) individual red-cheeked and \\(n=132\\) pygmy salamanders captured during the course of the survey.\n\ndef get_histories():\n    '''Read, augment, and recombine the salamander histories.'''\n    \n    # read in salamander data \n    sal_data = pd.read_csv('sal_data.csv')\n    \n    # labels for capture history columns \n    col_labs = [f'y{t}' for t in range(1, 5)]\n\n    # subset each dataset before augmenting \n    is_pyg = sal_data.spp == 1\n    is_red = sal_data.spp == 0\n\n    pyg = sal_data.loc[is_pyg, col_labs].to_numpy()\n    red = sal_data.loc[is_red, col_labs].to_numpy()\n\n    return {'pyg': pyg, 'red': red}\n\ndef augment_histories(histories, M):\n\n    pyg_augmented = augment_history(histories['pyg'], M=M)\n    red_augmented = augment_history(histories['red'], M=M)\n\n    # recombine into one history \n    history = np.concatenate((pyg_augmented, red_augmented))\n\n    return history\n\nhistories = get_histories()\n\nn_red, T = histories['red'].shape\nn_pyg, T = histories['pyg'].shape\n\n# # summarize into binomial data\nM = 1500\nhistory_augmented = augment_histories(histories, M=M)\nhistory_summarized = history_augmented.sum(axis=1)\n\nFor this model, I use the pm.ZeroInflatedBinomial class, just as I did in the occupancy notebook. That said, the parameters here are different. First, \\(p\\) represents the probability of capturing a given individual during the survey. Second, \\(\\psi\\) represents a mysterious entity known as the inclusion probability. That is, the probability that an individual from the hypothetical superpopulation \\(M\\) is included in the population of interest \\(N.\\) Then, we can simulate the posterior distribution for \\(N\\) using \\(M\\) and the posterior distributions of \\(\\psi.\\)\nIn this example, I combine the two species into one pm.Model object, making use of coords. That said, the parameters for each species are treated as independent. In other words, this is a “no-pooling” model.\n\n# index for each species\nspecies_idx = np.repeat([0, 1], M)\n\n# coordinates identifying parameter each species  \ncoords = {'species': ['pygmy', 'red_cheeked']}\n\nwith pm.Model(coords=coords) as M0:\n\n    # priors for the capture and inclusion probabilities\n    psi = pm.Uniform('psi', 0, 1, dims='species')\n    p = pm.Uniform('p', 0, 1, dims='species')\n\n    # likelihood for the summarized data\n    pm.ZeroInflatedBinomial(\n        'history', \n        p=p[species_idx], \n        psi=psi[species_idx], \n        n=T,\n        observed=history_summarized\n    )\n    \npm.model_to_graphviz(M0)\n\n\n\n\n\n\n\nFigure 1: Visual representation of model \\(M_0.\\) MarginalMixture refers to the zero-inflated binomial distribution.\n\n\n\n\n\n\nwith M0:\n    M0_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, p]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 19 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:18&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(M0_idata, figsize=(8,4));\n\n\n\n\n\n\n\nFigure 2: Traceplots for the salamander \\(M_0\\) model. The red-cheeked salamander is in blue while the pygmy salamander is in red.\n\n\n\n\n\nFor faster sampling, it’s better to separate the two species into two separate models. On my machine, the individual species models finish sampling in 2-3 seconds, compared to 15-20 seconds for the two species model. That said, the two species model is somewhat more convenient.\nOf course, the trace plots lack our true parameter of interest: the population size \\(N.\\) We can simulate the posterior of \\(N\\) as a derived quantity, using \\(M\\) and the posterior distribution of \\(\\psi\\).\n\n# az.extract flattens the chains\nposterior = az.extract(M0_idata)\npsi_samps = posterior.psi.values\np_samps = posterior.p.values\n\n# conditional probability that the animal is in the sample \nnot_p = (1 - p_samps)\np_included = (psi_samps * (not_p) ** T) / (psi_samps * (not_p) ** T + (1 - psi_samps))\n\n# simulate the number of undetected animals in each population\nnumber_undetected_pyg = RNG.binomial(M - n_pyg, p_included[0])\nnumber_undetected_red = RNG.binomial(M - n_red, p_included[1])\n\n# simulate N\nN_pyg = n_pyg + number_undetected_pyg\nN_red = n_red + number_undetected_red\n\nBelow I plotted the posterior distributions of \\(N\\) for both species, adding the estimates from Hooten and Hefley (2019), Chapter 24. Although note that they used a different prior for \\(\\psi.\\)\n\nN_hooten = [229.6, 450.9]\nfig, ax = plt.subplots(figsize=(6,4))\nax.hist(N_pyg, color='C0', edgecolor='white', alpha=0.9, bins=30, label='Pygmy')\nax.hist(N_red, color='C1', edgecolor='white', alpha=0.9, bins=30, label='Red-cheeked')\nax.axvline(N_hooten[0], linestyle='--', color='black', linewidth=2)\nax.axvline(N_hooten[1], linestyle='--', color='black', linewidth=2)\nax.set_title('Posterior distributions of $N$')\nax.set_ylabel('Number of samples')\nax.legend()\nplt.show()\n\n\n\n\n\n\n\nFigure 3: Posterior distributions of \\(N\\) from the \\(M_0\\) model. Estimates from Hooten and Hefley (2019) are shown by the vertical lines’\n\n\n\n\n\nWe might expect estimates of capture probability \\(p\\) and the abundance \\(N\\) to be somewhat correlated. We can explore this relationship visually by plotting the posterior draws.\n\n# create the plot\nfig, ax = plt.subplots(1, 1, figsize=(4, 4))\n\n# add the scatter for each species\nlabs = ['Pygmy', 'Red-backed']\nax.scatter(p_samps[0], N_pyg, s=10, alpha=0.2, label=labs[0])\nax.scatter(p_samps[1], N_red, s=10, alpha=0.2, label=labs[1])\n\n# this removes the opacity for the dots in the legend\nleg = ax.legend()\nfor lh in leg.legend_handles: \n    lh.set(sizes=[25], alpha=[1])\n\n# update aesthetics \nax.spines.right.set_visible(False)\nax.spines.top.set_visible(False)\n\nax.set_ylabel(r'$N$')\nax.set_xlabel(r'$p$')\nax.set_title('Posterior draws')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 4: Posterior draws of \\(N\\) and \\(p\\) for both species of salamander.\n\n\n\n\n\n\n\nModel \\(M_b\\)\nNext, I fit model \\(M_b,\\) which accounts for the possibility that the capture probability changes after the animal is first caught. This could be from trap happiness, whereby animals are more likely to be trapped after their first time. Conversely, this could be from subsequent trap avoidance.\nMirroring (Royle and Dorazio 2008, chap. 5), I fit this model to the Microtus dataset reported in (Williams, Nichols, and Conroy 2002, 525). This version of the dataset includes encounter histories of \\(n=56\\) adult males that were captured on \\(T=5\\) consecutive days.\n\n# read in the microtus data\nmicrotus = np.loadtxt('microtus.data.txt').astype(int)\n\n# the last column is not relevant\nmicro_hist = microtus[:,:-1]\nn, T = micro_hist.shape\n\n# augment with all zero histories\nM = 100\nmicro_augmented = augment_history(micro_hist, M=M)\n\n# note the occasion when each individual was first seen\nfirst_seen = (micro_hist != 0).argmax(axis=1)\n\n# create the covariate for the behavior effect\nbehavior_effect = np.zeros((M, T))\nfor i, f in enumerate(first_seen):\n    behavior_effect[i, (f + 1):] = 1\n\n# covariate matrix\nx_int = np.ones((M, T))\nX = np.stack((x_int, behavior_effect), axis=2)\n\nI use the same custom distribution as the occupancy notebook, the zero-inflated model, except the zero-inflation happens at the row-level.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\ncoords = {'alpha_coeffs': ['Intercept', 'B_Response']}\nwith pm.Model(coords=coords) as mb:\n\n    # priors for the capture and inclusion probabilities\n    psi = pm.Uniform('psi', 0, 1)\n    Alpha = pm.Normal('Alpha', 0, 2, dims='alpha_coeffs')\n\n    nu = pm.math.dot(X, Alpha)\n    p = pm.Deterministic('p', pm.math.invlogit(nu))\n\n    # likelihood \n    pm.CustomDist(\n        'y',\n        1,\n        p,\n        psi,\n        logp=logp,\n        observed=micro_augmented\n    )\n    \npm.model_to_graphviz(mb)\n\n\n\n\n\n\n\nFigure 5: Visual representation of model \\(M_b.\\)\n\n\n\n\n\n\nwith mb:\n    mb_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, Alpha]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(mb_idata, var_names=['Alpha', 'psi'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\nAlpha[Intercept]\n0.095\n0.257\n-0.388\n0.561\n0.006\n0.004\n1861.0\n1800.0\n1.0\n\n\nAlpha[B_Response]\n0.632\n0.300\n0.079\n1.217\n0.007\n0.005\n1898.0\n1827.0\n1.0\n\n\npsi\n0.576\n0.051\n0.486\n0.678\n0.001\n0.001\n2292.0\n2250.0\n1.0\n\n\n\n\n\n\n\n\n\naz.plot_forest(mb_idata, var_names=['Alpha'], combined=True, ess=True, figsize=(6,2));\n\n\n\n\n\n\n\nFigure 6: Forest plot showing the catchability parameters from model \\(M_b.\\)\n\n\n\n\n\nThe forest plot indicates that there is some evidence of a weak, positive behavioral response. Although note that the 94% credible intervals between the baseline capture rate and the behavioral effect overlap considerably.\n\n# simulate draws of N \npsi_samps = az.extract(mb_idata).psi.values\np_samps = az.extract(mb_idata).p.values[:n]\n\nnot_p = (1 - p_samps)\nbin_prod = not_p.prod(axis=1)\np_included = (psi_samps * bin_prod) / (psi_samps * bin_prod + (1 - psi_samps))\n\nnumber_undetected = RNG.binomial(1, p_included).sum(axis=0)\nN_samples = n + number_undetected\n\n# use a bar chart since the posterior is bunched up \nN_values, N_counts = np.unique(N_samples, return_counts=True)\n\n# create the plot\nfig, ax = plt.subplots(figsize=(4, 4))\n\nax.bar(N_values, N_counts)\n\nax.annotate(\n    'Number\\ndetected $n$', \n    ha='left',\n    xy=(N_values[0], N_counts[0]), \n    color='black',\n    xytext=(n+1, 1600), \n    arrowprops=dict(arrowstyle=\"-&gt;\", color='black', linewidth=1,\n                    connectionstyle=\"angle3,angleA=90,angleB=0\")\n)\n\nax.set_ylabel('Number of samples')\nax.set_title('Posterior of $N$')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 7: Posterior distribution of \\(N\\) from model \\(M_b.\\) The number voles that were detected \\(n\\) is shown by the vertical red line.\n\n\n\n\n\nMost of the posterior density of \\(N\\) is at \\(n,\\) the number of animals detected. The discovery curve hints at why this may be the case. It seems that all the voles in the population may have been captured by the end of the study.\n\n# how many voles have been seen?\ntotal_seen = micro_hist.sum(axis=0).cumsum()\ntotal_seen = np.insert(total_seen, 0, 0)\n\n# how many new voles have been seen?\nfirst_seen = (micro_hist != 0).argmax(axis=1)\nnewbies = [sum(first_seen == t) for t in range(T)]\ntotal_newbies = np.cumsum(newbies)\ntotal_newbies = np.insert(total_newbies, 0, 0)\n\nfig, ax = plt.subplots(figsize=(5, 3.5))\nax.plot(total_seen, total_newbies)\nax.fill_between(total_seen, total_newbies, alpha=0.2)\nax.set_title('Discovery curve')\nax.set_xlabel('Total voles captured')\nax.set_ylabel('Unique voles captured')\nplt.show()\n\n\n\n\n\n\n\nFigure 8: Discovery curve for the Microtus study.\n\n\n\n\n\nWe can also look at the behavioral effect by visualizing the posterior distributions of \\(p.\\) As we can see, the voles who have been captured before are more likely to be captured again.\n\np_samples = az.extract(mb_idata).p.values.reshape(500, 4000)\nb_cov = X[:,:,1].flatten()\n\nzero_p = p_samples[b_cov==0,].flatten()\none_p = p_samples[b_cov==1,].flatten() \n\nfig, ax = plt.subplots(figsize=(5, 3.5))\naz.plot_dist(zero_p, ax=ax, label='First detection', color='C0')\naz.plot_dist(one_p, ax=ax, label='Seen before', color='C1')\nax.set_title('Posterior distributions of $p$')\nax.set_xlim((0,1))\nax.set_yticks([])\nax.legend()\nplt.show()\n\n\n\n\n\n\n\nFigure 9: Posterior distributions for the probability of detection given the behavioral effect.\n\n\n\n\n\n\n\n\n\n\nReferences\n\nBailey, Larissa L, Theodore R Simons, and Kenneth H Pollock. 2004. “Estimating Detection Probability Parameters for Plethodon Salamanders Using the Robust Capture-Recapture Design.” The Journal of Wildlife Management 68 (1): 1–13.\n\n\nHooten, Mevin B, and Trevor Hefley. 2019. Bringing Bayesian Models to Life. CRC Press.\n\n\nOtis, David L, Kenneth P Burnham, Gary C White, and David R Anderson. 1978. “Statistical Inference from Capture Data on Closed Animal Populations.” Wildlife Monographs, no. 62: 3–135.\n\n\nRoyle, J Andrew, and Robert M Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier.\n\n\nWilliams, Byron K, James D Nichols, and Michael J Conroy. 2002. Analysis and Management of Animal Populations. Academic press.",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -205,7 +205,7 @@
     "href": "scr.html",
     "title": "Spatial capture-recapture",
     "section": "",
-    "text": "In this notebook, I present an approach to fitting spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs.\nIn this notebook, I train the simplest possible SCR model, SCR0 (Royle et al. 2013, chap. 5), where the goal is estimating the true population size \\(N\\). Similar to the other closed population notebooks, I do so using parameter-expanded data-augmentation (PX-DA). I also borrow the concept of the detection function from the distance sampling notebook.\nAs a motivating example, I use the ovenbird mist netting dataset provided by Murray Efford via the secr package in R. The design of the study is outlined in Efford, Dawson, and Robbins (2004) and Borchers and Efford (2008). In this dataset, ovenbirds were trapped in 44 mist nets over 8 to 10 consecutive days during the summers of 2005 to 2009.\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pytensor.tensor as pt \nimport pymc as pm\nimport arviz as az\nfrom pymc.distributions.dist_math import binomln, logpow\n\n# hyper parameters\nSEED = 42\nRNG = np.random.default_rng(SEED)\nBUFFER = 100\nM = 200\n\n# plotting defaults\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\n\ndef invlogit(x):\n    '''Inverse logit function'''\n    return 1 / (1 + np.exp(-x))\n\ndef euclid_dist(X, S, library='np'):\n    '''Pairwise euclidian distance between points in (M, 2) and (N, 2) arrays'''\n    diff = X[np.newaxis, :, :] - S[:, np.newaxis, :]\n    \n    if library == 'np':\n        return np.sqrt(np.sum(diff ** 2, axis=-1))\n        \n    elif library == 'pm': \n        return pm.math.sqrt(pm.math.sum(diff ** 2, axis=-1))\n\ndef half_normal(d, s, library='np'):\n    '''Half normal detection function.'''\n    if library == 'np':\n        return np.exp( - (d ** 2) / (2 * s ** 2))\n        \n    elif library == 'pm':\n        return pm.math.exp( - (d ** 2) / (2 * s ** 2))\n\ndef exponential(d, s, library='np'):\n    '''Negative exponential detection function.'''    \n    if library == 'np':\n        return np.exp(- d / s)\n        \n    elif library == 'pm':\n        return pm.math.exp(- d / s)\n\n# coordinates for each trap \novenbird_trap = pd.read_csv('ovenbirdtrap.txt', delimiter=' ')\ntrap_count, _ = ovenbird_trap.shape\n\n# information about each trap \ntrap_x = ovenbird_trap.x\ntrap_y = ovenbird_trap.y\nX = ovenbird_trap[['x', 'y']].to_numpy()\n\n# define the state space around the traps\nx_max = trap_x.max() + BUFFER\ny_max = trap_y.max() + BUFFER\nx_min = trap_x.min() - BUFFER\ny_min = trap_y.min() - BUFFER\n\n# plot the trap locations\nfig, ax = plt.subplots(figsize=(4, 4))\n\n# plot the traps\nax.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax.set_ylim((y_min, y_max))\nax.set_xlim((x_min, x_max))\n\nax.annotate(\n    '44 nets\\n30m apart', ha='center',\n    xy=(55, -150), xycoords='data', color='black',\n    xytext=(40, 30), textcoords='offset points',\n    arrowprops=dict(arrowstyle=\"-&gt;\", color='black', linewidth=1,\n                    connectionstyle=\"angle3,angleA=90,angleB=0\"))\n\n# aesthetics \nax.set_title('Mist net locations')\nax.grid(False)\nplt.show()\n\n\n\n\n\n\n\nFigure 1: Locations of the mist nets in the ovenbird dataset (Efford, Dawson, and Robbins 2004)\nOne difference between spatial and traditional (non-spatial) capture is the addition of the trap identifier in the capture history. Whereas a traditional capture history is [individual, occasion], a spatial capture history might be [individual, occasion, trap].\nIn the ovenbird example, I ignore the year dimension, pooling parameters across years, which allows for better estimation of the detection parameters. My hack for doing so is treating every band/year combination as a unique individual in a combined year capture history. This is easy to implement, creates an awkward interpretation of \\(N\\) (see below).\n# ovenbird capture history\noven_ch = pd.read_csv('ovenbirdcapt.txt', delimiter=' ')\n\n# create a unique bird/year identifier for each individual\noven_ch['ID'] = oven_ch.groupby(['Year','Band']).ngroup()\noccasion_count = oven_ch.Day.max()\n\n# merge the datasets, making sure that traps with no detections are included \novenbird = (\n    ovenbird_trap.merge(oven_ch[['ID', 'Net', 'Day']], how='left')\n      [['ID', 'Day', 'Net', 'x', 'y']]\n      .sort_values('ID')\n      .reset_index(drop=True)\n)\n\novenbird.head(10)\n\n\n\n\n\n\n\n\nID\nDay\nNet\nx\ny\n\n\n\n\n0\n0.0\n1.0\n2\n-50.0\n-255.0\n\n\n1\n1.0\n9.0\n20\n-50.0\n285.0\n\n\n2\n1.0\n1.0\n15\n-50.0\n135.0\n\n\n3\n2.0\n6.0\n17\n-50.0\n195.0\n\n\n4\n2.0\n9.0\n27\n49.0\n165.0\n\n\n5\n2.0\n1.0\n15\n-50.0\n135.0\n\n\n6\n2.0\n1.0\n14\n-50.0\n105.0\n\n\n7\n3.0\n1.0\n41\n49.0\n-255.0\n\n\n8\n3.0\n3.0\n39\n49.0\n-195.0\n\n\n9\n3.0\n1.0\n42\n49.0\n-285.0",
+    "text": "In this notebook, I show how to train spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs.\nIn this notebook, I train the simplest possible SCR model, SCR0 (Royle et al. 2013, chap. 5), where the goal is estimating the true population size \\(N\\). Similar to the other closed population notebooks, I do so using parameter-expanded data-augmentation (PX-DA). I also borrow the concept of the detection function from the distance sampling notebook.\nAs a motivating example, I use the ovenbird mist netting dataset provided by Murray Efford via the secr package in R. The design of the study is outlined in Efford, Dawson, and Robbins (2004) and Borchers and Efford (2008). In this dataset, ovenbirds were trapped in 44 mist nets over 8 to 10 consecutive days during the summers of 2005 to 2009.\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pytensor.tensor as pt \nimport pymc as pm\nimport arviz as az\nfrom pymc.distributions.dist_math import binomln, logpow\n\n# hyper parameters\nSEED = 42\nRNG = np.random.default_rng(SEED)\nBUFFER = 100\nM = 200\n\n# plotting defaults\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\n\ndef invlogit(x):\n    '''Inverse logit function'''\n    return 1 / (1 + np.exp(-x))\n\ndef euclid_dist(X, S, library='np'):\n    '''Pairwise euclidian distance between points in (M, 2) and (N, 2) arrays'''\n    diff = X[np.newaxis, :, :] - S[:, np.newaxis, :]\n    \n    if library == 'np':\n        return np.sqrt(np.sum(diff ** 2, axis=-1))\n        \n    elif library == 'pm': \n        return pm.math.sqrt(pm.math.sum(diff ** 2, axis=-1))\n\ndef half_normal(d, s, library='np'):\n    '''Half normal detection function.'''\n    if library == 'np':\n        return np.exp( - (d ** 2) / (2 * s ** 2))\n        \n    elif library == 'pm':\n        return pm.math.exp( - (d ** 2) / (2 * s ** 2))\n\ndef exponential(d, s, library='np'):\n    '''Negative exponential detection function.'''    \n    if library == 'np':\n        return np.exp(- d / s)\n        \n    elif library == 'pm':\n        return pm.math.exp(- d / s)\n\n# coordinates for each trap \novenbird_trap = pd.read_csv('ovenbirdtrap.txt', delimiter=' ')\ntrap_count, _ = ovenbird_trap.shape\n\n# information about each trap \ntrap_x = ovenbird_trap.x\ntrap_y = ovenbird_trap.y\nX = ovenbird_trap[['x', 'y']].to_numpy()\n\n# define the state space around the traps\nx_max = trap_x.max() + BUFFER\ny_max = trap_y.max() + BUFFER\nx_min = trap_x.min() - BUFFER\ny_min = trap_y.min() - BUFFER\n\n# plot the trap locations\nfig, ax = plt.subplots(figsize=(4, 4))\n\n# plot the traps\nax.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax.set_ylim((y_min, y_max))\nax.set_xlim((x_min, x_max))\n\nax.annotate(\n    '44 nets\\n30m apart', ha='center',\n    xy=(55, -150), xycoords='data', color='black',\n    xytext=(40, 30), textcoords='offset points',\n    arrowprops=dict(arrowstyle=\"-&gt;\", color='black', linewidth=1,\n                    connectionstyle=\"angle3,angleA=90,angleB=0\"))\n\n# aesthetics \nax.set_title('Mist net locations')\nax.grid(False)\nplt.show()\n\n\n\n\n\n\n\nFigure 1: Locations of the mist nets in the ovenbird dataset (Efford, Dawson, and Robbins 2004)\nOne difference between spatial and traditional (non-spatial) capture is the addition of the trap identifier in the capture history. Whereas a traditional capture history is [individual, occasion], a spatial capture history might be [individual, occasion, trap].\nIn the ovenbird example, I ignore the year dimension, pooling parameters across years, which allows for better estimation of the detection parameters. My hack for doing so is treating every band/year combination as a unique individual in a combined year capture history. This is easy to implement, creates an awkward interpretation of \\(N\\) (see below).\n# ovenbird capture history\noven_ch = pd.read_csv('ovenbirdcapt.txt', delimiter=' ')\n\n# create a unique bird/year identifier for each individual\noven_ch['ID'] = oven_ch.groupby(['Year','Band']).ngroup()\noccasion_count = oven_ch.Day.max()\n\n# merge the datasets, making sure that traps with no detections are included \novenbird = (\n    ovenbird_trap.merge(oven_ch[['ID', 'Net', 'Day']], how='left')\n      [['ID', 'Day', 'Net', 'x', 'y']]\n      .sort_values('ID')\n      .reset_index(drop=True)\n)\n\novenbird.head(10)\n\n\n\n\n\n\n\n\n\nID\nDay\nNet\nx\ny\n\n\n\n\n0\n0.0\n1.0\n2\n-50.0\n-255.0\n\n\n1\n1.0\n9.0\n20\n-50.0\n285.0\n\n\n2\n1.0\n1.0\n15\n-50.0\n135.0\n\n\n3\n2.0\n6.0\n17\n-50.0\n195.0\n\n\n4\n2.0\n9.0\n27\n49.0\n165.0\n\n\n5\n2.0\n1.0\n15\n-50.0\n135.0\n\n\n6\n2.0\n1.0\n14\n-50.0\n105.0\n\n\n7\n3.0\n1.0\n41\n49.0\n-255.0\n\n\n8\n3.0\n3.0\n39\n49.0\n-195.0\n\n\n9\n3.0\n1.0\n42\n49.0\n-285.0",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -217,7 +217,7 @@
     "href": "scr.html#simulation",
     "title": "Spatial capture-recapture",
     "section": "Simulation",
-    "text": "Simulation\nBefore estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the activity center. The activity center \\(\\mathbf{s}_i\\) is the most likely place that you’d find an individual \\(i\\) over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space. (The study takes place in homogenous habitat.)\nI compute the probability of detection for individual \\(i\\) at trap \\(j\\) as \\(p_{i,j}=g_0 \\exp(-d_{i,j}^2/2\\sigma^2),\\) where \\(g_0\\) is the probability of detecting an individual when it’s activity center is at the trap, \\(d_{i,j}\\) is the euclidean distance between the trap and the activity center, and \\(\\sigma\\) is the detection range parameter.\n\n# true population size\nN = 150\n\n# simulate activity centers\nsx_true = RNG.uniform(x_min, x_max, N)\nsy_true = RNG.uniform(y_min, y_max, N)\nS_true = np.column_stack((sx_true, sy_true))\n\n# true distance between the trap and the activity centers\nd_true = euclid_dist(X, S_true)\n\n# detection parameters\ng0_true = 0.025     \nsigma_true = 73     \n\n# simulate the number of captures at each trap for each individual\ncapture_probability = g0_true * half_normal(d_true, sigma_true)\nsim_Y = RNG.binomial(occasion_count, capture_probability)\n\n# filter out undetected individuals\nwas_detected = sim_Y.sum(axis=1) &gt; 0\nsim_Y_det = sim_Y[was_detected]\nn_detected = int(was_detected.sum())\n\nFollowing Royle et al. (2013), Chapter 5, I first fit the version of the model where we assume that we know the true population size. In this case, I’m only estimating the detection parameters and the activity center locations.\n\n# upper bound for the uniform prior on sigma\nU_SIGMA = 150\n\nwith pm.Model() as known:\n\n    # priors for the activity centers\n    sx = pm.Uniform('sx', x_min, x_max, shape=n_detected)\n    sy = pm.Uniform('sy', y_min, y_max, shape=n_detected)\n    S = pt.stack([sx, sy], axis=1)\n\n    # priors for the detection parameters\n    g0 = pm.Uniform('g0', 0, 1)\n    sigma = pm.Uniform('sigma', 0, U_SIGMA)\n    \n    # probability of capture for each individual at each trap\n    distance = euclid_dist(X, S, 'pm')\n    p = pm.Deterministic('p', g0 * half_normal(distance, sigma))\n\n    # likelihood\n    pm.Binomial(\n        'y',\n        p=p,\n        n=occasion_count,\n        observed=sim_Y_det\n    )\n\npm.model_to_graphviz(known)\n\n\n\n\n\n\n\nFigure 2: Visual representation of the model where \\(N\\) is known.\n\n\n\n\n\n\nwith known:\n    known_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [sx, sy, g0, sigma]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 26 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:25&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(known_idata, var_names=['g0', 'sigma'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\ng0\n0.033\n0.004\n0.026\n0.041\n0.000\n0.000\n1735.0\n2548.0\n1.0\n\n\nsigma\n79.366\n5.218\n69.491\n88.931\n0.174\n0.123\n892.0\n1932.0\n1.0\n\n\n\n\n\n\n\n\naz.plot_trace(\n    known_idata, \n    var_names=['g0', 'sigma'],\n    figsize=(8,4),\n    lines=[(\"g0\", {}, [g0_true]), (\"sigma\", {}, [sigma_true])] \n);\n\n\n\n\n\n\n\nFigure 3: Trace plots for model where \\(N\\) is known. The true parameter values are shown by vertical and horizontal lines.\n\n\n\n\n\nThe trace plots show reasonable agreement between the true parameter values and the estimated values, although \\(g_0\\) appears to be overestimated.",
+    "text": "Simulation\nBefore estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the activity center. The activity center \\(\\mathbf{s}_i\\) is the most likely place that you’d find an individual \\(i\\) over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space.\nI compute the probability of detection for individual \\(i\\) at trap \\(j\\) as \\(p_{i,j}=g_0 \\exp(-d_{i,j}^2/2\\sigma^2),\\) where \\(g_0\\) is the probability of detecting an individual when it’s activity center is at the trap, \\(d_{i,j}\\) is the euclidean distance between the trap and the activity center, and \\(\\sigma\\) is the detection range parameter.\n\n# true population size\nN = 150\n\n# simulate activity centers\nsx_true = RNG.uniform(x_min, x_max, N)\nsy_true = RNG.uniform(y_min, y_max, N)\nS_true = np.column_stack((sx_true, sy_true))\n\n# true distance between the trap and the activity centers\nd_true = euclid_dist(X, S_true)\n\n# detection parameters\ng0_true = 0.025     \nsigma_true = 73     \n\n# simulate the number of captures at each trap for each individual\ncapture_probability = g0_true * half_normal(d_true, sigma_true)\nsim_Y = RNG.binomial(occasion_count, capture_probability)\n\n# filter out undetected individuals\nwas_detected = sim_Y.sum(axis=1) &gt; 0\nsim_Y_det = sim_Y[was_detected]\nn_detected = int(was_detected.sum())\n\nFollowing Royle et al. (2013), Chapter 5, I first fit the version of the model where we assume that we know the true population size. In this case, I’m only estimating the detection parameters and the activity center locations.\n\n# upper bound for the uniform prior on sigma\nU_SIGMA = 150\n\nwith pm.Model() as known:\n\n    # priors for the activity centers\n    sx = pm.Uniform('sx', x_min, x_max, shape=n_detected)\n    sy = pm.Uniform('sy', y_min, y_max, shape=n_detected)\n    S = pt.stack([sx, sy], axis=1)\n\n    # priors for the detection parameters\n    g0 = pm.Uniform('g0', 0, 1)\n    sigma = pm.Uniform('sigma', 0, U_SIGMA)\n    \n    # probability of capture for each individual at each trap\n    distance = euclid_dist(X, S, 'pm')\n    p = pm.Deterministic('p', g0 * half_normal(distance, sigma))\n\n    # likelihood\n    pm.Binomial(\n        'y',\n        p=p,\n        n=occasion_count,\n        observed=sim_Y_det\n    )\n\npm.model_to_graphviz(known)\n\n\n\n\n\n\n\nFigure 2: Visual representation of the model where \\(N\\) is known.\n\n\n\n\n\n\nwith known:\n    known_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [sx, sy, g0, sigma]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 26 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:26&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(known_idata, var_names=['g0', 'sigma'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\ng0\n0.034\n0.004\n0.027\n0.041\n0.000\n0.000\n1589.0\n2642.0\n1.0\n\n\nsigma\n78.940\n5.087\n69.510\n88.420\n0.173\n0.122\n866.0\n1694.0\n1.0\n\n\n\n\n\n\n\n\n\naz.plot_trace(\n    known_idata, \n    var_names=['g0', 'sigma'],\n    figsize=(8,4),\n    lines=[(\"g0\", {}, [g0_true]), (\"sigma\", {}, [sigma_true])] \n);\n\n\n\n\n\n\n\nFigure 3: Trace plots for model where \\(N\\) is known. The true parameter values are shown by vertical and horizontal lines.\n\n\n\n\n\nThe trace plots show reasonable agreement between the true parameter values and the estimated values, although \\(g_0\\) appears to be overestimated.",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -229,7 +229,7 @@
     "href": "scr.html#ovenbird-density",
     "title": "Spatial capture-recapture",
     "section": "Ovenbird density",
-    "text": "Ovenbird density\nNow, I estimate the density \\(D\\) for the ovenbird population. Like distance sampling, SCR can robustly estimate the density of the population, regardless of the size of the state space. The difference between the model above and this one is that we use PX-DA to estimate the inclusion probability \\(\\psi,\\) and subsequently \\(N.\\) First, I convert the DataFrame to a (n_detected, n_traps) array of binomial counts.\n\ndef get_Y(ch):\n    '''Get a (individual_count, trap_count) array of detections.'''\n\n    # count the number of detections per individual per trap\n    detection_counts = pd.crosstab(ch.ID, ch.Net, dropna=False)\n\n    # remove the ghost nan individual \n    detection_counts = detection_counts.loc[~detection_counts.index.isna()]\n    \n    Y = detection_counts.to_numpy()\n    return Y\n\nY = get_Y(ovenbird)\ndetected_count, trap_count = Y.shape\n\n# augmented spatial capture histories with all zero histories\nall_zero_history = np.zeros((M - detected_count, trap_count))\nY_augmented = np.row_stack((Y, all_zero_history))\n\nSimilar to the occupancy notebook, I use a custom distribution to model the zero-inflated data. This is necessary because the zero inflation happens at the individual (row) level. This is, in fact, the same distribution as the occupancy model, although including the binomial coefficient.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\nwith pm.Model() as oven:\n\n    # Priors\n    # activity centers\n    sx = pm.Uniform('sx', x_min, x_max, shape=M)\n    sy = pm.Uniform('sy', y_min, y_max, shape=M)\n    S = pt.stack([sx, sy], axis=1)\n\n    # capture parameters\n    g0 = pm.Uniform('g0', 0, 1, initval=0.05)\n    sigma = pm.Uniform('sigma', 0, U_SIGMA)\n\n    # inclusion probability \n    psi = pm.Uniform('psi', 0, 1)\n\n    # compute the capture probability \n    distance = euclid_dist(X, S, 'pm')\n    p = pm.Deterministic('p', g0 * half_normal(distance, sigma))\n\n    # likelihood \n    pm.CustomDist(\n        'y',\n        occasion_count,\n        p,\n        psi,\n        logp=logp,\n        observed=Y_augmented\n    )\n\npm.model_to_graphviz(oven)\n\n\n\n\n\n\n\nFigure 4: Visual representation of the ovenbird model using data augmentation.\n\n\n\n\n\n\nwith oven:\n    oven_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [sx, sy, g0, sigma, psi]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 48 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:48&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(oven_idata, var_names=['g0', 'sigma', 'psi'])\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\ng0\n0.029\n0.004\n0.022\n0.036\n0.000\n0.000\n1678.0\n2463.0\n1.0\n\n\nsigma\n71.117\n4.668\n62.261\n79.867\n0.147\n0.104\n1011.0\n1764.0\n1.0\n\n\npsi\n0.705\n0.056\n0.602\n0.814\n0.001\n0.001\n3321.0\n2507.0\n1.0\n\n\n\n\n\n\n\n\ng0_mle = [0.025]\nsigma_mle = [73]\n\naz.plot_trace(\n    oven_idata, \n    var_names=['g0', 'sigma'],\n    figsize=(8,4),\n    lines=[(\"g0\", {}, [g0_mle]), (\"sigma\", {}, [sigma_mle])] \n);\n\n\n\n\n\n\n\nFigure 5: Trace plots for the ovenbird model using data augmentation. Maximum likelihood estimates are shown by vertical and horizontal lines.\n\n\n\n\n\nThe estimates are quite close to the maximum likelihood estimates, which I estimated using the secr package in R.\nFinally, I estimate density \\(D\\) using the results. As in the closed capture-recapture and distance sampling notebooks, I use the posterior samples of \\(\\psi\\) and \\(M\\) to sample the posterior of \\(N.\\) This \\(N,\\) however, has an awkward interpretation because I pooled across the years by combining all the detection histories. To get around this, I compute the average annual abundance by dividing by the total number of years in the sample. Then, I divide by the area of the state space.\n\ndef sim_N(idata, n, K):\n\n    psi_samps = az.extract(idata).psi.to_numpy()\n    p_samps = az.extract(idata).p\n    p_samps_undet = p_samps[n:, :, :]\n    \n    bin_probs = (1 - p_samps_undet) ** K\n    bin_prod = bin_probs.prod(axis=1)\n    p_included = (bin_prod * psi_samps) / (bin_prod * psi_samps  + (1 - psi_samps))\n    \n    number_undetected = RNG.binomial(1, p_included).sum(axis=0)\n    N_samps = n + number_undetected\n\n    return N_samps\n\n\nN_samps = sim_N(oven_idata, detected_count, occasion_count)\n\n# kludgy way of calculating avergage abundance \nyear_count = 5\naverage_annual_abundance = N_samps // year_count\n\n# area of the state space in terms of hectares \nha = 100 * 100\nmask_area = (x_max - x_min) * (y_max - y_min) / ha\n\n# density \nD_samples = average_annual_abundance / mask_area \nD_mle = 1.262946\n\nfig, ax = plt.subplots(figsize=(4,4))\nax.hist(D_samples, edgecolor='white', bins=13)\nax.axvline(D_mle, linestyle='--',color='C1')\nax.set_xlabel('Ovenbirds per hectare')\nax.set_ylabel('Number of samples')\nax.text(1.4, 800, f'$\\hat{{D}}$={D_samples.mean():.2f}', va='bottom', ha='left')\nplt.show()\n\n\n\n\n\n\n\nFigure 6: Posterior distribution of the density \\(D\\) of ovenbirds. The maximum likelihood estimate is shown by the dotted red line.\n\n\n\n\n\nI also plot the estimated activity centers for every detected individual, as well as the posterior distribution for two of the detected individuals.\n\nsx_samps = az.extract(oven_idata).sx\nsy_samps = az.extract(oven_idata).sy\n\nsx_mean = sx_samps[:detected_count].mean(axis=1)\nsy_mean = sy_samps[:detected_count].mean(axis=1)\n\none = 49\nsx1 = sx_samps[one]\nsy1 = sy_samps[one]\n\ntwo = 2\nsx2 = sx_samps[two]\nsy2 = sy_samps[two]\n\n# plot the trap locations\nfig, (ax0, ax1) = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(5, 10),\n                               tight_layout=True)\n\n# plot the traps\nax0.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax0.set_ylim((y_min, y_max))\nax0.set_xlim((x_min, x_max))\n\n# plot the mean activity centers\nax0.scatter(sx_mean, sy_mean, marker='o', s=4, color='black')\n\n# aesthetics \nax0.set_title('Estimated activity centers')\nax0.grid(False)\n\n# plot the traps\nax1.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax1.set_ylim((y_min, y_max))\nax1.set_xlim((x_min, x_max))\n\n# plot the distributions of the activity centers\nax1.scatter(sx1, sy1, marker='o', s=1, color='gray', alpha=0.2)\nax1.scatter(sx2, sy2, marker='o', s=1, color='gray', alpha=0.2)\n\n# plot the mean\nax1.scatter(sx1.mean(), sy1.mean(), marker='o', s=20, color='black')\nax1.scatter(sx2.mean(), sy2.mean(), marker='o', s=20, color='black')\n\n# add the label\nax1.text(sx1.mean(), sy1.mean() + 5, f'{one}', ha='center', va='bottom')\nax1.text(sx2.mean(), sy2.mean() + 5, f'{two}', ha='center', va='bottom')\n\n# aesthetics \nax1.set_title('Posterior of two activity centers')\nax1.grid(False)\nplt.show()\n\n\n\n\n\n\n\nFigure 7: Estimated activity centers for the detected individuals, and posterior distributions for two of them.\n\n\n\n\n\nFinally, I plot the posterior distribution of the detection function.\n\nxx = np.arange(BUFFER * 2)\n\nsigma_samps = az.extract(oven_idata).sigma.values.flatten()\ng0_samps = az.extract(oven_idata).g0.values.flatten()\n\np_samps = np.array(\n    [g * half_normal(xx, s) for g, s in zip(g0_samps, sigma_samps)]\n)\n\np_mean = p_samps.mean(axis=0)\np_low = np.quantile(p_samps, 0.02, axis=0)\np_high = np.quantile(p_samps, 0.98, axis=0)\n\nfig, ax = plt.subplots(figsize=(5,4))\n\nax.plot(xx, p_mean, '-')\nax.fill_between(xx, p_low, p_high, alpha=0.2)\n\nax.set_title('Detection function')\nax.set_ylabel(r'$p$')\nax.set_xlabel(r'Distance (m)')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 8: Posterior distribution for the detection function. The line represents the posterior mean while the shaded area is the 96% interval.",
+    "text": "Ovenbird density\nNow, I estimate the density \\(D\\) for the ovenbird population. Like distance sampling, SCR can robustly estimate the density of the population, regardless of the size of the state space. The difference between the model above and this one is that we use PX-DA to estimate the inclusion probability \\(\\psi,\\) and subsequently \\(N.\\) First, I convert the DataFrame to a (n_detected, n_traps) array of binomial counts.\n\ndef get_Y(ch):\n    '''Get a (individual_count, trap_count) array of detections.'''\n\n    # count the number of detections per individual per trap\n    detection_counts = pd.crosstab(ch.ID, ch.Net, dropna=False)\n\n    # remove the ghost nan individual \n    detection_counts = detection_counts.loc[~detection_counts.index.isna()]\n    \n    Y = detection_counts.to_numpy()\n    return Y\n\nY = get_Y(ovenbird)\ndetected_count, trap_count = Y.shape\n\n# augmented spatial capture histories with all zero histories\nall_zero_history = np.zeros((M - detected_count, trap_count))\nY_augmented = np.row_stack((Y, all_zero_history))\n\nSimilar to the occupancy notebook, I use a custom distribution to model the zero-inflated data. This is necessary because the zero inflation happens at the individual (row) level. This is, in fact, the same distribution as the occupancy model, although including the binomial coefficient.\n\ndef logp(value, n, p, psi):\n    \n    binom = binomln(n, value) + logpow(p, value) + logpow(1 - p, n - value)\n    bin_sum = pm.math.sum(binom, axis=1)\n    bin_exp = pm.math.exp(bin_sum)\n\n    res = pm.math.switch(\n        value.sum(axis=1) &gt; 0,\n        bin_exp * psi,\n        bin_exp * psi + (1 - psi)\n    )\n    \n    return pm.math.log(res)\n\nwith pm.Model() as oven:\n\n    # Priors\n    # activity centers\n    sx = pm.Uniform('sx', x_min, x_max, shape=M)\n    sy = pm.Uniform('sy', y_min, y_max, shape=M)\n    S = pt.stack([sx, sy], axis=1)\n\n    # capture parameters\n    g0 = pm.Uniform('g0', 0, 1, initval=0.05)\n    sigma = pm.Uniform('sigma', 0, U_SIGMA)\n\n    # inclusion probability \n    psi = pm.Uniform('psi', 0, 1)\n\n    # compute the capture probability \n    distance = euclid_dist(X, S, 'pm')\n    p = pm.Deterministic('p', g0 * half_normal(distance, sigma))\n\n    # likelihood \n    pm.CustomDist(\n        'y',\n        occasion_count,\n        p,\n        psi,\n        logp=logp,\n        observed=Y_augmented\n    )\n\npm.model_to_graphviz(oven)\n\n\n\n\n\n\n\nFigure 4: Visual representation of the ovenbird model using data augmentation.\n\n\n\n\n\n\nwith oven:\n    oven_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [sx, sy, g0, sigma, psi]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 50 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:49&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.summary(oven_idata, var_names=['g0', 'sigma', 'psi'])\n\n\n\n\n\n\n\n\n\nmean\nsd\nhdi_3%\nhdi_97%\nmcse_mean\nmcse_sd\ness_bulk\ness_tail\nr_hat\n\n\n\n\ng0\n0.029\n0.004\n0.022\n0.036\n0.000\n0.000\n1682.0\n2575.0\n1.0\n\n\nsigma\n71.114\n4.651\n62.763\n80.019\n0.148\n0.105\n972.0\n1848.0\n1.0\n\n\npsi\n0.704\n0.055\n0.606\n0.812\n0.001\n0.001\n3082.0\n2321.0\n1.0\n\n\n\n\n\n\n\n\n\ng0_mle = [0.025]\nsigma_mle = [73]\n\naz.plot_trace(\n    oven_idata, \n    var_names=['g0', 'sigma'],\n    figsize=(8,4),\n    lines=[(\"g0\", {}, [g0_mle]), (\"sigma\", {}, [sigma_mle])] \n);\n\n\n\n\n\n\n\nFigure 5: Trace plots for the ovenbird model using data augmentation. Maximum likelihood estimates are shown by vertical and horizontal lines.\n\n\n\n\n\nThe estimates are quite close to the maximum likelihood estimates, which I estimated using the secr package in R.\nFinally, I estimate density \\(D\\) using the results. As in the closed capture-recapture and distance sampling notebooks, I use the posterior samples of \\(\\psi\\) and \\(M\\) to sample the posterior of \\(N.\\) This \\(N,\\) however, has an awkward interpretation because I pooled across the years by combining all the detection histories. To get around this, I compute the average annual abundance by dividing by the total number of years in the sample. Then, I divide by the area of the state space.\n\ndef sim_N(idata, n, K):\n\n    psi_samps = az.extract(idata).psi.to_numpy()\n    p_samps = az.extract(idata).p\n    p_samps_undet = p_samps[n:, :, :]\n    \n    bin_probs = (1 - p_samps_undet) ** K\n    bin_prod = bin_probs.prod(axis=1)\n    p_included = (bin_prod * psi_samps) / (bin_prod * psi_samps  + (1 - psi_samps))\n    \n    number_undetected = RNG.binomial(1, p_included).sum(axis=0)\n    N_samps = n + number_undetected\n\n    return N_samps\n\n\nN_samps = sim_N(oven_idata, detected_count, occasion_count)\n\n# kludgy way of calculating avergage abundance \nyear_count = 5\naverage_annual_abundance = N_samps // year_count\n\n# area of the state space in terms of hectares \nha = 100 * 100\nmask_area = (x_max - x_min) * (y_max - y_min) / ha\n\n# density \nD_samples = average_annual_abundance / mask_area \nD_mle = 1.262946\n\nfig, ax = plt.subplots(figsize=(4,4))\nax.hist(D_samples, edgecolor='white', bins=13)\nax.axvline(D_mle, linestyle='--',color='C1')\nax.set_xlabel('Ovenbirds per hectare')\nax.set_ylabel('Number of samples')\nax.text(1.4, 800, f'$\\hat{{D}}$={D_samples.mean():.2f}', va='bottom', ha='left')\nplt.show()\n\n\n\n\n\n\n\nFigure 6: Posterior distribution of the density \\(D\\) of ovenbirds. The maximum likelihood estimate is shown by the dotted red line.\n\n\n\n\n\nI also plot the estimated activity centers for every detected individual, as well as the posterior distribution for two of the detected individuals.\n\nsx_samps = az.extract(oven_idata).sx\nsy_samps = az.extract(oven_idata).sy\n\nsx_mean = sx_samps[:detected_count].mean(axis=1)\nsy_mean = sy_samps[:detected_count].mean(axis=1)\n\none = 49\nsx1 = sx_samps[one]\nsy1 = sy_samps[one]\n\ntwo = 2\nsx2 = sx_samps[two]\nsy2 = sy_samps[two]\n\n# plot the trap locations\nfig, (ax0, ax1) = plt.subplots(2, 1, sharex=True, sharey=True, figsize=(5, 10),\n                               tight_layout=True)\n\n# plot the traps\nax0.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax0.set_ylim((y_min, y_max))\nax0.set_xlim((x_min, x_max))\n\n# plot the mean activity centers\nax0.scatter(sx_mean, sy_mean, marker='o', s=4, color='black')\n\n# aesthetics \nax0.set_title('Estimated activity centers')\nax0.grid(False)\n\n# plot the traps\nax1.scatter(trap_x, trap_y, marker='x', s=40, linewidth=1.5, color='C1')\nax1.set_ylim((y_min, y_max))\nax1.set_xlim((x_min, x_max))\n\n# plot the distributions of the activity centers\nax1.scatter(sx1, sy1, marker='o', s=1, color='gray', alpha=0.2)\nax1.scatter(sx2, sy2, marker='o', s=1, color='gray', alpha=0.2)\n\n# plot the mean\nax1.scatter(sx1.mean(), sy1.mean(), marker='o', s=20, color='black')\nax1.scatter(sx2.mean(), sy2.mean(), marker='o', s=20, color='black')\n\n# add the label\nax1.text(sx1.mean(), sy1.mean() + 5, f'{one}', ha='center', va='bottom')\nax1.text(sx2.mean(), sy2.mean() + 5, f'{two}', ha='center', va='bottom')\n\n# aesthetics \nax1.set_title('Posterior of two activity centers')\nax1.grid(False)\nplt.show()\n\n\n\n\n\n\n\nFigure 7: Estimated activity centers for the detected individuals, and posterior distributions for two of them.\n\n\n\n\n\nFinally, I plot the posterior distribution of the detection function.\n\nxx = np.arange(BUFFER * 2)\n\nsigma_samps = az.extract(oven_idata).sigma.values.flatten()\ng0_samps = az.extract(oven_idata).g0.values.flatten()\n\np_samps = np.array(\n    [g * half_normal(xx, s) for g, s in zip(g0_samps, sigma_samps)]\n)\n\np_mean = p_samps.mean(axis=0)\np_low = np.quantile(p_samps, 0.02, axis=0)\np_high = np.quantile(p_samps, 0.98, axis=0)\n\nfig, ax = plt.subplots(figsize=(5,4))\n\nax.plot(xx, p_mean, '-')\nax.fill_between(xx, p_low, p_high, alpha=0.2)\n\nax.set_title('Detection function')\nax.set_ylabel(r'$p$')\nax.set_xlabel(r'Distance (m)')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 8: Posterior distribution for the detection function. The line represents the posterior mean while the shaded area is the 96% interval.",
     "crumbs": [
       "Notebooks",
       "PyMC",
@@ -283,7 +283,7 @@
     "href": "cv.html#presentations",
     "title": "Philip T. Patton",
     "section": "Presentations",
-    "text": "Presentations\n\nPatton, P.T. Some hierarchical and machine learning models for wildlife science. Invited talk at University of Natural Resources and Life Sciences, Vienna (BOKU). July 2023.\nPatton, P.T. et al. The effect of fully automated photo–identification on mark-recapture estimates. Paper presented at the EURING Analytical Meeting. Montpellier, France. April 2023\nPatton, P.T. Assessing populations of resident cetaceans. HIMB Scholarship Symposium. K=aneohe, Hawaii. April 2022.\nPatton, P. T. & Gardner, B. Misspecifying movement models in spatial capture recapture studies. Paper presented at The Ecological Society of America Conference. Portland, OR, USA. August 2017\nPatton, P. T. et al. Modeling and estimating co–occurrence between generalist brood parasites and host communities. Paper presented at the EURING Analytical Meeting. Barcelona, Spain. June 2017\nPatton, P. T. et al. Multi–species occupancy models that incorporate false positive and false negative sampling errors. Paper presented at The Wildlife Society Conference. Raleigh, NC, USA. October 2016\nPatton, P. T. et al. Joint host–parasite occurrence models can improve predictions and reveal ecological traps. Paper presented at the International Statistical Ecology Conference. Seattle, WA, USA. July 2016"
+    "text": "Presentations\n\nPatton, P.T. Some hierarchical and machine learning models for wildlife science. Invited talk at University of Natural Resources and Life Sciences, Vienna (BOKU). July 2023.\nPatton, P.T. et al. The effect of fully automated photo–identification on mark-recapture estimates. Paper presented at the EURING Analytical Meeting. Montpellier, France. April 2023\nPatton, P.T. Assessing populations of resident cetaceans. HIMB Scholarship Symposium. Kāneʻohe, Hawaiʻi. April 2022.\nPatton, P. T. & Gardner, B. Misspecifying movement models in spatial capture recapture studies. Paper presented at The Ecological Society of America Conference. Portland, OR, USA. August 2017\nPatton, P. T. et al. Modeling and estimating co–occurrence between generalist brood parasites and host communities. Paper presented at the EURING Analytical Meeting. Barcelona, Spain. June 2017\nPatton, P. T. et al. Multi–species occupancy models that incorporate false positive and false negative sampling errors. Paper presented at The Wildlife Society Conference. Raleigh, NC, USA. October 2016\nPatton, P. T. et al. Joint host–parasite occurrence models can improve predictions and reveal ecological traps. Paper presented at the International Statistical Ecology Conference. Seattle, WA, USA. July 2016"
   },
   {
     "objectID": "cv.html#teaching-experience",
@@ -304,6 +304,18 @@
     "href": "cv.html#professional-service",
     "title": "Philip T. Patton",
     "section": "Professional Service",
-    "text": "Professional Service\n\nReferee: Wildlife Society Bulletin, Marine Mammal Science\nMember: British Ecological Society, The Wildlife Society (biometrics working group), The Ecological Society of America (statistical ecology section)\nRepresentative to the Faculty, Marine Biology Graduate Program, University of Hawai`i at Mānoa\nRepresentative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawai`i at Mānoa"
+    "text": "Professional Service\n\nReferee: Wildlife Society Bulletin, Marine Mammal Science\nMember: British Ecological Society, The Wildlife Society (biometrics working group), The Ecological Society of America (statistical ecology section)\nRepresentative to the Faculty, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa\nRepresentative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa"
+  },
+  {
+    "objectID": "distance.html",
+    "href": "distance.html",
+    "title": "Distance sampling",
+    "section": "",
+    "text": "In this notebook, I explore how to fit distance sampling models for estimating the size of a closed population. Similar to the occupancy and closed capture-recapture notebooks, I use parameter-expanded data-augmentation (PX-DA) and the zero-inflated binomial model in this notebook.\nThe idea with distance sampling, also known as line-transect sampling, is that a surveyer traverses a transect, typically in a boat or a plane. As they survey, they note when they detect an individual, or a group, from the species of interest, and further note the distance from the transect to the animal. Further, they note the angle to the animal(s), such that they can calculate the perpendicular distance from the animal to the transect. We assume that probability of detecting an animal \\(p\\) decreases monotonically as the distance from the transect grows, e.g., \\(p=\\exp(-x^2/\\sigma^2),\\) where \\(x\\) is the distance and \\(\\sigma\\) is a scale parameter to be estimated. These simple assumptions permit the estimation of the population size \\(N\\) as well as density \\(D.\\)\nFollowing Hooten and Hefley (2019), Chapter 24 and Royle and Dorazio (2008), Chapter 7, I use the impala data from Burnham, Anderson, and Laake (1980), who credits P. Hemingway with the dataset. In this dataset, 73 impalas were observed along a 60km transect. The distance values below are the perpendicular distances, in meters, from the transect.\n\nimport pymc as pm\nimport pytensor.tensor as pt\nimport matplotlib.pyplot as plt\nimport arviz as az\nimport numpy as np\n\n# plotting defaults\nplt.style.use('fivethirtyeight')\nplt.rcParams['axes.facecolor'] = 'white'\nplt.rcParams['figure.facecolor'] = 'white'\n\n# hyper parameters\nM = 500\nU_X = 400\nU_SIGMA = 400\n\n# burnham impala dataset with distances in m\nx_observed = np.array(\n    [71.933980, 26.047227, 58.474341, 92.349221, 163.830409, 84.523652\n    ,163.830409, 157.330098, 22.267696, 72.105330, 86.986979, 50.795047\n    ,0.000000, 73.135370,  0.000000, 128.557522, 163.830409,  71.845104\n    ,30.467336, 71.073909, 150.960702, 68.829172, 90.000000, 64.983827\n    ,165.690874, 38.008322, 378.207430, 78.146226, 42.127052, 0.000000\n    ,400.000000, 175.386612, 30.467336, 35.069692, 86.036465, 31.686029\n    ,200.000000, 271.892336, 26.047227, 76.604444, 41.042417, 200.000000\n    ,86.036465, 0.000000, 93.969262, 55.127471, 10.458689, 84.523652\n    ,0.000000, 77.645714, 0.000000, 96.418141, 0.000000, 64.278761\n    ,187.938524, 0.000000, 160.696902, 150.453756, 63.603607, 193.185165\n    ,106.066017, 114.906666, 143.394109, 128.557522, 245.745613, 123.127252\n    ,123.127252, 153.208889, 143.394109, 34.202014, 96.418141, 259.807621\n    ,8.715574]\n)\n\n# plot the distances \nfig, ax = plt.subplots(figsize=(4,4))\n\nax.hist(x_observed, edgecolor='white')\n\nax.set_title('Hemingway Impala Data')\nax.set_ylabel('Number of detections')\nax.set_xlabel('Distance (m)')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 1: Histogram of the number of detected impalas at varying distances.\n\n\n\n\n\nAgain, we treat this as a zero-inflated binomial model using PX-DA. The trick for doing so is to create a binary vector of length \\(M\\), \\(y,\\) that represents whether the individual was detected during the study. Then, combine the indicator with the distance vector \\(x\\) to create a the full dataset \\((x,y).\\)\n\nn = len(x_observed)\nunobserved_count = M - n\nzeros = np.zeros(unobserved_count)\n\ny = np.ones(n)\ny_augmented = np.concatenate((y, zeros))\n\nThe issue is that \\(x\\) is unobserved for the undetected individuals. To work around this, we put a uniform prior on the unobserved \\(x,\\) i.e., \\(x \\sim \\text{Uniform}(0, U_x).\\) With this “complete” \\(x,\\) we can construct the detection function \\(p\\) for the unobserved individuals.\n\nwith pm.Model() as distance:\n    \n    psi = pm.Uniform('psi', 0, 1)\n    sigma = pm.Uniform('sigma', 0, U_SIGMA)\n    \n    x_unobserved = pm.Uniform('x_unobserved', 0, U_X, shape=unobserved_count)\n    x_complete = pt.concatenate((x_observed, x_unobserved))\n\n    p = pm.Deterministic('p', pm.math.exp(- x_complete ** 2 / sigma ** 2))\n    \n    pm.ZeroInflatedBinomial('y', p=p, psi=psi, n=1, observed=y_augmented)\n\npm.model_to_graphviz(distance)\n\n\n\n\n\n\n\nFigure 2: Visual representation of the distance sampling model.\n\n\n\n\n\n\nwith distance:\n    distance_idata = pm.sample()\n\nAuto-assigning NUTS sampler...\nInitializing NUTS using jitter+adapt_diag...\nMultiprocess sampling (4 chains in 4 jobs)\nNUTS: [psi, sigma, x_unobserved]\nSampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 12 seconds.\n\n\n\n\n\n\n\n    \n      \n      100.00% [8000/8000 00:12&lt;00:00 Sampling 4 chains, 0 divergences]\n    \n    \n\n\n\naz.plot_trace(\n    distance_idata, \n    figsize=(10,4),\n    var_names=['psi', 'sigma']\n);\n\n\n\n\n\n\n\nFigure 3: Traceplots for the distance sampling model.\n\n\n\n\n\nThis model samples slower than the models in the other notebooks, presumably because of the unobserved \\(x.\\) As in the closed capture-recapture notebook, we will have to simulate the posterior for \\(N\\) using the posterior distribution of \\(\\psi\\) and \\(M.\\)\n\nRNG = np.random.default_rng()\n\nposterior = az.extract(distance_idata)\npsi_samples = posterior.psi.to_numpy()\np_samples = posterior.p.to_numpy()\n\nnot_p = (1 - p_samples[n:])\np_included = (not_p * psi_samples) / (not_p * psi_samples + (1 - psi_samples))\nn_undetected = RNG.binomial(1, p_included).sum(axis=0)\nN_samples = n + n_undetected\n\n\nsigma_samples = posterior.sigma.to_numpy()\n\n# plot the results\nfig, (ax0, ax1) = plt.subplots(1, 2, sharey=True, figsize=(8,4))\n\n# histograms of the posteriors\nax0.hist(N_samples, edgecolor='white', bins=30)\nax1.hist(sigma_samples, edgecolor='white', bins=30)\n\n# show the abundance dist in terms of M\n# ax0.set_xlim((100, M))\n\n# axes labels \nax0.set_xlabel('Abundance $N$')\nax0.set_ylabel('Number of samples')\nax1.set_xlabel('Detection range $\\sigma$')\n\n# add the point estimates\nN_hat = N_samples.mean()\nsigma_hat = sigma_samples.mean()\nax0.text(200, 350, f'$\\hat{{N}}$={N_hat:.1f}', ha='left', va='center')\nax1.text(205, 350, f'$\\hat{{\\sigma}}$={sigma_hat:.1f}', ha='left', va='center')\n\n# the results from royle and dorazio (2008) for comparison\nN_hat_royle = 179.9\nsigma_hat_royle = 187\n\nax0.axvline(N_hat_royle, linestyle='--', linewidth=3, color='C1')\nax1.axvline(sigma_hat_royle, linestyle='--', linewidth=3, color='C1')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 4: Posterior distributions for \\(N\\) and \\(\\sigma.\\) Estimates from Royle and Dorazio (2008) are shown with vertical lines.\n\n\n\n\n\nThe model shows a high level of agreement with Royle and Dorazio (2008), Chapter 7, although note that they reported \\(\\sigma\\) in terms of 100m units. It is also possible to plot the posterior distribution of the detection function.\n\nxx = np.arange(400)\n\ndef det_func(x, s):\n    return np.exp(- (x ** 2) / (s ** 2))\n\np_samps = np.array([det_func(xx, s) for s in sigma_samples])\n\np_mean = p_samps.mean(axis=0)\np_low = np.quantile(p_samps, 0.02, axis=0)\np_high = np.quantile(p_samps, 0.98, axis=0)\n\nfig, ax = plt.subplots(figsize=(5,4))\n\nax.plot(xx, p_mean, '-')\nax.fill_between(xx, p_low, p_high, alpha=0.2)\n\nax.set_title('Detection function')\nax.set_ylabel(r'$p$')\nax.set_xlabel(r'Distance (m)')\n\nplt.show()\n\n\n\n\n\n\n\nFigure 5: Posterior distribution for the detection function. The line represents the posterior mean while the shaded area is the 96% interval.\n\n\n\n\n\n\n\n\n\nReferences\n\nBurnham, Kenneth P, David R Anderson, and Jeffrey L Laake. 1980. “Estimation of Density from Line Transect Sampling of Biological Populations.” Wildlife Monographs, no. 72: 3–202.\n\n\nHooten, Mevin B, and Trevor Hefley. 2019. Bringing Bayesian Models to Life. CRC Press.\n\n\nRoyle, J Andrew, and Robert M Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier.",
+    "crumbs": [
+      "Notebooks",
+      "PyMC",
+      "Distance sampling"
+    ]
   }
 ]
\ No newline at end of file
diff --git a/docs/site_libs/.DS_Store b/docs/site_libs/.DS_Store
new file mode 100644
index 0000000..74009a0
Binary files /dev/null and b/docs/site_libs/.DS_Store differ
diff --git a/docs/site_libs/bootstrap/bootstrap.min.css b/docs/site_libs/bootstrap/bootstrap.min.css
index 876abf2..7ac005e 100644
--- a/docs/site_libs/bootstrap/bootstrap.min.css
+++ b/docs/site_libs/bootstrap/bootstrap.min.css
@@ -2,7 +2,7 @@
  * Bootstrap  v5.3.1 (https://getbootstrap.com/)
  * Copyright 2011-2023 The Bootstrap Authors
  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 0, 57;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 52, 58, 64;--bs-primary-text-emphasis: #10335b;--bs-secondary-text-emphasis: #15171a;--bs-success-text-emphasis: #19490a;--bs-info-text-emphasis: #3d224b;--bs-warning-text-emphasis: #662f0a;--bs-danger-text-emphasis: #660017;--bs-light-text-emphasis: #495057;--bs-dark-text-emphasis: #495057;--bs-primary-bg-subtle: #d4e6f9;--bs-secondary-bg-subtle: #d6d8d9;--bs-success-bg-subtle: #d9f0d1;--bs-info-bg-subtle: #ebddf1;--bs-warning-bg-subtle: #ffe3d1;--bs-danger-bg-subtle: #ffccd7;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #a9ccf4;--bs-secondary-border-subtle: #aeb0b3;--bs-success-border-subtle: #b2e2a3;--bs-info-border-subtle: #d6bbe4;--bs-warning-border-subtle: #ffc8a3;--bs-danger-border-subtle: #ff99b0;--bs-light-border-subtle: #e9ecef;--bs-dark-border-subtle: #adb5bd;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 0;--bs-font-sans-serif: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));--bs-root-font-size: 17px;--bs-body-font-family: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-body-font-size:1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #343a40;--bs-body-color-rgb: 52, 58, 64;--bs-body-bg: #fff;--bs-body-bg-rgb: 255, 255, 255;--bs-emphasis-color: #000;--bs-emphasis-color-rgb: 0, 0, 0;--bs-secondary-color: rgba(52, 58, 64, 0.75);--bs-secondary-color-rgb: 52, 58, 64;--bs-secondary-bg: #e9ecef;--bs-secondary-bg-rgb: 233, 236, 239;--bs-tertiary-color: rgba(52, 58, 64, 0.5);--bs-tertiary-color-rgb: 52, 58, 64;--bs-tertiary-bg: #f8f9fa;--bs-tertiary-bg-rgb: 248, 249, 250;--bs-heading-color: inherit;--bs-link-color: #2761e3;--bs-link-color-rgb: 39, 97, 227;--bs-link-decoration: underline;--bs-link-hover-color: #1f4eb6;--bs-link-hover-color-rgb: 31, 78, 182;--bs-code-color: #7d12ba;--bs-highlight-bg: #ffe3d1;--bs-border-width: 1px;--bs-border-style: solid;--bs-border-color: #dee2e6;--bs-border-color-translucent: rgba(0, 0, 0, 0.175);--bs-border-radius: 0.25rem;--bs-border-radius-sm: 0.2em;--bs-border-radius-lg: 0.5rem;--bs-border-radius-xl: 1rem;--bs-border-radius-xxl: 2rem;--bs-border-radius-2xl: var(--bs-border-radius-xxl);--bs-border-radius-pill: 50rem;--bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);--bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-focus-ring-width: 0.25rem;--bs-focus-ring-opacity: 0.25;--bs-focus-ring-color: rgba(39, 128, 227, 0.25);--bs-form-valid-color: #3fb618;--bs-form-valid-border-color: #3fb618;--bs-form-invalid-color: #ff0039;--bs-form-invalid-border-color: #ff0039}[data-bs-theme=dark]{color-scheme:dark;--bs-body-color: #dee2e6;--bs-body-color-rgb: 222, 226, 230;--bs-body-bg: #212529;--bs-body-bg-rgb: 33, 37, 41;--bs-emphasis-color: #fff;--bs-emphasis-color-rgb: 255, 255, 255;--bs-secondary-color: rgba(222, 226, 230, 0.75);--bs-secondary-color-rgb: 222, 226, 230;--bs-secondary-bg: #343a40;--bs-secondary-bg-rgb: 52, 58, 64;--bs-tertiary-color: rgba(222, 226, 230, 0.5);--bs-tertiary-color-rgb: 222, 226, 230;--bs-tertiary-bg: #2b3035;--bs-tertiary-bg-rgb: 43, 48, 53;--bs-primary-text-emphasis: #7db3ee;--bs-secondary-text-emphasis: #85898c;--bs-success-text-emphasis: #8cd374;--bs-info-text-emphasis: #c298d6;--bs-warning-text-emphasis: #ffac74;--bs-danger-text-emphasis: #ff6688;--bs-light-text-emphasis: #f8f9fa;--bs-dark-text-emphasis: #dee2e6;--bs-primary-bg-subtle: #081a2d;--bs-secondary-bg-subtle: #0a0c0d;--bs-success-bg-subtle: #0d2405;--bs-info-bg-subtle: #1f1125;--bs-warning-bg-subtle: #331705;--bs-danger-bg-subtle: #33000b;--bs-light-bg-subtle: #343a40;--bs-dark-bg-subtle: #1a1d20;--bs-primary-border-subtle: #174d88;--bs-secondary-border-subtle: #1f2326;--bs-success-border-subtle: #266d0e;--bs-info-border-subtle: #5c3270;--bs-warning-border-subtle: #99460e;--bs-danger-border-subtle: #990022;--bs-light-border-subtle: #495057;--bs-dark-border-subtle: #343a40;--bs-heading-color: inherit;--bs-link-color: #7db3ee;--bs-link-hover-color: #97c2f1;--bs-link-color-rgb: 125, 179, 238;--bs-link-hover-color-rgb: 151, 194, 241;--bs-code-color: white;--bs-border-color: #495057;--bs-border-color-translucent: rgba(255, 255, 255, 0.15);--bs-form-valid-color: #8cd374;--bs-form-valid-border-color: #8cd374;--bs-form-invalid-color: #ff6688;--bs-form-invalid-border-color: #ff6688}*,*::before,*::after{box-sizing:border-box}:root{font-size:var(--bs-root-font-size)}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;border:0;border-top:1px solid;opacity:.25}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color)}h1,.h1{font-size:calc(1.325rem + 0.9vw)}@media(min-width: 1200px){h1,.h1{font-size:2rem}}h2,.h2{font-size:calc(1.29rem + 0.48vw)}@media(min-width: 1200px){h2,.h2{font-size:1.65rem}}h3,.h3{font-size:calc(1.27rem + 0.24vw)}@media(min-width: 1200px){h3,.h3{font-size:1.45rem}}h4,.h4{font-size:1.25rem}h5,.h5{font-size:1.1rem}h6,.h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[title]{text-decoration:underline dotted;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;-ms-text-decoration:underline dotted;-o-text-decoration:underline dotted;cursor:help;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}ol,ul,dl{margin-top:0;margin-bottom:1rem}ol ol,ul ul,ol ul,ul ol{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem;padding:.625rem 1.25rem;border-left:.25rem solid #e9ecef}blockquote p:last-child,blockquote ul:last-child,blockquote ol:last-child{margin-bottom:0}b,strong{font-weight:bolder}small,.small{font-size:0.875em}mark,.mark{padding:.1875em;background-color:var(--bs-highlight-bg)}sub,sup{position:relative;font-size:0.75em;line-height:0;vertical-align:baseline}sub{bottom:-0.25em}sup{top:-0.5em}a{color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}a:hover{--bs-link-color-rgb: var(--bs-link-hover-color-rgb)}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code,kbd,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:0.875em;color:#000;background-color:#f8f9fa;padding:.5rem;border:1px solid var(--bs-border-color, #dee2e6)}pre code{background-color:rgba(0,0,0,0);font-size:inherit;color:inherit;word-break:normal}code{font-size:0.875em;color:var(--bs-code-color);background-color:#f8f9fa;padding:.125rem .25rem;word-wrap:break-word}a>code{color:inherit}kbd{padding:.4rem .4rem;font-size:0.875em;color:#fff;background-color:#343a40}kbd kbd{padding:0;font-size:1em}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:rgba(52,58,64,.75);text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]:not([type=date]):not([type=datetime-local]):not([type=month]):not([type=week]):not([type=time])::-webkit-calendar-picker-indicator{display:none !important}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + 0.3vw);line-height:inherit}@media(min-width: 1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none !important}.lead{font-size:1.25rem;font-weight:300}.display-1{font-size:calc(1.625rem + 4.5vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-1{font-size:5rem}}.display-2{font-size:calc(1.575rem + 3.9vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-2{font-size:4.5rem}}.display-3{font-size:calc(1.525rem + 3.3vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-3{font-size:4rem}}.display-4{font-size:calc(1.475rem + 2.7vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-4{font-size:3.5rem}}.display-5{font-size:calc(1.425rem + 2.1vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-5{font-size:3rem}}.display-6{font-size:calc(1.375rem + 1.5vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-6{font-size:2.5rem}}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;list-style:none}.list-inline-item{display:inline-block}.list-inline-item:not(:last-child){margin-right:.5rem}.initialism{font-size:0.875em;text-transform:uppercase}.blockquote{margin-bottom:1rem;font-size:1.25rem}.blockquote>:last-child{margin-bottom:0}.blockquote-footer{margin-top:-1rem;margin-bottom:1rem;font-size:0.875em;color:#6c757d}.blockquote-footer::before{content:"— "}.img-fluid{max-width:100%;height:auto}.img-thumbnail{padding:.25rem;background-color:#fff;border:1px solid #dee2e6;max-width:100%;height:auto}.figure{display:inline-block}.figure-img{margin-bottom:.5rem;line-height:1}.figure-caption{font-size:0.875em;color:rgba(52,58,64,.75)}.container,.container-fluid,.container-xxl,.container-xl,.container-lg,.container-md,.container-sm{--bs-gutter-x: 1.5rem;--bs-gutter-y: 0;width:100%;padding-right:calc(var(--bs-gutter-x)*.5);padding-left:calc(var(--bs-gutter-x)*.5);margin-right:auto;margin-left:auto}@media(min-width: 576px){.container-sm,.container{max-width:540px}}@media(min-width: 768px){.container-md,.container-sm,.container{max-width:720px}}@media(min-width: 992px){.container-lg,.container-md,.container-sm,.container{max-width:960px}}@media(min-width: 1200px){.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1140px}}@media(min-width: 1400px){.container-xxl,.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1320px}}:root{--bs-breakpoint-xs: 0;--bs-breakpoint-sm: 576px;--bs-breakpoint-md: 768px;--bs-breakpoint-lg: 992px;--bs-breakpoint-xl: 1200px;--bs-breakpoint-xxl: 1400px}.grid{display:grid;grid-template-rows:repeat(var(--bs-rows, 1), 1fr);grid-template-columns:repeat(var(--bs-columns, 12), 1fr);gap:var(--bs-gap, 1.5rem)}.grid .g-col-1{grid-column:auto/span 1}.grid .g-col-2{grid-column:auto/span 2}.grid .g-col-3{grid-column:auto/span 3}.grid .g-col-4{grid-column:auto/span 4}.grid .g-col-5{grid-column:auto/span 5}.grid .g-col-6{grid-column:auto/span 6}.grid .g-col-7{grid-column:auto/span 7}.grid .g-col-8{grid-column:auto/span 8}.grid .g-col-9{grid-column:auto/span 9}.grid .g-col-10{grid-column:auto/span 10}.grid .g-col-11{grid-column:auto/span 11}.grid .g-col-12{grid-column:auto/span 12}.grid .g-start-1{grid-column-start:1}.grid .g-start-2{grid-column-start:2}.grid .g-start-3{grid-column-start:3}.grid .g-start-4{grid-column-start:4}.grid .g-start-5{grid-column-start:5}.grid .g-start-6{grid-column-start:6}.grid .g-start-7{grid-column-start:7}.grid .g-start-8{grid-column-start:8}.grid .g-start-9{grid-column-start:9}.grid .g-start-10{grid-column-start:10}.grid .g-start-11{grid-column-start:11}@media(min-width: 576px){.grid .g-col-sm-1{grid-column:auto/span 1}.grid .g-col-sm-2{grid-column:auto/span 2}.grid .g-col-sm-3{grid-column:auto/span 3}.grid .g-col-sm-4{grid-column:auto/span 4}.grid .g-col-sm-5{grid-column:auto/span 5}.grid .g-col-sm-6{grid-column:auto/span 6}.grid .g-col-sm-7{grid-column:auto/span 7}.grid .g-col-sm-8{grid-column:auto/span 8}.grid .g-col-sm-9{grid-column:auto/span 9}.grid .g-col-sm-10{grid-column:auto/span 10}.grid .g-col-sm-11{grid-column:auto/span 11}.grid .g-col-sm-12{grid-column:auto/span 12}.grid .g-start-sm-1{grid-column-start:1}.grid .g-start-sm-2{grid-column-start:2}.grid .g-start-sm-3{grid-column-start:3}.grid .g-start-sm-4{grid-column-start:4}.grid .g-start-sm-5{grid-column-start:5}.grid .g-start-sm-6{grid-column-start:6}.grid .g-start-sm-7{grid-column-start:7}.grid .g-start-sm-8{grid-column-start:8}.grid .g-start-sm-9{grid-column-start:9}.grid .g-start-sm-10{grid-column-start:10}.grid .g-start-sm-11{grid-column-start:11}}@media(min-width: 768px){.grid .g-col-md-1{grid-column:auto/span 1}.grid .g-col-md-2{grid-column:auto/span 2}.grid .g-col-md-3{grid-column:auto/span 3}.grid .g-col-md-4{grid-column:auto/span 4}.grid .g-col-md-5{grid-column:auto/span 5}.grid .g-col-md-6{grid-column:auto/span 6}.grid .g-col-md-7{grid-column:auto/span 7}.grid .g-col-md-8{grid-column:auto/span 8}.grid .g-col-md-9{grid-column:auto/span 9}.grid .g-col-md-10{grid-column:auto/span 10}.grid .g-col-md-11{grid-column:auto/span 11}.grid .g-col-md-12{grid-column:auto/span 12}.grid .g-start-md-1{grid-column-start:1}.grid .g-start-md-2{grid-column-start:2}.grid .g-start-md-3{grid-column-start:3}.grid .g-start-md-4{grid-column-start:4}.grid .g-start-md-5{grid-column-start:5}.grid .g-start-md-6{grid-column-start:6}.grid .g-start-md-7{grid-column-start:7}.grid .g-start-md-8{grid-column-start:8}.grid .g-start-md-9{grid-column-start:9}.grid .g-start-md-10{grid-column-start:10}.grid .g-start-md-11{grid-column-start:11}}@media(min-width: 992px){.grid .g-col-lg-1{grid-column:auto/span 1}.grid .g-col-lg-2{grid-column:auto/span 2}.grid .g-col-lg-3{grid-column:auto/span 3}.grid .g-col-lg-4{grid-column:auto/span 4}.grid .g-col-lg-5{grid-column:auto/span 5}.grid .g-col-lg-6{grid-column:auto/span 6}.grid .g-col-lg-7{grid-column:auto/span 7}.grid .g-col-lg-8{grid-column:auto/span 8}.grid .g-col-lg-9{grid-column:auto/span 9}.grid .g-col-lg-10{grid-column:auto/span 10}.grid .g-col-lg-11{grid-column:auto/span 11}.grid .g-col-lg-12{grid-column:auto/span 12}.grid .g-start-lg-1{grid-column-start:1}.grid .g-start-lg-2{grid-column-start:2}.grid .g-start-lg-3{grid-column-start:3}.grid .g-start-lg-4{grid-column-start:4}.grid .g-start-lg-5{grid-column-start:5}.grid .g-start-lg-6{grid-column-start:6}.grid .g-start-lg-7{grid-column-start:7}.grid .g-start-lg-8{grid-column-start:8}.grid .g-start-lg-9{grid-column-start:9}.grid .g-start-lg-10{grid-column-start:10}.grid .g-start-lg-11{grid-column-start:11}}@media(min-width: 1200px){.grid .g-col-xl-1{grid-column:auto/span 1}.grid .g-col-xl-2{grid-column:auto/span 2}.grid .g-col-xl-3{grid-column:auto/span 3}.grid .g-col-xl-4{grid-column:auto/span 4}.grid .g-col-xl-5{grid-column:auto/span 5}.grid .g-col-xl-6{grid-column:auto/span 6}.grid .g-col-xl-7{grid-column:auto/span 7}.grid .g-col-xl-8{grid-column:auto/span 8}.grid .g-col-xl-9{grid-column:auto/span 9}.grid .g-col-xl-10{grid-column:auto/span 10}.grid .g-col-xl-11{grid-column:auto/span 11}.grid .g-col-xl-12{grid-column:auto/span 12}.grid .g-start-xl-1{grid-column-start:1}.grid .g-start-xl-2{grid-column-start:2}.grid .g-start-xl-3{grid-column-start:3}.grid .g-start-xl-4{grid-column-start:4}.grid .g-start-xl-5{grid-column-start:5}.grid .g-start-xl-6{grid-column-start:6}.grid .g-start-xl-7{grid-column-start:7}.grid .g-start-xl-8{grid-column-start:8}.grid .g-start-xl-9{grid-column-start:9}.grid .g-start-xl-10{grid-column-start:10}.grid .g-start-xl-11{grid-column-start:11}}@media(min-width: 1400px){.grid .g-col-xxl-1{grid-column:auto/span 1}.grid .g-col-xxl-2{grid-column:auto/span 2}.grid .g-col-xxl-3{grid-column:auto/span 3}.grid .g-col-xxl-4{grid-column:auto/span 4}.grid .g-col-xxl-5{grid-column:auto/span 5}.grid .g-col-xxl-6{grid-column:auto/span 6}.grid .g-col-xxl-7{grid-column:auto/span 7}.grid .g-col-xxl-8{grid-column:auto/span 8}.grid .g-col-xxl-9{grid-column:auto/span 9}.grid .g-col-xxl-10{grid-column:auto/span 10}.grid .g-col-xxl-11{grid-column:auto/span 11}.grid .g-col-xxl-12{grid-column:auto/span 12}.grid .g-start-xxl-1{grid-column-start:1}.grid .g-start-xxl-2{grid-column-start:2}.grid .g-start-xxl-3{grid-column-start:3}.grid .g-start-xxl-4{grid-column-start:4}.grid .g-start-xxl-5{grid-column-start:5}.grid .g-start-xxl-6{grid-column-start:6}.grid .g-start-xxl-7{grid-column-start:7}.grid .g-start-xxl-8{grid-column-start:8}.grid .g-start-xxl-9{grid-column-start:9}.grid .g-start-xxl-10{grid-column-start:10}.grid .g-start-xxl-11{grid-column-start:11}}.table{--bs-table-color-type: initial;--bs-table-bg-type: initial;--bs-table-color-state: initial;--bs-table-bg-state: initial;--bs-table-color: #343a40;--bs-table-bg: #fff;--bs-table-border-color: #dee2e6;--bs-table-accent-bg: transparent;--bs-table-striped-color: #343a40;--bs-table-striped-bg: rgba(0, 0, 0, 0.05);--bs-table-active-color: #343a40;--bs-table-active-bg: rgba(0, 0, 0, 0.1);--bs-table-hover-color: #343a40;--bs-table-hover-bg: rgba(0, 0, 0, 0.075);width:100%;margin-bottom:1rem;vertical-align:top;border-color:var(--bs-table-border-color)}.table>:not(caption)>*>*{padding:.5rem .5rem;color:var(--bs-table-color-state, var(--bs-table-color-type, var(--bs-table-color)));background-color:var(--bs-table-bg);border-bottom-width:1px;box-shadow:inset 0 0 0 9999px var(--bs-table-bg-state, var(--bs-table-bg-type, var(--bs-table-accent-bg)))}.table>tbody{vertical-align:inherit}.table>thead{vertical-align:bottom}.table-group-divider{border-top:calc(1px*2) solid #b2bac1}.caption-top{caption-side:top}.table-sm>:not(caption)>*>*{padding:.25rem .25rem}.table-bordered>:not(caption)>*{border-width:1px 0}.table-bordered>:not(caption)>*>*{border-width:0 1px}.table-borderless>:not(caption)>*>*{border-bottom-width:0}.table-borderless>:not(:first-child){border-top-width:0}.table-striped>tbody>tr:nth-of-type(odd)>*{--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-striped-columns>:not(caption)>tr>:nth-child(even){--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-active{--bs-table-color-state: var(--bs-table-active-color);--bs-table-bg-state: var(--bs-table-active-bg)}.table-hover>tbody>tr:hover>*{--bs-table-color-state: var(--bs-table-hover-color);--bs-table-bg-state: var(--bs-table-hover-bg)}.table-primary{--bs-table-color: #000;--bs-table-bg: #d4e6f9;--bs-table-border-color: #bfcfe0;--bs-table-striped-bg: #c9dbed;--bs-table-striped-color: #000;--bs-table-active-bg: #bfcfe0;--bs-table-active-color: #000;--bs-table-hover-bg: #c4d5e6;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-secondary{--bs-table-color: #000;--bs-table-bg: #d6d8d9;--bs-table-border-color: #c1c2c3;--bs-table-striped-bg: #cbcdce;--bs-table-striped-color: #000;--bs-table-active-bg: #c1c2c3;--bs-table-active-color: #000;--bs-table-hover-bg: #c6c8c9;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-success{--bs-table-color: #000;--bs-table-bg: #d9f0d1;--bs-table-border-color: #c3d8bc;--bs-table-striped-bg: #cee4c7;--bs-table-striped-color: #000;--bs-table-active-bg: #c3d8bc;--bs-table-active-color: #000;--bs-table-hover-bg: #c9dec1;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-info{--bs-table-color: #000;--bs-table-bg: #ebddf1;--bs-table-border-color: #d4c7d9;--bs-table-striped-bg: #dfd2e5;--bs-table-striped-color: #000;--bs-table-active-bg: #d4c7d9;--bs-table-active-color: #000;--bs-table-hover-bg: #d9ccdf;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-warning{--bs-table-color: #000;--bs-table-bg: #ffe3d1;--bs-table-border-color: #e6ccbc;--bs-table-striped-bg: #f2d8c7;--bs-table-striped-color: #000;--bs-table-active-bg: #e6ccbc;--bs-table-active-color: #000;--bs-table-hover-bg: #ecd2c1;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-danger{--bs-table-color: #000;--bs-table-bg: #ffccd7;--bs-table-border-color: #e6b8c2;--bs-table-striped-bg: #f2c2cc;--bs-table-striped-color: #000;--bs-table-active-bg: #e6b8c2;--bs-table-active-color: #000;--bs-table-hover-bg: #ecbdc7;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-light{--bs-table-color: #000;--bs-table-bg: #f8f9fa;--bs-table-border-color: #dfe0e1;--bs-table-striped-bg: #ecedee;--bs-table-striped-color: #000;--bs-table-active-bg: #dfe0e1;--bs-table-active-color: #000;--bs-table-hover-bg: #e5e6e7;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-dark{--bs-table-color: #fff;--bs-table-bg: #343a40;--bs-table-border-color: #484e53;--bs-table-striped-bg: #3e444a;--bs-table-striped-color: #fff;--bs-table-active-bg: #484e53;--bs-table-active-color: #fff;--bs-table-hover-bg: #43494e;--bs-table-hover-color: #fff;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-responsive{overflow-x:auto;-webkit-overflow-scrolling:touch}@media(max-width: 575.98px){.table-responsive-sm{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 767.98px){.table-responsive-md{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 991.98px){.table-responsive-lg{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 1199.98px){.table-responsive-xl{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 1399.98px){.table-responsive-xxl{overflow-x:auto;-webkit-overflow-scrolling:touch}}.form-label,.shiny-input-container .control-label{margin-bottom:.5rem}.col-form-label{padding-top:calc(0.375rem + 1px);padding-bottom:calc(0.375rem + 1px);margin-bottom:0;font-size:inherit;line-height:1.5}.col-form-label-lg{padding-top:calc(0.5rem + 1px);padding-bottom:calc(0.5rem + 1px);font-size:1.25rem}.col-form-label-sm{padding-top:calc(0.25rem + 1px);padding-bottom:calc(0.25rem + 1px);font-size:0.875rem}.form-text{margin-top:.25rem;font-size:0.875em;color:rgba(52,58,64,.75)}.form-control{display:block;width:100%;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#fff;background-clip:padding-box;border:1px solid #dee2e6;border-radius:0;transition:border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-control{transition:none}}.form-control[type=file]{overflow:hidden}.form-control[type=file]:not(:disabled):not([readonly]){cursor:pointer}.form-control:focus{color:#343a40;background-color:#fff;border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-control::-webkit-date-and-time-value{min-width:85px;height:1.5em;margin:0}.form-control::-webkit-datetime-edit{display:block;padding:0}.form-control::placeholder{color:rgba(52,58,64,.75);opacity:1}.form-control:disabled{background-color:#e9ecef;opacity:1}.form-control::file-selector-button{padding:.375rem .75rem;margin:-0.375rem -0.75rem;margin-inline-end:.75rem;color:#343a40;background-color:#f8f9fa;pointer-events:none;border-color:inherit;border-style:solid;border-width:0;border-inline-end-width:1px;border-radius:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-control::file-selector-button{transition:none}}.form-control:hover:not(:disabled):not([readonly])::file-selector-button{background-color:#e9ecef}.form-control-plaintext{display:block;width:100%;padding:.375rem 0;margin-bottom:0;line-height:1.5;color:#343a40;background-color:rgba(0,0,0,0);border:solid rgba(0,0,0,0);border-width:1px 0}.form-control-plaintext:focus{outline:0}.form-control-plaintext.form-control-sm,.form-control-plaintext.form-control-lg{padding-right:0;padding-left:0}.form-control-sm{min-height:calc(1.5em + 0.5rem + calc(1px * 2));padding:.25rem .5rem;font-size:0.875rem}.form-control-sm::file-selector-button{padding:.25rem .5rem;margin:-0.25rem -0.5rem;margin-inline-end:.5rem}.form-control-lg{min-height:calc(1.5em + 1rem + calc(1px * 2));padding:.5rem 1rem;font-size:1.25rem}.form-control-lg::file-selector-button{padding:.5rem 1rem;margin:-0.5rem -1rem;margin-inline-end:1rem}textarea.form-control{min-height:calc(1.5em + 0.75rem + calc(1px * 2))}textarea.form-control-sm{min-height:calc(1.5em + 0.5rem + calc(1px * 2))}textarea.form-control-lg{min-height:calc(1.5em + 1rem + calc(1px * 2))}.form-control-color{width:3rem;height:calc(1.5em + 0.75rem + calc(1px * 2));padding:.375rem}.form-control-color:not(:disabled):not([readonly]){cursor:pointer}.form-control-color::-moz-color-swatch{border:0 !important}.form-control-color::-webkit-color-swatch{border:0 !important}.form-control-color.form-control-sm{height:calc(1.5em + 0.5rem + calc(1px * 2))}.form-control-color.form-control-lg{height:calc(1.5em + 1rem + calc(1px * 2))}.form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e");display:block;width:100%;padding:.375rem 2.25rem .375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#fff;background-image:var(--bs-form-select-bg-img),var(--bs-form-select-bg-icon, none);background-repeat:no-repeat;background-position:right .75rem center;background-size:16px 12px;border:1px solid #dee2e6;border-radius:0;transition:border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-select{transition:none}}.form-select:focus{border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-select[multiple],.form-select[size]:not([size="1"]){padding-right:.75rem;background-image:none}.form-select:disabled{background-color:#e9ecef}.form-select:-moz-focusring{color:rgba(0,0,0,0);text-shadow:0 0 0 #343a40}.form-select-sm{padding-top:.25rem;padding-bottom:.25rem;padding-left:.5rem;font-size:0.875rem}.form-select-lg{padding-top:.5rem;padding-bottom:.5rem;padding-left:1rem;font-size:1.25rem}[data-bs-theme=dark] .form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23dee2e6' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e")}.form-check,.shiny-input-container .checkbox,.shiny-input-container .radio{display:block;min-height:1.5rem;padding-left:0;margin-bottom:.125rem}.form-check .form-check-input,.form-check .shiny-input-container .checkbox input,.form-check .shiny-input-container .radio input,.shiny-input-container .checkbox .form-check-input,.shiny-input-container .checkbox .shiny-input-container .checkbox input,.shiny-input-container .checkbox .shiny-input-container .radio input,.shiny-input-container .radio .form-check-input,.shiny-input-container .radio .shiny-input-container .checkbox input,.shiny-input-container .radio .shiny-input-container .radio input{float:left;margin-left:0}.form-check-reverse{padding-right:0;padding-left:0;text-align:right}.form-check-reverse .form-check-input{float:right;margin-right:0;margin-left:0}.form-check-input,.shiny-input-container .checkbox input,.shiny-input-container .checkbox-inline input,.shiny-input-container .radio input,.shiny-input-container .radio-inline input{--bs-form-check-bg: #fff;width:1em;height:1em;margin-top:.25em;vertical-align:top;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:var(--bs-form-check-bg);background-image:var(--bs-form-check-bg-image);background-repeat:no-repeat;background-position:center;background-size:contain;border:1px solid #dee2e6;print-color-adjust:exact}.form-check-input[type=radio],.shiny-input-container .checkbox input[type=radio],.shiny-input-container .checkbox-inline input[type=radio],.shiny-input-container .radio input[type=radio],.shiny-input-container .radio-inline input[type=radio]{border-radius:50%}.form-check-input:active,.shiny-input-container .checkbox input:active,.shiny-input-container .checkbox-inline input:active,.shiny-input-container .radio input:active,.shiny-input-container .radio-inline input:active{filter:brightness(90%)}.form-check-input:focus,.shiny-input-container .checkbox input:focus,.shiny-input-container .checkbox-inline input:focus,.shiny-input-container .radio input:focus,.shiny-input-container .radio-inline input:focus{border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-check-input:checked,.shiny-input-container .checkbox input:checked,.shiny-input-container .checkbox-inline input:checked,.shiny-input-container .radio input:checked,.shiny-input-container .radio-inline input:checked{background-color:#2780e3;border-color:#2780e3}.form-check-input:checked[type=checkbox],.shiny-input-container .checkbox input:checked[type=checkbox],.shiny-input-container .checkbox-inline input:checked[type=checkbox],.shiny-input-container .radio input:checked[type=checkbox],.shiny-input-container .radio-inline input:checked[type=checkbox]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/%3e%3c/svg%3e")}.form-check-input:checked[type=radio],.shiny-input-container .checkbox input:checked[type=radio],.shiny-input-container .checkbox-inline input:checked[type=radio],.shiny-input-container .radio input:checked[type=radio],.shiny-input-container .radio-inline input:checked[type=radio]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e")}.form-check-input[type=checkbox]:indeterminate,.shiny-input-container .checkbox input[type=checkbox]:indeterminate,.shiny-input-container .checkbox-inline input[type=checkbox]:indeterminate,.shiny-input-container .radio input[type=checkbox]:indeterminate,.shiny-input-container .radio-inline input[type=checkbox]:indeterminate{background-color:#2780e3;border-color:#2780e3;--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e")}.form-check-input:disabled,.shiny-input-container .checkbox input:disabled,.shiny-input-container .checkbox-inline input:disabled,.shiny-input-container .radio input:disabled,.shiny-input-container .radio-inline input:disabled{pointer-events:none;filter:none;opacity:.5}.form-check-input[disabled]~.form-check-label,.form-check-input[disabled]~span,.form-check-input:disabled~.form-check-label,.form-check-input:disabled~span,.shiny-input-container .checkbox input[disabled]~.form-check-label,.shiny-input-container .checkbox input[disabled]~span,.shiny-input-container .checkbox input:disabled~.form-check-label,.shiny-input-container .checkbox input:disabled~span,.shiny-input-container .checkbox-inline input[disabled]~.form-check-label,.shiny-input-container .checkbox-inline input[disabled]~span,.shiny-input-container .checkbox-inline input:disabled~.form-check-label,.shiny-input-container .checkbox-inline input:disabled~span,.shiny-input-container .radio input[disabled]~.form-check-label,.shiny-input-container .radio input[disabled]~span,.shiny-input-container .radio input:disabled~.form-check-label,.shiny-input-container .radio input:disabled~span,.shiny-input-container .radio-inline input[disabled]~.form-check-label,.shiny-input-container .radio-inline input[disabled]~span,.shiny-input-container .radio-inline input:disabled~.form-check-label,.shiny-input-container .radio-inline input:disabled~span{cursor:default;opacity:.5}.form-check-label,.shiny-input-container .checkbox label,.shiny-input-container .checkbox-inline label,.shiny-input-container .radio label,.shiny-input-container .radio-inline label{cursor:pointer}.form-switch{padding-left:2.5em}.form-switch .form-check-input{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280, 0, 0, 0.25%29'/%3e%3c/svg%3e");width:2em;margin-left:-2.5em;background-image:var(--bs-form-switch-bg);background-position:left center;transition:background-position .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-switch .form-check-input{transition:none}}.form-switch .form-check-input:focus{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%2393c0f1'/%3e%3c/svg%3e")}.form-switch .form-check-input:checked{background-position:right center;--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e")}.form-switch.form-check-reverse{padding-right:2.5em;padding-left:0}.form-switch.form-check-reverse .form-check-input{margin-right:-2.5em;margin-left:0}.form-check-inline{display:inline-block;margin-right:1rem}.btn-check{position:absolute;clip:rect(0, 0, 0, 0);pointer-events:none}.btn-check[disabled]+.btn,.btn-check:disabled+.btn{pointer-events:none;filter:none;opacity:.65}[data-bs-theme=dark] .form-switch .form-check-input:not(:checked):not(:focus){--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%28255, 255, 255, 0.25%29'/%3e%3c/svg%3e")}.form-range{width:100%;height:1.5rem;padding:0;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:rgba(0,0,0,0)}.form-range:focus{outline:0}.form-range:focus::-webkit-slider-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(39,128,227,.25)}.form-range:focus::-moz-range-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(39,128,227,.25)}.form-range::-moz-focus-outer{border:0}.form-range::-webkit-slider-thumb{width:1rem;height:1rem;margin-top:-0.25rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#2780e3;border:0;transition:background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-range::-webkit-slider-thumb{transition:none}}.form-range::-webkit-slider-thumb:active{background-color:#bed9f7}.form-range::-webkit-slider-runnable-track{width:100%;height:.5rem;color:rgba(0,0,0,0);cursor:pointer;background-color:#f8f9fa;border-color:rgba(0,0,0,0)}.form-range::-moz-range-thumb{width:1rem;height:1rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#2780e3;border:0;transition:background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-range::-moz-range-thumb{transition:none}}.form-range::-moz-range-thumb:active{background-color:#bed9f7}.form-range::-moz-range-track{width:100%;height:.5rem;color:rgba(0,0,0,0);cursor:pointer;background-color:#f8f9fa;border-color:rgba(0,0,0,0)}.form-range:disabled{pointer-events:none}.form-range:disabled::-webkit-slider-thumb{background-color:rgba(52,58,64,.75)}.form-range:disabled::-moz-range-thumb{background-color:rgba(52,58,64,.75)}.form-floating{position:relative}.form-floating>.form-control,.form-floating>.form-control-plaintext,.form-floating>.form-select{height:calc(3.5rem + calc(1px * 2));min-height:calc(3.5rem + calc(1px * 2));line-height:1.25}.form-floating>label{position:absolute;top:0;left:0;z-index:2;height:100%;padding:1rem .75rem;overflow:hidden;text-align:start;text-overflow:ellipsis;white-space:nowrap;pointer-events:none;border:1px solid rgba(0,0,0,0);transform-origin:0 0;transition:opacity .1s ease-in-out,transform .1s ease-in-out}@media(prefers-reduced-motion: reduce){.form-floating>label{transition:none}}.form-floating>.form-control,.form-floating>.form-control-plaintext{padding:1rem .75rem}.form-floating>.form-control::placeholder,.form-floating>.form-control-plaintext::placeholder{color:rgba(0,0,0,0)}.form-floating>.form-control:focus,.form-floating>.form-control:not(:placeholder-shown),.form-floating>.form-control-plaintext:focus,.form-floating>.form-control-plaintext:not(:placeholder-shown){padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:-webkit-autofill,.form-floating>.form-control-plaintext:-webkit-autofill{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-select{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:focus~label,.form-floating>.form-control:not(:placeholder-shown)~label,.form-floating>.form-control-plaintext~label,.form-floating>.form-select~label{color:rgba(var(--bs-body-color-rgb), 0.65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control:focus~label::after,.form-floating>.form-control:not(:placeholder-shown)~label::after,.form-floating>.form-control-plaintext~label::after,.form-floating>.form-select~label::after{position:absolute;inset:1rem .375rem;z-index:-1;height:1.5em;content:"";background-color:#fff}.form-floating>.form-control:-webkit-autofill~label{color:rgba(var(--bs-body-color-rgb), 0.65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control-plaintext~label{border-width:1px 0}.form-floating>:disabled~label,.form-floating>.form-control:disabled~label{color:#6c757d}.form-floating>:disabled~label::after,.form-floating>.form-control:disabled~label::after{background-color:#e9ecef}.input-group{position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:stretch;-webkit-align-items:stretch;width:100%}.input-group>.form-control,.input-group>.form-select,.input-group>.form-floating{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;width:1%;min-width:0}.input-group>.form-control:focus,.input-group>.form-select:focus,.input-group>.form-floating:focus-within{z-index:5}.input-group .btn{position:relative;z-index:2}.input-group .btn:focus{z-index:5}.input-group-text{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;text-align:center;white-space:nowrap;background-color:#f8f9fa;border:1px solid #dee2e6}.input-group-lg>.form-control,.input-group-lg>.form-select,.input-group-lg>.input-group-text,.input-group-lg>.btn{padding:.5rem 1rem;font-size:1.25rem}.input-group-sm>.form-control,.input-group-sm>.form-select,.input-group-sm>.input-group-text,.input-group-sm>.btn{padding:.25rem .5rem;font-size:0.875rem}.input-group-lg>.form-select,.input-group-sm>.form-select{padding-right:3rem}.input-group>:not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback){margin-left:calc(1px*-1)}.valid-feedback{display:none;width:100%;margin-top:.25rem;font-size:0.875em;color:#3fb618}.valid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:0.875rem;color:#fff;background-color:#3fb618}.was-validated :valid~.valid-feedback,.was-validated :valid~.valid-tooltip,.is-valid~.valid-feedback,.is-valid~.valid-tooltip{display:block}.was-validated .form-control:valid,.form-control.is-valid{border-color:#3fb618;padding-right:calc(1.5em + 0.75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233fb618' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(0.375em + 0.1875rem) center;background-size:calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-control:valid:focus,.form-control.is-valid:focus{border-color:#3fb618;box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated textarea.form-control:valid,textarea.form-control.is-valid{padding-right:calc(1.5em + 0.75rem);background-position:top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem)}.was-validated .form-select:valid,.form-select.is-valid{border-color:#3fb618}.was-validated .form-select:valid:not([multiple]):not([size]),.was-validated .form-select:valid:not([multiple])[size="1"],.form-select.is-valid:not([multiple]):not([size]),.form-select.is-valid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233fb618' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-select:valid:focus,.form-select.is-valid:focus{border-color:#3fb618;box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated .form-control-color:valid,.form-control-color.is-valid{width:calc(3rem + calc(1.5em + 0.75rem))}.was-validated .form-check-input:valid,.form-check-input.is-valid{border-color:#3fb618}.was-validated .form-check-input:valid:checked,.form-check-input.is-valid:checked{background-color:#3fb618}.was-validated .form-check-input:valid:focus,.form-check-input.is-valid:focus{box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated .form-check-input:valid~.form-check-label,.form-check-input.is-valid~.form-check-label{color:#3fb618}.form-check-inline .form-check-input~.valid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):valid,.input-group>.form-control:not(:focus).is-valid,.was-validated .input-group>.form-select:not(:focus):valid,.input-group>.form-select:not(:focus).is-valid,.was-validated .input-group>.form-floating:not(:focus-within):valid,.input-group>.form-floating:not(:focus-within).is-valid{z-index:3}.invalid-feedback{display:none;width:100%;margin-top:.25rem;font-size:0.875em;color:#ff0039}.invalid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:0.875rem;color:#fff;background-color:#ff0039}.was-validated :invalid~.invalid-feedback,.was-validated :invalid~.invalid-tooltip,.is-invalid~.invalid-feedback,.is-invalid~.invalid-tooltip{display:block}.was-validated .form-control:invalid,.form-control.is-invalid{border-color:#ff0039;padding-right:calc(1.5em + 0.75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23ff0039'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23ff0039' stroke='none'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(0.375em + 0.1875rem) center;background-size:calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-control:invalid:focus,.form-control.is-invalid:focus{border-color:#ff0039;box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated textarea.form-control:invalid,textarea.form-control.is-invalid{padding-right:calc(1.5em + 0.75rem);background-position:top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem)}.was-validated .form-select:invalid,.form-select.is-invalid{border-color:#ff0039}.was-validated .form-select:invalid:not([multiple]):not([size]),.was-validated .form-select:invalid:not([multiple])[size="1"],.form-select.is-invalid:not([multiple]):not([size]),.form-select.is-invalid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23ff0039'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23ff0039' stroke='none'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-select:invalid:focus,.form-select.is-invalid:focus{border-color:#ff0039;box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated .form-control-color:invalid,.form-control-color.is-invalid{width:calc(3rem + calc(1.5em + 0.75rem))}.was-validated .form-check-input:invalid,.form-check-input.is-invalid{border-color:#ff0039}.was-validated .form-check-input:invalid:checked,.form-check-input.is-invalid:checked{background-color:#ff0039}.was-validated .form-check-input:invalid:focus,.form-check-input.is-invalid:focus{box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated .form-check-input:invalid~.form-check-label,.form-check-input.is-invalid~.form-check-label{color:#ff0039}.form-check-inline .form-check-input~.invalid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):invalid,.input-group>.form-control:not(:focus).is-invalid,.was-validated .input-group>.form-select:not(:focus):invalid,.input-group>.form-select:not(:focus).is-invalid,.was-validated .input-group>.form-floating:not(:focus-within):invalid,.input-group>.form-floating:not(:focus-within).is-invalid{z-index:4}.btn{--bs-btn-padding-x: 0.75rem;--bs-btn-padding-y: 0.375rem;--bs-btn-font-family: ;--bs-btn-font-size:1rem;--bs-btn-font-weight: 400;--bs-btn-line-height: 1.5;--bs-btn-color: #343a40;--bs-btn-bg: transparent;--bs-btn-border-width: 1px;--bs-btn-border-color: transparent;--bs-btn-border-radius: 0.25rem;--bs-btn-hover-border-color: transparent;--bs-btn-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);--bs-btn-disabled-opacity: 0.65;--bs-btn-focus-box-shadow: 0 0 0 0.25rem rgba(var(--bs-btn-focus-shadow-rgb), .5);display:inline-block;padding:var(--bs-btn-padding-y) var(--bs-btn-padding-x);font-family:var(--bs-btn-font-family);font-size:var(--bs-btn-font-size);font-weight:var(--bs-btn-font-weight);line-height:var(--bs-btn-line-height);color:var(--bs-btn-color);text-align:center;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;vertical-align:middle;cursor:pointer;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;border:var(--bs-btn-border-width) solid var(--bs-btn-border-color);background-color:var(--bs-btn-bg);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.btn{transition:none}}.btn:hover{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color)}.btn-check+.btn:hover{color:var(--bs-btn-color);background-color:var(--bs-btn-bg);border-color:var(--bs-btn-border-color)}.btn:focus-visible{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:focus-visible+.btn{border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:checked+.btn,:not(.btn-check)+.btn:active,.btn:first-child:active,.btn.active,.btn.show{color:var(--bs-btn-active-color);background-color:var(--bs-btn-active-bg);border-color:var(--bs-btn-active-border-color)}.btn-check:checked+.btn:focus-visible,:not(.btn-check)+.btn:active:focus-visible,.btn:first-child:active:focus-visible,.btn.active:focus-visible,.btn.show:focus-visible{box-shadow:var(--bs-btn-focus-box-shadow)}.btn:disabled,.btn.disabled,fieldset:disabled .btn{color:var(--bs-btn-disabled-color);pointer-events:none;background-color:var(--bs-btn-disabled-bg);border-color:var(--bs-btn-disabled-border-color);opacity:var(--bs-btn-disabled-opacity)}.btn-default{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3136;--bs-btn-hover-border-color: #2a2e33;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a2e33;--bs-btn-active-border-color: #272c30;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-primary{--bs-btn-color: #fff;--bs-btn-bg: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #216dc1;--bs-btn-hover-border-color: #1f66b6;--bs-btn-focus-shadow-rgb: 71, 147, 231;--bs-btn-active-color: #fff;--bs-btn-active-bg: #1f66b6;--bs-btn-active-border-color: #1d60aa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #2780e3;--bs-btn-disabled-border-color: #2780e3}.btn-secondary{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3136;--bs-btn-hover-border-color: #2a2e33;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a2e33;--bs-btn-active-border-color: #272c30;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #3fb618;--bs-btn-border-color: #3fb618;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #369b14;--bs-btn-hover-border-color: #329213;--bs-btn-focus-shadow-rgb: 92, 193, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #329213;--bs-btn-active-border-color: #2f8912;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3fb618;--bs-btn-disabled-border-color: #3fb618}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #82479f;--bs-btn-hover-border-color: #7a4396;--bs-btn-focus-shadow-rgb: 168, 110, 197;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a4396;--bs-btn-active-border-color: #733f8c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #9954bb;--bs-btn-disabled-border-color: #9954bb}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d96314;--bs-btn-hover-border-color: #cc5e13;--bs-btn-focus-shadow-rgb: 255, 138, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc5e13;--bs-btn-active-border-color: #bf5812;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #ff7518;--bs-btn-disabled-border-color: #ff7518}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #ff0039;--bs-btn-border-color: #ff0039;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d90030;--bs-btn-hover-border-color: #cc002e;--bs-btn-focus-shadow-rgb: 255, 38, 87;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc002e;--bs-btn-active-border-color: #bf002b;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #ff0039;--bs-btn-disabled-border-color: #ff0039}.btn-light{--bs-btn-color: #000;--bs-btn-bg: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #d3d4d5;--bs-btn-hover-border-color: #c6c7c8;--bs-btn-focus-shadow-rgb: 211, 212, 213;--bs-btn-active-color: #000;--bs-btn-active-bg: #c6c7c8;--bs-btn-active-border-color: #babbbc;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #f8f9fa;--bs-btn-disabled-border-color: #f8f9fa}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #52585d;--bs-btn-hover-border-color: #484e53;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #5d6166;--bs-btn-active-border-color: #484e53;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-outline-default{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #3fb618;--bs-btn-border-color: #3fb618;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3fb618;--bs-btn-hover-border-color: #3fb618;--bs-btn-focus-shadow-rgb: 63, 182, 24;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3fb618;--bs-btn-active-border-color: #3fb618;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #3fb618;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #3fb618;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #9954bb;--bs-btn-hover-border-color: #9954bb;--bs-btn-focus-shadow-rgb: 153, 84, 187;--bs-btn-active-color: #fff;--bs-btn-active-bg: #9954bb;--bs-btn-active-border-color: #9954bb;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #9954bb;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #9954bb;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-warning{--bs-btn-color: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #ff7518;--bs-btn-hover-border-color: #ff7518;--bs-btn-focus-shadow-rgb: 255, 117, 24;--bs-btn-active-color: #fff;--bs-btn-active-bg: #ff7518;--bs-btn-active-border-color: #ff7518;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ff7518;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff7518;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-danger{--bs-btn-color: #ff0039;--bs-btn-border-color: #ff0039;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #ff0039;--bs-btn-hover-border-color: #ff0039;--bs-btn-focus-shadow-rgb: 255, 0, 57;--bs-btn-active-color: #fff;--bs-btn-active-bg: #ff0039;--bs-btn-active-border-color: #ff0039;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: #2761e3;--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: #1f4eb6;--bs-btn-hover-border-color: transparent;--bs-btn-active-color: #1f4eb6;--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 71, 121, 231;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: 0.5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: 0.5rem}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: 0.25rem;--bs-btn-padding-x: 0.5rem;--bs-btn-font-size:0.875rem;--bs-btn-border-radius: 0.2em}.fade{transition:opacity .15s linear}@media(prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height .2s ease}@media(prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width .35s ease}@media(prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid rgba(0,0,0,0);border-bottom:0;border-left:.3em solid rgba(0,0,0,0)}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: 0.5rem;--bs-dropdown-spacer: 0.125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: #343a40;--bs-dropdown-bg: #fff;--bs-dropdown-border-color: rgba(0, 0, 0, 0.175);--bs-dropdown-border-radius: 0.25rem;--bs-dropdown-border-width: 1px;--bs-dropdown-inner-border-radius: calc(0.25rem - 1px);--bs-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--bs-dropdown-divider-margin-y: 0.5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-dropdown-link-color: #343a40;--bs-dropdown-link-hover-color: #343a40;--bs-dropdown-link-hover-bg: #f8f9fa;--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2780e3;--bs-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: 0.25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: 0.5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media(min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid rgba(0,0,0,0);border-bottom:.3em solid;border-left:.3em solid rgba(0,0,0,0)}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid rgba(0,0,0,0);border-right:0;border-bottom:.3em solid rgba(0,0,0,0);border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid rgba(0,0,0,0);border-right:.3em solid;border-bottom:.3em solid rgba(0,0,0,0)}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap;background-color:rgba(0,0,0,0);border:0}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: rgba(0, 0, 0, 0.175);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2780e3;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #2761e3;--bs-nav-link-hover-color: #1f4eb6;--bs-nav-link-disabled-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: 1px;--bs-nav-tabs-border-color: #dee2e6;--bs-nav-tabs-border-radius: 0.25rem;--bs-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--bs-nav-tabs-link-active-color: #000;--bs-nav-tabs-link-active-bg: #fff;--bs-nav-tabs-link-active-border-color: #dee2e6 #dee2e6 #fff;border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1*var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid rgba(0,0,0,0)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1*var(--bs-nav-tabs-border-width))}.nav-pills{--bs-nav-pills-border-radius: 0.25rem;--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #2780e3}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: 0.125rem;--bs-nav-underline-link-active-color: #000;gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid rgba(0,0,0,0)}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 0.5rem;--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) var(--bs-navbar-padding-x)}.navbar>.container,.navbar>.container-fluid,.navbar>.container-sm,.navbar>.container-md,.navbar>.container-lg,.navbar>.container-xl,.navbar>.container-xxl{display:flex;display:-webkit-flex;flex-wrap:inherit;-webkit-flex-wrap:inherit;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between}.navbar-brand{padding-top:var(--bs-navbar-brand-padding-y);padding-bottom:var(--bs-navbar-brand-padding-y);margin-right:var(--bs-navbar-brand-margin-end);font-size:var(--bs-navbar-brand-font-size);color:var(--bs-navbar-brand-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap}.navbar-brand:hover,.navbar-brand:focus{color:var(--bs-navbar-brand-hover-color)}.navbar-nav{--bs-nav-link-padding-x: 0;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-navbar-color);--bs-nav-link-hover-color: var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:rgba(0,0,0,0);border:var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);transition:var(--bs-navbar-toggler-transition)}@media(prefers-reduced-motion: reduce){.navbar-toggler{transition:none}}.navbar-toggler:hover{text-decoration:none}.navbar-toggler:focus{text-decoration:none;outline:0;box-shadow:0 0 0 var(--bs-navbar-toggler-focus-width)}.navbar-toggler-icon{display:inline-block;width:1.5em;height:1.5em;vertical-align:middle;background-image:var(--bs-navbar-toggler-icon-bg);background-repeat:no-repeat;background-position:center;background-size:100%}.navbar-nav-scroll{max-height:var(--bs-scroll-height, 75vh);overflow-y:auto}@media(min-width: 576px){.navbar-expand-sm{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-sm .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-sm .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-sm .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #343a40;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover .popover-arrow{display:block;width:var(--bs-popover-arrow-width);height:var(--bs-popover-arrow-height)}.popover .popover-arrow::before,.popover .popover-arrow::after{position:absolute;display:block;content:"";border-color:rgba(0,0,0,0);border-style:solid;border-width:0}.bs-popover-top>.popover-arrow,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow{bottom:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow{left:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::before,.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::before{left:0;border-right-color:var(--bs-popover-arrow-border)}.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::after{left:var(--bs-popover-border-width);border-right-color:var(--bs-popover-bg)}.bs-popover-bottom>.popover-arrow,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow{top:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::before,.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::after{border-width:0 calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height)}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::before{top:0;border-bottom-color:var(--bs-popover-arrow-border)}.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::after{top:var(--bs-popover-border-width);border-bottom-color:var(--bs-popover-bg)}.bs-popover-bottom .popover-header::before,.bs-popover-auto[data-popper-placement^=bottom] .popover-header::before{position:absolute;top:0;left:50%;display:block;width:var(--bs-popover-arrow-width);margin-left:calc(-0.5*var(--bs-popover-arrow-width));content:"";border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-header-bg)}.bs-popover-start>.popover-arrow,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow{right:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::before,.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width)*.5) 0 calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::before{right:0;border-left-color:var(--bs-popover-arrow-border)}.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::after{right:var(--bs-popover-border-width);border-left-color:var(--bs-popover-bg)}.popover-header{padding:var(--bs-popover-header-padding-y) var(--bs-popover-header-padding-x);margin-bottom:0;font-size:var(--bs-popover-header-font-size);color:var(--bs-popover-header-color);background-color:var(--bs-popover-header-bg);border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover-header:empty{display:none}.popover-body{padding:var(--bs-popover-body-padding-y) var(--bs-popover-body-padding-x);color:var(--bs-popover-body-color)}.carousel{position:relative}.carousel.pointer-event{touch-action:pan-y;-webkit-touch-action:pan-y;-moz-touch-action:pan-y;-ms-touch-action:pan-y;-o-touch-action:pan-y}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner::after{display:block;clear:both;content:""}.carousel-item{position:relative;display:none;float:left;width:100%;margin-right:-100%;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden;transition:transform .6s ease-in-out}@media(prefers-reduced-motion: reduce){.carousel-item{transition:none}}.carousel-item.active,.carousel-item-next,.carousel-item-prev{display:block}.carousel-item-next:not(.carousel-item-start),.active.carousel-item-end{transform:translateX(100%)}.carousel-item-prev:not(.carousel-item-end),.active.carousel-item-start{transform:translateX(-100%)}.carousel-fade .carousel-item{opacity:0;transition-property:opacity;transform:none}.carousel-fade .carousel-item.active,.carousel-fade .carousel-item-next.carousel-item-start,.carousel-fade .carousel-item-prev.carousel-item-end{z-index:1;opacity:1}.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{z-index:0;opacity:0;transition:opacity 0s .6s}@media(prefers-reduced-motion: reduce){.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{transition:none}}.carousel-control-prev,.carousel-control-next{position:absolute;top:0;bottom:0;z-index:1;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:center;-webkit-justify-content:center;width:15%;padding:0;color:#fff;text-align:center;background:none;border:0;opacity:.5;transition:opacity .15s ease}@media(prefers-reduced-motion: reduce){.carousel-control-prev,.carousel-control-next{transition:none}}.carousel-control-prev:hover,.carousel-control-prev:focus,.carousel-control-next:hover,.carousel-control-next:focus{color:#fff;text-decoration:none;outline:0;opacity:.9}.carousel-control-prev{left:0}.carousel-control-next{right:0}.carousel-control-prev-icon,.carousel-control-next-icon{display:inline-block;width:2rem;height:2rem;background-repeat:no-repeat;background-position:50%;background-size:100% 100%}.carousel-control-prev-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e")}.carousel-control-next-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.carousel-indicators{position:absolute;right:0;bottom:0;left:0;z-index:2;display:flex;display:-webkit-flex;justify-content:center;-webkit-justify-content:center;padding:0;margin-right:15%;margin-bottom:1rem;margin-left:15%}.carousel-indicators [data-bs-target]{box-sizing:content-box;flex:0 1 auto;-webkit-flex:0 1 auto;width:30px;height:3px;padding:0;margin-right:3px;margin-left:3px;text-indent:-999px;cursor:pointer;background-color:#fff;background-clip:padding-box;border:0;border-top:10px solid rgba(0,0,0,0);border-bottom:10px solid rgba(0,0,0,0);opacity:.5;transition:opacity .6s ease}@media(prefers-reduced-motion: reduce){.carousel-indicators [data-bs-target]{transition:none}}.carousel-indicators .active{opacity:1}.carousel-caption{position:absolute;right:15%;bottom:1.25rem;left:15%;padding-top:1.25rem;padding-bottom:1.25rem;color:#fff;text-align:center}.carousel-dark .carousel-control-prev-icon,.carousel-dark .carousel-control-next-icon{filter:invert(1) grayscale(100)}.carousel-dark .carousel-indicators [data-bs-target]{background-color:#000}.carousel-dark .carousel-caption{color:#000}[data-bs-theme=dark] .carousel .carousel-control-prev-icon,[data-bs-theme=dark] .carousel .carousel-control-next-icon,[data-bs-theme=dark].carousel .carousel-control-prev-icon,[data-bs-theme=dark].carousel .carousel-control-next-icon{filter:invert(1) grayscale(100)}[data-bs-theme=dark] .carousel .carousel-indicators [data-bs-target],[data-bs-theme=dark].carousel .carousel-indicators [data-bs-target]{background-color:#000}[data-bs-theme=dark] .carousel .carousel-caption,[data-bs-theme=dark].carousel .carousel-caption{color:#000}.spinner-grow,.spinner-border{display:inline-block;width:var(--bs-spinner-width);height:var(--bs-spinner-height);vertical-align:var(--bs-spinner-vertical-align);border-radius:50%;animation:var(--bs-spinner-animation-speed) linear infinite var(--bs-spinner-animation-name)}@keyframes spinner-border{to{transform:rotate(360deg) /* rtl:ignore */}}.spinner-border{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -0.125em;--bs-spinner-border-width: 0.25em;--bs-spinner-animation-speed: 0.75s;--bs-spinner-animation-name: spinner-border;border:var(--bs-spinner-border-width) solid currentcolor;border-right-color:rgba(0,0,0,0)}.spinner-border-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem;--bs-spinner-border-width: 0.2em}@keyframes spinner-grow{0%{transform:scale(0)}50%{opacity:1;transform:none}}.spinner-grow{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -0.125em;--bs-spinner-animation-speed: 0.75s;--bs-spinner-animation-name: spinner-grow;background-color:currentcolor;opacity:0}.spinner-grow-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem}@media(prefers-reduced-motion: reduce){.spinner-border,.spinner-grow{--bs-spinner-animation-speed: 1.5s}}.offcanvas,.offcanvas-xxl,.offcanvas-xl,.offcanvas-lg,.offcanvas-md,.offcanvas-sm{--bs-offcanvas-zindex: 1045;--bs-offcanvas-width: 400px;--bs-offcanvas-height: 30vh;--bs-offcanvas-padding-x: 1rem;--bs-offcanvas-padding-y: 1rem;--bs-offcanvas-color: #343a40;--bs-offcanvas-bg: #fff;--bs-offcanvas-border-width: 1px;--bs-offcanvas-border-color: rgba(0, 0, 0, 0.175);--bs-offcanvas-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-offcanvas-transition: transform 0.3s ease-in-out;--bs-offcanvas-title-line-height: 1.5}@media(max-width: 575.98px){.offcanvas-sm{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 575.98px)and (prefers-reduced-motion: reduce){.offcanvas-sm{transition:none}}@media(max-width: 575.98px){.offcanvas-sm.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-sm.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-sm.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-sm.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-sm.showing,.offcanvas-sm.show:not(.hiding){transform:none}.offcanvas-sm.showing,.offcanvas-sm.hiding,.offcanvas-sm.show{visibility:visible}}@media(min-width: 576px){.offcanvas-sm{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-sm .offcanvas-header{display:none}.offcanvas-sm .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 767.98px){.offcanvas-md{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 767.98px)and (prefers-reduced-motion: reduce){.offcanvas-md{transition:none}}@media(max-width: 767.98px){.offcanvas-md.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-md.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-md.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-md.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-md.showing,.offcanvas-md.show:not(.hiding){transform:none}.offcanvas-md.showing,.offcanvas-md.hiding,.offcanvas-md.show{visibility:visible}}@media(min-width: 768px){.offcanvas-md{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-md .offcanvas-header{display:none}.offcanvas-md .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 991.98px){.offcanvas-lg{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 991.98px)and (prefers-reduced-motion: reduce){.offcanvas-lg{transition:none}}@media(max-width: 991.98px){.offcanvas-lg.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-lg.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-lg.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-lg.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-lg.showing,.offcanvas-lg.show:not(.hiding){transform:none}.offcanvas-lg.showing,.offcanvas-lg.hiding,.offcanvas-lg.show{visibility:visible}}@media(min-width: 992px){.offcanvas-lg{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-lg .offcanvas-header{display:none}.offcanvas-lg .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 1199.98px){.offcanvas-xl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 1199.98px)and (prefers-reduced-motion: reduce){.offcanvas-xl{transition:none}}@media(max-width: 1199.98px){.offcanvas-xl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xl.showing,.offcanvas-xl.show:not(.hiding){transform:none}.offcanvas-xl.showing,.offcanvas-xl.hiding,.offcanvas-xl.show{visibility:visible}}@media(min-width: 1200px){.offcanvas-xl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-xl .offcanvas-header{display:none}.offcanvas-xl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 1399.98px){.offcanvas-xxl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 1399.98px)and (prefers-reduced-motion: reduce){.offcanvas-xxl{transition:none}}@media(max-width: 1399.98px){.offcanvas-xxl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xxl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xxl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xxl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xxl.showing,.offcanvas-xxl.show:not(.hiding){transform:none}.offcanvas-xxl.showing,.offcanvas-xxl.hiding,.offcanvas-xxl.show{visibility:visible}}@media(min-width: 1400px){.offcanvas-xxl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-xxl .offcanvas-header{display:none}.offcanvas-xxl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}.offcanvas{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}@media(prefers-reduced-motion: reduce){.offcanvas{transition:none}}.offcanvas.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas.showing,.offcanvas.show:not(.hiding){transform:none}.offcanvas.showing,.offcanvas.hiding,.offcanvas.show{visibility:visible}.offcanvas-backdrop{position:fixed;top:0;left:0;z-index:1040;width:100vw;height:100vh;background-color:#000}.offcanvas-backdrop.fade{opacity:0}.offcanvas-backdrop.show{opacity:.5}.offcanvas-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x)}.offcanvas-header .btn-close{padding:calc(var(--bs-offcanvas-padding-y)*.5) calc(var(--bs-offcanvas-padding-x)*.5);margin-top:calc(-0.5*var(--bs-offcanvas-padding-y));margin-right:calc(-0.5*var(--bs-offcanvas-padding-x));margin-bottom:calc(-0.5*var(--bs-offcanvas-padding-y))}.offcanvas-title{margin-bottom:0;line-height:var(--bs-offcanvas-title-line-height)}.offcanvas-body{flex-grow:1;-webkit-flex-grow:1;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);overflow-y:auto}.placeholder{display:inline-block;min-height:1em;vertical-align:middle;cursor:wait;background-color:currentcolor;opacity:.5}.placeholder.btn::before{display:inline-block;content:""}.placeholder-xs{min-height:.6em}.placeholder-sm{min-height:.8em}.placeholder-lg{min-height:1.2em}.placeholder-glow .placeholder{animation:placeholder-glow 2s ease-in-out infinite}@keyframes placeholder-glow{50%{opacity:.2}}.placeholder-wave{mask-image:linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);-webkit-mask-image:linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);mask-size:200% 100%;-webkit-mask-size:200% 100%;animation:placeholder-wave 2s linear infinite}@keyframes placeholder-wave{100%{mask-position:-200% 0%;-webkit-mask-position:-200% 0%}}.clearfix::after{display:block;clear:both;content:""}.text-bg-default{color:#fff !important;background-color:RGBA(var(--bs-default-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-primary{color:#fff !important;background-color:RGBA(var(--bs-primary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-secondary{color:#fff !important;background-color:RGBA(var(--bs-secondary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-success{color:#fff !important;background-color:RGBA(var(--bs-success-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-info{color:#fff !important;background-color:RGBA(var(--bs-info-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-warning{color:#fff !important;background-color:RGBA(var(--bs-warning-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-danger{color:#fff !important;background-color:RGBA(var(--bs-danger-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-light{color:#000 !important;background-color:RGBA(var(--bs-light-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-dark{color:#fff !important;background-color:RGBA(var(--bs-dark-rgb), var(--bs-bg-opacity, 1)) !important}.link-default{color:RGBA(var(--bs-default-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-default-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-default:hover,.link-default:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-primary{color:RGBA(var(--bs-primary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-primary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-primary:hover,.link-primary:focus{color:RGBA(31, 102, 182, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(31, 102, 182, var(--bs-link-underline-opacity, 1)) !important}.link-secondary{color:RGBA(var(--bs-secondary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-secondary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-secondary:hover,.link-secondary:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-success{color:RGBA(var(--bs-success-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-success-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-success:hover,.link-success:focus{color:RGBA(50, 146, 19, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(50, 146, 19, var(--bs-link-underline-opacity, 1)) !important}.link-info{color:RGBA(var(--bs-info-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-info-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-info:hover,.link-info:focus{color:RGBA(122, 67, 150, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(122, 67, 150, var(--bs-link-underline-opacity, 1)) !important}.link-warning{color:RGBA(var(--bs-warning-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-warning-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-warning:hover,.link-warning:focus{color:RGBA(204, 94, 19, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(204, 94, 19, var(--bs-link-underline-opacity, 1)) !important}.link-danger{color:RGBA(var(--bs-danger-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-danger-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-danger:hover,.link-danger:focus{color:RGBA(204, 0, 46, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(204, 0, 46, var(--bs-link-underline-opacity, 1)) !important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis:hover,.link-body-emphasis:focus{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important}.focus-ring:focus{outline:0;box-shadow:var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color)}.icon-link{display:inline-flex;gap:.375rem;align-items:center;-webkit-align-items:center;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));text-underline-offset:.25em;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden}.icon-link>.bi{flex-shrink:0;-webkit-flex-shrink:0;width:1em;height:1em;fill:currentcolor;transition:.2s ease-in-out transform}@media(prefers-reduced-motion: reduce){.icon-link>.bi{transition:none}}.icon-link-hover:hover>.bi,.icon-link-hover:focus-visible>.bi{transform:var(--bs-icon-link-transform, translate3d(0.25em, 0, 0))}.ratio{position:relative;width:100%}.ratio::before{display:block;padding-top:var(--bs-aspect-ratio);content:""}.ratio>*{position:absolute;top:0;left:0;width:100%;height:100%}.ratio-1x1{--bs-aspect-ratio: 100%}.ratio-4x3{--bs-aspect-ratio: 75%}.ratio-16x9{--bs-aspect-ratio: 56.25%}.ratio-21x9{--bs-aspect-ratio: 42.8571428571%}.fixed-top{position:fixed;top:0;right:0;left:0;z-index:1030}.fixed-bottom{position:fixed;right:0;bottom:0;left:0;z-index:1030}.sticky-top{position:sticky;top:0;z-index:1020}.sticky-bottom{position:sticky;bottom:0;z-index:1020}@media(min-width: 576px){.sticky-sm-top{position:sticky;top:0;z-index:1020}.sticky-sm-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 768px){.sticky-md-top{position:sticky;top:0;z-index:1020}.sticky-md-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 992px){.sticky-lg-top{position:sticky;top:0;z-index:1020}.sticky-lg-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 1200px){.sticky-xl-top{position:sticky;top:0;z-index:1020}.sticky-xl-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 1400px){.sticky-xxl-top{position:sticky;top:0;z-index:1020}.sticky-xxl-bottom{position:sticky;bottom:0;z-index:1020}}.hstack{display:flex;display:-webkit-flex;flex-direction:row;-webkit-flex-direction:row;align-items:center;-webkit-align-items:center;align-self:stretch;-webkit-align-self:stretch}.vstack{display:flex;display:-webkit-flex;flex:1 1 auto;-webkit-flex:1 1 auto;flex-direction:column;-webkit-flex-direction:column;align-self:stretch;-webkit-align-self:stretch}.visually-hidden,.visually-hidden-focusable:not(:focus):not(:focus-within){width:1px !important;height:1px !important;padding:0 !important;margin:-1px !important;overflow:hidden !important;clip:rect(0, 0, 0, 0) !important;white-space:nowrap !important;border:0 !important}.visually-hidden:not(caption),.visually-hidden-focusable:not(:focus):not(:focus-within):not(caption){position:absolute !important}.stretched-link::after{position:absolute;top:0;right:0;bottom:0;left:0;z-index:1;content:""}.text-truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.vr{display:inline-block;align-self:stretch;-webkit-align-self:stretch;width:1px;min-height:1em;background-color:currentcolor;opacity:.25}.align-baseline{vertical-align:baseline !important}.align-top{vertical-align:top !important}.align-middle{vertical-align:middle !important}.align-bottom{vertical-align:bottom !important}.align-text-bottom{vertical-align:text-bottom !important}.align-text-top{vertical-align:text-top !important}.float-start{float:left !important}.float-end{float:right !important}.float-none{float:none !important}.object-fit-contain{object-fit:contain !important}.object-fit-cover{object-fit:cover !important}.object-fit-fill{object-fit:fill !important}.object-fit-scale{object-fit:scale-down !important}.object-fit-none{object-fit:none !important}.opacity-0{opacity:0 !important}.opacity-25{opacity:.25 !important}.opacity-50{opacity:.5 !important}.opacity-75{opacity:.75 !important}.opacity-100{opacity:1 !important}.overflow-auto{overflow:auto !important}.overflow-hidden{overflow:hidden !important}.overflow-visible{overflow:visible !important}.overflow-scroll{overflow:scroll !important}.overflow-x-auto{overflow-x:auto !important}.overflow-x-hidden{overflow-x:hidden !important}.overflow-x-visible{overflow-x:visible !important}.overflow-x-scroll{overflow-x:scroll !important}.overflow-y-auto{overflow-y:auto !important}.overflow-y-hidden{overflow-y:hidden !important}.overflow-y-visible{overflow-y:visible !important}.overflow-y-scroll{overflow-y:scroll !important}.d-inline{display:inline !important}.d-inline-block{display:inline-block !important}.d-block{display:block !important}.d-grid{display:grid !important}.d-inline-grid{display:inline-grid !important}.d-table{display:table !important}.d-table-row{display:table-row !important}.d-table-cell{display:table-cell !important}.d-flex{display:flex !important}.d-inline-flex{display:inline-flex !important}.d-none{display:none !important}.shadow{box-shadow:0 .5rem 1rem rgba(0,0,0,.15) !important}.shadow-sm{box-shadow:0 .125rem .25rem rgba(0,0,0,.075) !important}.shadow-lg{box-shadow:0 1rem 3rem rgba(0,0,0,.175) !important}.shadow-none{box-shadow:none !important}.focus-ring-default{--bs-focus-ring-color: rgba(var(--bs-default-rgb), var(--bs-focus-ring-opacity))}.focus-ring-primary{--bs-focus-ring-color: rgba(var(--bs-primary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-secondary{--bs-focus-ring-color: rgba(var(--bs-secondary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-success{--bs-focus-ring-color: rgba(var(--bs-success-rgb), var(--bs-focus-ring-opacity))}.focus-ring-info{--bs-focus-ring-color: rgba(var(--bs-info-rgb), var(--bs-focus-ring-opacity))}.focus-ring-warning{--bs-focus-ring-color: rgba(var(--bs-warning-rgb), var(--bs-focus-ring-opacity))}.focus-ring-danger{--bs-focus-ring-color: rgba(var(--bs-danger-rgb), var(--bs-focus-ring-opacity))}.focus-ring-light{--bs-focus-ring-color: rgba(var(--bs-light-rgb), var(--bs-focus-ring-opacity))}.focus-ring-dark{--bs-focus-ring-color: rgba(var(--bs-dark-rgb), var(--bs-focus-ring-opacity))}.position-static{position:static !important}.position-relative{position:relative !important}.position-absolute{position:absolute !important}.position-fixed{position:fixed !important}.position-sticky{position:sticky !important}.top-0{top:0 !important}.top-50{top:50% !important}.top-100{top:100% !important}.bottom-0{bottom:0 !important}.bottom-50{bottom:50% !important}.bottom-100{bottom:100% !important}.start-0{left:0 !important}.start-50{left:50% !important}.start-100{left:100% !important}.end-0{right:0 !important}.end-50{right:50% !important}.end-100{right:100% !important}.translate-middle{transform:translate(-50%, -50%) !important}.translate-middle-x{transform:translateX(-50%) !important}.translate-middle-y{transform:translateY(-50%) !important}.border{border:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-0{border:0 !important}.border-top{border-top:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-top-0{border-top:0 !important}.border-end{border-right:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-end-0{border-right:0 !important}.border-bottom{border-bottom:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-bottom-0{border-bottom:0 !important}.border-start{border-left:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-start-0{border-left:0 !important}.border-default{--bs-border-opacity: 1;border-color:rgba(var(--bs-default-rgb), var(--bs-border-opacity)) !important}.border-primary{--bs-border-opacity: 1;border-color:rgba(var(--bs-primary-rgb), var(--bs-border-opacity)) !important}.border-secondary{--bs-border-opacity: 1;border-color:rgba(var(--bs-secondary-rgb), var(--bs-border-opacity)) !important}.border-success{--bs-border-opacity: 1;border-color:rgba(var(--bs-success-rgb), var(--bs-border-opacity)) !important}.border-info{--bs-border-opacity: 1;border-color:rgba(var(--bs-info-rgb), var(--bs-border-opacity)) !important}.border-warning{--bs-border-opacity: 1;border-color:rgba(var(--bs-warning-rgb), var(--bs-border-opacity)) !important}.border-danger{--bs-border-opacity: 1;border-color:rgba(var(--bs-danger-rgb), var(--bs-border-opacity)) !important}.border-light{--bs-border-opacity: 1;border-color:rgba(var(--bs-light-rgb), var(--bs-border-opacity)) !important}.border-dark{--bs-border-opacity: 1;border-color:rgba(var(--bs-dark-rgb), var(--bs-border-opacity)) !important}.border-black{--bs-border-opacity: 1;border-color:rgba(var(--bs-black-rgb), var(--bs-border-opacity)) !important}.border-white{--bs-border-opacity: 1;border-color:rgba(var(--bs-white-rgb), var(--bs-border-opacity)) !important}.border-primary-subtle{border-color:var(--bs-primary-border-subtle) !important}.border-secondary-subtle{border-color:var(--bs-secondary-border-subtle) !important}.border-success-subtle{border-color:var(--bs-success-border-subtle) !important}.border-info-subtle{border-color:var(--bs-info-border-subtle) !important}.border-warning-subtle{border-color:var(--bs-warning-border-subtle) !important}.border-danger-subtle{border-color:var(--bs-danger-border-subtle) !important}.border-light-subtle{border-color:var(--bs-light-border-subtle) !important}.border-dark-subtle{border-color:var(--bs-dark-border-subtle) !important}.border-1{border-width:1px !important}.border-2{border-width:2px !important}.border-3{border-width:3px !important}.border-4{border-width:4px !important}.border-5{border-width:5px !important}.border-opacity-10{--bs-border-opacity: 0.1}.border-opacity-25{--bs-border-opacity: 0.25}.border-opacity-50{--bs-border-opacity: 0.5}.border-opacity-75{--bs-border-opacity: 0.75}.border-opacity-100{--bs-border-opacity: 1}.w-25{width:25% !important}.w-50{width:50% !important}.w-75{width:75% !important}.w-100{width:100% !important}.w-auto{width:auto !important}.mw-100{max-width:100% !important}.vw-100{width:100vw !important}.min-vw-100{min-width:100vw !important}.h-25{height:25% !important}.h-50{height:50% !important}.h-75{height:75% !important}.h-100{height:100% !important}.h-auto{height:auto !important}.mh-100{max-height:100% !important}.vh-100{height:100vh !important}.min-vh-100{min-height:100vh !important}.flex-fill{flex:1 1 auto !important}.flex-row{flex-direction:row !important}.flex-column{flex-direction:column !important}.flex-row-reverse{flex-direction:row-reverse !important}.flex-column-reverse{flex-direction:column-reverse !important}.flex-grow-0{flex-grow:0 !important}.flex-grow-1{flex-grow:1 !important}.flex-shrink-0{flex-shrink:0 !important}.flex-shrink-1{flex-shrink:1 !important}.flex-wrap{flex-wrap:wrap !important}.flex-nowrap{flex-wrap:nowrap !important}.flex-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-start{justify-content:flex-start !important}.justify-content-end{justify-content:flex-end !important}.justify-content-center{justify-content:center !important}.justify-content-between{justify-content:space-between !important}.justify-content-around{justify-content:space-around !important}.justify-content-evenly{justify-content:space-evenly !important}.align-items-start{align-items:flex-start !important}.align-items-end{align-items:flex-end !important}.align-items-center{align-items:center !important}.align-items-baseline{align-items:baseline !important}.align-items-stretch{align-items:stretch !important}.align-content-start{align-content:flex-start !important}.align-content-end{align-content:flex-end !important}.align-content-center{align-content:center !important}.align-content-between{align-content:space-between !important}.align-content-around{align-content:space-around !important}.align-content-stretch{align-content:stretch !important}.align-self-auto{align-self:auto !important}.align-self-start{align-self:flex-start !important}.align-self-end{align-self:flex-end !important}.align-self-center{align-self:center !important}.align-self-baseline{align-self:baseline !important}.align-self-stretch{align-self:stretch !important}.order-first{order:-1 !important}.order-0{order:0 !important}.order-1{order:1 !important}.order-2{order:2 !important}.order-3{order:3 !important}.order-4{order:4 !important}.order-5{order:5 !important}.order-last{order:6 !important}.m-0{margin:0 !important}.m-1{margin:.25rem !important}.m-2{margin:.5rem !important}.m-3{margin:1rem !important}.m-4{margin:1.5rem !important}.m-5{margin:3rem !important}.m-auto{margin:auto !important}.mx-0{margin-right:0 !important;margin-left:0 !important}.mx-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-3{margin-right:1rem !important;margin-left:1rem !important}.mx-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-5{margin-right:3rem !important;margin-left:3rem !important}.mx-auto{margin-right:auto !important;margin-left:auto !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.my-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-0{margin-top:0 !important}.mt-1{margin-top:.25rem !important}.mt-2{margin-top:.5rem !important}.mt-3{margin-top:1rem !important}.mt-4{margin-top:1.5rem !important}.mt-5{margin-top:3rem !important}.mt-auto{margin-top:auto !important}.me-0{margin-right:0 !important}.me-1{margin-right:.25rem !important}.me-2{margin-right:.5rem !important}.me-3{margin-right:1rem !important}.me-4{margin-right:1.5rem !important}.me-5{margin-right:3rem !important}.me-auto{margin-right:auto !important}.mb-0{margin-bottom:0 !important}.mb-1{margin-bottom:.25rem !important}.mb-2{margin-bottom:.5rem !important}.mb-3{margin-bottom:1rem !important}.mb-4{margin-bottom:1.5rem !important}.mb-5{margin-bottom:3rem !important}.mb-auto{margin-bottom:auto !important}.ms-0{margin-left:0 !important}.ms-1{margin-left:.25rem !important}.ms-2{margin-left:.5rem !important}.ms-3{margin-left:1rem !important}.ms-4{margin-left:1.5rem !important}.ms-5{margin-left:3rem !important}.ms-auto{margin-left:auto !important}.p-0{padding:0 !important}.p-1{padding:.25rem !important}.p-2{padding:.5rem !important}.p-3{padding:1rem !important}.p-4{padding:1.5rem !important}.p-5{padding:3rem !important}.px-0{padding-right:0 !important;padding-left:0 !important}.px-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-3{padding-right:1rem !important;padding-left:1rem !important}.px-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-5{padding-right:3rem !important;padding-left:3rem !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.py-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-0{padding-top:0 !important}.pt-1{padding-top:.25rem !important}.pt-2{padding-top:.5rem !important}.pt-3{padding-top:1rem !important}.pt-4{padding-top:1.5rem !important}.pt-5{padding-top:3rem !important}.pe-0{padding-right:0 !important}.pe-1{padding-right:.25rem !important}.pe-2{padding-right:.5rem !important}.pe-3{padding-right:1rem !important}.pe-4{padding-right:1.5rem !important}.pe-5{padding-right:3rem !important}.pb-0{padding-bottom:0 !important}.pb-1{padding-bottom:.25rem !important}.pb-2{padding-bottom:.5rem !important}.pb-3{padding-bottom:1rem !important}.pb-4{padding-bottom:1.5rem !important}.pb-5{padding-bottom:3rem !important}.ps-0{padding-left:0 !important}.ps-1{padding-left:.25rem !important}.ps-2{padding-left:.5rem !important}.ps-3{padding-left:1rem !important}.ps-4{padding-left:1.5rem !important}.ps-5{padding-left:3rem !important}.gap-0{gap:0 !important}.gap-1{gap:.25rem !important}.gap-2{gap:.5rem !important}.gap-3{gap:1rem !important}.gap-4{gap:1.5rem !important}.gap-5{gap:3rem !important}.row-gap-0{row-gap:0 !important}.row-gap-1{row-gap:.25rem !important}.row-gap-2{row-gap:.5rem !important}.row-gap-3{row-gap:1rem !important}.row-gap-4{row-gap:1.5rem !important}.row-gap-5{row-gap:3rem !important}.column-gap-0{column-gap:0 !important}.column-gap-1{column-gap:.25rem !important}.column-gap-2{column-gap:.5rem !important}.column-gap-3{column-gap:1rem !important}.column-gap-4{column-gap:1.5rem !important}.column-gap-5{column-gap:3rem !important}.font-monospace{font-family:var(--bs-font-monospace) !important}.fs-1{font-size:calc(1.325rem + 0.9vw) !important}.fs-2{font-size:calc(1.29rem + 0.48vw) !important}.fs-3{font-size:calc(1.27rem + 0.24vw) !important}.fs-4{font-size:1.25rem !important}.fs-5{font-size:1.1rem !important}.fs-6{font-size:1rem !important}.fst-italic{font-style:italic !important}.fst-normal{font-style:normal !important}.fw-lighter{font-weight:lighter !important}.fw-light{font-weight:300 !important}.fw-normal{font-weight:400 !important}.fw-medium{font-weight:500 !important}.fw-semibold{font-weight:600 !important}.fw-bold{font-weight:700 !important}.fw-bolder{font-weight:bolder !important}.lh-1{line-height:1 !important}.lh-sm{line-height:1.25 !important}.lh-base{line-height:1.5 !important}.lh-lg{line-height:2 !important}.text-start{text-align:left !important}.text-end{text-align:right !important}.text-center{text-align:center !important}.text-decoration-none{text-decoration:none !important}.text-decoration-underline{text-decoration:underline !important}.text-decoration-line-through{text-decoration:line-through !important}.text-lowercase{text-transform:lowercase !important}.text-uppercase{text-transform:uppercase !important}.text-capitalize{text-transform:capitalize !important}.text-wrap{white-space:normal !important}.text-nowrap{white-space:nowrap !important}.text-break{word-wrap:break-word !important;word-break:break-word !important}.text-default{--bs-text-opacity: 1;color:rgba(var(--bs-default-rgb), var(--bs-text-opacity)) !important}.text-primary{--bs-text-opacity: 1;color:rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important}.text-secondary{--bs-text-opacity: 1;color:rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important}.text-success{--bs-text-opacity: 1;color:rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important}.text-info{--bs-text-opacity: 1;color:rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important}.text-warning{--bs-text-opacity: 1;color:rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important}.text-danger{--bs-text-opacity: 1;color:rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important}.text-light{--bs-text-opacity: 1;color:rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important}.text-dark{--bs-text-opacity: 1;color:rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important}.text-black{--bs-text-opacity: 1;color:rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important}.text-white{--bs-text-opacity: 1;color:rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important}.text-body{--bs-text-opacity: 1;color:rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important}.text-muted{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-black-50{--bs-text-opacity: 1;color:rgba(0,0,0,.5) !important}.text-white-50{--bs-text-opacity: 1;color:rgba(255,255,255,.5) !important}.text-body-secondary{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-body-tertiary{--bs-text-opacity: 1;color:var(--bs-tertiary-color) !important}.text-body-emphasis{--bs-text-opacity: 1;color:var(--bs-emphasis-color) !important}.text-reset{--bs-text-opacity: 1;color:inherit !important}.text-opacity-25{--bs-text-opacity: 0.25}.text-opacity-50{--bs-text-opacity: 0.5}.text-opacity-75{--bs-text-opacity: 0.75}.text-opacity-100{--bs-text-opacity: 1}.text-primary-emphasis{color:var(--bs-primary-text-emphasis) !important}.text-secondary-emphasis{color:var(--bs-secondary-text-emphasis) !important}.text-success-emphasis{color:var(--bs-success-text-emphasis) !important}.text-info-emphasis{color:var(--bs-info-text-emphasis) !important}.text-warning-emphasis{color:var(--bs-warning-text-emphasis) !important}.text-danger-emphasis{color:var(--bs-danger-text-emphasis) !important}.text-light-emphasis{color:var(--bs-light-text-emphasis) !important}.text-dark-emphasis{color:var(--bs-dark-text-emphasis) !important}.link-opacity-10{--bs-link-opacity: 0.1}.link-opacity-10-hover:hover{--bs-link-opacity: 0.1}.link-opacity-25{--bs-link-opacity: 0.25}.link-opacity-25-hover:hover{--bs-link-opacity: 0.25}.link-opacity-50{--bs-link-opacity: 0.5}.link-opacity-50-hover:hover{--bs-link-opacity: 0.5}.link-opacity-75{--bs-link-opacity: 0.75}.link-opacity-75-hover:hover{--bs-link-opacity: 0.75}.link-opacity-100{--bs-link-opacity: 1}.link-opacity-100-hover:hover{--bs-link-opacity: 1}.link-offset-1{text-underline-offset:.125em !important}.link-offset-1-hover:hover{text-underline-offset:.125em !important}.link-offset-2{text-underline-offset:.25em !important}.link-offset-2-hover:hover{text-underline-offset:.25em !important}.link-offset-3{text-underline-offset:.375em !important}.link-offset-3-hover:hover{text-underline-offset:.375em !important}.link-underline-default{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-default-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-primary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-primary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-secondary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-secondary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-success{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-success-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-info{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-info-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-warning{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-warning-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-danger{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-danger-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-light{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-light-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-dark{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-dark-rgb), var(--bs-link-underline-opacity)) !important}.link-underline{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-underline-opacity-0{--bs-link-underline-opacity: 0}.link-underline-opacity-0-hover:hover{--bs-link-underline-opacity: 0}.link-underline-opacity-10{--bs-link-underline-opacity: 0.1}.link-underline-opacity-10-hover:hover{--bs-link-underline-opacity: 0.1}.link-underline-opacity-25{--bs-link-underline-opacity: 0.25}.link-underline-opacity-25-hover:hover{--bs-link-underline-opacity: 0.25}.link-underline-opacity-50{--bs-link-underline-opacity: 0.5}.link-underline-opacity-50-hover:hover{--bs-link-underline-opacity: 0.5}.link-underline-opacity-75{--bs-link-underline-opacity: 0.75}.link-underline-opacity-75-hover:hover{--bs-link-underline-opacity: 0.75}.link-underline-opacity-100{--bs-link-underline-opacity: 1}.link-underline-opacity-100-hover:hover{--bs-link-underline-opacity: 1}.bg-default{--bs-bg-opacity: 1;background-color:rgba(var(--bs-default-rgb), var(--bs-bg-opacity)) !important}.bg-primary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important}.bg-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important}.bg-success{--bs-bg-opacity: 1;background-color:rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important}.bg-info{--bs-bg-opacity: 1;background-color:rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important}.bg-warning{--bs-bg-opacity: 1;background-color:rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important}.bg-danger{--bs-bg-opacity: 1;background-color:rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important}.bg-light{--bs-bg-opacity: 1;background-color:rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important}.bg-dark{--bs-bg-opacity: 1;background-color:rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important}.bg-black{--bs-bg-opacity: 1;background-color:rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important}.bg-white{--bs-bg-opacity: 1;background-color:rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important}.bg-body{--bs-bg-opacity: 1;background-color:rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important}.bg-transparent{--bs-bg-opacity: 1;background-color:rgba(0,0,0,0) !important}.bg-body-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-body-tertiary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-tertiary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-opacity-10{--bs-bg-opacity: 0.1}.bg-opacity-25{--bs-bg-opacity: 0.25}.bg-opacity-50{--bs-bg-opacity: 0.5}.bg-opacity-75{--bs-bg-opacity: 0.75}.bg-opacity-100{--bs-bg-opacity: 1}.bg-primary-subtle{background-color:var(--bs-primary-bg-subtle) !important}.bg-secondary-subtle{background-color:var(--bs-secondary-bg-subtle) !important}.bg-success-subtle{background-color:var(--bs-success-bg-subtle) !important}.bg-info-subtle{background-color:var(--bs-info-bg-subtle) !important}.bg-warning-subtle{background-color:var(--bs-warning-bg-subtle) !important}.bg-danger-subtle{background-color:var(--bs-danger-bg-subtle) !important}.bg-light-subtle{background-color:var(--bs-light-bg-subtle) !important}.bg-dark-subtle{background-color:var(--bs-dark-bg-subtle) !important}.bg-gradient{background-image:var(--bs-gradient) !important}.user-select-all{user-select:all !important}.user-select-auto{user-select:auto !important}.user-select-none{user-select:none !important}.pe-none{pointer-events:none !important}.pe-auto{pointer-events:auto !important}.rounded{border-radius:var(--bs-border-radius) !important}.rounded-0{border-radius:0 !important}.rounded-1{border-radius:var(--bs-border-radius-sm) !important}.rounded-2{border-radius:var(--bs-border-radius) !important}.rounded-3{border-radius:var(--bs-border-radius-lg) !important}.rounded-4{border-radius:var(--bs-border-radius-xl) !important}.rounded-5{border-radius:var(--bs-border-radius-xxl) !important}.rounded-circle{border-radius:50% !important}.rounded-pill{border-radius:var(--bs-border-radius-pill) !important}.rounded-top{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-0{border-top-left-radius:0 !important;border-top-right-radius:0 !important}.rounded-top-1{border-top-left-radius:var(--bs-border-radius-sm) !important;border-top-right-radius:var(--bs-border-radius-sm) !important}.rounded-top-2{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-3{border-top-left-radius:var(--bs-border-radius-lg) !important;border-top-right-radius:var(--bs-border-radius-lg) !important}.rounded-top-4{border-top-left-radius:var(--bs-border-radius-xl) !important;border-top-right-radius:var(--bs-border-radius-xl) !important}.rounded-top-5{border-top-left-radius:var(--bs-border-radius-xxl) !important;border-top-right-radius:var(--bs-border-radius-xxl) !important}.rounded-top-circle{border-top-left-radius:50% !important;border-top-right-radius:50% !important}.rounded-top-pill{border-top-left-radius:var(--bs-border-radius-pill) !important;border-top-right-radius:var(--bs-border-radius-pill) !important}.rounded-end{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-0{border-top-right-radius:0 !important;border-bottom-right-radius:0 !important}.rounded-end-1{border-top-right-radius:var(--bs-border-radius-sm) !important;border-bottom-right-radius:var(--bs-border-radius-sm) !important}.rounded-end-2{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-3{border-top-right-radius:var(--bs-border-radius-lg) !important;border-bottom-right-radius:var(--bs-border-radius-lg) !important}.rounded-end-4{border-top-right-radius:var(--bs-border-radius-xl) !important;border-bottom-right-radius:var(--bs-border-radius-xl) !important}.rounded-end-5{border-top-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-right-radius:var(--bs-border-radius-xxl) !important}.rounded-end-circle{border-top-right-radius:50% !important;border-bottom-right-radius:50% !important}.rounded-end-pill{border-top-right-radius:var(--bs-border-radius-pill) !important;border-bottom-right-radius:var(--bs-border-radius-pill) !important}.rounded-bottom{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-0{border-bottom-right-radius:0 !important;border-bottom-left-radius:0 !important}.rounded-bottom-1{border-bottom-right-radius:var(--bs-border-radius-sm) !important;border-bottom-left-radius:var(--bs-border-radius-sm) !important}.rounded-bottom-2{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-3{border-bottom-right-radius:var(--bs-border-radius-lg) !important;border-bottom-left-radius:var(--bs-border-radius-lg) !important}.rounded-bottom-4{border-bottom-right-radius:var(--bs-border-radius-xl) !important;border-bottom-left-radius:var(--bs-border-radius-xl) !important}.rounded-bottom-5{border-bottom-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-left-radius:var(--bs-border-radius-xxl) !important}.rounded-bottom-circle{border-bottom-right-radius:50% !important;border-bottom-left-radius:50% !important}.rounded-bottom-pill{border-bottom-right-radius:var(--bs-border-radius-pill) !important;border-bottom-left-radius:var(--bs-border-radius-pill) !important}.rounded-start{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-0{border-bottom-left-radius:0 !important;border-top-left-radius:0 !important}.rounded-start-1{border-bottom-left-radius:var(--bs-border-radius-sm) !important;border-top-left-radius:var(--bs-border-radius-sm) !important}.rounded-start-2{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-3{border-bottom-left-radius:var(--bs-border-radius-lg) !important;border-top-left-radius:var(--bs-border-radius-lg) !important}.rounded-start-4{border-bottom-left-radius:var(--bs-border-radius-xl) !important;border-top-left-radius:var(--bs-border-radius-xl) !important}.rounded-start-5{border-bottom-left-radius:var(--bs-border-radius-xxl) !important;border-top-left-radius:var(--bs-border-radius-xxl) !important}.rounded-start-circle{border-bottom-left-radius:50% !important;border-top-left-radius:50% !important}.rounded-start-pill{border-bottom-left-radius:var(--bs-border-radius-pill) !important;border-top-left-radius:var(--bs-border-radius-pill) !important}.visible{visibility:visible !important}.invisible{visibility:hidden !important}.z-n1{z-index:-1 !important}.z-0{z-index:0 !important}.z-1{z-index:1 !important}.z-2{z-index:2 !important}.z-3{z-index:3 !important}@media(min-width: 576px){.float-sm-start{float:left !important}.float-sm-end{float:right !important}.float-sm-none{float:none !important}.object-fit-sm-contain{object-fit:contain !important}.object-fit-sm-cover{object-fit:cover !important}.object-fit-sm-fill{object-fit:fill !important}.object-fit-sm-scale{object-fit:scale-down !important}.object-fit-sm-none{object-fit:none !important}.d-sm-inline{display:inline !important}.d-sm-inline-block{display:inline-block !important}.d-sm-block{display:block !important}.d-sm-grid{display:grid !important}.d-sm-inline-grid{display:inline-grid !important}.d-sm-table{display:table !important}.d-sm-table-row{display:table-row !important}.d-sm-table-cell{display:table-cell !important}.d-sm-flex{display:flex !important}.d-sm-inline-flex{display:inline-flex !important}.d-sm-none{display:none !important}.flex-sm-fill{flex:1 1 auto !important}.flex-sm-row{flex-direction:row !important}.flex-sm-column{flex-direction:column !important}.flex-sm-row-reverse{flex-direction:row-reverse !important}.flex-sm-column-reverse{flex-direction:column-reverse !important}.flex-sm-grow-0{flex-grow:0 !important}.flex-sm-grow-1{flex-grow:1 !important}.flex-sm-shrink-0{flex-shrink:0 !important}.flex-sm-shrink-1{flex-shrink:1 !important}.flex-sm-wrap{flex-wrap:wrap !important}.flex-sm-nowrap{flex-wrap:nowrap !important}.flex-sm-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-sm-start{justify-content:flex-start !important}.justify-content-sm-end{justify-content:flex-end !important}.justify-content-sm-center{justify-content:center !important}.justify-content-sm-between{justify-content:space-between !important}.justify-content-sm-around{justify-content:space-around !important}.justify-content-sm-evenly{justify-content:space-evenly !important}.align-items-sm-start{align-items:flex-start !important}.align-items-sm-end{align-items:flex-end !important}.align-items-sm-center{align-items:center !important}.align-items-sm-baseline{align-items:baseline !important}.align-items-sm-stretch{align-items:stretch !important}.align-content-sm-start{align-content:flex-start !important}.align-content-sm-end{align-content:flex-end !important}.align-content-sm-center{align-content:center !important}.align-content-sm-between{align-content:space-between !important}.align-content-sm-around{align-content:space-around !important}.align-content-sm-stretch{align-content:stretch !important}.align-self-sm-auto{align-self:auto !important}.align-self-sm-start{align-self:flex-start !important}.align-self-sm-end{align-self:flex-end !important}.align-self-sm-center{align-self:center !important}.align-self-sm-baseline{align-self:baseline !important}.align-self-sm-stretch{align-self:stretch !important}.order-sm-first{order:-1 !important}.order-sm-0{order:0 !important}.order-sm-1{order:1 !important}.order-sm-2{order:2 !important}.order-sm-3{order:3 !important}.order-sm-4{order:4 !important}.order-sm-5{order:5 !important}.order-sm-last{order:6 !important}.m-sm-0{margin:0 !important}.m-sm-1{margin:.25rem !important}.m-sm-2{margin:.5rem !important}.m-sm-3{margin:1rem !important}.m-sm-4{margin:1.5rem !important}.m-sm-5{margin:3rem !important}.m-sm-auto{margin:auto !important}.mx-sm-0{margin-right:0 !important;margin-left:0 !important}.mx-sm-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-sm-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-sm-3{margin-right:1rem !important;margin-left:1rem !important}.mx-sm-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-sm-5{margin-right:3rem !important;margin-left:3rem !important}.mx-sm-auto{margin-right:auto !important;margin-left:auto !important}.my-sm-0{margin-top:0 !important;margin-bottom:0 !important}.my-sm-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-sm-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-sm-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-sm-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-sm-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-sm-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-sm-0{margin-top:0 !important}.mt-sm-1{margin-top:.25rem !important}.mt-sm-2{margin-top:.5rem !important}.mt-sm-3{margin-top:1rem !important}.mt-sm-4{margin-top:1.5rem !important}.mt-sm-5{margin-top:3rem !important}.mt-sm-auto{margin-top:auto !important}.me-sm-0{margin-right:0 !important}.me-sm-1{margin-right:.25rem !important}.me-sm-2{margin-right:.5rem !important}.me-sm-3{margin-right:1rem !important}.me-sm-4{margin-right:1.5rem !important}.me-sm-5{margin-right:3rem !important}.me-sm-auto{margin-right:auto !important}.mb-sm-0{margin-bottom:0 !important}.mb-sm-1{margin-bottom:.25rem !important}.mb-sm-2{margin-bottom:.5rem !important}.mb-sm-3{margin-bottom:1rem !important}.mb-sm-4{margin-bottom:1.5rem !important}.mb-sm-5{margin-bottom:3rem !important}.mb-sm-auto{margin-bottom:auto !important}.ms-sm-0{margin-left:0 !important}.ms-sm-1{margin-left:.25rem !important}.ms-sm-2{margin-left:.5rem !important}.ms-sm-3{margin-left:1rem !important}.ms-sm-4{margin-left:1.5rem !important}.ms-sm-5{margin-left:3rem !important}.ms-sm-auto{margin-left:auto !important}.p-sm-0{padding:0 !important}.p-sm-1{padding:.25rem !important}.p-sm-2{padding:.5rem !important}.p-sm-3{padding:1rem !important}.p-sm-4{padding:1.5rem !important}.p-sm-5{padding:3rem !important}.px-sm-0{padding-right:0 !important;padding-left:0 !important}.px-sm-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-sm-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-sm-3{padding-right:1rem !important;padding-left:1rem !important}.px-sm-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-sm-5{padding-right:3rem !important;padding-left:3rem !important}.py-sm-0{padding-top:0 !important;padding-bottom:0 !important}.py-sm-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-sm-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-sm-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-sm-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-sm-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-sm-0{padding-top:0 !important}.pt-sm-1{padding-top:.25rem !important}.pt-sm-2{padding-top:.5rem !important}.pt-sm-3{padding-top:1rem !important}.pt-sm-4{padding-top:1.5rem !important}.pt-sm-5{padding-top:3rem !important}.pe-sm-0{padding-right:0 !important}.pe-sm-1{padding-right:.25rem !important}.pe-sm-2{padding-right:.5rem !important}.pe-sm-3{padding-right:1rem !important}.pe-sm-4{padding-right:1.5rem !important}.pe-sm-5{padding-right:3rem !important}.pb-sm-0{padding-bottom:0 !important}.pb-sm-1{padding-bottom:.25rem !important}.pb-sm-2{padding-bottom:.5rem !important}.pb-sm-3{padding-bottom:1rem !important}.pb-sm-4{padding-bottom:1.5rem !important}.pb-sm-5{padding-bottom:3rem !important}.ps-sm-0{padding-left:0 !important}.ps-sm-1{padding-left:.25rem !important}.ps-sm-2{padding-left:.5rem !important}.ps-sm-3{padding-left:1rem !important}.ps-sm-4{padding-left:1.5rem !important}.ps-sm-5{padding-left:3rem !important}.gap-sm-0{gap:0 !important}.gap-sm-1{gap:.25rem !important}.gap-sm-2{gap:.5rem !important}.gap-sm-3{gap:1rem !important}.gap-sm-4{gap:1.5rem !important}.gap-sm-5{gap:3rem !important}.row-gap-sm-0{row-gap:0 !important}.row-gap-sm-1{row-gap:.25rem !important}.row-gap-sm-2{row-gap:.5rem !important}.row-gap-sm-3{row-gap:1rem !important}.row-gap-sm-4{row-gap:1.5rem !important}.row-gap-sm-5{row-gap:3rem !important}.column-gap-sm-0{column-gap:0 !important}.column-gap-sm-1{column-gap:.25rem !important}.column-gap-sm-2{column-gap:.5rem !important}.column-gap-sm-3{column-gap:1rem !important}.column-gap-sm-4{column-gap:1.5rem !important}.column-gap-sm-5{column-gap:3rem !important}.text-sm-start{text-align:left !important}.text-sm-end{text-align:right !important}.text-sm-center{text-align:center !important}}@media(min-width: 768px){.float-md-start{float:left !important}.float-md-end{float:right !important}.float-md-none{float:none !important}.object-fit-md-contain{object-fit:contain !important}.object-fit-md-cover{object-fit:cover !important}.object-fit-md-fill{object-fit:fill !important}.object-fit-md-scale{object-fit:scale-down !important}.object-fit-md-none{object-fit:none !important}.d-md-inline{display:inline !important}.d-md-inline-block{display:inline-block !important}.d-md-block{display:block !important}.d-md-grid{display:grid !important}.d-md-inline-grid{display:inline-grid !important}.d-md-table{display:table !important}.d-md-table-row{display:table-row !important}.d-md-table-cell{display:table-cell !important}.d-md-flex{display:flex !important}.d-md-inline-flex{display:inline-flex !important}.d-md-none{display:none !important}.flex-md-fill{flex:1 1 auto !important}.flex-md-row{flex-direction:row !important}.flex-md-column{flex-direction:column !important}.flex-md-row-reverse{flex-direction:row-reverse !important}.flex-md-column-reverse{flex-direction:column-reverse !important}.flex-md-grow-0{flex-grow:0 !important}.flex-md-grow-1{flex-grow:1 !important}.flex-md-shrink-0{flex-shrink:0 !important}.flex-md-shrink-1{flex-shrink:1 !important}.flex-md-wrap{flex-wrap:wrap !important}.flex-md-nowrap{flex-wrap:nowrap !important}.flex-md-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-md-start{justify-content:flex-start !important}.justify-content-md-end{justify-content:flex-end !important}.justify-content-md-center{justify-content:center !important}.justify-content-md-between{justify-content:space-between !important}.justify-content-md-around{justify-content:space-around !important}.justify-content-md-evenly{justify-content:space-evenly !important}.align-items-md-start{align-items:flex-start !important}.align-items-md-end{align-items:flex-end !important}.align-items-md-center{align-items:center !important}.align-items-md-baseline{align-items:baseline !important}.align-items-md-stretch{align-items:stretch !important}.align-content-md-start{align-content:flex-start !important}.align-content-md-end{align-content:flex-end !important}.align-content-md-center{align-content:center !important}.align-content-md-between{align-content:space-between !important}.align-content-md-around{align-content:space-around !important}.align-content-md-stretch{align-content:stretch !important}.align-self-md-auto{align-self:auto !important}.align-self-md-start{align-self:flex-start !important}.align-self-md-end{align-self:flex-end !important}.align-self-md-center{align-self:center !important}.align-self-md-baseline{align-self:baseline !important}.align-self-md-stretch{align-self:stretch !important}.order-md-first{order:-1 !important}.order-md-0{order:0 !important}.order-md-1{order:1 !important}.order-md-2{order:2 !important}.order-md-3{order:3 !important}.order-md-4{order:4 !important}.order-md-5{order:5 !important}.order-md-last{order:6 !important}.m-md-0{margin:0 !important}.m-md-1{margin:.25rem !important}.m-md-2{margin:.5rem !important}.m-md-3{margin:1rem !important}.m-md-4{margin:1.5rem !important}.m-md-5{margin:3rem !important}.m-md-auto{margin:auto !important}.mx-md-0{margin-right:0 !important;margin-left:0 !important}.mx-md-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-md-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-md-3{margin-right:1rem !important;margin-left:1rem !important}.mx-md-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-md-5{margin-right:3rem !important;margin-left:3rem !important}.mx-md-auto{margin-right:auto !important;margin-left:auto !important}.my-md-0{margin-top:0 !important;margin-bottom:0 !important}.my-md-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-md-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-md-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-md-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-md-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-md-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-md-0{margin-top:0 !important}.mt-md-1{margin-top:.25rem !important}.mt-md-2{margin-top:.5rem !important}.mt-md-3{margin-top:1rem !important}.mt-md-4{margin-top:1.5rem !important}.mt-md-5{margin-top:3rem !important}.mt-md-auto{margin-top:auto !important}.me-md-0{margin-right:0 !important}.me-md-1{margin-right:.25rem !important}.me-md-2{margin-right:.5rem !important}.me-md-3{margin-right:1rem !important}.me-md-4{margin-right:1.5rem !important}.me-md-5{margin-right:3rem !important}.me-md-auto{margin-right:auto !important}.mb-md-0{margin-bottom:0 !important}.mb-md-1{margin-bottom:.25rem !important}.mb-md-2{margin-bottom:.5rem !important}.mb-md-3{margin-bottom:1rem !important}.mb-md-4{margin-bottom:1.5rem !important}.mb-md-5{margin-bottom:3rem !important}.mb-md-auto{margin-bottom:auto !important}.ms-md-0{margin-left:0 !important}.ms-md-1{margin-left:.25rem !important}.ms-md-2{margin-left:.5rem !important}.ms-md-3{margin-left:1rem !important}.ms-md-4{margin-left:1.5rem !important}.ms-md-5{margin-left:3rem !important}.ms-md-auto{margin-left:auto !important}.p-md-0{padding:0 !important}.p-md-1{padding:.25rem !important}.p-md-2{padding:.5rem !important}.p-md-3{padding:1rem !important}.p-md-4{padding:1.5rem !important}.p-md-5{padding:3rem !important}.px-md-0{padding-right:0 !important;padding-left:0 !important}.px-md-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-md-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-md-3{padding-right:1rem !important;padding-left:1rem !important}.px-md-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-md-5{padding-right:3rem !important;padding-left:3rem !important}.py-md-0{padding-top:0 !important;padding-bottom:0 !important}.py-md-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-md-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-md-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-md-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-md-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-md-0{padding-top:0 !important}.pt-md-1{padding-top:.25rem !important}.pt-md-2{padding-top:.5rem !important}.pt-md-3{padding-top:1rem !important}.pt-md-4{padding-top:1.5rem !important}.pt-md-5{padding-top:3rem !important}.pe-md-0{padding-right:0 !important}.pe-md-1{padding-right:.25rem !important}.pe-md-2{padding-right:.5rem !important}.pe-md-3{padding-right:1rem !important}.pe-md-4{padding-right:1.5rem !important}.pe-md-5{padding-right:3rem !important}.pb-md-0{padding-bottom:0 !important}.pb-md-1{padding-bottom:.25rem !important}.pb-md-2{padding-bottom:.5rem !important}.pb-md-3{padding-bottom:1rem !important}.pb-md-4{padding-bottom:1.5rem !important}.pb-md-5{padding-bottom:3rem !important}.ps-md-0{padding-left:0 !important}.ps-md-1{padding-left:.25rem !important}.ps-md-2{padding-left:.5rem !important}.ps-md-3{padding-left:1rem !important}.ps-md-4{padding-left:1.5rem !important}.ps-md-5{padding-left:3rem !important}.gap-md-0{gap:0 !important}.gap-md-1{gap:.25rem !important}.gap-md-2{gap:.5rem !important}.gap-md-3{gap:1rem !important}.gap-md-4{gap:1.5rem !important}.gap-md-5{gap:3rem !important}.row-gap-md-0{row-gap:0 !important}.row-gap-md-1{row-gap:.25rem !important}.row-gap-md-2{row-gap:.5rem !important}.row-gap-md-3{row-gap:1rem !important}.row-gap-md-4{row-gap:1.5rem !important}.row-gap-md-5{row-gap:3rem !important}.column-gap-md-0{column-gap:0 !important}.column-gap-md-1{column-gap:.25rem !important}.column-gap-md-2{column-gap:.5rem !important}.column-gap-md-3{column-gap:1rem !important}.column-gap-md-4{column-gap:1.5rem !important}.column-gap-md-5{column-gap:3rem !important}.text-md-start{text-align:left !important}.text-md-end{text-align:right !important}.text-md-center{text-align:center !important}}@media(min-width: 992px){.float-lg-start{float:left !important}.float-lg-end{float:right !important}.float-lg-none{float:none !important}.object-fit-lg-contain{object-fit:contain !important}.object-fit-lg-cover{object-fit:cover !important}.object-fit-lg-fill{object-fit:fill !important}.object-fit-lg-scale{object-fit:scale-down !important}.object-fit-lg-none{object-fit:none !important}.d-lg-inline{display:inline !important}.d-lg-inline-block{display:inline-block !important}.d-lg-block{display:block !important}.d-lg-grid{display:grid !important}.d-lg-inline-grid{display:inline-grid !important}.d-lg-table{display:table !important}.d-lg-table-row{display:table-row !important}.d-lg-table-cell{display:table-cell !important}.d-lg-flex{display:flex !important}.d-lg-inline-flex{display:inline-flex !important}.d-lg-none{display:none !important}.flex-lg-fill{flex:1 1 auto !important}.flex-lg-row{flex-direction:row !important}.flex-lg-column{flex-direction:column !important}.flex-lg-row-reverse{flex-direction:row-reverse !important}.flex-lg-column-reverse{flex-direction:column-reverse !important}.flex-lg-grow-0{flex-grow:0 !important}.flex-lg-grow-1{flex-grow:1 !important}.flex-lg-shrink-0{flex-shrink:0 !important}.flex-lg-shrink-1{flex-shrink:1 !important}.flex-lg-wrap{flex-wrap:wrap !important}.flex-lg-nowrap{flex-wrap:nowrap !important}.flex-lg-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-lg-start{justify-content:flex-start !important}.justify-content-lg-end{justify-content:flex-end !important}.justify-content-lg-center{justify-content:center !important}.justify-content-lg-between{justify-content:space-between !important}.justify-content-lg-around{justify-content:space-around !important}.justify-content-lg-evenly{justify-content:space-evenly !important}.align-items-lg-start{align-items:flex-start !important}.align-items-lg-end{align-items:flex-end !important}.align-items-lg-center{align-items:center !important}.align-items-lg-baseline{align-items:baseline !important}.align-items-lg-stretch{align-items:stretch !important}.align-content-lg-start{align-content:flex-start !important}.align-content-lg-end{align-content:flex-end !important}.align-content-lg-center{align-content:center !important}.align-content-lg-between{align-content:space-between !important}.align-content-lg-around{align-content:space-around !important}.align-content-lg-stretch{align-content:stretch !important}.align-self-lg-auto{align-self:auto !important}.align-self-lg-start{align-self:flex-start !important}.align-self-lg-end{align-self:flex-end !important}.align-self-lg-center{align-self:center !important}.align-self-lg-baseline{align-self:baseline !important}.align-self-lg-stretch{align-self:stretch !important}.order-lg-first{order:-1 !important}.order-lg-0{order:0 !important}.order-lg-1{order:1 !important}.order-lg-2{order:2 !important}.order-lg-3{order:3 !important}.order-lg-4{order:4 !important}.order-lg-5{order:5 !important}.order-lg-last{order:6 !important}.m-lg-0{margin:0 !important}.m-lg-1{margin:.25rem !important}.m-lg-2{margin:.5rem !important}.m-lg-3{margin:1rem !important}.m-lg-4{margin:1.5rem !important}.m-lg-5{margin:3rem !important}.m-lg-auto{margin:auto !important}.mx-lg-0{margin-right:0 !important;margin-left:0 !important}.mx-lg-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-lg-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-lg-3{margin-right:1rem !important;margin-left:1rem !important}.mx-lg-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-lg-5{margin-right:3rem !important;margin-left:3rem !important}.mx-lg-auto{margin-right:auto !important;margin-left:auto !important}.my-lg-0{margin-top:0 !important;margin-bottom:0 !important}.my-lg-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-lg-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-lg-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-lg-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-lg-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-lg-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-lg-0{margin-top:0 !important}.mt-lg-1{margin-top:.25rem !important}.mt-lg-2{margin-top:.5rem !important}.mt-lg-3{margin-top:1rem !important}.mt-lg-4{margin-top:1.5rem !important}.mt-lg-5{margin-top:3rem !important}.mt-lg-auto{margin-top:auto !important}.me-lg-0{margin-right:0 !important}.me-lg-1{margin-right:.25rem !important}.me-lg-2{margin-right:.5rem !important}.me-lg-3{margin-right:1rem !important}.me-lg-4{margin-right:1.5rem !important}.me-lg-5{margin-right:3rem !important}.me-lg-auto{margin-right:auto !important}.mb-lg-0{margin-bottom:0 !important}.mb-lg-1{margin-bottom:.25rem !important}.mb-lg-2{margin-bottom:.5rem !important}.mb-lg-3{margin-bottom:1rem !important}.mb-lg-4{margin-bottom:1.5rem !important}.mb-lg-5{margin-bottom:3rem !important}.mb-lg-auto{margin-bottom:auto !important}.ms-lg-0{margin-left:0 !important}.ms-lg-1{margin-left:.25rem !important}.ms-lg-2{margin-left:.5rem !important}.ms-lg-3{margin-left:1rem !important}.ms-lg-4{margin-left:1.5rem !important}.ms-lg-5{margin-left:3rem !important}.ms-lg-auto{margin-left:auto !important}.p-lg-0{padding:0 !important}.p-lg-1{padding:.25rem !important}.p-lg-2{padding:.5rem !important}.p-lg-3{padding:1rem !important}.p-lg-4{padding:1.5rem !important}.p-lg-5{padding:3rem !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.px-lg-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-lg-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-lg-3{padding-right:1rem !important;padding-left:1rem !important}.px-lg-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-lg-5{padding-right:3rem !important;padding-left:3rem !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}.py-lg-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-lg-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-lg-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-lg-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-lg-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1400px){.float-xxl-start{float:left !important}.float-xxl-end{float:right !important}.float-xxl-none{float:none !important}.object-fit-xxl-contain{object-fit:contain !important}.object-fit-xxl-cover{object-fit:cover !important}.object-fit-xxl-fill{object-fit:fill !important}.object-fit-xxl-scale{object-fit:scale-down !important}.object-fit-xxl-none{object-fit:none !important}.d-xxl-inline{display:inline !important}.d-xxl-inline-block{display:inline-block !important}.d-xxl-block{display:block !important}.d-xxl-grid{display:grid !important}.d-xxl-inline-grid{display:inline-grid !important}.d-xxl-table{display:table !important}.d-xxl-table-row{display:table-row !important}.d-xxl-table-cell{display:table-cell !important}.d-xxl-flex{display:flex !important}.d-xxl-inline-flex{display:inline-flex !important}.d-xxl-none{display:none !important}.flex-xxl-fill{flex:1 1 auto !important}.flex-xxl-row{flex-direction:row !important}.flex-xxl-column{flex-direction:column !important}.flex-xxl-row-reverse{flex-direction:row-reverse !important}.flex-xxl-column-reverse{flex-direction:column-reverse !important}.flex-xxl-grow-0{flex-grow:0 !important}.flex-xxl-grow-1{flex-grow:1 !important}.flex-xxl-shrink-0{flex-shrink:0 !important}.flex-xxl-shrink-1{flex-shrink:1 !important}.flex-xxl-wrap{flex-wrap:wrap !important}.flex-xxl-nowrap{flex-wrap:nowrap !important}.flex-xxl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xxl-start{justify-content:flex-start !important}.justify-content-xxl-end{justify-content:flex-end !important}.justify-content-xxl-center{justify-content:center !important}.justify-content-xxl-between{justify-content:space-between !important}.justify-content-xxl-around{justify-content:space-around !important}.justify-content-xxl-evenly{justify-content:space-evenly !important}.align-items-xxl-start{align-items:flex-start !important}.align-items-xxl-end{align-items:flex-end !important}.align-items-xxl-center{align-items:center !important}.align-items-xxl-baseline{align-items:baseline !important}.align-items-xxl-stretch{align-items:stretch !important}.align-content-xxl-start{align-content:flex-start !important}.align-content-xxl-end{align-content:flex-end !important}.align-content-xxl-center{align-content:center !important}.align-content-xxl-between{align-content:space-between !important}.align-content-xxl-around{align-content:space-around !important}.align-content-xxl-stretch{align-content:stretch !important}.align-self-xxl-auto{align-self:auto !important}.align-self-xxl-start{align-self:flex-start !important}.align-self-xxl-end{align-self:flex-end !important}.align-self-xxl-center{align-self:center !important}.align-self-xxl-baseline{align-self:baseline !important}.align-self-xxl-stretch{align-self:stretch !important}.order-xxl-first{order:-1 !important}.order-xxl-0{order:0 !important}.order-xxl-1{order:1 !important}.order-xxl-2{order:2 !important}.order-xxl-3{order:3 !important}.order-xxl-4{order:4 !important}.order-xxl-5{order:5 !important}.order-xxl-last{order:6 !important}.m-xxl-0{margin:0 !important}.m-xxl-1{margin:.25rem !important}.m-xxl-2{margin:.5rem !important}.m-xxl-3{margin:1rem !important}.m-xxl-4{margin:1.5rem !important}.m-xxl-5{margin:3rem !important}.m-xxl-auto{margin:auto !important}.mx-xxl-0{margin-right:0 !important;margin-left:0 !important}.mx-xxl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xxl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xxl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xxl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xxl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xxl-auto{margin-right:auto !important;margin-left:auto !important}.my-xxl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xxl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xxl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xxl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xxl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xxl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xxl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xxl-0{margin-top:0 !important}.mt-xxl-1{margin-top:.25rem !important}.mt-xxl-2{margin-top:.5rem !important}.mt-xxl-3{margin-top:1rem !important}.mt-xxl-4{margin-top:1.5rem !important}.mt-xxl-5{margin-top:3rem !important}.mt-xxl-auto{margin-top:auto !important}.me-xxl-0{margin-right:0 !important}.me-xxl-1{margin-right:.25rem !important}.me-xxl-2{margin-right:.5rem !important}.me-xxl-3{margin-right:1rem !important}.me-xxl-4{margin-right:1.5rem !important}.me-xxl-5{margin-right:3rem !important}.me-xxl-auto{margin-right:auto !important}.mb-xxl-0{margin-bottom:0 !important}.mb-xxl-1{margin-bottom:.25rem !important}.mb-xxl-2{margin-bottom:.5rem !important}.mb-xxl-3{margin-bottom:1rem !important}.mb-xxl-4{margin-bottom:1.5rem !important}.mb-xxl-5{margin-bottom:3rem !important}.mb-xxl-auto{margin-bottom:auto !important}.ms-xxl-0{margin-left:0 !important}.ms-xxl-1{margin-left:.25rem !important}.ms-xxl-2{margin-left:.5rem !important}.ms-xxl-3{margin-left:1rem !important}.ms-xxl-4{margin-left:1.5rem !important}.ms-xxl-5{margin-left:3rem !important}.ms-xxl-auto{margin-left:auto !important}.p-xxl-0{padding:0 !important}.p-xxl-1{padding:.25rem !important}.p-xxl-2{padding:.5rem !important}.p-xxl-3{padding:1rem !important}.p-xxl-4{padding:1.5rem !important}.p-xxl-5{padding:3rem !important}.px-xxl-0{padding-right:0 !important;padding-left:0 !important}.px-xxl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xxl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xxl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xxl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xxl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xxl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xxl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xxl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xxl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xxl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xxl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xxl-0{padding-top:0 !important}.pt-xxl-1{padding-top:.25rem !important}.pt-xxl-2{padding-top:.5rem !important}.pt-xxl-3{padding-top:1rem !important}.pt-xxl-4{padding-top:1.5rem !important}.pt-xxl-5{padding-top:3rem !important}.pe-xxl-0{padding-right:0 !important}.pe-xxl-1{padding-right:.25rem !important}.pe-xxl-2{padding-right:.5rem !important}.pe-xxl-3{padding-right:1rem !important}.pe-xxl-4{padding-right:1.5rem !important}.pe-xxl-5{padding-right:3rem !important}.pb-xxl-0{padding-bottom:0 !important}.pb-xxl-1{padding-bottom:.25rem !important}.pb-xxl-2{padding-bottom:.5rem !important}.pb-xxl-3{padding-bottom:1rem !important}.pb-xxl-4{padding-bottom:1.5rem !important}.pb-xxl-5{padding-bottom:3rem !important}.ps-xxl-0{padding-left:0 !important}.ps-xxl-1{padding-left:.25rem !important}.ps-xxl-2{padding-left:.5rem !important}.ps-xxl-3{padding-left:1rem !important}.ps-xxl-4{padding-left:1.5rem !important}.ps-xxl-5{padding-left:3rem !important}.gap-xxl-0{gap:0 !important}.gap-xxl-1{gap:.25rem !important}.gap-xxl-2{gap:.5rem !important}.gap-xxl-3{gap:1rem !important}.gap-xxl-4{gap:1.5rem !important}.gap-xxl-5{gap:3rem !important}.row-gap-xxl-0{row-gap:0 !important}.row-gap-xxl-1{row-gap:.25rem !important}.row-gap-xxl-2{row-gap:.5rem !important}.row-gap-xxl-3{row-gap:1rem !important}.row-gap-xxl-4{row-gap:1.5rem !important}.row-gap-xxl-5{row-gap:3rem !important}.column-gap-xxl-0{column-gap:0 !important}.column-gap-xxl-1{column-gap:.25rem !important}.column-gap-xxl-2{column-gap:.5rem !important}.column-gap-xxl-3{column-gap:1rem !important}.column-gap-xxl-4{column-gap:1.5rem !important}.column-gap-xxl-5{column-gap:3rem !important}.text-xxl-start{text-align:left !important}.text-xxl-end{text-align:right !important}.text-xxl-center{text-align:center !important}}.bg-default{color:#fff}.bg-primary{color:#fff}.bg-secondary{color:#fff}.bg-success{color:#fff}.bg-info{color:#fff}.bg-warning{color:#fff}.bg-danger{color:#fff}.bg-light{color:#000}.bg-dark{color:#fff}@media(min-width: 1200px){.fs-1{font-size:2rem !important}.fs-2{font-size:1.65rem !important}.fs-3{font-size:1.45rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.bg-blue{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-blue{--bslib-color-fg: #2780e3;color:var(--bslib-color-fg)}.bg-indigo{--bslib-color-bg: #6610f2;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-indigo{--bslib-color-fg: #6610f2;color:var(--bslib-color-fg)}.bg-purple{--bslib-color-bg: #613d7c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-purple{--bslib-color-fg: #613d7c;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #ff0039;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #f0ad4e;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #f0ad4e;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #ff7518;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: #3fb618;color:var(--bslib-color-fg)}.bg-teal{--bslib-color-bg: #20c997;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-teal{--bslib-color-fg: #20c997;color:var(--bslib-color-fg)}.bg-cyan{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-cyan{--bslib-color-fg: #9954bb;color:var(--bslib-color-fg)}.text-default{--bslib-color-fg: #343a40}.bg-default{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-primary{--bslib-color-fg: #2780e3}.bg-primary{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff}.text-secondary{--bslib-color-fg: #343a40}.bg-secondary{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-success{--bslib-color-fg: #3fb618}.bg-success{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #9954bb}.bg-info{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #ff7518}.bg-warning{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #ff0039}.bg-danger{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #f8f9fa}.bg-light{--bslib-color-bg: #f8f9fa;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #343a40}.bg-dark{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4053e9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4053e9;color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3e65ba;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3e65ba;color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7466c0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7466c0;color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: #7d4d9f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #7d4d9f;color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: #7792a7;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #7792a7;color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #7d7c92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #7d7c92;color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: #319692;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #319692;color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: #249dc5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #249dc5;color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #556ed3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #556ed3;color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4d3dec;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4d3dec;color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: #6422c3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #6422c3;color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9a22c9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9a22c9;color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: #a30aa8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a30aa8;color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9d4fb0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9d4fb0;color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a3389b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a3389b;color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: #56529b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #56529b;color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4a5ace;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4a5ace;color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #7a2bdc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #7a2bdc;color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4a58a5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4a58a5;color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #632bab;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #632bab;color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: #973d82;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #973d82;color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: #a02561;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a02561;color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9a6a6a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9a6a6a;color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a05354;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a05354;color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: #536d54;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #536d54;color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: #477587;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #477587;color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #774695;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #774695;color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: #9b58af;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #9b58af;color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b42cb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b42cb5;color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b23e86;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b23e86;color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: #f1256b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f1256b;color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: #eb6a73;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #eb6a73;color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #f1545e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f1545e;color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: #a46e5e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a46e5e;color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: #987690;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #987690;color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #c8479f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #c8479f;color:#fff}.bg-gradient-red-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a9337d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a9337d;color:#fff}.bg-gradient-red-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c20683;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c20683;color:#fff}.bg-gradient-red-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c01854;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c01854;color:#fff}.bg-gradient-red-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f6195a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f6195a;color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: #f94541;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f94541;color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #ff2f2c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #ff2f2c;color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: #b2492c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b2492c;color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6505f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6505f;color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d6226d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d6226d;color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a09b8a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a09b8a;color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b96e90;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b96e90;color:#fff}.bg-gradient-orange-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b78060;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b78060;color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: #ed8167;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #ed8167;color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: #f66846;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f66846;color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #000;--bslib-color-bg: #f69738;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f69738;color:#000}.bg-gradient-orange-green{--bslib-color-fg: #000;--bslib-color-bg: #a9b138;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a9b138;color:#000}.bg-gradient-orange-teal{--bslib-color-fg: #000;--bslib-color-bg: #9db86b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #9db86b;color:#000}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #cd897a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #cd897a;color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a97969;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a97969;color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c24d6f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c24d6f;color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c05f40;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c05f40;color:#fff}.bg-gradient-yellow-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f65f46;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f65f46;color:#fff}.bg-gradient-yellow-red{--bslib-color-fg: #fff;--bslib-color-bg: #ff4625;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #ff4625;color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #000;--bslib-color-bg: #f98b2e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f98b2e;color:#000}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: #b28f18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b28f18;color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6974b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6974b;color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d66859;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d66859;color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: #35a069;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #35a069;color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4f746f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4f746f;color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4d8640;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #4d8640;color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: #838646;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #838646;color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: #8c6d25;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #8c6d25;color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #000;--bslib-color-bg: #86b22e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #86b22e;color:#000}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #8c9c18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #8c9c18;color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #33be4b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #33be4b;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #638f59;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #638f59;color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: #23acb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #23acb5;color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3a918c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3a918c;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #797971;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #797971;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #000;--bslib-color-bg: #73be7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #73be7a;color:#000}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #79a764;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #79a764;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #2cc164;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #2cc164;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #509aa5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #509aa5;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #6b66cb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #6b66cb;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #8539d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #8539d1;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #834ba2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #834ba2;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #b94ba8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #b94ba8;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #c23287;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #c23287;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #bc788f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #bc788f;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #c2617a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #c2617a;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #757b7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.bg-blue{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-blue{--bslib-color-fg: #2780e3;color:var(--bslib-color-fg)}.bg-indigo{--bslib-color-bg: #6610f2;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-indigo{--bslib-color-fg: #6610f2;color:var(--bslib-color-fg)}.bg-purple{--bslib-color-bg: #613d7c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-purple{--bslib-color-fg: #613d7c;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #ff0039;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #f0ad4e;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #f0ad4e;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #ff7518;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: #3fb618;color:var(--bslib-color-fg)}.bg-teal{--bslib-color-bg: #20c997;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-teal{--bslib-color-fg: #20c997;color:var(--bslib-color-fg)}.bg-cyan{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-cyan{--bslib-color-fg: #9954bb;color:var(--bslib-color-fg)}.text-default{--bslib-color-fg: #343a40}.bg-default{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-primary{--bslib-color-fg: #2780e3}.bg-primary{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff}.text-secondary{--bslib-color-fg: #343a40}.bg-secondary{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-success{--bslib-color-fg: #3fb618}.bg-success{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #9954bb}.bg-info{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #ff7518}.bg-warning{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #ff0039}.bg-danger{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #f8f9fa}.bg-light{--bslib-color-bg: #f8f9fa;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #343a40}.bg-dark{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4053e9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4053e9;color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3e65ba;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3e65ba;color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7466c0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7466c0;color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: #7d4d9f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #7d4d9f;color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: #7792a7;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #7792a7;color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #7d7c92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #7d7c92;color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: #319692;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #319692;color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: #249dc5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #249dc5;color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #556ed3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #556ed3;color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4d3dec;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4d3dec;color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: #6422c3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #6422c3;color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9a22c9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9a22c9;color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: #a30aa8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a30aa8;color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9d4fb0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9d4fb0;color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a3389b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a3389b;color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: #56529b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #56529b;color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4a5ace;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4a5ace;color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #7a2bdc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #7a2bdc;color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4a58a5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4a58a5;color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #632bab;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #632bab;color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: #973d82;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #973d82;color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: #a02561;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a02561;color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9a6a6a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9a6a6a;color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a05354;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a05354;color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: #536d54;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #536d54;color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: #477587;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #477587;color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #774695;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #774695;color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: #9b58af;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #9b58af;color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b42cb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b42cb5;color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b23e86;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b23e86;color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: #f1256b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f1256b;color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: #eb6a73;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #eb6a73;color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #f1545e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f1545e;color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: #a46e5e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a46e5e;color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: #987690;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #987690;color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #c8479f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #c8479f;color:#fff}.bg-gradient-red-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a9337d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a9337d;color:#fff}.bg-gradient-red-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c20683;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c20683;color:#fff}.bg-gradient-red-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c01854;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c01854;color:#fff}.bg-gradient-red-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f6195a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f6195a;color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: #f94541;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f94541;color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #ff2f2c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #ff2f2c;color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: #b2492c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b2492c;color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6505f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6505f;color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d6226d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d6226d;color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a09b8a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a09b8a;color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b96e90;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b96e90;color:#fff}.bg-gradient-orange-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b78060;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b78060;color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: #ed8167;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #ed8167;color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: #f66846;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f66846;color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #000;--bslib-color-bg: #f69738;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f69738;color:#000}.bg-gradient-orange-green{--bslib-color-fg: #000;--bslib-color-bg: #a9b138;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a9b138;color:#000}.bg-gradient-orange-teal{--bslib-color-fg: #000;--bslib-color-bg: #9db86b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #9db86b;color:#000}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #cd897a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #cd897a;color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a97969;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a97969;color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c24d6f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c24d6f;color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c05f40;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c05f40;color:#fff}.bg-gradient-yellow-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f65f46;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f65f46;color:#fff}.bg-gradient-yellow-red{--bslib-color-fg: #fff;--bslib-color-bg: #ff4625;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #ff4625;color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #000;--bslib-color-bg: #f98b2e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f98b2e;color:#000}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: #b28f18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b28f18;color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6974b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6974b;color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d66859;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d66859;color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: #35a069;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #35a069;color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4f746f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4f746f;color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4d8640;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #4d8640;color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: #838646;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #838646;color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: #8c6d25;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #8c6d25;color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #000;--bslib-color-bg: #86b22e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #86b22e;color:#000}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #8c9c18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #8c9c18;color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #33be4b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #33be4b;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #638f59;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #638f59;color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: #23acb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #23acb5;color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3a918c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3a918c;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #797971;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #797971;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #000;--bslib-color-bg: #73be7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #73be7a;color:#000}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #79a764;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #79a764;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #2cc164;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #2cc164;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #509aa5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #509aa5;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #6b66cb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #6b66cb;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #8539d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #8539d1;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #834ba2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #834ba2;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #b94ba8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #b94ba8;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #c23287;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #c23287;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #bc788f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #bc788f;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #c2617a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #c2617a;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #757b7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xs)}@media(min-width: 576px){.bslib-grid.grid.bslib-grid--row-heights--sm{--bslib-grid--row-heights: var(--bslib-grid--row-heights--sm)}}@media(min-width: 768px){.bslib-grid.grid.bslib-grid--row-heights--md{--bslib-grid--row-heights: var(--bslib-grid--row-heights--md)}}@media(min-width: 992px){.bslib-grid.grid.bslib-grid--row-heights--lg{--bslib-grid--row-heights: var(--bslib-grid--row-heights--lg)}}@media(min-width: 1200px){.bslib-grid.grid.bslib-grid--row-heights--xl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xl)}}@media(min-width: 1400px){.bslib-grid.grid.bslib-grid--row-heights--xxl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xxl)}}.bslib-grid>*>.shiny-input-container{width:100%}.bslib-grid-item{grid-column:auto/span 1}@media(max-width: 767.98px){.bslib-grid-item{grid-column:1/-1}}@media(max-width: 575.98px){.bslib-grid{grid-template-columns:1fr !important;height:var(--bslib-grid-height-mobile)}.bslib-grid.grid{height:unset !important;grid-auto-rows:var(--bslib-grid--row-heights--xs, auto)}}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.navbar+.container-fluid:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-sm:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-md:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-lg:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xl:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xxl:has(>.tab-content>.tab-pane.active.html-fill-container){padding-left:0;padding-right:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container{padding:var(--bslib-spacer, 1rem);gap:var(--bslib-spacer, 1rem)}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child){padding:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]){border-left:none;border-right:none;border-bottom:none}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]){border-radius:0}.navbar+div>.bslib-sidebar-layout{border-top:var(--bslib-sidebar-border)}html{height:100%}.bslib-page-fill{width:100%;height:100%;margin:0;padding:var(--bslib-spacer, 1rem);gap:var(--bslib-spacer, 1rem)}@media(max-width: 575.98px){.bslib-page-fill{height:var(--bslib-page-fill-mobile-height, auto)}}:root{--bslib-page-sidebar-title-bg: #f8f9fa;--bslib-page-sidebar-title-color: #000}.bslib-page-title{background-color:var(--bslib-page-sidebar-title-bg);color:var(--bslib-page-sidebar-title-color);font-size:1.25rem;font-weight:300;padding:var(--bslib-spacer, 1rem);padding-left:1.5rem;margin-bottom:0;border-bottom:1px solid #dee2e6}.bslib-sidebar-layout{--bslib-sidebar-transition-duration: 500ms;--bslib-sidebar-transition-easing-x: cubic-bezier(0.8, 0.78, 0.22, 1.07);--bslib-sidebar-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-border-radius: var(--bs-border-radius);--bslib-sidebar-vert-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--bslib-sidebar-fg: var(--bs-emphasis-color, black);--bslib-sidebar-main-fg: var(--bs-card-color, var(--bs-body-color));--bslib-sidebar-main-bg: var(--bs-card-bg, var(--bs-body-bg));--bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--bslib-sidebar-padding: calc(var(--bslib-spacer) * 1.5);--bslib-sidebar-icon-size: var(--bslib-spacer, 1rem);--bslib-sidebar-icon-button-size: calc(var(--bslib-sidebar-icon-size, 1rem) * 2);--bslib-sidebar-padding-icon: calc(var(--bslib-sidebar-icon-button-size, 2rem) * 1.5);--bslib-collapse-toggle-border-radius: var(--bs-border-radius, 0.25rem);--bslib-collapse-toggle-transform: 0deg;--bslib-sidebar-toggle-transition-easing: cubic-bezier(1, 0, 0, 1);--bslib-collapse-toggle-right-transform: 180deg;--bslib-sidebar-column-main: minmax(0, 1fr);display:grid !important;grid-template-columns:min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 250px)) var(--bslib-sidebar-column-main);position:relative;transition:grid-template-columns ease-in-out var(--bslib-sidebar-transition-duration);border:var(--bslib-sidebar-border);border-radius:var(--bslib-sidebar-border-radius)}@media(prefers-reduced-motion: reduce){.bslib-sidebar-layout{transition:none}}.bslib-sidebar-layout[data-bslib-sidebar-border=false]{border:none}.bslib-sidebar-layout[data-bslib-sidebar-border-radius=false]{border-radius:initial}.bslib-sidebar-layout>.main,.bslib-sidebar-layout>.sidebar{grid-row:1/2;border-radius:inherit;overflow:auto}.bslib-sidebar-layout>.main{grid-column:2/3;border-top-left-radius:0;border-bottom-left-radius:0;padding:var(--bslib-sidebar-padding);transition:padding var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration);color:var(--bslib-sidebar-main-fg);background-color:var(--bslib-sidebar-main-bg)}.bslib-sidebar-layout>.sidebar{grid-column:1/2;width:100%;height:100%;border-right:var(--bslib-sidebar-vert-border);border-top-right-radius:0;border-bottom-right-radius:0;color:var(--bslib-sidebar-fg);background-color:var(--bslib-sidebar-bg);backdrop-filter:blur(5px)}.bslib-sidebar-layout>.sidebar>.sidebar-content{display:flex;flex-direction:column;gap:var(--bslib-spacer, 1rem);padding:var(--bslib-sidebar-padding);padding-top:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout>.sidebar>.sidebar-content>:last-child:not(.sidebar-title){margin-bottom:0}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion{margin-left:calc(-1*var(--bslib-sidebar-padding));margin-right:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:last-child{margin-bottom:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child){margin-bottom:1rem}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-body{display:flex;flex-direction:column}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:first-child) .accordion-item:first-child{border-top:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child) .accordion-item:last-child{border-bottom:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content.has-accordion>.sidebar-title{border-bottom:none;padding-bottom:0}.bslib-sidebar-layout>.sidebar .shiny-input-container{width:100%}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar>.sidebar-content{padding-top:var(--bslib-sidebar-padding)}.bslib-sidebar-layout>.collapse-toggle{grid-row:1/2;grid-column:1/2;display:inline-flex;align-items:center;position:absolute;right:calc(var(--bslib-sidebar-icon-size));top:calc(var(--bslib-sidebar-icon-size, 1rem)/2);border:none;border-radius:var(--bslib-collapse-toggle-border-radius);height:var(--bslib-sidebar-icon-button-size, 2rem);width:var(--bslib-sidebar-icon-button-size, 2rem);display:flex;align-items:center;justify-content:center;padding:0;color:var(--bslib-sidebar-fg);background-color:unset;transition:color var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),top var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),right var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),left var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover{background-color:var(--bslib-sidebar-toggle-bg)}.bslib-sidebar-layout>.collapse-toggle>.collapse-icon{opacity:.8;width:var(--bslib-sidebar-icon-size);height:var(--bslib-sidebar-icon-size);transform:rotateY(var(--bslib-collapse-toggle-transform));transition:transform var(--bslib-sidebar-toggle-transition-easing) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover>.collapse-icon{opacity:1}.bslib-sidebar-layout .sidebar-title{font-size:1.25rem;line-height:1.25;margin-top:0;margin-bottom:1rem;padding-bottom:1rem;border-bottom:var(--bslib-sidebar-border)}.bslib-sidebar-layout.sidebar-right{grid-template-columns:var(--bslib-sidebar-column-main) min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 250px))}.bslib-sidebar-layout.sidebar-right>.main{grid-column:1/2;border-top-right-radius:0;border-bottom-right-radius:0;border-top-left-radius:inherit;border-bottom-left-radius:inherit}.bslib-sidebar-layout.sidebar-right>.sidebar{grid-column:2/3;border-right:none;border-left:var(--bslib-sidebar-vert-border);border-top-left-radius:0;border-bottom-left-radius:0}.bslib-sidebar-layout.sidebar-right>.collapse-toggle{grid-column:2/3;left:var(--bslib-sidebar-icon-size);right:unset;border:var(--bslib-collapse-toggle-border)}.bslib-sidebar-layout.sidebar-right>.collapse-toggle>.collapse-icon{transform:rotateY(var(--bslib-collapse-toggle-right-transform))}.bslib-sidebar-layout.sidebar-collapsed{--bslib-collapse-toggle-transform: 180deg;--bslib-collapse-toggle-right-transform: 0deg;--bslib-sidebar-vert-border: none;grid-template-columns:0 minmax(0, 1fr)}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right{grid-template-columns:minmax(0, 1fr) 0}.bslib-sidebar-layout.sidebar-collapsed:not(.transitioning)>.sidebar>*{display:none}.bslib-sidebar-layout.sidebar-collapsed>.main{border-radius:inherit}.bslib-sidebar-layout.sidebar-collapsed:not(.sidebar-right)>.main{padding-left:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.main{padding-right:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed>.collapse-toggle{color:var(--bslib-sidebar-main-fg);top:calc(var(--bslib-sidebar-overlap-counter, 0)*(var(--bslib-sidebar-icon-size) + var(--bslib-sidebar-padding)) + var(--bslib-sidebar-icon-size, 1rem)/2);right:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px))}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.collapse-toggle{left:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px));right:unset}@media(min-width: 576px){.bslib-sidebar-layout.transitioning>.sidebar>.sidebar-content{display:none}}@media(max-width: 575.98px){.bslib-sidebar-layout[data-bslib-sidebar-open=desktop]{--bslib-sidebar-js-init-collapsed: true}.bslib-sidebar-layout>.sidebar,.bslib-sidebar-layout.sidebar-right>.sidebar{border:none}.bslib-sidebar-layout>.main,.bslib-sidebar-layout.sidebar-right>.main{grid-column:1/3}.bslib-sidebar-layout[data-bslib-sidebar-open=always]{display:block !important}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar{max-height:var(--bslib-sidebar-max-height-mobile);overflow-y:auto;border-top:var(--bslib-sidebar-vert-border)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]){grid-template-columns:100% 0}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.sidebar{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.collapse-toggle{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-right{grid-template-columns:0 100%}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed{grid-template-columns:0 100%}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed.sidebar-right{grid-template-columns:100% 0}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-right)>.main{padding-left:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-right>.main{padding-right:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always])>.main{opacity:0;transition:opacity var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed>.main{opacity:1}}.html-fill-container{display:flex;flex-direction:column;min-height:0;min-width:0}.html-fill-container>.html-fill-item{flex:1 1 auto;min-height:0;min-width:0}.html-fill-container>:not(.html-fill-item){flex:0 0 auto}.quarto-container{min-height:calc(100vh - 132px)}body.hypothesis-enabled #quarto-header{margin-right:16px}footer.footer .nav-footer,#quarto-header>nav{padding-left:1em;padding-right:1em}footer.footer div.nav-footer p:first-child{margin-top:0}footer.footer div.nav-footer p:last-child{margin-bottom:0}#quarto-content>*{padding-top:14px}#quarto-content>#quarto-sidebar-glass{padding-top:0px}@media(max-width: 991.98px){#quarto-content>*{padding-top:0}#quarto-content .subtitle{padding-top:14px}#quarto-content section:first-of-type h2:first-of-type,#quarto-content section:first-of-type .h2:first-of-type{margin-top:1rem}}.headroom-target,header.headroom{will-change:transform;transition:position 200ms linear;transition:all 200ms linear}header.headroom--pinned{transform:translateY(0%)}header.headroom--unpinned{transform:translateY(-100%)}.navbar-container{width:100%}.navbar-brand{overflow:hidden;text-overflow:ellipsis}.navbar-brand-container{max-width:calc(100% - 115px);min-width:0;display:flex;align-items:center}@media(min-width: 992px){.navbar-brand-container{margin-right:1em}}.navbar-brand.navbar-brand-logo{margin-right:4px;display:inline-flex}.navbar-toggler{flex-basis:content;flex-shrink:0}.navbar .navbar-brand-container{order:2}.navbar .navbar-toggler{order:1}.navbar .navbar-container>.navbar-nav{order:20}.navbar .navbar-container>.navbar-brand-container{margin-left:0 !important;margin-right:0 !important}.navbar .navbar-collapse{order:20}.navbar #quarto-search{order:4;margin-left:auto}.navbar .navbar-toggler{margin-right:.5em}.navbar-logo{max-height:24px;width:auto;padding-right:4px}nav .nav-item:not(.compact){padding-top:1px}nav .nav-link i,nav .dropdown-item i{padding-right:1px}.navbar-expand-lg .navbar-nav .nav-link{padding-left:.6rem;padding-right:.6rem}nav .nav-item.compact .nav-link{padding-left:.5rem;padding-right:.5rem;font-size:1.1rem}.navbar .quarto-navbar-tools{order:3}.navbar .quarto-navbar-tools div.dropdown{display:inline-block}.navbar .quarto-navbar-tools .quarto-navigation-tool{color:#545555}.navbar .quarto-navbar-tools .quarto-navigation-tool:hover{color:#1f4eb6}.navbar-nav .dropdown-menu{min-width:220px;font-size:.9rem}.navbar .navbar-nav .nav-link.dropdown-toggle::after{opacity:.75;vertical-align:.175em}.navbar ul.dropdown-menu{padding-top:0;padding-bottom:0}.navbar .dropdown-header{text-transform:uppercase;font-size:.8rem;padding:0 .5rem}.navbar .dropdown-item{padding:.4rem .5rem}.navbar .dropdown-item>i.bi{margin-left:.1rem;margin-right:.25em}.sidebar #quarto-search{margin-top:-1px}.sidebar #quarto-search svg.aa-SubmitIcon{width:16px;height:16px}.sidebar-navigation a{color:inherit}.sidebar-title{margin-top:.25rem;padding-bottom:.5rem;font-size:1.3rem;line-height:1.6rem;visibility:visible}.sidebar-title>a{font-size:inherit;text-decoration:none}.sidebar-title .sidebar-tools-main{margin-top:-6px}@media(max-width: 991.98px){#quarto-sidebar div.sidebar-header{padding-top:.2em}}.sidebar-header-stacked .sidebar-title{margin-top:.6rem}.sidebar-logo{max-width:90%;padding-bottom:.5rem}.sidebar-logo-link{text-decoration:none}.sidebar-navigation li a{text-decoration:none}.sidebar-navigation .quarto-navigation-tool{opacity:.7;font-size:.875rem}#quarto-sidebar>nav>.sidebar-tools-main{margin-left:14px}.sidebar-tools-main{display:inline-flex;margin-left:0px;order:2}.sidebar-tools-main:not(.tools-wide){vertical-align:middle}.sidebar-navigation .quarto-navigation-tool.dropdown-toggle::after{display:none}.sidebar.sidebar-navigation>*{padding-top:1em}.sidebar-item{margin-bottom:.2em;line-height:1rem;margin-top:.4rem}.sidebar-section{padding-left:.5em;padding-bottom:.2em}.sidebar-item .sidebar-item-container{display:flex;justify-content:space-between;cursor:pointer}.sidebar-item-toggle:hover{cursor:pointer}.sidebar-item .sidebar-item-toggle .bi{font-size:.7rem;text-align:center}.sidebar-item .sidebar-item-toggle .bi-chevron-right::before{transition:transform 200ms ease}.sidebar-item .sidebar-item-toggle[aria-expanded=false] .bi-chevron-right::before{transform:none}.sidebar-item .sidebar-item-toggle[aria-expanded=true] .bi-chevron-right::before{transform:rotate(90deg)}.sidebar-item-text{width:100%}.sidebar-navigation .sidebar-divider{margin-left:0;margin-right:0;margin-top:.5rem;margin-bottom:.5rem}@media(max-width: 991.98px){.quarto-secondary-nav{display:block}.quarto-secondary-nav button.quarto-search-button{padding-right:0em;padding-left:2em}.quarto-secondary-nav button.quarto-btn-toggle{margin-left:-0.75rem;margin-right:.15rem}.quarto-secondary-nav nav.quarto-title-breadcrumbs{display:none}.quarto-secondary-nav nav.quarto-page-breadcrumbs{display:flex;align-items:center;padding-right:1em;margin-left:-0.25em}.quarto-secondary-nav nav.quarto-page-breadcrumbs a{text-decoration:none}.quarto-secondary-nav nav.quarto-page-breadcrumbs ol.breadcrumb{margin-bottom:0}}@media(min-width: 992px){.quarto-secondary-nav{display:none}}.quarto-title-breadcrumbs .breadcrumb{margin-bottom:.5em;font-size:.9rem}.quarto-title-breadcrumbs .breadcrumb li:last-of-type a{color:#6c757d}.quarto-secondary-nav .quarto-btn-toggle{color:#595959}.quarto-secondary-nav[aria-expanded=false] .quarto-btn-toggle .bi-chevron-right::before{transform:none}.quarto-secondary-nav[aria-expanded=true] .quarto-btn-toggle .bi-chevron-right::before{transform:rotate(90deg)}.quarto-secondary-nav .quarto-btn-toggle .bi-chevron-right::before{transition:transform 200ms ease}.quarto-secondary-nav{cursor:pointer}.no-decor{text-decoration:none}.quarto-secondary-nav-title{margin-top:.3em;color:#595959;padding-top:4px}.quarto-secondary-nav nav.quarto-page-breadcrumbs{color:#595959}.quarto-secondary-nav nav.quarto-page-breadcrumbs a{color:#595959}.quarto-secondary-nav nav.quarto-page-breadcrumbs a:hover{color:rgba(33,81,191,.8)}.quarto-secondary-nav nav.quarto-page-breadcrumbs .breadcrumb-item::before{color:#8c8c8c}.breadcrumb-item{line-height:1.2rem}div.sidebar-item-container{color:#595959}div.sidebar-item-container:hover,div.sidebar-item-container:focus{color:rgba(33,81,191,.8)}div.sidebar-item-container.disabled{color:rgba(89,89,89,.75)}div.sidebar-item-container .active,div.sidebar-item-container .show>.nav-link,div.sidebar-item-container .sidebar-link>code{color:#2151bf}div.sidebar.sidebar-navigation.rollup.quarto-sidebar-toggle-contents,nav.sidebar.sidebar-navigation:not(.rollup){background-color:#fff}.sidebar.sidebar-navigation:not(.rollup){border-right:1px solid #dee2e6 !important}@media(max-width: 991.98px){.sidebar-navigation .sidebar-item a,.nav-page .nav-page-text,.sidebar-navigation{font-size:1rem}.sidebar-navigation ul.sidebar-section.depth1 .sidebar-section-item{font-size:1.1rem}.sidebar-logo{display:none}.sidebar.sidebar-navigation{position:static;border-bottom:1px solid #dee2e6}.sidebar.sidebar-navigation.collapsing{position:fixed;z-index:1000}.sidebar.sidebar-navigation.show{position:fixed;z-index:1000}.sidebar.sidebar-navigation{min-height:100%}nav.quarto-secondary-nav{background-color:#fff;border-bottom:1px solid #dee2e6}.quarto-banner nav.quarto-secondary-nav{background-color:#f8f9fa;color:#545555;border-top:1px solid #dee2e6}.sidebar .sidebar-footer{visibility:visible;padding-top:1rem;position:inherit}.sidebar-tools-collapse{display:block}}#quarto-sidebar{transition:width .15s ease-in}#quarto-sidebar>*{padding-right:1em}@media(max-width: 991.98px){#quarto-sidebar .sidebar-menu-container{white-space:nowrap;min-width:225px}#quarto-sidebar.show{transition:width .15s ease-out}}@media(min-width: 992px){#quarto-sidebar{display:flex;flex-direction:column}.nav-page .nav-page-text,.sidebar-navigation .sidebar-section .sidebar-item{font-size:.875rem}.sidebar-navigation .sidebar-item{font-size:.925rem}.sidebar.sidebar-navigation{display:block;position:sticky}.sidebar-search{width:100%}.sidebar .sidebar-footer{visibility:visible}}@media(max-width: 991.98px){#quarto-sidebar-glass{position:fixed;top:0;bottom:0;left:0;right:0;background-color:rgba(255,255,255,0);transition:background-color .15s ease-in;z-index:-1}#quarto-sidebar-glass.collapsing{z-index:1000}#quarto-sidebar-glass.show{transition:background-color .15s ease-out;background-color:rgba(102,102,102,.4);z-index:1000}}.sidebar .sidebar-footer{padding:.5rem 1rem;align-self:flex-end;color:#6c757d;width:100%}.quarto-page-breadcrumbs .breadcrumb-item+.breadcrumb-item,.quarto-page-breadcrumbs .breadcrumb-item{padding-right:.33em;padding-left:0}.quarto-page-breadcrumbs .breadcrumb-item::before{padding-right:.33em}.quarto-sidebar-footer{font-size:.875em}.sidebar-section .bi-chevron-right{vertical-align:middle}.sidebar-section .bi-chevron-right::before{font-size:.9em}.notransition{-webkit-transition:none !important;-moz-transition:none !important;-o-transition:none !important;transition:none !important}.btn:focus:not(:focus-visible){box-shadow:none}.page-navigation{display:flex;justify-content:space-between}.nav-page{padding-bottom:.75em}.nav-page .bi{font-size:1.8rem;vertical-align:middle}.nav-page .nav-page-text{padding-left:.25em;padding-right:.25em}.nav-page a{color:#6c757d;text-decoration:none;display:flex;align-items:center}.nav-page a:hover{color:#1f4eb6}.nav-footer .toc-actions{padding-bottom:.5em;padding-top:.5em}.nav-footer .toc-actions a,.nav-footer .toc-actions a:hover{text-decoration:none}.nav-footer .toc-actions ul{display:flex;list-style:none}.nav-footer .toc-actions ul :first-child{margin-left:auto}.nav-footer .toc-actions ul :last-child{margin-right:auto}.nav-footer .toc-actions ul li{padding-right:1.5em}.nav-footer .toc-actions ul li i.bi{padding-right:.4em}.nav-footer .toc-actions ul li:last-of-type{padding-right:0}.nav-footer{display:flex;flex-direction:row;flex-wrap:wrap;justify-content:space-between;align-items:baseline;text-align:center;padding-top:.5rem;padding-bottom:.5rem;background-color:#fff}body.nav-fixed{padding-top:64px}.nav-footer-contents{color:#6c757d;margin-top:.25rem}.nav-footer{min-height:3.5em;color:#757575}.nav-footer a{color:#757575}.nav-footer .nav-footer-left{font-size:.825em}.nav-footer .nav-footer-center{font-size:.825em}.nav-footer .nav-footer-right{font-size:.825em}.nav-footer-left .footer-items,.nav-footer-center .footer-items,.nav-footer-right .footer-items{display:inline-flex;padding-top:.3em;padding-bottom:.3em;margin-bottom:0em}.nav-footer-left .footer-items .nav-link,.nav-footer-center .footer-items .nav-link,.nav-footer-right .footer-items .nav-link{padding-left:.6em;padding-right:.6em}.nav-footer-left{flex:1 1 0px;text-align:left}.nav-footer-right{flex:1 1 0px;text-align:right}.nav-footer-center{flex:1 1 0px;min-height:3em;text-align:center}.nav-footer-center .footer-items{justify-content:center}@media(max-width: 767.98px){.nav-footer-center{margin-top:3em}}.navbar .quarto-reader-toggle.reader .quarto-reader-toggle-btn{background-color:#545555;border-radius:3px}@media(max-width: 991.98px){.quarto-reader-toggle{display:none}}.quarto-reader-toggle.reader.quarto-navigation-tool .quarto-reader-toggle-btn{background-color:#595959;border-radius:3px}.quarto-reader-toggle .quarto-reader-toggle-btn{display:inline-flex;padding-left:.2em;padding-right:.2em;margin-left:-0.2em;margin-right:-0.2em;text-align:center}.navbar .quarto-reader-toggle:not(.reader) .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(84, 85, 85, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.navbar .quarto-reader-toggle.reader .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(248, 249, 250, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.sidebar-navigation .quarto-reader-toggle:not(.reader) .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(89, 89, 89, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.sidebar-navigation .quarto-reader-toggle.reader .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(255, 255, 255, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}#quarto-back-to-top{display:none;position:fixed;bottom:50px;background-color:#fff;border-radius:.25rem;box-shadow:0 .2rem .5rem #6c757d,0 0 .05rem #6c757d;color:#6c757d;text-decoration:none;font-size:.9em;text-align:center;left:50%;padding:.4rem .8rem;transform:translate(-50%, 0)}.aa-DetachedSearchButtonQuery{display:none}.aa-DetachedOverlay ul.aa-List,#quarto-search-results ul.aa-List{list-style:none;padding-left:0}.aa-DetachedOverlay .aa-Panel,#quarto-search-results .aa-Panel{background-color:#fff;position:absolute;z-index:2000}#quarto-search-results .aa-Panel{max-width:400px}#quarto-search input{font-size:.925rem}@media(min-width: 992px){.navbar #quarto-search{margin-left:.25rem;order:999}}.navbar.navbar-expand-sm #quarto-search,.navbar.navbar-expand-md #quarto-search{order:999}@media(min-width: 992px){.navbar .quarto-navbar-tools{margin-left:auto;order:900}}@media(max-width: 991.98px){#quarto-sidebar .sidebar-search{display:none}}#quarto-sidebar .sidebar-search .aa-Autocomplete{width:100%}.navbar .aa-Autocomplete .aa-Form{width:180px}.navbar #quarto-search.type-overlay .aa-Autocomplete{width:40px}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form{background-color:inherit;border:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form:focus-within{box-shadow:none;outline:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-InputWrapper{display:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-InputWrapper:focus-within{display:inherit}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-Label svg,.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-LoadingIndicator svg{width:26px;height:26px;color:#545555;opacity:1}.navbar #quarto-search.type-overlay .aa-Autocomplete svg.aa-SubmitIcon{width:26px;height:26px;color:#545555;opacity:1}.aa-Autocomplete .aa-Form,.aa-DetachedFormContainer .aa-Form{align-items:center;background-color:#fff;border:1px solid #dee2e6;border-radius:.25rem;color:#343a40;display:flex;line-height:1em;margin:0;position:relative;width:100%}.aa-Autocomplete .aa-Form:focus-within,.aa-DetachedFormContainer .aa-Form:focus-within{box-shadow:rgba(39,128,227,.6) 0 0 0 1px;outline:currentColor none medium}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix{align-items:center;display:flex;flex-shrink:0;order:1}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-Label,.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-Label,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator{cursor:initial;flex-shrink:0;padding:0;text-align:left}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-Label svg,.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-Label svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator svg{color:#343a40;opacity:.5}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-SubmitButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-SubmitButton{appearance:none;background:none;border:0;margin:0}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator{align-items:center;display:flex;justify-content:center}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator[hidden]{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapper,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper{order:3;position:relative;width:100%}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input{appearance:none;background:none;border:0;color:#343a40;font:inherit;height:calc(1.5em + .1rem + 2px);padding:0;width:100%}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::placeholder,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::placeholder{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input:focus{border-color:none;box-shadow:none;outline:none}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-decoration,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-cancel-button,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-button,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-decoration,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-decoration,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-cancel-button,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-button,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-decoration{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix{align-items:center;display:flex;order:4}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton{align-items:center;background:none;border:0;color:#343a40;opacity:.8;cursor:pointer;display:flex;margin:0;width:calc(1.5em + .1rem + 2px)}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:hover,.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:hover,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:focus{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton[hidden]{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton svg{width:calc(1.5em + 0.75rem + calc(1px * 2))}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton{border:none;align-items:center;background:none;color:#343a40;opacity:.4;font-size:.7rem;cursor:pointer;display:none;margin:0;width:calc(1em + .1rem + 2px)}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:hover,.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:hover,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:focus{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton[hidden]{display:none}.aa-PanelLayout:empty{display:none}.quarto-search-no-results.no-query{display:none}.aa-Source:has(.no-query){display:none}#quarto-search-results .aa-Panel{border:solid #dee2e6 1px}#quarto-search-results .aa-SourceNoResults{width:398px}.aa-DetachedOverlay .aa-Panel,#quarto-search-results .aa-Panel{max-height:65vh;overflow-y:auto;font-size:.925rem}.aa-DetachedOverlay .aa-SourceNoResults,#quarto-search-results .aa-SourceNoResults{height:60px;display:flex;justify-content:center;align-items:center}.aa-DetachedOverlay .search-error,#quarto-search-results .search-error{padding-top:10px;padding-left:20px;padding-right:20px;cursor:default}.aa-DetachedOverlay .search-error .search-error-title,#quarto-search-results .search-error .search-error-title{font-size:1.1rem;margin-bottom:.5rem}.aa-DetachedOverlay .search-error .search-error-title .search-error-icon,#quarto-search-results .search-error .search-error-title .search-error-icon{margin-right:8px}.aa-DetachedOverlay .search-error .search-error-text,#quarto-search-results .search-error .search-error-text{font-weight:300}.aa-DetachedOverlay .search-result-text,#quarto-search-results .search-result-text{font-weight:300;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical;line-height:1.2rem;max-height:2.4rem}.aa-DetachedOverlay .aa-SourceHeader .search-result-header,#quarto-search-results .aa-SourceHeader .search-result-header{font-size:.875rem;background-color:#f2f2f2;padding-left:14px;padding-bottom:4px;padding-top:4px}.aa-DetachedOverlay .aa-SourceHeader .search-result-header-no-results,#quarto-search-results .aa-SourceHeader .search-result-header-no-results{display:none}.aa-DetachedOverlay .aa-SourceFooter .algolia-search-logo,#quarto-search-results .aa-SourceFooter .algolia-search-logo{width:110px;opacity:.85;margin:8px;float:right}.aa-DetachedOverlay .search-result-section,#quarto-search-results .search-result-section{font-size:.925em}.aa-DetachedOverlay a.search-result-link,#quarto-search-results a.search-result-link{color:inherit;text-decoration:none}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item,#quarto-search-results li.aa-Item[aria-selected=true] .search-item{background-color:#2780e3}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item.search-result-more,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-section,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-text,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-title-container,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-text-container,#quarto-search-results li.aa-Item[aria-selected=true] .search-item.search-result-more,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-section,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-text,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-title-container,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-text-container{color:#fff;background-color:#2780e3}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item mark.search-match,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-match.mark,#quarto-search-results li.aa-Item[aria-selected=true] .search-item mark.search-match,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-match.mark{color:#fff;background-color:#4b95e8}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item,#quarto-search-results li.aa-Item[aria-selected=false] .search-item{background-color:#fff}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item.search-result-more,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-section,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-text,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-title-container,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-text-container,#quarto-search-results li.aa-Item[aria-selected=false] .search-item.search-result-more,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-section,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-text,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-title-container,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-text-container{color:#343a40}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item mark.search-match,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-match.mark,#quarto-search-results li.aa-Item[aria-selected=false] .search-item mark.search-match,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-match.mark{color:inherit;background-color:#e5effc}.aa-DetachedOverlay .aa-Item .search-result-doc:not(.document-selectable) .search-result-title-container,#quarto-search-results .aa-Item .search-result-doc:not(.document-selectable) .search-result-title-container{background-color:#fff;color:#343a40}.aa-DetachedOverlay .aa-Item .search-result-doc:not(.document-selectable) .search-result-text-container,#quarto-search-results .aa-Item .search-result-doc:not(.document-selectable) .search-result-text-container{padding-top:0px}.aa-DetachedOverlay li.aa-Item .search-result-doc.document-selectable .search-result-text-container,#quarto-search-results li.aa-Item .search-result-doc.document-selectable .search-result-text-container{margin-top:-4px}.aa-DetachedOverlay .aa-Item,#quarto-search-results .aa-Item{cursor:pointer}.aa-DetachedOverlay .aa-Item .search-item,#quarto-search-results .aa-Item .search-item{border-left:none;border-right:none;border-top:none;background-color:#fff;border-color:#dee2e6;color:#343a40}.aa-DetachedOverlay .aa-Item .search-item p,#quarto-search-results .aa-Item .search-item p{margin-top:0;margin-bottom:0}.aa-DetachedOverlay .aa-Item .search-item i.bi,#quarto-search-results .aa-Item .search-item i.bi{padding-left:8px;padding-right:8px;font-size:1.3em}.aa-DetachedOverlay .aa-Item .search-item .search-result-title,#quarto-search-results .aa-Item .search-item .search-result-title{margin-top:.3em;margin-bottom:0em}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs,#quarto-search-results .aa-Item .search-item .search-result-crumbs{white-space:nowrap;text-overflow:ellipsis;font-size:.8em;font-weight:300;margin-right:1em}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs:not(.search-result-crumbs-wrap),#quarto-search-results .aa-Item .search-item .search-result-crumbs:not(.search-result-crumbs-wrap){max-width:30%;margin-left:auto;margin-top:.5em;margin-bottom:.1rem}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs.search-result-crumbs-wrap,#quarto-search-results .aa-Item .search-item .search-result-crumbs.search-result-crumbs-wrap{flex-basis:100%;margin-top:0em;margin-bottom:.2em;margin-left:37px}.aa-DetachedOverlay .aa-Item .search-result-title-container,#quarto-search-results .aa-Item .search-result-title-container{font-size:1em;display:flex;flex-wrap:wrap;padding:6px 4px 6px 4px}.aa-DetachedOverlay .aa-Item .search-result-text-container,#quarto-search-results .aa-Item .search-result-text-container{padding-bottom:8px;padding-right:8px;margin-left:42px}.aa-DetachedOverlay .aa-Item .search-result-doc-section,.aa-DetachedOverlay .aa-Item .search-result-more,#quarto-search-results .aa-Item .search-result-doc-section,#quarto-search-results .aa-Item .search-result-more{padding-top:8px;padding-bottom:8px;padding-left:44px}.aa-DetachedOverlay .aa-Item .search-result-more,#quarto-search-results .aa-Item .search-result-more{font-size:.8em;font-weight:400}.aa-DetachedOverlay .aa-Item .search-result-doc,#quarto-search-results .aa-Item .search-result-doc{border-top:1px solid #dee2e6}.aa-DetachedSearchButton{background:none;border:none}.aa-DetachedSearchButton .aa-DetachedSearchButtonPlaceholder{display:none}.navbar .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon{color:#545555}.sidebar-tools-collapse #quarto-search,.sidebar-tools-main #quarto-search{display:inline}.sidebar-tools-collapse #quarto-search .aa-Autocomplete,.sidebar-tools-main #quarto-search .aa-Autocomplete{display:inline}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton{padding-left:4px;padding-right:4px}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon{color:#595959}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon .aa-SubmitIcon,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon .aa-SubmitIcon{margin-top:-3px}.aa-DetachedContainer{background:rgba(255,255,255,.65);width:90%;bottom:0;box-shadow:rgba(222,226,230,.6) 0 0 0 1px;outline:currentColor none medium;display:flex;flex-direction:column;left:0;margin:0;overflow:hidden;padding:0;position:fixed;right:0;top:0;z-index:1101}.aa-DetachedContainer::after{height:32px}.aa-DetachedContainer .aa-SourceHeader{margin:var(--aa-spacing-half) 0 var(--aa-spacing-half) 2px}.aa-DetachedContainer .aa-Panel{background-color:#fff;border-radius:0;box-shadow:none;flex-grow:1;margin:0;padding:0;position:relative}.aa-DetachedContainer .aa-PanelLayout{bottom:0;box-shadow:none;left:0;margin:0;max-height:none;overflow-y:auto;position:absolute;right:0;top:0;width:100%}.aa-DetachedFormContainer{background-color:#fff;border-bottom:1px solid #dee2e6;display:flex;flex-direction:row;justify-content:space-between;margin:0;padding:.5em}.aa-DetachedCancelButton{background:none;font-size:.8em;border:0;border-radius:3px;color:#343a40;cursor:pointer;margin:0 0 0 .5em;padding:0 .5em}.aa-DetachedCancelButton:hover,.aa-DetachedCancelButton:focus{box-shadow:rgba(39,128,227,.6) 0 0 0 1px;outline:currentColor none medium}.aa-DetachedContainer--modal{bottom:inherit;height:auto;margin:0 auto;position:absolute;top:100px;border-radius:6px;max-width:850px}@media(max-width: 575.98px){.aa-DetachedContainer--modal{width:100%;top:0px;border-radius:0px;border:none}}.aa-DetachedContainer--modal .aa-PanelLayout{max-height:var(--aa-detached-modal-max-height);padding-bottom:var(--aa-spacing-half);position:static}.aa-Detached{height:100vh;overflow:hidden}.aa-DetachedOverlay{background-color:rgba(52,58,64,.4);position:fixed;left:0;right:0;top:0;margin:0;padding:0;height:100vh;z-index:1100}.quarto-dashboard.nav-fixed.dashboard-sidebar #quarto-content.quarto-dashboard-content{padding:0em}.quarto-dashboard #quarto-content.quarto-dashboard-content{padding:1em}.quarto-dashboard #quarto-content.quarto-dashboard-content>*{padding-top:0}@media(min-width: 576px){.quarto-dashboard{height:100%}}.quarto-dashboard .card.valuebox.bslib-card.bg-primary{background-color:#5397e9 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-secondary{background-color:#343a40 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-success{background-color:#3aa716 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-info{background-color:rgba(153,84,187,.7019607843) !important}.quarto-dashboard .card.valuebox.bslib-card.bg-warning{background-color:#fa6400 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-danger{background-color:rgba(255,0,57,.7019607843) !important}.quarto-dashboard .card.valuebox.bslib-card.bg-light{background-color:#f8f9fa !important}.quarto-dashboard .card.valuebox.bslib-card.bg-dark{background-color:#343a40 !important}.quarto-dashboard.dashboard-fill{display:flex;flex-direction:column}.quarto-dashboard #quarto-appendix{display:none}.quarto-dashboard #quarto-header #quarto-dashboard-header{border-top:solid 1px #dae0e5;border-bottom:solid 1px #dae0e5}.quarto-dashboard #quarto-header #quarto-dashboard-header>nav{padding-left:1em;padding-right:1em}.quarto-dashboard #quarto-header #quarto-dashboard-header>nav .navbar-brand-container{padding-left:0}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-toggler{margin-right:0}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-toggler-icon{height:1em;width:1em;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgb(84, 85, 85)" class="bi bi-menu-button-wide" viewBox="0 0 16 16"><path d="M0 1.5A1.5 1.5 0 0 1 1.5 0h13A1.5 1.5 0 0 1 16 1.5v2A1.5 1.5 0 0 1 14.5 5h-13A1.5 1.5 0 0 1 0 3.5v-2zM1.5 1a.5.5 0 0 0-.5.5v2a.5.5 0 0 0 .5.5h13a.5.5 0 0 0 .5-.5v-2a.5.5 0 0 0-.5-.5h-13z"/><path d="M2 2.5a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5zm10.823.323-.396-.396A.25.25 0 0 1 12.604 2h.792a.25.25 0 0 1 .177.427l-.396.396a.25.25 0 0 1-.354 0zM0 8a2 2 0 0 1 2-2h12a2 2 0 0 1 2 2v5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2V8zm1 3v2a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2H1zm14-1V8a1 1 0 0 0-1-1H2a1 1 0 0 0-1 1v2h14zM2 8.5a.5.5 0 0 1 .5-.5h9a.5.5 0 0 1 0 1h-9a.5.5 0 0 1-.5-.5zm0 4a.5.5 0 0 1 .5-.5h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5z"/></svg>')}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-brand-container{padding-right:1em}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-title{font-size:1.1em}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-nav{font-size:.9em}.quarto-dashboard #quarto-dashboard-header .navbar{padding:0}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-container{padding-left:1em}.quarto-dashboard #quarto-dashboard-header .navbar.slim .navbar-brand-container .nav-link,.quarto-dashboard #quarto-dashboard-header .navbar.slim .navbar-nav .nav-link{padding:.7em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-color-scheme-toggle{order:9}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-toggler{margin-left:.5em;order:10}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-nav .nav-link{padding:.5em;height:100%;display:flex;align-items:center}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-nav .active{background-color:#e0e5e9}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-brand-container{padding:.5em .5em .5em 0;display:flex;flex-direction:row;margin-right:2em;align-items:center}@media(max-width: 767.98px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-brand-container{margin-right:auto}}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{align-self:stretch}@media(min-width: 768px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{order:8}}@media(max-width: 767.98px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{order:1000;padding-bottom:.5em}}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse .navbar-nav{align-self:stretch}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title{font-size:1.25em;line-height:1.1em;display:flex;flex-direction:row;flex-wrap:wrap;align-items:baseline}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title .navbar-title-text{margin-right:.4em}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title a{text-decoration:none;color:inherit}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-subtitle,.quarto-dashboard #quarto-dashboard-header .navbar .navbar-author{font-size:.9rem;margin-right:.5em}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-author{margin-left:auto}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-logo{max-height:48px;min-height:30px;object-fit:cover;margin-right:1em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-links{order:9;padding-right:1em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-link-text{margin-left:.25em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-link{padding-right:0em;padding-left:.7em;text-decoration:none;color:#545555}.quarto-dashboard .page-layout-custom .tab-content{padding:0;border:none}.quarto-dashboard-img-contain{height:100%;width:100%;object-fit:contain}@media(max-width: 575.98px){.quarto-dashboard .bslib-grid{grid-template-rows:minmax(1em, max-content) !important}.quarto-dashboard .sidebar-content{height:inherit}.quarto-dashboard .page-layout-custom{min-height:100vh}}.quarto-dashboard.dashboard-toolbar>.page-layout-custom,.quarto-dashboard.dashboard-sidebar>.page-layout-custom{padding:0}.quarto-dashboard .quarto-dashboard-content.quarto-dashboard-pages{padding:0}.quarto-dashboard .callout{margin-bottom:0;margin-top:0}.quarto-dashboard .html-fill-container figure{overflow:hidden}.quarto-dashboard bslib-tooltip .rounded-pill{border:solid #6c757d 1px}.quarto-dashboard bslib-tooltip .rounded-pill .svg{fill:#343a40}.quarto-dashboard .tabset .dashboard-card-no-title .nav-tabs{margin-left:0;margin-right:auto}.quarto-dashboard .tabset .tab-content{border:none}.quarto-dashboard .tabset .card-header .nav-link[role=tab]{margin-top:-6px;padding-top:6px;padding-bottom:6px}.quarto-dashboard .card.valuebox,.quarto-dashboard .card.bslib-value-box{min-height:3rem}.quarto-dashboard .card.valuebox .card-body,.quarto-dashboard .card.bslib-value-box .card-body{padding:0}.quarto-dashboard .bslib-value-box .value-box-value{font-size:clamp(.1em,15cqw,5em)}.quarto-dashboard .bslib-value-box .value-box-showcase .bi{font-size:clamp(.1em,max(18cqw,5.2cqh),5em);text-align:center;height:1em}.quarto-dashboard .bslib-value-box .value-box-showcase .bi::before{vertical-align:1em}.quarto-dashboard .bslib-value-box .value-box-area{margin-top:auto;margin-bottom:auto}.quarto-dashboard .card figure.quarto-float{display:flex;flex-direction:column;align-items:center}.quarto-dashboard .dashboard-scrolling{padding:1em}.quarto-dashboard .full-height{height:100%}.quarto-dashboard .showcase-bottom .value-box-grid{display:grid;grid-template-columns:1fr;grid-template-rows:1fr auto;grid-template-areas:"top" "bottom"}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-showcase i.bi{font-size:4rem}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-area{grid-area:top}.quarto-dashboard .tab-content{margin-bottom:0}.quarto-dashboard .bslib-card .bslib-navs-card-title{justify-content:stretch;align-items:end}.quarto-dashboard .card-header{display:flex;flex-wrap:wrap;justify-content:space-between}.quarto-dashboard .card-header .card-title{display:flex;flex-direction:column;justify-content:center;margin-bottom:0}.quarto-dashboard .tabset .card-toolbar{margin-bottom:1em}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout{border:none;gap:var(--bslib-spacer, 1rem)}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.main{padding:0}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.sidebar{border-radius:.25rem;border:1px solid rgba(0,0,0,.175)}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.collapse-toggle{display:none}@media(max-width: 767.98px){.quarto-dashboard .bslib-grid>.bslib-sidebar-layout{grid-template-columns:1fr;grid-template-rows:max-content 1fr}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.main{grid-column:1;grid-row:2}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout .sidebar{grid-column:1;grid-row:1}}.quarto-dashboard .sidebar-right .sidebar{padding-left:2.5em}.quarto-dashboard .sidebar-right .collapse-toggle{left:2px}.quarto-dashboard .quarto-dashboard .sidebar-right button.collapse-toggle:not(.transitioning){left:unset}.quarto-dashboard aside.sidebar{padding-left:1em;padding-right:1em;background-color:rgba(52,58,64,.25);color:#343a40}.quarto-dashboard .bslib-sidebar-layout>div.main{padding:.7em}.quarto-dashboard .bslib-sidebar-layout button.collapse-toggle{margin-top:.3em}.quarto-dashboard .bslib-sidebar-layout .collapse-toggle{top:0}.quarto-dashboard .bslib-sidebar-layout.sidebar-collapsed:not(.transitioning):not(.sidebar-right) .collapse-toggle{left:2px}.quarto-dashboard .sidebar>section>.h3:first-of-type{margin-top:0em}.quarto-dashboard .sidebar .h3,.quarto-dashboard .sidebar .h4,.quarto-dashboard .sidebar .h5,.quarto-dashboard .sidebar .h6{margin-top:.5em}.quarto-dashboard .sidebar form{flex-direction:column;align-items:start;margin-bottom:1em}.quarto-dashboard .sidebar form div[class*=oi-][class$=-input]{flex-direction:column}.quarto-dashboard .sidebar form[class*=oi-][class$=-toggle]{flex-direction:row-reverse;align-items:center;justify-content:start}.quarto-dashboard .sidebar form input[type=range]{margin-top:.5em;margin-right:.8em;margin-left:1em}.quarto-dashboard .sidebar label{width:fit-content}.quarto-dashboard .sidebar .card-body{margin-bottom:2em}.quarto-dashboard .sidebar .shiny-input-container{margin-bottom:1em}.quarto-dashboard .sidebar .shiny-options-group{margin-top:0}.quarto-dashboard .sidebar .control-label{margin-bottom:.3em}.quarto-dashboard .card .card-body .quarto-layout-row{align-items:stretch}.quarto-dashboard .toolbar{font-size:.9em;display:flex;flex-direction:row;border-top:solid 1px #bcbfc0;padding:1em;flex-wrap:wrap;background-color:rgba(52,58,64,.25)}.quarto-dashboard .toolbar .cell-output-display{display:flex}.quarto-dashboard .toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .toolbar>*:last-child{margin-right:0}.quarto-dashboard .toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .toolbar .shiny-input-container{padding-bottom:0;margin-bottom:0}.quarto-dashboard .toolbar .shiny-input-container>*{flex-shrink:0;flex-grow:0}.quarto-dashboard .toolbar .form-group.shiny-input-container:not([role=group])>label{margin-bottom:0}.quarto-dashboard .toolbar .shiny-input-container.no-baseline{align-items:start;padding-top:6px}.quarto-dashboard .toolbar .shiny-input-container{display:flex;align-items:baseline}.quarto-dashboard .toolbar .shiny-input-container label{padding-right:.4em}.quarto-dashboard .toolbar .shiny-input-container .bslib-input-switch{margin-top:6px}.quarto-dashboard .toolbar input[type=text]{line-height:1;width:inherit}.quarto-dashboard .toolbar .input-daterange{width:inherit}.quarto-dashboard .toolbar .input-daterange input[type=text]{height:2.4em;width:10em}.quarto-dashboard .toolbar .input-daterange .input-group-addon{height:auto;padding:0;margin-left:-5px !important;margin-right:-5px}.quarto-dashboard .toolbar .input-daterange .input-group-addon .input-group-text{padding-top:0;padding-bottom:0;height:100%}.quarto-dashboard .toolbar span.irs.irs--shiny{width:10em}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-line{top:9px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-min,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-max,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-from,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-to,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-single{top:20px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-bar{top:8px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-handle{top:0px}.quarto-dashboard .toolbar .shiny-input-checkboxgroup>label{margin-top:6px}.quarto-dashboard .toolbar .shiny-input-checkboxgroup>.shiny-options-group{margin-top:0;align-items:baseline}.quarto-dashboard .toolbar .shiny-input-radiogroup>label{margin-top:6px}.quarto-dashboard .toolbar .shiny-input-radiogroup>.shiny-options-group{align-items:baseline;margin-top:0}.quarto-dashboard .toolbar .shiny-input-radiogroup>.shiny-options-group>.radio{margin-right:.3em}.quarto-dashboard .toolbar .form-select{padding-top:.2em;padding-bottom:.2em}.quarto-dashboard .toolbar .shiny-input-select{min-width:6em}.quarto-dashboard .toolbar div.checkbox{margin-bottom:0px}.quarto-dashboard .toolbar>.checkbox:first-child{margin-top:6px}.quarto-dashboard .toolbar form{width:fit-content}.quarto-dashboard .toolbar form label{padding-top:.2em;padding-bottom:.2em;width:fit-content}.quarto-dashboard .toolbar form input[type=date]{width:fit-content}.quarto-dashboard .toolbar form input[type=color]{width:3em}.quarto-dashboard .toolbar form button{padding:.4em}.quarto-dashboard .toolbar form select{width:fit-content}.quarto-dashboard .toolbar>*{font-size:.9em;flex-grow:0}.quarto-dashboard .toolbar .shiny-input-container label{margin-bottom:1px}.quarto-dashboard .toolbar-bottom{margin-top:1em;margin-bottom:0 !important;order:2}.quarto-dashboard .quarto-dashboard-content>.dashboard-toolbar-container>.toolbar-content>.tab-content>.tab-pane>*:not(.bslib-sidebar-layout){padding:1em}.quarto-dashboard .quarto-dashboard-content>.dashboard-toolbar-container>.toolbar-content>*:not(.tab-content){padding:1em}.quarto-dashboard .quarto-dashboard-content>.tab-content>.dashboard-page>.dashboard-toolbar-container>.toolbar-content,.quarto-dashboard .quarto-dashboard-content>.tab-content>.dashboard-page:not(.dashboard-sidebar-container)>*:not(.dashboard-toolbar-container){padding:1em}.quarto-dashboard .toolbar-content{padding:0}.quarto-dashboard .quarto-dashboard-content.quarto-dashboard-pages .tab-pane>.dashboard-toolbar-container .toolbar{border-radius:0;margin-bottom:0}.quarto-dashboard .dashboard-toolbar-container.toolbar-toplevel .toolbar{border-bottom:1px solid rgba(0,0,0,.175)}.quarto-dashboard .dashboard-toolbar-container.toolbar-toplevel .toolbar-bottom{margin-top:0}.quarto-dashboard .dashboard-toolbar-container:not(.toolbar-toplevel) .toolbar{margin-bottom:1em;border-top:none;border-radius:.25rem;border:1px solid rgba(0,0,0,.175)}.quarto-dashboard .vega-embed.has-actions details{width:1.7em;height:2em;position:absolute !important;top:0;right:0}.quarto-dashboard .dashboard-toolbar-container{padding:0}.quarto-dashboard .card .card-header p:last-child,.quarto-dashboard .card .card-footer p:last-child{margin-bottom:0}.quarto-dashboard .card .card-body>.h4:first-child{margin-top:0}.quarto-dashboard .card .card-body{z-index:1000}@media(max-width: 767.98px){.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_length,.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_info,.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_paginate{text-align:initial}.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_filter{text-align:right}.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_paginate ul.pagination{justify-content:initial}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center;padding-top:0}.quarto-dashboard .card .card-body .itables .dataTables_wrapper table{flex-shrink:0}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons{margin-bottom:.5em;margin-left:auto;width:fit-content;float:right}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons.btn-group{background:#fff;border:none}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons .btn-secondary{background-color:#fff;background-image:none;border:solid #dee2e6 1px;padding:.2em .7em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons .btn span{font-size:.8em;color:#343a40}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{margin-left:.5em;margin-bottom:.5em;padding-top:0}@media(min-width: 768px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{font-size:.875em}}@media(max-width: 767.98px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{font-size:.8em}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_filter{margin-bottom:.5em;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_filter input[type=search]{padding:1px 5px 1px 5px;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_length{flex-basis:1 1 50%;margin-bottom:.5em;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_length select{padding:.4em 3em .4em .5em;font-size:.875em;margin-left:.2em;margin-right:.2em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate{flex-shrink:0}@media(min-width: 768px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate{margin-left:auto}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate ul.pagination .paginate_button .page-link{font-size:.8em}.quarto-dashboard .card .card-footer{font-size:.9em}.quarto-dashboard .card .card-toolbar{display:flex;flex-grow:1;flex-direction:row;width:100%;flex-wrap:wrap}.quarto-dashboard .card .card-toolbar>*{font-size:.8em;flex-grow:0}.quarto-dashboard .card .card-toolbar>.card-title{font-size:1em;flex-grow:1;align-self:flex-start;margin-top:.1em}.quarto-dashboard .card .card-toolbar .cell-output-display{display:flex}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .card .card-toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card .card-toolbar>*:last-child{margin-right:0}.quarto-dashboard .card .card-toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .card .card-toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .card .card-toolbar form{width:fit-content}.quarto-dashboard .card .card-toolbar form label{padding-top:.2em;padding-bottom:.2em;width:fit-content}.quarto-dashboard .card .card-toolbar form input[type=date]{width:fit-content}.quarto-dashboard .card .card-toolbar form input[type=color]{width:3em}.quarto-dashboard .card .card-toolbar form button{padding:.4em}.quarto-dashboard .card .card-toolbar form select{width:fit-content}.quarto-dashboard .card .card-toolbar .cell-output-display{display:flex}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .card .card-toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card .card-toolbar>*:last-child{margin-right:0}.quarto-dashboard .card .card-toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .card .card-toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:0;margin-bottom:0}.quarto-dashboard .card .card-toolbar .shiny-input-container>*{flex-shrink:0;flex-grow:0}.quarto-dashboard .card .card-toolbar .form-group.shiny-input-container:not([role=group])>label{margin-bottom:0}.quarto-dashboard .card .card-toolbar .shiny-input-container.no-baseline{align-items:start;padding-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-container{display:flex;align-items:baseline}.quarto-dashboard .card .card-toolbar .shiny-input-container label{padding-right:.4em}.quarto-dashboard .card .card-toolbar .shiny-input-container .bslib-input-switch{margin-top:6px}.quarto-dashboard .card .card-toolbar input[type=text]{line-height:1;width:inherit}.quarto-dashboard .card .card-toolbar .input-daterange{width:inherit}.quarto-dashboard .card .card-toolbar .input-daterange input[type=text]{height:2.4em;width:10em}.quarto-dashboard .card .card-toolbar .input-daterange .input-group-addon{height:auto;padding:0;margin-left:-5px !important;margin-right:-5px}.quarto-dashboard .card .card-toolbar .input-daterange .input-group-addon .input-group-text{padding-top:0;padding-bottom:0;height:100%}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny{width:10em}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-line{top:9px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-min,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-max,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-from,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-to,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-single{top:20px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-bar{top:8px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-handle{top:0px}.quarto-dashboard .card .card-toolbar .shiny-input-checkboxgroup>label{margin-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-checkboxgroup>.shiny-options-group{margin-top:0;align-items:baseline}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>label{margin-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>.shiny-options-group{align-items:baseline;margin-top:0}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>.shiny-options-group>.radio{margin-right:.3em}.quarto-dashboard .card .card-toolbar .form-select{padding-top:.2em;padding-bottom:.2em}.quarto-dashboard .card .card-toolbar .shiny-input-select{min-width:6em}.quarto-dashboard .card .card-toolbar div.checkbox{margin-bottom:0px}.quarto-dashboard .card .card-toolbar>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card-body>table>thead{border-top:none}.quarto-dashboard .card-body>.table>:not(caption)>*>*{background-color:#fff}.quarto-listing{padding-bottom:1em}.listing-pagination{padding-top:.5em}ul.pagination{float:right;padding-left:8px;padding-top:.5em}ul.pagination li{padding-right:.75em}ul.pagination li.disabled a,ul.pagination li.active a{color:#fff;text-decoration:none}ul.pagination li:last-of-type{padding-right:0}.listing-actions-group{display:flex}.quarto-listing-filter{margin-bottom:1em;width:200px;margin-left:auto}.quarto-listing-sort{margin-bottom:1em;margin-right:auto;width:auto}.quarto-listing-sort .input-group-text{font-size:.8em}.input-group-text{border-right:none}.quarto-listing-sort select.form-select{font-size:.8em}.listing-no-matching{text-align:center;padding-top:2em;padding-bottom:3em;font-size:1em}#quarto-margin-sidebar .quarto-listing-category{padding-top:0;font-size:1rem}#quarto-margin-sidebar .quarto-listing-category-title{cursor:pointer;font-weight:600;font-size:1rem}.quarto-listing-category .category{cursor:pointer}.quarto-listing-category .category.active{font-weight:600}.quarto-listing-category.category-cloud{display:flex;flex-wrap:wrap;align-items:baseline}.quarto-listing-category.category-cloud .category{padding-right:5px}.quarto-listing-category.category-cloud .category-cloud-1{font-size:.75em}.quarto-listing-category.category-cloud .category-cloud-2{font-size:.95em}.quarto-listing-category.category-cloud .category-cloud-3{font-size:1.15em}.quarto-listing-category.category-cloud .category-cloud-4{font-size:1.35em}.quarto-listing-category.category-cloud .category-cloud-5{font-size:1.55em}.quarto-listing-category.category-cloud .category-cloud-6{font-size:1.75em}.quarto-listing-category.category-cloud .category-cloud-7{font-size:1.95em}.quarto-listing-category.category-cloud .category-cloud-8{font-size:2.15em}.quarto-listing-category.category-cloud .category-cloud-9{font-size:2.35em}.quarto-listing-category.category-cloud .category-cloud-10{font-size:2.55em}.quarto-listing-cols-1{grid-template-columns:repeat(1, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-1{grid-template-columns:repeat(1, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-1{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-2{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-2{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-2{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-3{grid-template-columns:repeat(3, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-3{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-3{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-4{grid-template-columns:repeat(4, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-4{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-4{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-5{grid-template-columns:repeat(5, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-5{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-5{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-6{grid-template-columns:repeat(6, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-6{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-6{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-7{grid-template-columns:repeat(7, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-7{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-7{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-8{grid-template-columns:repeat(8, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-8{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-8{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-9{grid-template-columns:repeat(9, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-9{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-9{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-10{grid-template-columns:repeat(10, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-10{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-10{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-11{grid-template-columns:repeat(11, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-11{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-11{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-12{grid-template-columns:repeat(12, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-12{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-12{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-grid{gap:1.5em}.quarto-grid-item.borderless{border:none}.quarto-grid-item.borderless .listing-categories .listing-category:last-of-type,.quarto-grid-item.borderless .listing-categories .listing-category:first-of-type{padding-left:0}.quarto-grid-item.borderless .listing-categories .listing-category{border:0}.quarto-grid-link{text-decoration:none;color:inherit}.quarto-grid-link:hover{text-decoration:none;color:inherit}.quarto-grid-item h5.title,.quarto-grid-item .title.h5{margin-top:0;margin-bottom:0}.quarto-grid-item .card-footer{display:flex;justify-content:space-between;font-size:.8em}.quarto-grid-item .card-footer p{margin-bottom:0}.quarto-grid-item p.card-img-top{margin-bottom:0}.quarto-grid-item p.card-img-top>img{object-fit:cover}.quarto-grid-item .card-other-values{margin-top:.5em;font-size:.8em}.quarto-grid-item .card-other-values tr{margin-bottom:.5em}.quarto-grid-item .card-other-values tr>td:first-of-type{font-weight:600;padding-right:1em;padding-left:1em;vertical-align:top}.quarto-grid-item div.post-contents{display:flex;flex-direction:column;text-decoration:none;height:100%}.quarto-grid-item .listing-item-img-placeholder{background-color:rgba(52,58,64,.25);flex-shrink:0}.quarto-grid-item .card-attribution{padding-top:1em;display:flex;gap:1em;text-transform:uppercase;color:#6c757d;font-weight:500;flex-grow:10;align-items:flex-end}.quarto-grid-item .description{padding-bottom:1em}.quarto-grid-item .card-attribution .date{align-self:flex-end}.quarto-grid-item .card-attribution.justify{justify-content:space-between}.quarto-grid-item .card-attribution.start{justify-content:flex-start}.quarto-grid-item .card-attribution.end{justify-content:flex-end}.quarto-grid-item .card-title{margin-bottom:.1em}.quarto-grid-item .card-subtitle{padding-top:.25em}.quarto-grid-item .card-text{font-size:.9em}.quarto-grid-item .listing-reading-time{padding-bottom:.25em}.quarto-grid-item .card-text-small{font-size:.8em}.quarto-grid-item .card-subtitle.subtitle{font-size:.9em;font-weight:600;padding-bottom:.5em}.quarto-grid-item .listing-categories{display:flex;flex-wrap:wrap;padding-bottom:5px}.quarto-grid-item .listing-categories .listing-category{color:#6c757d;border:solid 1px #dee2e6;border-radius:.25rem;text-transform:uppercase;font-size:.65em;padding-left:.5em;padding-right:.5em;padding-top:.15em;padding-bottom:.15em;cursor:pointer;margin-right:4px;margin-bottom:4px}.quarto-grid-item.card-right{text-align:right}.quarto-grid-item.card-right .listing-categories{justify-content:flex-end}.quarto-grid-item.card-left{text-align:left}.quarto-grid-item.card-center{text-align:center}.quarto-grid-item.card-center .listing-description{text-align:justify}.quarto-grid-item.card-center .listing-categories{justify-content:center}table.quarto-listing-table td.image{padding:0px}table.quarto-listing-table td.image img{width:100%;max-width:50px;object-fit:contain}table.quarto-listing-table a{text-decoration:none;word-break:keep-all}table.quarto-listing-table th a{color:inherit}table.quarto-listing-table th a.asc:after{margin-bottom:-2px;margin-left:5px;display:inline-block;height:1rem;width:1rem;background-repeat:no-repeat;background-size:1rem 1rem;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-sort-up" viewBox="0 0 16 16"><path d="M3.5 12.5a.5.5 0 0 1-1 0V3.707L1.354 4.854a.5.5 0 1 1-.708-.708l2-1.999.007-.007a.498.498 0 0 1 .7.006l2 2a.5.5 0 1 1-.707.708L3.5 3.707V12.5zm3.5-9a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5zM7.5 6a.5.5 0 0 0 0 1h5a.5.5 0 0 0 0-1h-5zm0 3a.5.5 0 0 0 0 1h3a.5.5 0 0 0 0-1h-3zm0 3a.5.5 0 0 0 0 1h1a.5.5 0 0 0 0-1h-1z"/></svg>');content:""}table.quarto-listing-table th a.desc:after{margin-bottom:-2px;margin-left:5px;display:inline-block;height:1rem;width:1rem;background-repeat:no-repeat;background-size:1rem 1rem;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-sort-down" viewBox="0 0 16 16"><path d="M3.5 2.5a.5.5 0 0 0-1 0v8.793l-1.146-1.147a.5.5 0 0 0-.708.708l2 1.999.007.007a.497.497 0 0 0 .7-.006l2-2a.5.5 0 0 0-.707-.708L3.5 11.293V2.5zm3.5 1a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5zM7.5 6a.5.5 0 0 0 0 1h5a.5.5 0 0 0 0-1h-5zm0 3a.5.5 0 0 0 0 1h3a.5.5 0 0 0 0-1h-3zm0 3a.5.5 0 0 0 0 1h1a.5.5 0 0 0 0-1h-1z"/></svg>');content:""}table.quarto-listing-table.table-hover td{cursor:pointer}.quarto-post.image-left{flex-direction:row}.quarto-post.image-right{flex-direction:row-reverse}@media(max-width: 767.98px){.quarto-post.image-right,.quarto-post.image-left{gap:0em;flex-direction:column}.quarto-post .metadata{padding-bottom:1em;order:2}.quarto-post .body{order:1}.quarto-post .thumbnail{order:3}}.list.quarto-listing-default div:last-of-type{border-bottom:none}@media(min-width: 992px){.quarto-listing-container-default{margin-right:2em}}div.quarto-post{display:flex;gap:2em;margin-bottom:1.5em;border-bottom:1px solid #dee2e6}@media(max-width: 767.98px){div.quarto-post{padding-bottom:1em}}div.quarto-post .metadata{flex-basis:20%;flex-grow:0;margin-top:.2em;flex-shrink:10}div.quarto-post .thumbnail{flex-basis:30%;flex-grow:0;flex-shrink:0}div.quarto-post .thumbnail img{margin-top:.4em;width:100%;object-fit:cover}div.quarto-post .body{flex-basis:45%;flex-grow:1;flex-shrink:0}div.quarto-post .body h3.listing-title,div.quarto-post .body .listing-title.h3{margin-top:0px;margin-bottom:0px;border-bottom:none}div.quarto-post .body .listing-subtitle{font-size:.875em;margin-bottom:.5em;margin-top:.2em}div.quarto-post .body .description{font-size:.9em}div.quarto-post .body pre code{white-space:pre-wrap}div.quarto-post a{color:#343a40;text-decoration:none}div.quarto-post .metadata{display:flex;flex-direction:column;font-size:.8em;font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";flex-basis:33%}div.quarto-post .listing-categories{display:flex;flex-wrap:wrap;padding-bottom:5px}div.quarto-post .listing-categories .listing-category{color:#6c757d;border:solid 1px #dee2e6;border-radius:.25rem;text-transform:uppercase;font-size:.65em;padding-left:.5em;padding-right:.5em;padding-top:.15em;padding-bottom:.15em;cursor:pointer;margin-right:4px;margin-bottom:4px}div.quarto-post .listing-description{margin-bottom:.5em}div.quarto-about-jolla{display:flex !important;flex-direction:column;align-items:center;margin-top:10%;padding-bottom:1em}div.quarto-about-jolla .about-image{object-fit:cover;margin-left:auto;margin-right:auto;margin-bottom:1.5em}div.quarto-about-jolla img.round{border-radius:50%}div.quarto-about-jolla img.rounded{border-radius:10px}div.quarto-about-jolla .quarto-title h1.title,div.quarto-about-jolla .quarto-title .title.h1{text-align:center}div.quarto-about-jolla .quarto-title .description{text-align:center}div.quarto-about-jolla h2,div.quarto-about-jolla .h2{border-bottom:none}div.quarto-about-jolla .about-sep{width:60%}div.quarto-about-jolla main{text-align:center}div.quarto-about-jolla .about-links{display:flex}@media(min-width: 992px){div.quarto-about-jolla .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-jolla .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-jolla .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-jolla .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-jolla .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-jolla .about-link:hover{color:#2761e3}div.quarto-about-jolla .about-link i.bi{margin-right:.15em}div.quarto-about-solana{display:flex !important;flex-direction:column;padding-top:3em !important;padding-bottom:1em}div.quarto-about-solana .about-entity{display:flex !important;align-items:start;justify-content:space-between}@media(min-width: 992px){div.quarto-about-solana .about-entity{flex-direction:row}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity{flex-direction:column-reverse;align-items:center;text-align:center}}div.quarto-about-solana .about-entity .entity-contents{display:flex;flex-direction:column}@media(max-width: 767.98px){div.quarto-about-solana .about-entity .entity-contents{width:100%}}div.quarto-about-solana .about-entity .about-image{object-fit:cover}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-image{margin-bottom:1.5em}}div.quarto-about-solana .about-entity img.round{border-radius:50%}div.quarto-about-solana .about-entity img.rounded{border-radius:10px}div.quarto-about-solana .about-entity .about-links{display:flex;justify-content:left;padding-bottom:1.2em}@media(min-width: 992px){div.quarto-about-solana .about-entity .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-solana .about-entity .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-solana .about-entity .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-solana .about-entity .about-link:hover{color:#2761e3}div.quarto-about-solana .about-entity .about-link i.bi{margin-right:.15em}div.quarto-about-solana .about-contents{padding-right:1.5em;flex-basis:0;flex-grow:1}div.quarto-about-solana .about-contents main.content{margin-top:0}div.quarto-about-solana .about-contents h2,div.quarto-about-solana .about-contents .h2{border-bottom:none}div.quarto-about-trestles{display:flex !important;flex-direction:row;padding-top:3em !important;padding-bottom:1em}@media(max-width: 991.98px){div.quarto-about-trestles{flex-direction:column;padding-top:0em !important}}div.quarto-about-trestles .about-entity{display:flex !important;flex-direction:column;align-items:center;text-align:center;padding-right:1em}@media(min-width: 992px){div.quarto-about-trestles .about-entity{flex:0 0 42%}}div.quarto-about-trestles .about-entity .about-image{object-fit:cover;margin-bottom:1.5em}div.quarto-about-trestles .about-entity img.round{border-radius:50%}div.quarto-about-trestles .about-entity img.rounded{border-radius:10px}div.quarto-about-trestles .about-entity .about-links{display:flex;justify-content:center}@media(min-width: 992px){div.quarto-about-trestles .about-entity .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-trestles .about-entity .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-trestles .about-entity .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-trestles .about-entity .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-trestles .about-entity .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-trestles .about-entity .about-link:hover{color:#2761e3}div.quarto-about-trestles .about-entity .about-link i.bi{margin-right:.15em}div.quarto-about-trestles .about-contents{flex-basis:0;flex-grow:1}div.quarto-about-trestles .about-contents h2,div.quarto-about-trestles .about-contents .h2{border-bottom:none}@media(min-width: 992px){div.quarto-about-trestles .about-contents{border-left:solid 1px #dee2e6;padding-left:1.5em}}div.quarto-about-trestles .about-contents main.content{margin-top:0}div.quarto-about-marquee{padding-bottom:1em}div.quarto-about-marquee .about-contents{display:flex;flex-direction:column}div.quarto-about-marquee .about-image{max-height:550px;margin-bottom:1.5em;object-fit:cover}div.quarto-about-marquee img.round{border-radius:50%}div.quarto-about-marquee img.rounded{border-radius:10px}div.quarto-about-marquee h2,div.quarto-about-marquee .h2{border-bottom:none}div.quarto-about-marquee .about-links{display:flex;justify-content:center;padding-top:1.5em}@media(min-width: 992px){div.quarto-about-marquee .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-marquee .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-marquee .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-marquee .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-marquee .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-marquee .about-link:hover{color:#2761e3}div.quarto-about-marquee .about-link i.bi{margin-right:.15em}@media(min-width: 992px){div.quarto-about-marquee .about-link{border:none}}div.quarto-about-broadside{display:flex;flex-direction:column;padding-bottom:1em}div.quarto-about-broadside .about-main{display:flex !important;padding-top:0 !important}@media(min-width: 992px){div.quarto-about-broadside .about-main{flex-direction:row;align-items:flex-start}}@media(max-width: 991.98px){div.quarto-about-broadside .about-main{flex-direction:column}}@media(max-width: 991.98px){div.quarto-about-broadside .about-main .about-entity{flex-shrink:0;width:100%;height:450px;margin-bottom:1.5em;background-size:cover;background-repeat:no-repeat}}@media(min-width: 992px){div.quarto-about-broadside .about-main .about-entity{flex:0 10 50%;margin-right:1.5em;width:100%;height:100%;background-size:100%;background-repeat:no-repeat}}div.quarto-about-broadside .about-main .about-contents{padding-top:14px;flex:0 0 50%}div.quarto-about-broadside h2,div.quarto-about-broadside .h2{border-bottom:none}div.quarto-about-broadside .about-sep{margin-top:1.5em;width:60%;align-self:center}div.quarto-about-broadside .about-links{display:flex;justify-content:center;column-gap:20px;padding-top:1.5em}@media(min-width: 992px){div.quarto-about-broadside .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-broadside .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-broadside .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-broadside .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-broadside .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-broadside .about-link:hover{color:#2761e3}div.quarto-about-broadside .about-link i.bi{margin-right:.15em}@media(min-width: 992px){div.quarto-about-broadside .about-link{border:none}}.tippy-box[data-theme~=quarto]{background-color:#fff;border:solid 1px #dee2e6;border-radius:.25rem;color:#343a40;font-size:.875rem}.tippy-box[data-theme~=quarto]>.tippy-backdrop{background-color:#fff}.tippy-box[data-theme~=quarto]>.tippy-arrow:after,.tippy-box[data-theme~=quarto]>.tippy-svg-arrow:after{content:"";position:absolute;z-index:-1}.tippy-box[data-theme~=quarto]>.tippy-arrow:after{border-color:rgba(0,0,0,0);border-style:solid}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-6px}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-6px}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-6px}.tippy-box[data-placement^=left]>.tippy-arrow:before{right:-6px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-arrow:before{border-top-color:#fff}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-arrow:after{border-top-color:#dee2e6;border-width:7px 7px 0;top:17px;left:1px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-svg-arrow>svg{top:16px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-svg-arrow:after{top:17px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-arrow:before{border-bottom-color:#fff;bottom:16px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-arrow:after{border-bottom-color:#dee2e6;border-width:0 7px 7px;bottom:17px;left:1px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-svg-arrow>svg{bottom:15px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-svg-arrow:after{bottom:17px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-arrow:before{border-left-color:#fff}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-arrow:after{border-left-color:#dee2e6;border-width:7px 0 7px 7px;left:17px;top:1px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-svg-arrow>svg{left:11px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-svg-arrow:after{left:12px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-arrow:before{border-right-color:#fff;right:16px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-arrow:after{border-width:7px 7px 7px 0;right:17px;top:1px;border-right-color:#dee2e6}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-svg-arrow>svg{right:11px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-svg-arrow:after{right:12px}.tippy-box[data-theme~=quarto]>.tippy-svg-arrow{fill:#343a40}.tippy-box[data-theme~=quarto]>.tippy-svg-arrow:after{background-image:url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMCA2czEuNzk2LS4wMTMgNC42Ny0zLjYxNUM1Ljg1MS45IDYuOTMuMDA2IDggMGMxLjA3LS4wMDYgMi4xNDguODg3IDMuMzQzIDIuMzg1QzE0LjIzMyA2LjAwNSAxNiA2IDE2IDZIMHoiIGZpbGw9InJnYmEoMCwgOCwgMTYsIDAuMikiLz48L3N2Zz4=);background-size:16px 6px;width:16px;height:6px}.top-right{position:absolute;top:1em;right:1em}.visually-hidden{border:0;clip:rect(0 0 0 0);height:auto;margin:0;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap}.hidden{display:none !important}.zindex-bottom{z-index:-1 !important}figure.figure{display:block}.quarto-layout-panel{margin-bottom:1em}.quarto-layout-panel>figure{width:100%}.quarto-layout-panel>figure>figcaption,.quarto-layout-panel>.panel-caption{margin-top:10pt}.quarto-layout-panel>.table-caption{margin-top:0px}.table-caption p{margin-bottom:.5em}.quarto-layout-row{display:flex;flex-direction:row;align-items:flex-start}.quarto-layout-valign-top{align-items:flex-start}.quarto-layout-valign-bottom{align-items:flex-end}.quarto-layout-valign-center{align-items:center}.quarto-layout-cell{position:relative;margin-right:20px}.quarto-layout-cell:last-child{margin-right:0}.quarto-layout-cell figure,.quarto-layout-cell>p{margin:.2em}.quarto-layout-cell img{max-width:100%}.quarto-layout-cell .html-widget{width:100% !important}.quarto-layout-cell div figure p{margin:0}.quarto-layout-cell figure{display:block;margin-inline-start:0;margin-inline-end:0}.quarto-layout-cell table{display:inline-table}.quarto-layout-cell-subref figcaption,figure .quarto-layout-row figure figcaption{text-align:center;font-style:italic}.quarto-figure{position:relative;margin-bottom:1em}.quarto-figure>figure{width:100%;margin-bottom:0}.quarto-figure-left>figure>p,.quarto-figure-left>figure>div{text-align:left}.quarto-figure-center>figure>p,.quarto-figure-center>figure>div{text-align:center}.quarto-figure-right>figure>p,.quarto-figure-right>figure>div{text-align:right}.quarto-figure>figure>div.cell-annotation,.quarto-figure>figure>div code{text-align:left}figure>p:empty{display:none}figure>p:first-child{margin-top:0;margin-bottom:0}figure>figcaption.quarto-float-caption-bottom{margin-bottom:.5em}figure>figcaption.quarto-float-caption-top{margin-top:.5em}div[id^=tbl-]{position:relative}.quarto-figure>.anchorjs-link{position:absolute;top:.6em;right:.5em}div[id^=tbl-]>.anchorjs-link{position:absolute;top:.7em;right:.3em}.quarto-figure:hover>.anchorjs-link,div[id^=tbl-]:hover>.anchorjs-link,h2:hover>.anchorjs-link,.h2:hover>.anchorjs-link,h3:hover>.anchorjs-link,.h3:hover>.anchorjs-link,h4:hover>.anchorjs-link,.h4:hover>.anchorjs-link,h5:hover>.anchorjs-link,.h5:hover>.anchorjs-link,h6:hover>.anchorjs-link,.h6:hover>.anchorjs-link,.reveal-anchorjs-link>.anchorjs-link{opacity:1}#title-block-header{margin-block-end:1rem;position:relative;margin-top:-1px}#title-block-header .abstract{margin-block-start:1rem}#title-block-header .abstract .abstract-title{font-weight:600}#title-block-header a{text-decoration:none}#title-block-header .author,#title-block-header .date,#title-block-header .doi{margin-block-end:.2rem}#title-block-header .quarto-title-block>div{display:flex}#title-block-header .quarto-title-block>div>h1,#title-block-header .quarto-title-block>div>.h1{flex-grow:1}#title-block-header .quarto-title-block>div>button{flex-shrink:0;height:2.25rem;margin-top:0}@media(min-width: 992px){#title-block-header .quarto-title-block>div>button{margin-top:5px}}tr.header>th>p:last-of-type{margin-bottom:0px}table,table.table{margin-top:.5rem;margin-bottom:.5rem}caption,.table-caption{padding-top:.5rem;padding-bottom:.5rem;text-align:center}figure.quarto-float-tbl figcaption.quarto-float-caption-top{margin-top:.5rem;margin-bottom:.25rem;text-align:center}figure.quarto-float-tbl figcaption.quarto-float-caption-bottom{padding-top:.25rem;margin-bottom:.5rem;text-align:center}.utterances{max-width:none;margin-left:-8px}iframe{margin-bottom:1em}details{margin-bottom:1em}details[show]{margin-bottom:0}details>summary{color:#6c757d}details>summary>p:only-child{display:inline}pre.sourceCode,code.sourceCode{position:relative}p code:not(.sourceCode){white-space:pre-wrap}code{white-space:pre}@media print{code{white-space:pre-wrap}}pre>code{display:block}pre>code.sourceCode{white-space:pre}pre>code.sourceCode>span>a:first-child::before{text-decoration:none}pre.code-overflow-wrap>code.sourceCode{white-space:pre-wrap}pre.code-overflow-scroll>code.sourceCode{white-space:pre}code a:any-link{color:inherit;text-decoration:none}code a:hover{color:inherit;text-decoration:underline}ul.task-list{padding-left:1em}[data-tippy-root]{display:inline-block}.tippy-content .footnote-back{display:none}.footnote-back{margin-left:.2em}.tippy-content{overflow-x:auto}.quarto-embedded-source-code{display:none}.quarto-unresolved-ref{font-weight:600}.quarto-cover-image{max-width:35%;float:right;margin-left:30px}.cell-output-display .widget-subarea{margin-bottom:1em}.cell-output-display:not(.no-overflow-x),.knitsql-table:not(.no-overflow-x){overflow-x:auto}.panel-input{margin-bottom:1em}.panel-input>div,.panel-input>div>div{display:inline-block;vertical-align:top;padding-right:12px}.panel-input>p:last-child{margin-bottom:0}.layout-sidebar{margin-bottom:1em}.layout-sidebar .tab-content{border:none}.tab-content>.page-columns.active{display:grid}div.sourceCode>iframe{width:100%;height:300px;margin-bottom:-0.5em}a{text-underline-offset:3px}div.ansi-escaped-output{font-family:monospace;display:block}/*!
+ */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 0, 57;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 52, 58, 64;--bs-primary-text-emphasis: #10335b;--bs-secondary-text-emphasis: #15171a;--bs-success-text-emphasis: #19490a;--bs-info-text-emphasis: #3d224b;--bs-warning-text-emphasis: #662f0a;--bs-danger-text-emphasis: #660017;--bs-light-text-emphasis: #495057;--bs-dark-text-emphasis: #495057;--bs-primary-bg-subtle: #d4e6f9;--bs-secondary-bg-subtle: #d6d8d9;--bs-success-bg-subtle: #d9f0d1;--bs-info-bg-subtle: #ebddf1;--bs-warning-bg-subtle: #ffe3d1;--bs-danger-bg-subtle: #ffccd7;--bs-light-bg-subtle: #fcfcfd;--bs-dark-bg-subtle: #ced4da;--bs-primary-border-subtle: #a9ccf4;--bs-secondary-border-subtle: #aeb0b3;--bs-success-border-subtle: #b2e2a3;--bs-info-border-subtle: #d6bbe4;--bs-warning-border-subtle: #ffc8a3;--bs-danger-border-subtle: #ff99b0;--bs-light-border-subtle: #e9ecef;--bs-dark-border-subtle: #adb5bd;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 0;--bs-font-sans-serif: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0));--bs-root-font-size: 17px;--bs-body-font-family: "Source Sans Pro", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--bs-body-font-size:1rem;--bs-body-font-weight: 400;--bs-body-line-height: 1.5;--bs-body-color: #343a40;--bs-body-color-rgb: 52, 58, 64;--bs-body-bg: #fff;--bs-body-bg-rgb: 255, 255, 255;--bs-emphasis-color: #000;--bs-emphasis-color-rgb: 0, 0, 0;--bs-secondary-color: rgba(52, 58, 64, 0.75);--bs-secondary-color-rgb: 52, 58, 64;--bs-secondary-bg: #e9ecef;--bs-secondary-bg-rgb: 233, 236, 239;--bs-tertiary-color: rgba(52, 58, 64, 0.5);--bs-tertiary-color-rgb: 52, 58, 64;--bs-tertiary-bg: #f8f9fa;--bs-tertiary-bg-rgb: 248, 249, 250;--bs-heading-color: inherit;--bs-link-color: #2761e3;--bs-link-color-rgb: 39, 97, 227;--bs-link-decoration: underline;--bs-link-hover-color: #1f4eb6;--bs-link-hover-color-rgb: 31, 78, 182;--bs-code-color: #7d12ba;--bs-highlight-bg: #ffe3d1;--bs-border-width: 1px;--bs-border-style: solid;--bs-border-color: #dee2e6;--bs-border-color-translucent: rgba(0, 0, 0, 0.175);--bs-border-radius: 0.25rem;--bs-border-radius-sm: 0.2em;--bs-border-radius-lg: 0.5rem;--bs-border-radius-xl: 1rem;--bs-border-radius-xxl: 2rem;--bs-border-radius-2xl: var(--bs-border-radius-xxl);--bs-border-radius-pill: 50rem;--bs-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-box-shadow-sm: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-box-shadow-lg: 0 1rem 3rem rgba(0, 0, 0, 0.175);--bs-box-shadow-inset: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-focus-ring-width: 0.25rem;--bs-focus-ring-opacity: 0.25;--bs-focus-ring-color: rgba(39, 128, 227, 0.25);--bs-form-valid-color: #3fb618;--bs-form-valid-border-color: #3fb618;--bs-form-invalid-color: #ff0039;--bs-form-invalid-border-color: #ff0039}[data-bs-theme=dark]{color-scheme:dark;--bs-body-color: #dee2e6;--bs-body-color-rgb: 222, 226, 230;--bs-body-bg: #212529;--bs-body-bg-rgb: 33, 37, 41;--bs-emphasis-color: #fff;--bs-emphasis-color-rgb: 255, 255, 255;--bs-secondary-color: rgba(222, 226, 230, 0.75);--bs-secondary-color-rgb: 222, 226, 230;--bs-secondary-bg: #343a40;--bs-secondary-bg-rgb: 52, 58, 64;--bs-tertiary-color: rgba(222, 226, 230, 0.5);--bs-tertiary-color-rgb: 222, 226, 230;--bs-tertiary-bg: #2b3035;--bs-tertiary-bg-rgb: 43, 48, 53;--bs-primary-text-emphasis: #7db3ee;--bs-secondary-text-emphasis: #85898c;--bs-success-text-emphasis: #8cd374;--bs-info-text-emphasis: #c298d6;--bs-warning-text-emphasis: #ffac74;--bs-danger-text-emphasis: #ff6688;--bs-light-text-emphasis: #f8f9fa;--bs-dark-text-emphasis: #dee2e6;--bs-primary-bg-subtle: #081a2d;--bs-secondary-bg-subtle: #0a0c0d;--bs-success-bg-subtle: #0d2405;--bs-info-bg-subtle: #1f1125;--bs-warning-bg-subtle: #331705;--bs-danger-bg-subtle: #33000b;--bs-light-bg-subtle: #343a40;--bs-dark-bg-subtle: #1a1d20;--bs-primary-border-subtle: #174d88;--bs-secondary-border-subtle: #1f2326;--bs-success-border-subtle: #266d0e;--bs-info-border-subtle: #5c3270;--bs-warning-border-subtle: #99460e;--bs-danger-border-subtle: #990022;--bs-light-border-subtle: #495057;--bs-dark-border-subtle: #343a40;--bs-heading-color: inherit;--bs-link-color: #7db3ee;--bs-link-hover-color: #97c2f1;--bs-link-color-rgb: 125, 179, 238;--bs-link-hover-color-rgb: 151, 194, 241;--bs-code-color: white;--bs-border-color: #495057;--bs-border-color-translucent: rgba(255, 255, 255, 0.15);--bs-form-valid-color: #8cd374;--bs-form-valid-border-color: #8cd374;--bs-form-invalid-color: #ff6688;--bs-form-invalid-border-color: #ff6688}*,*::before,*::after{box-sizing:border-box}:root{font-size:var(--bs-root-font-size)}body{margin:0;font-family:var(--bs-body-font-family);font-size:var(--bs-body-font-size);font-weight:var(--bs-body-font-weight);line-height:var(--bs-body-line-height);color:var(--bs-body-color);text-align:var(--bs-body-text-align);background-color:var(--bs-body-bg);-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}hr{margin:1rem 0;color:inherit;border:0;border-top:1px solid;opacity:.25}h6,.h6,h5,.h5,h4,.h4,h3,.h3,h2,.h2,h1,.h1{margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color)}h1,.h1{font-size:calc(1.325rem + 0.9vw)}@media(min-width: 1200px){h1,.h1{font-size:2rem}}h2,.h2{font-size:calc(1.29rem + 0.48vw)}@media(min-width: 1200px){h2,.h2{font-size:1.65rem}}h3,.h3{font-size:calc(1.27rem + 0.24vw)}@media(min-width: 1200px){h3,.h3{font-size:1.45rem}}h4,.h4{font-size:1.25rem}h5,.h5{font-size:1.1rem}h6,.h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}abbr[title]{text-decoration:underline dotted;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;-ms-text-decoration:underline dotted;-o-text-decoration:underline dotted;cursor:help;text-decoration-skip-ink:none}address{margin-bottom:1rem;font-style:normal;line-height:inherit}ol,ul{padding-left:2rem}ol,ul,dl{margin-top:0;margin-bottom:1rem}ol ol,ul ul,ol ul,ul ol{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem;padding:.625rem 1.25rem;border-left:.25rem solid #e9ecef}blockquote p:last-child,blockquote ul:last-child,blockquote ol:last-child{margin-bottom:0}b,strong{font-weight:bolder}small,.small{font-size:0.875em}mark,.mark{padding:.1875em;background-color:var(--bs-highlight-bg)}sub,sup{position:relative;font-size:0.75em;line-height:0;vertical-align:baseline}sub{bottom:-0.25em}sup{top:-0.5em}a{color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 1));text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}a:hover{--bs-link-color-rgb: var(--bs-link-hover-color-rgb)}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code,kbd,samp{font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;font-size:1em}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:0.875em;color:#000;background-color:#f8f9fa;padding:.5rem;border:1px solid var(--bs-border-color, #dee2e6)}pre code{background-color:rgba(0,0,0,0);font-size:inherit;color:inherit;word-break:normal}code{font-size:0.875em;color:var(--bs-code-color);background-color:#f8f9fa;padding:.125rem .25rem;word-wrap:break-word}a>code{color:inherit}kbd{padding:.4rem .4rem;font-size:0.875em;color:#fff;background-color:#343a40}kbd kbd{padding:0;font-size:1em}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}caption{padding-top:.5rem;padding-bottom:.5rem;color:rgba(52,58,64,.75);text-align:left}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}select:disabled{opacity:1}[list]:not([type=date]):not([type=datetime-local]):not([type=month]):not([type=week]):not([type=time])::-webkit-calendar-picker-indicator{display:none !important}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{float:left;width:100%;padding:0;margin-bottom:.5rem;font-size:calc(1.275rem + 0.3vw);line-height:inherit}@media(min-width: 1200px){legend{font-size:1.5rem}}legend+*{clear:left}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit;-webkit-appearance:button}output{display:inline-block}iframe{border:0}summary{display:list-item;cursor:pointer}progress{vertical-align:baseline}[hidden]{display:none !important}.lead{font-size:1.25rem;font-weight:300}.display-1{font-size:calc(1.625rem + 4.5vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-1{font-size:5rem}}.display-2{font-size:calc(1.575rem + 3.9vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-2{font-size:4.5rem}}.display-3{font-size:calc(1.525rem + 3.3vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-3{font-size:4rem}}.display-4{font-size:calc(1.475rem + 2.7vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-4{font-size:3.5rem}}.display-5{font-size:calc(1.425rem + 2.1vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-5{font-size:3rem}}.display-6{font-size:calc(1.375rem + 1.5vw);font-weight:300;line-height:1.2}@media(min-width: 1200px){.display-6{font-size:2.5rem}}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;list-style:none}.list-inline-item{display:inline-block}.list-inline-item:not(:last-child){margin-right:.5rem}.initialism{font-size:0.875em;text-transform:uppercase}.blockquote{margin-bottom:1rem;font-size:1.25rem}.blockquote>:last-child{margin-bottom:0}.blockquote-footer{margin-top:-1rem;margin-bottom:1rem;font-size:0.875em;color:#6c757d}.blockquote-footer::before{content:"— "}.img-fluid{max-width:100%;height:auto}.img-thumbnail{padding:.25rem;background-color:#fff;border:1px solid #dee2e6;max-width:100%;height:auto}.figure{display:inline-block}.figure-img{margin-bottom:.5rem;line-height:1}.figure-caption{font-size:0.875em;color:rgba(52,58,64,.75)}.container,.container-fluid,.container-xxl,.container-xl,.container-lg,.container-md,.container-sm{--bs-gutter-x: 1.5rem;--bs-gutter-y: 0;width:100%;padding-right:calc(var(--bs-gutter-x)*.5);padding-left:calc(var(--bs-gutter-x)*.5);margin-right:auto;margin-left:auto}@media(min-width: 576px){.container-sm,.container{max-width:540px}}@media(min-width: 768px){.container-md,.container-sm,.container{max-width:720px}}@media(min-width: 992px){.container-lg,.container-md,.container-sm,.container{max-width:960px}}@media(min-width: 1200px){.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1140px}}@media(min-width: 1400px){.container-xxl,.container-xl,.container-lg,.container-md,.container-sm,.container{max-width:1320px}}:root{--bs-breakpoint-xs: 0;--bs-breakpoint-sm: 576px;--bs-breakpoint-md: 768px;--bs-breakpoint-lg: 992px;--bs-breakpoint-xl: 1200px;--bs-breakpoint-xxl: 1400px}.grid{display:grid;grid-template-rows:repeat(var(--bs-rows, 1), 1fr);grid-template-columns:repeat(var(--bs-columns, 12), 1fr);gap:var(--bs-gap, 1.5rem)}.grid .g-col-1{grid-column:auto/span 1}.grid .g-col-2{grid-column:auto/span 2}.grid .g-col-3{grid-column:auto/span 3}.grid .g-col-4{grid-column:auto/span 4}.grid .g-col-5{grid-column:auto/span 5}.grid .g-col-6{grid-column:auto/span 6}.grid .g-col-7{grid-column:auto/span 7}.grid .g-col-8{grid-column:auto/span 8}.grid .g-col-9{grid-column:auto/span 9}.grid .g-col-10{grid-column:auto/span 10}.grid .g-col-11{grid-column:auto/span 11}.grid .g-col-12{grid-column:auto/span 12}.grid .g-start-1{grid-column-start:1}.grid .g-start-2{grid-column-start:2}.grid .g-start-3{grid-column-start:3}.grid .g-start-4{grid-column-start:4}.grid .g-start-5{grid-column-start:5}.grid .g-start-6{grid-column-start:6}.grid .g-start-7{grid-column-start:7}.grid .g-start-8{grid-column-start:8}.grid .g-start-9{grid-column-start:9}.grid .g-start-10{grid-column-start:10}.grid .g-start-11{grid-column-start:11}@media(min-width: 576px){.grid .g-col-sm-1{grid-column:auto/span 1}.grid .g-col-sm-2{grid-column:auto/span 2}.grid .g-col-sm-3{grid-column:auto/span 3}.grid .g-col-sm-4{grid-column:auto/span 4}.grid .g-col-sm-5{grid-column:auto/span 5}.grid .g-col-sm-6{grid-column:auto/span 6}.grid .g-col-sm-7{grid-column:auto/span 7}.grid .g-col-sm-8{grid-column:auto/span 8}.grid .g-col-sm-9{grid-column:auto/span 9}.grid .g-col-sm-10{grid-column:auto/span 10}.grid .g-col-sm-11{grid-column:auto/span 11}.grid .g-col-sm-12{grid-column:auto/span 12}.grid .g-start-sm-1{grid-column-start:1}.grid .g-start-sm-2{grid-column-start:2}.grid .g-start-sm-3{grid-column-start:3}.grid .g-start-sm-4{grid-column-start:4}.grid .g-start-sm-5{grid-column-start:5}.grid .g-start-sm-6{grid-column-start:6}.grid .g-start-sm-7{grid-column-start:7}.grid .g-start-sm-8{grid-column-start:8}.grid .g-start-sm-9{grid-column-start:9}.grid .g-start-sm-10{grid-column-start:10}.grid .g-start-sm-11{grid-column-start:11}}@media(min-width: 768px){.grid .g-col-md-1{grid-column:auto/span 1}.grid .g-col-md-2{grid-column:auto/span 2}.grid .g-col-md-3{grid-column:auto/span 3}.grid .g-col-md-4{grid-column:auto/span 4}.grid .g-col-md-5{grid-column:auto/span 5}.grid .g-col-md-6{grid-column:auto/span 6}.grid .g-col-md-7{grid-column:auto/span 7}.grid .g-col-md-8{grid-column:auto/span 8}.grid .g-col-md-9{grid-column:auto/span 9}.grid .g-col-md-10{grid-column:auto/span 10}.grid .g-col-md-11{grid-column:auto/span 11}.grid .g-col-md-12{grid-column:auto/span 12}.grid .g-start-md-1{grid-column-start:1}.grid .g-start-md-2{grid-column-start:2}.grid .g-start-md-3{grid-column-start:3}.grid .g-start-md-4{grid-column-start:4}.grid .g-start-md-5{grid-column-start:5}.grid .g-start-md-6{grid-column-start:6}.grid .g-start-md-7{grid-column-start:7}.grid .g-start-md-8{grid-column-start:8}.grid .g-start-md-9{grid-column-start:9}.grid .g-start-md-10{grid-column-start:10}.grid .g-start-md-11{grid-column-start:11}}@media(min-width: 992px){.grid .g-col-lg-1{grid-column:auto/span 1}.grid .g-col-lg-2{grid-column:auto/span 2}.grid .g-col-lg-3{grid-column:auto/span 3}.grid .g-col-lg-4{grid-column:auto/span 4}.grid .g-col-lg-5{grid-column:auto/span 5}.grid .g-col-lg-6{grid-column:auto/span 6}.grid .g-col-lg-7{grid-column:auto/span 7}.grid .g-col-lg-8{grid-column:auto/span 8}.grid .g-col-lg-9{grid-column:auto/span 9}.grid .g-col-lg-10{grid-column:auto/span 10}.grid .g-col-lg-11{grid-column:auto/span 11}.grid .g-col-lg-12{grid-column:auto/span 12}.grid .g-start-lg-1{grid-column-start:1}.grid .g-start-lg-2{grid-column-start:2}.grid .g-start-lg-3{grid-column-start:3}.grid .g-start-lg-4{grid-column-start:4}.grid .g-start-lg-5{grid-column-start:5}.grid .g-start-lg-6{grid-column-start:6}.grid .g-start-lg-7{grid-column-start:7}.grid .g-start-lg-8{grid-column-start:8}.grid .g-start-lg-9{grid-column-start:9}.grid .g-start-lg-10{grid-column-start:10}.grid .g-start-lg-11{grid-column-start:11}}@media(min-width: 1200px){.grid .g-col-xl-1{grid-column:auto/span 1}.grid .g-col-xl-2{grid-column:auto/span 2}.grid .g-col-xl-3{grid-column:auto/span 3}.grid .g-col-xl-4{grid-column:auto/span 4}.grid .g-col-xl-5{grid-column:auto/span 5}.grid .g-col-xl-6{grid-column:auto/span 6}.grid .g-col-xl-7{grid-column:auto/span 7}.grid .g-col-xl-8{grid-column:auto/span 8}.grid .g-col-xl-9{grid-column:auto/span 9}.grid .g-col-xl-10{grid-column:auto/span 10}.grid .g-col-xl-11{grid-column:auto/span 11}.grid .g-col-xl-12{grid-column:auto/span 12}.grid .g-start-xl-1{grid-column-start:1}.grid .g-start-xl-2{grid-column-start:2}.grid .g-start-xl-3{grid-column-start:3}.grid .g-start-xl-4{grid-column-start:4}.grid .g-start-xl-5{grid-column-start:5}.grid .g-start-xl-6{grid-column-start:6}.grid .g-start-xl-7{grid-column-start:7}.grid .g-start-xl-8{grid-column-start:8}.grid .g-start-xl-9{grid-column-start:9}.grid .g-start-xl-10{grid-column-start:10}.grid .g-start-xl-11{grid-column-start:11}}@media(min-width: 1400px){.grid .g-col-xxl-1{grid-column:auto/span 1}.grid .g-col-xxl-2{grid-column:auto/span 2}.grid .g-col-xxl-3{grid-column:auto/span 3}.grid .g-col-xxl-4{grid-column:auto/span 4}.grid .g-col-xxl-5{grid-column:auto/span 5}.grid .g-col-xxl-6{grid-column:auto/span 6}.grid .g-col-xxl-7{grid-column:auto/span 7}.grid .g-col-xxl-8{grid-column:auto/span 8}.grid .g-col-xxl-9{grid-column:auto/span 9}.grid .g-col-xxl-10{grid-column:auto/span 10}.grid .g-col-xxl-11{grid-column:auto/span 11}.grid .g-col-xxl-12{grid-column:auto/span 12}.grid .g-start-xxl-1{grid-column-start:1}.grid .g-start-xxl-2{grid-column-start:2}.grid .g-start-xxl-3{grid-column-start:3}.grid .g-start-xxl-4{grid-column-start:4}.grid .g-start-xxl-5{grid-column-start:5}.grid .g-start-xxl-6{grid-column-start:6}.grid .g-start-xxl-7{grid-column-start:7}.grid .g-start-xxl-8{grid-column-start:8}.grid .g-start-xxl-9{grid-column-start:9}.grid .g-start-xxl-10{grid-column-start:10}.grid .g-start-xxl-11{grid-column-start:11}}.table{--bs-table-color-type: initial;--bs-table-bg-type: initial;--bs-table-color-state: initial;--bs-table-bg-state: initial;--bs-table-color: #343a40;--bs-table-bg: #fff;--bs-table-border-color: #dee2e6;--bs-table-accent-bg: transparent;--bs-table-striped-color: #343a40;--bs-table-striped-bg: rgba(0, 0, 0, 0.05);--bs-table-active-color: #343a40;--bs-table-active-bg: rgba(0, 0, 0, 0.1);--bs-table-hover-color: #343a40;--bs-table-hover-bg: rgba(0, 0, 0, 0.075);width:100%;margin-bottom:1rem;vertical-align:top;border-color:var(--bs-table-border-color)}.table>:not(caption)>*>*{padding:.5rem .5rem;color:var(--bs-table-color-state, var(--bs-table-color-type, var(--bs-table-color)));background-color:var(--bs-table-bg);border-bottom-width:1px;box-shadow:inset 0 0 0 9999px var(--bs-table-bg-state, var(--bs-table-bg-type, var(--bs-table-accent-bg)))}.table>tbody{vertical-align:inherit}.table>thead{vertical-align:bottom}.table-group-divider{border-top:calc(1px*2) solid #b2bac1}.caption-top{caption-side:top}.table-sm>:not(caption)>*>*{padding:.25rem .25rem}.table-bordered>:not(caption)>*{border-width:1px 0}.table-bordered>:not(caption)>*>*{border-width:0 1px}.table-borderless>:not(caption)>*>*{border-bottom-width:0}.table-borderless>:not(:first-child){border-top-width:0}.table-striped>tbody>tr:nth-of-type(odd)>*{--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-striped-columns>:not(caption)>tr>:nth-child(even){--bs-table-color-type: var(--bs-table-striped-color);--bs-table-bg-type: var(--bs-table-striped-bg)}.table-active{--bs-table-color-state: var(--bs-table-active-color);--bs-table-bg-state: var(--bs-table-active-bg)}.table-hover>tbody>tr:hover>*{--bs-table-color-state: var(--bs-table-hover-color);--bs-table-bg-state: var(--bs-table-hover-bg)}.table-primary{--bs-table-color: #000;--bs-table-bg: #d4e6f9;--bs-table-border-color: #bfcfe0;--bs-table-striped-bg: #c9dbed;--bs-table-striped-color: #000;--bs-table-active-bg: #bfcfe0;--bs-table-active-color: #000;--bs-table-hover-bg: #c4d5e6;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-secondary{--bs-table-color: #000;--bs-table-bg: #d6d8d9;--bs-table-border-color: #c1c2c3;--bs-table-striped-bg: #cbcdce;--bs-table-striped-color: #000;--bs-table-active-bg: #c1c2c3;--bs-table-active-color: #000;--bs-table-hover-bg: #c6c8c9;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-success{--bs-table-color: #000;--bs-table-bg: #d9f0d1;--bs-table-border-color: #c3d8bc;--bs-table-striped-bg: #cee4c7;--bs-table-striped-color: #000;--bs-table-active-bg: #c3d8bc;--bs-table-active-color: #000;--bs-table-hover-bg: #c9dec1;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-info{--bs-table-color: #000;--bs-table-bg: #ebddf1;--bs-table-border-color: #d4c7d9;--bs-table-striped-bg: #dfd2e5;--bs-table-striped-color: #000;--bs-table-active-bg: #d4c7d9;--bs-table-active-color: #000;--bs-table-hover-bg: #d9ccdf;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-warning{--bs-table-color: #000;--bs-table-bg: #ffe3d1;--bs-table-border-color: #e6ccbc;--bs-table-striped-bg: #f2d8c7;--bs-table-striped-color: #000;--bs-table-active-bg: #e6ccbc;--bs-table-active-color: #000;--bs-table-hover-bg: #ecd2c1;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-danger{--bs-table-color: #000;--bs-table-bg: #ffccd7;--bs-table-border-color: #e6b8c2;--bs-table-striped-bg: #f2c2cc;--bs-table-striped-color: #000;--bs-table-active-bg: #e6b8c2;--bs-table-active-color: #000;--bs-table-hover-bg: #ecbdc7;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-light{--bs-table-color: #000;--bs-table-bg: #f8f9fa;--bs-table-border-color: #dfe0e1;--bs-table-striped-bg: #ecedee;--bs-table-striped-color: #000;--bs-table-active-bg: #dfe0e1;--bs-table-active-color: #000;--bs-table-hover-bg: #e5e6e7;--bs-table-hover-color: #000;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-dark{--bs-table-color: #fff;--bs-table-bg: #343a40;--bs-table-border-color: #484e53;--bs-table-striped-bg: #3e444a;--bs-table-striped-color: #fff;--bs-table-active-bg: #484e53;--bs-table-active-color: #fff;--bs-table-hover-bg: #43494e;--bs-table-hover-color: #fff;color:var(--bs-table-color);border-color:var(--bs-table-border-color)}.table-responsive{overflow-x:auto;-webkit-overflow-scrolling:touch}@media(max-width: 575.98px){.table-responsive-sm{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 767.98px){.table-responsive-md{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 991.98px){.table-responsive-lg{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 1199.98px){.table-responsive-xl{overflow-x:auto;-webkit-overflow-scrolling:touch}}@media(max-width: 1399.98px){.table-responsive-xxl{overflow-x:auto;-webkit-overflow-scrolling:touch}}.form-label,.shiny-input-container .control-label{margin-bottom:.5rem}.col-form-label{padding-top:calc(0.375rem + 1px);padding-bottom:calc(0.375rem + 1px);margin-bottom:0;font-size:inherit;line-height:1.5}.col-form-label-lg{padding-top:calc(0.5rem + 1px);padding-bottom:calc(0.5rem + 1px);font-size:1.25rem}.col-form-label-sm{padding-top:calc(0.25rem + 1px);padding-bottom:calc(0.25rem + 1px);font-size:0.875rem}.form-text{margin-top:.25rem;font-size:0.875em;color:rgba(52,58,64,.75)}.form-control{display:block;width:100%;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#fff;background-clip:padding-box;border:1px solid #dee2e6;border-radius:0;transition:border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-control{transition:none}}.form-control[type=file]{overflow:hidden}.form-control[type=file]:not(:disabled):not([readonly]){cursor:pointer}.form-control:focus{color:#343a40;background-color:#fff;border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-control::-webkit-date-and-time-value{min-width:85px;height:1.5em;margin:0}.form-control::-webkit-datetime-edit{display:block;padding:0}.form-control::placeholder{color:rgba(52,58,64,.75);opacity:1}.form-control:disabled{background-color:#e9ecef;opacity:1}.form-control::file-selector-button{padding:.375rem .75rem;margin:-0.375rem -0.75rem;margin-inline-end:.75rem;color:#343a40;background-color:#f8f9fa;pointer-events:none;border-color:inherit;border-style:solid;border-width:0;border-inline-end-width:1px;border-radius:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-control::file-selector-button{transition:none}}.form-control:hover:not(:disabled):not([readonly])::file-selector-button{background-color:#e9ecef}.form-control-plaintext{display:block;width:100%;padding:.375rem 0;margin-bottom:0;line-height:1.5;color:#343a40;background-color:rgba(0,0,0,0);border:solid rgba(0,0,0,0);border-width:1px 0}.form-control-plaintext:focus{outline:0}.form-control-plaintext.form-control-sm,.form-control-plaintext.form-control-lg{padding-right:0;padding-left:0}.form-control-sm{min-height:calc(1.5em + 0.5rem + calc(1px * 2));padding:.25rem .5rem;font-size:0.875rem}.form-control-sm::file-selector-button{padding:.25rem .5rem;margin:-0.25rem -0.5rem;margin-inline-end:.5rem}.form-control-lg{min-height:calc(1.5em + 1rem + calc(1px * 2));padding:.5rem 1rem;font-size:1.25rem}.form-control-lg::file-selector-button{padding:.5rem 1rem;margin:-0.5rem -1rem;margin-inline-end:1rem}textarea.form-control{min-height:calc(1.5em + 0.75rem + calc(1px * 2))}textarea.form-control-sm{min-height:calc(1.5em + 0.5rem + calc(1px * 2))}textarea.form-control-lg{min-height:calc(1.5em + 1rem + calc(1px * 2))}.form-control-color{width:3rem;height:calc(1.5em + 0.75rem + calc(1px * 2));padding:.375rem}.form-control-color:not(:disabled):not([readonly]){cursor:pointer}.form-control-color::-moz-color-swatch{border:0 !important}.form-control-color::-webkit-color-swatch{border:0 !important}.form-control-color.form-control-sm{height:calc(1.5em + 0.5rem + calc(1px * 2))}.form-control-color.form-control-lg{height:calc(1.5em + 1rem + calc(1px * 2))}.form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e");display:block;width:100%;padding:.375rem 2.25rem .375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#fff;background-image:var(--bs-form-select-bg-img),var(--bs-form-select-bg-icon, none);background-repeat:no-repeat;background-position:right .75rem center;background-size:16px 12px;border:1px solid #dee2e6;border-radius:0;transition:border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-select{transition:none}}.form-select:focus{border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-select[multiple],.form-select[size]:not([size="1"]){padding-right:.75rem;background-image:none}.form-select:disabled{background-color:#e9ecef}.form-select:-moz-focusring{color:rgba(0,0,0,0);text-shadow:0 0 0 #343a40}.form-select-sm{padding-top:.25rem;padding-bottom:.25rem;padding-left:.5rem;font-size:0.875rem}.form-select-lg{padding-top:.5rem;padding-bottom:.5rem;padding-left:1rem;font-size:1.25rem}[data-bs-theme=dark] .form-select{--bs-form-select-bg-img: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23dee2e6' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e")}.form-check,.shiny-input-container .checkbox,.shiny-input-container .radio{display:block;min-height:1.5rem;padding-left:0;margin-bottom:.125rem}.form-check .form-check-input,.form-check .shiny-input-container .checkbox input,.form-check .shiny-input-container .radio input,.shiny-input-container .checkbox .form-check-input,.shiny-input-container .checkbox .shiny-input-container .checkbox input,.shiny-input-container .checkbox .shiny-input-container .radio input,.shiny-input-container .radio .form-check-input,.shiny-input-container .radio .shiny-input-container .checkbox input,.shiny-input-container .radio .shiny-input-container .radio input{float:left;margin-left:0}.form-check-reverse{padding-right:0;padding-left:0;text-align:right}.form-check-reverse .form-check-input{float:right;margin-right:0;margin-left:0}.form-check-input,.shiny-input-container .checkbox input,.shiny-input-container .checkbox-inline input,.shiny-input-container .radio input,.shiny-input-container .radio-inline input{--bs-form-check-bg: #fff;width:1em;height:1em;margin-top:.25em;vertical-align:top;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:var(--bs-form-check-bg);background-image:var(--bs-form-check-bg-image);background-repeat:no-repeat;background-position:center;background-size:contain;border:1px solid #dee2e6;print-color-adjust:exact}.form-check-input[type=radio],.shiny-input-container .checkbox input[type=radio],.shiny-input-container .checkbox-inline input[type=radio],.shiny-input-container .radio input[type=radio],.shiny-input-container .radio-inline input[type=radio]{border-radius:50%}.form-check-input:active,.shiny-input-container .checkbox input:active,.shiny-input-container .checkbox-inline input:active,.shiny-input-container .radio input:active,.shiny-input-container .radio-inline input:active{filter:brightness(90%)}.form-check-input:focus,.shiny-input-container .checkbox input:focus,.shiny-input-container .checkbox-inline input:focus,.shiny-input-container .radio input:focus,.shiny-input-container .radio-inline input:focus{border-color:#93c0f1;outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.form-check-input:checked,.shiny-input-container .checkbox input:checked,.shiny-input-container .checkbox-inline input:checked,.shiny-input-container .radio input:checked,.shiny-input-container .radio-inline input:checked{background-color:#2780e3;border-color:#2780e3}.form-check-input:checked[type=checkbox],.shiny-input-container .checkbox input:checked[type=checkbox],.shiny-input-container .checkbox-inline input:checked[type=checkbox],.shiny-input-container .radio input:checked[type=checkbox],.shiny-input-container .radio-inline input:checked[type=checkbox]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/%3e%3c/svg%3e")}.form-check-input:checked[type=radio],.shiny-input-container .checkbox input:checked[type=radio],.shiny-input-container .checkbox-inline input:checked[type=radio],.shiny-input-container .radio input:checked[type=radio],.shiny-input-container .radio-inline input:checked[type=radio]{--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='2' fill='%23fff'/%3e%3c/svg%3e")}.form-check-input[type=checkbox]:indeterminate,.shiny-input-container .checkbox input[type=checkbox]:indeterminate,.shiny-input-container .checkbox-inline input[type=checkbox]:indeterminate,.shiny-input-container .radio input[type=checkbox]:indeterminate,.shiny-input-container .radio-inline input[type=checkbox]:indeterminate{background-color:#2780e3;border-color:#2780e3;--bs-form-check-bg-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'%3e%3cpath fill='none' stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/%3e%3c/svg%3e")}.form-check-input:disabled,.shiny-input-container .checkbox input:disabled,.shiny-input-container .checkbox-inline input:disabled,.shiny-input-container .radio input:disabled,.shiny-input-container .radio-inline input:disabled{pointer-events:none;filter:none;opacity:.5}.form-check-input[disabled]~.form-check-label,.form-check-input[disabled]~span,.form-check-input:disabled~.form-check-label,.form-check-input:disabled~span,.shiny-input-container .checkbox input[disabled]~.form-check-label,.shiny-input-container .checkbox input[disabled]~span,.shiny-input-container .checkbox input:disabled~.form-check-label,.shiny-input-container .checkbox input:disabled~span,.shiny-input-container .checkbox-inline input[disabled]~.form-check-label,.shiny-input-container .checkbox-inline input[disabled]~span,.shiny-input-container .checkbox-inline input:disabled~.form-check-label,.shiny-input-container .checkbox-inline input:disabled~span,.shiny-input-container .radio input[disabled]~.form-check-label,.shiny-input-container .radio input[disabled]~span,.shiny-input-container .radio input:disabled~.form-check-label,.shiny-input-container .radio input:disabled~span,.shiny-input-container .radio-inline input[disabled]~.form-check-label,.shiny-input-container .radio-inline input[disabled]~span,.shiny-input-container .radio-inline input:disabled~.form-check-label,.shiny-input-container .radio-inline input:disabled~span{cursor:default;opacity:.5}.form-check-label,.shiny-input-container .checkbox label,.shiny-input-container .checkbox-inline label,.shiny-input-container .radio label,.shiny-input-container .radio-inline label{cursor:pointer}.form-switch{padding-left:2.5em}.form-switch .form-check-input{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%280, 0, 0, 0.25%29'/%3e%3c/svg%3e");width:2em;margin-left:-2.5em;background-image:var(--bs-form-switch-bg);background-position:left center;transition:background-position .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-switch .form-check-input{transition:none}}.form-switch .form-check-input:focus{--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%2393c0f1'/%3e%3c/svg%3e")}.form-switch .form-check-input:checked{background-position:right center;--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='%23fff'/%3e%3c/svg%3e")}.form-switch.form-check-reverse{padding-right:2.5em;padding-left:0}.form-switch.form-check-reverse .form-check-input{margin-right:-2.5em;margin-left:0}.form-check-inline{display:inline-block;margin-right:1rem}.btn-check{position:absolute;clip:rect(0, 0, 0, 0);pointer-events:none}.btn-check[disabled]+.btn,.btn-check:disabled+.btn{pointer-events:none;filter:none;opacity:.65}[data-bs-theme=dark] .form-switch .form-check-input:not(:checked):not(:focus){--bs-form-switch-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3e%3ccircle r='3' fill='rgba%28255, 255, 255, 0.25%29'/%3e%3c/svg%3e")}.form-range{width:100%;height:1.5rem;padding:0;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:rgba(0,0,0,0)}.form-range:focus{outline:0}.form-range:focus::-webkit-slider-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(39,128,227,.25)}.form-range:focus::-moz-range-thumb{box-shadow:0 0 0 1px #fff,0 0 0 .25rem rgba(39,128,227,.25)}.form-range::-moz-focus-outer{border:0}.form-range::-webkit-slider-thumb{width:1rem;height:1rem;margin-top:-0.25rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#2780e3;border:0;transition:background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-range::-webkit-slider-thumb{transition:none}}.form-range::-webkit-slider-thumb:active{background-color:#bed9f7}.form-range::-webkit-slider-runnable-track{width:100%;height:.5rem;color:rgba(0,0,0,0);cursor:pointer;background-color:#f8f9fa;border-color:rgba(0,0,0,0)}.form-range::-moz-range-thumb{width:1rem;height:1rem;appearance:none;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;-o-appearance:none;background-color:#2780e3;border:0;transition:background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.form-range::-moz-range-thumb{transition:none}}.form-range::-moz-range-thumb:active{background-color:#bed9f7}.form-range::-moz-range-track{width:100%;height:.5rem;color:rgba(0,0,0,0);cursor:pointer;background-color:#f8f9fa;border-color:rgba(0,0,0,0)}.form-range:disabled{pointer-events:none}.form-range:disabled::-webkit-slider-thumb{background-color:rgba(52,58,64,.75)}.form-range:disabled::-moz-range-thumb{background-color:rgba(52,58,64,.75)}.form-floating{position:relative}.form-floating>.form-control,.form-floating>.form-control-plaintext,.form-floating>.form-select{height:calc(3.5rem + calc(1px * 2));min-height:calc(3.5rem + calc(1px * 2));line-height:1.25}.form-floating>label{position:absolute;top:0;left:0;z-index:2;height:100%;padding:1rem .75rem;overflow:hidden;text-align:start;text-overflow:ellipsis;white-space:nowrap;pointer-events:none;border:1px solid rgba(0,0,0,0);transform-origin:0 0;transition:opacity .1s ease-in-out,transform .1s ease-in-out}@media(prefers-reduced-motion: reduce){.form-floating>label{transition:none}}.form-floating>.form-control,.form-floating>.form-control-plaintext{padding:1rem .75rem}.form-floating>.form-control::placeholder,.form-floating>.form-control-plaintext::placeholder{color:rgba(0,0,0,0)}.form-floating>.form-control:focus,.form-floating>.form-control:not(:placeholder-shown),.form-floating>.form-control-plaintext:focus,.form-floating>.form-control-plaintext:not(:placeholder-shown){padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:-webkit-autofill,.form-floating>.form-control-plaintext:-webkit-autofill{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-select{padding-top:1.625rem;padding-bottom:.625rem}.form-floating>.form-control:focus~label,.form-floating>.form-control:not(:placeholder-shown)~label,.form-floating>.form-control-plaintext~label,.form-floating>.form-select~label{color:rgba(var(--bs-body-color-rgb), 0.65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control:focus~label::after,.form-floating>.form-control:not(:placeholder-shown)~label::after,.form-floating>.form-control-plaintext~label::after,.form-floating>.form-select~label::after{position:absolute;inset:1rem .375rem;z-index:-1;height:1.5em;content:"";background-color:#fff}.form-floating>.form-control:-webkit-autofill~label{color:rgba(var(--bs-body-color-rgb), 0.65);transform:scale(0.85) translateY(-0.5rem) translateX(0.15rem)}.form-floating>.form-control-plaintext~label{border-width:1px 0}.form-floating>:disabled~label,.form-floating>.form-control:disabled~label{color:#6c757d}.form-floating>:disabled~label::after,.form-floating>.form-control:disabled~label::after{background-color:#e9ecef}.input-group{position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:stretch;-webkit-align-items:stretch;width:100%}.input-group>.form-control,.input-group>.form-select,.input-group>.form-floating{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;width:1%;min-width:0}.input-group>.form-control:focus,.input-group>.form-select:focus,.input-group>.form-floating:focus-within{z-index:5}.input-group .btn{position:relative;z-index:2}.input-group .btn:focus{z-index:5}.input-group-text{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:.375rem .75rem;font-size:1rem;font-weight:400;line-height:1.5;color:#343a40;text-align:center;white-space:nowrap;background-color:#f8f9fa;border:1px solid #dee2e6}.input-group-lg>.form-control,.input-group-lg>.form-select,.input-group-lg>.input-group-text,.input-group-lg>.btn{padding:.5rem 1rem;font-size:1.25rem}.input-group-sm>.form-control,.input-group-sm>.form-select,.input-group-sm>.input-group-text,.input-group-sm>.btn{padding:.25rem .5rem;font-size:0.875rem}.input-group-lg>.form-select,.input-group-sm>.form-select{padding-right:3rem}.input-group>:not(:first-child):not(.dropdown-menu):not(.valid-tooltip):not(.valid-feedback):not(.invalid-tooltip):not(.invalid-feedback){margin-left:calc(1px*-1)}.valid-feedback{display:none;width:100%;margin-top:.25rem;font-size:0.875em;color:#3fb618}.valid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:0.875rem;color:#fff;background-color:#3fb618}.was-validated :valid~.valid-feedback,.was-validated :valid~.valid-tooltip,.is-valid~.valid-feedback,.is-valid~.valid-tooltip{display:block}.was-validated .form-control:valid,.form-control.is-valid{border-color:#3fb618;padding-right:calc(1.5em + 0.75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233fb618' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(0.375em + 0.1875rem) center;background-size:calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-control:valid:focus,.form-control.is-valid:focus{border-color:#3fb618;box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated textarea.form-control:valid,textarea.form-control.is-valid{padding-right:calc(1.5em + 0.75rem);background-position:top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem)}.was-validated .form-select:valid,.form-select.is-valid{border-color:#3fb618}.was-validated .form-select:valid:not([multiple]):not([size]),.was-validated .form-select:valid:not([multiple])[size="1"],.form-select.is-valid:not([multiple]):not([size]),.form-select.is-valid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3e%3cpath fill='%233fb618' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-select:valid:focus,.form-select.is-valid:focus{border-color:#3fb618;box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated .form-control-color:valid,.form-control-color.is-valid{width:calc(3rem + calc(1.5em + 0.75rem))}.was-validated .form-check-input:valid,.form-check-input.is-valid{border-color:#3fb618}.was-validated .form-check-input:valid:checked,.form-check-input.is-valid:checked{background-color:#3fb618}.was-validated .form-check-input:valid:focus,.form-check-input.is-valid:focus{box-shadow:0 0 0 .25rem rgba(63,182,24,.25)}.was-validated .form-check-input:valid~.form-check-label,.form-check-input.is-valid~.form-check-label{color:#3fb618}.form-check-inline .form-check-input~.valid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):valid,.input-group>.form-control:not(:focus).is-valid,.was-validated .input-group>.form-select:not(:focus):valid,.input-group>.form-select:not(:focus).is-valid,.was-validated .input-group>.form-floating:not(:focus-within):valid,.input-group>.form-floating:not(:focus-within).is-valid{z-index:3}.invalid-feedback{display:none;width:100%;margin-top:.25rem;font-size:0.875em;color:#ff0039}.invalid-tooltip{position:absolute;top:100%;z-index:5;display:none;max-width:100%;padding:.25rem .5rem;margin-top:.1rem;font-size:0.875rem;color:#fff;background-color:#ff0039}.was-validated :invalid~.invalid-feedback,.was-validated :invalid~.invalid-tooltip,.is-invalid~.invalid-feedback,.is-invalid~.invalid-tooltip{display:block}.was-validated .form-control:invalid,.form-control.is-invalid{border-color:#ff0039;padding-right:calc(1.5em + 0.75rem);background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23ff0039'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23ff0039' stroke='none'/%3e%3c/svg%3e");background-repeat:no-repeat;background-position:right calc(0.375em + 0.1875rem) center;background-size:calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-control:invalid:focus,.form-control.is-invalid:focus{border-color:#ff0039;box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated textarea.form-control:invalid,textarea.form-control.is-invalid{padding-right:calc(1.5em + 0.75rem);background-position:top calc(0.375em + 0.1875rem) right calc(0.375em + 0.1875rem)}.was-validated .form-select:invalid,.form-select.is-invalid{border-color:#ff0039}.was-validated .form-select:invalid:not([multiple]):not([size]),.was-validated .form-select:invalid:not([multiple])[size="1"],.form-select.is-invalid:not([multiple]):not([size]),.form-select.is-invalid:not([multiple])[size="1"]{--bs-form-select-bg-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='%23ff0039'%3e%3ccircle cx='6' cy='6' r='4.5'/%3e%3cpath stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/%3e%3ccircle cx='6' cy='8.2' r='.6' fill='%23ff0039' stroke='none'/%3e%3c/svg%3e");padding-right:4.125rem;background-position:right .75rem center,center right 2.25rem;background-size:16px 12px,calc(0.75em + 0.375rem) calc(0.75em + 0.375rem)}.was-validated .form-select:invalid:focus,.form-select.is-invalid:focus{border-color:#ff0039;box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated .form-control-color:invalid,.form-control-color.is-invalid{width:calc(3rem + calc(1.5em + 0.75rem))}.was-validated .form-check-input:invalid,.form-check-input.is-invalid{border-color:#ff0039}.was-validated .form-check-input:invalid:checked,.form-check-input.is-invalid:checked{background-color:#ff0039}.was-validated .form-check-input:invalid:focus,.form-check-input.is-invalid:focus{box-shadow:0 0 0 .25rem rgba(255,0,57,.25)}.was-validated .form-check-input:invalid~.form-check-label,.form-check-input.is-invalid~.form-check-label{color:#ff0039}.form-check-inline .form-check-input~.invalid-feedback{margin-left:.5em}.was-validated .input-group>.form-control:not(:focus):invalid,.input-group>.form-control:not(:focus).is-invalid,.was-validated .input-group>.form-select:not(:focus):invalid,.input-group>.form-select:not(:focus).is-invalid,.was-validated .input-group>.form-floating:not(:focus-within):invalid,.input-group>.form-floating:not(:focus-within).is-invalid{z-index:4}.btn{--bs-btn-padding-x: 0.75rem;--bs-btn-padding-y: 0.375rem;--bs-btn-font-family: ;--bs-btn-font-size:1rem;--bs-btn-font-weight: 400;--bs-btn-line-height: 1.5;--bs-btn-color: #343a40;--bs-btn-bg: transparent;--bs-btn-border-width: 1px;--bs-btn-border-color: transparent;--bs-btn-border-radius: 0.25rem;--bs-btn-hover-border-color: transparent;--bs-btn-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);--bs-btn-disabled-opacity: 0.65;--bs-btn-focus-box-shadow: 0 0 0 0.25rem rgba(var(--bs-btn-focus-shadow-rgb), .5);display:inline-block;padding:var(--bs-btn-padding-y) var(--bs-btn-padding-x);font-family:var(--bs-btn-font-family);font-size:var(--bs-btn-font-size);font-weight:var(--bs-btn-font-weight);line-height:var(--bs-btn-line-height);color:var(--bs-btn-color);text-align:center;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;vertical-align:middle;cursor:pointer;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;border:var(--bs-btn-border-width) solid var(--bs-btn-border-color);background-color:var(--bs-btn-bg);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.btn{transition:none}}.btn:hover{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color)}.btn-check+.btn:hover{color:var(--bs-btn-color);background-color:var(--bs-btn-bg);border-color:var(--bs-btn-border-color)}.btn:focus-visible{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:focus-visible+.btn{border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:checked+.btn,:not(.btn-check)+.btn:active,.btn:first-child:active,.btn.active,.btn.show{color:var(--bs-btn-active-color);background-color:var(--bs-btn-active-bg);border-color:var(--bs-btn-active-border-color)}.btn-check:checked+.btn:focus-visible,:not(.btn-check)+.btn:active:focus-visible,.btn:first-child:active:focus-visible,.btn.active:focus-visible,.btn.show:focus-visible{box-shadow:var(--bs-btn-focus-box-shadow)}.btn:disabled,.btn.disabled,fieldset:disabled .btn{color:var(--bs-btn-disabled-color);pointer-events:none;background-color:var(--bs-btn-disabled-bg);border-color:var(--bs-btn-disabled-border-color);opacity:var(--bs-btn-disabled-opacity)}.btn-default{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3136;--bs-btn-hover-border-color: #2a2e33;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a2e33;--bs-btn-active-border-color: #272c30;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-primary{--bs-btn-color: #fff;--bs-btn-bg: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #216dc1;--bs-btn-hover-border-color: #1f66b6;--bs-btn-focus-shadow-rgb: 71, 147, 231;--bs-btn-active-color: #fff;--bs-btn-active-bg: #1f66b6;--bs-btn-active-border-color: #1d60aa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #2780e3;--bs-btn-disabled-border-color: #2780e3}.btn-secondary{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c3136;--bs-btn-hover-border-color: #2a2e33;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a2e33;--bs-btn-active-border-color: #272c30;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #3fb618;--bs-btn-border-color: #3fb618;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #369b14;--bs-btn-hover-border-color: #329213;--bs-btn-focus-shadow-rgb: 92, 193, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #329213;--bs-btn-active-border-color: #2f8912;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3fb618;--bs-btn-disabled-border-color: #3fb618}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #82479f;--bs-btn-hover-border-color: #7a4396;--bs-btn-focus-shadow-rgb: 168, 110, 197;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a4396;--bs-btn-active-border-color: #733f8c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #9954bb;--bs-btn-disabled-border-color: #9954bb}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d96314;--bs-btn-hover-border-color: #cc5e13;--bs-btn-focus-shadow-rgb: 255, 138, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc5e13;--bs-btn-active-border-color: #bf5812;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #ff7518;--bs-btn-disabled-border-color: #ff7518}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #ff0039;--bs-btn-border-color: #ff0039;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d90030;--bs-btn-hover-border-color: #cc002e;--bs-btn-focus-shadow-rgb: 255, 38, 87;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc002e;--bs-btn-active-border-color: #bf002b;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #ff0039;--bs-btn-disabled-border-color: #ff0039}.btn-light{--bs-btn-color: #000;--bs-btn-bg: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #d3d4d5;--bs-btn-hover-border-color: #c6c7c8;--bs-btn-focus-shadow-rgb: 211, 212, 213;--bs-btn-active-color: #000;--bs-btn-active-bg: #c6c7c8;--bs-btn-active-border-color: #babbbc;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #f8f9fa;--bs-btn-disabled-border-color: #f8f9fa}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #52585d;--bs-btn-hover-border-color: #484e53;--bs-btn-focus-shadow-rgb: 82, 88, 93;--bs-btn-active-color: #fff;--bs-btn-active-bg: #5d6166;--bs-btn-active-border-color: #484e53;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #343a40;--bs-btn-disabled-border-color: #343a40}.btn-outline-default{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #3fb618;--bs-btn-border-color: #3fb618;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #3fb618;--bs-btn-hover-border-color: #3fb618;--bs-btn-focus-shadow-rgb: 63, 182, 24;--bs-btn-active-color: #fff;--bs-btn-active-bg: #3fb618;--bs-btn-active-border-color: #3fb618;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #3fb618;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #3fb618;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #9954bb;--bs-btn-hover-border-color: #9954bb;--bs-btn-focus-shadow-rgb: 153, 84, 187;--bs-btn-active-color: #fff;--bs-btn-active-bg: #9954bb;--bs-btn-active-border-color: #9954bb;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #9954bb;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #9954bb;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-warning{--bs-btn-color: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #ff7518;--bs-btn-hover-border-color: #ff7518;--bs-btn-focus-shadow-rgb: 255, 117, 24;--bs-btn-active-color: #fff;--bs-btn-active-bg: #ff7518;--bs-btn-active-border-color: #ff7518;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ff7518;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff7518;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-danger{--bs-btn-color: #ff0039;--bs-btn-border-color: #ff0039;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #ff0039;--bs-btn-hover-border-color: #ff0039;--bs-btn-focus-shadow-rgb: 255, 0, 57;--bs-btn-active-color: #fff;--bs-btn-active-bg: #ff0039;--bs-btn-active-border-color: #ff0039;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: #2761e3;--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: #1f4eb6;--bs-btn-hover-border-color: transparent;--bs-btn-active-color: #1f4eb6;--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 71, 121, 231;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: 0.5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: 0.5rem}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: 0.25rem;--bs-btn-padding-x: 0.5rem;--bs-btn-font-size:0.875rem;--bs-btn-border-radius: 0.2em}.fade{transition:opacity .15s linear}@media(prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height .2s ease}@media(prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width .35s ease}@media(prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid rgba(0,0,0,0);border-bottom:0;border-left:.3em solid rgba(0,0,0,0)}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: 0.5rem;--bs-dropdown-spacer: 0.125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: #343a40;--bs-dropdown-bg: #fff;--bs-dropdown-border-color: rgba(0, 0, 0, 0.175);--bs-dropdown-border-radius: 0.25rem;--bs-dropdown-border-width: 1px;--bs-dropdown-inner-border-radius: calc(0.25rem - 1px);--bs-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--bs-dropdown-divider-margin-y: 0.5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-dropdown-link-color: #343a40;--bs-dropdown-link-hover-color: #343a40;--bs-dropdown-link-hover-bg: #f8f9fa;--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2780e3;--bs-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: 0.25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: 0.5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media(min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media(min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid rgba(0,0,0,0);border-bottom:.3em solid;border-left:.3em solid rgba(0,0,0,0)}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid rgba(0,0,0,0);border-right:0;border-bottom:.3em solid rgba(0,0,0,0);border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid rgba(0,0,0,0);border-right:.3em solid;border-bottom:.3em solid rgba(0,0,0,0)}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap;background-color:rgba(0,0,0,0);border:0}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: rgba(0, 0, 0, 0.175);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #2780e3;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #2761e3;--bs-nav-link-hover-color: #1f4eb6;--bs-nav-link-disabled-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(39,128,227,.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: 1px;--bs-nav-tabs-border-color: #dee2e6;--bs-nav-tabs-border-radius: 0.25rem;--bs-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--bs-nav-tabs-link-active-color: #000;--bs-nav-tabs-link-active-bg: #fff;--bs-nav-tabs-link-active-border-color: #dee2e6 #dee2e6 #fff;border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1*var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid rgba(0,0,0,0)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1*var(--bs-nav-tabs-border-width))}.nav-pills{--bs-nav-pills-border-radius: 0.25rem;--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #2780e3}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: 0.125rem;--bs-nav-underline-link-active-color: #000;gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid rgba(0,0,0,0)}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 0.5rem;--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) var(--bs-navbar-padding-x)}.navbar>.container,.navbar>.container-fluid,.navbar>.container-sm,.navbar>.container-md,.navbar>.container-lg,.navbar>.container-xl,.navbar>.container-xxl{display:flex;display:-webkit-flex;flex-wrap:inherit;-webkit-flex-wrap:inherit;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between}.navbar-brand{padding-top:var(--bs-navbar-brand-padding-y);padding-bottom:var(--bs-navbar-brand-padding-y);margin-right:var(--bs-navbar-brand-margin-end);font-size:var(--bs-navbar-brand-font-size);color:var(--bs-navbar-brand-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap}.navbar-brand:hover,.navbar-brand:focus{color:var(--bs-navbar-brand-hover-color)}.navbar-nav{--bs-nav-link-padding-x: 0;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-navbar-color);--bs-nav-link-hover-color: var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:rgba(0,0,0,0);border:var(--bs-border-width) solid var(--bs-navbar-toggler-border-color);transition:var(--bs-navbar-toggler-transition)}@media(prefers-reduced-motion: reduce){.navbar-toggler{transition:none}}.navbar-toggler:hover{text-decoration:none}.navbar-toggler:focus{text-decoration:none;outline:0;box-shadow:0 0 0 var(--bs-navbar-toggler-focus-width)}.navbar-toggler-icon{display:inline-block;width:1.5em;height:1.5em;vertical-align:middle;background-image:var(--bs-navbar-toggler-icon-bg);background-repeat:no-repeat;background-position:center;background-size:100%}.navbar-nav-scroll{max-height:var(--bs-scroll-height, 75vh);overflow-y:auto}@media(min-width: 576px){.navbar-expand-sm{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-sm .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-sm .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-sm .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #343a40;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover .popover-arrow{display:block;width:var(--bs-popover-arrow-width);height:var(--bs-popover-arrow-height)}.popover .popover-arrow::before,.popover .popover-arrow::after{position:absolute;display:block;content:"";border-color:rgba(0,0,0,0);border-style:solid;border-width:0}.bs-popover-top>.popover-arrow,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow{bottom:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow{left:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::before,.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-end>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::before{left:0;border-right-color:var(--bs-popover-arrow-border)}.bs-popover-end>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow::after{left:var(--bs-popover-border-width);border-right-color:var(--bs-popover-bg)}.bs-popover-bottom>.popover-arrow,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow{top:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width))}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::before,.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::after{border-width:0 calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height)}.bs-popover-bottom>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::before{top:0;border-bottom-color:var(--bs-popover-arrow-border)}.bs-popover-bottom>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=bottom]>.popover-arrow::after{top:var(--bs-popover-border-width);border-bottom-color:var(--bs-popover-bg)}.bs-popover-bottom .popover-header::before,.bs-popover-auto[data-popper-placement^=bottom] .popover-header::before{position:absolute;top:0;left:50%;display:block;width:var(--bs-popover-arrow-width);margin-left:calc(-0.5*var(--bs-popover-arrow-width));content:"";border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-header-bg)}.bs-popover-start>.popover-arrow,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow{right:calc(-1*(var(--bs-popover-arrow-height)) - var(--bs-popover-border-width));width:var(--bs-popover-arrow-height);height:var(--bs-popover-arrow-width)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::before,.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::after{border-width:calc(var(--bs-popover-arrow-width)*.5) 0 calc(var(--bs-popover-arrow-width)*.5) var(--bs-popover-arrow-height)}.bs-popover-start>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::before{right:0;border-left-color:var(--bs-popover-arrow-border)}.bs-popover-start>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=left]>.popover-arrow::after{right:var(--bs-popover-border-width);border-left-color:var(--bs-popover-bg)}.popover-header{padding:var(--bs-popover-header-padding-y) var(--bs-popover-header-padding-x);margin-bottom:0;font-size:var(--bs-popover-header-font-size);color:var(--bs-popover-header-color);background-color:var(--bs-popover-header-bg);border-bottom:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover-header:empty{display:none}.popover-body{padding:var(--bs-popover-body-padding-y) var(--bs-popover-body-padding-x);color:var(--bs-popover-body-color)}.carousel{position:relative}.carousel.pointer-event{touch-action:pan-y;-webkit-touch-action:pan-y;-moz-touch-action:pan-y;-ms-touch-action:pan-y;-o-touch-action:pan-y}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner::after{display:block;clear:both;content:""}.carousel-item{position:relative;display:none;float:left;width:100%;margin-right:-100%;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden;transition:transform .6s ease-in-out}@media(prefers-reduced-motion: reduce){.carousel-item{transition:none}}.carousel-item.active,.carousel-item-next,.carousel-item-prev{display:block}.carousel-item-next:not(.carousel-item-start),.active.carousel-item-end{transform:translateX(100%)}.carousel-item-prev:not(.carousel-item-end),.active.carousel-item-start{transform:translateX(-100%)}.carousel-fade .carousel-item{opacity:0;transition-property:opacity;transform:none}.carousel-fade .carousel-item.active,.carousel-fade .carousel-item-next.carousel-item-start,.carousel-fade .carousel-item-prev.carousel-item-end{z-index:1;opacity:1}.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{z-index:0;opacity:0;transition:opacity 0s .6s}@media(prefers-reduced-motion: reduce){.carousel-fade .active.carousel-item-start,.carousel-fade .active.carousel-item-end{transition:none}}.carousel-control-prev,.carousel-control-next{position:absolute;top:0;bottom:0;z-index:1;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:center;-webkit-justify-content:center;width:15%;padding:0;color:#fff;text-align:center;background:none;border:0;opacity:.5;transition:opacity .15s ease}@media(prefers-reduced-motion: reduce){.carousel-control-prev,.carousel-control-next{transition:none}}.carousel-control-prev:hover,.carousel-control-prev:focus,.carousel-control-next:hover,.carousel-control-next:focus{color:#fff;text-decoration:none;outline:0;opacity:.9}.carousel-control-prev{left:0}.carousel-control-next{right:0}.carousel-control-prev-icon,.carousel-control-next-icon{display:inline-block;width:2rem;height:2rem;background-repeat:no-repeat;background-position:50%;background-size:100% 100%}.carousel-control-prev-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/%3e%3c/svg%3e")}.carousel-control-next-icon{background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.carousel-indicators{position:absolute;right:0;bottom:0;left:0;z-index:2;display:flex;display:-webkit-flex;justify-content:center;-webkit-justify-content:center;padding:0;margin-right:15%;margin-bottom:1rem;margin-left:15%}.carousel-indicators [data-bs-target]{box-sizing:content-box;flex:0 1 auto;-webkit-flex:0 1 auto;width:30px;height:3px;padding:0;margin-right:3px;margin-left:3px;text-indent:-999px;cursor:pointer;background-color:#fff;background-clip:padding-box;border:0;border-top:10px solid rgba(0,0,0,0);border-bottom:10px solid rgba(0,0,0,0);opacity:.5;transition:opacity .6s ease}@media(prefers-reduced-motion: reduce){.carousel-indicators [data-bs-target]{transition:none}}.carousel-indicators .active{opacity:1}.carousel-caption{position:absolute;right:15%;bottom:1.25rem;left:15%;padding-top:1.25rem;padding-bottom:1.25rem;color:#fff;text-align:center}.carousel-dark .carousel-control-prev-icon,.carousel-dark .carousel-control-next-icon{filter:invert(1) grayscale(100)}.carousel-dark .carousel-indicators [data-bs-target]{background-color:#000}.carousel-dark .carousel-caption{color:#000}[data-bs-theme=dark] .carousel .carousel-control-prev-icon,[data-bs-theme=dark] .carousel .carousel-control-next-icon,[data-bs-theme=dark].carousel .carousel-control-prev-icon,[data-bs-theme=dark].carousel .carousel-control-next-icon{filter:invert(1) grayscale(100)}[data-bs-theme=dark] .carousel .carousel-indicators [data-bs-target],[data-bs-theme=dark].carousel .carousel-indicators [data-bs-target]{background-color:#000}[data-bs-theme=dark] .carousel .carousel-caption,[data-bs-theme=dark].carousel .carousel-caption{color:#000}.spinner-grow,.spinner-border{display:inline-block;width:var(--bs-spinner-width);height:var(--bs-spinner-height);vertical-align:var(--bs-spinner-vertical-align);border-radius:50%;animation:var(--bs-spinner-animation-speed) linear infinite var(--bs-spinner-animation-name)}@keyframes spinner-border{to{transform:rotate(360deg) /* rtl:ignore */}}.spinner-border{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -0.125em;--bs-spinner-border-width: 0.25em;--bs-spinner-animation-speed: 0.75s;--bs-spinner-animation-name: spinner-border;border:var(--bs-spinner-border-width) solid currentcolor;border-right-color:rgba(0,0,0,0)}.spinner-border-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem;--bs-spinner-border-width: 0.2em}@keyframes spinner-grow{0%{transform:scale(0)}50%{opacity:1;transform:none}}.spinner-grow{--bs-spinner-width: 2rem;--bs-spinner-height: 2rem;--bs-spinner-vertical-align: -0.125em;--bs-spinner-animation-speed: 0.75s;--bs-spinner-animation-name: spinner-grow;background-color:currentcolor;opacity:0}.spinner-grow-sm{--bs-spinner-width: 1rem;--bs-spinner-height: 1rem}@media(prefers-reduced-motion: reduce){.spinner-border,.spinner-grow{--bs-spinner-animation-speed: 1.5s}}.offcanvas,.offcanvas-xxl,.offcanvas-xl,.offcanvas-lg,.offcanvas-md,.offcanvas-sm{--bs-offcanvas-zindex: 1045;--bs-offcanvas-width: 400px;--bs-offcanvas-height: 30vh;--bs-offcanvas-padding-x: 1rem;--bs-offcanvas-padding-y: 1rem;--bs-offcanvas-color: #343a40;--bs-offcanvas-bg: #fff;--bs-offcanvas-border-width: 1px;--bs-offcanvas-border-color: rgba(0, 0, 0, 0.175);--bs-offcanvas-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-offcanvas-transition: transform 0.3s ease-in-out;--bs-offcanvas-title-line-height: 1.5}@media(max-width: 575.98px){.offcanvas-sm{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 575.98px)and (prefers-reduced-motion: reduce){.offcanvas-sm{transition:none}}@media(max-width: 575.98px){.offcanvas-sm.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-sm.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-sm.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-sm.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-sm.showing,.offcanvas-sm.show:not(.hiding){transform:none}.offcanvas-sm.showing,.offcanvas-sm.hiding,.offcanvas-sm.show{visibility:visible}}@media(min-width: 576px){.offcanvas-sm{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-sm .offcanvas-header{display:none}.offcanvas-sm .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 767.98px){.offcanvas-md{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 767.98px)and (prefers-reduced-motion: reduce){.offcanvas-md{transition:none}}@media(max-width: 767.98px){.offcanvas-md.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-md.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-md.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-md.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-md.showing,.offcanvas-md.show:not(.hiding){transform:none}.offcanvas-md.showing,.offcanvas-md.hiding,.offcanvas-md.show{visibility:visible}}@media(min-width: 768px){.offcanvas-md{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-md .offcanvas-header{display:none}.offcanvas-md .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 991.98px){.offcanvas-lg{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 991.98px)and (prefers-reduced-motion: reduce){.offcanvas-lg{transition:none}}@media(max-width: 991.98px){.offcanvas-lg.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-lg.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-lg.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-lg.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-lg.showing,.offcanvas-lg.show:not(.hiding){transform:none}.offcanvas-lg.showing,.offcanvas-lg.hiding,.offcanvas-lg.show{visibility:visible}}@media(min-width: 992px){.offcanvas-lg{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-lg .offcanvas-header{display:none}.offcanvas-lg .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 1199.98px){.offcanvas-xl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 1199.98px)and (prefers-reduced-motion: reduce){.offcanvas-xl{transition:none}}@media(max-width: 1199.98px){.offcanvas-xl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xl.showing,.offcanvas-xl.show:not(.hiding){transform:none}.offcanvas-xl.showing,.offcanvas-xl.hiding,.offcanvas-xl.show{visibility:visible}}@media(min-width: 1200px){.offcanvas-xl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-xl .offcanvas-header{display:none}.offcanvas-xl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}@media(max-width: 1399.98px){.offcanvas-xxl{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 1399.98px)and (prefers-reduced-motion: reduce){.offcanvas-xxl{transition:none}}@media(max-width: 1399.98px){.offcanvas-xxl.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-xxl.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-xxl.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas-xxl.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas-xxl.showing,.offcanvas-xxl.show:not(.hiding){transform:none}.offcanvas-xxl.showing,.offcanvas-xxl.hiding,.offcanvas-xxl.show{visibility:visible}}@media(min-width: 1400px){.offcanvas-xxl{--bs-offcanvas-height: auto;--bs-offcanvas-border-width: 0;background-color:rgba(0,0,0,0) !important}.offcanvas-xxl .offcanvas-header{display:none}.offcanvas-xxl .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible;background-color:rgba(0,0,0,0) !important}}.offcanvas{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}@media(prefers-reduced-motion: reduce){.offcanvas{transition:none}}.offcanvas.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(-100%)}.offcanvas.offcanvas-bottom{right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-top:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateY(100%)}.offcanvas.showing,.offcanvas.show:not(.hiding){transform:none}.offcanvas.showing,.offcanvas.hiding,.offcanvas.show{visibility:visible}.offcanvas-backdrop{position:fixed;top:0;left:0;z-index:1040;width:100vw;height:100vh;background-color:#000}.offcanvas-backdrop.fade{opacity:0}.offcanvas-backdrop.show{opacity:.5}.offcanvas-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x)}.offcanvas-header .btn-close{padding:calc(var(--bs-offcanvas-padding-y)*.5) calc(var(--bs-offcanvas-padding-x)*.5);margin-top:calc(-0.5*var(--bs-offcanvas-padding-y));margin-right:calc(-0.5*var(--bs-offcanvas-padding-x));margin-bottom:calc(-0.5*var(--bs-offcanvas-padding-y))}.offcanvas-title{margin-bottom:0;line-height:var(--bs-offcanvas-title-line-height)}.offcanvas-body{flex-grow:1;-webkit-flex-grow:1;padding:var(--bs-offcanvas-padding-y) var(--bs-offcanvas-padding-x);overflow-y:auto}.placeholder{display:inline-block;min-height:1em;vertical-align:middle;cursor:wait;background-color:currentcolor;opacity:.5}.placeholder.btn::before{display:inline-block;content:""}.placeholder-xs{min-height:.6em}.placeholder-sm{min-height:.8em}.placeholder-lg{min-height:1.2em}.placeholder-glow .placeholder{animation:placeholder-glow 2s ease-in-out infinite}@keyframes placeholder-glow{50%{opacity:.2}}.placeholder-wave{mask-image:linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);-webkit-mask-image:linear-gradient(130deg, #000 55%, rgba(0, 0, 0, 0.8) 75%, #000 95%);mask-size:200% 100%;-webkit-mask-size:200% 100%;animation:placeholder-wave 2s linear infinite}@keyframes placeholder-wave{100%{mask-position:-200% 0%;-webkit-mask-position:-200% 0%}}.clearfix::after{display:block;clear:both;content:""}.text-bg-default{color:#fff !important;background-color:RGBA(var(--bs-default-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-primary{color:#fff !important;background-color:RGBA(var(--bs-primary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-secondary{color:#fff !important;background-color:RGBA(var(--bs-secondary-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-success{color:#fff !important;background-color:RGBA(var(--bs-success-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-info{color:#fff !important;background-color:RGBA(var(--bs-info-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-warning{color:#fff !important;background-color:RGBA(var(--bs-warning-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-danger{color:#fff !important;background-color:RGBA(var(--bs-danger-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-light{color:#000 !important;background-color:RGBA(var(--bs-light-rgb), var(--bs-bg-opacity, 1)) !important}.text-bg-dark{color:#fff !important;background-color:RGBA(var(--bs-dark-rgb), var(--bs-bg-opacity, 1)) !important}.link-default{color:RGBA(var(--bs-default-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-default-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-default:hover,.link-default:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-primary{color:RGBA(var(--bs-primary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-primary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-primary:hover,.link-primary:focus{color:RGBA(31, 102, 182, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(31, 102, 182, var(--bs-link-underline-opacity, 1)) !important}.link-secondary{color:RGBA(var(--bs-secondary-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-secondary-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-secondary:hover,.link-secondary:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-success{color:RGBA(var(--bs-success-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-success-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-success:hover,.link-success:focus{color:RGBA(50, 146, 19, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(50, 146, 19, var(--bs-link-underline-opacity, 1)) !important}.link-info{color:RGBA(var(--bs-info-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-info-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-info:hover,.link-info:focus{color:RGBA(122, 67, 150, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(122, 67, 150, var(--bs-link-underline-opacity, 1)) !important}.link-warning{color:RGBA(var(--bs-warning-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-warning-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-warning:hover,.link-warning:focus{color:RGBA(204, 94, 19, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(204, 94, 19, var(--bs-link-underline-opacity, 1)) !important}.link-danger{color:RGBA(var(--bs-danger-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-danger-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-danger:hover,.link-danger:focus{color:RGBA(204, 0, 46, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(204, 0, 46, var(--bs-link-underline-opacity, 1)) !important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis:hover,.link-body-emphasis:focus{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 0.75)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 0.75)) !important}.focus-ring:focus{outline:0;box-shadow:var(--bs-focus-ring-x, 0) var(--bs-focus-ring-y, 0) var(--bs-focus-ring-blur, 0) var(--bs-focus-ring-width) var(--bs-focus-ring-color)}.icon-link{display:inline-flex;gap:.375rem;align-items:center;-webkit-align-items:center;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-opacity, 0.5));text-underline-offset:.25em;backface-visibility:hidden;-webkit-backface-visibility:hidden;-moz-backface-visibility:hidden;-ms-backface-visibility:hidden;-o-backface-visibility:hidden}.icon-link>.bi{flex-shrink:0;-webkit-flex-shrink:0;width:1em;height:1em;fill:currentcolor;transition:.2s ease-in-out transform}@media(prefers-reduced-motion: reduce){.icon-link>.bi{transition:none}}.icon-link-hover:hover>.bi,.icon-link-hover:focus-visible>.bi{transform:var(--bs-icon-link-transform, translate3d(0.25em, 0, 0))}.ratio{position:relative;width:100%}.ratio::before{display:block;padding-top:var(--bs-aspect-ratio);content:""}.ratio>*{position:absolute;top:0;left:0;width:100%;height:100%}.ratio-1x1{--bs-aspect-ratio: 100%}.ratio-4x3{--bs-aspect-ratio: 75%}.ratio-16x9{--bs-aspect-ratio: 56.25%}.ratio-21x9{--bs-aspect-ratio: 42.8571428571%}.fixed-top{position:fixed;top:0;right:0;left:0;z-index:1030}.fixed-bottom{position:fixed;right:0;bottom:0;left:0;z-index:1030}.sticky-top{position:sticky;top:0;z-index:1020}.sticky-bottom{position:sticky;bottom:0;z-index:1020}@media(min-width: 576px){.sticky-sm-top{position:sticky;top:0;z-index:1020}.sticky-sm-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 768px){.sticky-md-top{position:sticky;top:0;z-index:1020}.sticky-md-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 992px){.sticky-lg-top{position:sticky;top:0;z-index:1020}.sticky-lg-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 1200px){.sticky-xl-top{position:sticky;top:0;z-index:1020}.sticky-xl-bottom{position:sticky;bottom:0;z-index:1020}}@media(min-width: 1400px){.sticky-xxl-top{position:sticky;top:0;z-index:1020}.sticky-xxl-bottom{position:sticky;bottom:0;z-index:1020}}.hstack{display:flex;display:-webkit-flex;flex-direction:row;-webkit-flex-direction:row;align-items:center;-webkit-align-items:center;align-self:stretch;-webkit-align-self:stretch}.vstack{display:flex;display:-webkit-flex;flex:1 1 auto;-webkit-flex:1 1 auto;flex-direction:column;-webkit-flex-direction:column;align-self:stretch;-webkit-align-self:stretch}.visually-hidden,.visually-hidden-focusable:not(:focus):not(:focus-within){width:1px !important;height:1px !important;padding:0 !important;margin:-1px !important;overflow:hidden !important;clip:rect(0, 0, 0, 0) !important;white-space:nowrap !important;border:0 !important}.visually-hidden:not(caption),.visually-hidden-focusable:not(:focus):not(:focus-within):not(caption){position:absolute !important}.stretched-link::after{position:absolute;top:0;right:0;bottom:0;left:0;z-index:1;content:""}.text-truncate{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.vr{display:inline-block;align-self:stretch;-webkit-align-self:stretch;width:1px;min-height:1em;background-color:currentcolor;opacity:.25}.align-baseline{vertical-align:baseline !important}.align-top{vertical-align:top !important}.align-middle{vertical-align:middle !important}.align-bottom{vertical-align:bottom !important}.align-text-bottom{vertical-align:text-bottom !important}.align-text-top{vertical-align:text-top !important}.float-start{float:left !important}.float-end{float:right !important}.float-none{float:none !important}.object-fit-contain{object-fit:contain !important}.object-fit-cover{object-fit:cover !important}.object-fit-fill{object-fit:fill !important}.object-fit-scale{object-fit:scale-down !important}.object-fit-none{object-fit:none !important}.opacity-0{opacity:0 !important}.opacity-25{opacity:.25 !important}.opacity-50{opacity:.5 !important}.opacity-75{opacity:.75 !important}.opacity-100{opacity:1 !important}.overflow-auto{overflow:auto !important}.overflow-hidden{overflow:hidden !important}.overflow-visible{overflow:visible !important}.overflow-scroll{overflow:scroll !important}.overflow-x-auto{overflow-x:auto !important}.overflow-x-hidden{overflow-x:hidden !important}.overflow-x-visible{overflow-x:visible !important}.overflow-x-scroll{overflow-x:scroll !important}.overflow-y-auto{overflow-y:auto !important}.overflow-y-hidden{overflow-y:hidden !important}.overflow-y-visible{overflow-y:visible !important}.overflow-y-scroll{overflow-y:scroll !important}.d-inline{display:inline !important}.d-inline-block{display:inline-block !important}.d-block{display:block !important}.d-grid{display:grid !important}.d-inline-grid{display:inline-grid !important}.d-table{display:table !important}.d-table-row{display:table-row !important}.d-table-cell{display:table-cell !important}.d-flex{display:flex !important}.d-inline-flex{display:inline-flex !important}.d-none{display:none !important}.shadow{box-shadow:0 .5rem 1rem rgba(0,0,0,.15) !important}.shadow-sm{box-shadow:0 .125rem .25rem rgba(0,0,0,.075) !important}.shadow-lg{box-shadow:0 1rem 3rem rgba(0,0,0,.175) !important}.shadow-none{box-shadow:none !important}.focus-ring-default{--bs-focus-ring-color: rgba(var(--bs-default-rgb), var(--bs-focus-ring-opacity))}.focus-ring-primary{--bs-focus-ring-color: rgba(var(--bs-primary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-secondary{--bs-focus-ring-color: rgba(var(--bs-secondary-rgb), var(--bs-focus-ring-opacity))}.focus-ring-success{--bs-focus-ring-color: rgba(var(--bs-success-rgb), var(--bs-focus-ring-opacity))}.focus-ring-info{--bs-focus-ring-color: rgba(var(--bs-info-rgb), var(--bs-focus-ring-opacity))}.focus-ring-warning{--bs-focus-ring-color: rgba(var(--bs-warning-rgb), var(--bs-focus-ring-opacity))}.focus-ring-danger{--bs-focus-ring-color: rgba(var(--bs-danger-rgb), var(--bs-focus-ring-opacity))}.focus-ring-light{--bs-focus-ring-color: rgba(var(--bs-light-rgb), var(--bs-focus-ring-opacity))}.focus-ring-dark{--bs-focus-ring-color: rgba(var(--bs-dark-rgb), var(--bs-focus-ring-opacity))}.position-static{position:static !important}.position-relative{position:relative !important}.position-absolute{position:absolute !important}.position-fixed{position:fixed !important}.position-sticky{position:sticky !important}.top-0{top:0 !important}.top-50{top:50% !important}.top-100{top:100% !important}.bottom-0{bottom:0 !important}.bottom-50{bottom:50% !important}.bottom-100{bottom:100% !important}.start-0{left:0 !important}.start-50{left:50% !important}.start-100{left:100% !important}.end-0{right:0 !important}.end-50{right:50% !important}.end-100{right:100% !important}.translate-middle{transform:translate(-50%, -50%) !important}.translate-middle-x{transform:translateX(-50%) !important}.translate-middle-y{transform:translateY(-50%) !important}.border{border:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-0{border:0 !important}.border-top{border-top:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-top-0{border-top:0 !important}.border-end{border-right:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-end-0{border-right:0 !important}.border-bottom{border-bottom:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-bottom-0{border-bottom:0 !important}.border-start{border-left:var(--bs-border-width) var(--bs-border-style) var(--bs-border-color) !important}.border-start-0{border-left:0 !important}.border-default{--bs-border-opacity: 1;border-color:rgba(var(--bs-default-rgb), var(--bs-border-opacity)) !important}.border-primary{--bs-border-opacity: 1;border-color:rgba(var(--bs-primary-rgb), var(--bs-border-opacity)) !important}.border-secondary{--bs-border-opacity: 1;border-color:rgba(var(--bs-secondary-rgb), var(--bs-border-opacity)) !important}.border-success{--bs-border-opacity: 1;border-color:rgba(var(--bs-success-rgb), var(--bs-border-opacity)) !important}.border-info{--bs-border-opacity: 1;border-color:rgba(var(--bs-info-rgb), var(--bs-border-opacity)) !important}.border-warning{--bs-border-opacity: 1;border-color:rgba(var(--bs-warning-rgb), var(--bs-border-opacity)) !important}.border-danger{--bs-border-opacity: 1;border-color:rgba(var(--bs-danger-rgb), var(--bs-border-opacity)) !important}.border-light{--bs-border-opacity: 1;border-color:rgba(var(--bs-light-rgb), var(--bs-border-opacity)) !important}.border-dark{--bs-border-opacity: 1;border-color:rgba(var(--bs-dark-rgb), var(--bs-border-opacity)) !important}.border-black{--bs-border-opacity: 1;border-color:rgba(var(--bs-black-rgb), var(--bs-border-opacity)) !important}.border-white{--bs-border-opacity: 1;border-color:rgba(var(--bs-white-rgb), var(--bs-border-opacity)) !important}.border-primary-subtle{border-color:var(--bs-primary-border-subtle) !important}.border-secondary-subtle{border-color:var(--bs-secondary-border-subtle) !important}.border-success-subtle{border-color:var(--bs-success-border-subtle) !important}.border-info-subtle{border-color:var(--bs-info-border-subtle) !important}.border-warning-subtle{border-color:var(--bs-warning-border-subtle) !important}.border-danger-subtle{border-color:var(--bs-danger-border-subtle) !important}.border-light-subtle{border-color:var(--bs-light-border-subtle) !important}.border-dark-subtle{border-color:var(--bs-dark-border-subtle) !important}.border-1{border-width:1px !important}.border-2{border-width:2px !important}.border-3{border-width:3px !important}.border-4{border-width:4px !important}.border-5{border-width:5px !important}.border-opacity-10{--bs-border-opacity: 0.1}.border-opacity-25{--bs-border-opacity: 0.25}.border-opacity-50{--bs-border-opacity: 0.5}.border-opacity-75{--bs-border-opacity: 0.75}.border-opacity-100{--bs-border-opacity: 1}.w-25{width:25% !important}.w-50{width:50% !important}.w-75{width:75% !important}.w-100{width:100% !important}.w-auto{width:auto !important}.mw-100{max-width:100% !important}.vw-100{width:100vw !important}.min-vw-100{min-width:100vw !important}.h-25{height:25% !important}.h-50{height:50% !important}.h-75{height:75% !important}.h-100{height:100% !important}.h-auto{height:auto !important}.mh-100{max-height:100% !important}.vh-100{height:100vh !important}.min-vh-100{min-height:100vh !important}.flex-fill{flex:1 1 auto !important}.flex-row{flex-direction:row !important}.flex-column{flex-direction:column !important}.flex-row-reverse{flex-direction:row-reverse !important}.flex-column-reverse{flex-direction:column-reverse !important}.flex-grow-0{flex-grow:0 !important}.flex-grow-1{flex-grow:1 !important}.flex-shrink-0{flex-shrink:0 !important}.flex-shrink-1{flex-shrink:1 !important}.flex-wrap{flex-wrap:wrap !important}.flex-nowrap{flex-wrap:nowrap !important}.flex-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-start{justify-content:flex-start !important}.justify-content-end{justify-content:flex-end !important}.justify-content-center{justify-content:center !important}.justify-content-between{justify-content:space-between !important}.justify-content-around{justify-content:space-around !important}.justify-content-evenly{justify-content:space-evenly !important}.align-items-start{align-items:flex-start !important}.align-items-end{align-items:flex-end !important}.align-items-center{align-items:center !important}.align-items-baseline{align-items:baseline !important}.align-items-stretch{align-items:stretch !important}.align-content-start{align-content:flex-start !important}.align-content-end{align-content:flex-end !important}.align-content-center{align-content:center !important}.align-content-between{align-content:space-between !important}.align-content-around{align-content:space-around !important}.align-content-stretch{align-content:stretch !important}.align-self-auto{align-self:auto !important}.align-self-start{align-self:flex-start !important}.align-self-end{align-self:flex-end !important}.align-self-center{align-self:center !important}.align-self-baseline{align-self:baseline !important}.align-self-stretch{align-self:stretch !important}.order-first{order:-1 !important}.order-0{order:0 !important}.order-1{order:1 !important}.order-2{order:2 !important}.order-3{order:3 !important}.order-4{order:4 !important}.order-5{order:5 !important}.order-last{order:6 !important}.m-0{margin:0 !important}.m-1{margin:.25rem !important}.m-2{margin:.5rem !important}.m-3{margin:1rem !important}.m-4{margin:1.5rem !important}.m-5{margin:3rem !important}.m-auto{margin:auto !important}.mx-0{margin-right:0 !important;margin-left:0 !important}.mx-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-3{margin-right:1rem !important;margin-left:1rem !important}.mx-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-5{margin-right:3rem !important;margin-left:3rem !important}.mx-auto{margin-right:auto !important;margin-left:auto !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.my-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-0{margin-top:0 !important}.mt-1{margin-top:.25rem !important}.mt-2{margin-top:.5rem !important}.mt-3{margin-top:1rem !important}.mt-4{margin-top:1.5rem !important}.mt-5{margin-top:3rem !important}.mt-auto{margin-top:auto !important}.me-0{margin-right:0 !important}.me-1{margin-right:.25rem !important}.me-2{margin-right:.5rem !important}.me-3{margin-right:1rem !important}.me-4{margin-right:1.5rem !important}.me-5{margin-right:3rem !important}.me-auto{margin-right:auto !important}.mb-0{margin-bottom:0 !important}.mb-1{margin-bottom:.25rem !important}.mb-2{margin-bottom:.5rem !important}.mb-3{margin-bottom:1rem !important}.mb-4{margin-bottom:1.5rem !important}.mb-5{margin-bottom:3rem !important}.mb-auto{margin-bottom:auto !important}.ms-0{margin-left:0 !important}.ms-1{margin-left:.25rem !important}.ms-2{margin-left:.5rem !important}.ms-3{margin-left:1rem !important}.ms-4{margin-left:1.5rem !important}.ms-5{margin-left:3rem !important}.ms-auto{margin-left:auto !important}.p-0{padding:0 !important}.p-1{padding:.25rem !important}.p-2{padding:.5rem !important}.p-3{padding:1rem !important}.p-4{padding:1.5rem !important}.p-5{padding:3rem !important}.px-0{padding-right:0 !important;padding-left:0 !important}.px-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-3{padding-right:1rem !important;padding-left:1rem !important}.px-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-5{padding-right:3rem !important;padding-left:3rem !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.py-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-0{padding-top:0 !important}.pt-1{padding-top:.25rem !important}.pt-2{padding-top:.5rem !important}.pt-3{padding-top:1rem !important}.pt-4{padding-top:1.5rem !important}.pt-5{padding-top:3rem !important}.pe-0{padding-right:0 !important}.pe-1{padding-right:.25rem !important}.pe-2{padding-right:.5rem !important}.pe-3{padding-right:1rem !important}.pe-4{padding-right:1.5rem !important}.pe-5{padding-right:3rem !important}.pb-0{padding-bottom:0 !important}.pb-1{padding-bottom:.25rem !important}.pb-2{padding-bottom:.5rem !important}.pb-3{padding-bottom:1rem !important}.pb-4{padding-bottom:1.5rem !important}.pb-5{padding-bottom:3rem !important}.ps-0{padding-left:0 !important}.ps-1{padding-left:.25rem !important}.ps-2{padding-left:.5rem !important}.ps-3{padding-left:1rem !important}.ps-4{padding-left:1.5rem !important}.ps-5{padding-left:3rem !important}.gap-0{gap:0 !important}.gap-1{gap:.25rem !important}.gap-2{gap:.5rem !important}.gap-3{gap:1rem !important}.gap-4{gap:1.5rem !important}.gap-5{gap:3rem !important}.row-gap-0{row-gap:0 !important}.row-gap-1{row-gap:.25rem !important}.row-gap-2{row-gap:.5rem !important}.row-gap-3{row-gap:1rem !important}.row-gap-4{row-gap:1.5rem !important}.row-gap-5{row-gap:3rem !important}.column-gap-0{column-gap:0 !important}.column-gap-1{column-gap:.25rem !important}.column-gap-2{column-gap:.5rem !important}.column-gap-3{column-gap:1rem !important}.column-gap-4{column-gap:1.5rem !important}.column-gap-5{column-gap:3rem !important}.font-monospace{font-family:var(--bs-font-monospace) !important}.fs-1{font-size:calc(1.325rem + 0.9vw) !important}.fs-2{font-size:calc(1.29rem + 0.48vw) !important}.fs-3{font-size:calc(1.27rem + 0.24vw) !important}.fs-4{font-size:1.25rem !important}.fs-5{font-size:1.1rem !important}.fs-6{font-size:1rem !important}.fst-italic{font-style:italic !important}.fst-normal{font-style:normal !important}.fw-lighter{font-weight:lighter !important}.fw-light{font-weight:300 !important}.fw-normal{font-weight:400 !important}.fw-medium{font-weight:500 !important}.fw-semibold{font-weight:600 !important}.fw-bold{font-weight:700 !important}.fw-bolder{font-weight:bolder !important}.lh-1{line-height:1 !important}.lh-sm{line-height:1.25 !important}.lh-base{line-height:1.5 !important}.lh-lg{line-height:2 !important}.text-start{text-align:left !important}.text-end{text-align:right !important}.text-center{text-align:center !important}.text-decoration-none{text-decoration:none !important}.text-decoration-underline{text-decoration:underline !important}.text-decoration-line-through{text-decoration:line-through !important}.text-lowercase{text-transform:lowercase !important}.text-uppercase{text-transform:uppercase !important}.text-capitalize{text-transform:capitalize !important}.text-wrap{white-space:normal !important}.text-nowrap{white-space:nowrap !important}.text-break{word-wrap:break-word !important;word-break:break-word !important}.text-default{--bs-text-opacity: 1;color:rgba(var(--bs-default-rgb), var(--bs-text-opacity)) !important}.text-primary{--bs-text-opacity: 1;color:rgba(var(--bs-primary-rgb), var(--bs-text-opacity)) !important}.text-secondary{--bs-text-opacity: 1;color:rgba(var(--bs-secondary-rgb), var(--bs-text-opacity)) !important}.text-success{--bs-text-opacity: 1;color:rgba(var(--bs-success-rgb), var(--bs-text-opacity)) !important}.text-info{--bs-text-opacity: 1;color:rgba(var(--bs-info-rgb), var(--bs-text-opacity)) !important}.text-warning{--bs-text-opacity: 1;color:rgba(var(--bs-warning-rgb), var(--bs-text-opacity)) !important}.text-danger{--bs-text-opacity: 1;color:rgba(var(--bs-danger-rgb), var(--bs-text-opacity)) !important}.text-light{--bs-text-opacity: 1;color:rgba(var(--bs-light-rgb), var(--bs-text-opacity)) !important}.text-dark{--bs-text-opacity: 1;color:rgba(var(--bs-dark-rgb), var(--bs-text-opacity)) !important}.text-black{--bs-text-opacity: 1;color:rgba(var(--bs-black-rgb), var(--bs-text-opacity)) !important}.text-white{--bs-text-opacity: 1;color:rgba(var(--bs-white-rgb), var(--bs-text-opacity)) !important}.text-body{--bs-text-opacity: 1;color:rgba(var(--bs-body-color-rgb), var(--bs-text-opacity)) !important}.text-muted{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-black-50{--bs-text-opacity: 1;color:rgba(0,0,0,.5) !important}.text-white-50{--bs-text-opacity: 1;color:rgba(255,255,255,.5) !important}.text-body-secondary{--bs-text-opacity: 1;color:var(--bs-secondary-color) !important}.text-body-tertiary{--bs-text-opacity: 1;color:var(--bs-tertiary-color) !important}.text-body-emphasis{--bs-text-opacity: 1;color:var(--bs-emphasis-color) !important}.text-reset{--bs-text-opacity: 1;color:inherit !important}.text-opacity-25{--bs-text-opacity: 0.25}.text-opacity-50{--bs-text-opacity: 0.5}.text-opacity-75{--bs-text-opacity: 0.75}.text-opacity-100{--bs-text-opacity: 1}.text-primary-emphasis{color:var(--bs-primary-text-emphasis) !important}.text-secondary-emphasis{color:var(--bs-secondary-text-emphasis) !important}.text-success-emphasis{color:var(--bs-success-text-emphasis) !important}.text-info-emphasis{color:var(--bs-info-text-emphasis) !important}.text-warning-emphasis{color:var(--bs-warning-text-emphasis) !important}.text-danger-emphasis{color:var(--bs-danger-text-emphasis) !important}.text-light-emphasis{color:var(--bs-light-text-emphasis) !important}.text-dark-emphasis{color:var(--bs-dark-text-emphasis) !important}.link-opacity-10{--bs-link-opacity: 0.1}.link-opacity-10-hover:hover{--bs-link-opacity: 0.1}.link-opacity-25{--bs-link-opacity: 0.25}.link-opacity-25-hover:hover{--bs-link-opacity: 0.25}.link-opacity-50{--bs-link-opacity: 0.5}.link-opacity-50-hover:hover{--bs-link-opacity: 0.5}.link-opacity-75{--bs-link-opacity: 0.75}.link-opacity-75-hover:hover{--bs-link-opacity: 0.75}.link-opacity-100{--bs-link-opacity: 1}.link-opacity-100-hover:hover{--bs-link-opacity: 1}.link-offset-1{text-underline-offset:.125em !important}.link-offset-1-hover:hover{text-underline-offset:.125em !important}.link-offset-2{text-underline-offset:.25em !important}.link-offset-2-hover:hover{text-underline-offset:.25em !important}.link-offset-3{text-underline-offset:.375em !important}.link-offset-3-hover:hover{text-underline-offset:.375em !important}.link-underline-default{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-default-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-primary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-primary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-secondary{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-secondary-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-success{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-success-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-info{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-info-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-warning{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-warning-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-danger{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-danger-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-light{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-light-rgb), var(--bs-link-underline-opacity)) !important}.link-underline-dark{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-dark-rgb), var(--bs-link-underline-opacity)) !important}.link-underline{--bs-link-underline-opacity: 1;text-decoration-color:rgba(var(--bs-link-color-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-underline-opacity-0{--bs-link-underline-opacity: 0}.link-underline-opacity-0-hover:hover{--bs-link-underline-opacity: 0}.link-underline-opacity-10{--bs-link-underline-opacity: 0.1}.link-underline-opacity-10-hover:hover{--bs-link-underline-opacity: 0.1}.link-underline-opacity-25{--bs-link-underline-opacity: 0.25}.link-underline-opacity-25-hover:hover{--bs-link-underline-opacity: 0.25}.link-underline-opacity-50{--bs-link-underline-opacity: 0.5}.link-underline-opacity-50-hover:hover{--bs-link-underline-opacity: 0.5}.link-underline-opacity-75{--bs-link-underline-opacity: 0.75}.link-underline-opacity-75-hover:hover{--bs-link-underline-opacity: 0.75}.link-underline-opacity-100{--bs-link-underline-opacity: 1}.link-underline-opacity-100-hover:hover{--bs-link-underline-opacity: 1}.bg-default{--bs-bg-opacity: 1;background-color:rgba(var(--bs-default-rgb), var(--bs-bg-opacity)) !important}.bg-primary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-primary-rgb), var(--bs-bg-opacity)) !important}.bg-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-rgb), var(--bs-bg-opacity)) !important}.bg-success{--bs-bg-opacity: 1;background-color:rgba(var(--bs-success-rgb), var(--bs-bg-opacity)) !important}.bg-info{--bs-bg-opacity: 1;background-color:rgba(var(--bs-info-rgb), var(--bs-bg-opacity)) !important}.bg-warning{--bs-bg-opacity: 1;background-color:rgba(var(--bs-warning-rgb), var(--bs-bg-opacity)) !important}.bg-danger{--bs-bg-opacity: 1;background-color:rgba(var(--bs-danger-rgb), var(--bs-bg-opacity)) !important}.bg-light{--bs-bg-opacity: 1;background-color:rgba(var(--bs-light-rgb), var(--bs-bg-opacity)) !important}.bg-dark{--bs-bg-opacity: 1;background-color:rgba(var(--bs-dark-rgb), var(--bs-bg-opacity)) !important}.bg-black{--bs-bg-opacity: 1;background-color:rgba(var(--bs-black-rgb), var(--bs-bg-opacity)) !important}.bg-white{--bs-bg-opacity: 1;background-color:rgba(var(--bs-white-rgb), var(--bs-bg-opacity)) !important}.bg-body{--bs-bg-opacity: 1;background-color:rgba(var(--bs-body-bg-rgb), var(--bs-bg-opacity)) !important}.bg-transparent{--bs-bg-opacity: 1;background-color:rgba(0,0,0,0) !important}.bg-body-secondary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-secondary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-body-tertiary{--bs-bg-opacity: 1;background-color:rgba(var(--bs-tertiary-bg-rgb), var(--bs-bg-opacity)) !important}.bg-opacity-10{--bs-bg-opacity: 0.1}.bg-opacity-25{--bs-bg-opacity: 0.25}.bg-opacity-50{--bs-bg-opacity: 0.5}.bg-opacity-75{--bs-bg-opacity: 0.75}.bg-opacity-100{--bs-bg-opacity: 1}.bg-primary-subtle{background-color:var(--bs-primary-bg-subtle) !important}.bg-secondary-subtle{background-color:var(--bs-secondary-bg-subtle) !important}.bg-success-subtle{background-color:var(--bs-success-bg-subtle) !important}.bg-info-subtle{background-color:var(--bs-info-bg-subtle) !important}.bg-warning-subtle{background-color:var(--bs-warning-bg-subtle) !important}.bg-danger-subtle{background-color:var(--bs-danger-bg-subtle) !important}.bg-light-subtle{background-color:var(--bs-light-bg-subtle) !important}.bg-dark-subtle{background-color:var(--bs-dark-bg-subtle) !important}.bg-gradient{background-image:var(--bs-gradient) !important}.user-select-all{user-select:all !important}.user-select-auto{user-select:auto !important}.user-select-none{user-select:none !important}.pe-none{pointer-events:none !important}.pe-auto{pointer-events:auto !important}.rounded{border-radius:var(--bs-border-radius) !important}.rounded-0{border-radius:0 !important}.rounded-1{border-radius:var(--bs-border-radius-sm) !important}.rounded-2{border-radius:var(--bs-border-radius) !important}.rounded-3{border-radius:var(--bs-border-radius-lg) !important}.rounded-4{border-radius:var(--bs-border-radius-xl) !important}.rounded-5{border-radius:var(--bs-border-radius-xxl) !important}.rounded-circle{border-radius:50% !important}.rounded-pill{border-radius:var(--bs-border-radius-pill) !important}.rounded-top{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-0{border-top-left-radius:0 !important;border-top-right-radius:0 !important}.rounded-top-1{border-top-left-radius:var(--bs-border-radius-sm) !important;border-top-right-radius:var(--bs-border-radius-sm) !important}.rounded-top-2{border-top-left-radius:var(--bs-border-radius) !important;border-top-right-radius:var(--bs-border-radius) !important}.rounded-top-3{border-top-left-radius:var(--bs-border-radius-lg) !important;border-top-right-radius:var(--bs-border-radius-lg) !important}.rounded-top-4{border-top-left-radius:var(--bs-border-radius-xl) !important;border-top-right-radius:var(--bs-border-radius-xl) !important}.rounded-top-5{border-top-left-radius:var(--bs-border-radius-xxl) !important;border-top-right-radius:var(--bs-border-radius-xxl) !important}.rounded-top-circle{border-top-left-radius:50% !important;border-top-right-radius:50% !important}.rounded-top-pill{border-top-left-radius:var(--bs-border-radius-pill) !important;border-top-right-radius:var(--bs-border-radius-pill) !important}.rounded-end{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-0{border-top-right-radius:0 !important;border-bottom-right-radius:0 !important}.rounded-end-1{border-top-right-radius:var(--bs-border-radius-sm) !important;border-bottom-right-radius:var(--bs-border-radius-sm) !important}.rounded-end-2{border-top-right-radius:var(--bs-border-radius) !important;border-bottom-right-radius:var(--bs-border-radius) !important}.rounded-end-3{border-top-right-radius:var(--bs-border-radius-lg) !important;border-bottom-right-radius:var(--bs-border-radius-lg) !important}.rounded-end-4{border-top-right-radius:var(--bs-border-radius-xl) !important;border-bottom-right-radius:var(--bs-border-radius-xl) !important}.rounded-end-5{border-top-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-right-radius:var(--bs-border-radius-xxl) !important}.rounded-end-circle{border-top-right-radius:50% !important;border-bottom-right-radius:50% !important}.rounded-end-pill{border-top-right-radius:var(--bs-border-radius-pill) !important;border-bottom-right-radius:var(--bs-border-radius-pill) !important}.rounded-bottom{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-0{border-bottom-right-radius:0 !important;border-bottom-left-radius:0 !important}.rounded-bottom-1{border-bottom-right-radius:var(--bs-border-radius-sm) !important;border-bottom-left-radius:var(--bs-border-radius-sm) !important}.rounded-bottom-2{border-bottom-right-radius:var(--bs-border-radius) !important;border-bottom-left-radius:var(--bs-border-radius) !important}.rounded-bottom-3{border-bottom-right-radius:var(--bs-border-radius-lg) !important;border-bottom-left-radius:var(--bs-border-radius-lg) !important}.rounded-bottom-4{border-bottom-right-radius:var(--bs-border-radius-xl) !important;border-bottom-left-radius:var(--bs-border-radius-xl) !important}.rounded-bottom-5{border-bottom-right-radius:var(--bs-border-radius-xxl) !important;border-bottom-left-radius:var(--bs-border-radius-xxl) !important}.rounded-bottom-circle{border-bottom-right-radius:50% !important;border-bottom-left-radius:50% !important}.rounded-bottom-pill{border-bottom-right-radius:var(--bs-border-radius-pill) !important;border-bottom-left-radius:var(--bs-border-radius-pill) !important}.rounded-start{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-0{border-bottom-left-radius:0 !important;border-top-left-radius:0 !important}.rounded-start-1{border-bottom-left-radius:var(--bs-border-radius-sm) !important;border-top-left-radius:var(--bs-border-radius-sm) !important}.rounded-start-2{border-bottom-left-radius:var(--bs-border-radius) !important;border-top-left-radius:var(--bs-border-radius) !important}.rounded-start-3{border-bottom-left-radius:var(--bs-border-radius-lg) !important;border-top-left-radius:var(--bs-border-radius-lg) !important}.rounded-start-4{border-bottom-left-radius:var(--bs-border-radius-xl) !important;border-top-left-radius:var(--bs-border-radius-xl) !important}.rounded-start-5{border-bottom-left-radius:var(--bs-border-radius-xxl) !important;border-top-left-radius:var(--bs-border-radius-xxl) !important}.rounded-start-circle{border-bottom-left-radius:50% !important;border-top-left-radius:50% !important}.rounded-start-pill{border-bottom-left-radius:var(--bs-border-radius-pill) !important;border-top-left-radius:var(--bs-border-radius-pill) !important}.visible{visibility:visible !important}.invisible{visibility:hidden !important}.z-n1{z-index:-1 !important}.z-0{z-index:0 !important}.z-1{z-index:1 !important}.z-2{z-index:2 !important}.z-3{z-index:3 !important}@media(min-width: 576px){.float-sm-start{float:left !important}.float-sm-end{float:right !important}.float-sm-none{float:none !important}.object-fit-sm-contain{object-fit:contain !important}.object-fit-sm-cover{object-fit:cover !important}.object-fit-sm-fill{object-fit:fill !important}.object-fit-sm-scale{object-fit:scale-down !important}.object-fit-sm-none{object-fit:none !important}.d-sm-inline{display:inline !important}.d-sm-inline-block{display:inline-block !important}.d-sm-block{display:block !important}.d-sm-grid{display:grid !important}.d-sm-inline-grid{display:inline-grid !important}.d-sm-table{display:table !important}.d-sm-table-row{display:table-row !important}.d-sm-table-cell{display:table-cell !important}.d-sm-flex{display:flex !important}.d-sm-inline-flex{display:inline-flex !important}.d-sm-none{display:none !important}.flex-sm-fill{flex:1 1 auto !important}.flex-sm-row{flex-direction:row !important}.flex-sm-column{flex-direction:column !important}.flex-sm-row-reverse{flex-direction:row-reverse !important}.flex-sm-column-reverse{flex-direction:column-reverse !important}.flex-sm-grow-0{flex-grow:0 !important}.flex-sm-grow-1{flex-grow:1 !important}.flex-sm-shrink-0{flex-shrink:0 !important}.flex-sm-shrink-1{flex-shrink:1 !important}.flex-sm-wrap{flex-wrap:wrap !important}.flex-sm-nowrap{flex-wrap:nowrap !important}.flex-sm-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-sm-start{justify-content:flex-start !important}.justify-content-sm-end{justify-content:flex-end !important}.justify-content-sm-center{justify-content:center !important}.justify-content-sm-between{justify-content:space-between !important}.justify-content-sm-around{justify-content:space-around !important}.justify-content-sm-evenly{justify-content:space-evenly !important}.align-items-sm-start{align-items:flex-start !important}.align-items-sm-end{align-items:flex-end !important}.align-items-sm-center{align-items:center !important}.align-items-sm-baseline{align-items:baseline !important}.align-items-sm-stretch{align-items:stretch !important}.align-content-sm-start{align-content:flex-start !important}.align-content-sm-end{align-content:flex-end !important}.align-content-sm-center{align-content:center !important}.align-content-sm-between{align-content:space-between !important}.align-content-sm-around{align-content:space-around !important}.align-content-sm-stretch{align-content:stretch !important}.align-self-sm-auto{align-self:auto !important}.align-self-sm-start{align-self:flex-start !important}.align-self-sm-end{align-self:flex-end !important}.align-self-sm-center{align-self:center !important}.align-self-sm-baseline{align-self:baseline !important}.align-self-sm-stretch{align-self:stretch !important}.order-sm-first{order:-1 !important}.order-sm-0{order:0 !important}.order-sm-1{order:1 !important}.order-sm-2{order:2 !important}.order-sm-3{order:3 !important}.order-sm-4{order:4 !important}.order-sm-5{order:5 !important}.order-sm-last{order:6 !important}.m-sm-0{margin:0 !important}.m-sm-1{margin:.25rem !important}.m-sm-2{margin:.5rem !important}.m-sm-3{margin:1rem !important}.m-sm-4{margin:1.5rem !important}.m-sm-5{margin:3rem !important}.m-sm-auto{margin:auto !important}.mx-sm-0{margin-right:0 !important;margin-left:0 !important}.mx-sm-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-sm-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-sm-3{margin-right:1rem !important;margin-left:1rem !important}.mx-sm-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-sm-5{margin-right:3rem !important;margin-left:3rem !important}.mx-sm-auto{margin-right:auto !important;margin-left:auto !important}.my-sm-0{margin-top:0 !important;margin-bottom:0 !important}.my-sm-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-sm-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-sm-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-sm-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-sm-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-sm-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-sm-0{margin-top:0 !important}.mt-sm-1{margin-top:.25rem !important}.mt-sm-2{margin-top:.5rem !important}.mt-sm-3{margin-top:1rem !important}.mt-sm-4{margin-top:1.5rem !important}.mt-sm-5{margin-top:3rem !important}.mt-sm-auto{margin-top:auto !important}.me-sm-0{margin-right:0 !important}.me-sm-1{margin-right:.25rem !important}.me-sm-2{margin-right:.5rem !important}.me-sm-3{margin-right:1rem !important}.me-sm-4{margin-right:1.5rem !important}.me-sm-5{margin-right:3rem !important}.me-sm-auto{margin-right:auto !important}.mb-sm-0{margin-bottom:0 !important}.mb-sm-1{margin-bottom:.25rem !important}.mb-sm-2{margin-bottom:.5rem !important}.mb-sm-3{margin-bottom:1rem !important}.mb-sm-4{margin-bottom:1.5rem !important}.mb-sm-5{margin-bottom:3rem !important}.mb-sm-auto{margin-bottom:auto !important}.ms-sm-0{margin-left:0 !important}.ms-sm-1{margin-left:.25rem !important}.ms-sm-2{margin-left:.5rem !important}.ms-sm-3{margin-left:1rem !important}.ms-sm-4{margin-left:1.5rem !important}.ms-sm-5{margin-left:3rem !important}.ms-sm-auto{margin-left:auto !important}.p-sm-0{padding:0 !important}.p-sm-1{padding:.25rem !important}.p-sm-2{padding:.5rem !important}.p-sm-3{padding:1rem !important}.p-sm-4{padding:1.5rem !important}.p-sm-5{padding:3rem !important}.px-sm-0{padding-right:0 !important;padding-left:0 !important}.px-sm-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-sm-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-sm-3{padding-right:1rem !important;padding-left:1rem !important}.px-sm-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-sm-5{padding-right:3rem !important;padding-left:3rem !important}.py-sm-0{padding-top:0 !important;padding-bottom:0 !important}.py-sm-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-sm-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-sm-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-sm-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-sm-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-sm-0{padding-top:0 !important}.pt-sm-1{padding-top:.25rem !important}.pt-sm-2{padding-top:.5rem !important}.pt-sm-3{padding-top:1rem !important}.pt-sm-4{padding-top:1.5rem !important}.pt-sm-5{padding-top:3rem !important}.pe-sm-0{padding-right:0 !important}.pe-sm-1{padding-right:.25rem !important}.pe-sm-2{padding-right:.5rem !important}.pe-sm-3{padding-right:1rem !important}.pe-sm-4{padding-right:1.5rem !important}.pe-sm-5{padding-right:3rem !important}.pb-sm-0{padding-bottom:0 !important}.pb-sm-1{padding-bottom:.25rem !important}.pb-sm-2{padding-bottom:.5rem !important}.pb-sm-3{padding-bottom:1rem !important}.pb-sm-4{padding-bottom:1.5rem !important}.pb-sm-5{padding-bottom:3rem !important}.ps-sm-0{padding-left:0 !important}.ps-sm-1{padding-left:.25rem !important}.ps-sm-2{padding-left:.5rem !important}.ps-sm-3{padding-left:1rem !important}.ps-sm-4{padding-left:1.5rem !important}.ps-sm-5{padding-left:3rem !important}.gap-sm-0{gap:0 !important}.gap-sm-1{gap:.25rem !important}.gap-sm-2{gap:.5rem !important}.gap-sm-3{gap:1rem !important}.gap-sm-4{gap:1.5rem !important}.gap-sm-5{gap:3rem !important}.row-gap-sm-0{row-gap:0 !important}.row-gap-sm-1{row-gap:.25rem !important}.row-gap-sm-2{row-gap:.5rem !important}.row-gap-sm-3{row-gap:1rem !important}.row-gap-sm-4{row-gap:1.5rem !important}.row-gap-sm-5{row-gap:3rem !important}.column-gap-sm-0{column-gap:0 !important}.column-gap-sm-1{column-gap:.25rem !important}.column-gap-sm-2{column-gap:.5rem !important}.column-gap-sm-3{column-gap:1rem !important}.column-gap-sm-4{column-gap:1.5rem !important}.column-gap-sm-5{column-gap:3rem !important}.text-sm-start{text-align:left !important}.text-sm-end{text-align:right !important}.text-sm-center{text-align:center !important}}@media(min-width: 768px){.float-md-start{float:left !important}.float-md-end{float:right !important}.float-md-none{float:none !important}.object-fit-md-contain{object-fit:contain !important}.object-fit-md-cover{object-fit:cover !important}.object-fit-md-fill{object-fit:fill !important}.object-fit-md-scale{object-fit:scale-down !important}.object-fit-md-none{object-fit:none !important}.d-md-inline{display:inline !important}.d-md-inline-block{display:inline-block !important}.d-md-block{display:block !important}.d-md-grid{display:grid !important}.d-md-inline-grid{display:inline-grid !important}.d-md-table{display:table !important}.d-md-table-row{display:table-row !important}.d-md-table-cell{display:table-cell !important}.d-md-flex{display:flex !important}.d-md-inline-flex{display:inline-flex !important}.d-md-none{display:none !important}.flex-md-fill{flex:1 1 auto !important}.flex-md-row{flex-direction:row !important}.flex-md-column{flex-direction:column !important}.flex-md-row-reverse{flex-direction:row-reverse !important}.flex-md-column-reverse{flex-direction:column-reverse !important}.flex-md-grow-0{flex-grow:0 !important}.flex-md-grow-1{flex-grow:1 !important}.flex-md-shrink-0{flex-shrink:0 !important}.flex-md-shrink-1{flex-shrink:1 !important}.flex-md-wrap{flex-wrap:wrap !important}.flex-md-nowrap{flex-wrap:nowrap !important}.flex-md-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-md-start{justify-content:flex-start !important}.justify-content-md-end{justify-content:flex-end !important}.justify-content-md-center{justify-content:center !important}.justify-content-md-between{justify-content:space-between !important}.justify-content-md-around{justify-content:space-around !important}.justify-content-md-evenly{justify-content:space-evenly !important}.align-items-md-start{align-items:flex-start !important}.align-items-md-end{align-items:flex-end !important}.align-items-md-center{align-items:center !important}.align-items-md-baseline{align-items:baseline !important}.align-items-md-stretch{align-items:stretch !important}.align-content-md-start{align-content:flex-start !important}.align-content-md-end{align-content:flex-end !important}.align-content-md-center{align-content:center !important}.align-content-md-between{align-content:space-between !important}.align-content-md-around{align-content:space-around !important}.align-content-md-stretch{align-content:stretch !important}.align-self-md-auto{align-self:auto !important}.align-self-md-start{align-self:flex-start !important}.align-self-md-end{align-self:flex-end !important}.align-self-md-center{align-self:center !important}.align-self-md-baseline{align-self:baseline !important}.align-self-md-stretch{align-self:stretch !important}.order-md-first{order:-1 !important}.order-md-0{order:0 !important}.order-md-1{order:1 !important}.order-md-2{order:2 !important}.order-md-3{order:3 !important}.order-md-4{order:4 !important}.order-md-5{order:5 !important}.order-md-last{order:6 !important}.m-md-0{margin:0 !important}.m-md-1{margin:.25rem !important}.m-md-2{margin:.5rem !important}.m-md-3{margin:1rem !important}.m-md-4{margin:1.5rem !important}.m-md-5{margin:3rem !important}.m-md-auto{margin:auto !important}.mx-md-0{margin-right:0 !important;margin-left:0 !important}.mx-md-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-md-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-md-3{margin-right:1rem !important;margin-left:1rem !important}.mx-md-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-md-5{margin-right:3rem !important;margin-left:3rem !important}.mx-md-auto{margin-right:auto !important;margin-left:auto !important}.my-md-0{margin-top:0 !important;margin-bottom:0 !important}.my-md-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-md-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-md-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-md-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-md-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-md-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-md-0{margin-top:0 !important}.mt-md-1{margin-top:.25rem !important}.mt-md-2{margin-top:.5rem !important}.mt-md-3{margin-top:1rem !important}.mt-md-4{margin-top:1.5rem !important}.mt-md-5{margin-top:3rem !important}.mt-md-auto{margin-top:auto !important}.me-md-0{margin-right:0 !important}.me-md-1{margin-right:.25rem !important}.me-md-2{margin-right:.5rem !important}.me-md-3{margin-right:1rem !important}.me-md-4{margin-right:1.5rem !important}.me-md-5{margin-right:3rem !important}.me-md-auto{margin-right:auto !important}.mb-md-0{margin-bottom:0 !important}.mb-md-1{margin-bottom:.25rem !important}.mb-md-2{margin-bottom:.5rem !important}.mb-md-3{margin-bottom:1rem !important}.mb-md-4{margin-bottom:1.5rem !important}.mb-md-5{margin-bottom:3rem !important}.mb-md-auto{margin-bottom:auto !important}.ms-md-0{margin-left:0 !important}.ms-md-1{margin-left:.25rem !important}.ms-md-2{margin-left:.5rem !important}.ms-md-3{margin-left:1rem !important}.ms-md-4{margin-left:1.5rem !important}.ms-md-5{margin-left:3rem !important}.ms-md-auto{margin-left:auto !important}.p-md-0{padding:0 !important}.p-md-1{padding:.25rem !important}.p-md-2{padding:.5rem !important}.p-md-3{padding:1rem !important}.p-md-4{padding:1.5rem !important}.p-md-5{padding:3rem !important}.px-md-0{padding-right:0 !important;padding-left:0 !important}.px-md-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-md-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-md-3{padding-right:1rem !important;padding-left:1rem !important}.px-md-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-md-5{padding-right:3rem !important;padding-left:3rem !important}.py-md-0{padding-top:0 !important;padding-bottom:0 !important}.py-md-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-md-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-md-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-md-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-md-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-md-0{padding-top:0 !important}.pt-md-1{padding-top:.25rem !important}.pt-md-2{padding-top:.5rem !important}.pt-md-3{padding-top:1rem !important}.pt-md-4{padding-top:1.5rem !important}.pt-md-5{padding-top:3rem !important}.pe-md-0{padding-right:0 !important}.pe-md-1{padding-right:.25rem !important}.pe-md-2{padding-right:.5rem !important}.pe-md-3{padding-right:1rem !important}.pe-md-4{padding-right:1.5rem !important}.pe-md-5{padding-right:3rem !important}.pb-md-0{padding-bottom:0 !important}.pb-md-1{padding-bottom:.25rem !important}.pb-md-2{padding-bottom:.5rem !important}.pb-md-3{padding-bottom:1rem !important}.pb-md-4{padding-bottom:1.5rem !important}.pb-md-5{padding-bottom:3rem !important}.ps-md-0{padding-left:0 !important}.ps-md-1{padding-left:.25rem !important}.ps-md-2{padding-left:.5rem !important}.ps-md-3{padding-left:1rem !important}.ps-md-4{padding-left:1.5rem !important}.ps-md-5{padding-left:3rem !important}.gap-md-0{gap:0 !important}.gap-md-1{gap:.25rem !important}.gap-md-2{gap:.5rem !important}.gap-md-3{gap:1rem !important}.gap-md-4{gap:1.5rem !important}.gap-md-5{gap:3rem !important}.row-gap-md-0{row-gap:0 !important}.row-gap-md-1{row-gap:.25rem !important}.row-gap-md-2{row-gap:.5rem !important}.row-gap-md-3{row-gap:1rem !important}.row-gap-md-4{row-gap:1.5rem !important}.row-gap-md-5{row-gap:3rem !important}.column-gap-md-0{column-gap:0 !important}.column-gap-md-1{column-gap:.25rem !important}.column-gap-md-2{column-gap:.5rem !important}.column-gap-md-3{column-gap:1rem !important}.column-gap-md-4{column-gap:1.5rem !important}.column-gap-md-5{column-gap:3rem !important}.text-md-start{text-align:left !important}.text-md-end{text-align:right !important}.text-md-center{text-align:center !important}}@media(min-width: 992px){.float-lg-start{float:left !important}.float-lg-end{float:right !important}.float-lg-none{float:none !important}.object-fit-lg-contain{object-fit:contain !important}.object-fit-lg-cover{object-fit:cover !important}.object-fit-lg-fill{object-fit:fill !important}.object-fit-lg-scale{object-fit:scale-down !important}.object-fit-lg-none{object-fit:none !important}.d-lg-inline{display:inline !important}.d-lg-inline-block{display:inline-block !important}.d-lg-block{display:block !important}.d-lg-grid{display:grid !important}.d-lg-inline-grid{display:inline-grid !important}.d-lg-table{display:table !important}.d-lg-table-row{display:table-row !important}.d-lg-table-cell{display:table-cell !important}.d-lg-flex{display:flex !important}.d-lg-inline-flex{display:inline-flex !important}.d-lg-none{display:none !important}.flex-lg-fill{flex:1 1 auto !important}.flex-lg-row{flex-direction:row !important}.flex-lg-column{flex-direction:column !important}.flex-lg-row-reverse{flex-direction:row-reverse !important}.flex-lg-column-reverse{flex-direction:column-reverse !important}.flex-lg-grow-0{flex-grow:0 !important}.flex-lg-grow-1{flex-grow:1 !important}.flex-lg-shrink-0{flex-shrink:0 !important}.flex-lg-shrink-1{flex-shrink:1 !important}.flex-lg-wrap{flex-wrap:wrap !important}.flex-lg-nowrap{flex-wrap:nowrap !important}.flex-lg-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-lg-start{justify-content:flex-start !important}.justify-content-lg-end{justify-content:flex-end !important}.justify-content-lg-center{justify-content:center !important}.justify-content-lg-between{justify-content:space-between !important}.justify-content-lg-around{justify-content:space-around !important}.justify-content-lg-evenly{justify-content:space-evenly !important}.align-items-lg-start{align-items:flex-start !important}.align-items-lg-end{align-items:flex-end !important}.align-items-lg-center{align-items:center !important}.align-items-lg-baseline{align-items:baseline !important}.align-items-lg-stretch{align-items:stretch !important}.align-content-lg-start{align-content:flex-start !important}.align-content-lg-end{align-content:flex-end !important}.align-content-lg-center{align-content:center !important}.align-content-lg-between{align-content:space-between !important}.align-content-lg-around{align-content:space-around !important}.align-content-lg-stretch{align-content:stretch !important}.align-self-lg-auto{align-self:auto !important}.align-self-lg-start{align-self:flex-start !important}.align-self-lg-end{align-self:flex-end !important}.align-self-lg-center{align-self:center !important}.align-self-lg-baseline{align-self:baseline !important}.align-self-lg-stretch{align-self:stretch !important}.order-lg-first{order:-1 !important}.order-lg-0{order:0 !important}.order-lg-1{order:1 !important}.order-lg-2{order:2 !important}.order-lg-3{order:3 !important}.order-lg-4{order:4 !important}.order-lg-5{order:5 !important}.order-lg-last{order:6 !important}.m-lg-0{margin:0 !important}.m-lg-1{margin:.25rem !important}.m-lg-2{margin:.5rem !important}.m-lg-3{margin:1rem !important}.m-lg-4{margin:1.5rem !important}.m-lg-5{margin:3rem !important}.m-lg-auto{margin:auto !important}.mx-lg-0{margin-right:0 !important;margin-left:0 !important}.mx-lg-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-lg-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-lg-3{margin-right:1rem !important;margin-left:1rem !important}.mx-lg-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-lg-5{margin-right:3rem !important;margin-left:3rem !important}.mx-lg-auto{margin-right:auto !important;margin-left:auto !important}.my-lg-0{margin-top:0 !important;margin-bottom:0 !important}.my-lg-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-lg-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-lg-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-lg-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-lg-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-lg-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-lg-0{margin-top:0 !important}.mt-lg-1{margin-top:.25rem !important}.mt-lg-2{margin-top:.5rem !important}.mt-lg-3{margin-top:1rem !important}.mt-lg-4{margin-top:1.5rem !important}.mt-lg-5{margin-top:3rem !important}.mt-lg-auto{margin-top:auto !important}.me-lg-0{margin-right:0 !important}.me-lg-1{margin-right:.25rem !important}.me-lg-2{margin-right:.5rem !important}.me-lg-3{margin-right:1rem !important}.me-lg-4{margin-right:1.5rem !important}.me-lg-5{margin-right:3rem !important}.me-lg-auto{margin-right:auto !important}.mb-lg-0{margin-bottom:0 !important}.mb-lg-1{margin-bottom:.25rem !important}.mb-lg-2{margin-bottom:.5rem !important}.mb-lg-3{margin-bottom:1rem !important}.mb-lg-4{margin-bottom:1.5rem !important}.mb-lg-5{margin-bottom:3rem !important}.mb-lg-auto{margin-bottom:auto !important}.ms-lg-0{margin-left:0 !important}.ms-lg-1{margin-left:.25rem !important}.ms-lg-2{margin-left:.5rem !important}.ms-lg-3{margin-left:1rem !important}.ms-lg-4{margin-left:1.5rem !important}.ms-lg-5{margin-left:3rem !important}.ms-lg-auto{margin-left:auto !important}.p-lg-0{padding:0 !important}.p-lg-1{padding:.25rem !important}.p-lg-2{padding:.5rem !important}.p-lg-3{padding:1rem !important}.p-lg-4{padding:1.5rem !important}.p-lg-5{padding:3rem !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.px-lg-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-lg-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-lg-3{padding-right:1rem !important;padding-left:1rem !important}.px-lg-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-lg-5{padding-right:3rem !important;padding-left:3rem !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}.py-lg-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-lg-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-lg-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-lg-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-lg-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1400px){.float-xxl-start{float:left !important}.float-xxl-end{float:right !important}.float-xxl-none{float:none !important}.object-fit-xxl-contain{object-fit:contain !important}.object-fit-xxl-cover{object-fit:cover !important}.object-fit-xxl-fill{object-fit:fill !important}.object-fit-xxl-scale{object-fit:scale-down !important}.object-fit-xxl-none{object-fit:none !important}.d-xxl-inline{display:inline !important}.d-xxl-inline-block{display:inline-block !important}.d-xxl-block{display:block !important}.d-xxl-grid{display:grid !important}.d-xxl-inline-grid{display:inline-grid !important}.d-xxl-table{display:table !important}.d-xxl-table-row{display:table-row !important}.d-xxl-table-cell{display:table-cell !important}.d-xxl-flex{display:flex !important}.d-xxl-inline-flex{display:inline-flex !important}.d-xxl-none{display:none !important}.flex-xxl-fill{flex:1 1 auto !important}.flex-xxl-row{flex-direction:row !important}.flex-xxl-column{flex-direction:column !important}.flex-xxl-row-reverse{flex-direction:row-reverse !important}.flex-xxl-column-reverse{flex-direction:column-reverse !important}.flex-xxl-grow-0{flex-grow:0 !important}.flex-xxl-grow-1{flex-grow:1 !important}.flex-xxl-shrink-0{flex-shrink:0 !important}.flex-xxl-shrink-1{flex-shrink:1 !important}.flex-xxl-wrap{flex-wrap:wrap !important}.flex-xxl-nowrap{flex-wrap:nowrap !important}.flex-xxl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xxl-start{justify-content:flex-start !important}.justify-content-xxl-end{justify-content:flex-end !important}.justify-content-xxl-center{justify-content:center !important}.justify-content-xxl-between{justify-content:space-between !important}.justify-content-xxl-around{justify-content:space-around !important}.justify-content-xxl-evenly{justify-content:space-evenly !important}.align-items-xxl-start{align-items:flex-start !important}.align-items-xxl-end{align-items:flex-end !important}.align-items-xxl-center{align-items:center !important}.align-items-xxl-baseline{align-items:baseline !important}.align-items-xxl-stretch{align-items:stretch !important}.align-content-xxl-start{align-content:flex-start !important}.align-content-xxl-end{align-content:flex-end !important}.align-content-xxl-center{align-content:center !important}.align-content-xxl-between{align-content:space-between !important}.align-content-xxl-around{align-content:space-around !important}.align-content-xxl-stretch{align-content:stretch !important}.align-self-xxl-auto{align-self:auto !important}.align-self-xxl-start{align-self:flex-start !important}.align-self-xxl-end{align-self:flex-end !important}.align-self-xxl-center{align-self:center !important}.align-self-xxl-baseline{align-self:baseline !important}.align-self-xxl-stretch{align-self:stretch !important}.order-xxl-first{order:-1 !important}.order-xxl-0{order:0 !important}.order-xxl-1{order:1 !important}.order-xxl-2{order:2 !important}.order-xxl-3{order:3 !important}.order-xxl-4{order:4 !important}.order-xxl-5{order:5 !important}.order-xxl-last{order:6 !important}.m-xxl-0{margin:0 !important}.m-xxl-1{margin:.25rem !important}.m-xxl-2{margin:.5rem !important}.m-xxl-3{margin:1rem !important}.m-xxl-4{margin:1.5rem !important}.m-xxl-5{margin:3rem !important}.m-xxl-auto{margin:auto !important}.mx-xxl-0{margin-right:0 !important;margin-left:0 !important}.mx-xxl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xxl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xxl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xxl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xxl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xxl-auto{margin-right:auto !important;margin-left:auto !important}.my-xxl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xxl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xxl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xxl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xxl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xxl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xxl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xxl-0{margin-top:0 !important}.mt-xxl-1{margin-top:.25rem !important}.mt-xxl-2{margin-top:.5rem !important}.mt-xxl-3{margin-top:1rem !important}.mt-xxl-4{margin-top:1.5rem !important}.mt-xxl-5{margin-top:3rem !important}.mt-xxl-auto{margin-top:auto !important}.me-xxl-0{margin-right:0 !important}.me-xxl-1{margin-right:.25rem !important}.me-xxl-2{margin-right:.5rem !important}.me-xxl-3{margin-right:1rem !important}.me-xxl-4{margin-right:1.5rem !important}.me-xxl-5{margin-right:3rem !important}.me-xxl-auto{margin-right:auto !important}.mb-xxl-0{margin-bottom:0 !important}.mb-xxl-1{margin-bottom:.25rem !important}.mb-xxl-2{margin-bottom:.5rem !important}.mb-xxl-3{margin-bottom:1rem !important}.mb-xxl-4{margin-bottom:1.5rem !important}.mb-xxl-5{margin-bottom:3rem !important}.mb-xxl-auto{margin-bottom:auto !important}.ms-xxl-0{margin-left:0 !important}.ms-xxl-1{margin-left:.25rem !important}.ms-xxl-2{margin-left:.5rem !important}.ms-xxl-3{margin-left:1rem !important}.ms-xxl-4{margin-left:1.5rem !important}.ms-xxl-5{margin-left:3rem !important}.ms-xxl-auto{margin-left:auto !important}.p-xxl-0{padding:0 !important}.p-xxl-1{padding:.25rem !important}.p-xxl-2{padding:.5rem !important}.p-xxl-3{padding:1rem !important}.p-xxl-4{padding:1.5rem !important}.p-xxl-5{padding:3rem !important}.px-xxl-0{padding-right:0 !important;padding-left:0 !important}.px-xxl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xxl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xxl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xxl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xxl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xxl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xxl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xxl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xxl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xxl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xxl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xxl-0{padding-top:0 !important}.pt-xxl-1{padding-top:.25rem !important}.pt-xxl-2{padding-top:.5rem !important}.pt-xxl-3{padding-top:1rem !important}.pt-xxl-4{padding-top:1.5rem !important}.pt-xxl-5{padding-top:3rem !important}.pe-xxl-0{padding-right:0 !important}.pe-xxl-1{padding-right:.25rem !important}.pe-xxl-2{padding-right:.5rem !important}.pe-xxl-3{padding-right:1rem !important}.pe-xxl-4{padding-right:1.5rem !important}.pe-xxl-5{padding-right:3rem !important}.pb-xxl-0{padding-bottom:0 !important}.pb-xxl-1{padding-bottom:.25rem !important}.pb-xxl-2{padding-bottom:.5rem !important}.pb-xxl-3{padding-bottom:1rem !important}.pb-xxl-4{padding-bottom:1.5rem !important}.pb-xxl-5{padding-bottom:3rem !important}.ps-xxl-0{padding-left:0 !important}.ps-xxl-1{padding-left:.25rem !important}.ps-xxl-2{padding-left:.5rem !important}.ps-xxl-3{padding-left:1rem !important}.ps-xxl-4{padding-left:1.5rem !important}.ps-xxl-5{padding-left:3rem !important}.gap-xxl-0{gap:0 !important}.gap-xxl-1{gap:.25rem !important}.gap-xxl-2{gap:.5rem !important}.gap-xxl-3{gap:1rem !important}.gap-xxl-4{gap:1.5rem !important}.gap-xxl-5{gap:3rem !important}.row-gap-xxl-0{row-gap:0 !important}.row-gap-xxl-1{row-gap:.25rem !important}.row-gap-xxl-2{row-gap:.5rem !important}.row-gap-xxl-3{row-gap:1rem !important}.row-gap-xxl-4{row-gap:1.5rem !important}.row-gap-xxl-5{row-gap:3rem !important}.column-gap-xxl-0{column-gap:0 !important}.column-gap-xxl-1{column-gap:.25rem !important}.column-gap-xxl-2{column-gap:.5rem !important}.column-gap-xxl-3{column-gap:1rem !important}.column-gap-xxl-4{column-gap:1.5rem !important}.column-gap-xxl-5{column-gap:3rem !important}.text-xxl-start{text-align:left !important}.text-xxl-end{text-align:right !important}.text-xxl-center{text-align:center !important}}.bg-default{color:#fff}.bg-primary{color:#fff}.bg-secondary{color:#fff}.bg-success{color:#fff}.bg-info{color:#fff}.bg-warning{color:#fff}.bg-danger{color:#fff}.bg-light{color:#000}.bg-dark{color:#fff}@media(min-width: 1200px){.fs-1{font-size:2rem !important}.fs-2{font-size:1.65rem !important}.fs-3{font-size:1.45rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.bg-blue{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-blue{--bslib-color-fg: #2780e3;color:var(--bslib-color-fg)}.bg-indigo{--bslib-color-bg: #6610f2;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-indigo{--bslib-color-fg: #6610f2;color:var(--bslib-color-fg)}.bg-purple{--bslib-color-bg: #613d7c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-purple{--bslib-color-fg: #613d7c;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #ff0039;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #f0ad4e;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #f0ad4e;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #ff7518;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: #3fb618;color:var(--bslib-color-fg)}.bg-teal{--bslib-color-bg: #20c997;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-teal{--bslib-color-fg: #20c997;color:var(--bslib-color-fg)}.bg-cyan{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-cyan{--bslib-color-fg: #9954bb;color:var(--bslib-color-fg)}.text-default{--bslib-color-fg: #343a40}.bg-default{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-primary{--bslib-color-fg: #2780e3}.bg-primary{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff}.text-secondary{--bslib-color-fg: #343a40}.bg-secondary{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-success{--bslib-color-fg: #3fb618}.bg-success{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #9954bb}.bg-info{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #ff7518}.bg-warning{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #ff0039}.bg-danger{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #f8f9fa}.bg-light{--bslib-color-bg: #f8f9fa;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #343a40}.bg-dark{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4053e9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4053e9;color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3e65ba;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3e65ba;color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7466c0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7466c0;color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: #7d4d9f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #7d4d9f;color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: #7792a7;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #7792a7;color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #7d7c92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #7d7c92;color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: #319692;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #319692;color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: #249dc5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #249dc5;color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #556ed3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #556ed3;color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4d3dec;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4d3dec;color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: #6422c3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #6422c3;color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9a22c9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9a22c9;color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: #a30aa8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a30aa8;color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9d4fb0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9d4fb0;color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a3389b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a3389b;color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: #56529b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #56529b;color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4a5ace;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4a5ace;color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #7a2bdc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #7a2bdc;color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4a58a5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4a58a5;color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #632bab;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #632bab;color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: #973d82;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #973d82;color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: #a02561;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a02561;color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9a6a6a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9a6a6a;color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a05354;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a05354;color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: #536d54;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #536d54;color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: #477587;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #477587;color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #774695;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #774695;color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: #9b58af;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #9b58af;color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b42cb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b42cb5;color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b23e86;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b23e86;color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: #f1256b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f1256b;color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: #eb6a73;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #eb6a73;color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #f1545e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f1545e;color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: #a46e5e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a46e5e;color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: #987690;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #987690;color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #c8479f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #c8479f;color:#fff}.bg-gradient-red-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a9337d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a9337d;color:#fff}.bg-gradient-red-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c20683;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c20683;color:#fff}.bg-gradient-red-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c01854;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c01854;color:#fff}.bg-gradient-red-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f6195a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f6195a;color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: #f94541;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f94541;color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #ff2f2c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #ff2f2c;color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: #b2492c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b2492c;color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6505f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6505f;color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d6226d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d6226d;color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a09b8a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a09b8a;color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b96e90;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b96e90;color:#fff}.bg-gradient-orange-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b78060;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b78060;color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: #ed8167;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #ed8167;color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: #f66846;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f66846;color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #000;--bslib-color-bg: #f69738;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f69738;color:#000}.bg-gradient-orange-green{--bslib-color-fg: #000;--bslib-color-bg: #a9b138;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a9b138;color:#000}.bg-gradient-orange-teal{--bslib-color-fg: #000;--bslib-color-bg: #9db86b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #9db86b;color:#000}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #cd897a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #cd897a;color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a97969;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a97969;color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c24d6f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c24d6f;color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c05f40;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c05f40;color:#fff}.bg-gradient-yellow-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f65f46;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f65f46;color:#fff}.bg-gradient-yellow-red{--bslib-color-fg: #fff;--bslib-color-bg: #ff4625;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #ff4625;color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #000;--bslib-color-bg: #f98b2e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f98b2e;color:#000}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: #b28f18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b28f18;color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6974b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6974b;color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d66859;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d66859;color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: #35a069;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #35a069;color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4f746f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4f746f;color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4d8640;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #4d8640;color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: #838646;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #838646;color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: #8c6d25;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #8c6d25;color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #000;--bslib-color-bg: #86b22e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #86b22e;color:#000}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #8c9c18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #8c9c18;color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #33be4b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #33be4b;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #638f59;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #638f59;color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: #23acb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #23acb5;color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3a918c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3a918c;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #797971;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #797971;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #000;--bslib-color-bg: #73be7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #73be7a;color:#000}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #79a764;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #79a764;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #2cc164;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #2cc164;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #509aa5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #509aa5;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #6b66cb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #6b66cb;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #8539d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #8539d1;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #834ba2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #834ba2;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #b94ba8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #b94ba8;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #c23287;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #c23287;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #bc788f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #bc788f;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #c2617a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #c2617a;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #757b7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.bg-blue{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-blue{--bslib-color-fg: #2780e3;color:var(--bslib-color-fg)}.bg-indigo{--bslib-color-bg: #6610f2;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-indigo{--bslib-color-fg: #6610f2;color:var(--bslib-color-fg)}.bg-purple{--bslib-color-bg: #613d7c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-purple{--bslib-color-fg: #613d7c;color:var(--bslib-color-fg)}.bg-pink{--bslib-color-bg: #e83e8c;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-pink{--bslib-color-fg: #e83e8c;color:var(--bslib-color-fg)}.bg-red{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-red{--bslib-color-fg: #ff0039;color:var(--bslib-color-fg)}.bg-orange{--bslib-color-bg: #f0ad4e;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-orange{--bslib-color-fg: #f0ad4e;color:var(--bslib-color-fg)}.bg-yellow{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-yellow{--bslib-color-fg: #ff7518;color:var(--bslib-color-fg)}.bg-green{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-green{--bslib-color-fg: #3fb618;color:var(--bslib-color-fg)}.bg-teal{--bslib-color-bg: #20c997;--bslib-color-fg: #000;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-teal{--bslib-color-fg: #20c997;color:var(--bslib-color-fg)}.bg-cyan{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff;background-color:var(--bslib-color-bg);color:var(--bslib-color-fg)}.text-cyan{--bslib-color-fg: #9954bb;color:var(--bslib-color-fg)}.text-default{--bslib-color-fg: #343a40}.bg-default{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-primary{--bslib-color-fg: #2780e3}.bg-primary{--bslib-color-bg: #2780e3;--bslib-color-fg: #fff}.text-secondary{--bslib-color-fg: #343a40}.bg-secondary{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.text-success{--bslib-color-fg: #3fb618}.bg-success{--bslib-color-bg: #3fb618;--bslib-color-fg: #fff}.text-info{--bslib-color-fg: #9954bb}.bg-info{--bslib-color-bg: #9954bb;--bslib-color-fg: #fff}.text-warning{--bslib-color-fg: #ff7518}.bg-warning{--bslib-color-bg: #ff7518;--bslib-color-fg: #fff}.text-danger{--bslib-color-fg: #ff0039}.bg-danger{--bslib-color-bg: #ff0039;--bslib-color-fg: #fff}.text-light{--bslib-color-fg: #f8f9fa}.bg-light{--bslib-color-bg: #f8f9fa;--bslib-color-fg: #000}.text-dark{--bslib-color-fg: #343a40}.bg-dark{--bslib-color-bg: #343a40;--bslib-color-fg: #fff}.bg-gradient-blue-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4053e9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4053e9;color:#fff}.bg-gradient-blue-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3e65ba;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3e65ba;color:#fff}.bg-gradient-blue-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7466c0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7466c0;color:#fff}.bg-gradient-blue-red{--bslib-color-fg: #fff;--bslib-color-bg: #7d4d9f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #7d4d9f;color:#fff}.bg-gradient-blue-orange{--bslib-color-fg: #fff;--bslib-color-bg: #7792a7;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #7792a7;color:#fff}.bg-gradient-blue-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #7d7c92;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #7d7c92;color:#fff}.bg-gradient-blue-green{--bslib-color-fg: #fff;--bslib-color-bg: #319692;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #319692;color:#fff}.bg-gradient-blue-teal{--bslib-color-fg: #fff;--bslib-color-bg: #249dc5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #249dc5;color:#fff}.bg-gradient-blue-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #556ed3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #2780e3 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #556ed3;color:#fff}.bg-gradient-indigo-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4d3dec;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4d3dec;color:#fff}.bg-gradient-indigo-purple{--bslib-color-fg: #fff;--bslib-color-bg: #6422c3;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #6422c3;color:#fff}.bg-gradient-indigo-pink{--bslib-color-fg: #fff;--bslib-color-bg: #9a22c9;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #9a22c9;color:#fff}.bg-gradient-indigo-red{--bslib-color-fg: #fff;--bslib-color-bg: #a30aa8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a30aa8;color:#fff}.bg-gradient-indigo-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9d4fb0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9d4fb0;color:#fff}.bg-gradient-indigo-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a3389b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a3389b;color:#fff}.bg-gradient-indigo-green{--bslib-color-fg: #fff;--bslib-color-bg: #56529b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #56529b;color:#fff}.bg-gradient-indigo-teal{--bslib-color-fg: #fff;--bslib-color-bg: #4a5ace;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #4a5ace;color:#fff}.bg-gradient-indigo-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #7a2bdc;background:linear-gradient(var(--bg-gradient-deg, 140deg), #6610f2 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #7a2bdc;color:#fff}.bg-gradient-purple-blue{--bslib-color-fg: #fff;--bslib-color-bg: #4a58a5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #4a58a5;color:#fff}.bg-gradient-purple-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #632bab;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #632bab;color:#fff}.bg-gradient-purple-pink{--bslib-color-fg: #fff;--bslib-color-bg: #973d82;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #973d82;color:#fff}.bg-gradient-purple-red{--bslib-color-fg: #fff;--bslib-color-bg: #a02561;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #a02561;color:#fff}.bg-gradient-purple-orange{--bslib-color-fg: #fff;--bslib-color-bg: #9a6a6a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #9a6a6a;color:#fff}.bg-gradient-purple-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #a05354;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #a05354;color:#fff}.bg-gradient-purple-green{--bslib-color-fg: #fff;--bslib-color-bg: #536d54;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #536d54;color:#fff}.bg-gradient-purple-teal{--bslib-color-fg: #fff;--bslib-color-bg: #477587;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #477587;color:#fff}.bg-gradient-purple-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #774695;background:linear-gradient(var(--bg-gradient-deg, 140deg), #613d7c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #774695;color:#fff}.bg-gradient-pink-blue{--bslib-color-fg: #fff;--bslib-color-bg: #9b58af;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #9b58af;color:#fff}.bg-gradient-pink-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b42cb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b42cb5;color:#fff}.bg-gradient-pink-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b23e86;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b23e86;color:#fff}.bg-gradient-pink-red{--bslib-color-fg: #fff;--bslib-color-bg: #f1256b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f1256b;color:#fff}.bg-gradient-pink-orange{--bslib-color-fg: #fff;--bslib-color-bg: #eb6a73;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #eb6a73;color:#fff}.bg-gradient-pink-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #f1545e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f1545e;color:#fff}.bg-gradient-pink-green{--bslib-color-fg: #fff;--bslib-color-bg: #a46e5e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a46e5e;color:#fff}.bg-gradient-pink-teal{--bslib-color-fg: #fff;--bslib-color-bg: #987690;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #987690;color:#fff}.bg-gradient-pink-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #c8479f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #e83e8c var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #c8479f;color:#fff}.bg-gradient-red-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a9337d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a9337d;color:#fff}.bg-gradient-red-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c20683;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c20683;color:#fff}.bg-gradient-red-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c01854;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c01854;color:#fff}.bg-gradient-red-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f6195a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f6195a;color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: #f94541;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f94541;color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #ff2f2c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #ff2f2c;color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: #b2492c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b2492c;color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6505f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6505f;color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d6226d;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff0039 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d6226d;color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a09b8a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a09b8a;color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #b96e90;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #b96e90;color:#fff}.bg-gradient-orange-purple{--bslib-color-fg: #fff;--bslib-color-bg: #b78060;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #b78060;color:#fff}.bg-gradient-orange-pink{--bslib-color-fg: #fff;--bslib-color-bg: #ed8167;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #ed8167;color:#fff}.bg-gradient-orange-red{--bslib-color-fg: #fff;--bslib-color-bg: #f66846;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #f66846;color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #000;--bslib-color-bg: #f69738;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #f69738;color:#000}.bg-gradient-orange-green{--bslib-color-fg: #000;--bslib-color-bg: #a9b138;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #a9b138;color:#000}.bg-gradient-orange-teal{--bslib-color-fg: #000;--bslib-color-bg: #9db86b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #9db86b;color:#000}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #cd897a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #f0ad4e var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #cd897a;color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: #a97969;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #a97969;color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #c24d6f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #c24d6f;color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: #c05f40;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #c05f40;color:#fff}.bg-gradient-yellow-pink{--bslib-color-fg: #fff;--bslib-color-bg: #f65f46;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #f65f46;color:#fff}.bg-gradient-yellow-red{--bslib-color-fg: #fff;--bslib-color-bg: #ff4625;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #ff4625;color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #000;--bslib-color-bg: #f98b2e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #f98b2e;color:#000}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: #b28f18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #b28f18;color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: #a6974b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #a6974b;color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #d66859;background:linear-gradient(var(--bg-gradient-deg, 140deg), #ff7518 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #d66859;color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: #35a069;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #35a069;color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #4f746f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #4f746f;color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: #4d8640;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #4d8640;color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: #838646;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #838646;color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: #8c6d25;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #8c6d25;color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #000;--bslib-color-bg: #86b22e;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #86b22e;color:#000}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #8c9c18;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #8c9c18;color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #000;--bslib-color-bg: #33be4b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #33be4b;color:#000}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #638f59;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3fb618 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #638f59;color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: #23acb5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #23acb5;color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #3c7fbb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #3c7fbb;color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: #3a918c;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #3a918c;color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: #709193;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #709193;color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: #797971;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #797971;color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #000;--bslib-color-bg: #73be7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #73be7a;color:#000}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #79a764;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #79a764;color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #000;--bslib-color-bg: #2cc164;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #2cc164;color:#000}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: #509aa5;background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #9954bb var(--bg-gradient-end, 180%)) #509aa5;color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: #6b66cb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #2780e3 var(--bg-gradient-end, 180%)) #6b66cb;color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: #8539d1;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) #8539d1;color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: #834ba2;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #613d7c var(--bg-gradient-end, 180%)) #834ba2;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #b94ba8;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #b94ba8;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #c23287;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff0039 var(--bg-gradient-end, 180%)) #c23287;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #bc788f;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #f0ad4e var(--bg-gradient-end, 180%)) #bc788f;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #c2617a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #ff7518 var(--bg-gradient-end, 180%)) #c2617a;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #757b7a;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #3fb618 var(--bg-gradient-end, 180%)) #757b7a;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #6983ad;background:linear-gradient(var(--bg-gradient-deg, 140deg), #9954bb var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #6983ad;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #fff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xs)}@media(min-width: 576px){.bslib-grid.grid.bslib-grid--row-heights--sm{--bslib-grid--row-heights: var(--bslib-grid--row-heights--sm)}}@media(min-width: 768px){.bslib-grid.grid.bslib-grid--row-heights--md{--bslib-grid--row-heights: var(--bslib-grid--row-heights--md)}}@media(min-width: 992px){.bslib-grid.grid.bslib-grid--row-heights--lg{--bslib-grid--row-heights: var(--bslib-grid--row-heights--lg)}}@media(min-width: 1200px){.bslib-grid.grid.bslib-grid--row-heights--xl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xl)}}@media(min-width: 1400px){.bslib-grid.grid.bslib-grid--row-heights--xxl{--bslib-grid--row-heights: var(--bslib-grid--row-heights--xxl)}}.bslib-grid>*>.shiny-input-container{width:100%}.bslib-grid-item{grid-column:auto/span 1}@media(max-width: 767.98px){.bslib-grid-item{grid-column:1/-1}}@media(max-width: 575.98px){.bslib-grid{grid-template-columns:1fr !important;height:var(--bslib-grid-height-mobile)}.bslib-grid.grid{height:unset !important;grid-auto-rows:var(--bslib-grid--row-heights--xs, auto)}}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.navbar+.container-fluid:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-sm:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-md:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-lg:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xl:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xxl:has(>.tab-content>.tab-pane.active.html-fill-container){padding-left:0;padding-right:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container{padding:var(--bslib-spacer, 1rem);gap:var(--bslib-spacer, 1rem)}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container:has(>.bslib-sidebar-layout:only-child){padding:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border=true]){border-left:none;border-right:none;border-bottom:none}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]),.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container>.bslib-sidebar-layout:only-child:not([data-bslib-sidebar-border-radius=true]){border-radius:0}.navbar+div>.bslib-sidebar-layout{border-top:var(--bslib-sidebar-border)}html{height:100%}.bslib-page-fill{width:100%;height:100%;margin:0;padding:var(--bslib-spacer, 1rem);gap:var(--bslib-spacer, 1rem)}@media(max-width: 575.98px){.bslib-page-fill{height:var(--bslib-page-fill-mobile-height, auto)}}:root{--bslib-page-sidebar-title-bg: #f8f9fa;--bslib-page-sidebar-title-color: #000}.bslib-page-title{background-color:var(--bslib-page-sidebar-title-bg);color:var(--bslib-page-sidebar-title-color);font-size:1.25rem;font-weight:300;padding:var(--bslib-spacer, 1rem);padding-left:1.5rem;margin-bottom:0;border-bottom:1px solid #dee2e6}.bslib-sidebar-layout{--bslib-sidebar-transition-duration: 500ms;--bslib-sidebar-transition-easing-x: cubic-bezier(0.8, 0.78, 0.22, 1.07);--bslib-sidebar-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-border-radius: var(--bs-border-radius);--bslib-sidebar-vert-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--bslib-sidebar-fg: var(--bs-emphasis-color, black);--bslib-sidebar-main-fg: var(--bs-card-color, var(--bs-body-color));--bslib-sidebar-main-bg: var(--bs-card-bg, var(--bs-body-bg));--bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--bslib-sidebar-padding: calc(var(--bslib-spacer) * 1.5);--bslib-sidebar-icon-size: var(--bslib-spacer, 1rem);--bslib-sidebar-icon-button-size: calc(var(--bslib-sidebar-icon-size, 1rem) * 2);--bslib-sidebar-padding-icon: calc(var(--bslib-sidebar-icon-button-size, 2rem) * 1.5);--bslib-collapse-toggle-border-radius: var(--bs-border-radius, 0.25rem);--bslib-collapse-toggle-transform: 0deg;--bslib-sidebar-toggle-transition-easing: cubic-bezier(1, 0, 0, 1);--bslib-collapse-toggle-right-transform: 180deg;--bslib-sidebar-column-main: minmax(0, 1fr);display:grid !important;grid-template-columns:min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 250px)) var(--bslib-sidebar-column-main);position:relative;transition:grid-template-columns ease-in-out var(--bslib-sidebar-transition-duration);border:var(--bslib-sidebar-border);border-radius:var(--bslib-sidebar-border-radius)}@media(prefers-reduced-motion: reduce){.bslib-sidebar-layout{transition:none}}.bslib-sidebar-layout[data-bslib-sidebar-border=false]{border:none}.bslib-sidebar-layout[data-bslib-sidebar-border-radius=false]{border-radius:initial}.bslib-sidebar-layout>.main,.bslib-sidebar-layout>.sidebar{grid-row:1/2;border-radius:inherit;overflow:auto}.bslib-sidebar-layout>.main{grid-column:2/3;border-top-left-radius:0;border-bottom-left-radius:0;padding:var(--bslib-sidebar-padding);transition:padding var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration);color:var(--bslib-sidebar-main-fg);background-color:var(--bslib-sidebar-main-bg)}.bslib-sidebar-layout>.sidebar{grid-column:1/2;width:100%;height:100%;border-right:var(--bslib-sidebar-vert-border);border-top-right-radius:0;border-bottom-right-radius:0;color:var(--bslib-sidebar-fg);background-color:var(--bslib-sidebar-bg);backdrop-filter:blur(5px)}.bslib-sidebar-layout>.sidebar>.sidebar-content{display:flex;flex-direction:column;gap:var(--bslib-spacer, 1rem);padding:var(--bslib-sidebar-padding);padding-top:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout>.sidebar>.sidebar-content>:last-child:not(.sidebar-title){margin-bottom:0}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion{margin-left:calc(-1*var(--bslib-sidebar-padding));margin-right:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:last-child{margin-bottom:calc(-1*var(--bslib-sidebar-padding))}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child){margin-bottom:1rem}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-body{display:flex;flex-direction:column}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:first-child) .accordion-item:first-child{border-top:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion:not(:last-child) .accordion-item:last-child{border-bottom:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.bslib-sidebar-layout>.sidebar>.sidebar-content.has-accordion>.sidebar-title{border-bottom:none;padding-bottom:0}.bslib-sidebar-layout>.sidebar .shiny-input-container{width:100%}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar>.sidebar-content{padding-top:var(--bslib-sidebar-padding)}.bslib-sidebar-layout>.collapse-toggle{grid-row:1/2;grid-column:1/2;display:inline-flex;align-items:center;position:absolute;right:calc(var(--bslib-sidebar-icon-size));top:calc(var(--bslib-sidebar-icon-size, 1rem)/2);border:none;border-radius:var(--bslib-collapse-toggle-border-radius);height:var(--bslib-sidebar-icon-button-size, 2rem);width:var(--bslib-sidebar-icon-button-size, 2rem);display:flex;align-items:center;justify-content:center;padding:0;color:var(--bslib-sidebar-fg);background-color:unset;transition:color var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),top var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),right var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration),left var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover{background-color:var(--bslib-sidebar-toggle-bg)}.bslib-sidebar-layout>.collapse-toggle>.collapse-icon{opacity:.8;width:var(--bslib-sidebar-icon-size);height:var(--bslib-sidebar-icon-size);transform:rotateY(var(--bslib-collapse-toggle-transform));transition:transform var(--bslib-sidebar-toggle-transition-easing) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout>.collapse-toggle:hover>.collapse-icon{opacity:1}.bslib-sidebar-layout .sidebar-title{font-size:1.25rem;line-height:1.25;margin-top:0;margin-bottom:1rem;padding-bottom:1rem;border-bottom:var(--bslib-sidebar-border)}.bslib-sidebar-layout.sidebar-right{grid-template-columns:var(--bslib-sidebar-column-main) min(100% - var(--bslib-sidebar-icon-size),var(--bslib-sidebar-width, 250px))}.bslib-sidebar-layout.sidebar-right>.main{grid-column:1/2;border-top-right-radius:0;border-bottom-right-radius:0;border-top-left-radius:inherit;border-bottom-left-radius:inherit}.bslib-sidebar-layout.sidebar-right>.sidebar{grid-column:2/3;border-right:none;border-left:var(--bslib-sidebar-vert-border);border-top-left-radius:0;border-bottom-left-radius:0}.bslib-sidebar-layout.sidebar-right>.collapse-toggle{grid-column:2/3;left:var(--bslib-sidebar-icon-size);right:unset;border:var(--bslib-collapse-toggle-border)}.bslib-sidebar-layout.sidebar-right>.collapse-toggle>.collapse-icon{transform:rotateY(var(--bslib-collapse-toggle-right-transform))}.bslib-sidebar-layout.sidebar-collapsed{--bslib-collapse-toggle-transform: 180deg;--bslib-collapse-toggle-right-transform: 0deg;--bslib-sidebar-vert-border: none;grid-template-columns:0 minmax(0, 1fr)}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right{grid-template-columns:minmax(0, 1fr) 0}.bslib-sidebar-layout.sidebar-collapsed:not(.transitioning)>.sidebar>*{display:none}.bslib-sidebar-layout.sidebar-collapsed>.main{border-radius:inherit}.bslib-sidebar-layout.sidebar-collapsed:not(.sidebar-right)>.main{padding-left:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.main{padding-right:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout.sidebar-collapsed>.collapse-toggle{color:var(--bslib-sidebar-main-fg);top:calc(var(--bslib-sidebar-overlap-counter, 0)*(var(--bslib-sidebar-icon-size) + var(--bslib-sidebar-padding)) + var(--bslib-sidebar-icon-size, 1rem)/2);right:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px))}.bslib-sidebar-layout.sidebar-collapsed.sidebar-right>.collapse-toggle{left:calc(-2.5*var(--bslib-sidebar-icon-size) - var(--bs-card-border-width, 1px));right:unset}@media(min-width: 576px){.bslib-sidebar-layout.transitioning>.sidebar>.sidebar-content{display:none}}@media(max-width: 575.98px){.bslib-sidebar-layout[data-bslib-sidebar-open=desktop]{--bslib-sidebar-js-init-collapsed: true}.bslib-sidebar-layout>.sidebar,.bslib-sidebar-layout.sidebar-right>.sidebar{border:none}.bslib-sidebar-layout>.main,.bslib-sidebar-layout.sidebar-right>.main{grid-column:1/3}.bslib-sidebar-layout[data-bslib-sidebar-open=always]{display:block !important}.bslib-sidebar-layout[data-bslib-sidebar-open=always]>.sidebar{max-height:var(--bslib-sidebar-max-height-mobile);overflow-y:auto;border-top:var(--bslib-sidebar-vert-border)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]){grid-template-columns:100% 0}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.sidebar{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-collapsed)>.collapse-toggle{z-index:1}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-right{grid-template-columns:0 100%}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed{grid-template-columns:0 100%}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed.sidebar-right{grid-template-columns:100% 0}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]):not(.sidebar-right)>.main{padding-left:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-right>.main{padding-right:var(--bslib-sidebar-padding-icon)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always])>.main{opacity:0;transition:opacity var(--bslib-sidebar-transition-easing-x) var(--bslib-sidebar-transition-duration)}.bslib-sidebar-layout:not([data-bslib-sidebar-open=always]).sidebar-collapsed>.main{opacity:1}}.html-fill-container{display:flex;flex-direction:column;min-height:0;min-width:0}.html-fill-container>.html-fill-item{flex:1 1 auto;min-height:0;min-width:0}.html-fill-container>:not(.html-fill-item){flex:0 0 auto}.quarto-container{min-height:calc(100vh - 132px)}body.hypothesis-enabled #quarto-header{margin-right:16px}footer.footer .nav-footer,#quarto-header>nav{padding-left:1em;padding-right:1em}footer.footer div.nav-footer p:first-child{margin-top:0}footer.footer div.nav-footer p:last-child{margin-bottom:0}#quarto-content>*{padding-top:14px}#quarto-content>#quarto-sidebar-glass{padding-top:0px}@media(max-width: 991.98px){#quarto-content>*{padding-top:0}#quarto-content .subtitle{padding-top:14px}#quarto-content section:first-of-type h2:first-of-type,#quarto-content section:first-of-type .h2:first-of-type{margin-top:1rem}}.headroom-target,header.headroom{will-change:transform;transition:position 200ms linear;transition:all 200ms linear}header.headroom--pinned{transform:translateY(0%)}header.headroom--unpinned{transform:translateY(-100%)}.navbar-container{width:100%}.navbar-brand{overflow:hidden;text-overflow:ellipsis}.navbar-brand-container{max-width:calc(100% - 115px);min-width:0;display:flex;align-items:center}@media(min-width: 992px){.navbar-brand-container{margin-right:1em}}.navbar-brand.navbar-brand-logo{margin-right:4px;display:inline-flex}.navbar-toggler{flex-basis:content;flex-shrink:0}.navbar .navbar-brand-container{order:2}.navbar .navbar-toggler{order:1}.navbar .navbar-container>.navbar-nav{order:20}.navbar .navbar-container>.navbar-brand-container{margin-left:0 !important;margin-right:0 !important}.navbar .navbar-collapse{order:20}.navbar #quarto-search{order:4;margin-left:auto}.navbar .navbar-toggler{margin-right:.5em}.navbar-logo{max-height:24px;width:auto;padding-right:4px}nav .nav-item:not(.compact){padding-top:1px}nav .nav-link i,nav .dropdown-item i{padding-right:1px}.navbar-expand-lg .navbar-nav .nav-link{padding-left:.6rem;padding-right:.6rem}nav .nav-item.compact .nav-link{padding-left:.5rem;padding-right:.5rem;font-size:1.1rem}.navbar .quarto-navbar-tools{order:3}.navbar .quarto-navbar-tools div.dropdown{display:inline-block}.navbar .quarto-navbar-tools .quarto-navigation-tool{color:#545555}.navbar .quarto-navbar-tools .quarto-navigation-tool:hover{color:#1f4eb6}.navbar-nav .dropdown-menu{min-width:220px;font-size:.9rem}.navbar .navbar-nav .nav-link.dropdown-toggle::after{opacity:.75;vertical-align:.175em}.navbar ul.dropdown-menu{padding-top:0;padding-bottom:0}.navbar .dropdown-header{text-transform:uppercase;font-size:.8rem;padding:0 .5rem}.navbar .dropdown-item{padding:.4rem .5rem}.navbar .dropdown-item>i.bi{margin-left:.1rem;margin-right:.25em}.sidebar #quarto-search{margin-top:-1px}.sidebar #quarto-search svg.aa-SubmitIcon{width:16px;height:16px}.sidebar-navigation a{color:inherit}.sidebar-title{margin-top:.25rem;padding-bottom:.5rem;font-size:1.3rem;line-height:1.6rem;visibility:visible}.sidebar-title>a{font-size:inherit;text-decoration:none}.sidebar-title .sidebar-tools-main{margin-top:-6px}@media(max-width: 991.98px){#quarto-sidebar div.sidebar-header{padding-top:.2em}}.sidebar-header-stacked .sidebar-title{margin-top:.6rem}.sidebar-logo{max-width:90%;padding-bottom:.5rem}.sidebar-logo-link{text-decoration:none}.sidebar-navigation li a{text-decoration:none}.sidebar-navigation .quarto-navigation-tool{opacity:.7;font-size:.875rem}#quarto-sidebar>nav>.sidebar-tools-main{margin-left:14px}.sidebar-tools-main{display:inline-flex;margin-left:0px;order:2}.sidebar-tools-main:not(.tools-wide){vertical-align:middle}.sidebar-navigation .quarto-navigation-tool.dropdown-toggle::after{display:none}.sidebar.sidebar-navigation>*{padding-top:1em}.sidebar-item{margin-bottom:.2em;line-height:1rem;margin-top:.4rem}.sidebar-section{padding-left:.5em;padding-bottom:.2em}.sidebar-item .sidebar-item-container{display:flex;justify-content:space-between;cursor:pointer}.sidebar-item-toggle:hover{cursor:pointer}.sidebar-item .sidebar-item-toggle .bi{font-size:.7rem;text-align:center}.sidebar-item .sidebar-item-toggle .bi-chevron-right::before{transition:transform 200ms ease}.sidebar-item .sidebar-item-toggle[aria-expanded=false] .bi-chevron-right::before{transform:none}.sidebar-item .sidebar-item-toggle[aria-expanded=true] .bi-chevron-right::before{transform:rotate(90deg)}.sidebar-item-text{width:100%}.sidebar-navigation .sidebar-divider{margin-left:0;margin-right:0;margin-top:.5rem;margin-bottom:.5rem}@media(max-width: 991.98px){.quarto-secondary-nav{display:block}.quarto-secondary-nav button.quarto-search-button{padding-right:0em;padding-left:2em}.quarto-secondary-nav button.quarto-btn-toggle{margin-left:-0.75rem;margin-right:.15rem}.quarto-secondary-nav nav.quarto-title-breadcrumbs{display:none}.quarto-secondary-nav nav.quarto-page-breadcrumbs{display:flex;align-items:center;padding-right:1em;margin-left:-0.25em}.quarto-secondary-nav nav.quarto-page-breadcrumbs a{text-decoration:none}.quarto-secondary-nav nav.quarto-page-breadcrumbs ol.breadcrumb{margin-bottom:0}}@media(min-width: 992px){.quarto-secondary-nav{display:none}}.quarto-title-breadcrumbs .breadcrumb{margin-bottom:.5em;font-size:.9rem}.quarto-title-breadcrumbs .breadcrumb li:last-of-type a{color:#6c757d}.quarto-secondary-nav .quarto-btn-toggle{color:#595959}.quarto-secondary-nav[aria-expanded=false] .quarto-btn-toggle .bi-chevron-right::before{transform:none}.quarto-secondary-nav[aria-expanded=true] .quarto-btn-toggle .bi-chevron-right::before{transform:rotate(90deg)}.quarto-secondary-nav .quarto-btn-toggle .bi-chevron-right::before{transition:transform 200ms ease}.quarto-secondary-nav{cursor:pointer}.no-decor{text-decoration:none}.quarto-secondary-nav-title{margin-top:.3em;color:#595959;padding-top:4px}.quarto-secondary-nav nav.quarto-page-breadcrumbs{color:#595959}.quarto-secondary-nav nav.quarto-page-breadcrumbs a{color:#595959}.quarto-secondary-nav nav.quarto-page-breadcrumbs a:hover{color:rgba(33,81,191,.8)}.quarto-secondary-nav nav.quarto-page-breadcrumbs .breadcrumb-item::before{color:#8c8c8c}.breadcrumb-item{line-height:1.2rem}div.sidebar-item-container{color:#595959}div.sidebar-item-container:hover,div.sidebar-item-container:focus{color:rgba(33,81,191,.8)}div.sidebar-item-container.disabled{color:rgba(89,89,89,.75)}div.sidebar-item-container .active,div.sidebar-item-container .show>.nav-link,div.sidebar-item-container .sidebar-link>code{color:#2151bf}div.sidebar.sidebar-navigation.rollup.quarto-sidebar-toggle-contents,nav.sidebar.sidebar-navigation:not(.rollup){background-color:#fff}.sidebar.sidebar-navigation:not(.rollup){border-right:1px solid #dee2e6 !important}@media(max-width: 991.98px){.sidebar-navigation .sidebar-item a,.nav-page .nav-page-text,.sidebar-navigation{font-size:1rem}.sidebar-navigation ul.sidebar-section.depth1 .sidebar-section-item{font-size:1.1rem}.sidebar-logo{display:none}.sidebar.sidebar-navigation{position:static;border-bottom:1px solid #dee2e6}.sidebar.sidebar-navigation.collapsing{position:fixed;z-index:1000}.sidebar.sidebar-navigation.show{position:fixed;z-index:1000}.sidebar.sidebar-navigation{min-height:100%}nav.quarto-secondary-nav{background-color:#fff;border-bottom:1px solid #dee2e6}.quarto-banner nav.quarto-secondary-nav{background-color:#f8f9fa;color:#545555;border-top:1px solid #dee2e6}.sidebar .sidebar-footer{visibility:visible;padding-top:1rem;position:inherit}.sidebar-tools-collapse{display:block}}#quarto-sidebar{transition:width .15s ease-in}#quarto-sidebar>*{padding-right:1em}@media(max-width: 991.98px){#quarto-sidebar .sidebar-menu-container{white-space:nowrap;min-width:225px}#quarto-sidebar.show{transition:width .15s ease-out}}@media(min-width: 992px){#quarto-sidebar{display:flex;flex-direction:column}.nav-page .nav-page-text,.sidebar-navigation .sidebar-section .sidebar-item{font-size:.875rem}.sidebar-navigation .sidebar-item{font-size:.925rem}.sidebar.sidebar-navigation{display:block;position:sticky}.sidebar-search{width:100%}.sidebar .sidebar-footer{visibility:visible}}@media(max-width: 991.98px){#quarto-sidebar-glass{position:fixed;top:0;bottom:0;left:0;right:0;background-color:rgba(255,255,255,0);transition:background-color .15s ease-in;z-index:-1}#quarto-sidebar-glass.collapsing{z-index:1000}#quarto-sidebar-glass.show{transition:background-color .15s ease-out;background-color:rgba(102,102,102,.4);z-index:1000}}.sidebar .sidebar-footer{padding:.5rem 1rem;align-self:flex-end;color:#6c757d;width:100%}.quarto-page-breadcrumbs .breadcrumb-item+.breadcrumb-item,.quarto-page-breadcrumbs .breadcrumb-item{padding-right:.33em;padding-left:0}.quarto-page-breadcrumbs .breadcrumb-item::before{padding-right:.33em}.quarto-sidebar-footer{font-size:.875em}.sidebar-section .bi-chevron-right{vertical-align:middle}.sidebar-section .bi-chevron-right::before{font-size:.9em}.notransition{-webkit-transition:none !important;-moz-transition:none !important;-o-transition:none !important;transition:none !important}.btn:focus:not(:focus-visible){box-shadow:none}.page-navigation{display:flex;justify-content:space-between}.nav-page{padding-bottom:.75em}.nav-page .bi{font-size:1.8rem;vertical-align:middle}.nav-page .nav-page-text{padding-left:.25em;padding-right:.25em}.nav-page a{color:#6c757d;text-decoration:none;display:flex;align-items:center}.nav-page a:hover{color:#1f4eb6}.nav-footer .toc-actions{padding-bottom:.5em;padding-top:.5em}.nav-footer .toc-actions a,.nav-footer .toc-actions a:hover{text-decoration:none}.nav-footer .toc-actions ul{display:flex;list-style:none}.nav-footer .toc-actions ul :first-child{margin-left:auto}.nav-footer .toc-actions ul :last-child{margin-right:auto}.nav-footer .toc-actions ul li{padding-right:1.5em}.nav-footer .toc-actions ul li i.bi{padding-right:.4em}.nav-footer .toc-actions ul li:last-of-type{padding-right:0}.nav-footer{display:flex;flex-direction:row;flex-wrap:wrap;justify-content:space-between;align-items:baseline;text-align:center;padding-top:.5rem;padding-bottom:.5rem;background-color:#fff}body.nav-fixed{padding-top:64px}.nav-footer-contents{color:#6c757d;margin-top:.25rem}.nav-footer{min-height:3.5em;color:#757575}.nav-footer a{color:#757575}.nav-footer .nav-footer-left{font-size:.825em}.nav-footer .nav-footer-center{font-size:.825em}.nav-footer .nav-footer-right{font-size:.825em}.nav-footer-left .footer-items,.nav-footer-center .footer-items,.nav-footer-right .footer-items{display:inline-flex;padding-top:.3em;padding-bottom:.3em;margin-bottom:0em}.nav-footer-left .footer-items .nav-link,.nav-footer-center .footer-items .nav-link,.nav-footer-right .footer-items .nav-link{padding-left:.6em;padding-right:.6em}.nav-footer-left{flex:1 1 0px;text-align:left}.nav-footer-right{flex:1 1 0px;text-align:right}.nav-footer-center{flex:1 1 0px;min-height:3em;text-align:center}.nav-footer-center .footer-items{justify-content:center}@media(max-width: 767.98px){.nav-footer-center{margin-top:3em}}.navbar .quarto-reader-toggle.reader .quarto-reader-toggle-btn{background-color:#545555;border-radius:3px}@media(max-width: 991.98px){.quarto-reader-toggle{display:none}}.quarto-reader-toggle.reader.quarto-navigation-tool .quarto-reader-toggle-btn{background-color:#595959;border-radius:3px}.quarto-reader-toggle .quarto-reader-toggle-btn{display:inline-flex;padding-left:.2em;padding-right:.2em;margin-left:-0.2em;margin-right:-0.2em;text-align:center}.navbar .quarto-reader-toggle:not(.reader) .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(84, 85, 85, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.navbar .quarto-reader-toggle.reader .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(248, 249, 250, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.sidebar-navigation .quarto-reader-toggle:not(.reader) .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(89, 89, 89, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}.sidebar-navigation .quarto-reader-toggle.reader .bi::before{background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgba(255, 255, 255, 1)" class="bi bi-body-text" viewBox="0 0 16 16"><path fill-rule="evenodd" d="M0 .5A.5.5 0 0 1 .5 0h4a.5.5 0 0 1 0 1h-4A.5.5 0 0 1 0 .5Zm0 2A.5.5 0 0 1 .5 2h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm9 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-9 2A.5.5 0 0 1 .5 4h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm5 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5Zm-12 2A.5.5 0 0 1 .5 6h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5Zm8 0a.5.5 0 0 1 .5-.5h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm-8 2A.5.5 0 0 1 .5 8h5a.5.5 0 0 1 0 1h-5a.5.5 0 0 1-.5-.5Zm7 0a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5Zm-7 2a.5.5 0 0 1 .5-.5h8a.5.5 0 0 1 0 1h-8a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h4a.5.5 0 0 1 0 1h-4a.5.5 0 0 1-.5-.5Zm0 2a.5.5 0 0 1 .5-.5h2a.5.5 0 0 1 0 1h-2a.5.5 0 0 1-.5-.5Z"/></svg>')}#quarto-back-to-top{display:none;position:fixed;bottom:50px;background-color:#fff;border-radius:.25rem;box-shadow:0 .2rem .5rem #6c757d,0 0 .05rem #6c757d;color:#6c757d;text-decoration:none;font-size:.9em;text-align:center;left:50%;padding:.4rem .8rem;transform:translate(-50%, 0)}.aa-DetachedSearchButtonQuery{display:none}.aa-DetachedOverlay ul.aa-List,#quarto-search-results ul.aa-List{list-style:none;padding-left:0}.aa-DetachedOverlay .aa-Panel,#quarto-search-results .aa-Panel{background-color:#fff;position:absolute;z-index:2000}#quarto-search-results .aa-Panel{max-width:400px}#quarto-search input{font-size:.925rem}@media(min-width: 992px){.navbar #quarto-search{margin-left:.25rem;order:999}}.navbar.navbar-expand-sm #quarto-search,.navbar.navbar-expand-md #quarto-search{order:999}@media(min-width: 992px){.navbar .quarto-navbar-tools{order:900}}@media(min-width: 992px){.navbar .quarto-navbar-tools.tools-end{margin-left:auto !important}}@media(max-width: 991.98px){#quarto-sidebar .sidebar-search{display:none}}#quarto-sidebar .sidebar-search .aa-Autocomplete{width:100%}.navbar .aa-Autocomplete .aa-Form{width:180px}.navbar #quarto-search.type-overlay .aa-Autocomplete{width:40px}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form{background-color:inherit;border:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form:focus-within{box-shadow:none;outline:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-InputWrapper{display:none}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-InputWrapper:focus-within{display:inherit}.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-Label svg,.navbar #quarto-search.type-overlay .aa-Autocomplete .aa-Form .aa-LoadingIndicator svg{width:26px;height:26px;color:#545555;opacity:1}.navbar #quarto-search.type-overlay .aa-Autocomplete svg.aa-SubmitIcon{width:26px;height:26px;color:#545555;opacity:1}.aa-Autocomplete .aa-Form,.aa-DetachedFormContainer .aa-Form{align-items:center;background-color:#fff;border:1px solid #dee2e6;border-radius:.25rem;color:#343a40;display:flex;line-height:1em;margin:0;position:relative;width:100%}.aa-Autocomplete .aa-Form:focus-within,.aa-DetachedFormContainer .aa-Form:focus-within{box-shadow:rgba(39,128,227,.6) 0 0 0 1px;outline:currentColor none medium}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix{align-items:center;display:flex;flex-shrink:0;order:1}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-Label,.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-Label,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator{cursor:initial;flex-shrink:0;padding:0;text-align:left}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-Label svg,.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-Label svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator svg{color:#343a40;opacity:.5}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-SubmitButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-SubmitButton{appearance:none;background:none;border:0;margin:0}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator{align-items:center;display:flex;justify-content:center}.aa-Autocomplete .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperPrefix .aa-LoadingIndicator[hidden]{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapper,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper{order:3;position:relative;width:100%}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input{appearance:none;background:none;border:0;color:#343a40;font:inherit;height:calc(1.5em + .1rem + 2px);padding:0;width:100%}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::placeholder,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::placeholder{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input:focus{border-color:none;box-shadow:none;outline:none}.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-decoration,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-cancel-button,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-button,.aa-Autocomplete .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-decoration,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-decoration,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-cancel-button,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-button,.aa-DetachedFormContainer .aa-Form .aa-InputWrapper .aa-Input::-webkit-search-results-decoration{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix{align-items:center;display:flex;order:4}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton{align-items:center;background:none;border:0;color:#343a40;opacity:.8;cursor:pointer;display:flex;margin:0;width:calc(1.5em + .1rem + 2px)}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:hover,.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:hover,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton:focus{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton[hidden]{display:none}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-ClearButton svg,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-ClearButton svg{width:calc(1.5em + 0.75rem + calc(1px * 2))}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton{border:none;align-items:center;background:none;color:#343a40;opacity:.4;font-size:.7rem;cursor:pointer;display:none;margin:0;width:calc(1em + .1rem + 2px)}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:hover,.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:focus,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:hover,.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton:focus{color:#343a40;opacity:.8}.aa-Autocomplete .aa-Form .aa-InputWrapperSuffix .aa-CopyButton[hidden],.aa-DetachedFormContainer .aa-Form .aa-InputWrapperSuffix .aa-CopyButton[hidden]{display:none}.aa-PanelLayout:empty{display:none}.quarto-search-no-results.no-query{display:none}.aa-Source:has(.no-query){display:none}#quarto-search-results .aa-Panel{border:solid #dee2e6 1px}#quarto-search-results .aa-SourceNoResults{width:398px}.aa-DetachedOverlay .aa-Panel,#quarto-search-results .aa-Panel{max-height:65vh;overflow-y:auto;font-size:.925rem}.aa-DetachedOverlay .aa-SourceNoResults,#quarto-search-results .aa-SourceNoResults{height:60px;display:flex;justify-content:center;align-items:center}.aa-DetachedOverlay .search-error,#quarto-search-results .search-error{padding-top:10px;padding-left:20px;padding-right:20px;cursor:default}.aa-DetachedOverlay .search-error .search-error-title,#quarto-search-results .search-error .search-error-title{font-size:1.1rem;margin-bottom:.5rem}.aa-DetachedOverlay .search-error .search-error-title .search-error-icon,#quarto-search-results .search-error .search-error-title .search-error-icon{margin-right:8px}.aa-DetachedOverlay .search-error .search-error-text,#quarto-search-results .search-error .search-error-text{font-weight:300}.aa-DetachedOverlay .search-result-text,#quarto-search-results .search-result-text{font-weight:300;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical;line-height:1.2rem;max-height:2.4rem}.aa-DetachedOverlay .aa-SourceHeader .search-result-header,#quarto-search-results .aa-SourceHeader .search-result-header{font-size:.875rem;background-color:#f2f2f2;padding-left:14px;padding-bottom:4px;padding-top:4px}.aa-DetachedOverlay .aa-SourceHeader .search-result-header-no-results,#quarto-search-results .aa-SourceHeader .search-result-header-no-results{display:none}.aa-DetachedOverlay .aa-SourceFooter .algolia-search-logo,#quarto-search-results .aa-SourceFooter .algolia-search-logo{width:110px;opacity:.85;margin:8px;float:right}.aa-DetachedOverlay .search-result-section,#quarto-search-results .search-result-section{font-size:.925em}.aa-DetachedOverlay a.search-result-link,#quarto-search-results a.search-result-link{color:inherit;text-decoration:none}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item,#quarto-search-results li.aa-Item[aria-selected=true] .search-item{background-color:#2780e3}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item.search-result-more,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-section,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-text,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-title-container,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-result-text-container,#quarto-search-results li.aa-Item[aria-selected=true] .search-item.search-result-more,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-section,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-text,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-title-container,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-result-text-container{color:#fff;background-color:#2780e3}.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item mark.search-match,.aa-DetachedOverlay li.aa-Item[aria-selected=true] .search-item .search-match.mark,#quarto-search-results li.aa-Item[aria-selected=true] .search-item mark.search-match,#quarto-search-results li.aa-Item[aria-selected=true] .search-item .search-match.mark{color:#fff;background-color:#4b95e8}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item,#quarto-search-results li.aa-Item[aria-selected=false] .search-item{background-color:#fff}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item.search-result-more,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-section,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-text,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-title-container,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-result-text-container,#quarto-search-results li.aa-Item[aria-selected=false] .search-item.search-result-more,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-section,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-text,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-title-container,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-result-text-container{color:#343a40}.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item mark.search-match,.aa-DetachedOverlay li.aa-Item[aria-selected=false] .search-item .search-match.mark,#quarto-search-results li.aa-Item[aria-selected=false] .search-item mark.search-match,#quarto-search-results li.aa-Item[aria-selected=false] .search-item .search-match.mark{color:inherit;background-color:#e5effc}.aa-DetachedOverlay .aa-Item .search-result-doc:not(.document-selectable) .search-result-title-container,#quarto-search-results .aa-Item .search-result-doc:not(.document-selectable) .search-result-title-container{background-color:#fff;color:#343a40}.aa-DetachedOverlay .aa-Item .search-result-doc:not(.document-selectable) .search-result-text-container,#quarto-search-results .aa-Item .search-result-doc:not(.document-selectable) .search-result-text-container{padding-top:0px}.aa-DetachedOverlay li.aa-Item .search-result-doc.document-selectable .search-result-text-container,#quarto-search-results li.aa-Item .search-result-doc.document-selectable .search-result-text-container{margin-top:-4px}.aa-DetachedOverlay .aa-Item,#quarto-search-results .aa-Item{cursor:pointer}.aa-DetachedOverlay .aa-Item .search-item,#quarto-search-results .aa-Item .search-item{border-left:none;border-right:none;border-top:none;background-color:#fff;border-color:#dee2e6;color:#343a40}.aa-DetachedOverlay .aa-Item .search-item p,#quarto-search-results .aa-Item .search-item p{margin-top:0;margin-bottom:0}.aa-DetachedOverlay .aa-Item .search-item i.bi,#quarto-search-results .aa-Item .search-item i.bi{padding-left:8px;padding-right:8px;font-size:1.3em}.aa-DetachedOverlay .aa-Item .search-item .search-result-title,#quarto-search-results .aa-Item .search-item .search-result-title{margin-top:.3em;margin-bottom:0em}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs,#quarto-search-results .aa-Item .search-item .search-result-crumbs{white-space:nowrap;text-overflow:ellipsis;font-size:.8em;font-weight:300;margin-right:1em}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs:not(.search-result-crumbs-wrap),#quarto-search-results .aa-Item .search-item .search-result-crumbs:not(.search-result-crumbs-wrap){max-width:30%;margin-left:auto;margin-top:.5em;margin-bottom:.1rem}.aa-DetachedOverlay .aa-Item .search-item .search-result-crumbs.search-result-crumbs-wrap,#quarto-search-results .aa-Item .search-item .search-result-crumbs.search-result-crumbs-wrap{flex-basis:100%;margin-top:0em;margin-bottom:.2em;margin-left:37px}.aa-DetachedOverlay .aa-Item .search-result-title-container,#quarto-search-results .aa-Item .search-result-title-container{font-size:1em;display:flex;flex-wrap:wrap;padding:6px 4px 6px 4px}.aa-DetachedOverlay .aa-Item .search-result-text-container,#quarto-search-results .aa-Item .search-result-text-container{padding-bottom:8px;padding-right:8px;margin-left:42px}.aa-DetachedOverlay .aa-Item .search-result-doc-section,.aa-DetachedOverlay .aa-Item .search-result-more,#quarto-search-results .aa-Item .search-result-doc-section,#quarto-search-results .aa-Item .search-result-more{padding-top:8px;padding-bottom:8px;padding-left:44px}.aa-DetachedOverlay .aa-Item .search-result-more,#quarto-search-results .aa-Item .search-result-more{font-size:.8em;font-weight:400}.aa-DetachedOverlay .aa-Item .search-result-doc,#quarto-search-results .aa-Item .search-result-doc{border-top:1px solid #dee2e6}.aa-DetachedSearchButton{background:none;border:none}.aa-DetachedSearchButton .aa-DetachedSearchButtonPlaceholder{display:none}.navbar .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon{color:#545555}.sidebar-tools-collapse #quarto-search,.sidebar-tools-main #quarto-search{display:inline}.sidebar-tools-collapse #quarto-search .aa-Autocomplete,.sidebar-tools-main #quarto-search .aa-Autocomplete{display:inline}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton{padding-left:4px;padding-right:4px}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon{color:#595959}.sidebar-tools-collapse #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon .aa-SubmitIcon,.sidebar-tools-main #quarto-search .aa-DetachedSearchButton .aa-DetachedSearchButtonIcon .aa-SubmitIcon{margin-top:-3px}.aa-DetachedContainer{background:rgba(255,255,255,.65);width:90%;bottom:0;box-shadow:rgba(222,226,230,.6) 0 0 0 1px;outline:currentColor none medium;display:flex;flex-direction:column;left:0;margin:0;overflow:hidden;padding:0;position:fixed;right:0;top:0;z-index:1101}.aa-DetachedContainer::after{height:32px}.aa-DetachedContainer .aa-SourceHeader{margin:var(--aa-spacing-half) 0 var(--aa-spacing-half) 2px}.aa-DetachedContainer .aa-Panel{background-color:#fff;border-radius:0;box-shadow:none;flex-grow:1;margin:0;padding:0;position:relative}.aa-DetachedContainer .aa-PanelLayout{bottom:0;box-shadow:none;left:0;margin:0;max-height:none;overflow-y:auto;position:absolute;right:0;top:0;width:100%}.aa-DetachedFormContainer{background-color:#fff;border-bottom:1px solid #dee2e6;display:flex;flex-direction:row;justify-content:space-between;margin:0;padding:.5em}.aa-DetachedCancelButton{background:none;font-size:.8em;border:0;border-radius:3px;color:#343a40;cursor:pointer;margin:0 0 0 .5em;padding:0 .5em}.aa-DetachedCancelButton:hover,.aa-DetachedCancelButton:focus{box-shadow:rgba(39,128,227,.6) 0 0 0 1px;outline:currentColor none medium}.aa-DetachedContainer--modal{bottom:inherit;height:auto;margin:0 auto;position:absolute;top:100px;border-radius:6px;max-width:850px}@media(max-width: 575.98px){.aa-DetachedContainer--modal{width:100%;top:0px;border-radius:0px;border:none}}.aa-DetachedContainer--modal .aa-PanelLayout{max-height:var(--aa-detached-modal-max-height);padding-bottom:var(--aa-spacing-half);position:static}.aa-Detached{height:100vh;overflow:hidden}.aa-DetachedOverlay{background-color:rgba(52,58,64,.4);position:fixed;left:0;right:0;top:0;margin:0;padding:0;height:100vh;z-index:1100}.quarto-dashboard.nav-fixed.dashboard-sidebar #quarto-content.quarto-dashboard-content{padding:0em}.quarto-dashboard #quarto-content.quarto-dashboard-content{padding:1em}.quarto-dashboard #quarto-content.quarto-dashboard-content>*{padding-top:0}@media(min-width: 576px){.quarto-dashboard{height:100%}}.quarto-dashboard .card.valuebox.bslib-card.bg-primary{background-color:#5397e9 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-secondary{background-color:#343a40 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-success{background-color:#3aa716 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-info{background-color:rgba(153,84,187,.7019607843) !important}.quarto-dashboard .card.valuebox.bslib-card.bg-warning{background-color:#fa6400 !important}.quarto-dashboard .card.valuebox.bslib-card.bg-danger{background-color:rgba(255,0,57,.7019607843) !important}.quarto-dashboard .card.valuebox.bslib-card.bg-light{background-color:#f8f9fa !important}.quarto-dashboard .card.valuebox.bslib-card.bg-dark{background-color:#343a40 !important}.quarto-dashboard.dashboard-fill{display:flex;flex-direction:column}.quarto-dashboard #quarto-appendix{display:none}.quarto-dashboard #quarto-header #quarto-dashboard-header{border-top:solid 1px #dae0e5;border-bottom:solid 1px #dae0e5}.quarto-dashboard #quarto-header #quarto-dashboard-header>nav{padding-left:1em;padding-right:1em}.quarto-dashboard #quarto-header #quarto-dashboard-header>nav .navbar-brand-container{padding-left:0}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-toggler{margin-right:0}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-toggler-icon{height:1em;width:1em;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="rgb(84, 85, 85)" class="bi bi-menu-button-wide" viewBox="0 0 16 16"><path d="M0 1.5A1.5 1.5 0 0 1 1.5 0h13A1.5 1.5 0 0 1 16 1.5v2A1.5 1.5 0 0 1 14.5 5h-13A1.5 1.5 0 0 1 0 3.5v-2zM1.5 1a.5.5 0 0 0-.5.5v2a.5.5 0 0 0 .5.5h13a.5.5 0 0 0 .5-.5v-2a.5.5 0 0 0-.5-.5h-13z"/><path d="M2 2.5a.5.5 0 0 1 .5-.5h3a.5.5 0 0 1 0 1h-3a.5.5 0 0 1-.5-.5zm10.823.323-.396-.396A.25.25 0 0 1 12.604 2h.792a.25.25 0 0 1 .177.427l-.396.396a.25.25 0 0 1-.354 0zM0 8a2 2 0 0 1 2-2h12a2 2 0 0 1 2 2v5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2V8zm1 3v2a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2H1zm14-1V8a1 1 0 0 0-1-1H2a1 1 0 0 0-1 1v2h14zM2 8.5a.5.5 0 0 1 .5-.5h9a.5.5 0 0 1 0 1h-9a.5.5 0 0 1-.5-.5zm0 4a.5.5 0 0 1 .5-.5h6a.5.5 0 0 1 0 1h-6a.5.5 0 0 1-.5-.5z"/></svg>')}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-brand-container{padding-right:1em}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-title{font-size:1.1em}.quarto-dashboard #quarto-header #quarto-dashboard-header .navbar-nav{font-size:.9em}.quarto-dashboard #quarto-dashboard-header .navbar{padding:0}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-container{padding-left:1em}.quarto-dashboard #quarto-dashboard-header .navbar.slim .navbar-brand-container .nav-link,.quarto-dashboard #quarto-dashboard-header .navbar.slim .navbar-nav .nav-link{padding:.7em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-color-scheme-toggle{order:9}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-toggler{margin-left:.5em;order:10}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-nav .nav-link{padding:.5em;height:100%;display:flex;align-items:center}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-nav .active{background-color:#e0e5e9}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-brand-container{padding:.5em .5em .5em 0;display:flex;flex-direction:row;margin-right:2em;align-items:center}@media(max-width: 767.98px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-brand-container{margin-right:auto}}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{align-self:stretch}@media(min-width: 768px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{order:8}}@media(max-width: 767.98px){.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse{order:1000;padding-bottom:.5em}}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-collapse .navbar-nav{align-self:stretch}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title{font-size:1.25em;line-height:1.1em;display:flex;flex-direction:row;flex-wrap:wrap;align-items:baseline}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title .navbar-title-text{margin-right:.4em}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-title a{text-decoration:none;color:inherit}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-subtitle,.quarto-dashboard #quarto-dashboard-header .navbar .navbar-author{font-size:.9rem;margin-right:.5em}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-author{margin-left:auto}.quarto-dashboard #quarto-dashboard-header .navbar .navbar-logo{max-height:48px;min-height:30px;object-fit:cover;margin-right:1em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-links{order:9;padding-right:1em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-link-text{margin-left:.25em}.quarto-dashboard #quarto-dashboard-header .navbar .quarto-dashboard-link{padding-right:0em;padding-left:.7em;text-decoration:none;color:#545555}.quarto-dashboard .page-layout-custom .tab-content{padding:0;border:none}.quarto-dashboard-img-contain{height:100%;width:100%;object-fit:contain}@media(max-width: 575.98px){.quarto-dashboard .bslib-grid{grid-template-rows:minmax(1em, max-content) !important}.quarto-dashboard .sidebar-content{height:inherit}.quarto-dashboard .page-layout-custom{min-height:100vh}}.quarto-dashboard.dashboard-toolbar>.page-layout-custom,.quarto-dashboard.dashboard-sidebar>.page-layout-custom{padding:0}.quarto-dashboard .quarto-dashboard-content.quarto-dashboard-pages{padding:0}.quarto-dashboard .callout{margin-bottom:0;margin-top:0}.quarto-dashboard .html-fill-container figure{overflow:hidden}.quarto-dashboard bslib-tooltip .rounded-pill{border:solid #6c757d 1px}.quarto-dashboard bslib-tooltip .rounded-pill .svg{fill:#343a40}.quarto-dashboard .tabset .dashboard-card-no-title .nav-tabs{margin-left:0;margin-right:auto}.quarto-dashboard .tabset .tab-content{border:none}.quarto-dashboard .tabset .card-header .nav-link[role=tab]{margin-top:-6px;padding-top:6px;padding-bottom:6px}.quarto-dashboard .card.valuebox,.quarto-dashboard .card.bslib-value-box{min-height:3rem}.quarto-dashboard .card.valuebox .card-body,.quarto-dashboard .card.bslib-value-box .card-body{padding:0}.quarto-dashboard .bslib-value-box .value-box-value{font-size:clamp(.1em,15cqw,5em)}.quarto-dashboard .bslib-value-box .value-box-showcase .bi{font-size:clamp(.1em,max(18cqw,5.2cqh),5em);text-align:center;height:1em}.quarto-dashboard .bslib-value-box .value-box-showcase .bi::before{vertical-align:1em}.quarto-dashboard .bslib-value-box .value-box-area{margin-top:auto;margin-bottom:auto}.quarto-dashboard .card figure.quarto-float{display:flex;flex-direction:column;align-items:center}.quarto-dashboard .dashboard-scrolling{padding:1em}.quarto-dashboard .full-height{height:100%}.quarto-dashboard .showcase-bottom .value-box-grid{display:grid;grid-template-columns:1fr;grid-template-rows:1fr auto;grid-template-areas:"top" "bottom"}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-showcase i.bi{font-size:4rem}.quarto-dashboard .showcase-bottom .value-box-grid .value-box-area{grid-area:top}.quarto-dashboard .tab-content{margin-bottom:0}.quarto-dashboard .bslib-card .bslib-navs-card-title{justify-content:stretch;align-items:end}.quarto-dashboard .card-header{display:flex;flex-wrap:wrap;justify-content:space-between}.quarto-dashboard .card-header .card-title{display:flex;flex-direction:column;justify-content:center;margin-bottom:0}.quarto-dashboard .tabset .card-toolbar{margin-bottom:1em}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout{border:none;gap:var(--bslib-spacer, 1rem)}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.main{padding:0}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.sidebar{border-radius:.25rem;border:1px solid rgba(0,0,0,.175)}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.collapse-toggle{display:none}@media(max-width: 767.98px){.quarto-dashboard .bslib-grid>.bslib-sidebar-layout{grid-template-columns:1fr;grid-template-rows:max-content 1fr}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout>.main{grid-column:1;grid-row:2}.quarto-dashboard .bslib-grid>.bslib-sidebar-layout .sidebar{grid-column:1;grid-row:1}}.quarto-dashboard .sidebar-right .sidebar{padding-left:2.5em}.quarto-dashboard .sidebar-right .collapse-toggle{left:2px}.quarto-dashboard .quarto-dashboard .sidebar-right button.collapse-toggle:not(.transitioning){left:unset}.quarto-dashboard aside.sidebar{padding-left:1em;padding-right:1em;background-color:rgba(52,58,64,.25);color:#343a40}.quarto-dashboard .bslib-sidebar-layout>div.main{padding:.7em}.quarto-dashboard .bslib-sidebar-layout button.collapse-toggle{margin-top:.3em}.quarto-dashboard .bslib-sidebar-layout .collapse-toggle{top:0}.quarto-dashboard .bslib-sidebar-layout.sidebar-collapsed:not(.transitioning):not(.sidebar-right) .collapse-toggle{left:2px}.quarto-dashboard .sidebar>section>.h3:first-of-type{margin-top:0em}.quarto-dashboard .sidebar .h3,.quarto-dashboard .sidebar .h4,.quarto-dashboard .sidebar .h5,.quarto-dashboard .sidebar .h6{margin-top:.5em}.quarto-dashboard .sidebar form{flex-direction:column;align-items:start;margin-bottom:1em}.quarto-dashboard .sidebar form div[class*=oi-][class$=-input]{flex-direction:column}.quarto-dashboard .sidebar form[class*=oi-][class$=-toggle]{flex-direction:row-reverse;align-items:center;justify-content:start}.quarto-dashboard .sidebar form input[type=range]{margin-top:.5em;margin-right:.8em;margin-left:1em}.quarto-dashboard .sidebar label{width:fit-content}.quarto-dashboard .sidebar .card-body{margin-bottom:2em}.quarto-dashboard .sidebar .shiny-input-container{margin-bottom:1em}.quarto-dashboard .sidebar .shiny-options-group{margin-top:0}.quarto-dashboard .sidebar .control-label{margin-bottom:.3em}.quarto-dashboard .card .card-body .quarto-layout-row{align-items:stretch}.quarto-dashboard .toolbar{font-size:.9em;display:flex;flex-direction:row;border-top:solid 1px #bcbfc0;padding:1em;flex-wrap:wrap;background-color:rgba(52,58,64,.25)}.quarto-dashboard .toolbar .cell-output-display{display:flex}.quarto-dashboard .toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .toolbar>*:last-child{margin-right:0}.quarto-dashboard .toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .toolbar .shiny-input-container{padding-bottom:0;margin-bottom:0}.quarto-dashboard .toolbar .shiny-input-container>*{flex-shrink:0;flex-grow:0}.quarto-dashboard .toolbar .form-group.shiny-input-container:not([role=group])>label{margin-bottom:0}.quarto-dashboard .toolbar .shiny-input-container.no-baseline{align-items:start;padding-top:6px}.quarto-dashboard .toolbar .shiny-input-container{display:flex;align-items:baseline}.quarto-dashboard .toolbar .shiny-input-container label{padding-right:.4em}.quarto-dashboard .toolbar .shiny-input-container .bslib-input-switch{margin-top:6px}.quarto-dashboard .toolbar input[type=text]{line-height:1;width:inherit}.quarto-dashboard .toolbar .input-daterange{width:inherit}.quarto-dashboard .toolbar .input-daterange input[type=text]{height:2.4em;width:10em}.quarto-dashboard .toolbar .input-daterange .input-group-addon{height:auto;padding:0;margin-left:-5px !important;margin-right:-5px}.quarto-dashboard .toolbar .input-daterange .input-group-addon .input-group-text{padding-top:0;padding-bottom:0;height:100%}.quarto-dashboard .toolbar span.irs.irs--shiny{width:10em}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-line{top:9px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-min,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-max,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-from,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-to,.quarto-dashboard .toolbar span.irs.irs--shiny .irs-single{top:20px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-bar{top:8px}.quarto-dashboard .toolbar span.irs.irs--shiny .irs-handle{top:0px}.quarto-dashboard .toolbar .shiny-input-checkboxgroup>label{margin-top:6px}.quarto-dashboard .toolbar .shiny-input-checkboxgroup>.shiny-options-group{margin-top:0;align-items:baseline}.quarto-dashboard .toolbar .shiny-input-radiogroup>label{margin-top:6px}.quarto-dashboard .toolbar .shiny-input-radiogroup>.shiny-options-group{align-items:baseline;margin-top:0}.quarto-dashboard .toolbar .shiny-input-radiogroup>.shiny-options-group>.radio{margin-right:.3em}.quarto-dashboard .toolbar .form-select{padding-top:.2em;padding-bottom:.2em}.quarto-dashboard .toolbar .shiny-input-select{min-width:6em}.quarto-dashboard .toolbar div.checkbox{margin-bottom:0px}.quarto-dashboard .toolbar>.checkbox:first-child{margin-top:6px}.quarto-dashboard .toolbar form{width:fit-content}.quarto-dashboard .toolbar form label{padding-top:.2em;padding-bottom:.2em;width:fit-content}.quarto-dashboard .toolbar form input[type=date]{width:fit-content}.quarto-dashboard .toolbar form input[type=color]{width:3em}.quarto-dashboard .toolbar form button{padding:.4em}.quarto-dashboard .toolbar form select{width:fit-content}.quarto-dashboard .toolbar>*{font-size:.9em;flex-grow:0}.quarto-dashboard .toolbar .shiny-input-container label{margin-bottom:1px}.quarto-dashboard .toolbar-bottom{margin-top:1em;margin-bottom:0 !important;order:2}.quarto-dashboard .quarto-dashboard-content>.dashboard-toolbar-container>.toolbar-content>.tab-content>.tab-pane>*:not(.bslib-sidebar-layout){padding:1em}.quarto-dashboard .quarto-dashboard-content>.dashboard-toolbar-container>.toolbar-content>*:not(.tab-content){padding:1em}.quarto-dashboard .quarto-dashboard-content>.tab-content>.dashboard-page>.dashboard-toolbar-container>.toolbar-content,.quarto-dashboard .quarto-dashboard-content>.tab-content>.dashboard-page:not(.dashboard-sidebar-container)>*:not(.dashboard-toolbar-container){padding:1em}.quarto-dashboard .toolbar-content{padding:0}.quarto-dashboard .quarto-dashboard-content.quarto-dashboard-pages .tab-pane>.dashboard-toolbar-container .toolbar{border-radius:0;margin-bottom:0}.quarto-dashboard .dashboard-toolbar-container.toolbar-toplevel .toolbar{border-bottom:1px solid rgba(0,0,0,.175)}.quarto-dashboard .dashboard-toolbar-container.toolbar-toplevel .toolbar-bottom{margin-top:0}.quarto-dashboard .dashboard-toolbar-container:not(.toolbar-toplevel) .toolbar{margin-bottom:1em;border-top:none;border-radius:.25rem;border:1px solid rgba(0,0,0,.175)}.quarto-dashboard .vega-embed.has-actions details{width:1.7em;height:2em;position:absolute !important;top:0;right:0}.quarto-dashboard .dashboard-toolbar-container{padding:0}.quarto-dashboard .card .card-header p:last-child,.quarto-dashboard .card .card-footer p:last-child{margin-bottom:0}.quarto-dashboard .card .card-body>.h4:first-child{margin-top:0}.quarto-dashboard .card .card-body{z-index:4}@media(max-width: 767.98px){.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_length,.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_info,.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_paginate{text-align:initial}.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_filter{text-align:right}.quarto-dashboard .card .card-body .itables div.dataTables_wrapper div.dataTables_paginate ul.pagination{justify-content:initial}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center;padding-top:0}.quarto-dashboard .card .card-body .itables .dataTables_wrapper table{flex-shrink:0}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons{margin-bottom:.5em;margin-left:auto;width:fit-content;float:right}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons.btn-group{background:#fff;border:none}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons .btn-secondary{background-color:#fff;background-image:none;border:solid #dee2e6 1px;padding:.2em .7em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dt-buttons .btn span{font-size:.8em;color:#343a40}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{margin-left:.5em;margin-bottom:.5em;padding-top:0}@media(min-width: 768px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{font-size:.875em}}@media(max-width: 767.98px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_info{font-size:.8em}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_filter{margin-bottom:.5em;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_filter input[type=search]{padding:1px 5px 1px 5px;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_length{flex-basis:1 1 50%;margin-bottom:.5em;font-size:.875em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_length select{padding:.4em 3em .4em .5em;font-size:.875em;margin-left:.2em;margin-right:.2em}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate{flex-shrink:0}@media(min-width: 768px){.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate{margin-left:auto}}.quarto-dashboard .card .card-body .itables .dataTables_wrapper .dataTables_paginate ul.pagination .paginate_button .page-link{font-size:.8em}.quarto-dashboard .card .card-footer{font-size:.9em}.quarto-dashboard .card .card-toolbar{display:flex;flex-grow:1;flex-direction:row;width:100%;flex-wrap:wrap}.quarto-dashboard .card .card-toolbar>*{font-size:.8em;flex-grow:0}.quarto-dashboard .card .card-toolbar>.card-title{font-size:1em;flex-grow:1;align-self:flex-start;margin-top:.1em}.quarto-dashboard .card .card-toolbar .cell-output-display{display:flex}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .card .card-toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card .card-toolbar>*:last-child{margin-right:0}.quarto-dashboard .card .card-toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .card .card-toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .card .card-toolbar form{width:fit-content}.quarto-dashboard .card .card-toolbar form label{padding-top:.2em;padding-bottom:.2em;width:fit-content}.quarto-dashboard .card .card-toolbar form input[type=date]{width:fit-content}.quarto-dashboard .card .card-toolbar form input[type=color]{width:3em}.quarto-dashboard .card .card-toolbar form button{padding:.4em}.quarto-dashboard .card .card-toolbar form select{width:fit-content}.quarto-dashboard .card .card-toolbar .cell-output-display{display:flex}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:.5em;margin-bottom:.5em;width:inherit}.quarto-dashboard .card .card-toolbar .shiny-input-container>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card .card-toolbar>*:last-child{margin-right:0}.quarto-dashboard .card .card-toolbar>*>*{margin-right:1em;align-items:baseline}.quarto-dashboard .card .card-toolbar>*>*>a{text-decoration:none;margin-top:auto;margin-bottom:auto}.quarto-dashboard .card .card-toolbar .shiny-input-container{padding-bottom:0;margin-bottom:0}.quarto-dashboard .card .card-toolbar .shiny-input-container>*{flex-shrink:0;flex-grow:0}.quarto-dashboard .card .card-toolbar .form-group.shiny-input-container:not([role=group])>label{margin-bottom:0}.quarto-dashboard .card .card-toolbar .shiny-input-container.no-baseline{align-items:start;padding-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-container{display:flex;align-items:baseline}.quarto-dashboard .card .card-toolbar .shiny-input-container label{padding-right:.4em}.quarto-dashboard .card .card-toolbar .shiny-input-container .bslib-input-switch{margin-top:6px}.quarto-dashboard .card .card-toolbar input[type=text]{line-height:1;width:inherit}.quarto-dashboard .card .card-toolbar .input-daterange{width:inherit}.quarto-dashboard .card .card-toolbar .input-daterange input[type=text]{height:2.4em;width:10em}.quarto-dashboard .card .card-toolbar .input-daterange .input-group-addon{height:auto;padding:0;margin-left:-5px !important;margin-right:-5px}.quarto-dashboard .card .card-toolbar .input-daterange .input-group-addon .input-group-text{padding-top:0;padding-bottom:0;height:100%}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny{width:10em}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-line{top:9px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-min,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-max,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-from,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-to,.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-single{top:20px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-bar{top:8px}.quarto-dashboard .card .card-toolbar span.irs.irs--shiny .irs-handle{top:0px}.quarto-dashboard .card .card-toolbar .shiny-input-checkboxgroup>label{margin-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-checkboxgroup>.shiny-options-group{margin-top:0;align-items:baseline}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>label{margin-top:6px}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>.shiny-options-group{align-items:baseline;margin-top:0}.quarto-dashboard .card .card-toolbar .shiny-input-radiogroup>.shiny-options-group>.radio{margin-right:.3em}.quarto-dashboard .card .card-toolbar .form-select{padding-top:.2em;padding-bottom:.2em}.quarto-dashboard .card .card-toolbar .shiny-input-select{min-width:6em}.quarto-dashboard .card .card-toolbar div.checkbox{margin-bottom:0px}.quarto-dashboard .card .card-toolbar>.checkbox:first-child{margin-top:6px}.quarto-dashboard .card-body>table>thead{border-top:none}.quarto-dashboard .card-body>.table>:not(caption)>*>*{background-color:#fff}.tableFloatingHeaderOriginal{background-color:#fff;position:sticky !important;top:0 !important}.dashboard-data-table{margin-top:-1px}.quarto-listing{padding-bottom:1em}.listing-pagination{padding-top:.5em}ul.pagination{float:right;padding-left:8px;padding-top:.5em}ul.pagination li{padding-right:.75em}ul.pagination li.disabled a,ul.pagination li.active a{color:#fff;text-decoration:none}ul.pagination li:last-of-type{padding-right:0}.listing-actions-group{display:flex}.quarto-listing-filter{margin-bottom:1em;width:200px;margin-left:auto}.quarto-listing-sort{margin-bottom:1em;margin-right:auto;width:auto}.quarto-listing-sort .input-group-text{font-size:.8em}.input-group-text{border-right:none}.quarto-listing-sort select.form-select{font-size:.8em}.listing-no-matching{text-align:center;padding-top:2em;padding-bottom:3em;font-size:1em}#quarto-margin-sidebar .quarto-listing-category{padding-top:0;font-size:1rem}#quarto-margin-sidebar .quarto-listing-category-title{cursor:pointer;font-weight:600;font-size:1rem}.quarto-listing-category .category{cursor:pointer}.quarto-listing-category .category.active{font-weight:600}.quarto-listing-category.category-cloud{display:flex;flex-wrap:wrap;align-items:baseline}.quarto-listing-category.category-cloud .category{padding-right:5px}.quarto-listing-category.category-cloud .category-cloud-1{font-size:.75em}.quarto-listing-category.category-cloud .category-cloud-2{font-size:.95em}.quarto-listing-category.category-cloud .category-cloud-3{font-size:1.15em}.quarto-listing-category.category-cloud .category-cloud-4{font-size:1.35em}.quarto-listing-category.category-cloud .category-cloud-5{font-size:1.55em}.quarto-listing-category.category-cloud .category-cloud-6{font-size:1.75em}.quarto-listing-category.category-cloud .category-cloud-7{font-size:1.95em}.quarto-listing-category.category-cloud .category-cloud-8{font-size:2.15em}.quarto-listing-category.category-cloud .category-cloud-9{font-size:2.35em}.quarto-listing-category.category-cloud .category-cloud-10{font-size:2.55em}.quarto-listing-cols-1{grid-template-columns:repeat(1, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-1{grid-template-columns:repeat(1, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-1{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-2{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-2{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-2{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-3{grid-template-columns:repeat(3, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-3{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-3{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-4{grid-template-columns:repeat(4, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-4{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-4{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-5{grid-template-columns:repeat(5, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-5{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-5{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-6{grid-template-columns:repeat(6, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-6{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-6{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-7{grid-template-columns:repeat(7, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-7{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-7{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-8{grid-template-columns:repeat(8, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-8{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-8{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-9{grid-template-columns:repeat(9, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-9{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-9{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-10{grid-template-columns:repeat(10, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-10{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-10{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-11{grid-template-columns:repeat(11, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-11{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-11{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-cols-12{grid-template-columns:repeat(12, minmax(0, 1fr));gap:1.5em}@media(max-width: 767.98px){.quarto-listing-cols-12{grid-template-columns:repeat(2, minmax(0, 1fr));gap:1.5em}}@media(max-width: 575.98px){.quarto-listing-cols-12{grid-template-columns:minmax(0, 1fr);gap:1.5em}}.quarto-listing-grid{gap:1.5em}.quarto-grid-item.borderless{border:none}.quarto-grid-item.borderless .listing-categories .listing-category:last-of-type,.quarto-grid-item.borderless .listing-categories .listing-category:first-of-type{padding-left:0}.quarto-grid-item.borderless .listing-categories .listing-category{border:0}.quarto-grid-link{text-decoration:none;color:inherit}.quarto-grid-link:hover{text-decoration:none;color:inherit}.quarto-grid-item h5.title,.quarto-grid-item .title.h5{margin-top:0;margin-bottom:0}.quarto-grid-item .card-footer{display:flex;justify-content:space-between;font-size:.8em}.quarto-grid-item .card-footer p{margin-bottom:0}.quarto-grid-item p.card-img-top{margin-bottom:0}.quarto-grid-item p.card-img-top>img{object-fit:cover}.quarto-grid-item .card-other-values{margin-top:.5em;font-size:.8em}.quarto-grid-item .card-other-values tr{margin-bottom:.5em}.quarto-grid-item .card-other-values tr>td:first-of-type{font-weight:600;padding-right:1em;padding-left:1em;vertical-align:top}.quarto-grid-item div.post-contents{display:flex;flex-direction:column;text-decoration:none;height:100%}.quarto-grid-item .listing-item-img-placeholder{background-color:rgba(52,58,64,.25);flex-shrink:0}.quarto-grid-item .card-attribution{padding-top:1em;display:flex;gap:1em;text-transform:uppercase;color:#6c757d;font-weight:500;flex-grow:10;align-items:flex-end}.quarto-grid-item .description{padding-bottom:1em}.quarto-grid-item .card-attribution .date{align-self:flex-end}.quarto-grid-item .card-attribution.justify{justify-content:space-between}.quarto-grid-item .card-attribution.start{justify-content:flex-start}.quarto-grid-item .card-attribution.end{justify-content:flex-end}.quarto-grid-item .card-title{margin-bottom:.1em}.quarto-grid-item .card-subtitle{padding-top:.25em}.quarto-grid-item .card-text{font-size:.9em}.quarto-grid-item .listing-reading-time{padding-bottom:.25em}.quarto-grid-item .card-text-small{font-size:.8em}.quarto-grid-item .card-subtitle.subtitle{font-size:.9em;font-weight:600;padding-bottom:.5em}.quarto-grid-item .listing-categories{display:flex;flex-wrap:wrap;padding-bottom:5px}.quarto-grid-item .listing-categories .listing-category{color:#6c757d;border:solid 1px #dee2e6;border-radius:.25rem;text-transform:uppercase;font-size:.65em;padding-left:.5em;padding-right:.5em;padding-top:.15em;padding-bottom:.15em;cursor:pointer;margin-right:4px;margin-bottom:4px}.quarto-grid-item.card-right{text-align:right}.quarto-grid-item.card-right .listing-categories{justify-content:flex-end}.quarto-grid-item.card-left{text-align:left}.quarto-grid-item.card-center{text-align:center}.quarto-grid-item.card-center .listing-description{text-align:justify}.quarto-grid-item.card-center .listing-categories{justify-content:center}table.quarto-listing-table td.image{padding:0px}table.quarto-listing-table td.image img{width:100%;max-width:50px;object-fit:contain}table.quarto-listing-table a{text-decoration:none;word-break:keep-all}table.quarto-listing-table th a{color:inherit}table.quarto-listing-table th a.asc:after{margin-bottom:-2px;margin-left:5px;display:inline-block;height:1rem;width:1rem;background-repeat:no-repeat;background-size:1rem 1rem;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-sort-up" viewBox="0 0 16 16"><path d="M3.5 12.5a.5.5 0 0 1-1 0V3.707L1.354 4.854a.5.5 0 1 1-.708-.708l2-1.999.007-.007a.498.498 0 0 1 .7.006l2 2a.5.5 0 1 1-.707.708L3.5 3.707V12.5zm3.5-9a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5zM7.5 6a.5.5 0 0 0 0 1h5a.5.5 0 0 0 0-1h-5zm0 3a.5.5 0 0 0 0 1h3a.5.5 0 0 0 0-1h-3zm0 3a.5.5 0 0 0 0 1h1a.5.5 0 0 0 0-1h-1z"/></svg>');content:""}table.quarto-listing-table th a.desc:after{margin-bottom:-2px;margin-left:5px;display:inline-block;height:1rem;width:1rem;background-repeat:no-repeat;background-size:1rem 1rem;background-image:url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-sort-down" viewBox="0 0 16 16"><path d="M3.5 2.5a.5.5 0 0 0-1 0v8.793l-1.146-1.147a.5.5 0 0 0-.708.708l2 1.999.007.007a.497.497 0 0 0 .7-.006l2-2a.5.5 0 0 0-.707-.708L3.5 11.293V2.5zm3.5 1a.5.5 0 0 1 .5-.5h7a.5.5 0 0 1 0 1h-7a.5.5 0 0 1-.5-.5zM7.5 6a.5.5 0 0 0 0 1h5a.5.5 0 0 0 0-1h-5zm0 3a.5.5 0 0 0 0 1h3a.5.5 0 0 0 0-1h-3zm0 3a.5.5 0 0 0 0 1h1a.5.5 0 0 0 0-1h-1z"/></svg>');content:""}table.quarto-listing-table.table-hover td{cursor:pointer}.quarto-post.image-left{flex-direction:row}.quarto-post.image-right{flex-direction:row-reverse}@media(max-width: 767.98px){.quarto-post.image-right,.quarto-post.image-left{gap:0em;flex-direction:column}.quarto-post .metadata{padding-bottom:1em;order:2}.quarto-post .body{order:1}.quarto-post .thumbnail{order:3}}.list.quarto-listing-default div:last-of-type{border-bottom:none}@media(min-width: 992px){.quarto-listing-container-default{margin-right:2em}}div.quarto-post{display:flex;gap:2em;margin-bottom:1.5em;border-bottom:1px solid #dee2e6}@media(max-width: 767.98px){div.quarto-post{padding-bottom:1em}}div.quarto-post .metadata{flex-basis:20%;flex-grow:0;margin-top:.2em;flex-shrink:10}div.quarto-post .thumbnail{flex-basis:30%;flex-grow:0;flex-shrink:0}div.quarto-post .thumbnail img{margin-top:.4em;width:100%;object-fit:cover}div.quarto-post .body{flex-basis:45%;flex-grow:1;flex-shrink:0}div.quarto-post .body h3.listing-title,div.quarto-post .body .listing-title.h3{margin-top:0px;margin-bottom:0px;border-bottom:none}div.quarto-post .body .listing-subtitle{font-size:.875em;margin-bottom:.5em;margin-top:.2em}div.quarto-post .body .description{font-size:.9em}div.quarto-post .body pre code{white-space:pre-wrap}div.quarto-post a{color:#343a40;text-decoration:none}div.quarto-post .metadata{display:flex;flex-direction:column;font-size:.8em;font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";flex-basis:33%}div.quarto-post .listing-categories{display:flex;flex-wrap:wrap;padding-bottom:5px}div.quarto-post .listing-categories .listing-category{color:#6c757d;border:solid 1px #dee2e6;border-radius:.25rem;text-transform:uppercase;font-size:.65em;padding-left:.5em;padding-right:.5em;padding-top:.15em;padding-bottom:.15em;cursor:pointer;margin-right:4px;margin-bottom:4px}div.quarto-post .listing-description{margin-bottom:.5em}div.quarto-about-jolla{display:flex !important;flex-direction:column;align-items:center;margin-top:10%;padding-bottom:1em}div.quarto-about-jolla .about-image{object-fit:cover;margin-left:auto;margin-right:auto;margin-bottom:1.5em}div.quarto-about-jolla img.round{border-radius:50%}div.quarto-about-jolla img.rounded{border-radius:10px}div.quarto-about-jolla .quarto-title h1.title,div.quarto-about-jolla .quarto-title .title.h1{text-align:center}div.quarto-about-jolla .quarto-title .description{text-align:center}div.quarto-about-jolla h2,div.quarto-about-jolla .h2{border-bottom:none}div.quarto-about-jolla .about-sep{width:60%}div.quarto-about-jolla main{text-align:center}div.quarto-about-jolla .about-links{display:flex}@media(min-width: 992px){div.quarto-about-jolla .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-jolla .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-jolla .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-jolla .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-jolla .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-jolla .about-link:hover{color:#2761e3}div.quarto-about-jolla .about-link i.bi{margin-right:.15em}div.quarto-about-solana{display:flex !important;flex-direction:column;padding-top:3em !important;padding-bottom:1em}div.quarto-about-solana .about-entity{display:flex !important;align-items:start;justify-content:space-between}@media(min-width: 992px){div.quarto-about-solana .about-entity{flex-direction:row}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity{flex-direction:column-reverse;align-items:center;text-align:center}}div.quarto-about-solana .about-entity .entity-contents{display:flex;flex-direction:column}@media(max-width: 767.98px){div.quarto-about-solana .about-entity .entity-contents{width:100%}}div.quarto-about-solana .about-entity .about-image{object-fit:cover}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-image{margin-bottom:1.5em}}div.quarto-about-solana .about-entity img.round{border-radius:50%}div.quarto-about-solana .about-entity img.rounded{border-radius:10px}div.quarto-about-solana .about-entity .about-links{display:flex;justify-content:left;padding-bottom:1.2em}@media(min-width: 992px){div.quarto-about-solana .about-entity .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-solana .about-entity .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-solana .about-entity .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-solana .about-entity .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-solana .about-entity .about-link:hover{color:#2761e3}div.quarto-about-solana .about-entity .about-link i.bi{margin-right:.15em}div.quarto-about-solana .about-contents{padding-right:1.5em;flex-basis:0;flex-grow:1}div.quarto-about-solana .about-contents main.content{margin-top:0}div.quarto-about-solana .about-contents h2,div.quarto-about-solana .about-contents .h2{border-bottom:none}div.quarto-about-trestles{display:flex !important;flex-direction:row;padding-top:3em !important;padding-bottom:1em}@media(max-width: 991.98px){div.quarto-about-trestles{flex-direction:column;padding-top:0em !important}}div.quarto-about-trestles .about-entity{display:flex !important;flex-direction:column;align-items:center;text-align:center;padding-right:1em}@media(min-width: 992px){div.quarto-about-trestles .about-entity{flex:0 0 42%}}div.quarto-about-trestles .about-entity .about-image{object-fit:cover;margin-bottom:1.5em}div.quarto-about-trestles .about-entity img.round{border-radius:50%}div.quarto-about-trestles .about-entity img.rounded{border-radius:10px}div.quarto-about-trestles .about-entity .about-links{display:flex;justify-content:center}@media(min-width: 992px){div.quarto-about-trestles .about-entity .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-trestles .about-entity .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-trestles .about-entity .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-trestles .about-entity .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-trestles .about-entity .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-trestles .about-entity .about-link:hover{color:#2761e3}div.quarto-about-trestles .about-entity .about-link i.bi{margin-right:.15em}div.quarto-about-trestles .about-contents{flex-basis:0;flex-grow:1}div.quarto-about-trestles .about-contents h2,div.quarto-about-trestles .about-contents .h2{border-bottom:none}@media(min-width: 992px){div.quarto-about-trestles .about-contents{border-left:solid 1px #dee2e6;padding-left:1.5em}}div.quarto-about-trestles .about-contents main.content{margin-top:0}div.quarto-about-marquee{padding-bottom:1em}div.quarto-about-marquee .about-contents{display:flex;flex-direction:column}div.quarto-about-marquee .about-image{max-height:550px;margin-bottom:1.5em;object-fit:cover}div.quarto-about-marquee img.round{border-radius:50%}div.quarto-about-marquee img.rounded{border-radius:10px}div.quarto-about-marquee h2,div.quarto-about-marquee .h2{border-bottom:none}div.quarto-about-marquee .about-links{display:flex;justify-content:center;padding-top:1.5em}@media(min-width: 992px){div.quarto-about-marquee .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-marquee .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-marquee .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-marquee .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-marquee .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-marquee .about-link:hover{color:#2761e3}div.quarto-about-marquee .about-link i.bi{margin-right:.15em}@media(min-width: 992px){div.quarto-about-marquee .about-link{border:none}}div.quarto-about-broadside{display:flex;flex-direction:column;padding-bottom:1em}div.quarto-about-broadside .about-main{display:flex !important;padding-top:0 !important}@media(min-width: 992px){div.quarto-about-broadside .about-main{flex-direction:row;align-items:flex-start}}@media(max-width: 991.98px){div.quarto-about-broadside .about-main{flex-direction:column}}@media(max-width: 991.98px){div.quarto-about-broadside .about-main .about-entity{flex-shrink:0;width:100%;height:450px;margin-bottom:1.5em;background-size:cover;background-repeat:no-repeat}}@media(min-width: 992px){div.quarto-about-broadside .about-main .about-entity{flex:0 10 50%;margin-right:1.5em;width:100%;height:100%;background-size:100%;background-repeat:no-repeat}}div.quarto-about-broadside .about-main .about-contents{padding-top:14px;flex:0 0 50%}div.quarto-about-broadside h2,div.quarto-about-broadside .h2{border-bottom:none}div.quarto-about-broadside .about-sep{margin-top:1.5em;width:60%;align-self:center}div.quarto-about-broadside .about-links{display:flex;justify-content:center;column-gap:20px;padding-top:1.5em}@media(min-width: 992px){div.quarto-about-broadside .about-links{flex-direction:row;column-gap:.8em;row-gap:15px;flex-wrap:wrap}}@media(max-width: 991.98px){div.quarto-about-broadside .about-links{flex-direction:column;row-gap:1em;width:100%;padding-bottom:1.5em}}div.quarto-about-broadside .about-link{color:#626d78;text-decoration:none;border:solid 1px}@media(min-width: 992px){div.quarto-about-broadside .about-link{font-size:.8em;padding:.25em .5em;border-radius:4px}}@media(max-width: 991.98px){div.quarto-about-broadside .about-link{font-size:1.1em;padding:.5em .5em;text-align:center;border-radius:6px}}div.quarto-about-broadside .about-link:hover{color:#2761e3}div.quarto-about-broadside .about-link i.bi{margin-right:.15em}@media(min-width: 992px){div.quarto-about-broadside .about-link{border:none}}.tippy-box[data-theme~=quarto]{background-color:#fff;border:solid 1px #dee2e6;border-radius:.25rem;color:#343a40;font-size:.875rem}.tippy-box[data-theme~=quarto]>.tippy-backdrop{background-color:#fff}.tippy-box[data-theme~=quarto]>.tippy-arrow:after,.tippy-box[data-theme~=quarto]>.tippy-svg-arrow:after{content:"";position:absolute;z-index:-1}.tippy-box[data-theme~=quarto]>.tippy-arrow:after{border-color:rgba(0,0,0,0);border-style:solid}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-6px}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-6px}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-6px}.tippy-box[data-placement^=left]>.tippy-arrow:before{right:-6px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-arrow:before{border-top-color:#fff}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-arrow:after{border-top-color:#dee2e6;border-width:7px 7px 0;top:17px;left:1px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-svg-arrow>svg{top:16px}.tippy-box[data-theme~=quarto][data-placement^=top]>.tippy-svg-arrow:after{top:17px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-arrow:before{border-bottom-color:#fff;bottom:16px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-arrow:after{border-bottom-color:#dee2e6;border-width:0 7px 7px;bottom:17px;left:1px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-svg-arrow>svg{bottom:15px}.tippy-box[data-theme~=quarto][data-placement^=bottom]>.tippy-svg-arrow:after{bottom:17px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-arrow:before{border-left-color:#fff}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-arrow:after{border-left-color:#dee2e6;border-width:7px 0 7px 7px;left:17px;top:1px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-svg-arrow>svg{left:11px}.tippy-box[data-theme~=quarto][data-placement^=left]>.tippy-svg-arrow:after{left:12px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-arrow:before{border-right-color:#fff;right:16px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-arrow:after{border-width:7px 7px 7px 0;right:17px;top:1px;border-right-color:#dee2e6}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-svg-arrow>svg{right:11px}.tippy-box[data-theme~=quarto][data-placement^=right]>.tippy-svg-arrow:after{right:12px}.tippy-box[data-theme~=quarto]>.tippy-svg-arrow{fill:#343a40}.tippy-box[data-theme~=quarto]>.tippy-svg-arrow:after{background-image:url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJNMCA2czEuNzk2LS4wMTMgNC42Ny0zLjYxNUM1Ljg1MS45IDYuOTMuMDA2IDggMGMxLjA3LS4wMDYgMi4xNDguODg3IDMuMzQzIDIuMzg1QzE0LjIzMyA2LjAwNSAxNiA2IDE2IDZIMHoiIGZpbGw9InJnYmEoMCwgOCwgMTYsIDAuMikiLz48L3N2Zz4=);background-size:16px 6px;width:16px;height:6px}.top-right{position:absolute;top:1em;right:1em}.visually-hidden{border:0;clip:rect(0 0 0 0);height:auto;margin:0;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap}.hidden{display:none !important}.zindex-bottom{z-index:-1 !important}figure.figure{display:block}.quarto-layout-panel{margin-bottom:1em}.quarto-layout-panel>figure{width:100%}.quarto-layout-panel>figure>figcaption,.quarto-layout-panel>.panel-caption{margin-top:10pt}.quarto-layout-panel>.table-caption{margin-top:0px}.table-caption p{margin-bottom:.5em}.quarto-layout-row{display:flex;flex-direction:row;align-items:flex-start}.quarto-layout-valign-top{align-items:flex-start}.quarto-layout-valign-bottom{align-items:flex-end}.quarto-layout-valign-center{align-items:center}.quarto-layout-cell{position:relative;margin-right:20px}.quarto-layout-cell:last-child{margin-right:0}.quarto-layout-cell figure,.quarto-layout-cell>p{margin:.2em}.quarto-layout-cell img{max-width:100%}.quarto-layout-cell .html-widget{width:100% !important}.quarto-layout-cell div figure p{margin:0}.quarto-layout-cell figure{display:block;margin-inline-start:0;margin-inline-end:0}.quarto-layout-cell table{display:inline-table}.quarto-layout-cell-subref figcaption,figure .quarto-layout-row figure figcaption{text-align:center;font-style:italic}.quarto-figure{position:relative;margin-bottom:1em}.quarto-figure>figure{width:100%;margin-bottom:0}.quarto-figure-left>figure>p,.quarto-figure-left>figure>div{text-align:left}.quarto-figure-center>figure>p,.quarto-figure-center>figure>div{text-align:center}.quarto-figure-right>figure>p,.quarto-figure-right>figure>div{text-align:right}.quarto-figure>figure>div.cell-annotation,.quarto-figure>figure>div code{text-align:left}figure>p:empty{display:none}figure>p:first-child{margin-top:0;margin-bottom:0}figure>figcaption.quarto-float-caption-bottom{margin-bottom:.5em}figure>figcaption.quarto-float-caption-top{margin-top:.5em}div[id^=tbl-]{position:relative}.quarto-figure>.anchorjs-link{position:absolute;top:.6em;right:.5em}div[id^=tbl-]>.anchorjs-link{position:absolute;top:.7em;right:.3em}.quarto-figure:hover>.anchorjs-link,div[id^=tbl-]:hover>.anchorjs-link,h2:hover>.anchorjs-link,.h2:hover>.anchorjs-link,h3:hover>.anchorjs-link,.h3:hover>.anchorjs-link,h4:hover>.anchorjs-link,.h4:hover>.anchorjs-link,h5:hover>.anchorjs-link,.h5:hover>.anchorjs-link,h6:hover>.anchorjs-link,.h6:hover>.anchorjs-link,.reveal-anchorjs-link>.anchorjs-link{opacity:1}#title-block-header{margin-block-end:1rem;position:relative;margin-top:-1px}#title-block-header .abstract{margin-block-start:1rem}#title-block-header .abstract .abstract-title{font-weight:600}#title-block-header a{text-decoration:none}#title-block-header .author,#title-block-header .date,#title-block-header .doi{margin-block-end:.2rem}#title-block-header .quarto-title-block>div{display:flex}#title-block-header .quarto-title-block>div>h1,#title-block-header .quarto-title-block>div>.h1{flex-grow:1}#title-block-header .quarto-title-block>div>button{flex-shrink:0;height:2.25rem;margin-top:0}@media(min-width: 992px){#title-block-header .quarto-title-block>div>button{margin-top:5px}}tr.header>th>p:last-of-type{margin-bottom:0px}table,table.table{margin-top:.5rem;margin-bottom:.5rem}caption,.table-caption{padding-top:.5rem;padding-bottom:.5rem;text-align:center}figure.quarto-float-tbl figcaption.quarto-float-caption-top{margin-top:.5rem;margin-bottom:.25rem;text-align:center}figure.quarto-float-tbl figcaption.quarto-float-caption-bottom{padding-top:.25rem;margin-bottom:.5rem;text-align:center}.utterances{max-width:none;margin-left:-8px}iframe{margin-bottom:1em}details{margin-bottom:1em}details[show]{margin-bottom:0}details>summary{color:#6c757d}details>summary>p:only-child{display:inline}pre.sourceCode,code.sourceCode{position:relative}p code:not(.sourceCode){white-space:pre-wrap}code{white-space:pre}@media print{code{white-space:pre-wrap}}pre>code{display:block}pre>code.sourceCode{white-space:pre}pre>code.sourceCode>span>a:first-child::before{text-decoration:none}pre.code-overflow-wrap>code.sourceCode{white-space:pre-wrap}pre.code-overflow-scroll>code.sourceCode{white-space:pre}code a:any-link{color:inherit;text-decoration:none}code a:hover{color:inherit;text-decoration:underline}ul.task-list{padding-left:1em}[data-tippy-root]{display:inline-block}.tippy-content .footnote-back{display:none}.footnote-back{margin-left:.2em}.tippy-content{overflow-x:auto}.quarto-embedded-source-code{display:none}.quarto-unresolved-ref{font-weight:600}.quarto-cover-image{max-width:35%;float:right;margin-left:30px}.cell-output-display .widget-subarea{margin-bottom:1em}.cell-output-display:not(.no-overflow-x),.knitsql-table:not(.no-overflow-x){overflow-x:auto}.panel-input{margin-bottom:1em}.panel-input>div,.panel-input>div>div{display:inline-block;vertical-align:top;padding-right:12px}.panel-input>p:last-child{margin-bottom:0}.layout-sidebar{margin-bottom:1em}.layout-sidebar .tab-content{border:none}.tab-content>.page-columns.active{display:grid}div.sourceCode>iframe{width:100%;height:300px;margin-bottom:-0.5em}a{text-underline-offset:3px}div.ansi-escaped-output{font-family:monospace;display:block}/*!
 *
 * ansi colors from IPython notebook's
 *
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
index ebfc262..f6a53b1 100644
--- a/docs/site_libs/quarto-nav/quarto-nav.js
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -237,6 +237,7 @@ window.document.addEventListener("DOMContentLoaded", function () {
     const links = window.document.querySelectorAll("a");
     for (let i = 0; i < links.length; i++) {
       if (links[i].href) {
+        links[i].dataset.originalHref = links[i].href;
         links[i].href = links[i].href.replace(/\/index\.html/, "/");
       }
     }
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
index aefb6a5..5f723d7 100644
--- a/docs/site_libs/quarto-search/quarto-search.js
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -675,6 +675,18 @@ function showCopyLink(query, options) {
 // create the index
 var fuseIndex = undefined;
 var shownWarning = false;
+
+// fuse index options
+const kFuseIndexOptions = {
+  keys: [
+    { name: "title", weight: 20 },
+    { name: "section", weight: 20 },
+    { name: "text", weight: 10 },
+  ],
+  ignoreLocation: true,
+  threshold: 0.1,
+};
+
 async function readSearchData() {
   // Initialize the search index on demand
   if (fuseIndex === undefined) {
@@ -685,17 +697,7 @@ async function readSearchData() {
       shownWarning = true;
       return;
     }
-    // create fuse index
-    const options = {
-      keys: [
-        { name: "title", weight: 20 },
-        { name: "section", weight: 20 },
-        { name: "text", weight: 10 },
-      ],
-      ignoreLocation: true,
-      threshold: 0.1,
-    };
-    const fuse = new window.Fuse([], options);
+    const fuse = new window.Fuse([], kFuseIndexOptions);
 
     // fetch the main search.json
     const response = await fetch(offsetURL("search.json"));
@@ -1226,8 +1228,34 @@ function algoliaSearch(query, limit, algoliaOptions) {
   });
 }
 
-function fuseSearch(query, fuse, fuseOptions) {
-  return fuse.search(query, fuseOptions).map((result) => {
+let subSearchTerm = undefined;
+let subSearchFuse = undefined;
+const kFuseMaxWait = 125;
+
+async function fuseSearch(query, fuse, fuseOptions) {
+  let index = fuse;
+  // Fuse.js using the Bitap algorithm for text matching which runs in
+  // O(nm) time (no matter the structure of the text). In our case this
+  // means that long search terms mixed with large index gets very slow
+  //
+  // This injects a subIndex that will be used once the terms get long enough
+  // Usually making this subindex is cheap since there will typically be
+  // a subset of results matching the existing query
+  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
+    // Use the existing subSearchFuse
+    index = subSearchFuse;
+  } else if (subSearchFuse !== undefined) {
+    // The term changed, discard the existing fuse
+    subSearchFuse = undefined;
+    subSearchTerm = undefined;
+  }
+
+  // Search using the active fuse
+  const then = performance.now();
+  const resultsRaw = await index.search(query, fuseOptions);
+  const now = performance.now();
+
+  const results = resultsRaw.map((result) => {
     const addParam = (url, name, value) => {
       const anchorParts = url.split("#");
       const baseUrl = anchorParts[0];
@@ -1244,4 +1272,15 @@ function fuseSearch(query, fuse, fuseOptions) {
       crumbs: result.item.crumbs,
     };
   });
+
+  // If we don't have a subfuse and the query is long enough, go ahead
+  // and create a subfuse to use for subsequent queries
+  if (now - then > kFuseMaxWait && subSearchFuse === undefined) {
+    subSearchTerm = query;
+    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
+    resultsRaw.forEach((rr) => {
+      subSearchFuse.add(rr.item);
+    });
+  }
+  return results;
 }
diff --git a/msom.ipynb b/msom.ipynb
new file mode 100644
index 0000000..7b20006
--- /dev/null
+++ b/msom.ipynb
@@ -0,0 +1,364 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "title: Community occupancy\n",
+        "description: Static community occupancy models in PyMC\n",
+        "author:\n",
+        "  name: Philip T. Patton\n",
+        "  affiliation:\n",
+        "    - Marine Mammal Research Program\n",
+        "    - Hawaiʻi Institute of Marine Biology\n",
+        "date: 'February 15, 2024'\n",
+        "bibliography: refs.bib\n",
+        "execute:\n",
+        "  cache: true\n",
+        "---"
+      ],
+      "id": "61ee13ef"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this notebook, I explore fitting community occupancy models in PyMC. Community occupancy models are a multi-species extension of standard occupancy models. The benefit of these models is that, by treating each species as a random effect, they can estimate occupancy and detection more precisely than single species models. Further, through data augmentation, they can estimate the richness of the supercommunity, that is, the total number of species that use the study area during the surveys.\n",
+        "\n",
+        "# US Breeding Bird Survey\n",
+        "\n",
+        "As a motivating example, I use the breeding bird survey (BBS) data used by @dorazio2005 and @royle2008, Chapter 12. This is a $(n, J)$ matrix with the number of times each species was detected over $K$ surveys, where $n$ is the number of detected species and $J$ is the number surveyed sites. In this example, $n=99$ species were detected at the $J=50$ sites over the $K=11$ surveys in New Hampshire. The BBS occurs on routes across the US. This dataset represents one route.\n"
+      ],
+      "id": "a7bbaf0e"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Number of detections for each species at each site along this BBS route.\n",
+        "#| label: fig-bbs\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import pymc as pm\n",
+        "import arviz as az\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from matplotlib.patches import Patch\n",
+        "\n",
+        "SEED = 808\n",
+        "RNG = np.random.default_rng(SEED)\n",
+        "\n",
+        "plt.style.use('fivethirtyeight')\n",
+        "plt.rcParams['axes.facecolor'] = 'white'\n",
+        "plt.rcParams['figure.facecolor'] = 'white'\n",
+        "\n",
+        "def invlogit(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "# read in the detection data\n",
+        "nh17 = pd.read_csv('detectionFreq.NH17.csv')\n",
+        "Y = nh17.to_numpy()\n",
+        "n, J = Y.shape\n",
+        "K = Y.max()\n",
+        "\n",
+        "# convert the species names to ints \n",
+        "species_idx, lookup = nh17.index.factorize() # lookup[int] returns the actual name\n",
+        "\n",
+        "# plot the detection frequencies\n",
+        "fig, ax = plt.subplots(figsize=(4, 6))\n",
+        "im = ax.imshow(Y[np.argsort(Y.sum(axis=1))], aspect='auto')\n",
+        "ax.set_ylabel('Species')\n",
+        "ax.set_xlabel('Site')\n",
+        "\n",
+        "# add a legend\n",
+        "values = np.unique(Y.ravel())[1::2]\n",
+        "colors = [ im.cmap(im.norm(value)) for value in values]\n",
+        "patches = [ Patch(color=colors[i], label=f'{v}') for i, v in enumerate(values) ]\n",
+        "plt.legend(title='Detections', handles=patches, bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.)\n",
+        "ax.grid(False)\n",
+        "plt.show()"
+      ],
+      "id": "fig-bbs",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "@dorazio2005 draw each species-level effect from a multivariate normal distribution,\n",
+        "$$\n",
+        "{\\alpha_i \\choose \\beta_i} \\sim \\text{Normal} \\left( {\\mu_{\\,\\text{detection}} \\choose \\mu_{\\, \\text{occupancy}}}, \\; \\mathbf{\\Sigma} \\right),\n",
+        "$$\n",
+        "where $\\alpha_i$ is the logit-scale probability of detection for species $i=1,\\dots,n$, $\\beta_i$ is the logit-scale probability of occurrence, $\\mu$ is the community-level average, and $\\mathbf{\\Sigma}$ is the covariance matrix. We assume that there will be a positive correlation between occupancy and the probability of detection, since abundance is positively correlated with both. \n",
+        "\n",
+        "## Known $N$\n",
+        "\n",
+        "First, I fit the the known $N$ version of the model. The goal of this version is to estimate occurrence and detection for each species, without estimating species richness.\n",
+        "\n",
+        "This notebook makes extensive use of the `coords` feature in PyMC. `Coords` makes it easier to incorporate the species-level effects via the multivariate normal. I use a $\\text{Normal}(0, 2)$ prior for both $\\mu$ parameters, and a LKJ Cholesky covariance prior for $\\mathbf{\\Sigma}.$\n"
+      ],
+      "id": "e3e24346"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Visual representation of the known $N$ version of the community occupancy model.\n",
+        "#| label: fig-known\n",
+        "\n",
+        "coords = {'process': ['detection', 'occurrence'], \n",
+        "          'process_bis': ['detection', 'occurrence'],\n",
+        "          'species': lookup}\n",
+        "\n",
+        "with pm.Model(coords=coords) as known:\n",
+        "\n",
+        "    # priors for community-level means for detection and occurrence\n",
+        "    mu = pm.Normal('mu', 0, 2, dims='process')\n",
+        "\n",
+        "    # prior for covariance matrix for occurrence and detection\n",
+        "    chol, corr, stds = pm.LKJCholeskyCov(\n",
+        "        \"chol\", n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0, shape=2)\n",
+        "    )\n",
+        "    cov = pm.Deterministic(\"cov\", chol.dot(chol.T), dims=(\"process\", \"process_bis\"))\n",
+        "\n",
+        "    # species-level occurrence and detection probabilities on logit-scale  \n",
+        "    ab = pm.MvNormal(\"ab\", mu, chol=chol, dims=(\"species\", \"process\"))\n",
+        "\n",
+        "    # probability of detection. newaxis allows for broadcasting\n",
+        "    a = ab[:, 0][:, np.newaxis]\n",
+        "    p = pm.Deterministic(\"p\", pm.math.invlogit(a))\n",
+        "\n",
+        "    # probability of detection. newaxis allows for broadcasting\n",
+        "    b = ab[:, 1][:, np.newaxis]\n",
+        "    psi = pm.Deterministic(\"psi\", pm.math.invlogit(b))\n",
+        "\n",
+        "    # likelihood\n",
+        "    pm.ZeroInflatedBinomial('Y', p=p, psi=psi, n=K, observed=Y)\n",
+        "\n",
+        "pm.model_to_graphviz(known)"
+      ],
+      "id": "fig-known",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "with known:\n",
+        "    known_idata = pm.sample()"
+      ],
+      "id": "5aca72f1",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Trace plots for the community level means of occupancy and abundance in the known $N$ version of the BBS model. The estimates from @royle2008 are shown by vertical and horizontal lines.\n",
+        "#| label: fig-trace_known.\n",
+        "mu_hat_royle = [-1.11, -1.7]\n",
+        "az.plot_trace(known_idata, var_names=['mu'], figsize=(8,2),\n",
+        "              lines=[(\"mu\", {}, [mu_hat_royle])]);"
+      ],
+      "id": "fig-trace_known",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Species-level probabilities of detection and occupancy.\n",
+        "#| label: fig-probs\n",
+        "samps = az.extract(known_idata, var_names='ab')\n",
+        "ab_mean = samps.mean(axis=2)\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(5,4))\n",
+        "ax.scatter(invlogit(ab_mean[:, 1]), invlogit(ab_mean[:, 0]), alpha=0.5)\n",
+        "ax.set_xlim((0, 1))\n",
+        "ax.set_xlabel('Occupancy probability')\n",
+        "ax.set_ylim((0, 0.8))\n",
+        "ax.set_ylabel('Detection probability')\n",
+        "plt.show()"
+      ],
+      "id": "fig-probs",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The estimates of the community-level means is quite close to the estimates from @royle2008. We can visualize the species-level probabilities of detection and occupancy. Compare with Figure 12.3 in @royle2008.\n",
+        "\n",
+        "## Unknown $N$\n",
+        "\n",
+        "Next, I train the unknown $N$ version of the model. Like many other notebooks in this series, it relies on augmenting the detection histories with all-zero histories. These represent the detection histories for species that may use the study site, but were not detected over the $K=11$ surveys. I also augment the species names in the `coords` dict, such that we can still use the `dims` argument in the multivariate normal. Mirroring @royle2008, I augment the history $M - n$ all-zero histories, where $M=250$ and $n$ is the number of species detected during the survey. \n",
+        "\n",
+        "Similar to the occupancy notebook, I use a `CustomDist` to model the augmented history. This accounts for the \"row-level\" zero-inflation, whereby we know that the species is included in the super community if it was detected along the BBS route. The only difference with this `logp` is that it uses a `ZeroInflatedBinomial` distribution under the hood, rather than a Bernoulli, and uses the parameter $\\Omega$ to account for the row-level inflation. \n"
+      ],
+      "id": "c0a988ba"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Visual representation of the unknown $N$ version of the BBS model.\n",
+        "#| label: fig-unknown\n",
+        "M = 250\n",
+        "all_zero_history = np.zeros((M - n, J))\n",
+        "Y_augmented = np.row_stack((Y, all_zero_history))\n",
+        "\n",
+        "aug_names = [f'aug{i}' for i in np.arange(M - n)]\n",
+        "spp_aug = np.concatenate((lookup, aug_names))\n",
+        "\n",
+        "coords = {'process': ['detection', 'occurrence'], \n",
+        "          'process_bis': ['detection', 'occurrence'],\n",
+        "          'species_aug': spp_aug}\n",
+        "\n",
+        "def logp(x, psi, n, p, omega):\n",
+        "\n",
+        "    rv = pm.ZeroInflatedBinomial.dist(psi=psi, n=n, p=p)\n",
+        "    lp = pm.logp(rv, x)\n",
+        "    lp_sum = lp.sum(axis=1)\n",
+        "    lp_exp = pm.math.exp(lp_sum)\n",
+        "    \n",
+        "    res = pm.math.switch(\n",
+        "        x.sum(axis=1) > 0,\n",
+        "        lp_exp * omega,\n",
+        "        lp_exp * omega + (1 - omega)\n",
+        "    )\n",
+        "    \n",
+        "    return pm.math.log(res)\n",
+        "\n",
+        "with pm.Model(coords=coords) as unknown:\n",
+        "\n",
+        "    # priors for inclusion\n",
+        "    omega = pm.Uniform('omega', 0, 1)\n",
+        "    \n",
+        "    # priors for community-level means for detection and occurrence\n",
+        "    mu = pm.Normal('mu', 0, 2, dims='process')\n",
+        "\n",
+        "    # prior for covariance matrix for occurrence and detection\n",
+        "    chol, corr, stds = pm.LKJCholeskyCov(\n",
+        "        \"chol\", n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0, shape=2)\n",
+        "    )\n",
+        "    cov = pm.Deterministic(\"cov\", chol.dot(chol.T), dims=(\"process\", \"process_bis\"))\n",
+        "\n",
+        "    # species-level occurrence and detection probabilities on logit-scale  \n",
+        "    ab = pm.MvNormal(\"ab\", mu, chol=chol, dims=(\"species_aug\", \"process\"))\n",
+        "\n",
+        "    # probability of detection\n",
+        "    alpha = ab[:, 0]\n",
+        "    p = pm.Deterministic(\"p\", pm.math.invlogit(alpha))\n",
+        "\n",
+        "    # probability of occurrence\n",
+        "    beta = ab[:, 1]\n",
+        "    psi = pm.Deterministic(\"psi\", pm.math.invlogit(beta))\n",
+        "\n",
+        "    # likelihood\n",
+        "    pm.CustomDist(\n",
+        "        'Y',\n",
+        "        psi[:, np.newaxis],\n",
+        "        K,\n",
+        "        p[:, np.newaxis],\n",
+        "        omega,\n",
+        "        logp=logp,\n",
+        "        observed=Y_augmented\n",
+        "    )\n",
+        "    \n",
+        "pm.model_to_graphviz(unknown)"
+      ],
+      "id": "fig-unknown",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "with unknown:\n",
+        "    unknown_idata = pm.sample()"
+      ],
+      "id": "d1743cbc",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Trace plots for the inclusion parameter for the unknown $N$ version of the BBS model. The estimate from @royle2008 are shown by vertical and horizontal lines.\n",
+        "#| label: fig-trace_unknown.\n",
+        "omega_hat_royle = [0.55]\n",
+        "az.plot_trace(unknown_idata, var_names=['omega'], figsize=(8,2), \n",
+        "              lines=[(\"omega\", {}, [omega_hat_royle])]);"
+      ],
+      "id": "fig-trace_unknown",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I can plot the posterior distribution of species richness $N.$ This is slightly more complicated than before sinc there is an additional level of zero-inflation (included and never detected or not-included) in this model compared to the occupancy model (present and never detection or not present). \n"
+      ],
+      "id": "1056a428"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "#| fig-cap: Posterior distribution of species richness from the BBS model.\n",
+        "#| label: fig-postN\n",
+        "\n",
+        "# relevant posterior samples\n",
+        "post = az.extract(unknown_idata)\n",
+        "o_samps = post.omega.to_numpy()\n",
+        "psi_samps = post.psi.to_numpy()[n:, :]\n",
+        "p_samps = post.p.to_numpy()[n:, :]\n",
+        "\n",
+        "# probability that the animal was never detected during the survey if present\n",
+        "p_not_detected = (1 - p_samps) ** K\n",
+        "\n",
+        "# probability of a zero detection history \n",
+        "p_zero_hist = psi_samps * p_not_detected + (1 - psi_samps)\n",
+        "\n",
+        "# probability that the species was included in the given the all-zero history\n",
+        "p_included = (o_samps * p_zero_hist ** J) / (o_samps * p_zero_hist ** J + (1 - o_samps))\n",
+        "\n",
+        "# posterior samples of N\n",
+        "number_undetected = RNG.binomial(1, p_included).sum(axis=0)\n",
+        "N_samps = n + number_undetected\n",
+        "\n",
+        "# posterior distribution \n",
+        "N_hat_royle = 138\n",
+        "fig, ax = plt.subplots(figsize=(6, 4))\n",
+        "ax.hist(N_samps, edgecolor='white', bins=25)\n",
+        "ax.set_xlabel('Species richness $N$')\n",
+        "ax.set_ylabel('Posterior samples')\n",
+        "ax.axvline(N_hat_royle, linestyle='--', color='C1')\n",
+        "ax.axvline(N_samps.mean(), linestyle='--', color='C2')\n",
+        "plt.show()"
+      ],
+      "id": "fig-postN",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "pymc",
+      "language": "python",
+      "display_name": "PyMC"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/msom.qmd b/msom.qmd
index ef5e624..26ba3ba 100644
--- a/msom.qmd
+++ b/msom.qmd
@@ -13,7 +13,7 @@ execute:
   cache: true
 ---
 
-In this notebook, I explore fitting community occupancy models in PyMC. Community occupancy models are a multi-species extension of standard occupancy models. The benefit of these models is that they estimate occupancy and detection better that single species models by treating each species as a random effect. Further, through data augmentation, they can estimate the richness of the supercommunity, that is, the total number of species that use the study area during the surveys.
+In this notebook, I explore fitting community occupancy models in PyMC. Community occupancy models are a multi-species extension of standard occupancy models. The benefit of these models is that, by treating each species as a random effect, they can estimate occupancy and detection more precisely than single species models. Further, through data augmentation, they can estimate the richness of the supercommunity, that is, the total number of species that use the study area during the surveys.
 
 # US Breeding Bird Survey
 
diff --git a/notebooks.qmd b/notebooks.qmd
index fd469c3..704f076 100644
--- a/notebooks.qmd
+++ b/notebooks.qmd
@@ -3,10 +3,10 @@ title: "Notebooks"
 bibliography: refs.bib
 ---
 
-Please find here any notebooks that I may have thought would be of general interest. For now, these mainly consist of my attempts to port standard ecological models to [PyMC](https://www.pymc.io/welcome.html), a Python library for doing Bayesian analysis.  
+Please find here any jupyter notebooks that I may have thought would be of general interest. For now, these are all about [PyMC](https://www.pymc.io/welcome.html), a Python library for doing Bayesian analysis.  
 
 # PyMC
 
-There are many valuable tools for fitting hierarchical models in ecology. These tools are typically R libraries, such as [unmarked](https://rbchan.github.io/unmarked/), or programs called from R, such as [JAGS](https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/) or [Stan](https://fukamilab.github.io/BIO202/09-C-occupancy-models.html). There are relatively fewer examples of how to fit these models in Python. While most ecologists, and arguably statisticians, use R, there are some benefits to using Python generally. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Besides, Python is widely used for machine learning, which is increasingly applied in ecology.  
+There are many valuable tools for fitting hierarchical models in ecology, including [unmarked](https://rbchan.github.io/unmarked/), [JAGS](https://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/), [NIMBLE](https://r-nimble.org) and [Stan](https://fukamilab.github.io/BIO202/09-C-occupancy-models.html). These are, for the most part, R libraries or programs called from R. There are relatively fewer examples of how to fit these models in Python. While most ecologists use R, they may find some benefit from using Python. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Also, Python is widely used for machine learning, which is increasingly applied in ecology.  
 
-In the PyMC notebooks, I try to demonstrate how to use PyMC to train the most common hierarchcial models in ecology. For this, I have drawn considerable inspiration from @royle2008, @kery2011, @mccrea2014, and @hooten2019, oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC's core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.
\ No newline at end of file
+In the PyMC jupyter notebooks, I try to demonstrate how to use PyMC to train the most common hierarchical models in ecology. For this, I have drawn considerable inspiration from @royle2008, @kery2011, @mccrea2014, and @hooten2019, oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC's core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.
\ No newline at end of file
diff --git a/occupancy.qmd b/occupancy.qmd
index 1fb8c19..e883b66 100644
--- a/occupancy.qmd
+++ b/occupancy.qmd
@@ -29,26 +29,29 @@ which assumes a constant occurrence probability across sites and a constant dete
 To start, I demonstrate how to simulate this zero-inflated model using numpy. Throughout this section, I use hyperparameter values similar to those of @kery2011.
 
 ```{python}
+
+# relevant libraries
 import numpy as np
 import pymc as pm
 import arviz as az
 import pandas as pd
 import matplotlib.pyplot as plt
 
+# plotting styles
 plt.style.use('fivethirtyeight')
 plt.rcParams['axes.facecolor'] = 'white'
 plt.rcParams['figure.facecolor'] = 'white'
-# pal = sns.color_palette('Set2')
-# sns.set_palette(pal)ß
 
 def scale(x):
+    '''Scale x: 0 is the mean and 1 is one standard deviation from the mean.'''
     return (x - np.nanmean(x)) / np.nanstd(x)
 
 def invlogit(x):
+    '''Compute inverse logit of x.'''
     return 1 / (1 + np.exp(-x))
 
 def sim_y(p, z, site_count, visit_count):
-    
+    '''Simulate detections given detection probability p and occurrence state z.'''
     ones = np.ones((site_count, visit_count))
     p_array = p * ones 
 
@@ -261,7 +264,7 @@ y = sim_y(p_true, z_true, site_count, visit_count)
 y[:5]
 ```
 
-Our PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state $z_j.$ The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit painfully slowly.
+Our PyMC code will need to be a little uglier now. I could write the model in terms of the latent occurrence state $z_j.$ The NUTS sampler, however, does not jive with discrete latent states. As such, PyMC will assign it to a binary Gibbs sampler by default, which works, albeit slowly.
 
 Since I am impatient, I instead use the [marginalized version of the model](https://mbjoseph.github.io/posts/2020-04-28-a-step-by-step-guide-to-marginalizing-over-discrete-parameters-for-ecologists-using-stan/), that is, a model that does not include the discrete latent states. To do this in PyMC, I use the `CustomDist` class. This requires, first, defining the log probability of the distribution, `logp`, given the data and it's parameters. We can write `logp` using the likelihood of the occupancy model, 
 $$
@@ -271,7 +274,7 @@ P(\mathbf{y}_j)=
     P(\mathbf{y}_j | z_j = 1)\; \psi_j,  & \text{otherwise}
 \end{cases}
 $$
-where $P(\mathbf{y}_j | z_j = 1) = \prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}$ [@royle2008]. To do this in PyMC, I rely on the `pm.math.switch` function, which is similar to `ifelse()` in R or `np.where()`.
+where $P(\mathbf{y}_j | z_j = 1) = \prod_j p_{j,k}^{y_{j,k}} (1-p_{j,k})^{(1-y_{j,k})}$ [@royle2008]. To do this in PyMC, I rely on the `pm.math.switch` function, which is like the `ifelse()` function in R or `np.where()`.
 
 ```{python}
 # likelihood for y data
diff --git a/occupancy_files/.DS_Store b/occupancy_files/.DS_Store
new file mode 100644
index 0000000..e954052
Binary files /dev/null and b/occupancy_files/.DS_Store differ
diff --git a/occupancy_files/libs/.DS_Store b/occupancy_files/libs/.DS_Store
new file mode 100644
index 0000000..3245464
Binary files /dev/null and b/occupancy_files/libs/.DS_Store differ
diff --git a/scr.qmd b/scr.qmd
index 3c95918..52604ed 100644
--- a/scr.qmd
+++ b/scr.qmd
@@ -11,7 +11,7 @@ bibliography: refs.bib
 jupyter: pymc
 ---
 
-In this notebook, I present an approach to fitting spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs. 
+In this notebook, I show how to train spatial capture-recapture (SCR) models in PyMC. SCR expands upon traditional capture-recapture by incorporating the location of the traps in the analysis. This matters because, typically, animals that live near a particular trap are more likely to be caught in it. In doing so, SCR links individual-level processes to the population-level, expanding the scientific scope of simple designs. 
 
 In this notebook, I train the simplest possible SCR model, SCR0 [@royle2013,Chapter 5], where the goal is estimating the true population size $N$. Similar to the other closed population notebooks, I do so using parameter-expanded data-augmentation (PX-DA). I also borrow the concept of the *detection function* from the distance sampling notebook.
 
@@ -131,7 +131,7 @@ ovenbird.head(10)
 
 ## Simulation
 
-Before estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the *activity center*. The activity center $\mathbf{s}_i$ is the most likely place that you'd find an individual $i$ over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space. (The study takes place in homogenous habitat.)
+Before estimating the parameters, I perform a small simulation. The simulation starts with a core idea of SCR: the *activity center*. The activity center $\mathbf{s}_i$ is the most likely place that you'd find an individual $i$ over the course of the trapping study. In this case, I assume that activity centers are uniformly distributed across the sample space.
 
 I compute the probability of detection for individual $i$ at trap $j$ as $p_{i,j}=g_0 \exp(-d_{i,j}^2/2\sigma^2),$ where $g_0$ is the probability of detecting an individual when it's activity center is at the trap, $d_{i,j}$ is the euclidean distance between the trap and the activity center, and $\sigma$ is the detection range parameter.