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ahartikainen/arviz_idata_to_dataframe.ipynb

Created August 19, 2020 22:00
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arviz_idata_to_dataframe.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example with \"wide\" structure"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import arviz as az"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"idata = az.load_arviz_data(dataset=\"centered_eight\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
" * chain (chain) int64 0 1 2 3\n",
" * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
" * school (school) object &#x27;Choate&#x27; &#x27;Deerfield&#x27; ... &quot;St. Paul&#x27;s&quot; &#x27;Mt. Hermon&#x27;\n",
"Data variables:\n",
" mu (chain, draw) float64 -3.477 -2.456 -2.826 ... 4.597 5.899 0.1614\n",
" theta (chain, draw, school) float64 1.669 -8.537 -2.623 ... 10.59 4.523\n",
" tau (chain, draw) float64 3.73 2.075 3.703 4.146 ... 8.346 7.711 5.407\n",
"Attributes:\n",
" created_at: 2019-06-21T17:36:34.398087\n",
" inference_library: pymc3\n",
" inference_library_version: 3.7</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c4a84c4f-25a3-45bb-9fee-79d709cf4fee' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c4a84c4f-25a3-45bb-9fee-79d709cf4fee' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7a86d8a7-59b4-4671-96b9-86efbdb33c7b' class='xr-section-summary-in' type='checkbox' checked><label for='section-7a86d8a7-59b4-4671-96b9-86efbdb33c7b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0f316981-5bd6-44db-9265-a05472c7c304' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f316981-5bd6-44db-9265-a05472c7c304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d3e66d8-4325-4417-b54f-07bb923ae4d4' class='xr-var-data-in' type='checkbox'><label for='data-6d3e66d8-4325-4417-b54f-07bb923ae4d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-7d107c85-1a9b-4d77-91f8-c09da9b81316' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d107c85-1a9b-4d77-91f8-c09da9b81316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bc9bd2f-4d08-4c9e-86a5-dde5b810e3c6' class='xr-var-data-in' type='checkbox'><label for='data-6bc9bd2f-4d08-4c9e-86a5-dde5b810e3c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Choate&#x27; ... &#x27;Mt. Hermon&#x27;</div><input id='attrs-733a4701-942a-4893-a5db-3087ec0084f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-733a4701-942a-4893-a5db-3087ec0084f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-500a501a-8411-4f44-99a5-f35136080523' class='xr-var-data-in' type='checkbox'><label for='data-500a501a-8411-4f44-99a5-f35136080523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Choate&#x27;, &#x27;Deerfield&#x27;, &#x27;Phillips Andover&#x27;, &#x27;Phillips Exeter&#x27;,\n",
" &#x27;Hotchkiss&#x27;, &#x27;Lawrenceville&#x27;, &quot;St. Paul&#x27;s&quot;, &#x27;Mt. Hermon&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4143e2b2-75ee-4eff-ae1f-06ed721c585c' class='xr-section-summary-in' type='checkbox' checked><label for='section-4143e2b2-75ee-4eff-ae1f-06ed721c585c' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41bfab66-e754-4cca-9047-53274c5c756a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41bfab66-e754-4cca-9047-53274c5c756a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48de0c95-4d4c-4f12-84ee-d68dfbdbb64a' class='xr-var-data-in' type='checkbox'><label for='data-48de0c95-4d4c-4f12-84ee-d68dfbdbb64a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.476986, -2.455871, -2.826254, ..., 3.392022, 8.46255 , -0.238516],\n",
" [ 8.250863, 8.250863, 8.250863, ..., 2.527095, 0.276589, 5.655297],\n",
" [10.51707 , 9.887949, 8.500833, ..., -1.571177, -4.435385, 9.762948],\n",
" [ 4.532296, 4.532296, 3.914097, ..., 4.597058, 5.898506, 0.161389]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ebc88f3-9305-4cc5-a2da-6719668b7084' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ebc88f3-9305-4cc5-a2da-6719668b7084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b505695-9070-4ca3-889e-1aff3b7bcec4' class='xr-var-data-in' type='checkbox'><label for='data-6b505695-9070-4ca3-889e-1aff3b7bcec4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.668654, -8.537401, ..., 0.155234, -6.818251],\n",
" [-6.239359, 1.071411, ..., -4.462528, -1.110761],\n",
" ...,\n",
" [ 9.292977, 13.691033, ..., 8.176874, 5.888367],\n",
" [11.715418, 4.492172, ..., 12.300712, 9.22107 ]],\n",
"\n",
" [[ 8.096212, 7.756517, ..., 6.465884, 5.472468],\n",
" [ 8.096212, 7.756517, ..., 6.465884, 5.472468],\n",
" ...,\n",
" [14.735501, 7.546139, ..., 15.732696, -4.697359],\n",
" [-4.837035, 8.501408, ..., 5.850945, -0.426543]],\n",
"\n",
" [[14.570919, 15.029668, ..., 11.798422, 8.519339],\n",
" [12.686667, 7.679173, ..., 13.514133, 10.295221],\n",
" ...,\n",
" [ 5.361653, 2.78173 , ..., 7.224553, -7.416111],\n",
" [13.439111, 9.614329, ..., 12.008359, 16.673157]],\n",
"\n",
" [[ 4.326388, 5.198464, ..., 5.339654, 3.422931],\n",
" [ 4.326388, 5.198464, ..., 5.339654, 3.422931],\n",
" ...,\n",
" [-1.420946, -4.034405, ..., 15.850648, 4.013397],\n",
" [-0.050159, 0.063538, ..., 10.592933, 4.523389]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a77d884a-5cb4-48a4-96d4-bd7906c1dbc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a77d884a-5cb4-48a4-96d4-bd7906c1dbc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84016c64-895c-4675-abe7-56724b3a098c' class='xr-var-data-in' type='checkbox'><label for='data-84016c64-895c-4675-abe7-56724b3a098c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3.730101, 2.075383, 3.702993, ..., 10.107925, 8.079994, 7.728861],\n",
" [ 1.193334, 1.193334, 1.193334, ..., 13.922048, 8.869919, 4.763175],\n",
" [ 5.137247, 4.264381, 2.141432, ..., 2.811842, 12.179657, 4.452967],\n",
" [ 0.50007 , 0.50007 , 0.902267, ..., 8.345631, 7.71079 , 5.406798]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-57d43a0b-91bb-46eb-9fdf-e67c11f81645' class='xr-section-summary-in' type='checkbox' checked><label for='section-57d43a0b-91bb-46eb-9fdf-e67c11f81645' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2019-06-21T17:36:34.398087</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.7</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
" * chain (chain) int64 0 1 2 3\n",
" * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
" * school (school) object &#x27;Choate&#x27; &#x27;Deerfield&#x27; ... &quot;St. Paul&#x27;s&quot; &#x27;Mt. Hermon&#x27;\n",
"Data variables:\n",
" obs (chain, draw, school) float64 7.85 -19.03 -22.5 ... 4.698 -15.07\n",
"Attributes:\n",
" created_at: 2019-06-21T17:36:34.489022\n",
" inference_library: pymc3\n",
" inference_library_version: 3.7</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8e0ed6d6-7df5-4bb5-a632-312c0de9ace2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e0ed6d6-7df5-4bb5-a632-312c0de9ace2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-65e415ed-b67e-47d0-8c65-a7285e05b0ce' class='xr-section-summary-in' type='checkbox' checked><label for='section-65e415ed-b67e-47d0-8c65-a7285e05b0ce' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2738c6ef-ac21-40ce-a830-0394d723258e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2738c6ef-ac21-40ce-a830-0394d723258e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55cafce2-4cc5-46a1-b461-5b29844ac76d' class='xr-var-data-in' type='checkbox'><label for='data-55cafce2-4cc5-46a1-b461-5b29844ac76d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-ec0be2fb-b2a2-488c-9bfb-0017faadf5ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec0be2fb-b2a2-488c-9bfb-0017faadf5ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a364fb3-3988-466c-b6cd-71bcaed04ed6' class='xr-var-data-in' type='checkbox'><label for='data-7a364fb3-3988-466c-b6cd-71bcaed04ed6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Choate&#x27; ... &#x27;Mt. Hermon&#x27;</div><input id='attrs-ac541f97-dd48-48f2-805c-23f6a9395803' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac541f97-dd48-48f2-805c-23f6a9395803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c73e7b51-802e-4eee-ae9e-729216cfaec9' class='xr-var-data-in' type='checkbox'><label for='data-c73e7b51-802e-4eee-ae9e-729216cfaec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Choate&#x27;, &#x27;Deerfield&#x27;, &#x27;Phillips Andover&#x27;, &#x27;Phillips Exeter&#x27;,\n",
" &#x27;Hotchkiss&#x27;, &#x27;Lawrenceville&#x27;, &quot;St. Paul&#x27;s&quot;, &#x27;Mt. Hermon&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-361c4333-f6d8-4676-9d71-c901c5adcc12' class='xr-section-summary-in' type='checkbox' checked><label for='section-361c4333-f6d8-4676-9d71-c901c5adcc12' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>obs</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d3974e10-93da-4187-b58a-3e6e3aa0984c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3974e10-93da-4187-b58a-3e6e3aa0984c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4e71fb8-b3ad-46bf-b7e6-85001f721d27' class='xr-var-data-in' type='checkbox'><label for='data-c4e71fb8-b3ad-46bf-b7e6-85001f721d27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 7.850329e+00, -1.902792e+01, ..., -3.547030e+00, 1.619463e+01],\n",
" [ 2.931985e+00, 1.919950e-01, ..., -8.065696e-01, 1.518667e+01],\n",
" ...,\n",
" [-7.248618e-01, 5.924768e+00, ..., 1.173805e+01, -1.422732e+01],\n",
" [ 2.220263e+01, 1.548817e+01, ..., 8.783500e+00, 2.019629e+01]],\n",
"\n",
" [[-1.202312e+01, 1.233019e+01, ..., 2.131579e+01, 8.356886e+00],\n",
" [ 4.996825e+00, 1.506829e+01, ..., -1.342830e+00, -2.743757e+01],\n",
" ...,\n",
" [ 3.666123e+01, 1.349807e+01, ..., 4.540989e+01, -2.117575e+00],\n",
" [ 1.791875e+00, 1.501421e+01, ..., -2.182083e+00, -6.630969e+00]],\n",
"\n",
" [[ 3.377648e+01, 3.088294e+01, ..., 2.182889e+01, 4.625301e+00],\n",
" [-5.600531e-01, 5.228436e+00, ..., 9.387947e+00, 3.665830e+00],\n",
" ...,\n",
" [ 3.279823e+00, -1.301396e+01, ..., 1.089418e+01, -1.149742e+01],\n",
" [ 3.424522e+01, 2.320377e+01, ..., 9.892069e+00, 1.729264e+01]],\n",
"\n",
" [[-1.517826e-02, -5.597241e-01, ..., -2.986433e+00, 1.075464e+01],\n",
" [ 7.538687e+00, 2.524281e+01, ..., -8.230382e+00, -2.109873e+01],\n",
" ...,\n",
" [ 2.180411e+00, -1.861976e+01, ..., 2.564547e+01, -7.993703e+00],\n",
" [-2.096968e+01, 5.474909e+00, ..., 4.697547e+00, -1.506955e+01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f01871b-a770-4b39-8c5b-c52625ece709' class='xr-section-summary-in' type='checkbox' checked><label for='section-0f01871b-a770-4b39-8c5b-c52625ece709' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2019-06-21T17:36:34.489022</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.7</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 4, draw: 500, school: 8)\n",
"Coordinates:\n",
" * chain (chain) int64 0 1 2 3\n",
" * draw (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499\n",
" * school (school) object &#x27;Choate&#x27; &#x27;Deerfield&#x27; ... &#x27;Mt. Hermon&#x27;\n",
"Data variables:\n",
" tune (chain, draw) bool True False False ... False False False\n",
" depth (chain, draw) int64 5 3 3 4 5 5 4 4 5 ... 4 4 4 5 5 5 5 5\n",
" tree_size (chain, draw) float64 31.0 7.0 7.0 15.0 ... 31.0 31.0 31.0\n",
" lp (chain, draw) float64 -59.05 -56.19 ... -63.62 -58.35\n",
" energy_error (chain, draw) float64 0.07387 -0.1841 ... -0.087 -0.003652\n",
" step_size_bar (chain, draw) float64 0.2417 0.2417 ... 0.1502 0.1502\n",
" max_energy_error (chain, draw) float64 0.131 -0.2067 ... -0.101 -0.1757\n",
" energy (chain, draw) float64 60.76 62.76 64.4 ... 67.77 67.21\n",
" mean_tree_accept (chain, draw) float64 0.9506 0.9906 ... 0.9875 0.9967\n",
" step_size (chain, draw) float64 0.1275 0.1275 ... 0.1064 0.1064\n",
" diverging (chain, draw) bool False False False ... False False False\n",
" log_likelihood (chain, draw, school) float64 -5.168 -4.589 ... -3.896\n",
"Attributes:\n",
" created_at: 2019-06-21T17:36:34.485802\n",
" inference_library: pymc3\n",
" inference_library_version: 3.7</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0ee52eaa-3d7d-429f-8ccc-1c75d61b52a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0ee52eaa-3d7d-429f-8ccc-1c75d61b52a9' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-148b01ca-23d7-4f97-bb53-da850b79d766' class='xr-section-summary-in' type='checkbox' checked><label for='section-148b01ca-23d7-4f97-bb53-da850b79d766' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-d208d5a6-14c3-4abc-9800-5ce9eed60d30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d208d5a6-14c3-4abc-9800-5ce9eed60d30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f108d1b-0901-464d-b999-1da0ac39b4ee' class='xr-var-data-in' type='checkbox'><label for='data-5f108d1b-0901-464d-b999-1da0ac39b4ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-8c21bf22-ce98-44be-97e1-acfe72e9a8a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c21bf22-ce98-44be-97e1-acfe72e9a8a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bcd8309-cd7e-4238-b2eb-565e02126dc6' class='xr-var-data-in' type='checkbox'><label for='data-9bcd8309-cd7e-4238-b2eb-565e02126dc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Choate&#x27; ... &#x27;Mt. Hermon&#x27;</div><input id='attrs-087d84fd-94a2-463b-8477-125321255f55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-087d84fd-94a2-463b-8477-125321255f55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-097df179-e3fe-4a36-99a3-f95ae488c58f' class='xr-var-data-in' type='checkbox'><label for='data-097df179-e3fe-4a36-99a3-f95ae488c58f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Choate&#x27;, &#x27;Deerfield&#x27;, &#x27;Phillips Andover&#x27;, &#x27;Phillips Exeter&#x27;,\n",
" &#x27;Hotchkiss&#x27;, &#x27;Lawrenceville&#x27;, &quot;St. Paul&#x27;s&quot;, &#x27;Mt. Hermon&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-51ba5c7d-f0f7-4493-a4bd-3d7ac9fa3d6d' class='xr-section-summary-in' type='checkbox' checked><label for='section-51ba5c7d-f0f7-4493-a4bd-3d7ac9fa3d6d' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tune</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbc1e337-6a3d-4776-b510-709d76542e51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbc1e337-6a3d-4776-b510-709d76542e51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e6c61e-2fe5-463e-8b5e-f3adff4c161c' class='xr-var-data-in' type='checkbox'><label for='data-50e6c61e-2fe5-463e-8b5e-f3adff4c161c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ True, False, False, ..., False, False, False],\n",
" [ True, False, False, ..., False, False, False],\n",
" [ True, False, False, ..., False, False, False],\n",
" [ True, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcdba910-0b4d-4353-b3d3-77e7b7b86f65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcdba910-0b4d-4353-b3d3-77e7b7b86f65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e04bb23e-cf9e-4803-a864-f3fc5a8a45e9' class='xr-var-data-in' type='checkbox'><label for='data-e04bb23e-cf9e-4803-a864-f3fc5a8a45e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5, 3, 3, ..., 5, 5, 4],\n",
" [6, 3, 2, ..., 4, 4, 4],\n",
" [3, 5, 3, ..., 4, 4, 5],\n",
" [3, 4, 3, ..., 5, 5, 5]], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91d7bbae-c4d1-40b4-818d-b5d689bf95b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91d7bbae-c4d1-40b4-818d-b5d689bf95b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d95e142d-cd91-4c11-9f23-7a9c889b875a' class='xr-var-data-in' type='checkbox'><label for='data-d95e142d-cd91-4c11-9f23-7a9c889b875a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[31., 7., 7., ..., 31., 31., 15.],\n",
" [39., 7., 3., ..., 15., 15., 15.],\n",
" [ 7., 31., 7., ..., 15., 15., 31.],\n",
" [ 7., 11., 7., ..., 31., 31., 31.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8a86fcd-e542-4c5d-8c6b-43f6edded9d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8a86fcd-e542-4c5d-8c6b-43f6edded9d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67acce38-7660-4fcc-80ec-c13294b45f20' class='xr-var-data-in' type='checkbox'><label for='data-67acce38-7660-4fcc-80ec-c13294b45f20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-59.048452, -56.192829, -56.739609, ..., -63.171891, -62.871221,\n",
" -59.67573 ],\n",
" [-51.16655 , -51.16655 , -51.16655 , ..., -62.242981, -60.962775,\n",
" -61.120349],\n",
" [-57.1196 , -54.709673, -49.854318, ..., -58.202845, -63.100613,\n",
" -61.906641],\n",
" [-43.11603 , -43.11603 , -44.766386, ..., -60.530643, -63.616474,\n",
" -58.345072]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-370d5bec-3ce7-4777-bfab-44eed11a7208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-370d5bec-3ce7-4777-bfab-44eed11a7208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ded57d-0b8d-4334-b2ab-de72a9d4f6fe' class='xr-var-data-in' type='checkbox'><label for='data-c4ded57d-0b8d-4334-b2ab-de72a9d4f6fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.073872, -0.184094, 0.301398, ..., -0.024763, 0.015377, 0.011884],\n",
" [ 0.542861, 0. , 0. , ..., 0.035578, -0.144987, -0.023558],\n",
" [ 1.30834 , -0.068309, -0.343327, ..., -0.480097, 1.118238, -0.505195],\n",
" [-0.232345, 0. , 2.427791, ..., -0.007677, -0.087005, -0.003652]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d3ad7c79-8bea-4a80-b950-7c860fc3887b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3ad7c79-8bea-4a80-b950-7c860fc3887b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0d53b80-e5e2-4669-b045-3ad16ffc6b0d' class='xr-var-data-in' type='checkbox'><label for='data-d0d53b80-e5e2-4669-b045-3ad16ffc6b0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.241676, 0.241676, 0.241676, ..., 0.241676, 0.241676, 0.241676],\n",
" [0.233163, 0.233163, 0.233163, ..., 0.233163, 0.233163, 0.233163],\n",
" [0.25014 , 0.25014 , 0.25014 , ..., 0.25014 , 0.25014 , 0.25014 ],\n",
" [0.150248, 0.150248, 0.150248, ..., 0.150248, 0.150248, 0.150248]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4cac123-1ffc-4027-b326-42a0ebceef84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4cac123-1ffc-4027-b326-42a0ebceef84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36b62eef-bb82-48fc-aa16-0cde89dc1e7b' class='xr-var-data-in' type='checkbox'><label for='data-36b62eef-bb82-48fc-aa16-0cde89dc1e7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.310060e-01, -2.066764e-01, 6.362023e-01, ..., 1.272182e-01,\n",
" -3.155631e-01, -6.702092e-02],\n",
" [ 2.089505e+00, 3.848563e+01, 6.992369e+01, ..., -3.713299e-01,\n",
" -2.177462e-01, -1.621819e-01],\n",
" [ 1.458063e+00, 4.335779e+02, 2.788723e+00, ..., -4.800969e-01,\n",
" 4.380251e+00, -5.051946e-01],\n",
" [ 3.226553e-01, 2.736452e+02, 2.202908e+02, ..., -1.224747e-01,\n",
" -1.009818e-01, -1.756579e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f560db0b-f8f2-4848-8667-8f5038290d8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f560db0b-f8f2-4848-8667-8f5038290d8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e015c87-cce2-479b-8c76-7f889e8fe1e8' class='xr-var-data-in' type='checkbox'><label for='data-4e015c87-cce2-479b-8c76-7f889e8fe1e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[60.756731, 62.756232, 64.398717, ..., 67.394493, 66.923554, 65.031815],\n",
" [53.535435, 56.914649, 54.576739, ..., 63.760659, 64.405753, 66.210544],\n",
" [62.504616, 61.998659, 56.945798, ..., 64.477622, 68.892486, 67.322436],\n",
" [50.115409, 46.916088, 52.915592, ..., 66.27361 , 67.768307, 67.209852]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_tree_accept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a8b7daae-0c92-4973-b03a-76c3d176a34e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8b7daae-0c92-4973-b03a-76c3d176a34e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67205a7d-04d3-4db2-9a87-aae4b312e428' class='xr-var-data-in' type='checkbox'><label for='data-67205a7d-04d3-4db2-9a87-aae4b312e428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.950641, 0.990596, 0.725287, ..., 0.971847, 0.979623, 0.986629],\n",
" [0.78913 , 0.014034, 0.035809, ..., 0.989669, 0.987006, 0.991768],\n",
" [0.26802 , 0.392567, 0.839235, ..., 0.969229, 0.105422, 0.979116],\n",
" [0.909964, 0.157585, 0.061793, ..., 0.999467, 0.987537, 0.996704]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2aad7cf3-25a0-483e-882b-67e30bd8b763' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2aad7cf3-25a0-483e-882b-67e30bd8b763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63c13566-2d76-4560-bb43-cfa9996bae8a' class='xr-var-data-in' type='checkbox'><label for='data-63c13566-2d76-4560-bb43-cfa9996bae8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.127504, 0.127504, 0.127504, ..., 0.127504, 0.127504, 0.127504],\n",
" [0.12298 , 0.12298 , 0.12298 , ..., 0.12298 , 0.12298 , 0.12298 ],\n",
" [0.207479, 0.207479, 0.207479, ..., 0.207479, 0.207479, 0.207479],\n",
" [0.106445, 0.106445, 0.106445, ..., 0.106445, 0.106445, 0.106445]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f25d29d-c473-4570-8a11-705fadb0c54a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f25d29d-c473-4570-8a11-705fadb0c54a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82793a3c-992c-46a3-8ff7-762501a65477' class='xr-var-data-in' type='checkbox'><label for='data-82793a3c-992c-46a3-8ff7-762501a65477' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False],\n",
" [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_likelihood</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65c625e7-165a-4b7f-91df-6a8e759873aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65c625e7-165a-4b7f-91df-6a8e759873aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a696ab84-e3fc-4059-860d-2244015fdebe' class='xr-var-data-in' type='checkbox'><label for='data-a696ab84-e3fc-4059-860d-2244015fdebe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-5.167744, -4.588952, ..., -4.813702, -4.355802],\n",
" [-6.232175, -3.46155 , ..., -5.744349, -4.074576],\n",
" ...,\n",
" [-4.404661, -3.383463, ..., -3.703993, -3.866952],\n",
" [-4.216295, -3.283048, ..., -3.383933, -3.821228]],\n",
"\n",
" [[-4.507346, -3.22182 , ..., -3.886703, -3.875064],\n",
" [-4.507346, -3.22182 , ..., -3.886703, -3.875064],\n",
" ...,\n",
" [-4.017982, -3.222554, ..., -3.247227, -4.23956 ],\n",
" [-6.023146, -3.222781, ..., -3.959521, -4.047611]],\n",
"\n",
" [[-4.027745, -3.468605, ..., -3.413821, -3.828006],\n",
" [-4.148096, -3.222038, ..., -3.322139, -3.813795],\n",
" ...,\n",
" [-4.765866, -3.357675, ..., -3.802075, -4.391078],\n",
" [-4.098143, -3.234554, ..., -3.401022, -3.843012]],\n",
"\n",
" [[-4.872411, -3.260767, ..., -4.022945, -3.922838],\n",
" [-4.872411, -3.260767, ..., -4.022945, -3.922838],\n",
" ...,\n",
" [-5.550527, -3.945658, ..., -3.244622, -3.907745],\n",
" [-5.375459, -3.536461, ..., -3.495847, -3.895575]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea5cd95b-cde0-4b12-bbb1-577ded8cf997' class='xr-section-summary-in' type='checkbox' checked><label for='section-ea5cd95b-cde0-4b12-bbb1-577ded8cf997' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2019-06-21T17:36:34.485802</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.7</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (chain: 1, draw: 500, school: 8)\n",
"Coordinates:\n",
" * chain (chain) int64 0\n",
" * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499\n",
" * school (school) object &#x27;Choate&#x27; &#x27;Deerfield&#x27; ... &#x27;Mt. Hermon&#x27;\n",
"Data variables:\n",
" tau (chain, draw) float64 6.561 1.016 68.91 ... 1.56 5.949 0.7631\n",
" tau_log__ (chain, draw) float64 1.881 0.01593 4.233 ... 1.783 -0.2704\n",
" mu (chain, draw) float64 5.293 0.8137 0.7122 ... -1.658 -3.273\n",
" theta (chain, draw, school) float64 2.357 7.371 7.251 ... -3.775 -3.555\n",
" obs (chain, draw, school) float64 -3.54 6.769 19.68 ... -21.16 -6.071\n",
"Attributes:\n",
" created_at: 2019-06-21T17:36:34.490387\n",
" inference_library: pymc3\n",
" inference_library_version: 3.7</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3f1445d8-9c54-49a5-b2ec-7feaa67c54bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f1445d8-9c54-49a5-b2ec-7feaa67c54bf' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6ea46ce6-a805-4df1-9bc1-8f09af3d38f5' class='xr-section-summary-in' type='checkbox' checked><label for='section-6ea46ce6-a805-4df1-9bc1-8f09af3d38f5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-7e07351e-72f6-4bb2-be85-ecc12daa6b2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e07351e-72f6-4bb2-be85-ecc12daa6b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eda3742-ecd4-47ad-be92-49c774fd1077' class='xr-var-data-in' type='checkbox'><label for='data-7eda3742-ecd4-47ad-be92-49c774fd1077' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-c16ef3fa-f892-41f0-b23b-988e0ce56cf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c16ef3fa-f892-41f0-b23b-988e0ce56cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e24fdfa4-b080-44e2-8320-5182e5d968ad' class='xr-var-data-in' type='checkbox'><label for='data-e24fdfa4-b080-44e2-8320-5182e5d968ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0, 1, 2, ..., 497, 498, 499], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Choate&#x27; ... &#x27;Mt. Hermon&#x27;</div><input id='attrs-fc8ce874-7ec9-4a51-85b7-c41779ab407e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc8ce874-7ec9-4a51-85b7-c41779ab407e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc6f44e1-c435-4fb9-8e28-e4b2fed6fa49' class='xr-var-data-in' type='checkbox'><label for='data-fc6f44e1-c435-4fb9-8e28-e4b2fed6fa49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Choate&#x27;, &#x27;Deerfield&#x27;, &#x27;Phillips Andover&#x27;, &#x27;Phillips Exeter&#x27;,\n",
" &#x27;Hotchkiss&#x27;, &#x27;Lawrenceville&#x27;, &quot;St. Paul&#x27;s&quot;, &#x27;Mt. Hermon&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52c89710-c550-47f2-9311-263e77c4e7c1' class='xr-section-summary-in' type='checkbox' checked><label for='section-52c89710-c550-47f2-9311-263e77c4e7c1' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da02d53d-3edf-45a8-9410-1e6b73247029' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da02d53d-3edf-45a8-9410-1e6b73247029' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1d5cd52-5424-43d7-8719-e9c1f3bfc8f9' class='xr-var-data-in' type='checkbox'><label for='data-b1d5cd52-5424-43d7-8719-e9c1f3bfc8f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 6.560633, 1.016055, 68.91391 , ..., 1.560098, 5.948734, 0.763063]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau_log__</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c5a4cb57-e9e3-4a22-b0cf-c631d71abb8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5a4cb57-e9e3-4a22-b0cf-c631d71abb8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8082eb17-1a51-4442-aaa9-9c9c361b96a5' class='xr-var-data-in' type='checkbox'><label for='data-8082eb17-1a51-4442-aaa9-9c9c361b96a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.881087, 0.015927, 4.232858, ..., 0.444748, 1.783178, -0.270415]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9b86f3e-622b-4321-b441-ce27a52777fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9b86f3e-622b-4321-b441-ce27a52777fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-105052a4-39cd-4cfe-9e71-de835e7bbd8b' class='xr-var-data-in' type='checkbox'><label for='data-105052a4-39cd-4cfe-9e71-de835e7bbd8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 5.29345 , 0.813724, 0.712223, ..., -0.979857, -1.657547, -3.272668]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-130de19d-8f25-43b3-9497-12be521c004c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-130de19d-8f25-43b3-9497-12be521c004c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aba19afb-3df5-483a-a273-9595b6bedee1' class='xr-var-data-in' type='checkbox'><label for='data-aba19afb-3df5-483a-a273-9595b6bedee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 2.357357, 7.371371, ..., 6.135082, 3.984435],\n",
" [ 0.258399, -0.752515, ..., 1.73084 , -0.034163],\n",
" ...,\n",
" [-4.353289, 2.194643, ..., -7.819076, -6.21613 ],\n",
" [-4.131344, -4.093318, ..., -3.775218, -3.555126]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>obs</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0091cb95-4ef6-4b20-9fbe-717dbaf2a44b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0091cb95-4ef6-4b20-9fbe-717dbaf2a44b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57dac9b0-8a8b-4773-a61a-4f961530dc4a' class='xr-var-data-in' type='checkbox'><label for='data-57dac9b0-8a8b-4773-a61a-4f961530dc4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ -3.539971, 6.769448, ..., 8.26964 , -8.569042],\n",
" [-21.166369, 1.14605 , ..., -13.157913, -8.5424 ],\n",
" ...,\n",
" [ 29.354582, -5.511382, ..., -17.892521, 46.28878 ],\n",
" [ -6.379747, 6.538907, ..., -21.155214, -6.070767]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4732d82-1bef-4b99-b2af-bbff34a1f1df' class='xr-section-summary-in' type='checkbox' checked><label for='section-e4732d82-1bef-4b99-b2af-bbff34a1f1df' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2019-06-21T17:36:34.490387</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.7</dd></dl></div></li></ul></div></div><br></div>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (school: 8)\n",
"Coordinates:\n",
" * school (school) object &#x27;Choate&#x27; &#x27;Deerfield&#x27; ... &quot;St. Paul&#x27;s&quot; &#x27;Mt. Hermon&#x27;\n",
"Data variables:\n",
" obs (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
"Attributes:\n",
" created_at: 2019-06-21T17:36:34.491909\n",
" inference_library: pymc3\n",
" inference_library_version: 3.7</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c75eb4b5-21c3-42a4-b7d4-8782403747ec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c75eb4b5-21c3-42a4-b7d4-8782403747ec' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f7c80d4-7690-4101-993b-0a176886003c' class='xr-section-summary-in' type='checkbox' checked><label for='section-7f7c80d4-7690-4101-993b-0a176886003c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Choate&#x27; ... &#x27;Mt. Hermon&#x27;</div><input id='attrs-5bb02a39-aa4c-49fd-9050-b88ef5177bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bb02a39-aa4c-49fd-9050-b88ef5177bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa0f924e-2edb-4322-bca0-fac4bb6c39ea' class='xr-var-data-in' type='checkbox'><label for='data-aa0f924e-2edb-4322-bca0-fac4bb6c39ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Choate&#x27;, &#x27;Deerfield&#x27;, &#x27;Phillips Andover&#x27;, &#x27;Phillips Exeter&#x27;,\n",
" &#x27;Hotchkiss&#x27;, &#x27;Lawrenceville&#x27;, &quot;St. Paul&#x27;s&quot;, &#x27;Mt. Hermon&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6140b1f5-2458-48a4-af20-5742bad465f4' class='xr-section-summary-in' type='checkbox' checked><label for='section-6140b1f5-2458-48a4-af20-5742bad465f4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>obs</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e61bad9b-e809-4409-bfac-3829ee039268' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e61bad9b-e809-4409-bfac-3829ee039268' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76a24ef7-5912-4ce9-a16f-58756fc77727' class='xr-var-data-in' type='checkbox'><label for='data-76a24ef7-5912-4ce9-a16f-58756fc77727' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28., 8., -3., 7., -1., 1., 18., 12.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-808742eb-3995-48b3-a62d-681828047a20' class='xr-section-summary-in' type='checkbox' checked><label for='section-808742eb-3995-48b3-a62d-681828047a20' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2019-06-21T17:36:34.491909</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.7</dd></dl></div></li></ul></div></div><br></div>\n",
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"text/plain": [
"Inference data with groups:\n",
"\t> posterior\n",
"\t> posterior_predictive\n",
"\t> sample_stats\n",
"\t> prior\n",
"\t> observed_data"
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"c:\\users\\harti\\github\\arviz\\arviz\\data\\inference_data.py:294: UserWarning: The attributes are not same for all groups. Considering only the first group `attrs`\n",
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"idict = idata.to_dict()\n",
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"coords = idict[\"coords\"]\n",
"idfs = {}\n",
"coords_info = {}\n",
"for group, values in idict.items():\n",
" if group in (\"dims\", \"coords\"):\n",
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" chain = draw = None\n",
" for variable, array in values.items():\n",
" if chain is None:\n",
" chain, draw = array.shape[:2]\n",
" group_dict[\"chain\"] = (np.ones((chain, draw), dtype=int) * np.arange(chain)[:, None]).ravel()\n",
" group_dict[\"draw\"] = (np.ones((chain, draw), dtype=int) * np.arange(draw)).ravel()\n",
" if not array.shape[2:]:\n",
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" continue\n",
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" key = f\"{variable}[{','.join(map(str, idx))}]\"\n",
" idx_tuple = (slice(None), slice(None)) + idx\n",
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{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"posterior = pd.concat((idfs[key] for key in [\"posterior\", \"posterior_predictive\", \"sample_stats\", \"log_likelihood\"] if key in idfs), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"posterior = posterior.loc[:, ~posterior.columns.duplicated()]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
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" <th>log_likelihood[0]</th>\n",
" <th>log_likelihood[1]</th>\n",
" <th>log_likelihood[2]</th>\n",
" <th>log_likelihood[3]</th>\n",
" <th>log_likelihood[4]</th>\n",
" <th>log_likelihood[5]</th>\n",
" <th>log_likelihood[6]</th>\n",
" <th>log_likelihood[7]</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-3.476986</td>\n",
" <td>1.668654</td>\n",
" <td>-8.537401</td>\n",
" <td>-2.622619</td>\n",
" <td>-2.509951</td>\n",
" <td>-4.004844</td>\n",
" <td>-9.174019</td>\n",
" <td>0.155234</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.167744</td>\n",
" <td>-4.588952</td>\n",
" <td>-3.691805</td>\n",
" <td>-3.690549</td>\n",
" <td>-3.171898</td>\n",
" <td>-3.744564</td>\n",
" <td>-4.813702</td>\n",
" <td>-4.355802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>-2.455871</td>\n",
" <td>-6.239359</td>\n",
" <td>1.071411</td>\n",
" <td>-4.517927</td>\n",
" <td>-3.341560</td>\n",
" <td>-3.422806</td>\n",
" <td>0.399546</td>\n",
" <td>-4.462528</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-6.232175</td>\n",
" <td>-3.461550</td>\n",
" <td>-3.696027</td>\n",
" <td>-3.758767</td>\n",
" <td>-3.152398</td>\n",
" <td>-3.318324</td>\n",
" <td>-5.744349</td>\n",
" <td>-4.074576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>-2.826254</td>\n",
" <td>2.195098</td>\n",
" <td>-2.920843</td>\n",
" <td>-3.494201</td>\n",
" <td>2.136533</td>\n",
" <td>-7.229751</td>\n",
" <td>-2.720302</td>\n",
" <td>-3.487005</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.106751</td>\n",
" <td>-3.817848</td>\n",
" <td>-3.692004</td>\n",
" <td>-3.414575</td>\n",
" <td>-3.355730</td>\n",
" <td>-3.374027</td>\n",
" <td>-5.529981</td>\n",
" <td>-4.071174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>-1.995894</td>\n",
" <td>-3.736485</td>\n",
" <td>-6.342422</td>\n",
" <td>-5.017723</td>\n",
" <td>5.005665</td>\n",
" <td>-6.107046</td>\n",
" <td>-2.099592</td>\n",
" <td>1.145165</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.865221</td>\n",
" <td>-4.250049</td>\n",
" <td>-3.699479</td>\n",
" <td>-3.333269</td>\n",
" <td>-3.277163</td>\n",
" <td>-3.356534</td>\n",
" <td>-4.641951</td>\n",
" <td>-4.073257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>7.668982</td>\n",
" <td>11.738328</td>\n",
" <td>12.854003</td>\n",
" <td>-1.419241</td>\n",
" <td>20.278944</td>\n",
" <td>1.377388</td>\n",
" <td>18.534980</td>\n",
" <td>27.949800</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.214638</td>\n",
" <td>-3.339330</td>\n",
" <td>-3.696408</td>\n",
" <td>-4.045472</td>\n",
" <td>-3.151052</td>\n",
" <td>-4.587394</td>\n",
" <td>-3.716516</td>\n",
" <td>-3.939023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6.338959</td>\n",
" <td>14.080092</td>\n",
" <td>-10.883112</td>\n",
" <td>-0.791933</td>\n",
" <td>6.611586</td>\n",
" <td>-4.871253</td>\n",
" <td>2.052244</td>\n",
" <td>10.104293</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.057575</td>\n",
" <td>-5.004383</td>\n",
" <td>-3.701050</td>\n",
" <td>-3.317457</td>\n",
" <td>-3.208673</td>\n",
" <td>-3.321409</td>\n",
" <td>-3.533235</td>\n",
" <td>-3.930046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.230095</td>\n",
" <td>16.907672</td>\n",
" <td>5.831368</td>\n",
" <td>-1.682121</td>\n",
" <td>8.148128</td>\n",
" <td>-5.808090</td>\n",
" <td>-2.030019</td>\n",
" <td>16.190222</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.900410</td>\n",
" <td>-3.245038</td>\n",
" <td>-3.694919</td>\n",
" <td>-3.322281</td>\n",
" <td>-3.258865</td>\n",
" <td>-3.354772</td>\n",
" <td>-3.237900</td>\n",
" <td>-3.847260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>5.675400</td>\n",
" <td>12.945050</td>\n",
" <td>-6.521105</td>\n",
" <td>6.668133</td>\n",
" <td>4.996123</td>\n",
" <td>-10.796652</td>\n",
" <td>-0.202836</td>\n",
" <td>12.691089</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.130659</td>\n",
" <td>-4.275836</td>\n",
" <td>-3.874091</td>\n",
" <td>-3.333427</td>\n",
" <td>-3.708598</td>\n",
" <td>-3.322812</td>\n",
" <td>-3.362446</td>\n",
" <td>-3.849760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>-1.829137</td>\n",
" <td>0.280614</td>\n",
" <td>12.033005</td>\n",
" <td>-17.984199</td>\n",
" <td>6.739312</td>\n",
" <td>-0.521269</td>\n",
" <td>-5.681634</td>\n",
" <td>9.446477</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.334465</td>\n",
" <td>-3.302849</td>\n",
" <td>-4.130055</td>\n",
" <td>-3.317115</td>\n",
" <td>-3.117578</td>\n",
" <td>-3.501314</td>\n",
" <td>-3.587337</td>\n",
" <td>-4.774977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>13.390969</td>\n",
" <td>29.910117</td>\n",
" <td>2.813732</td>\n",
" <td>24.819013</td>\n",
" <td>5.592107</td>\n",
" <td>7.507567</td>\n",
" <td>10.394801</td>\n",
" <td>18.783279</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.635097</td>\n",
" <td>-3.356011</td>\n",
" <td>-5.203046</td>\n",
" <td>-3.325025</td>\n",
" <td>-3.562945</td>\n",
" <td>-3.681554</td>\n",
" <td>-3.224591</td>\n",
" <td>-4.235928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>4.904209</td>\n",
" <td>21.969318</td>\n",
" <td>11.325143</td>\n",
" <td>7.832334</td>\n",
" <td>9.025274</td>\n",
" <td>4.649066</td>\n",
" <td>2.714005</td>\n",
" <td>14.099440</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.707809</td>\n",
" <td>-3.276806</td>\n",
" <td>-3.920706</td>\n",
" <td>-3.333783</td>\n",
" <td>-3.313150</td>\n",
" <td>-3.328974</td>\n",
" <td>-3.297595</td>\n",
" <td>-3.810965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>7.811048</td>\n",
" <td>6.463812</td>\n",
" <td>0.537111</td>\n",
" <td>4.608454</td>\n",
" <td>8.556091</td>\n",
" <td>-5.420430</td>\n",
" <td>10.360583</td>\n",
" <td>15.244519</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.657672</td>\n",
" <td>-3.499997</td>\n",
" <td>-3.804591</td>\n",
" <td>-3.326840</td>\n",
" <td>-3.236782</td>\n",
" <td>-3.678902</td>\n",
" <td>-3.259487</td>\n",
" <td>-3.902212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>8.980132</td>\n",
" <td>15.928272</td>\n",
" <td>14.214414</td>\n",
" <td>2.836545</td>\n",
" <td>12.252855</td>\n",
" <td>8.883448</td>\n",
" <td>4.599082</td>\n",
" <td>8.688264</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.950826</td>\n",
" <td>-3.414618</td>\n",
" <td>-3.758061</td>\n",
" <td>-3.430852</td>\n",
" <td>-3.719142</td>\n",
" <td>-3.370360</td>\n",
" <td>-3.655066</td>\n",
" <td>-3.812546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>-0.370881</td>\n",
" <td>-2.147231</td>\n",
" <td>-2.525059</td>\n",
" <td>6.928848</td>\n",
" <td>-0.372222</td>\n",
" <td>-2.402938</td>\n",
" <td>4.181375</td>\n",
" <td>5.426032</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.646668</td>\n",
" <td>-3.775408</td>\n",
" <td>-3.884070</td>\n",
" <td>-3.541419</td>\n",
" <td>-3.128313</td>\n",
" <td>-3.358657</td>\n",
" <td>-4.012047</td>\n",
" <td>-4.104170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>2.631733</td>\n",
" <td>5.019647</td>\n",
" <td>-2.551684</td>\n",
" <td>7.908219</td>\n",
" <td>2.287241</td>\n",
" <td>-0.650687</td>\n",
" <td>-1.347565</td>\n",
" <td>0.746378</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.800537</td>\n",
" <td>-3.778214</td>\n",
" <td>-3.923928</td>\n",
" <td>-3.408611</td>\n",
" <td>-3.116916</td>\n",
" <td>-3.339607</td>\n",
" <td>-4.709961</td>\n",
" <td>-3.934577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>2.025024</td>\n",
" <td>2.276359</td>\n",
" <td>8.956007</td>\n",
" <td>-5.671905</td>\n",
" <td>3.950439</td>\n",
" <td>4.561990</td>\n",
" <td>5.775358</td>\n",
" <td>1.921816</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.097446</td>\n",
" <td>-3.226093</td>\n",
" <td>-3.705471</td>\n",
" <td>-3.355263</td>\n",
" <td>-3.307124</td>\n",
" <td>-3.411065</td>\n",
" <td>-4.514064</td>\n",
" <td>-3.879134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>1.743450</td>\n",
" <td>0.541453</td>\n",
" <td>10.247421</td>\n",
" <td>-3.423160</td>\n",
" <td>3.898326</td>\n",
" <td>5.113742</td>\n",
" <td>5.912515</td>\n",
" <td>2.018315</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.302482</td>\n",
" <td>-3.246778</td>\n",
" <td>-3.691877</td>\n",
" <td>-3.356587</td>\n",
" <td>-3.346891</td>\n",
" <td>-3.416556</td>\n",
" <td>-4.498595</td>\n",
" <td>-3.855039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>3.661774</td>\n",
" <td>4.235164</td>\n",
" <td>8.072691</td>\n",
" <td>-8.365203</td>\n",
" <td>7.818263</td>\n",
" <td>4.938846</td>\n",
" <td>4.878810</td>\n",
" <td>-0.935396</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.882027</td>\n",
" <td>-3.221550</td>\n",
" <td>-3.747749</td>\n",
" <td>-3.319601</td>\n",
" <td>-3.333878</td>\n",
" <td>-3.379004</td>\n",
" <td>-5.014270</td>\n",
" <td>-3.827656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>3.343410</td>\n",
" <td>7.232911</td>\n",
" <td>-3.358840</td>\n",
" <td>11.187876</td>\n",
" <td>4.009117</td>\n",
" <td>-0.925305</td>\n",
" <td>10.203626</td>\n",
" <td>12.306253</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.585371</td>\n",
" <td>-3.866640</td>\n",
" <td>-4.084683</td>\n",
" <td>-3.353798</td>\n",
" <td>-3.116198</td>\n",
" <td>-3.666862</td>\n",
" <td>-3.383617</td>\n",
" <td>-3.873922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>7.355920</td>\n",
" <td>16.851820</td>\n",
" <td>9.790524</td>\n",
" <td>14.988851</td>\n",
" <td>12.151725</td>\n",
" <td>3.385920</td>\n",
" <td>8.493480</td>\n",
" <td>6.989742</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.903171</td>\n",
" <td>-3.237554</td>\n",
" <td>-4.323556</td>\n",
" <td>-3.426504</td>\n",
" <td>-3.234906</td>\n",
" <td>-3.548868</td>\n",
" <td>-3.827652</td>\n",
" <td>-4.482538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>2.599059</td>\n",
" <td>5.799566</td>\n",
" <td>10.611846</td>\n",
" <td>-5.129603</td>\n",
" <td>5.777672</td>\n",
" <td>-6.020122</td>\n",
" <td>10.881908</td>\n",
" <td>6.962072</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.722232</td>\n",
" <td>-3.255632</td>\n",
" <td>-3.700385</td>\n",
" <td>-3.323008</td>\n",
" <td>-3.271729</td>\n",
" <td>-3.720355</td>\n",
" <td>-3.830703</td>\n",
" <td>-3.989067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>1.823955</td>\n",
" <td>0.034339</td>\n",
" <td>6.380960</td>\n",
" <td>-4.190662</td>\n",
" <td>6.327370</td>\n",
" <td>11.547312</td>\n",
" <td>-2.757497</td>\n",
" <td>6.341045</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.364940</td>\n",
" <td>-3.234630</td>\n",
" <td>-3.694296</td>\n",
" <td>-3.318703</td>\n",
" <td>-4.087984</td>\n",
" <td>-3.375176</td>\n",
" <td>-3.901180</td>\n",
" <td>-4.047399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>3.124835</td>\n",
" <td>6.781519</td>\n",
" <td>-1.974952</td>\n",
" <td>1.825302</td>\n",
" <td>5.164631</td>\n",
" <td>-1.351676</td>\n",
" <td>1.918796</td>\n",
" <td>4.962440</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.627486</td>\n",
" <td>-3.719022</td>\n",
" <td>-3.737003</td>\n",
" <td>-3.330754</td>\n",
" <td>-3.116927</td>\n",
" <td>-3.320322</td>\n",
" <td>-4.071413</td>\n",
" <td>-3.834715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>0.337915</td>\n",
" <td>7.337188</td>\n",
" <td>3.217786</td>\n",
" <td>2.077125</td>\n",
" <td>1.245161</td>\n",
" <td>-1.459427</td>\n",
" <td>4.061180</td>\n",
" <td>6.679039</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.575771</td>\n",
" <td>-3.335871</td>\n",
" <td>-3.741873</td>\n",
" <td>-3.453686</td>\n",
" <td>-3.117466</td>\n",
" <td>-3.355556</td>\n",
" <td>-3.862344</td>\n",
" <td>-3.878923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>3.156042</td>\n",
" <td>1.034565</td>\n",
" <td>0.240908</td>\n",
" <td>1.801479</td>\n",
" <td>1.873389</td>\n",
" <td>-3.124957</td>\n",
" <td>-0.049449</td>\n",
" <td>4.275170</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-5.242844</td>\n",
" <td>-3.522541</td>\n",
" <td>-3.736555</td>\n",
" <td>-3.425438</td>\n",
" <td>-3.144036</td>\n",
" <td>-3.321385</td>\n",
" <td>-4.163378</td>\n",
" <td>-3.963518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>6.216871</td>\n",
" <td>6.096077</td>\n",
" <td>7.240276</td>\n",
" <td>1.757184</td>\n",
" <td>0.882096</td>\n",
" <td>3.998537</td>\n",
" <td>4.254531</td>\n",
" <td>2.487774</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.693171</td>\n",
" <td>-3.224410</td>\n",
" <td>-3.735728</td>\n",
" <td>-3.471498</td>\n",
" <td>-3.270394</td>\n",
" <td>-3.360602</td>\n",
" <td>-4.424669</td>\n",
" <td>-3.915803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3.076418</td>\n",
" <td>9.877108</td>\n",
" <td>-0.558058</td>\n",
" <td>4.796189</td>\n",
" <td>4.603232</td>\n",
" <td>4.515727</td>\n",
" <td>9.455162</td>\n",
" <td>8.806070</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.356854</td>\n",
" <td>-3.587725</td>\n",
" <td>-3.810239</td>\n",
" <td>-3.340571</td>\n",
" <td>-3.303961</td>\n",
" <td>-3.612246</td>\n",
" <td>-3.644165</td>\n",
" <td>-4.035791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>8.358642</td>\n",
" <td>6.896625</td>\n",
" <td>20.145187</td>\n",
" <td>10.262220</td>\n",
" <td>13.117087</td>\n",
" <td>7.209109</td>\n",
" <td>6.396538</td>\n",
" <td>10.117781</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-4.616661</td>\n",
" <td>-3.959051</td>\n",
" <td>-4.035056</td>\n",
" <td>-3.471457</td>\n",
" <td>-3.532147</td>\n",
" <td>-3.437175</td>\n",
" <td>-3.532170</td>\n",
" <td>-3.821987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>11.274724</td>\n",
" <td>17.995885</td>\n",
" <td>4.129998</td>\n",
" <td>4.501676</td>\n",
" <td>11.698821</td>\n",
" <td>9.394247</td>\n",
" <td>8.937333</td>\n",
" <td>14.952057</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.849394</td>\n",
" <td>-3.296408</td>\n",
" <td>-3.801440</td>\n",
" <td>-3.408069</td>\n",
" <td>-3.783079</td>\n",
" <td>-3.577170</td>\n",
" <td>-3.267973</td>\n",
" <td>-3.877508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>11.613149</td>\n",
" <td>17.337053</td>\n",
" <td>6.051506</td>\n",
" <td>10.050024</td>\n",
" <td>12.858860</td>\n",
" <td>10.389515</td>\n",
" <td>8.853609</td>\n",
" <td>11.301829</td>\n",
" <td>...</td>\n",
" <td>0.127504</td>\n",
" <td>False</td>\n",
" <td>-3.879652</td>\n",
" <td>-3.240507</td>\n",
" <td>-4.024151</td>\n",
" <td>-3.458678</td>\n",
" <td>-3.916910</td>\n",
" <td>-3.571706</td>\n",
" <td>-3.445851</td>\n",
" <td>-3.845983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>3</td>\n",
" <td>470</td>\n",
" <td>1.869989</td>\n",
" <td>0.725330</td>\n",
" <td>2.393603</td>\n",
" <td>1.256708</td>\n",
" <td>3.686245</td>\n",
" <td>1.538459</td>\n",
" <td>2.158328</td>\n",
" <td>1.385548</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.280117</td>\n",
" <td>-3.378682</td>\n",
" <td>-3.726917</td>\n",
" <td>-3.362210</td>\n",
" <td>-3.155939</td>\n",
" <td>-3.322378</td>\n",
" <td>-4.601724</td>\n",
" <td>-3.976336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1971</th>\n",
" <td>3</td>\n",
" <td>471</td>\n",
" <td>1.909353</td>\n",
" <td>0.067436</td>\n",
" <td>6.646855</td>\n",
" <td>-0.113488</td>\n",
" <td>3.360973</td>\n",
" <td>0.202484</td>\n",
" <td>2.772774</td>\n",
" <td>1.625175</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.360829</td>\n",
" <td>-3.230679</td>\n",
" <td>-3.707801</td>\n",
" <td>-3.371555</td>\n",
" <td>-3.125089</td>\n",
" <td>-3.329820</td>\n",
" <td>-4.562198</td>\n",
" <td>-3.929513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1972</th>\n",
" <td>3</td>\n",
" <td>472</td>\n",
" <td>2.241014</td>\n",
" <td>5.269134</td>\n",
" <td>0.082023</td>\n",
" <td>2.809841</td>\n",
" <td>5.204529</td>\n",
" <td>5.565809</td>\n",
" <td>2.293236</td>\n",
" <td>1.932488</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.775194</td>\n",
" <td>-3.534995</td>\n",
" <td>-3.757454</td>\n",
" <td>-3.330155</td>\n",
" <td>-3.382273</td>\n",
" <td>-3.323745</td>\n",
" <td>-4.512348</td>\n",
" <td>-4.009309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>3</td>\n",
" <td>473</td>\n",
" <td>2.592713</td>\n",
" <td>4.642032</td>\n",
" <td>-1.050461</td>\n",
" <td>1.661159</td>\n",
" <td>1.869528</td>\n",
" <td>1.924598</td>\n",
" <td>-0.605397</td>\n",
" <td>3.965103</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.839421</td>\n",
" <td>-3.631078</td>\n",
" <td>-3.733962</td>\n",
" <td>-3.425601</td>\n",
" <td>-3.168961</td>\n",
" <td>-3.327484</td>\n",
" <td>-4.206415</td>\n",
" <td>-3.833742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1974</th>\n",
" <td>3</td>\n",
" <td>474</td>\n",
" <td>6.556429</td>\n",
" <td>5.741580</td>\n",
" <td>12.528967</td>\n",
" <td>8.822346</td>\n",
" <td>6.851722</td>\n",
" <td>5.257151</td>\n",
" <td>11.795488</td>\n",
" <td>6.129473</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.727960</td>\n",
" <td>-3.324081</td>\n",
" <td>-3.964511</td>\n",
" <td>-3.316925</td>\n",
" <td>-3.357842</td>\n",
" <td>-3.798415</td>\n",
" <td>-3.926071</td>\n",
" <td>-4.048533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>3</td>\n",
" <td>475</td>\n",
" <td>8.846856</td>\n",
" <td>18.256693</td>\n",
" <td>0.115580</td>\n",
" <td>7.400398</td>\n",
" <td>12.751221</td>\n",
" <td>11.461377</td>\n",
" <td>0.250558</td>\n",
" <td>15.751851</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-3.837949</td>\n",
" <td>-3.532344</td>\n",
" <td>-3.902793</td>\n",
" <td>-3.453514</td>\n",
" <td>-4.074718</td>\n",
" <td>-3.319155</td>\n",
" <td>-3.246794</td>\n",
" <td>-4.222112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1976</th>\n",
" <td>3</td>\n",
" <td>476</td>\n",
" <td>11.130817</td>\n",
" <td>15.893008</td>\n",
" <td>12.214955</td>\n",
" <td>6.201571</td>\n",
" <td>16.613614</td>\n",
" <td>12.453672</td>\n",
" <td>11.049515</td>\n",
" <td>6.769355</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-3.952720</td>\n",
" <td>-3.310353</td>\n",
" <td>-3.856896</td>\n",
" <td>-3.698741</td>\n",
" <td>-4.233455</td>\n",
" <td>-3.734159</td>\n",
" <td>-3.852161</td>\n",
" <td>-3.813775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1977</th>\n",
" <td>3</td>\n",
" <td>477</td>\n",
" <td>5.733472</td>\n",
" <td>3.546940</td>\n",
" <td>5.798428</td>\n",
" <td>11.307423</td>\n",
" <td>1.337032</td>\n",
" <td>1.855085</td>\n",
" <td>4.342916</td>\n",
" <td>9.972150</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.955771</td>\n",
" <td>-3.245758</td>\n",
" <td>-4.091337</td>\n",
" <td>-3.449351</td>\n",
" <td>-3.166481</td>\n",
" <td>-3.363012</td>\n",
" <td>-3.543756</td>\n",
" <td>-3.894211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1978</th>\n",
" <td>3</td>\n",
" <td>478</td>\n",
" <td>-0.597631</td>\n",
" <td>2.793760</td>\n",
" <td>-0.723780</td>\n",
" <td>-7.751555</td>\n",
" <td>4.236943</td>\n",
" <td>1.037038</td>\n",
" <td>-4.736183</td>\n",
" <td>-0.144301</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.038888</td>\n",
" <td>-3.602045</td>\n",
" <td>-3.735623</td>\n",
" <td>-3.348381</td>\n",
" <td>-3.141777</td>\n",
" <td>-3.452800</td>\n",
" <td>-4.867602</td>\n",
" <td>-4.020169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1979</th>\n",
" <td>3</td>\n",
" <td>479</td>\n",
" <td>-0.767514</td>\n",
" <td>0.497522</td>\n",
" <td>-4.498644</td>\n",
" <td>-1.922556</td>\n",
" <td>4.537264</td>\n",
" <td>-2.317449</td>\n",
" <td>-6.133446</td>\n",
" <td>10.922462</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.307847</td>\n",
" <td>-4.002604</td>\n",
" <td>-3.693795</td>\n",
" <td>-3.341896</td>\n",
" <td>-3.126877</td>\n",
" <td>-3.527107</td>\n",
" <td>-3.471981</td>\n",
" <td>-3.821819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>3</td>\n",
" <td>480</td>\n",
" <td>1.782292</td>\n",
" <td>8.135279</td>\n",
" <td>7.112862</td>\n",
" <td>-5.210512</td>\n",
" <td>0.450116</td>\n",
" <td>2.093041</td>\n",
" <td>3.401952</td>\n",
" <td>4.173563</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.503893</td>\n",
" <td>-3.225459</td>\n",
" <td>-3.701071</td>\n",
" <td>-3.494111</td>\n",
" <td>-3.175218</td>\n",
" <td>-3.340674</td>\n",
" <td>-4.177375</td>\n",
" <td>-4.016791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>3</td>\n",
" <td>481</td>\n",
" <td>3.970422</td>\n",
" <td>8.347139</td>\n",
" <td>4.587307</td>\n",
" <td>-4.110620</td>\n",
" <td>0.599184</td>\n",
" <td>2.831459</td>\n",
" <td>1.396461</td>\n",
" <td>6.625333</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.485289</td>\n",
" <td>-3.279756</td>\n",
" <td>-3.693936</td>\n",
" <td>-3.486133</td>\n",
" <td>-3.206781</td>\n",
" <td>-3.317483</td>\n",
" <td>-3.868439</td>\n",
" <td>-4.077986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3</td>\n",
" <td>482</td>\n",
" <td>8.679431</td>\n",
" <td>11.352388</td>\n",
" <td>10.249975</td>\n",
" <td>5.931753</td>\n",
" <td>3.453658</td>\n",
" <td>7.018939</td>\n",
" <td>1.339878</td>\n",
" <td>9.252671</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.242862</td>\n",
" <td>-3.246836</td>\n",
" <td>-3.847340</td>\n",
" <td>-3.368803</td>\n",
" <td>-3.513098</td>\n",
" <td>-3.317311</td>\n",
" <td>-3.604102</td>\n",
" <td>-3.841257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>3</td>\n",
" <td>483</td>\n",
" <td>3.432447</td>\n",
" <td>0.545929</td>\n",
" <td>0.490570</td>\n",
" <td>4.519641</td>\n",
" <td>5.369701</td>\n",
" <td>1.232052</td>\n",
" <td>7.857660</td>\n",
" <td>2.130660</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.301935</td>\n",
" <td>-3.503481</td>\n",
" <td>-3.801967</td>\n",
" <td>-3.327817</td>\n",
" <td>-3.146917</td>\n",
" <td>-3.511162</td>\n",
" <td>-4.480703</td>\n",
" <td>-3.969565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>3</td>\n",
" <td>484</td>\n",
" <td>3.687586</td>\n",
" <td>4.554860</td>\n",
" <td>1.637717</td>\n",
" <td>4.507513</td>\n",
" <td>7.128407</td>\n",
" <td>3.360117</td>\n",
" <td>6.954580</td>\n",
" <td>0.192653</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.848488</td>\n",
" <td>-3.423917</td>\n",
" <td>-3.801611</td>\n",
" <td>-3.316902</td>\n",
" <td>-3.233513</td>\n",
" <td>-3.463350</td>\n",
" <td>-4.807032</td>\n",
" <td>-3.974308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1985</th>\n",
" <td>3</td>\n",
" <td>485</td>\n",
" <td>5.116861</td>\n",
" <td>6.711324</td>\n",
" <td>8.721111</td>\n",
" <td>5.272585</td>\n",
" <td>5.163660</td>\n",
" <td>3.274869</td>\n",
" <td>2.765933</td>\n",
" <td>8.748047</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.634117</td>\n",
" <td>-3.224124</td>\n",
" <td>-3.825191</td>\n",
" <td>-3.330768</td>\n",
" <td>-3.228969</td>\n",
" <td>-3.329720</td>\n",
" <td>-3.649517</td>\n",
" <td>-3.941764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1986</th>\n",
" <td>3</td>\n",
" <td>486</td>\n",
" <td>5.288070</td>\n",
" <td>7.831502</td>\n",
" <td>10.202467</td>\n",
" <td>1.142689</td>\n",
" <td>8.169488</td>\n",
" <td>0.855962</td>\n",
" <td>2.664386</td>\n",
" <td>13.195699</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.530918</td>\n",
" <td>-3.245778</td>\n",
" <td>-3.725047</td>\n",
" <td>-3.322485</td>\n",
" <td>-3.137426</td>\n",
" <td>-3.328281</td>\n",
" <td>-3.336930</td>\n",
" <td>-3.967306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1987</th>\n",
" <td>3</td>\n",
" <td>487</td>\n",
" <td>3.486806</td>\n",
" <td>5.391694</td>\n",
" <td>9.067864</td>\n",
" <td>2.744689</td>\n",
" <td>10.806048</td>\n",
" <td>4.489098</td>\n",
" <td>2.863271</td>\n",
" <td>8.474816</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.762845</td>\n",
" <td>-3.227225</td>\n",
" <td>-3.755983</td>\n",
" <td>-3.376693</td>\n",
" <td>-3.302152</td>\n",
" <td>-3.331180</td>\n",
" <td>-3.675169</td>\n",
" <td>-3.831511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1988</th>\n",
" <td>3</td>\n",
" <td>488</td>\n",
" <td>7.381755</td>\n",
" <td>3.340344</td>\n",
" <td>4.984973</td>\n",
" <td>5.935046</td>\n",
" <td>8.151243</td>\n",
" <td>3.292452</td>\n",
" <td>4.116778</td>\n",
" <td>7.717448</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.978319</td>\n",
" <td>-3.266976</td>\n",
" <td>-3.847455</td>\n",
" <td>-3.322311</td>\n",
" <td>-3.229899</td>\n",
" <td>-3.356976</td>\n",
" <td>-3.750178</td>\n",
" <td>-3.894316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1989</th>\n",
" <td>3</td>\n",
" <td>489</td>\n",
" <td>4.988879</td>\n",
" <td>9.412453</td>\n",
" <td>3.814246</td>\n",
" <td>2.391239</td>\n",
" <td>5.513540</td>\n",
" <td>3.210410</td>\n",
" <td>5.670256</td>\n",
" <td>4.333049</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.394760</td>\n",
" <td>-3.309126</td>\n",
" <td>-3.748296</td>\n",
" <td>-3.325964</td>\n",
" <td>-3.225592</td>\n",
" <td>-3.406963</td>\n",
" <td>-4.155451</td>\n",
" <td>-3.836526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990</th>\n",
" <td>3</td>\n",
" <td>490</td>\n",
" <td>5.426869</td>\n",
" <td>8.796816</td>\n",
" <td>5.661044</td>\n",
" <td>2.936930</td>\n",
" <td>4.854316</td>\n",
" <td>1.483534</td>\n",
" <td>10.351022</td>\n",
" <td>3.236551</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.446460</td>\n",
" <td>-3.248877</td>\n",
" <td>-3.760369</td>\n",
" <td>-3.335858</td>\n",
" <td>-3.154237</td>\n",
" <td>-3.678163</td>\n",
" <td>-4.311321</td>\n",
" <td>-3.815643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1991</th>\n",
" <td>3</td>\n",
" <td>491</td>\n",
" <td>5.077233</td>\n",
" <td>3.823720</td>\n",
" <td>5.509849</td>\n",
" <td>7.470286</td>\n",
" <td>4.641470</td>\n",
" <td>8.971699</td>\n",
" <td>0.696856</td>\n",
" <td>7.993876</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.925861</td>\n",
" <td>-3.252528</td>\n",
" <td>-3.905642</td>\n",
" <td>-3.339820</td>\n",
" <td>-3.729958</td>\n",
" <td>-3.317214</td>\n",
" <td>-3.722136</td>\n",
" <td>-3.933980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1992</th>\n",
" <td>3</td>\n",
" <td>492</td>\n",
" <td>5.128699</td>\n",
" <td>8.333100</td>\n",
" <td>4.418502</td>\n",
" <td>8.179375</td>\n",
" <td>5.964116</td>\n",
" <td>4.128196</td>\n",
" <td>6.163289</td>\n",
" <td>5.937767</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.486515</td>\n",
" <td>-3.285659</td>\n",
" <td>-3.935626</td>\n",
" <td>-3.321268</td>\n",
" <td>-3.278499</td>\n",
" <td>-3.426997</td>\n",
" <td>-3.949011</td>\n",
" <td>-3.891992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1993</th>\n",
" <td>3</td>\n",
" <td>493</td>\n",
" <td>5.408870</td>\n",
" <td>7.288141</td>\n",
" <td>4.841726</td>\n",
" <td>5.621141</td>\n",
" <td>3.789775</td>\n",
" <td>7.849266</td>\n",
" <td>4.755630</td>\n",
" <td>7.126402</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.580280</td>\n",
" <td>-3.271397</td>\n",
" <td>-3.836691</td>\n",
" <td>-3.359419</td>\n",
" <td>-3.599555</td>\n",
" <td>-3.375118</td>\n",
" <td>-3.812699</td>\n",
" <td>-3.848277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1994</th>\n",
" <td>3</td>\n",
" <td>494</td>\n",
" <td>5.909746</td>\n",
" <td>5.575157</td>\n",
" <td>2.447253</td>\n",
" <td>6.350806</td>\n",
" <td>4.120710</td>\n",
" <td>7.385656</td>\n",
" <td>5.715122</td>\n",
" <td>5.971869</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.744486</td>\n",
" <td>-3.375689</td>\n",
" <td>-3.862304</td>\n",
" <td>-3.351091</td>\n",
" <td>-3.550232</td>\n",
" <td>-3.408703</td>\n",
" <td>-3.944903</td>\n",
" <td>-3.817422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1995</th>\n",
" <td>3</td>\n",
" <td>495</td>\n",
" <td>10.037602</td>\n",
" <td>11.161808</td>\n",
" <td>15.781574</td>\n",
" <td>9.490260</td>\n",
" <td>12.453176</td>\n",
" <td>7.582983</td>\n",
" <td>9.015609</td>\n",
" <td>11.001966</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.257044</td>\n",
" <td>-3.524288</td>\n",
" <td>-3.996228</td>\n",
" <td>-3.439715</td>\n",
" <td>-3.570901</td>\n",
" <td>-3.582330</td>\n",
" <td>-3.466386</td>\n",
" <td>-3.879737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996</th>\n",
" <td>3</td>\n",
" <td>496</td>\n",
" <td>9.920620</td>\n",
" <td>4.949339</td>\n",
" <td>0.661325</td>\n",
" <td>10.858709</td>\n",
" <td>4.483763</td>\n",
" <td>11.547203</td>\n",
" <td>9.790741</td>\n",
" <td>18.312519</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.807729</td>\n",
" <td>-3.490804</td>\n",
" <td>-4.066652</td>\n",
" <td>-3.342997</td>\n",
" <td>-4.087967</td>\n",
" <td>-3.636161</td>\n",
" <td>-3.222012</td>\n",
" <td>-3.826559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997</th>\n",
" <td>3</td>\n",
" <td>497</td>\n",
" <td>4.597058</td>\n",
" <td>14.186299</td>\n",
" <td>11.762716</td>\n",
" <td>4.684036</td>\n",
" <td>8.303899</td>\n",
" <td>-3.204281</td>\n",
" <td>3.357491</td>\n",
" <td>-0.586514</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-4.051029</td>\n",
" <td>-3.292314</td>\n",
" <td>-3.806848</td>\n",
" <td>-3.323859</td>\n",
" <td>-3.146156</td>\n",
" <td>-3.339800</td>\n",
" <td>-4.948816</td>\n",
" <td>-4.073941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998</th>\n",
" <td>3</td>\n",
" <td>498</td>\n",
" <td>5.898506</td>\n",
" <td>-1.420946</td>\n",
" <td>-4.034405</td>\n",
" <td>16.620064</td>\n",
" <td>7.119605</td>\n",
" <td>6.733222</td>\n",
" <td>-4.901925</td>\n",
" <td>15.850648</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.550527</td>\n",
" <td>-3.945658</td>\n",
" <td>-4.443377</td>\n",
" <td>-3.316893</td>\n",
" <td>-3.485316</td>\n",
" <td>-3.460771</td>\n",
" <td>-3.244622</td>\n",
" <td>-3.907745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999</th>\n",
" <td>3</td>\n",
" <td>499</td>\n",
" <td>0.161389</td>\n",
" <td>-0.050159</td>\n",
" <td>0.063538</td>\n",
" <td>7.608454</td>\n",
" <td>-2.338148</td>\n",
" <td>2.754583</td>\n",
" <td>-1.106142</td>\n",
" <td>10.592933</td>\n",
" <td>...</td>\n",
" <td>0.106445</td>\n",
" <td>False</td>\n",
" <td>-5.375459</td>\n",
" <td>-3.536461</td>\n",
" <td>-3.911331</td>\n",
" <td>-3.677169</td>\n",
" <td>-3.203181</td>\n",
" <td>-3.335164</td>\n",
" <td>-3.495847</td>\n",
" <td>-3.895575</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2000 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" chain draw mu theta[0] theta[1] theta[2] theta[3] \\\n",
"0 0 0 -3.476986 1.668654 -8.537401 -2.622619 -2.509951 \n",
"1 0 1 -2.455871 -6.239359 1.071411 -4.517927 -3.341560 \n",
"2 0 2 -2.826254 2.195098 -2.920843 -3.494201 2.136533 \n",
"3 0 3 -1.995894 -3.736485 -6.342422 -5.017723 5.005665 \n",
"4 0 4 7.668982 11.738328 12.854003 -1.419241 20.278944 \n",
"... ... ... ... ... ... ... ... \n",
"1995 3 495 10.037602 11.161808 15.781574 9.490260 12.453176 \n",
"1996 3 496 9.920620 4.949339 0.661325 10.858709 4.483763 \n",
"1997 3 497 4.597058 14.186299 11.762716 4.684036 8.303899 \n",
"1998 3 498 5.898506 -1.420946 -4.034405 16.620064 7.119605 \n",
"1999 3 499 0.161389 -0.050159 0.063538 7.608454 -2.338148 \n",
"\n",
" theta[4] theta[5] theta[6] ... step_size diverging \\\n",
"0 -4.004844 -9.174019 0.155234 ... 0.127504 False \n",
"1 -3.422806 0.399546 -4.462528 ... 0.127504 False \n",
"2 -7.229751 -2.720302 -3.487005 ... 0.127504 False \n",
"3 -6.107046 -2.099592 1.145165 ... 0.127504 False \n",
"4 1.377388 18.534980 27.949800 ... 0.127504 False \n",
"... ... ... ... ... ... ... \n",
"1995 7.582983 9.015609 11.001966 ... 0.106445 False \n",
"1996 11.547203 9.790741 18.312519 ... 0.106445 False \n",
"1997 -3.204281 3.357491 -0.586514 ... 0.106445 False \n",
"1998 6.733222 -4.901925 15.850648 ... 0.106445 False \n",
"1999 2.754583 -1.106142 10.592933 ... 0.106445 False \n",
"\n",
" log_likelihood[0] log_likelihood[1] log_likelihood[2] \\\n",
"0 -5.167744 -4.588952 -3.691805 \n",
"1 -6.232175 -3.461550 -3.696027 \n",
"2 -5.106751 -3.817848 -3.692004 \n",
"3 -5.865221 -4.250049 -3.699479 \n",
"4 -4.214638 -3.339330 -3.696408 \n",
"... ... ... ... \n",
"1995 -4.257044 -3.524288 -3.996228 \n",
"1996 -4.807729 -3.490804 -4.066652 \n",
"1997 -4.051029 -3.292314 -3.806848 \n",
"1998 -5.550527 -3.945658 -4.443377 \n",
"1999 -5.375459 -3.536461 -3.911331 \n",
"\n",
" log_likelihood[3] log_likelihood[4] log_likelihood[5] \\\n",
"0 -3.690549 -3.171898 -3.744564 \n",
"1 -3.758767 -3.152398 -3.318324 \n",
"2 -3.414575 -3.355730 -3.374027 \n",
"3 -3.333269 -3.277163 -3.356534 \n",
"4 -4.045472 -3.151052 -4.587394 \n",
"... ... ... ... \n",
"1995 -3.439715 -3.570901 -3.582330 \n",
"1996 -3.342997 -4.087967 -3.636161 \n",
"1997 -3.323859 -3.146156 -3.339800 \n",
"1998 -3.316893 -3.485316 -3.460771 \n",
"1999 -3.677169 -3.203181 -3.335164 \n",
"\n",
" log_likelihood[6] log_likelihood[7] \n",
"0 -4.813702 -4.355802 \n",
"1 -5.744349 -4.074576 \n",
"2 -5.529981 -4.071174 \n",
"3 -4.641951 -4.073257 \n",
"4 -3.716516 -3.939023 \n",
"... ... ... \n",
"1995 -3.466386 -3.879737 \n",
"1996 -3.222012 -3.826559 \n",
"1997 -4.948816 -4.073941 \n",
"1998 -3.244622 -3.907745 \n",
"1999 -3.495847 -3.895575 \n",
"\n",
"[2000 rows x 39 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"posterior"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"prior = pd.concat((idfs[key] for key in [\"prior\", \"prior_predictive\", \"sample_stats_prior\"] if key in idfs), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"prior = prior.loc[:, ~prior.columns.duplicated()]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>chain</th>\n",
" <th>draw</th>\n",
" <th>tau</th>\n",
" <th>tau_log__</th>\n",
" <th>mu</th>\n",
" <th>theta[0]</th>\n",
" <th>theta[1]</th>\n",
" <th>theta[2]</th>\n",
" <th>theta[3]</th>\n",
" <th>theta[4]</th>\n",
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" <th>obs[1]</th>\n",
" <th>obs[2]</th>\n",
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" <th>obs[7]</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6.560633</td>\n",
" <td>1.881087</td>\n",
" <td>5.293450</td>\n",
" <td>2.357357</td>\n",
" <td>7.371371</td>\n",
" <td>7.251098</td>\n",
" <td>-7.145438</td>\n",
" <td>-1.636578</td>\n",
" <td>...</td>\n",
" <td>6.135082</td>\n",
" <td>3.984435</td>\n",
" <td>-3.539971</td>\n",
" <td>6.769448</td>\n",
" <td>19.679771</td>\n",
" <td>-10.741723</td>\n",
" <td>1.594982</td>\n",
" <td>-13.157970</td>\n",
" <td>8.269640</td>\n",
" <td>-8.569042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.016055</td>\n",
" <td>0.015927</td>\n",
" <td>0.813724</td>\n",
" <td>0.258399</td>\n",
" <td>-0.752515</td>\n",
" <td>0.491562</td>\n",
" <td>1.312863</td>\n",
" <td>0.548235</td>\n",
" <td>...</td>\n",
" <td>1.730840</td>\n",
" <td>-0.034163</td>\n",
" <td>-21.166369</td>\n",
" <td>1.146050</td>\n",
" <td>-24.570783</td>\n",
" <td>-6.921033</td>\n",
" <td>-12.705194</td>\n",
" <td>-4.068690</td>\n",
" <td>-13.157913</td>\n",
" <td>-8.542400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>68.913910</td>\n",
" <td>4.232858</td>\n",
" <td>0.712223</td>\n",
" <td>-99.560305</td>\n",
" <td>7.754424</td>\n",
" <td>-18.362189</td>\n",
" <td>-33.324630</td>\n",
" <td>63.035509</td>\n",
" <td>...</td>\n",
" <td>-122.029976</td>\n",
" <td>-74.031724</td>\n",
" <td>-122.383964</td>\n",
" <td>22.385597</td>\n",
" <td>-15.761502</td>\n",
" <td>-52.800127</td>\n",
" <td>68.876046</td>\n",
" <td>44.242679</td>\n",
" <td>-129.804129</td>\n",
" <td>-108.183013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>7.796560</td>\n",
" <td>2.053683</td>\n",
" <td>7.626863</td>\n",
" <td>12.240061</td>\n",
" <td>21.038023</td>\n",
" <td>10.103392</td>\n",
" <td>17.679999</td>\n",
" <td>4.436022</td>\n",
" <td>...</td>\n",
" <td>13.318479</td>\n",
" <td>3.548044</td>\n",
" <td>2.144288</td>\n",
" <td>34.180500</td>\n",
" <td>4.411558</td>\n",
" <td>22.045692</td>\n",
" <td>13.628275</td>\n",
" <td>20.757049</td>\n",
" <td>6.063963</td>\n",
" <td>18.748404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4.510800</td>\n",
" <td>1.506474</td>\n",
" <td>3.116833</td>\n",
" <td>-1.576783</td>\n",
" <td>3.737056</td>\n",
" <td>-0.201018</td>\n",
" <td>-3.407841</td>\n",
" <td>9.793744</td>\n",
" <td>...</td>\n",
" <td>3.507706</td>\n",
" <td>6.524663</td>\n",
" <td>-7.138264</td>\n",
" <td>0.311008</td>\n",
" <td>9.011283</td>\n",
" <td>0.734600</td>\n",
" <td>5.968886</td>\n",
" <td>4.765805</td>\n",
" <td>11.512443</td>\n",
" <td>-10.845965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>33.761642</td>\n",
" <td>3.519325</td>\n",
" <td>11.439762</td>\n",
" <td>56.460314</td>\n",
" <td>30.250104</td>\n",
" <td>30.466880</td>\n",
" <td>53.240393</td>\n",
" <td>-54.117221</td>\n",
" <td>...</td>\n",
" <td>2.344470</td>\n",
" <td>-8.655776</td>\n",
" <td>53.917262</td>\n",
" <td>38.361119</td>\n",
" <td>41.877420</td>\n",
" <td>43.869795</td>\n",
" <td>-71.569702</td>\n",
" <td>16.268857</td>\n",
" <td>-4.346704</td>\n",
" <td>-40.679284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2.608374</td>\n",
" <td>0.958727</td>\n",
" <td>7.751772</td>\n",
" <td>6.455401</td>\n",
" <td>10.689210</td>\n",
" <td>7.461108</td>\n",
" <td>10.665410</td>\n",
" <td>9.925927</td>\n",
" <td>...</td>\n",
" <td>11.504375</td>\n",
" <td>5.407992</td>\n",
" <td>20.800882</td>\n",
" <td>9.232804</td>\n",
" <td>30.488915</td>\n",
" <td>14.338211</td>\n",
" <td>13.728470</td>\n",
" <td>15.813312</td>\n",
" <td>25.024535</td>\n",
" <td>25.858206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>7.959992</td>\n",
" <td>2.074428</td>\n",
" <td>2.915744</td>\n",
" <td>5.232191</td>\n",
" <td>-7.873472</td>\n",
" <td>-4.563376</td>\n",
" <td>5.601380</td>\n",
" <td>8.193365</td>\n",
" <td>...</td>\n",
" <td>-13.909074</td>\n",
" <td>8.516241</td>\n",
" <td>14.024149</td>\n",
" <td>-2.549788</td>\n",
" <td>-22.686269</td>\n",
" <td>0.932917</td>\n",
" <td>14.117347</td>\n",
" <td>14.747796</td>\n",
" <td>-14.978839</td>\n",
" <td>-2.303841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0.120884</td>\n",
" <td>-2.112928</td>\n",
" <td>2.961944</td>\n",
" <td>3.033356</td>\n",
" <td>3.038373</td>\n",
" <td>2.756354</td>\n",
" <td>2.977229</td>\n",
" <td>3.107519</td>\n",
" <td>...</td>\n",
" <td>2.977594</td>\n",
" <td>3.134178</td>\n",
" <td>-22.553359</td>\n",
" <td>9.872543</td>\n",
" <td>-1.144172</td>\n",
" <td>10.696390</td>\n",
" <td>-12.037462</td>\n",
" <td>10.887511</td>\n",
" <td>5.680613</td>\n",
" <td>1.070456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>16.193143</td>\n",
" <td>2.784588</td>\n",
" <td>-1.532129</td>\n",
" <td>16.554561</td>\n",
" <td>-26.626873</td>\n",
" <td>27.084693</td>\n",
" <td>25.233117</td>\n",
" <td>-11.879843</td>\n",
" <td>...</td>\n",
" <td>-20.502919</td>\n",
" <td>-24.886723</td>\n",
" <td>25.949726</td>\n",
" <td>-42.976733</td>\n",
" <td>42.955610</td>\n",
" <td>25.109167</td>\n",
" <td>-16.035370</td>\n",
" <td>-29.616280</td>\n",
" <td>0.563365</td>\n",
" <td>-3.847534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>8.170661</td>\n",
" <td>2.100550</td>\n",
" <td>8.031789</td>\n",
" <td>13.798509</td>\n",
" <td>14.200005</td>\n",
" <td>10.160351</td>\n",
" <td>14.708857</td>\n",
" <td>1.975722</td>\n",
" <td>...</td>\n",
" <td>0.593327</td>\n",
" <td>2.117313</td>\n",
" <td>20.633343</td>\n",
" <td>19.672257</td>\n",
" <td>21.484940</td>\n",
" <td>3.078450</td>\n",
" <td>-5.265239</td>\n",
" <td>16.640749</td>\n",
" <td>-3.318428</td>\n",
" <td>-26.008923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>7.182765</td>\n",
" <td>1.971684</td>\n",
" <td>-2.955081</td>\n",
" <td>-0.878595</td>\n",
" <td>-11.785655</td>\n",
" <td>-1.191283</td>\n",
" <td>-7.090599</td>\n",
" <td>-1.059072</td>\n",
" <td>...</td>\n",
" <td>9.090259</td>\n",
" <td>4.043708</td>\n",
" <td>-1.794033</td>\n",
" <td>-2.344010</td>\n",
" <td>-0.365157</td>\n",
" <td>-7.959975</td>\n",
" <td>1.503295</td>\n",
" <td>-13.960718</td>\n",
" <td>23.664589</td>\n",
" <td>27.307884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1.363685</td>\n",
" <td>0.310191</td>\n",
" <td>-3.256501</td>\n",
" <td>-4.173802</td>\n",
" <td>-5.123200</td>\n",
" <td>-4.480843</td>\n",
" <td>-3.512665</td>\n",
" <td>-4.528537</td>\n",
" <td>...</td>\n",
" <td>-5.386614</td>\n",
" <td>-4.057562</td>\n",
" <td>3.810700</td>\n",
" <td>11.139715</td>\n",
" <td>-23.221579</td>\n",
" <td>-20.619096</td>\n",
" <td>-3.168489</td>\n",
" <td>-5.772004</td>\n",
" <td>-9.258791</td>\n",
" <td>-13.877266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>1.410229</td>\n",
" <td>0.343752</td>\n",
" <td>5.728591</td>\n",
" <td>6.331716</td>\n",
" <td>4.149296</td>\n",
" <td>2.761171</td>\n",
" <td>4.239611</td>\n",
" <td>4.706318</td>\n",
" <td>...</td>\n",
" <td>7.933910</td>\n",
" <td>5.623044</td>\n",
" <td>18.344306</td>\n",
" <td>15.374503</td>\n",
" <td>5.250760</td>\n",
" <td>9.058417</td>\n",
" <td>1.281618</td>\n",
" <td>-0.023311</td>\n",
" <td>6.117792</td>\n",
" <td>16.934096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>5.165262</td>\n",
" <td>1.641956</td>\n",
" <td>-5.227733</td>\n",
" <td>-5.320889</td>\n",
" <td>-2.781087</td>\n",
" <td>-13.432292</td>\n",
" <td>-19.620800</td>\n",
" <td>-11.594680</td>\n",
" <td>...</td>\n",
" <td>-11.231068</td>\n",
" <td>-13.154996</td>\n",
" <td>4.060765</td>\n",
" <td>-3.681054</td>\n",
" <td>-6.481753</td>\n",
" <td>-34.842805</td>\n",
" <td>-3.848709</td>\n",
" <td>3.499045</td>\n",
" <td>-10.047960</td>\n",
" <td>-55.020731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>5.741674</td>\n",
" <td>1.747751</td>\n",
" <td>4.252949</td>\n",
" <td>0.554600</td>\n",
" <td>9.285609</td>\n",
" <td>11.577237</td>\n",
" <td>9.662400</td>\n",
" <td>7.295675</td>\n",
" <td>...</td>\n",
" <td>7.571399</td>\n",
" <td>5.765588</td>\n",
" <td>-25.124609</td>\n",
" <td>13.629558</td>\n",
" <td>6.704828</td>\n",
" <td>3.299332</td>\n",
" <td>8.079087</td>\n",
" <td>-3.384524</td>\n",
" <td>13.523727</td>\n",
" <td>14.308102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0.271361</td>\n",
" <td>-1.304305</td>\n",
" <td>0.169628</td>\n",
" <td>0.029389</td>\n",
" <td>0.028130</td>\n",
" <td>0.070015</td>\n",
" <td>0.025242</td>\n",
" <td>-0.025074</td>\n",
" <td>...</td>\n",
" <td>-0.006676</td>\n",
" <td>0.495928</td>\n",
" <td>-11.558838</td>\n",
" <td>-6.516273</td>\n",
" <td>-7.290362</td>\n",
" <td>4.571765</td>\n",
" <td>10.127870</td>\n",
" <td>-6.743130</td>\n",
" <td>5.066452</td>\n",
" <td>-1.733486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>7.660465</td>\n",
" <td>2.036073</td>\n",
" <td>-9.062582</td>\n",
" <td>-9.730234</td>\n",
" <td>-1.400790</td>\n",
" <td>-13.543988</td>\n",
" <td>-15.764141</td>\n",
" <td>-6.070616</td>\n",
" <td>...</td>\n",
" <td>-3.092961</td>\n",
" <td>-15.116670</td>\n",
" <td>-26.658732</td>\n",
" <td>-23.192429</td>\n",
" <td>-20.860144</td>\n",
" <td>-20.198991</td>\n",
" <td>-5.038987</td>\n",
" <td>-8.528661</td>\n",
" <td>-12.491317</td>\n",
" <td>-23.658137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>2.915961</td>\n",
" <td>1.070199</td>\n",
" <td>-11.656651</td>\n",
" <td>-12.244753</td>\n",
" <td>-11.049134</td>\n",
" <td>-15.803107</td>\n",
" <td>-9.314835</td>\n",
" <td>-12.778430</td>\n",
" <td>...</td>\n",
" <td>-7.431808</td>\n",
" <td>-11.996723</td>\n",
" <td>10.775864</td>\n",
" <td>-4.178131</td>\n",
" <td>-26.871527</td>\n",
" <td>-26.026018</td>\n",
" <td>-16.615280</td>\n",
" <td>-22.752978</td>\n",
" <td>2.802024</td>\n",
" <td>-22.500594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>76.151092</td>\n",
" <td>4.332719</td>\n",
" <td>-7.616270</td>\n",
" <td>-134.328137</td>\n",
" <td>33.061093</td>\n",
" <td>-63.953228</td>\n",
" <td>-76.395892</td>\n",
" <td>111.327077</td>\n",
" <td>...</td>\n",
" <td>47.126546</td>\n",
" <td>-28.769310</td>\n",
" <td>-145.480147</td>\n",
" <td>36.482682</td>\n",
" <td>-58.533319</td>\n",
" <td>-88.622723</td>\n",
" <td>102.536167</td>\n",
" <td>22.322297</td>\n",
" <td>56.518705</td>\n",
" <td>-36.832889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>3.543998</td>\n",
" <td>1.265255</td>\n",
" <td>1.307934</td>\n",
" <td>5.714568</td>\n",
" <td>4.423091</td>\n",
" <td>2.458268</td>\n",
" <td>-0.047398</td>\n",
" <td>1.084978</td>\n",
" <td>...</td>\n",
" <td>1.788977</td>\n",
" <td>4.969445</td>\n",
" <td>-27.625733</td>\n",
" <td>4.922689</td>\n",
" <td>12.806889</td>\n",
" <td>13.997133</td>\n",
" <td>11.385221</td>\n",
" <td>-0.762997</td>\n",
" <td>-7.766240</td>\n",
" <td>12.477679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>7.388257</td>\n",
" <td>1.999892</td>\n",
" <td>1.470810</td>\n",
" <td>8.088433</td>\n",
" <td>4.925547</td>\n",
" <td>-9.092982</td>\n",
" <td>0.628512</td>\n",
" <td>-8.381534</td>\n",
" <td>...</td>\n",
" <td>4.222624</td>\n",
" <td>0.521766</td>\n",
" <td>-5.690519</td>\n",
" <td>13.637686</td>\n",
" <td>-16.655794</td>\n",
" <td>1.983214</td>\n",
" <td>-3.811548</td>\n",
" <td>3.161430</td>\n",
" <td>5.834727</td>\n",
" <td>25.560290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0.696493</td>\n",
" <td>-0.361697</td>\n",
" <td>3.196542</td>\n",
" <td>3.032166</td>\n",
" <td>3.747064</td>\n",
" <td>2.736396</td>\n",
" <td>3.508000</td>\n",
" <td>3.653956</td>\n",
" <td>...</td>\n",
" <td>3.469593</td>\n",
" <td>3.029787</td>\n",
" <td>-17.271489</td>\n",
" <td>0.251286</td>\n",
" <td>18.159839</td>\n",
" <td>4.866415</td>\n",
" <td>-1.491114</td>\n",
" <td>24.601066</td>\n",
" <td>2.655559</td>\n",
" <td>12.328475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>81.966101</td>\n",
" <td>4.406306</td>\n",
" <td>7.245110</td>\n",
" <td>-227.070053</td>\n",
" <td>-135.720728</td>\n",
" <td>4.876975</td>\n",
" <td>119.172739</td>\n",
" <td>128.064290</td>\n",
" <td>...</td>\n",
" <td>2.722730</td>\n",
" <td>31.789969</td>\n",
" <td>-213.565294</td>\n",
" <td>-116.808285</td>\n",
" <td>-2.703074</td>\n",
" <td>110.878278</td>\n",
" <td>122.058264</td>\n",
" <td>122.254400</td>\n",
" <td>-0.200958</td>\n",
" <td>37.182862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>6.794145</td>\n",
" <td>1.916061</td>\n",
" <td>-1.470843</td>\n",
" <td>4.774059</td>\n",
" <td>9.843367</td>\n",
" <td>-12.777450</td>\n",
" <td>3.425381</td>\n",
" <td>-3.601761</td>\n",
" <td>...</td>\n",
" <td>12.804397</td>\n",
" <td>-2.389470</td>\n",
" <td>10.248392</td>\n",
" <td>17.722228</td>\n",
" <td>-24.618944</td>\n",
" <td>4.712715</td>\n",
" <td>-2.485321</td>\n",
" <td>4.410208</td>\n",
" <td>-1.767943</td>\n",
" <td>-3.041112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>4.311464</td>\n",
" <td>1.461278</td>\n",
" <td>-7.094384</td>\n",
" <td>-12.029309</td>\n",
" <td>-7.516458</td>\n",
" <td>-9.561586</td>\n",
" <td>-9.058443</td>\n",
" <td>-9.298908</td>\n",
" <td>...</td>\n",
" <td>-10.923809</td>\n",
" <td>-11.467660</td>\n",
" <td>6.683382</td>\n",
" <td>-15.726920</td>\n",
" <td>16.529032</td>\n",
" <td>7.041785</td>\n",
" <td>1.963378</td>\n",
" <td>-15.234580</td>\n",
" <td>-3.607250</td>\n",
" <td>-25.870307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>5.002228</td>\n",
" <td>1.609883</td>\n",
" <td>-4.235698</td>\n",
" <td>-1.067389</td>\n",
" <td>-7.641470</td>\n",
" <td>-7.774862</td>\n",
" <td>0.687901</td>\n",
" <td>-0.789762</td>\n",
" <td>...</td>\n",
" <td>-18.723082</td>\n",
" <td>1.872345</td>\n",
" <td>9.352822</td>\n",
" <td>-6.824400</td>\n",
" <td>2.080326</td>\n",
" <td>14.018978</td>\n",
" <td>-0.192193</td>\n",
" <td>-0.241210</td>\n",
" <td>-17.082475</td>\n",
" <td>-9.865937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>9.661047</td>\n",
" <td>2.268102</td>\n",
" <td>8.498927</td>\n",
" <td>6.392152</td>\n",
" <td>2.702416</td>\n",
" <td>18.803556</td>\n",
" <td>-8.966982</td>\n",
" <td>8.145869</td>\n",
" <td>...</td>\n",
" <td>4.052777</td>\n",
" <td>16.213002</td>\n",
" <td>36.276270</td>\n",
" <td>-13.602513</td>\n",
" <td>55.861696</td>\n",
" <td>-16.304163</td>\n",
" <td>-6.536352</td>\n",
" <td>7.571507</td>\n",
" <td>6.756140</td>\n",
" <td>13.517835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>1.928676</td>\n",
" <td>0.656834</td>\n",
" <td>-13.699625</td>\n",
" <td>-14.651738</td>\n",
" <td>-14.590033</td>\n",
" <td>-12.395309</td>\n",
" <td>-13.992828</td>\n",
" <td>-14.704500</td>\n",
" <td>...</td>\n",
" <td>-15.510113</td>\n",
" <td>-14.906267</td>\n",
" <td>-11.983508</td>\n",
" <td>-6.473960</td>\n",
" <td>-44.957191</td>\n",
" <td>-13.495872</td>\n",
" <td>-14.314067</td>\n",
" <td>-23.430062</td>\n",
" <td>-15.472657</td>\n",
" <td>22.979171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>21.543682</td>\n",
" <td>3.070083</td>\n",
" <td>6.090019</td>\n",
" <td>1.685980</td>\n",
" <td>0.495376</td>\n",
" <td>-0.027352</td>\n",
" <td>33.584188</td>\n",
" <td>24.434947</td>\n",
" <td>...</td>\n",
" <td>10.196277</td>\n",
" <td>2.708263</td>\n",
" <td>10.625418</td>\n",
" <td>21.148065</td>\n",
" <td>7.644087</td>\n",
" <td>36.009484</td>\n",
" <td>23.702621</td>\n",
" <td>19.821311</td>\n",
" <td>17.516030</td>\n",
" <td>26.346301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>470</th>\n",
" <td>0</td>\n",
" <td>470</td>\n",
" <td>9.610827</td>\n",
" <td>2.262890</td>\n",
" <td>0.288421</td>\n",
" <td>-3.943719</td>\n",
" <td>0.212089</td>\n",
" <td>9.389078</td>\n",
" <td>11.868646</td>\n",
" <td>4.797833</td>\n",
" <td>...</td>\n",
" <td>-9.798034</td>\n",
" <td>10.055472</td>\n",
" <td>-14.619929</td>\n",
" <td>-7.064588</td>\n",
" <td>-10.839712</td>\n",
" <td>13.898890</td>\n",
" <td>14.269219</td>\n",
" <td>-3.822183</td>\n",
" <td>-2.814990</td>\n",
" <td>21.550762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471</th>\n",
" <td>0</td>\n",
" <td>471</td>\n",
" <td>13.007924</td>\n",
" <td>2.565559</td>\n",
" <td>-11.569216</td>\n",
" <td>-2.740502</td>\n",
" <td>-16.536532</td>\n",
" <td>-18.272762</td>\n",
" <td>-20.150568</td>\n",
" <td>-25.360548</td>\n",
" <td>...</td>\n",
" <td>10.627980</td>\n",
" <td>-3.389223</td>\n",
" <td>9.929313</td>\n",
" <td>-26.490133</td>\n",
" <td>-13.665560</td>\n",
" <td>-5.937350</td>\n",
" <td>-26.417943</td>\n",
" <td>-1.430498</td>\n",
" <td>21.456395</td>\n",
" <td>36.825796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>472</th>\n",
" <td>0</td>\n",
" <td>472</td>\n",
" <td>28.578657</td>\n",
" <td>3.352660</td>\n",
" <td>-5.636283</td>\n",
" <td>-22.531519</td>\n",
" <td>6.468182</td>\n",
" <td>-21.384848</td>\n",
" <td>-5.738626</td>\n",
" <td>-4.027562</td>\n",
" <td>...</td>\n",
" <td>-1.474402</td>\n",
" <td>-10.623486</td>\n",
" <td>-13.368707</td>\n",
" <td>3.267763</td>\n",
" <td>-26.862845</td>\n",
" <td>6.158262</td>\n",
" <td>5.026947</td>\n",
" <td>15.734990</td>\n",
" <td>-9.865140</td>\n",
" <td>7.691505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>473</th>\n",
" <td>0</td>\n",
" <td>473</td>\n",
" <td>8.464342</td>\n",
" <td>2.135862</td>\n",
" <td>3.422550</td>\n",
" <td>12.387987</td>\n",
" <td>5.226755</td>\n",
" <td>4.400968</td>\n",
" <td>-8.258625</td>\n",
" <td>7.533114</td>\n",
" <td>...</td>\n",
" <td>11.324881</td>\n",
" <td>-11.988766</td>\n",
" <td>2.706351</td>\n",
" <td>-4.933192</td>\n",
" <td>10.462656</td>\n",
" <td>-13.797083</td>\n",
" <td>-8.031448</td>\n",
" <td>6.131281</td>\n",
" <td>9.313546</td>\n",
" <td>-38.494202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>474</th>\n",
" <td>0</td>\n",
" <td>474</td>\n",
" <td>0.330915</td>\n",
" <td>-1.105895</td>\n",
" <td>5.424351</td>\n",
" <td>5.410015</td>\n",
" <td>5.169467</td>\n",
" <td>5.383610</td>\n",
" <td>5.244975</td>\n",
" <td>4.973590</td>\n",
" <td>...</td>\n",
" <td>4.632844</td>\n",
" <td>5.622618</td>\n",
" <td>11.344342</td>\n",
" <td>19.642183</td>\n",
" <td>1.054149</td>\n",
" <td>12.969772</td>\n",
" <td>-6.612096</td>\n",
" <td>0.063574</td>\n",
" <td>12.863360</td>\n",
" <td>-0.737742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td>0</td>\n",
" <td>475</td>\n",
" <td>43.959515</td>\n",
" <td>3.783269</td>\n",
" <td>-5.447815</td>\n",
" <td>1.108691</td>\n",
" <td>6.084472</td>\n",
" <td>-70.817463</td>\n",
" <td>93.554623</td>\n",
" <td>-17.661112</td>\n",
" <td>...</td>\n",
" <td>32.379292</td>\n",
" <td>-16.739106</td>\n",
" <td>-33.504858</td>\n",
" <td>-6.944722</td>\n",
" <td>-78.087247</td>\n",
" <td>72.604803</td>\n",
" <td>-14.145269</td>\n",
" <td>-107.542404</td>\n",
" <td>39.849992</td>\n",
" <td>-9.712036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>476</th>\n",
" <td>0</td>\n",
" <td>476</td>\n",
" <td>237.579437</td>\n",
" <td>5.470502</td>\n",
" <td>5.837267</td>\n",
" <td>177.327123</td>\n",
" <td>688.724619</td>\n",
" <td>403.569012</td>\n",
" <td>2.507928</td>\n",
" <td>-107.062318</td>\n",
" <td>...</td>\n",
" <td>-254.875731</td>\n",
" <td>-326.342662</td>\n",
" <td>174.886643</td>\n",
" <td>689.343375</td>\n",
" <td>397.589301</td>\n",
" <td>-14.857776</td>\n",
" <td>-99.801511</td>\n",
" <td>60.126406</td>\n",
" <td>-238.501704</td>\n",
" <td>-349.470691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>0</td>\n",
" <td>477</td>\n",
" <td>4.382804</td>\n",
" <td>1.477689</td>\n",
" <td>-1.896759</td>\n",
" <td>-0.795049</td>\n",
" <td>-5.208624</td>\n",
" <td>3.751670</td>\n",
" <td>-7.852768</td>\n",
" <td>-1.679162</td>\n",
" <td>...</td>\n",
" <td>-3.163811</td>\n",
" <td>-0.787934</td>\n",
" <td>14.266851</td>\n",
" <td>-11.384817</td>\n",
" <td>15.239027</td>\n",
" <td>5.014109</td>\n",
" <td>9.273061</td>\n",
" <td>4.637090</td>\n",
" <td>9.682643</td>\n",
" <td>15.428316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478</th>\n",
" <td>0</td>\n",
" <td>478</td>\n",
" <td>0.382631</td>\n",
" <td>-0.960683</td>\n",
" <td>-8.481365</td>\n",
" <td>-8.686068</td>\n",
" <td>-8.104400</td>\n",
" <td>-8.991879</td>\n",
" <td>-8.665817</td>\n",
" <td>-8.455313</td>\n",
" <td>...</td>\n",
" <td>-8.538728</td>\n",
" <td>-8.536667</td>\n",
" <td>-21.939262</td>\n",
" <td>-23.391433</td>\n",
" <td>-5.126656</td>\n",
" <td>-25.829834</td>\n",
" <td>-18.836053</td>\n",
" <td>-11.688913</td>\n",
" <td>-36.133003</td>\n",
" <td>5.986280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479</th>\n",
" <td>0</td>\n",
" <td>479</td>\n",
" <td>1.650859</td>\n",
" <td>0.501296</td>\n",
" <td>-1.594511</td>\n",
" <td>-3.346207</td>\n",
" <td>0.287749</td>\n",
" <td>-3.431939</td>\n",
" <td>-4.707448</td>\n",
" <td>-1.979455</td>\n",
" <td>...</td>\n",
" <td>-4.847490</td>\n",
" <td>-4.389073</td>\n",
" <td>13.120532</td>\n",
" <td>6.523998</td>\n",
" <td>-25.402698</td>\n",
" <td>-13.271789</td>\n",
" <td>0.549801</td>\n",
" <td>1.907882</td>\n",
" <td>-4.562626</td>\n",
" <td>-15.021633</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>0</td>\n",
" <td>480</td>\n",
" <td>15.427733</td>\n",
" <td>2.736167</td>\n",
" <td>-8.754002</td>\n",
" <td>9.867471</td>\n",
" <td>5.697674</td>\n",
" <td>4.533577</td>\n",
" <td>-4.539834</td>\n",
" <td>-18.075832</td>\n",
" <td>...</td>\n",
" <td>9.502720</td>\n",
" <td>33.461231</td>\n",
" <td>11.783856</td>\n",
" <td>9.568180</td>\n",
" <td>20.107720</td>\n",
" <td>10.577091</td>\n",
" <td>-17.790920</td>\n",
" <td>-7.427808</td>\n",
" <td>-2.090270</td>\n",
" <td>64.417795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>481</th>\n",
" <td>0</td>\n",
" <td>481</td>\n",
" <td>11.682851</td>\n",
" <td>2.458122</td>\n",
" <td>3.086403</td>\n",
" <td>-1.163906</td>\n",
" <td>7.863673</td>\n",
" <td>-14.586508</td>\n",
" <td>-9.094895</td>\n",
" <td>10.135113</td>\n",
" <td>...</td>\n",
" <td>1.505152</td>\n",
" <td>3.148299</td>\n",
" <td>-25.811952</td>\n",
" <td>-2.334815</td>\n",
" <td>-34.534016</td>\n",
" <td>-27.225123</td>\n",
" <td>6.763458</td>\n",
" <td>-1.757050</td>\n",
" <td>7.078858</td>\n",
" <td>18.886379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>482</th>\n",
" <td>0</td>\n",
" <td>482</td>\n",
" <td>6.656079</td>\n",
" <td>1.895531</td>\n",
" <td>5.126957</td>\n",
" <td>4.188252</td>\n",
" <td>3.283221</td>\n",
" <td>4.657936</td>\n",
" <td>4.446673</td>\n",
" <td>6.669022</td>\n",
" <td>...</td>\n",
" <td>12.129612</td>\n",
" <td>11.044461</td>\n",
" <td>12.448328</td>\n",
" <td>-4.283685</td>\n",
" <td>1.821315</td>\n",
" <td>16.438630</td>\n",
" <td>16.158571</td>\n",
" <td>5.377514</td>\n",
" <td>15.788982</td>\n",
" <td>19.261735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>0</td>\n",
" <td>483</td>\n",
" <td>0.693580</td>\n",
" <td>-0.365889</td>\n",
" <td>2.543720</td>\n",
" <td>1.297385</td>\n",
" <td>1.646520</td>\n",
" <td>3.053167</td>\n",
" <td>1.467627</td>\n",
" <td>2.279813</td>\n",
" <td>...</td>\n",
" <td>3.039224</td>\n",
" <td>3.123106</td>\n",
" <td>7.569170</td>\n",
" <td>-9.857391</td>\n",
" <td>-0.258825</td>\n",
" <td>0.009939</td>\n",
" <td>2.368015</td>\n",
" <td>2.301022</td>\n",
" <td>3.083461</td>\n",
" <td>26.222346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>484</th>\n",
" <td>0</td>\n",
" <td>484</td>\n",
" <td>0.454379</td>\n",
" <td>-0.788823</td>\n",
" <td>3.257484</td>\n",
" <td>3.697353</td>\n",
" <td>3.525205</td>\n",
" <td>3.332442</td>\n",
" <td>4.111192</td>\n",
" <td>4.114524</td>\n",
" <td>...</td>\n",
" <td>3.974310</td>\n",
" <td>2.740719</td>\n",
" <td>18.300559</td>\n",
" <td>-1.307467</td>\n",
" <td>12.771414</td>\n",
" <td>7.301147</td>\n",
" <td>-18.570525</td>\n",
" <td>8.270955</td>\n",
" <td>-11.641383</td>\n",
" <td>-12.371095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>485</th>\n",
" <td>0</td>\n",
" <td>485</td>\n",
" <td>27.293289</td>\n",
" <td>3.306641</td>\n",
" <td>3.451067</td>\n",
" <td>24.787411</td>\n",
" <td>-46.869456</td>\n",
" <td>2.450013</td>\n",
" <td>2.381739</td>\n",
" <td>-13.204453</td>\n",
" <td>...</td>\n",
" <td>-10.777492</td>\n",
" <td>57.207163</td>\n",
" <td>37.232485</td>\n",
" <td>-34.533252</td>\n",
" <td>-27.382564</td>\n",
" <td>-17.100101</td>\n",
" <td>-6.485627</td>\n",
" <td>21.602734</td>\n",
" <td>-27.119148</td>\n",
" <td>60.420978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>486</th>\n",
" <td>0</td>\n",
" <td>486</td>\n",
" <td>18.701530</td>\n",
" <td>2.928605</td>\n",
" <td>-11.347522</td>\n",
" <td>-17.643361</td>\n",
" <td>-29.922906</td>\n",
" <td>33.218534</td>\n",
" <td>-58.526787</td>\n",
" <td>12.448562</td>\n",
" <td>...</td>\n",
" <td>5.792987</td>\n",
" <td>-5.607148</td>\n",
" <td>-29.162504</td>\n",
" <td>-33.498724</td>\n",
" <td>19.877020</td>\n",
" <td>-68.563867</td>\n",
" <td>14.121963</td>\n",
" <td>-19.740864</td>\n",
" <td>5.413735</td>\n",
" <td>-18.560331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>487</th>\n",
" <td>0</td>\n",
" <td>487</td>\n",
" <td>2.254237</td>\n",
" <td>0.812811</td>\n",
" <td>4.688342</td>\n",
" <td>7.409721</td>\n",
" <td>1.461081</td>\n",
" <td>2.920769</td>\n",
" <td>4.364836</td>\n",
" <td>2.819969</td>\n",
" <td>...</td>\n",
" <td>3.082651</td>\n",
" <td>4.198434</td>\n",
" <td>16.991770</td>\n",
" <td>-17.881896</td>\n",
" <td>9.880490</td>\n",
" <td>27.083708</td>\n",
" <td>-3.232190</td>\n",
" <td>7.570046</td>\n",
" <td>4.522625</td>\n",
" <td>-13.870306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488</th>\n",
" <td>0</td>\n",
" <td>488</td>\n",
" <td>0.124391</td>\n",
" <td>-2.084329</td>\n",
" <td>-7.899735</td>\n",
" <td>-7.700957</td>\n",
" <td>-7.871130</td>\n",
" <td>-7.760744</td>\n",
" <td>-7.909016</td>\n",
" <td>-8.123011</td>\n",
" <td>...</td>\n",
" <td>-7.916283</td>\n",
" <td>-7.715296</td>\n",
" <td>-22.449853</td>\n",
" <td>-26.445400</td>\n",
" <td>-16.035295</td>\n",
" <td>-18.996830</td>\n",
" <td>0.834944</td>\n",
" <td>-17.542543</td>\n",
" <td>-3.060642</td>\n",
" <td>12.804276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>489</th>\n",
" <td>0</td>\n",
" <td>489</td>\n",
" <td>3.838683</td>\n",
" <td>1.345129</td>\n",
" <td>2.080610</td>\n",
" <td>9.034221</td>\n",
" <td>-0.222111</td>\n",
" <td>3.330372</td>\n",
" <td>6.375071</td>\n",
" <td>5.273958</td>\n",
" <td>...</td>\n",
" <td>3.066750</td>\n",
" <td>0.054520</td>\n",
" <td>-5.398327</td>\n",
" <td>13.581943</td>\n",
" <td>28.683290</td>\n",
" <td>19.921959</td>\n",
" <td>-1.152186</td>\n",
" <td>-12.297958</td>\n",
" <td>10.497045</td>\n",
" <td>8.019944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>490</th>\n",
" <td>0</td>\n",
" <td>490</td>\n",
" <td>3.226941</td>\n",
" <td>1.171535</td>\n",
" <td>11.126901</td>\n",
" <td>10.680997</td>\n",
" <td>15.393856</td>\n",
" <td>11.925036</td>\n",
" <td>4.792132</td>\n",
" <td>10.354824</td>\n",
" <td>...</td>\n",
" <td>12.209596</td>\n",
" <td>10.046276</td>\n",
" <td>34.391521</td>\n",
" <td>6.244846</td>\n",
" <td>5.037269</td>\n",
" <td>18.029213</td>\n",
" <td>15.276817</td>\n",
" <td>3.937669</td>\n",
" <td>20.615054</td>\n",
" <td>0.236229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>491</th>\n",
" <td>0</td>\n",
" <td>491</td>\n",
" <td>62.589962</td>\n",
" <td>4.136605</td>\n",
" <td>3.176858</td>\n",
" <td>27.184103</td>\n",
" <td>-93.742788</td>\n",
" <td>36.733628</td>\n",
" <td>2.592292</td>\n",
" <td>51.109924</td>\n",
" <td>...</td>\n",
" <td>-18.529587</td>\n",
" <td>-76.102129</td>\n",
" <td>13.071470</td>\n",
" <td>-89.507330</td>\n",
" <td>42.526930</td>\n",
" <td>17.844865</td>\n",
" <td>48.596378</td>\n",
" <td>-97.987747</td>\n",
" <td>-25.161430</td>\n",
" <td>-101.439970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>492</th>\n",
" <td>0</td>\n",
" <td>492</td>\n",
" <td>102.291297</td>\n",
" <td>4.627825</td>\n",
" <td>-10.533095</td>\n",
" <td>19.882508</td>\n",
" <td>67.820855</td>\n",
" <td>-24.176477</td>\n",
" <td>44.437696</td>\n",
" <td>75.377519</td>\n",
" <td>...</td>\n",
" <td>90.248364</td>\n",
" <td>-38.973747</td>\n",
" <td>25.426051</td>\n",
" <td>65.809746</td>\n",
" <td>-3.110708</td>\n",
" <td>35.650789</td>\n",
" <td>96.102127</td>\n",
" <td>31.090508</td>\n",
" <td>81.152455</td>\n",
" <td>-65.778648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>0</td>\n",
" <td>493</td>\n",
" <td>6.321758</td>\n",
" <td>1.843997</td>\n",
" <td>12.644287</td>\n",
" <td>13.115552</td>\n",
" <td>15.493992</td>\n",
" <td>11.702251</td>\n",
" <td>-1.112843</td>\n",
" <td>12.162525</td>\n",
" <td>...</td>\n",
" <td>20.009053</td>\n",
" <td>9.033008</td>\n",
" <td>22.742412</td>\n",
" <td>31.628819</td>\n",
" <td>2.916960</td>\n",
" <td>5.535696</td>\n",
" <td>8.450868</td>\n",
" <td>3.034147</td>\n",
" <td>38.857156</td>\n",
" <td>22.597466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>0</td>\n",
" <td>494</td>\n",
" <td>5.114242</td>\n",
" <td>1.632029</td>\n",
" <td>-3.425001</td>\n",
" <td>-6.421901</td>\n",
" <td>0.917458</td>\n",
" <td>-4.366762</td>\n",
" <td>-4.114409</td>\n",
" <td>-3.128220</td>\n",
" <td>...</td>\n",
" <td>-1.534149</td>\n",
" <td>0.189685</td>\n",
" <td>-17.194494</td>\n",
" <td>3.186197</td>\n",
" <td>-28.506065</td>\n",
" <td>-13.390459</td>\n",
" <td>-3.186596</td>\n",
" <td>-13.966735</td>\n",
" <td>12.680172</td>\n",
" <td>18.725064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>0</td>\n",
" <td>495</td>\n",
" <td>4.445551</td>\n",
" <td>1.491904</td>\n",
" <td>0.232724</td>\n",
" <td>1.196588</td>\n",
" <td>3.060784</td>\n",
" <td>-10.675311</td>\n",
" <td>3.010120</td>\n",
" <td>-3.925280</td>\n",
" <td>...</td>\n",
" <td>4.596144</td>\n",
" <td>-5.641857</td>\n",
" <td>1.189139</td>\n",
" <td>-1.202329</td>\n",
" <td>21.560733</td>\n",
" <td>-22.004127</td>\n",
" <td>-8.514239</td>\n",
" <td>5.046773</td>\n",
" <td>18.906647</td>\n",
" <td>7.493907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>0</td>\n",
" <td>496</td>\n",
" <td>62.200798</td>\n",
" <td>4.130368</td>\n",
" <td>-0.250165</td>\n",
" <td>57.878999</td>\n",
" <td>35.706400</td>\n",
" <td>64.757216</td>\n",
" <td>70.822279</td>\n",
" <td>41.073614</td>\n",
" <td>...</td>\n",
" <td>35.916722</td>\n",
" <td>-111.451789</td>\n",
" <td>49.520322</td>\n",
" <td>45.249363</td>\n",
" <td>38.336500</td>\n",
" <td>68.830373</td>\n",
" <td>48.866306</td>\n",
" <td>44.479603</td>\n",
" <td>36.268265</td>\n",
" <td>-91.931933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>0</td>\n",
" <td>497</td>\n",
" <td>1.560098</td>\n",
" <td>0.444748</td>\n",
" <td>-0.979857</td>\n",
" <td>-0.351622</td>\n",
" <td>-2.290876</td>\n",
" <td>-2.296242</td>\n",
" <td>-2.450523</td>\n",
" <td>2.139087</td>\n",
" <td>...</td>\n",
" <td>-0.048620</td>\n",
" <td>-3.624284</td>\n",
" <td>-4.351460</td>\n",
" <td>4.514593</td>\n",
" <td>9.669734</td>\n",
" <td>-0.240080</td>\n",
" <td>3.758147</td>\n",
" <td>13.604036</td>\n",
" <td>-0.499152</td>\n",
" <td>-15.657440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>0</td>\n",
" <td>498</td>\n",
" <td>5.948734</td>\n",
" <td>1.783178</td>\n",
" <td>-1.657547</td>\n",
" <td>-4.353289</td>\n",
" <td>2.194643</td>\n",
" <td>-3.969012</td>\n",
" <td>-5.730340</td>\n",
" <td>-14.848460</td>\n",
" <td>...</td>\n",
" <td>-7.819076</td>\n",
" <td>-6.216130</td>\n",
" <td>29.354582</td>\n",
" <td>-5.511382</td>\n",
" <td>5.533544</td>\n",
" <td>-7.433232</td>\n",
" <td>-27.922372</td>\n",
" <td>6.957065</td>\n",
" <td>-17.892521</td>\n",
" <td>46.288780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>0</td>\n",
" <td>499</td>\n",
" <td>0.763063</td>\n",
" <td>-0.270415</td>\n",
" <td>-3.272668</td>\n",
" <td>-4.131344</td>\n",
" <td>-4.093318</td>\n",
" <td>-2.327147</td>\n",
" <td>-3.593630</td>\n",
" <td>-3.510077</td>\n",
" <td>...</td>\n",
" <td>-3.775218</td>\n",
" <td>-3.555126</td>\n",
" <td>-6.379747</td>\n",
" <td>6.538907</td>\n",
" <td>12.201444</td>\n",
" <td>-4.426332</td>\n",
" <td>0.787914</td>\n",
" <td>-6.721646</td>\n",
" <td>-21.155214</td>\n",
" <td>-6.070767</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" chain draw tau tau_log__ mu theta[0] theta[1] \\\n",
"0 0 0 6.560633 1.881087 5.293450 2.357357 7.371371 \n",
"1 0 1 1.016055 0.015927 0.813724 0.258399 -0.752515 \n",
"2 0 2 68.913910 4.232858 0.712223 -99.560305 7.754424 \n",
"3 0 3 7.796560 2.053683 7.626863 12.240061 21.038023 \n",
"4 0 4 4.510800 1.506474 3.116833 -1.576783 3.737056 \n",
".. ... ... ... ... ... ... ... \n",
"495 0 495 4.445551 1.491904 0.232724 1.196588 3.060784 \n",
"496 0 496 62.200798 4.130368 -0.250165 57.878999 35.706400 \n",
"497 0 497 1.560098 0.444748 -0.979857 -0.351622 -2.290876 \n",
"498 0 498 5.948734 1.783178 -1.657547 -4.353289 2.194643 \n",
"499 0 499 0.763063 -0.270415 -3.272668 -4.131344 -4.093318 \n",
"\n",
" theta[2] theta[3] theta[4] ... theta[6] theta[7] obs[0] \\\n",
"0 7.251098 -7.145438 -1.636578 ... 6.135082 3.984435 -3.539971 \n",
"1 0.491562 1.312863 0.548235 ... 1.730840 -0.034163 -21.166369 \n",
"2 -18.362189 -33.324630 63.035509 ... -122.029976 -74.031724 -122.383964 \n",
"3 10.103392 17.679999 4.436022 ... 13.318479 3.548044 2.144288 \n",
"4 -0.201018 -3.407841 9.793744 ... 3.507706 6.524663 -7.138264 \n",
".. ... ... ... ... ... ... ... \n",
"495 -10.675311 3.010120 -3.925280 ... 4.596144 -5.641857 1.189139 \n",
"496 64.757216 70.822279 41.073614 ... 35.916722 -111.451789 49.520322 \n",
"497 -2.296242 -2.450523 2.139087 ... -0.048620 -3.624284 -4.351460 \n",
"498 -3.969012 -5.730340 -14.848460 ... -7.819076 -6.216130 29.354582 \n",
"499 -2.327147 -3.593630 -3.510077 ... -3.775218 -3.555126 -6.379747 \n",
"\n",
" obs[1] obs[2] obs[3] obs[4] obs[5] obs[6] \\\n",
"0 6.769448 19.679771 -10.741723 1.594982 -13.157970 8.269640 \n",
"1 1.146050 -24.570783 -6.921033 -12.705194 -4.068690 -13.157913 \n",
"2 22.385597 -15.761502 -52.800127 68.876046 44.242679 -129.804129 \n",
"3 34.180500 4.411558 22.045692 13.628275 20.757049 6.063963 \n",
"4 0.311008 9.011283 0.734600 5.968886 4.765805 11.512443 \n",
".. ... ... ... ... ... ... \n",
"495 -1.202329 21.560733 -22.004127 -8.514239 5.046773 18.906647 \n",
"496 45.249363 38.336500 68.830373 48.866306 44.479603 36.268265 \n",
"497 4.514593 9.669734 -0.240080 3.758147 13.604036 -0.499152 \n",
"498 -5.511382 5.533544 -7.433232 -27.922372 6.957065 -17.892521 \n",
"499 6.538907 12.201444 -4.426332 0.787914 -6.721646 -21.155214 \n",
"\n",
" obs[7] \n",
"0 -8.569042 \n",
"1 -8.542400 \n",
"2 -108.183013 \n",
"3 18.748404 \n",
"4 -10.845965 \n",
".. ... \n",
"495 7.493907 \n",
"496 -91.931933 \n",
"497 -15.657440 \n",
"498 46.288780 \n",
"499 -6.070767 \n",
"\n",
"[500 rows x 21 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prior"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{('posterior', 'theta[0]'): ('school', 'Choate'),\n",
" ('posterior', 'theta[1]'): ('school', 'Deerfield'),\n",
" ('posterior', 'theta[2]'): ('school', 'Phillips Andover'),\n",
" ('posterior', 'theta[3]'): ('school', 'Phillips Exeter'),\n",
" ('posterior', 'theta[4]'): ('school', 'Hotchkiss'),\n",
" ('posterior', 'theta[5]'): ('school', 'Lawrenceville'),\n",
" ('posterior', 'theta[6]'): ('school', \"St. Paul's\"),\n",
" ('posterior', 'theta[7]'): ('school', 'Mt. Hermon'),\n",
" ('posterior_predictive', 'obs[0]'): ('school', 'Choate'),\n",
" ('posterior_predictive', 'obs[1]'): ('school', 'Deerfield'),\n",
" ('posterior_predictive', 'obs[2]'): ('school', 'Phillips Andover'),\n",
" ('posterior_predictive', 'obs[3]'): ('school', 'Phillips Exeter'),\n",
" ('posterior_predictive', 'obs[4]'): ('school', 'Hotchkiss'),\n",
" ('posterior_predictive', 'obs[5]'): ('school', 'Lawrenceville'),\n",
" ('posterior_predictive', 'obs[6]'): ('school', \"St. Paul's\"),\n",
" ('posterior_predictive', 'obs[7]'): ('school', 'Mt. Hermon'),\n",
" ('sample_stats', 'log_likelihood[0]'): ('school', 'Choate'),\n",
" ('sample_stats', 'log_likelihood[1]'): ('school', 'Deerfield'),\n",
" ('sample_stats', 'log_likelihood[2]'): ('school', 'Phillips Andover'),\n",
" ('sample_stats', 'log_likelihood[3]'): ('school', 'Phillips Exeter'),\n",
" ('sample_stats', 'log_likelihood[4]'): ('school', 'Hotchkiss'),\n",
" ('sample_stats', 'log_likelihood[5]'): ('school', 'Lawrenceville'),\n",
" ('sample_stats', 'log_likelihood[6]'): ('school', \"St. Paul's\"),\n",
" ('sample_stats', 'log_likelihood[7]'): ('school', 'Mt. Hermon'),\n",
" ('prior', 'theta[0]'): ('school', 'Choate'),\n",
" ('prior', 'theta[1]'): ('school', 'Deerfield'),\n",
" ('prior', 'theta[2]'): ('school', 'Phillips Andover'),\n",
" ('prior', 'theta[3]'): ('school', 'Phillips Exeter'),\n",
" ('prior', 'theta[4]'): ('school', 'Hotchkiss'),\n",
" ('prior', 'theta[5]'): ('school', 'Lawrenceville'),\n",
" ('prior', 'theta[6]'): ('school', \"St. Paul's\"),\n",
" ('prior', 'theta[7]'): ('school', 'Mt. Hermon'),\n",
" ('prior', 'obs[0]'): ('school', 'Choate'),\n",
" ('prior', 'obs[1]'): ('school', 'Deerfield'),\n",
" ('prior', 'obs[2]'): ('school', 'Phillips Andover'),\n",
" ('prior', 'obs[3]'): ('school', 'Phillips Exeter'),\n",
" ('prior', 'obs[4]'): ('school', 'Hotchkiss'),\n",
" ('prior', 'obs[5]'): ('school', 'Lawrenceville'),\n",
" ('prior', 'obs[6]'): ('school', \"St. Paul's\"),\n",
" ('prior', 'obs[7]'): ('school', 'Mt. Hermon')}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coords_info"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple plotting"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"ax = plt.gca()\n",
"for chain, subdf in posterior.groupby(\"chain\")[\"mu\"]:\n",
" subdf.plot(ax=ax, alpha=0.8)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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XAP5S//6XAP6rYa/XNgH0JkV/EHLcnQPmfgl0HXoVkrDYvoWVzgNstNcPbm4vfQjc+LZClIPE/9y8s2HiyPejVvpmfAoABJaLgzs7STpAZto9qPYDCZAQaF6+DNkaJqyr9CIACOQviMX2Fd9toMiHmwgBZ3sC4PDY9FihNMXhr9OrfAPJOsD1v3bx5bkaB/6YcglBvRx5cXweN51V+8EP0HjzLafcAHREB2utNWx1vE3Zdxc0mDo4tXK3s4HF5sr+Jx5BXL9Yj0Pfc82zUhGRF6kV7xrZ2hrSJ0/2bcdRww+fJ6IV3bgVAM/1O5Ex9q8ZY1cYY1cajQZaWsEwO6AHzBSDKjxlISRB6PAdSeTFwg4pK9dVMo7cD5ErJNgvQmNfGUCtlH1uYk0HIvJ+11y/i1fWX1e/D5M5KQWylVWkj5+gdfVq//MwYOEpOd5zbruwz+khEoICauUpavJaWuvhh4v3PNLdzbUP6GTulWLUSqEfi0700qQgjch7qBXq6ePjROSy2bTj3M9ytPcwt0qbwMPXVAb0QRB50Nanu+qXUSs7nR1cW7/We7LRUQaR67EQFcaC39fNX76D5nuX990r+GOLIyeif0NE3yCib8zMzKClEQKiSIUfauUoy5SkeXkeb0cEG+pDxA7v7BSeA670c63og7ExvHnX71yyxwsTxrRH9A8/7KtT2zsgbibj/ghBte2IA51IB634kQKFc7o19zuPdBKYCbM8PdRKV3Txkyc/wYfrH/Z8dnyI3CjyI069ngYVlXGBK8/LqJWeslnu2j2HjlGR+8o77V2ge2jS2hL2m099xWt2qW45ohT7JZc53l56Gw92HwRjFoDN7CzGkSeFXdL88R2dU5vTZ8vLqKU19JOjKvI1xtgLAKB/Dk38ZTLzIlb2i1rRn/mInMjDJOzwk9sgPd4niiDXL8NT9DRUiv5g6afIlQLfz9kZfofFrm3MWslDIN4jcOTSbq5hUEb/9gV/m3YdsGhWKp++s9OYuuutwcP4SPcvhGDe3rqN5cYhHJ8Uort9+1FbwH6UhUHkZeGHRTlWasWbP2WK3M5l/9kMRTgkiMpL5tdxVEmVkvDh/A5md+aw2d7sWXQyEuB6jPckrhlErn+a73LG8S9+81/gD17+g94bRi7k+SdPftK3XUeNgfpbAP8tgP9F//ybYb+YG0VuOtemEJdICSIXkuxxRqzUHB5KVrQJ1E+Rm7ok/ucHStHvd25h0Jqjmg9kYhDVVBjMUQTkQl3JT7QaeH8cHN1oMeGO6hrQqNxH5Pvwt0H7hmvDTseZlodRKiQEWDSYzmA9nLH3/WNG5G+svY9nqIV7O/cAAP/y8//yoBcKG7ZfAwfEkZeFHz5NZyd5tKHs9vqoBgYuDEmtdEtS848DkD/ZauLuSh1Li2/g1Wen8ZVnvxJ8nrU2UdmdB2ae7Y38MRy5YR88RV6JKi4xyG+nNIvt4Oc+SPjhXwN4F8AXGGOLjLH/DkqB/wlj7AGAP9F/D3OtHkXeiw48screp1bIW8HZ4aNWTPRAmSLv7KkqhcXPD7Jo9KNWSpEsACEsVRH0xfpdxzUX+oj5MclECDI2B/XL/R8DtYOFfQLMKXH/AQYpcl/sQjM8Im+kDaw0V/Ab534juOWwIptN7P5/30F3dnbgeaZgWdkYPDBHnraCiKdmN8ffXV9GR9ea30x3rRI/lFDhl2I/+n8zruZOASFaYqUQC132/o41jtzrl1KO3B3p/S4JtFMRZBiXSdvWijlmRF4Y7rKA/HOSiHdVTopvRQLwLJFQkbNCll0IJEKg208OErXyr4joBSJKiOhlIvq3RLRFRP+ciH5L/yxGtfSVTHiKnDG38pTe3NSLKFIrhiMPUVpPCc5hpEyR+8k6XqznwTY97sdz9xYxAgDS0SpMeg6ntKkK1s++Yb4cfMdQKwSlxBlk4Lgy0n30GLX3C2Gcm4OV2+Cn8NpCJcdK/9a/8+HDD+u6EJFJyz8ojSYaKkIqnZ/f50zTwsGIfCh9cOObylGnZXajgXonx8pu85g2QikuoEV6xLtHorclzB1n69daGSpqpaDIF+oLeP+QoX0+NUnZAETeB9R97+oSvnd18DaHZYj8uIwKHx3LAiDLSdody4rZsdYSodAaN2+ClVFbXoGtQXIiRbMYmOPIrTil3CN9qBUiNfQkIYhaOZTjs9TZ6TfG6+SDjIi+nsk+h02olf+9mg6hiqv6ku6zfGfHVVXTSJ6RdO31Bl3r/fch6mGY4YEicACwbiEawjftZRhe5Z3U52J8qHrRxmlkCvAf2szf52uDNr0uHmmnIqj3Uyp1F/pm98eUAlmf8dn+6CZ2vvmtwdd0jdU/yzNkf7V6BW/UvLh9fU7ZVm+9C1dJ1Erh72vr1/Ck9gRLjUPsG9uPIy8oONvribc/7pDUitDnXZv3KLlj0uS5VxlSSgkODn+E7OYdZLnEdz+cw1rNo7SMJV/CkQPl1J4FjcdFrRynKGolB5gu2QoXtVKKtqwdkwWHCBKcMRDJQHkfji8vWQ1N50WVYBU+kLNzQEIQECoNEkKtSoSQ0Gto+qMyU7wI6j/9hzAGnAr37DN4MynwWu0BGulwOxHZy/QoXk/5kfAPlbfB/z1K9g//hKfINYd4YMe2fbUEbD/aNwmsHJFT8Pn3ri6VV4JUJ/c2Qbdhs9bCXjctPadz+/bAdoX3KEzwwvUWGkvYzDX69xS5aw8Do9DE94tm9dyucOx89TwAHMpR61u0pc5OIrSyFu7vPFQfRIn/5eHugV7uXxyTIhcUlovgjNsxHzOOjAR2WxkEZXi05pKbHOAKAYNV5GXUntGLx0WtHKc4RO6FStmHK/mCpVYKzk5dylxq5wzrZpi6+QRZPkSpy373KDvm1waBGnyNX/wCoj5MVbXB1IovUm/qTJGbZABcZI104Uu1dobLj3fQLO7vaaiVMu7ak428iZ28jQdm95j90OogLtbw8v0icco4PwCIJ4barLYruuDgqPBK2JYhhTGmEmNWbgCPfqGSy0rEVyRFCTZA0r8vbPdpewmQiHRYaCfNcH1lcEzwcFJArYU2R/UWomaKXdEOEbnXd0aBUKOL2o9/DMrKK1sCvQ5mQ1+284NvSWjC8Fi1AipaeFALy1tLb+H61k10ZAb4RaV8WqOvRUQeFvLR7YGbWio5hVYEYwz/7IX/FP/Fuc/jjG5rOxOYqd3GZ+a/A0gR0kmWjSSN5staHzb6dFIrHiK3/Ihu6PeuLuFvrhXNNYPInSKXpIIOOVPmza8ebWL73fuoLm4im+8tgCX29nr2y7My9Qx6lQ/cm2dRgERlo4lsdQ3t6zf2f9h9olaCgamRtZycKKBYY5I5Rb7TVoOp3vEcR/ozZhSrf5+gSYSKppJSg07FYL+ClNosL6NNCKElMEzUCqAqSg6pyKtxdajCRH2lueEc253yeNxhr2t9g2XReje/U1oWwVArjIQOjQvvlRcc+UM3okix6Ps8894szl5bw07eLkXkaO8CjXU19Z5sQOzVkG0aF9f+CUHGkdfKnDX4o8c/wp2tO/u3XSs1Pj0N2enNDyAQGmnDHYkqXkPcM6T9kuZ0IIQ01q2W4/JNmEREAHiw+wARi/BM9RzORhOWHmlnApOtOcScqTb7VrypM0UycHSOHrXic+SGWrFoCGh2C4jGcuSeSUPKeGKMYb3exXqtg0ZHfU4lE6z245+g8dbb5Q2KkgMhcvscvGwm97lG8bDlA71jGpGLiQRB1Ip5mVbpClQaK5huLyGpKwdefOkSSFs2jETvBA9u7m6camfTfnTRT+Z/gNu1X4AXrrfTTLFltpYrKpfyB3e/JxM2dXyQpCJFNaoODA8cKIyFjut9nOFlERq+IjMKoeftE6lFYrk3S9YMFU4SOXqjLtKgFMQwz1d8v+F3zMIhQaUx++zBa0DWUorEha94TdhHkev2GkTeyTtoZA3c3BpQ28dcSyvgaGYGsu0WAp8jNxaAJAJiT5F71k4n6z9mfXaSyLvWEUXIkCN3N9Q0iR4VnUwgp9yCq2B+eZRWqYPTFxt+eBoROQqIHNhn8vdGrUgy4IOhlQoQhOuSPgq2lTXx7Tv/Ebud3dDRyuPy+5tjPCr/vN+mDAGftQ8i9z63iHxqwvoM1Cmhs5c2H6DaWMBUexVJU23rxiKu8qsIwL7UCln+MNMKzmad9ZFc5kip3XO9+a0GFnZasNFHANo3b0LUPNTbD03Ek0Mh8o7ohIr8MBNSdF1X9/GhCClxb7WOepGuKkhfhTCAe2c+IodEOw37O4hwGCYqah9nZxAVZRC57K3fw8EgiNDyNw8m9b3np57Hn3/2z3GheqGUWuHgyClHV3Sx2Vb+gul4iBLKUgCcgU9OgjrOGVhGbQnIvoi827dmvwJBRefpcXDkQhJEwa/DVjZQ+9lbICLn1CZC7vWZHztvI1HyHMmO27PXovMA3elzTyVHzhiygFrZJ7PTItOQI2dMUSsmptzyymX6NW1id28evJspT3ugyMsRd0CtlF27jyJ/tFHDZqOAUrXsdfcgpEBZ+KFst8GSBJQUImiK1Epzo7e3OAcxg8ipd6IH1/McQWaA7YPI7f3KLBNAc4WkPn/8Nhrf+8ue83okmVTKbx9aJxUpKlHFOYMOisiBQvGz8mfda2fYa2d4vNno+SzcWnCIexTEUivQiLyAJsOiYMNQKwWHtv1OMSYZ5c5O75ylbA93tu9ix5ZScNTKVDIFxsI8DSEV2jxXPQdA0SsmYets9ez+Tc8FWBSDTU6qcNs07DcXfqgXTV7u7Oxm/ZUbgfSC6+WbHAOzIokCZycAVG49gqjVIRqdIPiToMtXgALwYBYYdv0OZi7fteGxftu9k82NB7brxBC5IKFwo3moYRC5z5F7zk5AvXxXza3knus3EO3Og3cyVePAV8KMl3+rhFoJsgP71Mu4/GgLsxuN8BpQXO9P536KD9Y+sC8oMNlbbfCpSfUi/TjyYtGw4mYUqjHqmQy1UoL43XM5tMJMttl+9c8tmg2vx2wcLSlLyFgNu0slXy6IjW8ejMpzmSPhiXPODanIZbuNnW9+S/lG8tSjqsqftZObanSDzV3bd8XTBkTgmEsajjwtKCE/nXs/iyMXBY49cDSXXKNP+KFqF7NUZNN/D+Q4Ww4efM8sOmcqZ4C0ie7mfVs3ZCgROVgcgU+q9y87YdapH34ofGpIP2ukp12335gl39np+uJ4qBWCVK1CdWEDvNGGmFHRVPluw+kgMsocfakV2qsrEKTpzTKaxYQHt7pZL+XsycltvmzIEAIG1XhQp/ahVhQrDMOqWZ695BIsicHBwNsdl4zkPh1MrbDImTbeoCrjyKVXOqCZ+grVTda11lpp1Ioa4LG3OhXMZpnjtbnXMN8oKc3JPEUOH5H7EzBFTXaBT/2uReQszxXRsg8qtnivqATDEetZMSVx92deAD73x+64jovHPlFGkqRV4kFj9pHXb3wPD3buo3v3TojC+yByw7kmUe+0KO5IBZRMvCE2hWYkwTihmxK+cemf4tNnPq2+Sr0TvUwa3RzfurKIpR0/5LTXeRr8XebsNO3x0JAQzplOILDGGtCtgzMeLJ7G0TkVTQLL15HPv+spy/1pIRIS4BHYhFKANlpLX8P6C0gfK2wNWInV+9mo9xs3ijp0US2mbfs2bV8x9Excb2Hq1hzOvX0L0Al5otbyqBXzAPpnmSIHgXnjujz8UPXne4+2cGu5pE67lpNR5HlH1RbnfZBwUUiiPbuC1s2H9pDQCosxpb6JpGddehOsuQXszIElMSLGwFstCBJoP15D7Vd39fm8fPKQhMxy5HtN+7lfoKoIyaQkvHFv3S4otU4WPJ5BMtJHRr6DSQggjiHNC127Ddz7kVU8UmTY6ezgo9qjsNdIqkVFPwcjiTLu9N72PTzobII8aoUJqQZ8GUe+fA3I2oPNfnLHWXA/1nvS5/4YOP+KOzxkmj6BQnQ4pCZvpg3U0nov5dEHkRvONYlKkFFArfSh8IqKvIx6I4mLMxVwBmzXqvjSM1/S1+zlUGtpDQ92HgRfr7XVPTb8RBP/fbuDut1U7uy0VI+TTh5GkLDVj4Dbf9tDrWRZFzNvv4fqD34BQGUzms+HyuGQAizi4FOKT5c6jNtIiIgAACAASURBVNe8V3+8CUhg8rwCAfoZTPcv7rYHJmVJQJfT085OqXYSW6gt7N/GPqISEWWQSMfSXC1+MnReGnpFIfLe3A4zrgem6A/KcvXkhBR5V6Gx578My5EPbCihM7eG7sKqO0IEYmS7jSDLOfI7fwvM/kzvUcjA1p8g31tE59EyREtPhn4cPUnUrzxA/fJNh+K412WFF9DKBNZqXZ1ZCXQzEUweX5HbTFb/dlkOFsUu6mZvGaiv2omdkVBKSLr9PJXy1H3ItYUT8Kc+tZMqcEDCLSRCQkKCRMHsrq8By1fx0yv/O370+EcgIpzZ2AUr8JkMKo7cUF2OjipB5EXFNmThLBOr6/89lNhnLPdXFKWTmx1bBsf2+pe5tXkL253t8D5WWOl3kogh5hyZILvNVxkiv7N1B9c2rmG+VlZawF9Z8t4+7ketFMa5QuTq944tu6qoN6afiYGF1MrqdSRzT5DojV5y0k5UKcEfLewbAUVCAFGEaGYafHIC+fq6brK6h1sMNM/NIuALf6ryDkhCkqK/ckE9vgbz7JIIqal57nHk93fu41ervzq0Mhea8vSdjyzNAE2HcoTj1HabX1/GS7sPGN6RCz+cPA+89HXQmU8N94USdGlMF9URFKDcsulKLFIrYKeBfPVGSK0MQuTtLgBu7TJWqfSeZ9tE9nvVhKOThSZvqMjduVY0d0hGIcrwnJQEQAKxnxlnPmdMLTJSauXaLzkHyKVw/nTpKXDZuwDsZXV0RRdRu4sXHyxg4mYYWsdAYI0WOstboDzz0K4/tKypFDZkSEWu/B/qXM740NUPbeSPVxf6XmcD396+UVqPp5MNoJd8kORZU7e3b+P1eb2ZRw8id30QvAWmKoFLIksZ+YjXoL2peAoAgjR4KvlNKXL9/doysDUbekgGUivuSm1TIZEMV6atoAK1QtopaggPUzK2uriJ5M4jdO7e7bmPL5RrChFA/PynkBlFbigQuLErQHZxkmAwPH+sraZ+iLzWydDS1KZZWASRzRJu5YfbEUvRNWRLhAAA9yKQuF1I/QUmVPx26oPAvHlSGopo1udTici1GLMjHE5lJ5ond/HRtuAMU8aTuxbKww9ZZDFw3oPuWLkyseiSWwXFNa8HoCdMzIypzzwzgTPVWCE8H8noiS4hvagV73YaqRAAiMxNHpkDUUUp8ryDqCdQXgLMRK1AWwS9C4VBC0IKyN/8IyBKwMgrWTpgsLABMbt8s6Yc193UodIyjrwHkZtBv48iR8iRD43IJQHtXQhvi8B7nQ0AvYXVNhtdzG32n9z+He36WlxQis7OflYE02Fq5HaHEYWJH967FJp4983Dd/f4zXCeDFDknDlEnlonOkFKF87LEFIrUu82xKUqkZFpC49lQp23TygrpLSWbXT+HKibgjLf/yULvzPcX6vjvUebmL1zFZO7DxFrP0Z5dqdD6hO1Ni69+Q+ori5CEtlt1Q7knPXE7IPQ6uZYreldzszcIEcBmlBP0sfLEoIMIi9Wn7SA1AdspxGRG/EzBZk3MOMiR0mFQasPMTiM1zOpiveyRd01ReHCXfpGrdjBxbjtSF+RUyGzzLyAl85UUE0ipLkMOMOA+xO94YeUC4VUONNxzx41kEyqdq/eRMJ46MuUUncGB6Ni+KHH5ZmC9yQhq2dUQg71QeTuW+rftM/At3GuAOrLwOb94HvhpcoR+U+X3urhgu3lC4O8yCcW5frGdXz7/rfVH2kTaG0hqytEKyRZ+qSIcFb3OoEJ3tsO97u0SrLQXwOolaIwqKFXhshtUaV+pY6L7Zei5CSfWom884rtcLPILiZEYJ0OWFOhV8ZYYAVJjWYZCUTgyGEoG41SS6ipoGVebXjzk/LcUSteO3SVfTxcb4CLLjYbXVza+dBGFvmx4UIS/uq9eTxcryOVqgrhWQ1qZu7cUEheU1n5/GKY61AiK42Vni3bDEdeQRUvZlOB5UvkwJKiseA+K3N2FhKCehC5T9+cZkS+2+pCCrXi+tIT/hXQD4aeMIEaHn+nOanSZ85zUDIFNnEeuRT2niQ1LVGy4ommie/kNt6aTXiV2AoTw0UzKM86URgiZakVSXiwUUOzqByFMzkhMgR64twrSCvKORR5vCb0BGJB1Iqa2JkMLQLTrbnM9MRUlJGkflULnfASlCUNj8qZcp6mLh6W+ZNZ9+39nfvhDulaie11a7i2UbLHIdxCZ6gVhsG7Qd3fua/bJsF0/6mFO8LqXgfzO03sdTJIrXRX9tr44UcruLG4h0rM+9qGZfXIexV5jlvtNbzTmNP/P8G7y+9is73pKDCo1xQxBklkd1APnISF91D2vD7w6UHk3nf6USvBDNOvyuemp3/2ASofqlIXnIXhh6QROQMhYdw5O6Wx7hg+XPvQLahF8RC5Ge9G0fFGG5Xbj92p2v9TVAlGR/iIfKel3undlRrSXCKOeKAApWcB4cpHqP34x+Xt0/L2stqyLWg6KZxdTXdxobMFNLcwlUzqjnYJQSacgMw/fq2VYBOdXv+gs8Q1vdlO97Vaj7pD0KGlkwn8+NYavsJzvFr4jBdRV4BWdBq+doBVIgYIwzcBcAZNmGiSp6BkEmBd5Hpiq+sZaqVk+tptubjmLQl8whXw6YfIOSNUtOnXzXJM6c9NWJUgxel3DYeuzTUSEogiSAaF7vw2RTG6514Ctm4iQQTJASBXzSdSE4O7qJXNzg7e2L2Ff3rxt6H9/ZaPE4YXZwysLyJXx2aiCTSgzEfz/UZax3x9Ab91/gvqvEhN9GYqMBFzXSCql0a5vnEdEYvwF7/1F/aYvnFv35tPrPJj9ucw1IokCWjzOSMJVKYg23sgANvNFN/7cBFxUlOmsr7cVIWruOoy68zXmyW7tkiSmN9dwfu1VTxbrSBhEQCJRmMZMY9xgX/JOsFVsSS3IEQsKnDkzvQ25/cKuSABnyO37XXLTVkcObMoXClezrhOVFMWXeArLShyswhySYhZpPoX5nuqXbN7A+rcS+kW+lgl+1CWgRLCmSv3EWcM7JWzIBh1yHqMuSq5uWRks6EsiCRiaOYSMVdjxWZ2ZhnY/BNErKkWnCEZOl/UlCdwUu3/R2deAbv4RRUcIMkCAaYVu5JyjhykFb/+rIjIiUhnGzeAM4MzZk9MkUvdIZv1To8i77GeA0WuQ/F0Z0wkEWC2RSOjzvXf3vZWJCUoYhqBSgDapBNSOztLlIk0FIz6Hohs7KvfFvunvi2HtIo8TXsROfdwJRGwsttCmuY4C4VQCELtMFNwyGZ6QnICiFcAePsYMgbS5TTPtBawtaRizdfTmlXkZoHMSXOZehBJQ2WVUCsEhSYq2zUI3Wdz9Xl08g7aWUfdmwi5JGzWU1QTjhfPTRTMa6cYAi54iO3oiorHJJMVI1nsJU3AGUm7kBMATF6A3FOoezKJ8NlLk6hWZxAxhm4u8XC9URp26J7Ab5PuLm8BerDzAN95+C54KvDnFz6NS5VpIJnCGxMX0UgbuDBB2MvW8Uhs4AWmHOaGouGMl9bQL0tZD8JWeaIW/BJFHlIr/aNWMpKIGFDhFTQpRQ6JRIcz2j7PU8jVj4BP/zEQV7zysBIx48hJICYCyw1HngH9YwKssgfgwnnzHJR4i4F+BmkWrEK7Ey4AREFs+LbOps6ERCpyJFWOzC921+lA3ryDs/VFyInz/Rs4QIQ0fchVfIHikuyg4GZmk0HkGqj5Fm2RWjFjvBhHLiVS4fpikJwYtWLmeS6lctJ5DS1O0M1Gx+pqFxmgVPZEJYJxdDLSeNx0hL9PoUGgAHZaGVpd4R0HSjtKatrHIB+iILOzWP/AZalKqxS63gu0HPnaDTCpQ5ZAeOPOGt65p2qOsygC1XSUwsQ5d3EWITX3JiCLptw9NarS9Qow1fU2DyYJzL8HXPn3DoVJoZSQjtYhXhhA/jMB+I0lied21XsJwgCh3dTEdfghQ2brX7DgIlTGbQ+xk7ylVryazU9qT/CLxV8M/J4y9TVlxABMXgCIkEQMF6YS/O6LM/j6py/gq6+cxwvn1OKcFXZwCdoRmOgGKTvle3PzpkKAjCFv6+diDDOVGTTzJoiAR80PsUDbAAiRZ1lELAoVeT9EvjVrE7cYyNUgKeHIgwgus8l5yWKR62OVSBVqyz2rwQjbngW1NoHtWTVe7DiHpVaIJFimFlhfaZVaT6pR6hoeteI/r9qzlqzFUKyrEuvhJLy9bbv6/eWCVIhiBL3Tln6mdsfeo2+WJxGwNRuMASLCw52HWGuuQUjdPmi6h0KkzbX/ygvBUGL8WKYaIpS1oS6h29RsYur2HEiP241aBx8t7rnvD5ATS9EHtOlgJw96BjCgKJg7y3tYa+jBIX2OnGGysM2ZI6YQZAyS1GidgN1mF0u7OptMOoVWFMOf51LgZ7sP0M5boblQVOT6J4dEEnFwBhvLCniREt2GPVmZYcJlTEYRyHjUz37aXZxHyJh5VmmThnRDwbhB5IXnkDmwflu3i+t2GGpFnS8tw1GMwlHIlqUZWBCCRooXJ+NYVU6g/DdeLukN0+JeeVJbQE10yq0h2wb1Wa2dq8w2fdmN9kbf75jvmfIDePbzanMQ/3PPmjozoZRJErGelIbV5ipSkZY6O31KSELixepFfD3+nFdelWEqmUI7b0OQgC1nwEjHZqs/OeOhs7OMUmmsA4/fRHX5sr4yAWavVpmDpMC11jIaJtTSi4xQzqRwbLggASUVXgGThFvtNZAUwRxUSVhaRGYROSeDyLUiz3OlvHxFXgaQPEQO39kJT8lKRX0YRF6sdKiTOwOF7ABcBmQCMS9w5K2md48+UVgb91TUz4YLoZQkcXXjKt5celNTcVLpZO5oEWgaxzg7HUGnFiRoBy/jYaFA5gVatC9fQXV+A9hWynsuqPlzShG5NbXtoKfSVUfolPdUc1JkqvVpIF2JOSYShnNTiSVVVmttNLu5i3DRtIihHlRqkL5XchZ2WBfvr9FALW+iIzKsNtcUyvqjPwSfmupJfLDlTfUkSmKOrhebLElaZ4tpK+lHt0onitSHcSXcGYVxSP23Mg48xGXMT6OF/JCmkmy+TOYaAXKV2Wk8Sd6gb+UdfGfnI3QoBzujdybyxhJFJnJGTWuRJKBnzoAAZJKwsttyjiii0gn9/sZVvF57WLqI2vvo782uN3F9YQ9rtcHp/EFShaFWJs4AOqnFKjBvIp+fquA/ObuNf/TSZPCYqUjx1tJbeHf53eAe5rGK8eyfn3gJU3wKTo8zWw2wlfmVIxk4I3udiEUQ3QawcFk5jK3p7f9ULefdXdsztpiUzLGd1vGgs4n3m4vBM7iOiVRIXDcsCDY5t4vJhRoqUQWQEo+721jtbkNFbRulZMY2A6RT5DCKHFIv+DnQ2oTsupotK82V3r09NfAAAJboZzDKX49tZuK1SSKXhFwQ2Of/FHtnPgcAiBUJXYhaAc5PJTh75T1cvL6AOGLgngUhWi3bdtEPPJj66h4I9N+zK9CnqUo9joyCVjuWQb9Db/zr0GLfHyc1srd6x05D9ffZqr/h+ylE5EYIils1zCbvU+9DrW76oTKDyMnS16++WMfZqQyMFOK+NreLn9xahb8Rg7qfMWN0B1fPgn7jjzwTvzD8hYluYcGLSZ57DtHFCyUI1rRXHY+jOEDkRITETD5feZEEM9fy63ywKPhdMgAXXoV8/ssBorccOefa4eLQ+v3OJj7cnkO2VbccuXFqmWgd4gyZkPjulQWs69TvZuaV19TXa1z0Kttx5vhJxiG0n4EIWNxuY6+dYrft72QeUiuqjDEr3WTBF2rtAM1tJBq5NTrDbbMnSKjF0SA/s4g7Te5O7jbwm82rmFh+LzC4jAVVz+rh6/J5agBff+7r+Ccv/hNMoqLeg/f5jN6eryNatgcEJBh4wJHL7p6yNvO2NbWDxc+MUQ1kGMEt9DK3pVW5jobxlnj3/fXbyP/+/8b01Xv2nU4sqhh7Q60AsEuh/aofKSQyrx47IZYKkYMkWH0baO9BrN2z331n+R08qT1BIAFHrqmVPPfGdIjIDRp/9sXPoDulkggTrpWhR5JLIkxWIiRC1QGPuUvkk5RjbvWGReLFUgLvr76PhfqCGxd9cgAkEZ4/W8XZiq6IapzxkYmqMu/N+cAAuJBLxrz3KzX9X7in/jxivpWGgXKiihzQprl+gKih4jqLAI0RgUwsrDYdyavUtt3ZRlu0A3Omk0l7oWYnw5OtFrpSeY9thEtcBcBdBxZvbBSk/ox5moBFUS8i1y/AGKKMx0FNBgmJpKTLuTa9AO3s1CtU0ByjIM69CEqmwqRPw//7g0JLZb2J7V/eROP6rB2QucxtogWIIDlDM81BUuL2Sm9srTFPtz5j6l0AZBGuUtKyoKgZyGXdUS8eV/6C/Z2d8s7fAht3LfLKh6x8RKQQOYGh3VHbCtrFH+WmNXWb8LvQ8NaRzgq255mfTH3+zMQzeGnmJUCPLOefkjY7s5k3YKiYjAQi5hzAEYsgDBL0lIO7n6eYc5dGr7hvlaxm22qdbf53YReCfGEblaVeWirhSVAD31i8gF4cfEVuqRWyCweRcnYCgEz7l/NV1w7nkXquPOhj0xZBhE6ufp9IIgtuohJELqVqz0TMEHGAZ1JFcOXL2MpXsb03j5XaFp5sNdHJw+StxfoiNlobQKG/AAS0l2o6IeJq5AshIHQ4JekUfdV3XoihoYo4Dzhy9aFT7Ez78GyyoMk14dxa+f3kZBS50ZvBqg/EjXrPcbMTEBl+OE+B9bv41Px3EQnnzOTqi3py6O9rRLVZ74AIqHe8YjemDcahqf4I2ymFfqEMbnH0EJ4kzG408FfvzevSoqYthjcr1DknIDFKPkAfZDlyMtRKISQyhzPxiCQaooVMx4urBYcrQy2gXIDpB9v2GuZ6QpvCCUtUC7hvDvaKqsfBIfxgXs6Ueaq981lxKJF0zkNQj7PT7NkaeY7uR3uPeu5tWiQ1kV+2l8BCfcFubGDk3vY9sDxHrStwf3kPbv9G45/xLmS1e26LsAFuAnOm6qLAHqfwc+25NxSIX3VvMp5ExCK0s5Z9GkEKkbtwVQ5pFbkDIAG1YtoruuAyQ5Lu4olo4e/27kKK1GYqRszjs80djT/EHuxdxBLmEHmWdRXC1t3CoVE+CcXHGyBEqr6RCjBwtY5EaZllT6TXHovIC5teSGWpEcg6OieSSNGjcIjcT9EXpJzIz5+dwMxEAp7m4ETIWY4m6kA3t5uGtNJQkeek6UY7Ltx47VHkACK9Vs5vNXB9cQ8dSXi43kCt41gACbdY2xrsnhPGceo+IncWnSu4Gp12RA6N1vojckmKcyYWqZefdoG9BeQkwb0aGirTWPuKi/wwGSREHkLWk1oI9KNWjPfYj1qxejxSSOjhuqIgNhrdHo4cPA4UhoRE4qFU7yk9jpxbNge63GdOEt9beB0rTV2+ttHCUrqKObnpTVRAh+3oK/a+ebMQ5Br9xFGizdewn4rCJDR144xuFWpmeo1B+ghm6hkwhFEgxSsr2oJpB6y60AdrH5S0ORzUZSnZv1r5Fd5YeEO1VWufufocIAS6Qj2XsK+P6euUZdqFRZYMtcLB8dHSrj3dsmCpOmayBRVKRjD+GFMOz1beBCNgphojIwnOnNqKuIfIvSQuZ1+SdwyYbi9jOdvG++k2OiSRi66lClw0jG7kuVdUxI4fDmoiKDyJGLf0Xp6b8hAs6FMSGSBSZ9mQtOF2vhUgs0KpAnh9YvrbzCPGVAhingXnOI6c0NGKfDKJ7KJrolZ8dlNlygJnqjEmEw7eyVXUCueQnIOlvkNVbdkOOJpFkoSoNbDzs2vIay65rQgsCVJteajBTy4ItVS9t0ZbW9bF5VQKPbdDjjzwrheYAaN/FG1zGhE5tF70BqicrCDS+/eV8ZHEGMAiUNoCZI6cRG/GN4WJDAZ5tDWv2hGko1dcx5HPoxYQ6Up7C02ZAsyZejb8jkcgIfHsjAoBW691gzhyACAegUjvqCJVCnJM0k4Rgqqrwkh6USseoiKJ5t0VLKwu2TZWlrcQz6lNpG1UsFYYJoaesT7OHLugqXjfKFLOvbnaLJYbc8E6Ro0meNeEuqmnEh5/b+pqGLopN0OJATMTFQDkIfJeQJHLHOAakZt+35oFdr1Kf95mECbMLB9mGzTTRiFAUNx9booc2THTg1t1GB/Tlh1ZRU5gaKcSl85U9d+EZ3Y/wrPrbwFp29WBkRIEjtXn/0BfVl13Op5WEU8gPDNTQU7CoVwYRN62bSly5CYu2VwzEm3cylaAyhTAOYTInCIHh7TOMw688JWgVpB7zlAi7lBfdvluoFTNiJQiA2Ruk3SgozdMdJPp3CzrdUhTob+DTYfj2NGU5r5emYJORuiIBpIIllrhULuDBdSKkKj+5G/t/XhXKJ3AGGTEwbIs4MZNPxlaSpBAtqZop2x5zZ7nh4aqMaQpFO+JmnqjEFWXBp6lor7kOHLuwKSJI/fel3Jbaf1hdE7cZ6tJT04s/NAiSSNJjOm9uYAuMadYhBvFwOZDoL6KjEJWljEGRqoTyZuYzVSgplN3mxnDZj1Vw9B6nly6vpks33/4fbyz9A6u7s3ibnstbKvl9tTkMANyp+WUjm0vi8BI4IePf4jvP/w+JCSYzBGBY+fMF5FWzrmHtAqK2+bEa9tI1/ewee+JtRqivaZGBaaD9D9cKaw0OY/tC1+1SJZihvysVkC6XalIlRKOVfw07T7G3vpVdLI9+zzdn72Dc1dW9PNo+qWgBIV1HGtHqzoblVhRSmlesH48MTH1kR8WV18DHr7uTurW7XOa+OZsnxKpQXaccDtB5UKZ/qaV5YjcjSlJ0qv5ofveo/TP1+6qtomOS/vWz9maeB54/nfseJpOptESKhrFjH1V/VB9izMOkXnJXX7ooLmsje/2xpemvEhmdtGJ/OqQfpZq6u/J6SJSPld9Bp+pXgBnjpaTTbXomL6wu0iJzBZz41aRm7ks7PfzrJda6UXk3nuKY1BWwpGTQuS1bgsPmm/joy1XtZSRBOdMjUFdMkAKoR36OmUnkxqRMxVUnoUJUQbs5DIHSWB1r11qyQYJWbpfFARzx9u5am9qd28qDz/0EbgaLczlo2hK1SBxacoWxHFfa9nIyVErVumpruGxxNm9WUw3FkAA0sUlNC9fRl6vK8cB4/A3Qc4gwL2EDEWtAGoAmgkgsFbrgJEKSxJxxZnmTA0mG0euvwsoJbPUXHKRJBqlAd4A1Ijc8Zi9USuKWiF0RMeanpykdkipUCSzSFhum0O/FYZoeQPgCeo8t9YFmN4mT5vzBpGbDhAsgeQVq/hAgKz4HC6sdRBFutwAZwAJNDM/kcibVFph+JOv8fXPIfvK7yrlyLl1RhMYkjgCZx56JkKxYKOpBMlZBJRMHgBAe8dzsrlSpINAuSRnCZBF5DpJxDsvrCbnkK/NeocreMYZx8W9W6hkmvrTDkcJAkTmtqDT6C+3uQlOkeciCzAcYwxRWgN2niAiBmmS16THkZcgciKAe2MBLEIustDZqVFgmRBRgMjPxRN4Np4CA8erE89p5Rl2sC0tIDMVfkhkU+y5eT0k7HqdywzFPWADZ/HSh8CyK4fMorjH2ekQOaGedpFEHOutdXdfpvjw6tZt4PpfA90GZJ4H6wOXKoyRGNPhsuFz+Ui83s1wc3kHt5b2bD/bpvj0qNYDPDjHJVLlmgYKK5CqIAtwrsMujc7QlonFiAW+yFhsPDq9RbNcH6gi8FRRfC3Pukr53byJ9PET5Csr7mSWgIjQFVJvsuA62FDE0h/EUkKCoxIBF6YSZV5RyOe1r11HvqsjNYqrHgEmqsUgBONUMuFGInO8aoDImat9YiSTGRiRQqFwiwfXDi4yyNc8kJBIkwnkna47nyk0mcYzaE98ymu3N4IZd5Px7EuQ55/Xd1LHukIVzeKRRuScY4pVUO9u2Ku4ScVCx6zuluy580gvXQKIEEVOkXPOMFmJwYvOzgIitwjSLM5l2rm1FVArsd44QwzQ5As7LVyd31VV6rLc3lclQTkHHvllbD2EZCQVqSuYJAQu7N3GpeWf6w8VuhUkAZHawlfmPQhT0U1fcTqZ1j3peiEC8OLij4DZNxDJzGZY+s5OC6aFtCVTSaPzKotVSVbGQCSDypqkQ/eCUEgjSRUoLeFKeLnyDCIwXdtf6ggKsguHlJmNWmF+aCwAmafgUoBijlRkNoLFdbHXjm5TVcrUwioVtQGzdwoT0tZBT4V0hfT0oskhEXGGxGy6kbV01Ih5GkIkFKgjzsHiCEykiLw65EaR5zJHLpTDtmnyPoKwxtDZSRqGWSOdJASLEEEBBmOpBphdO3h3u3vYbK5jrjaHnMzCYzW5UuYiROQs5r3vsSAnHkeufjKgomJiebejOkCbZ6Q3SiAwgEeQgnBtfheZlD2IHNCoyN5AgHhsFdEXXnb1FUzYHACky6vmy2H7gogTQifv4odPfoS52pzdYEKkmf1qgMgZBwNHlNWAHTXYOqKjTEL10CEO9QpZecHOaE9dBK9cAipn7IOa74moqovtk34UshPYlP9k8QRQmYDgHAvbLeSSkGlO1SJJDpyNJtDJa0g1tRU4ngiaOgn7JycCYyo7jbgqlvXqM9OIeATOyFErRCimRNu6MwaRUzGAEUqR63tKITGpyzEMCkGs66iBvZbe5ELTC5lB5GacBM9SQOQA3l56G1udLUgJ7DR1kSizluoYe2KEyN/5RSPyTiZVyByp/6eTaWt52T71ohMij0ICpFcdTx29trCD92Y3rcHO9cblFZ2iLyjXFT1V29V1Q1RvJaqW97VuXwydZeqhRWYqjhqOnEcwBdSYNrXEwvvgrW3ImGMvb4EVwovsXC869gCwagWUdguI3KHeTApwzpxlCKU8z7TmEdtoJQYpHNUJAJEAmFDW4MWpFzHRXkHcWjenWwUtSCCXaqessvo9VpF7CJz7Y5BUqy57CAAAIABJREFUmRFTlkP4/W8seVJJUHd27+HW5i1cXr2sSAHwgCNXFzALp3YqR/EpDT+04pAHVXRBqLSr/taxqHYzY8Y1GtUmDGRgbvDWNqa6W4ia67iwp1LSVXF3DsGrwPlXwBLu0KV0+Vqi2XYUhSerOy1s6UnMJNASChWtNletIqdUx7WTG6ycCOARGGfgIgWvLblEDvvUmggi/ZL1AqTmhUPYEkDKJ9Hc1JsjmAgC03av3WQiJnxETgqRtCSQiVwX3Gfoii44MUydfQmV5Dl0WwycBGrZtmuXfU2qPUX1mQmhnE6cgaJYlxplyolJVAg/DL/rNnZQXGulgPAA4MrWTcxle1AuRIapilL6g/ZpnK7GOLe6jXRh0xVEA3TxJJcsVcaR69aAiFBLlZU2v93EozWluE09d6YRec5icG9XIB8cvHFv0157Jpmx/WAkli4Pgrd28WCjjtVaV23t9+Qd02sAgE4qrCNf+YwEJHRyGePINneQa7rHWYaqLfY9Vs8o55lOAisVqWg/CeneFxGYzZDOFSJn3Co87vUnBwPFHE3RcVFY9sndol4UXq2CuqGDlOn9LyUIOZyCZh4iv7T5npdPkYGE1NmWqq+4IOvsfOHM5xAj0phHW9MmistD5KQVhF9HySB3N+zI7qoIaEuLM1soTwh1zAUjKAe2ZFBujSJt6S1uDHBVVfXmHqc6/NCGLEldJSzRiryrNlSgXAC1ZVA3dNKYh8xJgHne5GjvCarpLljWxGRHe5xJQLII6y/+c+DcpxVHZTqE3C7b79+Yw/21Ooq9lWYCuQQaOrSItELjjDtE3u0iWV/GwtybaKW64D5USCNp03CSuxhdDkLTmsGe05fUXoQK9lnYCPb8s1jaa2P9dnnqdTMT2Gt17fkmFlXCIEJdyMebeM1ujpvLm+Dg+NLL/xkY/x1E4OCSIGEiNTzaiqC5yRDlZUKCMz25GFe1J/T9OQhZHmbW+mIQOemVzVzdX0AetzfwOKtZ5TWZKBeTr8iLJidJ4FOPlnDxo1kwYcoSQU9UP0U/tLacMMTclSpupW6nHG42Q0ibIBYhrcxgfaeBlT1/F3i90DoOB0mUIGJxsBRGwkU5xZ1dCEg0cj0da8rJbJCgMdJNvJVWtUh4gqjeReeDe6CHS/rcPhFLX/4L4Kv/jW1f77ZiiseNEUGQhEycZ9csVlRfAdKmWg7NeNLPIDVlSLF6k1Oohle3dJGGMV5OAqtWIbtdBBvNSLI5BrkQjjKxJS6UMjXJSD+Yew173RWAqV2HxIVJRa2QVNZiJbYx21xV9A848pxULXVbIXR7B8nqNiYfLHnOWzf+OXkRcqSyoxNtNQipgJl7QsL19au4snYFzt9mHh5B0iDAbASPFNrS5io0tNIKA0F8ObmiWT7gAwCjyLNUVTjMO8DOYyS6SJDpAGOiC0hEHhfOZY48mobkFesINAk9JpmHCpw1O3sGyUsvgQmJ3VZmP3u00cTqXkdnnWp6y0RoMASKnNIUlRtvI77yFm7vqM0ROEmARQ7BuacEk4Tfn/4sXpn4omeeerwoYyDuFAI7M4PW+RnM7NT1uWGIZc4YZrc28HDngUZRSpHmJs4VAOORUs9E4MSwuNXCSr3uBURHFv9b5eFbO2B2UdIfqntLsiiceOTqyDMVHZ7nGbC3BGw/ChD5TmfH279U9Q23k8QXZV1IUoi8migl4dM0sqC0bLKOlGBdF5GUG0RuKbgSZyepz88lF90nBGi7x6HBvAURTSIDR7OV4o27G/Y6XqxHcG1eVOREesciiSRtAqTLHJT0grqa2z1eJeUASZQgrndBJCG0snERTWT5c9UAVRuH/HDPogiJGLpPObfKnntx1qgtKXvRVqPULdbbvlGs5lgiWXDpXkTuKfJKVc3tLAUzaFQoRC6IUM/rmnFklt/imgYhUnvZNtMmlpu3wBkw8fnPgmIGZhA5Z4iSRPP/pOk8YK42h7TbgFi+jjzLVV+Z8dNsY+baI0zMriC7dSdoOmOmCLZ5EvWBoVZyMiwC7OLUydpoyY6KoPH6X/iVK827MvMyT3WfxiAQPnutfBct1R8nJAZ9MRBaqY66iDlYlgKpt6lC5u10T1LvKKSolanOFrB6E1i7CYCQxmch4kkw48zRiNxEkciIoWrKppEEe+Yi+JkZZzPpe242upjfbllT2VqqeiJELAoUOfmTDIYjZzCbIDA4c5aB8GL1Ii5UXvRoFgKz5Xz1s5q1Lo6RVRJEOalY6mJ9F8awKtbxaO+x14Bwg+JWRtjt5AplkwtJE0LfL0pUjXTykqk8ZcI8X+rCb/8e3nnlFfVqpHTUilHkOhOWM+3Bf/DTwtWA1+Zfs5tsSKbehUV2wZlKkStELlGJTMliqFDF3YWewlUW3EgJlgudCaiSNrbPfBH5xS+oK5fsWm++WuHedn4gy08aRE4ig+AJUqkWoMSr527eoclANO+dszDy2OQaXF/YRcwiEBikXjBsHLltF9kNQNRYVB6QmMfg7QxEEnmF2XP9yJdgYNrkN/89u672EbnPaduEIH1MMkdPGAtXaFqG9PxKZKhaejYv8fQ8r+r67D/9hQMfUiICx57oYLE167hry5E7RJ6RAMlch5fqeRdxRIJwrhqjmkR4/sK0W5hMljhj2Jp/G+3VW+jurevSzi7223ZbU1Fr0u+TwIekJkhFLzJSO1kZHLDwS5EUAyC8bDfVQuPvaDZVpN6kyvfgPVsJen3Y95OPSwhYrXWU4opjsDQFy0ytbt/sYMD5T1s6REIiZgyQuXq5kxch+YTqPsPr6QQNM7uJM1yaquLSTBVEBMkYhD+iCkjITGAJBHUoGGNWkcvU2yQZbpCBq7oQCtR7zLjmsA3a1w8JaC6vxzkVRyDOwYmQSQkqxMISfCvDcNkKyZjj9VRoJEMgyeyAtorCUAba4UPG8erdxSio/Ow51CeVoss1tQLt7LR7rTJFYQmPPy4a+6lIcfnxNjabqXmKoE36D43IFSWVxBrNdPZU8tDD1/ojcp14peMWkAsJgRhcRPpzn8N1ipwBSHxFrhUnABepIXMQi5CT8ockkY+kC4j88ZvAnb9DJLyRxpiy2rQkuqBXMTzOvWf1LgmGShAgMEWtdDJIkvZ51DnumQLL10u6KQ1nE2rrEIXI9Six49dfWBy14n/GGYeM1bPEPRuEF5rjOzv1XIKfNi/dAmJwBANzVhFlYEzljWQkle9M6qikiIMihcgTBnzmuRlMTFRtEo+yF9W1RXsH9zfqEHkG0rHwRduQemrHOOCh+oDAuCutm2vr3aVIkDbrmeXBjeTeNn12AdcRP1Svg6anbETYVGeltE+BE1fkGuZqXhVxBJ5nASK3HnzGgOoZZJ/5ZwAAAUKECLj0ReBTvwN56Qsw9UYAgJOKvSbmCs5QpF6lMYskGCz7ZpyqQfO8ECJybY1Y5MpvakSuhOti85rS8T3gpqCWVk7mfMW3QZchUGFGy7U21mt68CQxiDNwyZALwv2VPWzUvRKbjCt0R6TS0AFtkkqLliKurvHi+Qk8N1PFc2e0NSGMIo/VJCG9f6cI61Ezgm0zeeolJ5VqrpydERIbIqYRuVfCtxh+uNtR/oSNhnrX3EOfqrv1XfQCxElVsyMQoEPIap0Mu41WcF3Dn/sheHHE7E4rz717B/Fep3fB8Fpa4W5fVklAK9/Frmy57cm0Ijeb35kFzC8GZRH53iLQ3ESct2CXs8JkTphKFJEUhr4FiNz1DIiUwot5jKilKAGfT3cUQRg7reLqFahgAS/rPo9I5Sl4zgTFLV/4DKTeM9anVsx7YwQPkcshqBUnrFpFK80DCMOktI5UIsANLZ3PkLWR6ISglFTJaqatB6YzOSNJiqrhHDyJVF111Xi8PPMZgAFpexudPEck2tpi7bVWRLdjHgLmYYOKNiRt+5LIgSiVLKXfhaZ4tBlhyzpUoornP9DvKlPlClizCZqeUe/itEathApSUwlJBJ6mYHlmG24Hoh44O2kDu7IFIu0MSRR6UtSAUs5kEblQHnZjEupEGzPEBMw2xcx2tpRkN2Bgum3SoFuNorlW0ib+1Ud3dg8+pqJWXKapoVaUwnabaAAvrf0MPO9YRN5KhV3JKYkhuXJEZkKCS9KRJ8B0NUKSKLpBSgmpNK5C5HCInMCQJBE4CNUowoTekMAGtnClyLlGe91Hj1B0dqKgiAEgE6SxDQexCLGpJaH7R/gZfoWv77SUAo4TppWFEltbxSgj69ySei9QsjWj/8+FK/jpR3PBdR2joHo9naiiomunk16Ik602iCTSuTmkCwtwiFzF9PuIHCCkso1ZueHixaVKyJIIETkjiYmKV0PeE+5TYow7C4THSpGTx2eDkC4uge2Y/AbdM3YYKmolQQTeyUAys9FcBO1D0NMqQPm5znQ1n4c9pxS5HoPE9Uwhva8ki5AbipI5ZGzBikbuhlqJejARuTYAgbOzLjhuLtWwVvOcedIfE9AlKEx4aATkHcSRKr/clRIP1mpK+TMAnEFGyvLk3QznJi6AxzFiT/2eTc4BjCHNW5BEiGVHhR+WKEyDyO3Yglbk+hE45aiKBkCE87ra5Rk2WaBO1dgzOm8qmcJvX/wSXjnziuMDdQG/ex8+wp2fvQtqNyGnZjzg11+ORZEzxp4wxj5ijF1jjF0Z6kvG5LCrHAPiGCxLwTNPkXsOOQC4svEBrssFdJIZZC/8Y68NeiDBIAWDyCOLPgw9YJykgjxqRTsocumlwpDCQiq13t7JLgUfrbfQrDch9cQCmVKyKuyN/JA6nyvjOnaaacVFQJLVHbXiK70oAnGFMtI097JNgYk4wvmpCTCt5IWxbkhHLpjxwZjKvoRa/Bx9q3+J3F6glZVVtK9ddxOPwYsjN4uS+swWf2IqRT/mDMlzFwHNl3ORKcePe3wrex0V6RFHBnGHBq1dHLVDi0MVhIryJqKWy0BlMjR73WYWSvGJJA6oDwaGZLcDKQWav3oPzXfe9RY8IJIpYlbF7z3/e/izz/4ZKmxajwDptvkTOYhH0DUMLUdORDgzkeBLL5xx6F1LTMJTI9w6hiWLETMTDpdjJatjK2uh+ctfovrONchUKz5yUSsmoSduZQAYpBRWYUuy/wBg4Muur2ylT492CzuPwKUGLp41Oc0rAI/QMmGIzMEhbtcebXNp/05SqKRgfRk27NNd39iXLf2sVI1V+KEXassYg9DheKQRecw5Fp79z9EUQp1v/FG6DQDDl89+Fr/73FeQXLwANj2JbCICkdQ+C4ZmlkJAYgIpLs1U7Vxe9saYKcvr+284MdtHZxuPUc3VonuhUsUfxF/As9E56yOz/c1ddjQDw5lkRpV3IIn2tWvIVtf07CIsvH8DkAR57oLuh5L35clxIvI/IqKvEdE39j2TwTbYQgcoRM6yDMwMTINuzZcAQDJsn/syts5+Ebx6JrgmmKmLTWjnO5BCUysG5ekJF+u7CgJyQxuYamv+xhN6FRXWVCXddvV5LQdYt6tePkFXhWNWkYdOIqfITdo4YOJ89X6j5sXrQUgAkCS2PvleM+1BOlGsnLlZLvz0KAiS+Gz1AqaSSaVMdXYFB4Ohsq0hoTcoiAioLq/oNvvheZ630xtQmZBezRCOJGKY+drnMfnVryrPvuh4Pg3C1fkdbGpaqJ05/wPI0V2uhKdR5OqTiXQP1b2HmOhuqiZMXVAfexUwtzvbtmCTNmohGQ82Ve68/Cyidg75zjWvF8n+nN7cxNnXf4KX5TlMJ9PgLFaWGgicO45csgiSKdgQM79vtLOtMLUiP26dcxd/zRPELAIjji1Zx3Jaw+PUVVo899qHdvxY/UsqYzVudmDGielngt6wWEds8ev3IGoa2RtEHrzFULggSJHbKCMiwhRPAMbQkEahOSRudxEijZY5QMgRF7Zmc13d6+y0m0sQ0Hn1eYjpCbA0R9wyuSSKkchkhufOVgHGkcgu4ohB8grquUoahJS6RDuHmK6AAZjgFUSZQHzuHLq//Rl0pxNLYcU8RjNLkfMYl6YjnEkIJAW6MseKt5WgzNNCkIF2gHuLnVmYL51Rc+nChMqrkO4ruvAffLNRvZZmC+vXb5ne8O4CyIvPqPt9jIp8aLHkhpSIWw0XmharKiQs7YKkLjJjTEbO1RCVEuL/Z+7NviRJrvS+3zVz94jIPSsra6/u6g29YkcDAwyIAQYcUrPoUNQ5lHT0Ir2IL/qP9KI/gXrR4SYuQw5mxQxmADS23pfq7qquNSuXiHA3Mz3ca+YWWQkMKQ7V8HO6KzMywsPd3Ozad7/73Xv9lCjQ2MLa+t5rzN5V7XjEcSse8LPDP+fj+T11w6pzAEVK2ANDrt+cdPKHgw+LoS4bAKvBoZq/dUNf1AZDHMaGrOI04/EU/+VI6iWkuuAQLMMJrz/8hQU/4PazF0iTljibkrwu/EdHixEBofOobVTWtuhDQT3BaIUZDWvNmi48oyW8ZBUt42o2d9hFkMWcEAOPetXvu3mPzLUC5Pbhm3TL+2V8VH6YlM5yTjNFxSFtp4g89qU63ckQ6UPivXtKi+SuL0PeyPJYl6lhfLGh1d2DnzG9+adMF7cJzQy2rD9ohcj/zdv/kjQEewaJIPrMW+9ITcPtr/wGR89eYX5tk/jw0TiQldewdv8uEpac/NVfldckRTPkNUfeEM07a9wo2RSXQ9urXJKLYaXYVQnkSVtc/oGc0RdhsAxntdImUa2pFcHP++LVpZRg9wlGjjxSFCpDTuipJs+ZSVWasZhSgGS+atJCXLNmjWMzgJGKWhk/qr9lxcjNv149c4l5VXVi7GgOb9L2BzqvvCNszGgeHDH745+WvBFB9d5fvL7L55/YZcqSxjmC6zgY9H5VdRV58/gmw9aE/gtXARju39e6LKJNW0KyMs7OcxIHFs0aTqTw5PfDycq1x5issbQ9Y0x+WD9imxpbXcsrV7d57sI6qh5LDFltYoCsTkq8czjne9/7MT/76FHVl9QYg909jcWdilmddfxdGfIE/CsR+UsR+adnvUFE/qmIfF9Evn/3jtbQ2P75T+lufzwaFKvg53oNgIkbu2fgPH3oy+8xRZURpYR/dML6zz8oG8SCHiHwaDixYKdNIq8TzRvoDYg2KrDzcP9t3C/+JbO57sZZVhRi1K3VAhYlsOS0NKbSAImQhlJ9rcgPbXAKtRJjVfxLigfwyfJDHvYH3Dq+BSSO9zY5+PaXGNqGKML6VDu4ONvZ+0mLIHjv8SIs+oHBqKCc6OBMChiglO4r3b8pLS01Ki66caUYePPhmzxYqqHb/rP34OgEF5d0ywdcvPOnOv4WHF6mgeMwV8PisN1laoh8WYBMRuCz1hOjNgtoZaJbz/wA/8nPbSJlmmKMjSR0ohZKSBqjp4BoiPzgY3j/z9m5/2NaQkHkiKPxeo9hbQZdx8mTO/DSjXHyVmn2k+N77Nz9IfGdzBDmkFUsiDwZZReNYnBlkekzdcUzHA+pa7vI+Ayia2xjFYJx88vFHG7+pXLxZs3rYGemVrzVAUpiHsxkm3ThpdGdzxtPnv/DUAZRUjqViGKXZqn1qT80Q57o7z5i/9/9jKOHVjSMVMo7jIa8SrZyQrM4XcU0e7lZATT+bfL+f2Tn0evkWkPDVq5NI1qT3za+YKn6a12HJloZIl/2TJd3mZ7c5ebRe/zw4Rs8DHPYmAKJ7to1vBM6Gt34kgZWvXgWIbDwM0QEP5wgYcmiflYoLSQlcK+eh2OVfnJV39v1ztMgJeAdc2JcDnaWsUi89ckxkjfaigw4fP4V5l98VT8Swv9viPw3U0pfAn4X+N9F5Fun35BS+j9SSl9JKX1l7/weAJMH91ff4x2dd7hlX266YqxZhEUVcIiKyLOBzK5qDlTGQAqByGjIS4eboBTIECHkRZcSnDxgiLGU0s3u0xg4tE3kg7+Am39Jch4Z+mJ0+mhFcEoA15VdfNSRV8YKuDMc8XE4wCVRtJAnOlpFL6RE8o7WOVyM+CQc7m5y7/J5GxahdY5+OTDIiAwBmqhGJSS0yJe97rJqIsNfK77kEzAMHA+rShB1mY1Ht7EJxtm+2X/CO/ObBGt/BQJNZxNwWQpcHS31viatLx1fvLTEtOqij8HOEZFnLX6hI6TRa4bRkNvG44Yl0/4+zWCt/ZxXjjwHy/IcuHqeta98WcfL0sPTlS8wNOcY2k3SwzuklIhJCiIXnwt8aX5CyteUOaqYPTG9o5i04NWP70Zi348cuYzUSpJc2hROJhegm9H3S7PXWjVTkiLynOyTeVyfVEoX8qN1qk4fDbR9Y6VTLuagwP36BfB5TiRt/AHQ3z2g81OWFnzNGyRQ0GVBmaIBT0/VOBjGdRwe58hXrsA5wuZsfEdU1KU0halzzIA23hNdy/FcKxZOF3fo47JIXMQJO//NbzH93Odwooa8vv9GPIs4sPQdzjmah+/gwtFKjXMwFN6HlRLZLukmnI/TNVpyvaWYUumnmqpgJ5zykNDbCrubvP/VL7K48gQnwTy3Ycij80uPvxNDnlL60P69Dfwz4Kv/eWfQQXhyf4OdmdekILuyX8xv8TfhfRBhGcaa3ylFrf5W3Z+IM6MMLgVCUi6zyA/zA04qmQtJCkcuKSn3WT9EWzjB3LZsoNOjj7j//p9xt/8QiZEBlQuFqj652vEcIKyuMbu8ydjMBO+luyr9MzRd+mAm5aGTCI1zmrYc1BPAiRlYNfKLfsnJoJmMOUU7Z10uJfAQNcDO7stJ1ZLMqTHxEVK/ikYeO1Jg7eQjwnJhm1Nie9bh1uZsTFqjViaICOfu/6j0r8xKGycjrTJpWk76yKNFb+nYKGL76IerzRFsQEtPQ9cWQ56yIR/mxMkWi+48TZjj4wnJ6cbeOOUmFUmOcQuZKAKO2ZBPt8Ftsei2ScOS/vABmSjJHLkYmkviGbugV11hTskPj5eBO4uO5XIOlihWvDXxRXLa0oITtie70Ed6Aqk/1skTxxZkI0cOkgypkkwieqpKtm0QZc2cbqJyhrsursvDXYEScM6X82roKGdYjucTfYHkBb+i/KkMedmg679hz0fBXNhaI6zrHCKql7k33Qcsrd48o6bT75gboo2uK9egpxTEe0XbTkHNNb/HpclT+nmv1EoAnM3ZmJLVYLExa7x6/0Moa7a0a6vUJGVeVcF2xDHExL/9yUc654Uy/q+c/xx/8fbdx8Y/TVoGUz4tg/p1EsKZz6o+/osNuYisi8hm/hn4B8CPf+WHUhrlYPpB/ddSxV2/NDpJuBuOeZCOzaCGkVohWjnYtHKKXDK/6x8Qw8Biet6UIrkxgqXuSmKJMERdUERFWlllsd3ss8GUJE4RuVEr6pol/s3DN7gXbhEJBNP1Dinzs2MKA2ByMDOuKSqSN0SbXeccBc91ukU8IUaGEEuyjUbmIda9M3GsTxrEaionxs4zjaloYoLXwye62MR4XS+kgsjVkLuYLHC7Yg6QR7fGbFkSG8fvw8P3SUkrKDbecWVvNmp9vapWJMayWeSKhRoU1vettRMQuHO4rBA56u1klJvL4+bRFIi57R+CxIW6vf2c2Ew5nlwkbV3jZHqJw7WrqsF3UgxmNkAxxZJRmIwGSAJ+2RNMQra885Gx4GNgVkwNo4b8VLNc2yBFIPgpicTw4JDJX/yY9uFhZbvUqGjZAw18v9DeYL+7wfluBzcE+hRhWFh5CO3ElCWVMQVIqmF3zhEkUykmRS1p+bm8bn50maRixcCPf0u4kGDnmoEeqZxLP6rkMq25cm5DpSJqyFMD/UILgC0OK2pl1NKXr85G06gVRDh57qohcmik49Ka8t2h9NGFtpuyvzkhmuhBghWqKoHY8QK9Tc59t0krHSnBrJkxjwPLtECskuRr4abSMna5b9w/4f37R8WbSbZXN4ybtY51IZbsWnqSNMz7wNGy5/7xkqymubp2hXW/9xi1pQBhHJcQDXic0dHp9PF3gcgvAn8kIn8D/Dnwf6eU/sWv+kB88JDJnYePvS4mRZN+qRInGZseq4HK6hHFSCrdqT6PDQaOdjgkSsN8dhkhIs6ZzhqVWYkQIvQipf5EDH1JKNnw21yRbZJzIyI3xBXRziXJSXGbQAhWG0F/lQqRj+hHjVFG5Cq5iij3HUVYhrH2QkiqoonOKSJPCRejBpNoASFdvYRc2ebo8k6prpY5clPSqu7X59HRe22cMPQ2ltbwuRmqoEoe17BEHt5k9vDN8nJCG1ikNJRzDvWKblqd13GsVJivSQ2Rvtb5hqzu8PZvJHH3aMmDo1yaYUTkmN5dKQ81pjtv/5Cdf/49Zm9+QvQTJIFrO87vbjO0a7YwVqyNzYGATK0e+2LUCft+YLFl6O+BStDGhtZBa3OnZKoVQ6RloWmOgBNh0e0Sb/wWJ1wnih8NuX2/1q/xuiEkmLl1pn6D1rfIEOkNhY/p4KOxy7m9WszMGyJPIyLPcy0DnWzYw2qiz2MoL4EEwHWVobXN3zWF9hrSqODJiLyMkUCabuBChLmpbx59PBrrTK3UVESecjWvb4AjSyYbnzNyY5kT0kz4nZcu8uyXvs32zjlcHNSzy2OckbMdzmHSSb2azWaNmCK99DR+MqoW03hRyTsOF72tV0PknE7sGxF5wab9Ccl36vnFQYfbNkcBTpZnoOw8/nYMVsa2f+6F8cS/5Gh+5V//E46U0lvA5/9zP9ccnnBaosVkDUjqSphRKvUdjHaoUYQ/1TlDDEEcTy8AB/TnXyThSmZiRKViYum/PWJRcTMicSi0hKSknbKdIdxKlpgyOhKhT2oEGjchhL5CAWNkvxLaKaJjtQefJJ0YSYRlGEoCToyJn358oEV/nOBCVEMuwnZ7AZFbMFtneHYff7hQZIZmljk0087sKYtzM3gUdaIkYdJ45o8iP7r5kKPz+t0+xMJZrxwCUjUuAK2t0ceTQhWEcTUijdVdCbHwjXmY1Qjra9OmI8cLRAQaLZ3w5u1DfpobTWAhAAAgAElEQVRuso4+N3WoBLo1Q8C6pIKf4I6P4OgO7dAQ/AxJJ7p9ydiaTlGlrFArMQXcbIY0DYu33mHx8euktQNcCITNHTiEeHykz9DmhFa107SYJI05aGNrrhSVsitrevcphvWPSfJDff4FEWfVioy5A9ZOzjmP9JHYjAgc00hbb5NiUB2qjBpK020pJRZ0TI0OKa3aRn5ZG6U8/qhd0M0ulThKKtcV0U44KSW8a4GTcf2lbKKE1Hj8/JfJDx8Pdo6nSIX+LEW7onpRjRl4zTo1b6DRCouByIXtbWTjPm3vV2ib+hAb73ysNVP1bJxjf+08u8dzfppW6Y7oNDtWYrLrTMjyEH98h6WM5vP85gTujDcjKZkhr6W0opToAEcLXU8XtibcPsgSWs1aKTYvJmYnHyNxya0nrsGPzr4v+LQyO8XhTxaMhtEGt5uRo7OZWqknSohhJVPVn0q6MEqQRbfHve3P0m9dt801FbdGzJA7gaNl4v0HGY050tCPRW7McU4iREllp876gYhSHHeX7wGoAiMMI38GIyKv0E9Oxa9XUUJlX1FgYQoKEceQEg+Ol0TnmDQePwTW3BaXNl7hXKtFt5is49sZXf+QQKRPPb3xxt4MWBAhdQ0Pvv5s2WgmrWfZR06WoQTc/BBGuqu6uhpJLtpNEo4QA308KWsmVJ+TXE2xolZixfFm73rSNMp3A2myDVtXi+G/cvvf2snGKnzx4iuEdsZytk9KiQebn+He2vOwfQXfXYSHie54wVo3Va18hc4+/8QO13an6mLnwGfTMHn2GYZ7DwiHJ7B/jsMr+8yvPw1Af7I0r8Y2o4zIgehG1UqmVuys4/UmVQElM6iOMF6TeRTgrMZVRpFOmyEYkh4ReSwVKDMuFEZETqYPy9Xq34AxqJbqYOcZiBwUScNjlkGsHowqOoTGntuY2Tly5HinqfHl/FXa++m8EPIeZDkUpq5KTqkjiTqijcVEakROaawRaJoZDSHvh+M1V/PS5TlhY+jE0eBxzjFt1nh1/Qk2mKysgeitLEQsan7aT36so2+3cGVvjY3JKiYWIHltQp4yCDJwISkxN0M+a8ekQQ1Uj+eICfY++kN2b/3JygZ01vHp6Mid4OYjwlt0u3yy+yXSdLMEJn969C6vzW+NhWQQ4wbHO/XiT03GLIlKBN9ZgSHI3TkUFGryz1rXWEPhbNq0OFNubPLCxQ0rFG8LIwo61NGQUaoaDkMjU4gDWNK/LtjakGeOHJNEwpo10yCNrukQBjLTH2xRXdmd4Z2jWfY4HOtrl6r2a+DWztOEYyLwwfI93j38kX2X8O7dYzUu5uFkIzNtpKhWsvywCdriKyMPvZ7AEMYxvrf9smY1DgNDnJcNdxgLvUPTIrtPVAWIxvZstes/aVp6rwqF1CuHmPt81o+1OPmblznYedn4Zx3HGNBu8s2UrR+8waW3brI33cVby7yMeDenHV94Ysc8vbHhwvTqOmv7cza+8DR84UXuP3eNsL6l19wbd1+yjLUGfkrwMBwQssgw0y3mOdQhjBgiEY9LqqsvtAAQJdesH29YGL24MZPTEDlKJWZz7JPSd4PEioIcZYUiXu+zIPJAXiMpnSU/TCWDpSByA0G50NTBT99l53tv4LOKqbg9+s8z0z1N7EtROXI7x6haycFOqf42nqKsqRzMVyez5IyEFEpZjozIY4p07QwZhhVA4caqVfp7JRHMb3OGyBtRksiXjVA3HzXkUWXDKc9dKQqk/vPPsvEbL5b7SdvX7faE5BSRpxjs3mwRAieLAe+oso71uqSuXGnn+dUm3O7jP+E9f/eH8/h5VfjJdRxsPgNNzrmE+8MhD+P4noQ2ha0XeOsf58jrI5HofvEa7QfvqjTL2pJJSExbx8WtNXCOSatBp8gYKLpxbsbTe+u60AqKUoOs76O4gXvddXxcMnl0k5EPl6K11YsZkX5uzLs+yQpi48idsKwSYYbszhnCbXpFQ6lpsz3S79u4gABLqbZz1GgfzUNZlKWxQlIZYK74kawQkR/C2KbKxuFwPnD/qC/ISw2MI8bAPBwiAhPfjRy5UStsXwc8f3L8Nv/X/dfGWiBJi3sJ0DrH0JjUrF+ACMtMCxRq3JDPciAdLsmU00cPlEN/dPIB/Z4a3rzhaCa0FASUN9a6UuIvHr3Hvfk95OMfMrl8jmZnYzQo3ut8WfY2ipkcC0gciCny85MfofJDh8SR7yybZn7kMWhwO0bVkucd1RC5cuSjAc3cc0xRKZuUUWks9KAacn2mznvrEqU1gjJq1DHUUhCpkh8WaiWxajByqYS8ae9cg40LBoTGtm/zD+8ADu9zTZnsaSpyfmX9Cv/g0qv6e661Y2Omb8/BTvt4PXaJEjRMziHJqVQYKdRKTNHmFjBXOWRIgbaZaUyGUap32gTm9P1R1BxxSRG5NzCT/JbuZ5NW17lzxaPOY6scuc6Ld2/8I9Kzvz1SRZOd8l3RqBVs8y+sQILjxUDX+OyAkNq2bOi1R7BMAyepp0yQX3J8Sojc4edavnTldd8gIrTDISwtGFrUDu4xjtxxVgd2KS+FFOnefUtfE8fgsuuqg9J2Kv9qnBCTVhe8c7hAnDOO2RBdgYSQJXcp6d/OrXfsb07YPHqX6eHHRYNeu/X1FWrdcY9bzCtUAi5Gogh90IkoCEPQ7xLzDJrloLfXNCNKFUhrqss/QQ3P9kd3Wf/5HcJgFdic1VeR0fHvGldKcWqZTGH96DZuOIHju2WRZHd8dO1Ey/+GQGRgw3VMXGXIoaRcE+FePOKDg2MWuTQnoLUuTDpnMrU43VHVUEGT1fmS0Lz2Lif/7t/y2d1vslysa+ljNDgZZ+Zil0YXq5M+lWmRz6lG6we3f7DydLIRVjm4IwyDjpfNuZgi/uSIIfSGpPP7YzFQUnV4iikRB6VWXASfRkRePINMl2SE2HTVnDFgYf9F5wtHnjA0Kb7ERrxTbXsBDZlLrhB5udtMuM+siUZWuByagmdzH9m+Dle+SNq8qusman0doFArpxG5/tFoF2uDSFVHewx22gu3flQhcs2Z0BPnukX6a5sReQywpQoWdm+U59I2UyQlhtRX938WtWK0BwagEJzz+HPP0h+tcbjxGQCWFzZJ8RS1krL8UOv+SIoMzZrG9qQ2EnZ7rkVVRGG8AAvizpcDrZciu42TqTo/izltf1Cu+Q/nr/NHizeqATv7+JQQuYMYtThWfRgyXD/5sMiQ8hGTSeMqEX3rTiHyUzxSNEQrmMFrNAgmgxqnvc0pOGf8luPRfGDeR/bWOwSrf5KvQxwasIp2Lbpbr3Uebzp0DbpX/mAd7ExR1TKIeSQjv3zu/QP8ckFCWOYsMEaaAa+zz+dGz01brFMygyACS9TQb96+T3fnhOFR7uOYeeZc41lVK2UR5szOBM3yHifnUr4EFDpSFke0QFi0fp27zUyNSe0g5/rmSY1iXaI0K04UYOnY3t96icWlVyEF661JVR3Psbz4Re5uvIiIsNVuc667mk+GHwKxbej3twkp0ThX4hxOhJlxl+Kqcgm/jG8stKcgTUMcNOaRN5UhBLb+4k/gzY/Mi3GVaiWVk9QChtT3Ov7WxapKTyWRDS8FXPiJ0QUkUujpHr7L9q17SAjW+SpZQN40SSYd1Wnix3R9RkO+gsgZLy7d+Casj4ll3eV9pK+MDsDes3DhZf22mOgJRnV04xSxZyH23MQyLyXXFw99xZHnYKdoS7sP/3oMRld0ZTIwlYGcrznydgpf/l9hX41uiIHWNbo862dbq1ZSqloRJnM61ePxTnAf32d+S9i6ecuWllFzhVoZN1u9HoqnVj8DytIZ138yJVrKQeSU6PuexhqWA8ROn7u/8zp7D/5m5Rb0h9OAdfX49Ax5ogp82CEOaYRg8qckEIbxBlTCJtXbx6xNGL3wYnSqvyXRh+LEWxpy4sLOOr/3+Svsrnek5Fgez+l+/DYX7h/CoyNdpFInxyZUwW6I3DmKnUfvx1UceebkRl12LunlcPNja+gg+KXVABchxNqQm5tVUysJYqvp+c5QpipoPHNZTeZZfvKweAbFuCEQ1eBl1zNTK2Jjtri6wfzq5opHUc+jaNSKE82O884zZN2UueHS+KqgFmS1Qkoqq3SZYgCGZkZyY7MKfZbVZtKul6CoxIgXNSI+o/yu4fALTxPWd9jstrm6oYb+2YsbPHl+vUyOFd30wUda+z4l/uTwXf7s8D2y96sn1zhASolnzq9zY2+dFAaW4Yi37r4FOKOshNIYJCWdk/mWU9J69c6zl1SRdb7T8qkp1+4w+WGJoUxnI2WG/rv1yX3efOM/GLWrugaHVs1zZuAkaQ/PxNiLtiTxZBRcUSsK7WvjkOgunx/piOxZpEQK0SjJaButFGolv18MMCGCtGbIM0ceh9EbKtSKlJ9Xw1xS/s0qNDAaFRjSKY4dtQud61SJU/cCrc1biuX6irWNURutOE/TTXXe5k3QO5O4GhFjtqp5cA930uOr6od1pctUTVuKt3WKWkFYLgbNDcmIvM00DGcfvxqQf3rBzrP/kLP3EtELx8tQOrHHpDuvS2N0+LSO3E5Sfoq5JK1YMpH3ePG4YXSDN2YdjUX7Tz66h8yXbL1xm/jv/1QROVJx5JCXWB+iTbbKIIak9acKIjdElCfC8ggRIbYz/Mkxw9rYoNYPB7T3fsrQn9hQuFKHRbyiv8uNfU/TPr5Bu2b1Ya+f55F/iqy8yecqBlSqrDy7P+VYk6F8yqxMlVQtoJtGDAHvdAI58QynH4T3EMfNtMLrpIhllo5vDxHYvEJvrcbejndwi4HtP/4Fm9ZYGGDy3ltcunXMU3enNNlDaRvwnsnODa5vPEXnK3oiozIZmVFy0tWdnwHwwfIh75WKg5ZX6z3ROlWJqZZCDByE+2pMxWECQEY9PfpatQHG5ZIonmnyfHfyAputbiybP3ib7ifagzHlIBcgXaf8ahUEJCWYn7BIiZCsrDLKATnvVamRKNRKLp6Wva9ceCqFWGiPOuBXjrwxgZVOtvfFQJYCDujPvtpAyvkM+EjbquFcVMHOLMGr6tqcrrOdEoVaSZaNm8M+mSM/XTxKO0HpJiZDLD1DIWu7xw3EVbGukz7www/ukxJ473Dem52VMnZlMxUs2Ay7f/0XnPvrWyvrbwWRZ2rFvOQhRT5YvgMki1no9/fDoGWfrTJnnM4qhdJ4zdlD/NuOTw+RM6KOcohDvEb3U9My77aRaoGEFGmqGt+OVQ5MTt12fsiC1WxutEJfplbwHnGOWevp3vuQ41vKy+fmEIKi7pwRGieee49OmIfAo/nA3pYlEVip3BQTrVe0r5tSDtDo3XJ8Vxshr1/BLeaE2pD3j3TxVgZlRNHAziabk4adtVapFTtSHkfX2M+GYqbb9EmzLddnLVuzdjTeGXVkakWkUgmkleg6K99k3yeKyL2NrRNfgTs7d9PijEZZ+bzRUupN1JuugHPc3Xqa1+Mt5vT4wyXuZMnGu7fLZU7e/Dnrb/yUa+/cYesTHas4afm9p36P7enl1auWrNPO43kqGHxKM6+gyaiOxhP6wcZMj9DPrUVbYgWRG98ck1ELFejN1ApBudW86BsnXO+kfC7vaq7rsNlE5s596mmHIw163T3APzzCmSKlGJEEjdgcyHGGPBrZiMZQmpw/ZsStTkwxCMXb0I0gx5b6FDQw6HLDj5FSUAayMuR9XxQmKXuaNb1VNrxqm8+DZ9RKpoWy/HBIAx8ffczNQ93c+6NHzH7+Po1rcctAnIxr4ziOfHmhVtCZffdwydAHHIJ3jhSrol8ibLcbpAQbR+/R9Q9gcUx77w1cLptcLRFxdTew8TtEYE6PpMTUbyKiazAGHdPG6Xc/f2mD5d4Few4VJIhDtQx/TYOdeqTHXhcvSOxJ3Rqum45IMGnBnBqRu9OZnac5cvsKEQhhAEPkucIb4jTxQIRp1yKPThR9GVXSf/JQeUZziw62J7x/75AP7mtRqe313J1IXcsUlfUkJZYf3zWYaRM1RVge42Z7pEGfVqy6ySwJMKQi08t1H7JHFi+dr2+0zKNcw1zT1qubn3QMMTFtGp69uKU1s43SmPmxq1I+X0bkoO5pqj3QNCIjC79aNxlsIXgGiSW6DyBNo2NSENQoP4x2HTf21rUOCmbIgZ8evcGtpMEe11ecrv3kBB699HlAmFj2Z2q0XG+QEQ0/NifOADXiV7v5KKaUQmelMKgePd/D4S1mi3tlM7NKK1q+oNokCqpNSQOMzmsp4J+/j+t1jNRbcMjREQ9PekXkohuIIvLxmtbmtyBpJ3beuMnW67fxSWW0ropveOdV1VJq7VjRtjwPY4Qmp9avcr56OTKCrDpF32oItfeOWMagbcpK56Zq8Aoit3otMcJky74uJ01VtVZyo4oE8dyWjmsJdrqykcAoP0wp8R9v/kf++MM/JqXE0Z/8KdO3b9E8mhdDPrH66VqO9hS6rasPpmjBTkeMFg+xe95tN3h++2W2Q6AdDpBPXsed3GG6uG3PfNx0nHPjQBRqRcevR1U5a26PH3zwgDuH2s1JQsAbGvdOGLZ2kMtXOXlhvzwPl4Yy58fmAWcfn1JCkBDa1YJXejhkNsGngeRbTS0vG6oFgypELqzqRMtROPIxBBdzok3bItbAWBpfur7MrAFsOr/F8XOXRpfROY4u7TJszXi0v4GkyN3hmEBikgNpmStLSq2EozlHf/Ua8bWf1BcFRMQ1YIWpUjeqT+b0Fmga0VTM1IoI6foV3c0318t35TiCInJvBt6uezohxIT3MmbL2bm+vPcK37zyTTMeZnQEWsb+po8NacwSPKVWktWVUd201j4ZamPWaGem0nncXg8xaYs4B1vTji89uYuTsdfmogJuOfB2+OyTnDz5jI01hI1tBKE7UXQ0tKp2CuJXvXXzlhKRD48/Zh5OlVb17cr8yYg8JRDfkPrB7FqmmIJRVXnh2sKPscxPV6ofQhyCfdTBfEDuH8LB3BJ+9Lv8wSPtwRq1Bg/elUp/oPSHc7qRLoYDUlKOXAOZCecaKzFB6QOpDU5soydViDyWblCPc+SAGytMKsABUiSFUKS0fYoqMnis+mGxYGCGXEKCbsMefD9eg917MeQkwhMXef9rXx4D5VkDHlAq9BdvsP2HP6T/0U+YvX6T6es3ufvBGwwH5kX32hA5ThrOdVp75bnpRf3Oo7vw4D3zAvUbnVB6AnivCW7K5jjb0MFZADeZ8T+a94j1M5WMsMDiFPmhxzIMiLBMAy4m9aDEcbActFJCjDTiWP/G11n7vT8AEfxXv0bcnIwKptgjAufWOzamv5pe+S9O0f//esSueWwXSeLwGxMkDvhFYQI5t9YRSZwsAzGMN1R6KNqRnZr8jlCRsMrRNfimHRvPVptCZy7n/PIei6sz3N4T8Ocf4ZLw8KnLbF/v6Q+PcEtDCN7jckOGUvY20TAQDg4BIR2fkNpE+3BOMxwybDaI+NI6KrVqyFu87tx9Ja/MAUox1dJ0wv2//yUmy6dhTkHXp6mVYoinisiVW8xKCS00NvUTdjcuAz+qkprEaKtVnW9xelPlBosafC3vLqW580nqafMnrORrvpx3ho+AvVLCdtJ2xTg4EaI2iWTzZzfZfe8jPvmNa7hlJLWe+Y2ryCcXkTt3dYPrOlLT0h5lFY9tws4/NqfEOY76Y24fvMXh/dXpPshYKTLfq14L2uBgWKqqwALuKkccByflrFPLwCyv5bELYYwRiFpjcd6oPd18/eEjWEMNgHOIdwiOsD0l3TspRkUMkbfmMTgccQicBFjGSBsyIlfd+LgKEos338KfO6fqiZxJeEYyUM0RJ2tEgnkVtRlpq3rwj1U/zBw51rez9IfNO3QV7FzpmmTgDjP4TssP+JOByz99k3CvxZ0sSW+8zfTwI0hw9/a/YGuyrd91NFdKqmvx4vkn5z4P7RrceV3/wzYP482dEyQkrrldPpme59raFXpusdnusTFscn22z0fDPd3YBeaLgfky0CzvExqMljPq07lSEXos2KWIfCAgyZkhN+/T4g6NF9z6Om4yAx6awi6Pe8QllRN7EZ7cmZB7CJ11fDqIHIjN43uIiOC2NEEkdF3JgAR1F289mnO8GBferJ0+VmtFXREdsNyiTTBqBXDtGAiTKquq6xrC05eJnUq8XNVENtrum2IkY+i28cUIzh78Qr87gn/tJxy99o6tPt3dZ+8dMHvnHmA1rQedrNEyOye2n2Y1jd6LKzW/yyLyDmnHcbOwsN6PBTslu7CTTmurixsj+Slnu+mvT+yMpUbPbXTsTrtyvpqLcEhB5AEs0JfwkvAIW90WThreWtwrCNdNJriTObIYg1tZM7t+74B2MVY89M4RAoSQmN0aGw7LEIhdqyjJ+h2KaEJUMrlW8I7Foy/qzytcpY2QKQfO4lb6FFmuuKxjsE+8L82I84DFpClApeqdjPXIbz3MQepKiTCM9577t7oko4rEtfjDQ/1ciKpi6jSuEdbXePDVqyM4SbBIPQsCSwYkCt9/9z5v311w97jn3uECkhXNOjipDLkei5/9zBD5GOxMmQ4pD3oM3JOD4ilBiFTsuUr9xjsbn1e+f98izjGlK8i91BsJ1XhX1Eo5W06MsrjT9O4xs4NDJk88yf3f+RLxv/0uD/7hqzz47c8TfaVEOlQ5b5xaD1R5/HnnxieZrpAYmUrLt65/h85UUV4cl5ttGnGkfgGY15uSPiNGL2N73TwPV9GaBYfp9yyNWnFWBkOFByAhqHLMVRt/0hMkwKWw0oxEfi3lh0Bs8gKrDufw61OGF5/k6JlLRHE40zWVAACq+356f4PL1kmkHNkrzF5OhbbyRPKZv0uMSRBo3Y+4t63fo6FsPVcVEIwx0lmwQ9yYzCtN1k0n/EcfUthWZ5xzSFZ7Qst/5mp7fpINqSUJrLVMsrTONdaFJ08KW9KZCmlajXEZRdWvXWbZbrNsrI/pdMIQEo2XalIr6lq88x4pBJ6/tMG1vVGe11aoIY/l8/6iqlmq8sF52nhTMHS+Y3/tPLf6wzKe05dfRkJk/c7YpKLxahiu/exdLvzg9fId3inoPelD2bYSKCLvWjXGVTYrIkR7jqFtEHHEmIjW9Lk+nBU9yklDtYEb0kAfa2Mro+LEecJyoGlkpOpiLPYtH+IaCD1/9PonZRzzn2NVTTKJlWiOgvRWT3x7B2dzgaQaatm5gtu+Ttq8Mvb9EP37PA68HW4TSLRD4nje03jH9prmFRycBOXD7x3BtFm90KZR+qb7JaoVLTJe3b9h7ZRIYUCcENZ186wNeVq/qIHckMbYl1FEs8qQF0QeK/ngKUOeA7z1kWHN2pe+iG9a+tjru7qW+PJzsKVGVY7m6k11TbXprJ4rp+wXr9X+bX1TecJZJAxpWGrUSITk1iCZveg2cPsvcumV3+J/evU6dX/e7Olk9N+ngEuoXNTp3AgxIUHlh1i99Hz/OftwsrzPuYOfVfP11zHYiazUKRn/YA99Y0ZqrCdiGtUUoIbcO+FSv6C/eXNlO8/uTFEZ5I4oAjGcMuRQjDWAs59311sUOZvbH+0rxMFwwmxxx76L8aF5KZPeVWFnoy6Vi7YaCuKaQq1864nv8lyzjwC3PrvPgyd3+cbOV3jZXWHmp6WAlIjw2f3PcmPrBpfXtFflyfWnOHnlKdITVyw41/Jw8+kSrIpNSzJ+tSBIUyCkfuDkhz/Ujcfef7z/OZab10eqj8S9oyVDlofncrTEEV0Si9Z+vdvgpKqQ6Le3cV1Dswzsvf2AqWVilqJMx8uy4LwIMUiRmjoEtxhoDhZEa2mXXJaH2fVkRG7BQS0r7FeBmIA/XhJiYNjSGuN12YQhrhryFWjYeELfW70dM8YhPBaSSdKRwpilnGVn+n412MdPPcfiqSfLRclyofTI+uY4L3JQ1HvcZEuzNQupq7XiF90ueQRaC5pOuwlToykfHA+4gyPiEBj2NsitBgGkbdXIW8niogSo7l1LHY+7hwb+EsRIe+MGB68+xaOX9umyIX/i63D9a+z+6U2mHz4aH44oRbRG+5ghL8lJQsWRj0fJ/UgRWWrf2JuvPEczm+HEsaiabccnrtB+55t6ukfHSrtNGvOihNMPywmknQ2On3+BkytPkhN6mmas2VQkigBhYXNP4yaDX9f4RLemGbGzHUsyquWH5SnjBHoCTRrtTDLqTmJUr+AxRK4jcuX2HzJdfDKO6an2c6ePTw2RB+8ff1HGZAoQwsjAlZ3aoahh889+zuEffW/14zK6d2DVEgFS4u6xIiZn/J3aH739je98m62//y2++tQ5ntpbt0ltRqYMrisDHZ3KDoeddYbdDfwzV4zHzM8zL2rrNznEgsjD3QM4UZTazjasOYaQ1jqtfT5EzrtNnDhTrSRtHOsnvHrpVdocrHKO4folS9euAk125C4jXqQY93rJhIcPVWJpf1uuXWSY7XPZ7/CbGzdYhjFQCZQkE9WR62eaFOx+hUkzo09h1TBOJrTzgfV7J1z8+R3Ovfsxaw8P7dnEYlS9U537YEVYnIOd9zSIFTanOj6NanBLGVrrDhMbrzr2kEhNYwtRhyJszKxcMgQz5ILwD/c+z/Vuhz4O9BW1otrpTK00DP3ApNIla8wlUasWk2tJQ1VuolLOpEHn37C5Tb9/EQE2/RrS98ykRdbMe8rp8s7z1t0TFkPiYHlYjIMAS7fPcLQLQOcdTa+a4yaXtRDH4XzA3TtgkQbS7horh/GyePO6YiTlojfVUTyanOlmHLkr865C5DKWI4BxPWlin2ftQY+zOEbpDJRLGawg8hKJ4aOHS/oQkds/5eDRISfzgaHrcE7w4pkPY8A6pFA2PEkJab3JFmXlu1buzTn6CxdUSGGSy7Zqg1hAY0rErJ8XVVUdd9d4sPGMDkKMIwo/A5TWqpUmx5dsk0hJqRUnAlW5jZgSrTiOWbJIPXd3Psdi9/n818e+Y4h9cGsAACAASURBVOXefuVf/ysesTQ6GA+puK2f3z6mRyxyPHLB7cmC7qhSH1RBmzztM5+0+6//CICD/j4HC5O0ZY682nnbCxfwG5uMV5SQZgwCJgDnxg4pop3r8Z5Hrz6PbM4AwfcRX7isDB8TREVcsuiZ/+VPia+/TvIeZzVUBFMmAEOfOwSNqeY171tTAxrstIxCqSs5Qp8DTk7Gp1y5j2mpbfNyclYSAd+y5da40m3xG7MbPOMuVA8na+Jj+R5F5PrM1jqlaE7CiMpl2tHlhUxi/+YnXP35ewBc2tgv9+KddmpSD0QX4vTBnH53yvzpSwiuqIvyUCzPXyR4x4mpeAZT4AjQ7O/z6NXnOXn2CssXnyJszQgbszKWW1/6X9htZpASJ7GqA5JSuabejFjn6s3MyvxW0zY6Txr6EstxFRhJgzUU8J7YdCCw3+7y96ZX2W1myMY20XUQI4GW5B0/+eiAO4d1HaKMThxrb7wLCK13PLU9VdTqGu1sBCSE5v4RDycJ2q4iqSDN54bItaWcbh6ndPUr2b4VTAwRb7kLyQmdVLLNGvWWrEyPeMd5v8lzf6P1vUdqRd//+uIO/+yDP1xJ8Ekkbj9c8vYnR8j8AQcnPYlIMG24E8dJ1eE+miQ5to1SF5160z7bkdOGPO8zySJPSUFJbchLPh+JITeiNl4bIDilNOn7FWp2jEPVnpkYEHPjmDp9ppLljlVcJwGf6S4wELmdHvFg6/ky4eOvqyFP/gyO/JR2bB60urJLqci7JscnXP2rnzFtJjx+mIHJO6AFdFI1CE1rn3Pj+/X3KgszJTOyjK8hJJPT1U1XSaEE8Xyf+23myI/XRIOQjEfUMqIJNKmnKmmqS0gYhsFk3VImVJ0IW3twTsZgj1T/B+gtmKxVAOTUxBbl6WNlyNFNYtFp9TaPp9aALJ9SKZcWKPD2nsGoFcekVUN5UlWszK3UANanDVNTTDyxeY3fuPZ8mcCKyMfqhfkml3uzMl7lzuyH4dwF3vjay9y9rpvNEGyzdCBNQ9jbBudI++c4+MbLoz7aZJs5uHVwSpKYEenSpK2dUMYthjFTOB+BBpf64qansoEbtWJIOzYTva8I68Fou/UtFt0uH+99nePughUv0+4x0ds8NYaln06LhykCzvo4TjstZ+FRoOGWS+LUG1Woc6i9dJE4tx6r3qkiJSbNZ6h8XiqPhJojH4YCgHCyypFXAV2XLaXx7+IbLq9f1peqmugAPzq5xRCXLFNYtQJia2DcUkbv+BQiP+wPOVgeaNlccfSdvr8Vy6B8DCjmVZLrR1qxAycV5aNj5tamLEMgTlurLZRGWhCQflgRHsC4jvTXce1Mlxr/iJY9GhM0Oeiba/fbsK27sdYOjMo8eTRmN591fHqG3J1Brbi6qp+qA/wAF7//YTHGguPK7Ek+s6sFc2rVSjmEFQ1zrtQGoyFPtXWEkcuz/2dEnjW6iCucdZRKOhmD1m9BKRRnG4F6Bq5IrCREZBhbbaWmKYX/YdzQQ7AkgGJg0wqvWwfzXK7lYEHM2uWfm7Ftq5rr9cTWPpVpvG+Tzd3ffonhc/8zi6oGebh8ztCOGfIsG4yDBf8caxbJP7FKd8f9MYvJeEGdH5Fq12jp0BVDHnK8rZKPrnUjILUXm2u53jMrRx8jYTrDITT7Y1LF6SSx3HJu5hS9/eLkVvlbzGncwDKqIZjKqCRKGZFX4zg4r3XGM6Jz8hi1kpzTgleWQViUGUatzNs96yur4zrEnuPYwI1vlK96dP6clQI2L5LIixc3eOrCNjitTR4NNKTG4dZsDJzgz5/XDNMhmPoHM7YVyktoVmY2SHn+mYfouwms7Wk262x3/NhyyVpWwhSVwaAlgMUXumVFfph35hSZp2El2Kn9AvpVRU1hexx9FYe5dXyLH935Eck6Uh01uoFcnp4bv6s6SoPvlEhxYHZya5xIKxhC6C7tc/jCZwgXtpRaCdr8pbZDWWY5fq46T/W/2VFP2NzS529JWj5ZApirk9hWm4PHFMYA8q+vauVsQ34Q5hwlrQSYJGet1chT2Jrs0ubqa0O1S1b8qGesLV4z77RVZ5MV18j0rtkQnzb0VRfxJH6UA6UBbxSJHyJSPUkBctNWTVmO1iIrEQ2Ri91TXl+ly1BVfmDFeFdPLAejRoni+L6lGebcQT7znXbz6mbHWO4z2dgl5/j+B4fcORyRdX3UHLnPvKfzTBrlrBf22g/v/JBfxNtnniMb1zrYGSKsv/kRzglbfsITbo/zs00ytSUIu//j/8Dkq1+zO12duiEmwuY23Xd/h+kLL/C1y1/j3OTcyiYI2buCmevKeGxZpmvKnhTQT6YIidmjo7I4Y8jQYBxnkUYNecWR57/GEEdqxbVj8PLaN5BLL5d0eQlBg53e49uGSOCw2khFhDBtq6qHEIae8xsdjW9MbaO0gwwQr7yM7D2tz1PATauO9hmRhzSWlC2DkypErjMzWb0U301gugk3vgbdqBab/+znrJnSalm0woHUD0qxTJWrr4Od86R19UmR104+5p8f/aTqzOVYLnvevnNUxrHEUuq8Dzd6e6lT5dLm2jqQ2Gxmq/O9jKP9S2KSS2HkhKW81my+LfrAsD5j1nht92gedc0i1Ib8dMTS5VhJSswOl/Rbu+V90Qz5abowlzTKtXZeO/j3RYzwa9khSL/Z89geI45/f/QG3w/vgFgtC1F52thOS1bQmJis4vDLz3HnW79pLlmyspOpGLry/qxfz9Hm6noAHcmkhZJy6dqU0H6S5HNVF2BBD3HghpECMjOrKhES19stvjm5PhZ0mkzGtlZiOzxCyAWdal78FJ0yvp77PY6bWD4WQWgnHefX9nhm51nOcjVTCCNiEjON4vj44Xz1S2XkTmOqEHkabAyluIAZ8S7DkuOtcaIPW+PCy5MuvvUOoIi8OTxg/e0PEXHMaJlJy+XZNtka5cvJ9MxpnXSmZfyOJohc27zGd5/87or+uT6mfryebTPkMY2JNO8MDbQNszffQ0wiGEurtMSz61/l2uwlttt9S6WuNsmjI5oH9wrtkJwacsmIvJkh2xfxGczEoBu+eLh0hcNnLvL+5fPUAo8waUqCGMA1v6uJP7bQJ9Kw6I9U6zyd4rx6t0nArY2BzwQlWQvrzwmscNX5PiAVRJ7XzVlqsyvdJtt+yrmJGfhzT5PWLsD2E7j1DTt/IJ6ccPKT17VdnAAxcnN5QGjWtDF63kiieqUnz1/j4xf2qoKI43dPKmr1+IXrzF55ia8/81n+u+t/v3jEZwY79WJ07ib4wrf/sT2DHIRVW3Pc97gUmbaeZIh8q3M8e2GtvE/qXJgMiGLU2jSHS0SgO+5pk6bgV4OLj2EVSGKXbXkvA4EhLYuaLv7aInIHpw0Lvhk3NpGiZa2DnaexWDbkyTtTNhgiT0krHtr7+n2t+VAnBK3C24ojB9zhLZhsls9/7co3+KJ/Qukaqa7BUtURp6nEdYLJEEq9FUdiMzbkZsKxHRGh5HK4YDWwQSrapJ7AtfnSeiyxIMl6114MkXZtihPPFy99WT+ZzjDkORZgaedJhK45hWKrJrORQG6aLXEM+Lh2hhMh7D6p70uReRXGePTyBR49caFcN0D4m9c02cQJs9sfE6Ia9SybE9+AyMqmNmntu08h7SJd/NXApRyd5G5UiU3jo0MKo2srDvfkJeTwmMmtwzJeicQUz2a7x/7kSQ1+xaEqbSAs/s2/ZvsHf2ocOVoSVxpFw1EpF5wviE5C0C5DXmV7O5/7PIcBDhd92VjCpLNcCOFiu8k2U4iJ9XaNz65f5mq3h1/YNtM21ZwR3EZGncB0Qmoa3VDCUHIgxrUofGb3OdbaDSZ+QrRNTNqWWTPjxfMvPTaWrXiene7RZYrFN2z+k/8NLDEIEVLsefDJB9xbPORuOLZaPgFcwzDZ1u5faMKVJFWLxK0Zy/WuzPlcOKuRhraiS+PalLUXXqCdTUsDirOOwlKQsyZB8oawEnR1LPsIRCbm8UhKTBg7FQmygsideVdpOfDor95g8f038CHRHQ148VpLBSviFsGlOEqcKzolt34ZSq3zLDL4pbel7/vVf/6veJwlP1zJGKuqy7EatV1Bq/1YhGdEq4JLkWVY8HD5CYuXX+TwC8/ytUtfqxC5O4XIM7UCDCfaIWd2jmza19b22XEzLohJxoiapRY1MIOAC5H04P1yDRJDKU8KQlqOjQViN9FgyfY6j57eL7GRGAZO2yJf3+8pBUveeHSSjJ9ZDJGJudQlNTmNi1W/bESgZaxFVHJXfU/0Y0wg1jpy28D0Ihvcjb9HOPc0YGWDJfHhK/t89NJ5xAl9V2n4UWPsD47xIrjFnJSc1W5RWsl5MwJp5J0nZ1FyjLVa6rE6PV4rrztDpQk6GdurXdiccnl7ShLBX96jm23gFoGJa4yjTtQd9fIm54y71dRvC3weHqlHWBC5PQMzoHku5jwD1cEL//jF77DZbvHRwxMePnmO+69cpZ9MzNiBdE1pViBOuNRt4cVxvXmGiZ8oZ5xLpgJuvUqc297UHqdWQ0VMVtjs6VwH2Oy2+N2nf5fGtdpLFTXkf/D0H/DS/itnjiewkindnD9Pe+2qAhkRXvvwL/gPP/4/efv4FveHE0rw3YKLgZyFPdb7GZPg9JwT23Ab16zQLFCh9WQxjDOee1aPSIrmTYpmREPJk9ASDYnjhVYt7LSDBO2i58ZHn4wlgnFjSWAo3nU8PiE8OgaEpu9Ze7DAtRPWw20my7vFp/cmBa3vTx0Jb4bc7svek/4WS/3pGfIVzbgeqXo4uZtGqVVR6U9X6IXSQiwjN5PzGd+0iEdq4Lxjd7prfUHV5VzZOOrSusMCh7Dx7e/w/pc+QwKado2U4Fl3kb1mXwf//T+DGPDieLj1wqn7E+UKw5ggpAEgVWHEboJrJtz95n/P8txuUa2kMKpWwDQbK9RK9RUo55pSVraMfxwiNLPuFERNbH/zFaYv6bWmELT7kI1ffmtrkzcfsX4uKZzierKmWOmVHJdISfsSDZOGfqZZmaHNG4AtUKB9cIj3ggwBj0ecaANcDSzYuarF3Jw9ZfvSr/OUIbcbaaRZ/YCMVFOeiTFFvHPcOL+uzylGntx/jpf8HuebdUWxwKIbg6kxtzwrMsZx7OK9eyY5cyy6HYbJNsnPSmKOz4Y8DNoC0GnQfL2bcG1rn4cnA++88F2Onv8aadra2Aoy6ZSDzqVvJy0iwuzmTc5NdwsiFyN86sYHbG6Qtq+QLrwMzRrSNGy++jwbX//qSEnoQOrYL1eplTMBWB7SU7SLeK/XKJ44zInHD3hqbZS0dgiZQg2WEaHNHYJ54vlR6XkzLdn5bsVLdbjSYKP2LB67vnrapp4olS6r0pHPl4G3PjlEiExmG/geNrbO0eT4gp1+NdgpWvr44dimbfvtj5k+WhD3znPh7p+z8+gNIsqRuxhGjtzeH5MWpHMIh9b7M99nndNx1vHpqVZkRJPBeDSp3KWEW+nCfnkn++lnc+RUFLEacv1kIx1xYz1/ckwIcrLKURVqhfJQ28tX6NcnkBKNa7i9+yVuXfh7NNKOHG3QNN5gaCH58eKStHoPXt3DtLQgD4lkFM/R1rMEr11hBDE0MjZuzq/n47RqJZYOjqvUSozQTKdlQ/it7c/wD3dewHUNLqdph8jMfs51IxBWmxrk+yjGLvDMhS22Z52hleyv6jVnjjyksLqWBE527DkXQy7IoldEHgYNHNY67JTf9bcb8twW75ch8ozm6gv63f2v8Hs7L45UjymMNiYNCae9IGfrnI8TPFqiYdFuMDSbo8oo1+gwQy7O4WbGuT+4XwBI8DMeXfoySKObtfdjaYe+LhSlP+5aOjziS8Armv7ZTRpiLrA226F94fMsn36V7sMPdE4Yr3tqby/Xh/fI7LxtKI5mc7bK9zIOeHigQcFCA52VkZ2PxwQCjlznfPr+AV9+PbLbrOXhZ8LocUUil+98T5PqrBNYnnP5G6cWUPfiVxC5q2urWDLPWUf6zO/mn3BpULqryiStbhtISApsbO5wcfdpNs2GhK3RlkhVhpqX/hHy1NcJj47KSy/P95hON0hPPVNeiwZSfIplsyw68qSUXt9ucW/9ht5rRuS/thx5q+qNxeVrPPjqtwDbwStz/MTWhPWJ3sjmtGHSKMpYQeR9VbtBpCD9NbeOp+HOc08TtzUA5sQhVilvSeD+Ytw9V2kduwyfmzWonvNg4xmOZ5epin3CoM2ak/GDqUokkgQf7H2b2K4jQFwO5eHFRlGUOhFjjZDp4pMigRpv63FD3lQp1GdldoLQzGZls7rQbbOVI/154oSBzVnL73/2Mk9d2CyTOMTE8ezy+Kz8Kkc+aT2/c/EzfHn9ahk3Ec28y4i8DhyCehahbXjv5adIFpAU0V6W3hkiF0XkSTLrkaH447Vm8hh85qJSXblp82lEno/Or9I6iGPDT1j33QoiF4TNqfKiG51DphPislfNvjUv1trf9hlZNeQUzxBgtea3uEb58aiIPBtPZ/RF8uM8OLc2s/GORjcZTyogk9YQeVKu/dnvwme/Ur4/tQ3pFILb/oPfZ+v3f09de6f3ohTPGfP+1DjDKvp87Fg7B+eeXqFWoEbkjtl7D61hMTy7e5nf3n6O1p4DYNLehEtBFR3jpZR5OXZ+SqfWhGN18p9BrZx/Dr+puRCStH9qksayFBgROdotLL/mxLP4wtPlNKVmj7A6dmvnkPXtUVcvwma3yZW9J1c3P2v75mIYn391qSklgp8RsJIizmnw+NeVWuk317j70gscPvdyee00tbLV+XGRMfJmK0CvQuQ1WePNTewnumGUw3itN/v7/D/v/7vx9apwff4e3PjZxjWVRKryCpZH0G2UnpJUk1kAGRZlYqibailDlpor1fnyOVWOOCLys469DdVY58zO028WhOa5z7D+m785/i1XoMsTy1QP22ut6rrtOoeYOL70FU6m6gZH160gcgc8vXaRpyd7VQTpcUR+al2TEpxsb7D8+qtsfPu3cLMZRC1B4IYBJ81KklMe7dNe2Hde2Of3P3uZLz25W17PqpVfFux83JAbtZJSQfHZi5i2nq/cOMez+2u4aUdc9Hhc1fhbaDN9YkjfW2KRcwLDwPzak8iXXqW/8ex4/9asIg2KyF2mVrIhrxb8hqG9lLQmhxPh4f6OjoUZk9T3JaHLnz9vnq2QOgUgSNVfa30dn4OehpSVWquDnb8C9Z0y5H63UmHsvwibl8825CGWUrZaKTJy7bPPs9es5ZqfJKyGT/5cirYeVzfv7FWdVthIvXhOx4HKm5zJhPUbXVyeSuwbqZVizpP2Ku0v7nL80hPASHGc1pED2nS6OpxA3N6kHtfl/JiTZTDVyioij3kzQwgm43UisL7/txArnypHLry3ecgPHv0rYlZ61AMrY9AIsECT/akWm+Su3xkJiZaGz5nVg3NF8SIiI60gnCKcz6ht7pXbTSnRSDNGjusLWB6RJtvFE8ALj169xqMndnVzWVRFfpYDMp0iX/gyYTOj0vG7iyGXUz9Xk/LYPJDzG5NfKq2z0WCysU570ThJ3xb3caW7d/6iyq0fwmpma6wVAqdafZVxOwOR+1MLO//aNp724kXtkxijxp1DZLu9wPnpPjt+Vk1ceWwSX96eacU/xuWamzb7X8KR1yqHcqQEppDI15x/3l2f0oggE6PWfniT1AdySk4WzwbLxGssdTzhYOg1T+DqdRbPvVimmTQeq9erhtyCuW7I1Iov752akVDVkjby/viZa7z7hSfwO+vjndtD8yI8+MpvIt/65ioiP2tjK5mdQ+Fpy3ic+YFVhL79B7/P5ne+c+b7WDs3/uy9NuMwZ8XjSoATDGyJdlrCqfciaH6CejrjVg6jIY91P7TxCu0eImdTK1rXG3FMlvfxaaF8dAYNVbBTX4DGWflZYHlxh7AxZfnM1XI9p70Z13WAFMrMiZC2NrRlW36PGWipOHKgaBFSUg8mGiJXZ8P9LQn6n6YhryQWwaRAYxFTPaQy3rX8sDZsUolt679kRJ5krJ8hSEHkj2V2mmEad1xF5K/sfoPLs6dX3ip12UogTTbJXceTd/xZ/yE/WL6n17Ocj9ecEm62hlx/0s6Th6JSgYwXtPKefDx9foPnL23w0pWtIj888xChbaoPuzowY489RGqOe0TkVsc8o05XceRE5SfzputWDXnmmUMMq3z13rNc3V3j+u4al7dNTeO8IXKHCz0Oz7XNJ/DiCE9fKVJPWaErHrtNu+ZTC7H8PVNRZ3DAtuizJxcqo46o0ckboT9clJZpIo42c5ZG1TXByvVGw3NNS+5P2lTty1KISq9knbf3IyKvFnZpbUa0glECTghdi1ubjrJBQ3XOYj6ys2Of08d3qi2pUnHemeIqjs/vDOM3felxqSEouq/RaHvtqv7w3D+A53+/eqODmNj58/f01zzm+ZkwctvRPIK1+S2my/srnaryMyybcabd6uP0/Hjsd4dzwuCnrJ18hIsDwVeJUvWmIQofvnh9uxQLS5OOg2++wm1/pOUfeNyQ5zFxXcP0xmUmn/0sybmiaNJTG2ViQfDx9TE+dzrZDfc4mDl9fGodgorBrYKeOQGoHCHC5kVIY5bmaY48w+RcS1pldLlqoRC1p5N+VqpgZwkwpFXXLF+fOHCO7e4cm+25VR2nuNWM2clWQeSlC3huTLFYrLjMbjpjFU4IqaIuQE34GBBcfYRd4/jyk+fKWBQdeTlbXsRaXKkcNbVQkhcqRF7uXysRilWUA4jiyBUh1LA4yL1Ta0TuRkOeFSAADzf+3/a+LMaS6zzv+08t99Zde9+72T0bydk45CykSVqiaVGiSVukbBGxDceGEcB5MZAgAQIZBoLkMUa2lyCAswAGEscvSRzZMJDYkWPDiHaLiiXLlIbLkEMOh7PP9HaXqpOHs9Sp7d66vd3u6fqAme6u5dSpU1X/+c/3b8eB2iRcsjA7EgplZtugINDFMNpTM7CsNbCnjsNvBQBCg2maGH927llsbNr4xtut0NiZoZHHA4gAgvL51xkVI5OiEDpWvYrS3Djo2jpYW5iWCQQ9R1oOeJfDJpnMSb2PtrSvcMCxCO0uBI3R7gKMQJZwteTMAmvJ2qMs5HrVxKPSBmjunwiOzWA1atJFLdTIxW75/pFJT4Xg4GH2w64P8mJeKMYweWdOw1mYB9/YQC/YY2PoXP1QrPoMe4rWOM1qQjzQdJCtYheM/nqtW2jUy1hfT6ZtVRr5VGUK69312F5DI08Te3JcjrzwS/jm5Y8QBO/BW3tX9yVOrYx4NsoWl+MZbW/t/Ak0Fl5OXsKVKSVcB97xBeCxk8A7f67tJ8tsAj/ABixVBCahkatVSfJt37cBQRyi8+cfGTWW49GQYfIDEUJbqmvuFRTjyDM85RkX8ThmmlcC6YK7ysPDj11Ta+RWaNRQmpVuGywq991KSE0oIWmLZTJrt9AtOWHin/HxsO+S++WksrvpHfrFixs7I+NDYfbDxL6EIDeoBc2Rh0mziDGtnHcDLjlz6UrIbNhkwWUqf4sFyCRZRmfAiOHm5k38wdt/oI10AqJ/i/VF3TcAQtsJuE6z0BkZR+3nvghWkUto7VFACQULAOZr8xgtCw1UuR8mvFbUtWIBRDDz4MhNPvfDsZYauUo0xXyAugG6coKWLDS8jz+E/d23wWSlH7REnUUu88FzzvVzIMuWGnlXFhQAOqPjsNbX5TiH+dRVfzkURy62BySKZJORxAowJjBpFePKMpoGplI0+EZAkLhaHPboKJy5ufR24og/JK3AGIEvPNC8vE0MG50A9za64BTGTDfLDvyJmDsvhNfK55Y/h3NT55IrUfNdS82/JPowO1pHqdJEYMv3Vx1r1hKFXOHIQjAJWFZobzA3j40BINhNQX2pZ8KCNs4/MorH6xN4ynsSBAbf51FBrlW2FI0cyZVVHEPTyE1VS9EmNzaiuTk6Y3XYd8QHstlt6bMiHlCGoc9Sy3AOUCCi/QILgCHImW1L10N1esZMp7wxoISl2fOYldySme1IUTYc3BbBHay1icCzxKrAB+zx8cR7pgylREI9ITLcD5E+Q+u+xVIQmPvcLI3cFGq6qktoKu76XL7IPHL8YrOK+kxduCzOPye2V4zVjjwuXuRY32fcSCU5curKSD7bhmMzqLM3Oz78QH3g6WOgy/r18VpJCnLSglzduZnG1qReyGIiCCjgaDsMFTCUgy4AF+6dGwjIAW20wG7fR3f1/4LKNrhlazdOLWSVK1lX8KNEhNbMAkrXP9L7Ve9nqjMgECZKS2B0y7gvka2PHBvoQHtEmPs/uL2OarWEURA+edDC7379fX3bH29+jPbHq7Bv3RTvz1QDo/Xb+HDtKibv3ID98Srur97GLeMchelGCT/5+HTq+KYhEnynwAP9bdnE8MHdTcBy0Q1EgJPtt0DeOI7MHUdz/U3gQbTNhtvIuppsP+N7ptivavWv41RMjVzOjzyQ/uldLQeAFFpHwl1chPv6K8DbXwGgnjvBCtqwmUyjLBXHbsAjPvlKYQQXKT+WxiqRN76fRj5UakUNWsB9tIMNvHnjO5EObR6bQ3t2DMe+/QlubNyQ52Vz5CAZ3UiQBWOVkSAUNkQMftlGUBJX6vIuHEhtdfJR4MNvimOt0CjKOSIaOcAifDazS5pi4DGNHAACi8E5Pg/rvfuwp6YBqbxJ2Q/OXKGZaqIMBnWd/QB1rpX0vXAMn3ZTkEc0fH0hFh1XRpqnVC52VctB4InitnDKwNGfAL51I7xEmvbSA8xyoHK1AwC3bDhGVsS3b6zhsr2K5bqVqpEDoSDvBtF0v+GNpNyz2iEzNSoh3+XdKEcOCGOoLF4BAKvjVXh3LZSvvQ9MHhPPgNmChlprg6Stz/c8/d54KvDDNCzK3/1KGHXJjXv3bA/nRl7G/c5NMLptrDSkwqJSqBrGTkCUe7t2ZAsxXAAAIABJREFUbxN07x7mAay2u5irOJgfFYKker+Km9fLmPdFwQ5r1EOj7qE+W0OVeeg8eRy16qOYnY8KzFurbVy7t4mOH0RXej0Qut0ZY88DmfUzEJ5AJMzGbRW1Ch9kl2BbDPOVCqoPSpiuJrn69NwwAJT3TQpHHh5qZATVGnnUsCqymAYg+dwtstDl8UIYaTcdKgKWVOqER5MnKEspkQR9GeuT5OIIDOM1NzLO/YydQ9XIw6EO4POudukKDyEEXkm7jqmZNDpVhe5GSntl4Oh0xMMMTA8XeeL9J6aNCdx4KI88C75+A1h9TwcnSbEc1ciJRVehRiZDHSlpW1r4+5YFa6yGxswcUK6Ar24abZHwCgmgXwKh76cHBMVHMI1a2ayUQcRCIxsQpVZMgaLllvJpV0t1lQcOCCSlUmYOHiBFu5VIbNcBV3oU9T2L3VIjl55H3LbhWIR7j74BfPV3ME8juBI8Ao/PZ7oVajpI5mxJ7FfUChheWHgh7CMR0JEugwl1zWg4EFnqmKT0xibrOL58FHTtGjB5TIR0kwVrowW0OmBLy2g+/zSC71yDSEUqa5UCUQ1MaquBE10pmbfwmZNT+O5HG7hLoXzgJOMhZByGymCoZMKmHMsNPwDnHJuMYXGsgtPzstr8rRroAw8LXEwgToWhNuIBMw2gUwXmZoGjn06M44d3N3Dt3iburLcxVS8n9usxNWFZye08kA4HLcGRM5FPpxP4Mjs4gTkeCASL2Xiq9Cjuu+OII6nAGII51fuGEr+RPH7ta19H+8oV2VXhR84IIjJbrpKnK9MgIlxdvdqnyEP0upZvVjQSExUAjFXd6PsAaH0fKd9Xv8jO4Qlyo18BfGR6vMcn1vgGJcjJ1LoIflfkLAnMb5Skoc34WuIcuZqplUauZsq4Rh55N61yGOhjaOSCc+UILMPLxXLAuSHIYXDk5kY9s6dpk+H9xIX425dOokuExKtvCnKjvTDfcfQaRGHOC+XvXLUc3EC25p3YPnYEC1YZs7X51OOVV0OY+MyGbTGxwoEQWLPecbQ6QNVNb8Kkg+KGzsiliGGyMmlsIaAjjHipb55eLnZBFoMt763eKMOdGAd//zrY+hoo8EVgyfWbCCwXqNV06L3iyHW/DC8HPe7Gx2x6rQDAVL2MJxZH8WdXYSSDYrCJyajCtqhUg1AjX2uJZ7bpuFh77Dg+un8djbJZ0QehWy8jeGdOAh/9RbgzA2MV8QC+/+F9XK2IcbNuiyhG3/dg3V6Dz6vA+3f0OY17m8n3kAfCvtJpSc8o4S/+gG8KV0sQyC6DQFg7+Qt4953byTaQppGrp5hFrZjaL8Lny7kW4oCa1EmshHkoPhljmKnM4Orq1fT2c6DFymDwcWq+gaprRygn4dovq31FVQsAQMD2qyAnyYEA4DxAoGzwofzCidET8P0uKs796LmmdhwxQorZlAhSw2WSagk58jjiRhNlVAi9RsTlTEEuuGxjPWEKMKUFW0zkj6ZoZGR8CIgALn2RzdB1w0khEwTCWmdNL/kAIHBsmXslrh1lcOSmUDfpIgvaq2NlqoFj1VH85br4cCc9UyAa58Q1CcuGM7akiCucHD+J1c4q5qrCeCY4ci4FC4GrABlS7ZndTB8Ik1qJZ200kTg/ttTWv6dSK0yn6Q1sC2xsFD6uw7l/B3xzUweWBGDgZc+UESIcmwgWi1MryXciUTzagK2Pl9SKLQS5LvwgB2tD+myXrAq+jjJ8x0bTiwXzbAh7U+X8eVh1o7ZnDz9yz7UwWS/hxoMWbjwQ5zsrZ+FXqgg2LODCZ4A2AJkpMuAc1q37eDEIADtqGCfHAzp3YYNEqbwAWIfoeAMeiIT9wLLsVO0UEJ4rNzdvGo2aVEk/agV6ZZ/F0Ci5otQ8iyz9DvQEZXy4y8/jCPsrvNO5i4os+mK6Lypjp3ACiYc99ppiBYaokYeeCEIjF7/7s0/hdnAfREDdreNI8wiu0neNE9M1cl04l0ljJ+fCb1dRD5Q+QAmNXGptUa8VRN0PDUEr2uDSe8bIQEiEwBJXixeaNr1oAOGnzbn40LuoAdQSaU2haJZsjdwU4qq/ymAagaGRRyLwMgSaRSG1wpgDiwgr7hjWvEldnSmO+Fg6zIlUdKm7dfzk0k+GB8gIQ+r6mKh5uGuH3jsfzLyEEVoN+5Z6xajfeBq1ojS3VGOnOs9oPaReFLXSBSwmy4cJ+ofV6/CJYK2twt/cDCOSiYGaI3ocBbUi3Ftd20zsFHsGqq+MZT5rV/uVi/4qjlwJcnXvnY6Lo9WLeHJ+Ae/c/xu4Xhg8pdBtVoFbwvAOKNfCfqICeOlk3NC5mHnsB7fX8dU7t7He9gHLwebps8C7on6nUnaE26+NmlXX54kEWCIDYC8l5uT4SYyVx/AXejWhDs6gViKpoMk4w7hvg15kyj6E8P3JZwPK6HRlHCPVCs7OdvHgPdWl6OTCAxHoRKCkmOvzfHbE/ZCIXiait4joMhF9afAWAvhKI7fL8C3hVx26jokBrJZsEAhlJ73bJn1OMg+F8IgxgoJib8fd1t3I37wxDcycBRZE7gptrTbGkceEQsmWEWK16YgaqQR5YLGIsDC1gJlmGWCu0KiIoe3UwYhg6WhUnvlurHfivrTGWMRPYlnUiinQjMMt0/1Q7Jlwqnhh8QU4VlQwKMRXNyrJUWYflUbe9XF0soa/9WMruvdtdwTdkRVDwcmazIw+pxwSnzSNM8PzjN/DwCGlZQhjp/qIg4oD23IAtwTqdMA3W1oj37j4JHhNJQYLNXJGwKdPTOHEbDO8fNoqzbISz1pNRJ5097QUX+9Gk4ApaqXjcyw1Z/H0yhROzjUxPxLVhjk42kvTaL72eVjNJhDb21N6DoCxqgswhrW2pM1co84nqTqsBB8WTHc7BtIaeZYHEiDeh0apYW6QjXPhjZQWAKZ/h4xZSbYfMgKi31VZQWisnKw21Ruxtu2S6JOp7KQoeHLaSLytu+5+SMKs+28AvATgKoBvEtGXOed/3ftE/R8CBEYeE9Vu+PEpratedvDk5EhY5DXaEXmcmFVPTdVx5ebdaO4UJD/ob1//NhxysNiQ2gUHUK6BZDmrUCMPpS+PtdL0HDy1OILvt0eN7IeEri98iuMaudFlnJxtwC0t4M1vM2xUlnDTOoOp1l/B98aAO735uHute8k2FRkUf/ARasU0dpoHEtSJOnc2IITO0Rczl7kK3SC6OvAsDw/i/mMmDI1cBMlEI1wtxuBaDK1ukEsjj/uQA6EgT/UjBwC7JApk6Das6H4ZSq1yWYNZqLt1bLgu7PsPULOB9YkZjMx2EThu6LGJkJIjEoKt23KhrkRpedUpOd2o/qti44wxWZd0DGVrEqWjIrNe2bEwWnGw0fExFxPeaQjLv5mUxM6hWrJhVzxcubWOa5tdtJo1vHvzXQDAnR/dRmPjNupjHB3OorVrQWDMjtCLmR7CqRQhF6uohCCPRlFqBFHlQ8sJqZTMeJP47MwTaJaauLF+A30Ro1ZmmmVRbtFyAWZJ90NJrSg5dvcDWNwHDywAKggpxhzMnMKmm146EdgZauUSgMuc83dE/+n3ALwGoLcgN6YYzoPEsly2BSD6wFKFuNksAOJAxSbYJUdamMNsaWlL11ubt7Qgj1vDzQ/S7Fe8nVrJknx3KMiDivhY2mU3cnTUA4ZQK5WA5WfhPwA63SbuTH8WaL8r92dTKyatMlZ18eHdDYzXSvj4/kbyjGixz+R2KXAULMvCemkM5fWPhZ+8rPzTC3GN3CzHlQZTI4/ndgZEdkPXloI8Q5JH3EC3Qq3YJdhc/BTXtPV2AEBnXdAghiG96lTBHRf2/RuYGK9g4VPncP2dDdwMQk8lVaDAD7iOcI0EgBj3e//sBbi3bkTuPXmfYrtlyaKAxOCdChPOWYzwU2dmI+eYvs/meGS9T7048q3g6ZPzuPs9Dws4C3bkGMrv/g0AYHSyhttXCbdW2+CWB5u5aFRc3F5vg4FwdvwkqDaNbmcLfeFSkDuV2A5jFaoUPiAhyPUxOgCRoVkKs6f2R7TPj4zJfliO1MiN61mW8Jy6/CeYulVCe/lFHdmZmNAr47g3MZN51Z2gVuYBfGD8fVVui4CIfo2IvkVE37px44aoEC33BTxAwEVBhTFPOOIyUyNHXiMDSW8LDup0QbaToFbSYAqg+IevPEPubghdynMN1poImD+vThR8uBn7cP407l26hPUJUeYpXqMvXH3Ij9SI5gyvn933C9MX9O8V18allTFU3DCtbPRihhCJrBCyBAew5s3h5ug5aVjrj/hkPFoazThSXUTkWqGOH7lGqJGHZeeyBXk+jTzVjxwA7DLsc78ALIjnqPN5qKK8rQdaew7KFkCEmlPT+eQBoDIxAYDAydZi02KETjcQ7oexgCAAoJKY5H/2qXl0xqewduKU2auw/zpgRbVrIS2lRBpUwNiwMD9awdyIh7kRD8cWxjFScTBScbAyWcdoxYXdcjHnPY7Xjl/AiOdgtOJiftTDhDeJcW88abCPoWSV0HSbuDh9UWwgFgryuGEyrpErY2enEzlMpTAgpSQZ300iX09PpPTdcgBD+SLL0lRLqXVb/07xyHEAnPu4emoFWdgJQZ422om3h3P+25zzC5zzC5OTk+jyTkhrkQ+fd1FiJSw3johGDa23F1cW7YgmuEAdX2Sg4zGdhCC47MnQYGcKoLQAm7WWj6+/cxsA8MyRcbzwqMwo6HiAIzU3aexU7ocgAF4ZQaOBAIS7mx189d17aHeDFK1frhZ0ThhjUHsEBK00V1BzoqHC4eQXG7PIi21GdsprOw4YAb4UMEwlASMzR3xvLDeWI39PVabwzOwzmccrgx91uhENNRRahiDP6ENkFdFLI4+/6upQJvLCqwpCmlpRleJbD0AWQ+X0Udw/LRNoMUsUzwZENGZ9FItjHqZGqloD81wLq61upF/mBMrK4nxVTDrtfoDwfRz1XEzWSlgcq8q8NzukOScm/J3TyCPNmileiTA/6mFxzMNLj57H4ojgukc8B5PVsn5Xw8eZ/g0wYvjs8mcxW5s1+t6fI1cGTQJ0TVJxGWULC4PhIkbxPBp5ltcKIDXybphO2rLC6GLeNS4vBIBpXM0O/JNN9+9ZX1xF1Hy9AOCjfieZBg6lkVvMhqGnIpTLGQEolZALVP7f2n2n6wOOC45AaLUmtTJxFKhO6HNNbjc0N6j8ENFrWkRw0jhvGVrb1f6eYlnLZHXw63eFd4P6uIFQS4zkd1B9VNuQbpTRbcS4/zBXSPxAo8/GTSmezp6chHfpaawdPykPMY/J93EvNZZwfvq8cZlsLwxxgPxg250wUhFhJkOLEUq6OHR6E7o4B9K/nUyOXDUo+6Bcy7TW5ZTFh9cSHH9pbhLPTxzHMxNPiP1SMFG9DnLKcG0LZ5cmdRCW54TP2k4R5ImCDrpX0ZvQ/WcMK5NVuLbM0Z9HI6cUasWgGRHbs5sgx6TZCKWli5hrenhsbhyuTGcBQExSAxkVI1cxNHIbOP5SxlHqmwH8m1HeW3+9SiYYfdmW1wog+hQEwAdfA1avi29PaeEkKGbRgrjm0ZGjeP3Y6wDQJwhpZwT5NwEcJ6IVInIB/DyAL/c76bHmeZweeQ4uc8EhNHKHOXqwSjZlzoDemdMY+eLPgdxS6NMs1kviISlqxRUcufCrztbsTBe51KT1sb/DCu7hPlV0IDB2qSwhAdkgAu40HpXXiJ6t+qbkJSODJuhBrZiNJPoZP87UyCN8eXiku/yIXv4zU+gMoKTFS3D1nIRUIrH7GxGN3DcEeaiRZ8OkYuIIc2jENc/QeCnaF/sjy2e3CrRX9fEzTh2LNZVASgqD5ogYoJVPR1Z5JYdhraWqvCgap//SPGu4zBwwiuPt21YKR552lMYOc+QAUPvx51E6shK9d2LA7Fngwq9K7dOwgRHbuiDXGrkU5M0FQBYDjx+mFL/Nt34Y7pAaceSLMwV5Hj/y8CrJTcwOlaKNO1IjN4qzQzwCJV9ssrVM9LmPY5PJRF0K2zZ2cs67RPTrAP4nhDn2P3LOv9/vvJozCgRdtMFxY/N9cE5wrBXUSg6OTtbQ8Ozs5XS5LLQbRlhqLKETdHBHafBMhup3uuCeC2BTaOVxjdVAy28lthnfTQSMUjYCMlcKwQ/fAJkiV6SBvbb8BbRalZ6cpS5KbHgv9Pus4hp5PPlPeKAhmCOCPBYoofoSEYr5P25z8rVZ9jOUF9G/6hJZiApyrc32XZXwVI5caTIJbUpPlNHtEUFeqgOb96LHq/5KCgqzUrCPrUT2e47JrcrJqI+hHkiOtLbfEPBI/RFc2fy+LMg9mMD1Ax+/f/n34Vpu+jPZJS7dmZuDMzeH4O6tcGMPn37LsCX1eubpMDTyjCA8eZScQOTkPT6G7q3bsS7Jaxu060DUisLyj4d9GV0GPCkaucy1ErTC01QxbSXIVXIxZsP3fYzVMsKbsUMBQZzzPwLwR4OeRxDaMBFQtcZxfOQEGImEMWJ/1oNMoRHkZAwQmB8IatkRgjzgvOfSY7MbhszH/Y4T8pAIdUcEMBytTOPRlVfkiWKy8MPFmTxe5WkRf5uBRar7aRp5SLv01sjj+0KaIT4DpQtv83SzLWYk3MojgBRMgekYBSnSYLbr3w5Du5Xnh51TI9fjmO5ILvb1oVbiHw+AkCcHEgI/WFjC3Qs2aCo9G6DJfVtWr96nd0thwhMU4PGR4xj3xnGmMg/2o/+VPLBPWxvdDQQIsOlvantAEjvnR55AJL9P4qsyVqeUGOvccwwRoFbXvQyTUhlT13SPHg0FOVe6sep3SAkNRK2oe5wIS/2hVANOfh746g+BzppYpQTr+iyu0irLd1Fdz2FOurJpYEcCgrYCwWWHfy9Xn8BsNZr3ODMIREu8GEUgXRqZ0u5UNSCEeabTBEvbDw0eCb/zFI28UarjZ0Yex1P1FVQdmXxodhYEEQQU3qOYQLjRXsDDBFzxPplh+eq6q517Pb/ZuIDKpclH+G9zWRseYmXQL/2Q0Mh7nBtshhOoeyTUaFUFoYXRik7F28uDQbvmpVErWQFBMY1cUyuUIchT8q/79Wbm0EQ18uhBztxs/PCw2Vg/PdvDGyfewGRlEoyYDgzK67UCiHfapA+jB+2S4I4joiFnPAtAlIOTk2vFFT+XxuOuhFkgwO8vyBkRuo0R3Q1rJKw/6trC0FmyGTB9GmjOG+dtIyBIbS2Xhf2l2xIThpETn7otGXcgBTkLBXk/DC1En8f4OEsXVxhk+UKRbVogdrqgEmRiIUhXx+wleoAA3aALm4W1DintGno7ocyciGbrnT+PB94VVD95E+iK/nDOQQxYbXVxZ70DzyqjGyTzh2uNnIl4MtNjJ+93pnKZR/uZASudLzeFpZk5cZAl7iAvO6sKQbl6/hgqTz6pt4/XSvjFp0Wx2/V2NAdPajuK7k5VyPsZO6PumlGNPIWTpFA49oLnGhq50bGRN76YuJnPnZrGV9+5hfsb3f7PO8U+0w8cPKKsZB21Gxy5Alkxjjy6V9NjpkZediy8cWEhd9pcUH5Bzm0Ha8++iNqRRsQIPVErwxspYapeAsrxyNc86P1eEBHK5y5i88/+O+j+FaAslQUCSJa8VN+hUir2tyCHGPdnZp7BvVvXAfm3+cFlLst1xr6UfURgHR9UYoBbAgJEiupmoRN0Ih9xL4087WERY6CKB9+wLgsPgahGGGRUNDKvRTA8WAyhnn5Oxszfk1Nmkb/i1wcAx7FTjuiPQQS5u7iIe586g6CSHTiUz9hJkZ8m+hs7o0bwVGql5z2l96xkJPByzJwaKTTVeK2Ex2Ya+Ma7t/uPtS29aczVQmbPwkmnHYSCPDoW8vf3vwb0SPmwbUQEeWxfgiM3xi6vEFcNB2Eh68yuqCarVThTUwjW1iL7qyqfdg+ePRNGoZsseE+/gHL1DujBVaB0XPacQmoFB0gjV7JwsbGIsRJhsxPAzK8CRF+4pfqiyCZoIJ6ClXMOixxYnS5Klis48pYU5H1UnU7QgQcv8eHHzxLlqtTDitMaBF9rapQaRecHsi6fsZlpTozQlddQfuB9Bbnm8qPcXM+7jbgfpo+3Zcu8Hxy53Q/FPUQ/oH6CvZcQB4CSzmmdfYx2XUvppzJ2JvqhjYjR9iPHKWEZEQr5xqJkb40j7xsz4ZSBJ34hl5Axn2dfjby9pk7q2+5WQHGvFRNmBtMtux4ippFnC7/Etx13J1bvRg4B2qMzvfe6VVHYRPmRM0LgdyFWJVFX2KzcRiaGWFjC4HNThFr898nKlHEipf8EMObOY9mbxoRXE4mFWiIlF6VEh9adOqar07h893LkRc+aTESfpHRLux8iw9+TpEYevdturBKJeb1QCw8P6Pdaq3NPjJ7AamcVTQb86JO/7P0a9TQ8CTg5jknDtj7EFPQLCAJCDjqVR1fGzvhI6o9V3GfDaeBB+0FMkNeAkUVg6hRw4wdpzWbC1MjjHHkaViaquLPextmFHMv5nJqi1sjB+xrL9gRaCYqNh6GgWTknyowLCI8VoC+1oroDpKyS9LuR3sZYaaxHH3JaZomJ60ivGJsRup2OUPJUHdaYQO/5DeS76s4jLTBBBNAMkM/A5CL0LjLyZQjvl4AHiFvCAeDM5BmUrTIu372sg4J4LJx/JJbLWcjxbI28G/4htfsYtRKPNEUo/Ii49tRg7fB6PWkSuc+1XFwcv4g/fefNzGPDk8zIzvTxtg1j3XY48u0K9n4h+kAoNFOplawQ/ZhGfnHmIpbWl1B3w5SqIAKOfUb8/slfh9vQfwVtata9Cl6Yx1xc7iUgto44Rx4RCCkeJLsHvcSLbTc18m1cPzdHbvYHSf9+5dudogl/4dgXer/TOagV0QkpyIMw1qDbVRy5BcAPNXJdrWw/CnKeRXEb2nC/l6qX4YcAJqPvAvhgSC7hGZhetqgXPW7EmmlGU7GSyZHHHhYRGfSPiuxMo1Z46nMmEF4+M4OKY+H6lVA4DUathH3JPMcc44zMjJZBDfRyP2y+9vnI0njQup2vrrzac9mvfMl7vQmKxugZop9FrSge0nKwUF/I3/EBkObfvtfYFxq5QmJSDZUna7uOdOpdUnTY7Flg7QbQML1P1PXlJG8a/CsVuI/IIMOUySB/vpUcsosHegVhM0Lg+wg4ZIS7rwW4+tkrunO4glzLnlDLiRg7Mw156pfoT1MEk+tqq6/wVU+2xYjpQVLuWfFVQdmxMNMs4eN7LXmOwZHHPVpAkaEWRS1UXnFxrB9ENXTVD3W8KsuVwhqlIhGSb2Tf63FS+KubHmTgpqVZTUGYDjXWn5yoOBVUEpnqophulkV+6wyUMvLTAz28VjJWVf0Rf+b9kTdX0E4jDGqJcuQ9n9FeTDq7ppGzcIJW2Su9UeDMFyOHqWukOR41f+anw4LiObjpJLZGrViMQL6PbsAxak/g0uKj+rvIM3kM1Y9cIYsjj2uZcVCatFMeDK6LqiuS8XSDTqpW61gOXCYEhBLkaYbRFx8Lgz4YEVCRVQQnH030R3utSGrFD6JC3w9SysZmTDLqdgYTjjFDTr+jMwS5FTGC5r+60sitmH/2dvDpE5M4Pl3P3K98zdvdpMYS6PwVGdRKbg1reFkEtwqTI8/0I4+DZ2t9O4fYs6hOAq4QWsmSCoM0a8iDHsqBztwc257w79+KsTMvtUKW1MjFeNuMgXgXXT+AxWyMe2Gl0jxeK0MT5EDKCgsZ1ErCdUxZAllyvzJkuC5KrASXeZk5mJtuExazwMC0xtL221q4p4ERxEt34VeFIczsFijxcnS1IBHQXi3m5JXyGMLFRu8XIkGtqO3b1Mgjhw+gUaoJaEoapwcPsx4cSiNv+8mc9gqZHPlA+TMQcuQ5BLtIeTx8cPBE0Y9MBNljuH2o7zk2LpaDqWMvA9imRq7ad6o9Bam6hlljYORnv4Dqc8/FDtwOYTEYtWJZorxcN0hyznnsTEMNCFL+ksJXVOQnz0OthIhyK9zYTK4LRoSK1QSwmjquyk+zbJfx4eqHaPttfLz+MTw7WWFldqSMa3c3c3PPiiP3jaQ4gNDIs15WzpNLzH5jEI8OzUvJ6POdHm5a+md+gWQzGy8tvYSaDKbZCY28HyZqYhk9WklOSpmRnXH3w37IkNu9ns8rZ2ZTVwl7BfOeTUHe09iZUuBlx5EyZpdmL2Htgzd1bdRttdvHxz6NWkn9DgZITRFiAGol8A1qBSDuS3kRHZ88dqehBgSp/n76xCSu3FpHtWSD82SnE+k4E+6HyfbJdUEE2OToqhsK05VprTECwPnp83jzkzfx3v33wMHRdJMuYD9+bAKbfT5KJkslq05xzrXxk0xBHi/NKP8wubBqiaHiWijZOTXyuOE1pwDNMnZGjhmQ4x0phyHPeyXIX39yThfVMJEZELRVjTzNIJOBkm1F/Mn3GroIdK8Q/Th2USP3Hn0EzPKR9sHazEYzRYEaDHkFufiZ+QhrU8Bqdlm1nvBkMZXxo72P0xq5DxCDTYpaSXr15FnVDlkjF6iWbJycE3x2hFrpo16mCRjlpUquLOCcwtV+auFTkXNmqjN4eeVlfOX9r+DW5q1UTsq2GGp9osyiNQQFP9vxu3Kf6Mfd9Y5OCqbgMAdnJ85irhbmmqmUGE7PiwmlVzh4nFoZL4skS6OlicxzBsX2PML2xtCXJsQBkSP98t3LyURRWiPPq3VlxA7kPHuY6E2tZExwu4DyiePA6vUBxnyLSEutYIAZE1wqjn8u9H4Z+NpVQbv2g0mtWC4sqwvioqJU/JvZ1xr5ZifASMpS2EQadwwgqZGnJHgi2wYj0QZHPp6pbAkPjDyRVOndIsTpnk7QBYFpQX57rY3ba23tH63w6FjUcNqvalF40eifI6UxnGu+jJFS3kRDOTAkr4udwLnJczg9fjqZS3pQQV6fAe5dFaltDwjUO98Nun0LE2jsJkdeqglBnjVZHH+mYF0UAAAPSklEQVRpe7y0WnU45Z6H9eXhLXtr4fmDQPmR8wCwHNiMwLjKKxTt377myDc7fqLMVRxZias0YhxWRODpnMYst7+BKhacx0qc2p1YNREODovxnsbMLEQE+QAaOQCY+cx3Attpba808l7XT5+YB/RCmTkDjK4IYTT42UOBouo2uhv5T9pNjlxpyu0H6fub2/TjV8FAdh9BLj/HoMd3tetQssJvC42cCAxRe5rCvhXkfsARcKDcw/8XyOG1krKfFCfKRCUVBgZwnqjwnoaStU1BDqa7xMgC5xyPzVaw5vde6qUhT38BgweVoqWn99Op1/tyfxeWR7U7X/waW8FecORbwsDUCrQQN7EPYn0yod5jM98+EHue8RvYTY28KgNtrP6eUltCt5Wr/V5+5HsGRZd0W3qCcygac6KwbwX5PVmRvpxhCCpZpdRINO/sGbTf/wD2mAhljnPkzbKD9SAQBibLApESqP1r3gHRwJytwKRWLCY0csYCzI9U0ZKFZqolC2stH60+hlNTI+/Vd9NX2PyZKkC9UW2MKT/2qE4ja+KE4a8t7md7xQb2rSCvTgKb98PAkQExP+Lhyq11jHi7JJR2AHGNPFfpt930Ix9ZBI5/Fqhn52PfFnQwUD5qZbiC3NTIxYTrkvzSDxJHDkRzNptouk18svFJ+NLJG7PGxtB4/PHwQM2Ri59HJ2uwFpvw2puApBdIlnbolz9aNCMG19/i8jKNWukGXTiWjRaAhmfj1FwTX337VnYjEn5OzUhPPjyqkfejtb0nnsjVPrBNjXy/qqyPPCcKB8QLRuTE8kQVC6NeJG/7foPSyJUgL1klbPqbsck1rpHn9DffKoxCDbsGu49Gvp+ole6mWEEwCzZxtLE1jXyob2GWRv5jcz+Gi9MXdfWdEFkUS7i7Knl3YgzLE1UQWaiVnVxURdUW10vzI8+DSOZGJqgVIcjFB2UzljvvRqS/Od43NekFKQFH28Wg7oeRc/erRs4soLK9JFX7WYgDSUGey4g/VDV1h5BXI9+LvmRByQrOhYGXLLhMffODJ54bqkaelSPDtVwsN5fDDVmyQN2geaNaJWWYbpTx+rkFfO3a1VzUymJjEYwxzMVKzuWFXgPIRFccHD73US+VsbTYxPK4SFWaB+bD67UcTlIr0e07gX0qigv0QZxaKbESHuBB73djLwKCdhs5OfKhSnJTZlmi2pgtO+TEPNryUCtDUylOzTX6eq30hZpZlWbPYRixJB0jByEI8nF/87X5LdMBumSb+lQ4dAm5U3NNVEt2rpSmAHBm4gwaMldMr9UExV7KvKkeBsFDSa0cAihBvt4VlX9KVoo9YC+NnXuFPu+c+gSHSq2wmCBnFmwmxr5aiqfO7v8NDU2Qn5nPXw8vkeFP75A/jSUulwJbRSwq4Zrbj3YbYMQAEho5I1FkohN0IsEoeQW5Yzk4NX4KwGAaeZj/fOdAux3AUWBXwIjBIgsb3Q1YZOlsej0Fw8OgkfeByka5L6gVQCTnIgaSk2itHBXk+9bYSTRgas+MvCM6j7BZDiwIqRUg1EryGDu3CyICGrNgd6+CZOItpZErDJIUKO5a2OsYpbWHzNIOivKHkSM/JLCZDd/30XAbGQIh9nz6pBTe1zj6E0Bns+9h+8NrxXgWklrxpR98nKnYtyH6O7bcVu1YSWpFXWO7niiDgIEBjTmwsWOAZSHgAbq8G/FLz1P2K9JeH8QFZbDzts7tuR8W1MpQ4TAHLb+FhtvobzSbP98/R8h+xuhyrsP2RaByRCO3ARaW1PAy0k30wlAE+cAL9aycK2pmtVKMnVY0L/ZeaOSaIyfBkqv8FmZ4+CCaspn0qB+0Rq5Lm+W+TF9sKQmcRKGRDxcqHqNZamplJvN9Glvpm3DqYcD2UuXuEBLGTgtLYxVUXBuT9d5eN2kYCvk5uJaW6bYifhgZ/BRHrgZKGzv3IGG+mVKWQLi1KfzFzfzmg5T9yiMEE3VPefr27aGgVg4qVNbDxfpi+C2Y9qJIlOfwMjXuJfadIJccuWMxzDbLW9LChqKRl+wtzh/xG5SZBRHRyOWhLEqt7JmxEwAoFKQ22VhqLOlj8ho7zfbyaOSJEP3cV+kBGdm5rUlhH3wzhxkXpy+iG3RRcSr9V3iHxKi976gV6bVi7By4uaEI8njmv62Cy4owK+PHcNu5g+nqNNbUTskHJLLe7SIiiavk75OVyV01do6XRUmokZLIAd7wxLXSiiwMjJzFLXo2UUjyocKMx1A2l+j7ZGrkh0WQ74N30pRLzAamHgfufyT+Piga+cDI4Mh5V2jkdW8EL688DQBJQb6Hy8W06kZxg+UgGrkSgr1ooYX6Al4pv6KjYBdGK3jlzEzfFMF5wNU9FF4rDwWY/CYy36c9VHqGiWEVw47AlGVORSRkmzgB3PzhllL5HhBBrn7GqRWhkadVuYl7rewFdMFkQ3gxtnVBnrfv8VQGOyHEBSj2cwst7AftpwCAUKnJzONzSDTyfYEIRy5/X35OpEveQv6fgyHIM8C7UpDbKbcRCwjaC0QEuI7y3Pr1t5uNcbug7birqDaQvjIpsPdQRvdsY+fhmXTHqg6OTg6eXnrHkMUUlBtbau5ACPIsrY4c0f3USvAxAb7SWNnxfiUuaXitxLcdRHDV9ZzpDdJARHh87HHM1/Yg612Bnthq5auHES+f3qVUunmhY2B25pkcCEGepSl4Z8/CajTgzCWTXJkZ+9448caudc1EJPshts/RD+K1shtYe+IScOU9UHlwv1YTpydO71CPCmwHbmoyqYOraBxoqLw3C5d2pLmDIcglEiH6joPS8ePpB+eoDr/TiNAHsqvboXaSeVT2Fn6jic3HzhzoVUWBEGY8Q4Ehw7LzFWnOiYNBXG5BkAxD+JDhrqdraW6jH8PWyAs8XEjXyAscBIyXx3uu7g+URj4QdsBQN/Alc7gfDoJha8Kn55v41nt3dszvv8Bwkbo6LFZbBwIvLr3Yc/+2vlAi+idE9CERvSn/vbKd9npcafBThijIVYi+6Mb2qZW9SC+QhhPTdfzi00sDuUwWKFBg77ETGvm/4pz/8x1opy8GoRh2wnVuUJjeKorX3gn3wwIFdg/FJP0w4GBIigOy/IuUZ5OTTpowtq1895MnRL9AgQIFdkIj/3Ui+mUA3wLwDznnd9IOIqJfA/BrALC0tJR2SDYOhhyP+JFrjTxFkP/sk/l8qofttVLg4cPzc8/r9MoFHh701ciJ6E+I6Hsp/14D8G8BHAVwDsA1AP8iqx3O+W9zzi9wzi9MTk5urbf73HsjQq301MhZrgrsBbVSYKcxW5vFYmMx3HBAVrsFeqOvRs45/0yehojo3wH4w233KL1x1ZldaX7HQWaBhyLhVIECBXYX2/VaMeNcvwDge9vrzsGGSYFojXwH3A9nKjPb61iBApkolIWHAdvlyH+LiM5BlHN4D8Df3XaPUjBsf+qtoBdHPgheXXm1COQoUKBAT2xLkHPO//ZOdaQnBqBWKpcuInjwYJc71BtmBNZ2BXnlIFc1L1CgwJ7gQFjTvLNnwWpVWM1m32NLKyvwzp7dg14lMV4ex4nRE7g0c6mnsbNAgX2DA7jaLZDEgQjRtycn0Xz11WF3oy+ICE9MPgEgjMYsBHmBAgV2G4WU2WUUgrzA/kahkT8MKKTMLkFVYSkEeYECBXYbhZTZLUi7bOELXqBAgd1GIch3CTvlfligwK5CGTunHh9uPwpsCwfC2HkQUeRHKXAgQAQ89cuJGrcFDhYKQb5LKNwPCxwYsL0vi1hgZ1FImV2CMnYWKFCgwG6jEOS7BWXsLAIuChQosMsoBPkuwZLL1e0kzSpQoECBPCg48l3Cs3PP4sr9K6i5tWF3pUCBAg85CnVxl1B1qjg5fnLY3ShQoMAhQCHICxQoUOCAoxDkBQoUKHDAUQjyAgUKFDjgKAR5gQIFChxwFIK8QIECBQ44CkFeoECBAgcchSAvUKBAgQOOQpAXKFCgwAEH8RyV6Xf8okQPALy15xfen5gAcHPYndgnKMYiRDEWIYqxCPEI53wyvnFYIfpvcc4vDOna+wpE9K1iLASKsQhRjEWIYiz6o6BWChQoUOCAoxDkBQoUKHDAMSxB/ttDuu5+RDEWIYqxCFGMRYhiLPpgKMbOAgUKFCiwcyiolQIFChQ44CgEeYECBQoccOypICeil4noLSK6TERf2strDwtE9B+J6BMi+p6xbYyI/piIfiR/jhr7fkOOz1tE9Lnh9HrnQUSLRPSnRPQDIvo+Ef09uf0wjkWZiL5BRN+VY/FP5fZDNxYKRGQR0XeI6A/l34d2LLYEzvme/ANgAXgbwBEALoDvAji5V9cf1j8AnwLwFIDvGdt+C8CX5O9fAvDP5O8n5biUAKzI8bKGfQ87NA6zAJ6Sv9cB/FDe72EcCwJQk787AL4O4JnDOBbGmPwDAL8L4A/l34d2LLbyby818ksALnPO3+GctwH8HoDX9vD6QwHn/M8B3I5tfg3A78jffwfA68b23+Octzjn7wK4DDFuBx6c82uc87+Uvz8A8AMA8zicY8E556vyT0f+4ziEYwEARLQA4FUA/97YfCjHYqvYS0E+D+AD4++rctthxDTn/BogBByAKbn9UIwRES0DeBJCEz2UYyGphDcBfALgjznnh3YsAPxrAP8IQGBsO6xjsSXspSCnlG2F72MUD/0YEVENwH8F8Pc55/d7HZqy7aEZC865zzk/B2ABwCUiOt3j8Id2LIjopwF8wjn/dt5TUrY9FGOxHeylIL8KYNH4ewHAR3t4/f2E60Q0CwDy5ydy+0M9RkTkQAjx/8w5/29y86EcCwXO+V0A/wfAyzicY/EcgM8T0XsQdOuLRPSfcDjHYsvYS0H+TQDHiWiFiFwAPw/gy3t4/f2ELwP4Ffn7rwD4H8b2nyeiEhGtADgO4BtD6N+Og4gIwH8A8APO+b80dh3GsZgkohH5uwfgMwD+BodwLDjnv8E5X+CcL0PIhK9wzn8Jh3AstoW9tKwCeAXCW+FtAL85bEvvHt3zfwFwDUAHQpv4OwDGAfxvAD+SP8eM439Tjs9bAH5q2P3fwXF4HmIJ/P8AvCn/vXJIx+IsgO/IsfgegH8stx+6sYiNywsIvVYO9VgM+q8I0S9QoECBA44isrNAgQIFDjgKQV6gQIECBxyFIC9QoECBA45CkBcoUKDAAUchyAsUKFDggKMQ5AUKFChwwFEI8gIFChQ44Pj/s4vOQ7Fb/DgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"ax = plt.gca()\n",
"for chain, subdf in posterior.groupby(\"chain\")[\"mu\"]:\n",
" subdf.reset_index(drop=True).plot(ax=ax, alpha=0.4)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"chain 1.500000\n",
"draw 249.500000\n",
"mu 4.092611\n",
"theta[0] 6.025829\n",
"theta[1] 4.724150\n",
"theta[2] 3.576364\n",
"theta[3] 4.477782\n",
"theta[4] 3.064036\n",
"theta[5] 3.821032\n",
"theta[6] 6.250179\n",
"theta[7] 4.544409\n",
"tau 4.088983\n",
"obs[0] 5.581352\n",
"obs[1] 4.813570\n",
"obs[2] 3.428965\n",
"obs[3] 4.193547\n",
"obs[4] 3.290920\n",
"obs[5] 3.986031\n",
"obs[6] 6.459169\n",
"obs[7] 4.786532\n",
"tune 0.002000\n",
"depth 3.928000\n",
"tree_size 17.138000\n",
"lp -55.146905\n",
"energy_error -0.006913\n",
"step_size_bar 0.218807\n",
"max_energy_error 67.087863\n",
"energy 60.170759\n",
"mean_tree_accept 0.790789\n",
"step_size 0.141102\n",
"diverging 0.021500\n",
"log_likelihood[0] -4.774267\n",
"log_likelihood[1] -3.387256\n",
"log_likelihood[2] -3.836329\n",
"log_likelihood[3] -3.443855\n",
"log_likelihood[4] -3.351045\n",
"log_likelihood[5] -3.452097\n",
"log_likelihood[6] -4.059510\n",
"log_likelihood[7] -3.942111\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"posterior.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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