├── GeoCoding.ipynb
├── Intro_PyGIS.ipynb
└── ListaEnderecos.csv


/GeoCoding.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Geocoding com geopandas & geopy"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "Neste tutorial, vamos ver como realizar o *geocode* (obtenção dos pares de coordenadas a partir de endereços) para vários registros em uma tabela.\n",
 15 |     "Vamos usar o [pandas](https://pandas.pydata.org/pandas-docs/stable/index.html#) para carregar a tabela com os endereços, [Geopandas](https://geopandas.readthedocs.io/en/stable/) para acionar a biblioteca [geopy](https://geopy.readthedocs.io/en/stable/) que, por sua vez irá executar o geocode.\n",
 16 |     "Como exemplo usarei uma tabela com três registros de hoteis na cidade do Rio de Janeiro e, a partir de seus respectivos endereços obteremos suas localizações por pares de coordenadas. **[Baixar planilha usada no exemplo](https://github.com/FelipeSBarros/Python-para-Geo-I/blob/master/ListaEnderecos.csv)**;\n",
 17 |     "\n",
 18 |     "Aproveitando esse tema, vamos apresentar, na segunda parte deste tutorial como realizar o *geocode reverso* (obtenção do endereço a partir dos pares de coordenadas). Como não está implementado como ferramenta do geopandas, teremos que usar outros mecanismos, como a biblioteca [shapely](https://shapely.readthedocs.io/en/stable/) e, depois o **geopy**;\n",
 19 |     "\n",
 20 |     "Para o geocode, usaremos como provedor a **API** [Nominatim](https://wiki.openstreetmap.org/wiki/Nominatim), do **Open Street Map**."
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "markdown",
 25 |    "metadata": {},
 26 |    "source": [
 27 |     "## 1. Geocoding"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "### Importando as bibliotecas"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 1,
 40 |    "metadata": {},
 41 |    "outputs": [],
 42 |    "source": [
 43 |     "import geopandas as gpds\n",
 44 |     "import pandas as pds # Para carregar a planilha com os endereços"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 2,
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<table border=\"1\" class=\"dataframe\">\n",
 57 |        "  <thead>\n",
 58 |        "    <tr style=\"text-align: right;\">\n",
 59 |        "      <th></th>\n",
 60 |        "      <th>address</th>\n",
 61 |        "      <th>geometry</th>\n",
 62 |        "    </tr>\n",
 63 |        "  </thead>\n",
 64 |        "  <tbody>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>0</th>\n",
 67 |        "      <td>Rio de Janeiro, Microrregião Rio de Janeiro, R...</td>\n",
 68 |        "      <td>POINT (-43.2093727 -22.9110137)</td>\n",
 69 |        "    </tr>\n",
 70 |        "  </tbody>\n",
 71 |        "</table>\n",
 72 |        "</div>"
 73 |       ],
 74 |       "text/plain": [
 75 |        "                                             address  \\\n",
 76 |        "0  Rio de Janeiro, Microrregião Rio de Janeiro, R...   \n",
 77 |        "\n",
 78 |        "                          geometry  \n",
 79 |        "0  POINT (-43.2093727 -22.9110137)  "
 80 |       ]
 81 |      },
 82 |      "execution_count": 2,
 83 |      "metadata": {},
 84 |      "output_type": "execute_result"
 85 |     }
 86 |    ],
 87 |    "source": [
 88 |     "gpds.tools.geocode(\"Rio de Janeiro, RJ\", provider = \"nominatim\", user_agent=\"Intro Geocode\")"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 3,
 94 |    "metadata": {},
 95 |    "outputs": [
 96 |     {
 97 |      "data": {
 98 |       "text/html": [
 99 |        "<div>\n",
100 |        "<table border=\"1\" class=\"dataframe\">\n",
101 |        "  <thead>\n",
102 |        "    <tr style=\"text-align: right;\">\n",
103 |        "      <th></th>\n",
104 |        "      <th>Hotel</th>\n",
105 |        "      <th>Endereco</th>\n",
106 |        "    </tr>\n",
107 |        "  </thead>\n",
108 |        "  <tbody>\n",
109 |        "    <tr>\n",
110 |        "      <th>0</th>\n",
111 |        "      <td>Copacabana Palace</td>\n",
112 |        "      <td>Avenida Atlântica, 1702, Copacabana - Rio de J...</td>\n",
113 |        "    </tr>\n",
114 |        "    <tr>\n",
115 |        "      <th>1</th>\n",
116 |        "      <td>Hotel Marina</td>\n",
117 |        "      <td>Avenida Delfim Moreira 696, Leblon - Rio de Ja...</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>2</th>\n",
121 |        "      <td>Windsor</td>\n",
122 |        "      <td>Avenida Atlantica, 1020, Copacabana – Rio de J...</td>\n",
123 |        "    </tr>\n",
124 |        "  </tbody>\n",
125 |        "</table>\n",
126 |        "</div>"
127 |       ],
128 |       "text/plain": [
129 |        "               Hotel                                           Endereco\n",
130 |        "0  Copacabana Palace  Avenida Atlântica, 1702, Copacabana - Rio de J...\n",
131 |        "1       Hotel Marina  Avenida Delfim Moreira 696, Leblon - Rio de Ja...\n",
132 |        "2            Windsor  Avenida Atlantica, 1020, Copacabana – Rio de J..."
133 |       ]
134 |      },
135 |      "execution_count": 3,
136 |      "metadata": {},
137 |      "output_type": "execute_result"
138 |     }
139 |    ],
140 |    "source": [
141 |     "end = pds.read_csv(\"./ListaEnderecos.csv\", encoding='UTF8')\n",
142 |     "end"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 4,
148 |    "metadata": {},
149 |    "outputs": [
150 |     {
151 |      "data": {
152 |       "text/html": [
153 |        "<div>\n",
154 |        "<table border=\"1\" class=\"dataframe\">\n",
155 |        "  <thead>\n",
156 |        "    <tr style=\"text-align: right;\">\n",
157 |        "      <th></th>\n",
158 |        "      <th>address</th>\n",
159 |        "      <th>geometry</th>\n",
160 |        "    </tr>\n",
161 |        "  </thead>\n",
162 |        "  <tbody>\n",
163 |        "    <tr>\n",
164 |        "      <th>0</th>\n",
165 |        "      <td>Copacabana Palace, 1702, Avenida Atlântica, Co...</td>\n",
166 |        "      <td>POINT (-43.1790751 -22.9670027)</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>1</th>\n",
170 |        "      <td>696, Avenida Delfim Moreira, Leblon, Zona Sul ...</td>\n",
171 |        "      <td>POINT (-43.222646 -22.9869118)</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>2</th>\n",
175 |        "      <td>Avenida Atlântica, Copacabana, Zona Sul do Rio...</td>\n",
176 |        "      <td>POINT (-43.189305 -22.984457)</td>\n",
177 |        "    </tr>\n",
178 |        "  </tbody>\n",
179 |        "</table>\n",
180 |        "</div>"
181 |       ],
182 |       "text/plain": [
183 |        "                                             address  \\\n",
184 |        "0  Copacabana Palace, 1702, Avenida Atlântica, Co...   \n",
185 |        "1  696, Avenida Delfim Moreira, Leblon, Zona Sul ...   \n",
186 |        "2  Avenida Atlântica, Copacabana, Zona Sul do Rio...   \n",
187 |        "\n",
188 |        "                          geometry  \n",
189 |        "0  POINT (-43.1790751 -22.9670027)  \n",
190 |        "1   POINT (-43.222646 -22.9869118)  \n",
191 |        "2    POINT (-43.189305 -22.984457)  "
192 |       ]
193 |      },
194 |      "execution_count": 4,
195 |      "metadata": {},
196 |      "output_type": "execute_result"
197 |     }
198 |    ],
199 |    "source": [
200 |     "gpds.tools.geocode(end[\"Endereco\"], provider = \"nominatim\", user_agent=\"Intro Geocode\", country_bias=\"Brazil\")"
201 |    ]
202 |   },
203 |   {
204 |    "cell_type": "code",
205 |    "execution_count": 5,
206 |    "metadata": {},
207 |    "outputs": [],
208 |    "source": [
209 |     "end[\"Geom\"] = gpds.tools.geocode(end[\"Endereco\"], provider = \"nominatim\", user_agent=\"Intro Geocode\", country_bias=\"Brazil\")[\"geometry\"]"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "code",
214 |    "execution_count": 6,
215 |    "metadata": {},
216 |    "outputs": [
217 |     {
218 |      "data": {
219 |       "text/html": [
220 |        "<div>\n",
221 |        "<table border=\"1\" class=\"dataframe\">\n",
222 |        "  <thead>\n",
223 |        "    <tr style=\"text-align: right;\">\n",
224 |        "      <th></th>\n",
225 |        "      <th>Hotel</th>\n",
226 |        "      <th>Endereco</th>\n",
227 |        "      <th>Geom</th>\n",
228 |        "    </tr>\n",
229 |        "  </thead>\n",
230 |        "  <tbody>\n",
231 |        "    <tr>\n",
232 |        "      <th>0</th>\n",
233 |        "      <td>Copacabana Palace</td>\n",
234 |        "      <td>Avenida Atlântica, 1702, Copacabana - Rio de J...</td>\n",
235 |        "      <td>POINT (-43.1790751 -22.9670027)</td>\n",
236 |        "    </tr>\n",
237 |        "    <tr>\n",
238 |        "      <th>1</th>\n",
239 |        "      <td>Hotel Marina</td>\n",
240 |        "      <td>Avenida Delfim Moreira 696, Leblon - Rio de Ja...</td>\n",
241 |        "      <td>POINT (-43.222646 -22.9869118)</td>\n",
242 |        "    </tr>\n",
243 |        "    <tr>\n",
244 |        "      <th>2</th>\n",
245 |        "      <td>Windsor</td>\n",
246 |        "      <td>Avenida Atlantica, 1020, Copacabana – Rio de J...</td>\n",
247 |        "      <td>POINT (-43.189305 -22.984457)</td>\n",
248 |        "    </tr>\n",
249 |        "  </tbody>\n",
250 |        "</table>\n",
251 |        "</div>"
252 |       ],
253 |       "text/plain": [
254 |        "               Hotel                                           Endereco  \\\n",
255 |        "0  Copacabana Palace  Avenida Atlântica, 1702, Copacabana - Rio de J...   \n",
256 |        "1       Hotel Marina  Avenida Delfim Moreira 696, Leblon - Rio de Ja...   \n",
257 |        "2            Windsor  Avenida Atlantica, 1020, Copacabana – Rio de J...   \n",
258 |        "\n",
259 |        "                              Geom  \n",
260 |        "0  POINT (-43.1790751 -22.9670027)  \n",
261 |        "1   POINT (-43.222646 -22.9869118)  \n",
262 |        "2    POINT (-43.189305 -22.984457)  "
263 |       ]
264 |      },
265 |      "execution_count": 6,
266 |      "metadata": {},
267 |      "output_type": "execute_result"
268 |     }
269 |    ],
270 |    "source": [
271 |     "end.head()"
272 |    ]
273 |   },
274 |   {
275 |    "cell_type": "code",
276 |    "execution_count": 7,
277 |    "metadata": {},
278 |    "outputs": [
279 |     {
280 |      "data": {
281 |       "text/plain": [
282 |        "0    POINT (-43.1790751 -22.9670027)\n",
283 |        "1     POINT (-43.222646 -22.9869118)\n",
284 |        "2      POINT (-43.189305 -22.984457)\n",
285 |        "Name: Geom, dtype: object"
286 |       ]
287 |      },
288 |      "execution_count": 7,
289 |      "metadata": {},
290 |      "output_type": "execute_result"
291 |     }
292 |    ],
293 |    "source": [
294 |     "end.Geom"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "markdown",
299 |    "metadata": {},
300 |    "source": [
301 |     "## Geocode reverso"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": 8,
307 |    "metadata": {},
308 |    "outputs": [],
309 |    "source": [
310 |     "import shapely.geometry\n",
311 |     "import geopy"
312 |    ]
313 |   },
314 |   {
315 |    "cell_type": "code",
316 |    "execution_count": 9,
317 |    "metadata": {},
318 |    "outputs": [
319 |     {
320 |      "data": {
321 |       "text/plain": [
322 |        "-43.1790751"
323 |       ]
324 |      },
325 |      "execution_count": 9,
326 |      "metadata": {},
327 |      "output_type": "execute_result"
328 |     }
329 |    ],
330 |    "source": [
331 |     "end[\"Geom\"][0].x"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 10,
337 |    "metadata": {},
338 |    "outputs": [
339 |     {
340 |      "data": {
341 |       "text/plain": [
342 |        "-22.9670027"
343 |       ]
344 |      },
345 |      "execution_count": 10,
346 |      "metadata": {},
347 |      "output_type": "execute_result"
348 |     }
349 |    ],
350 |    "source": [
351 |     "end[\"Geom\"][0].y"
352 |    ]
353 |   },
354 |   {
355 |    "cell_type": "code",
356 |    "execution_count": 11,
357 |    "metadata": {},
358 |    "outputs": [
359 |     {
360 |      "data": {
361 |       "text/plain": [
362 |        "Location(Copacabana Palace, 1702, Avenida Atlântica, Copacabana, Zona Sul do Rio de Janeiro, Rio de Janeiro, Microrregião Rio de Janeiro, Região Metropolitana do Rio de Janeiro, RJ, Região Sudeste, 22021-001, Brasil, (-22.9670027, -43.1790751, 0.0))"
363 |       ]
364 |      },
365 |      "execution_count": 11,
366 |      "metadata": {},
367 |      "output_type": "execute_result"
368 |     }
369 |    ],
370 |    "source": [
371 |     "geopy.geocoders.osm.Nominatim(user_agent=\"Intro Geocode\").reverse(str(end[\"Geom\"][0].y) + \", \" + str(end[\"Geom\"][0].x))"
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "code",
376 |    "execution_count": 12,
377 |    "metadata": {},
378 |    "outputs": [],
379 |    "source": [
380 |     "end[\"y\"] = end[\"Geom\"].map(lambda p: p.y)\n",
381 |     "end[\"x\"] = end[\"Geom\"].map(lambda p: p.x)\n",
382 |     "end[\"yx\"] = end[\"Geom\"].map(lambda p: str(p.y) + \", \" + str(p.x))"
383 |    ]
384 |   },
385 |   {
386 |    "cell_type": "code",
387 |    "execution_count": 13,
388 |    "metadata": {},
389 |    "outputs": [
390 |     {
391 |      "data": {
392 |       "text/html": [
393 |        "<div>\n",
394 |        "<table border=\"1\" class=\"dataframe\">\n",
395 |        "  <thead>\n",
396 |        "    <tr style=\"text-align: right;\">\n",
397 |        "      <th></th>\n",
398 |        "      <th>Hotel</th>\n",
399 |        "      <th>Endereco</th>\n",
400 |        "      <th>Geom</th>\n",
401 |        "      <th>y</th>\n",
402 |        "      <th>x</th>\n",
403 |        "      <th>yx</th>\n",
404 |        "    </tr>\n",
405 |        "  </thead>\n",
406 |        "  <tbody>\n",
407 |        "    <tr>\n",
408 |        "      <th>0</th>\n",
409 |        "      <td>Copacabana Palace</td>\n",
410 |        "      <td>Avenida Atlântica, 1702, Copacabana - Rio de J...</td>\n",
411 |        "      <td>POINT (-43.1790751 -22.9670027)</td>\n",
412 |        "      <td>-22.967003</td>\n",
413 |        "      <td>-43.179075</td>\n",
414 |        "      <td>-22.9670027, -43.1790751</td>\n",
415 |        "    </tr>\n",
416 |        "    <tr>\n",
417 |        "      <th>1</th>\n",
418 |        "      <td>Hotel Marina</td>\n",
419 |        "      <td>Avenida Delfim Moreira 696, Leblon - Rio de Ja...</td>\n",
420 |        "      <td>POINT (-43.222646 -22.9869118)</td>\n",
421 |        "      <td>-22.986912</td>\n",
422 |        "      <td>-43.222646</td>\n",
423 |        "      <td>-22.9869118, -43.222646</td>\n",
424 |        "    </tr>\n",
425 |        "    <tr>\n",
426 |        "      <th>2</th>\n",
427 |        "      <td>Windsor</td>\n",
428 |        "      <td>Avenida Atlantica, 1020, Copacabana – Rio de J...</td>\n",
429 |        "      <td>POINT (-43.189305 -22.984457)</td>\n",
430 |        "      <td>-22.984457</td>\n",
431 |        "      <td>-43.189305</td>\n",
432 |        "      <td>-22.984457, -43.189305</td>\n",
433 |        "    </tr>\n",
434 |        "  </tbody>\n",
435 |        "</table>\n",
436 |        "</div>"
437 |       ],
438 |       "text/plain": [
439 |        "               Hotel                                           Endereco  \\\n",
440 |        "0  Copacabana Palace  Avenida Atlântica, 1702, Copacabana - Rio de J...   \n",
441 |        "1       Hotel Marina  Avenida Delfim Moreira 696, Leblon - Rio de Ja...   \n",
442 |        "2            Windsor  Avenida Atlantica, 1020, Copacabana – Rio de J...   \n",
443 |        "\n",
444 |        "                              Geom          y          x  \\\n",
445 |        "0  POINT (-43.1790751 -22.9670027) -22.967003 -43.179075   \n",
446 |        "1   POINT (-43.222646 -22.9869118) -22.986912 -43.222646   \n",
447 |        "2    POINT (-43.189305 -22.984457) -22.984457 -43.189305   \n",
448 |        "\n",
449 |        "                         yx  \n",
450 |        "0  -22.9670027, -43.1790751  \n",
451 |        "1   -22.9869118, -43.222646  \n",
452 |        "2    -22.984457, -43.189305  "
453 |       ]
454 |      },
455 |      "execution_count": 13,
456 |      "metadata": {},
457 |      "output_type": "execute_result"
458 |     }
459 |    ],
460 |    "source": [
461 |     "end.head()"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": 14,
467 |    "metadata": {},
468 |    "outputs": [
469 |     {
470 |      "data": {
471 |       "text/plain": [
472 |        "Location(Copacabana Palace, 1702, Avenida Atlântica, Copacabana, Zona Sul do Rio de Janeiro, Rio de Janeiro, Microrregião Rio de Janeiro, Região Metropolitana do Rio de Janeiro, RJ, Região Sudeste, 22021-001, Brasil, (-22.9670027, -43.1790751, 0.0))"
473 |       ]
474 |      },
475 |      "execution_count": 14,
476 |      "metadata": {},
477 |      "output_type": "execute_result"
478 |     }
479 |    ],
480 |    "source": [
481 |     "geopy.geocoders.osm.Nominatim(user_agent=\"Intro Geocode\").reverse(end[0:1][\"yx\"])"
482 |    ]
483 |   },
484 |   {
485 |    "cell_type": "code",
486 |    "execution_count": 15,
487 |    "metadata": {},
488 |    "outputs": [],
489 |    "source": [
490 |     "end['RevAddress'] = end.apply(lambda row: geopy.geocoders.osm.Nominatim(user_agent=\"Intro Geocode\").reverse(row['yx']),axis=1)"
491 |    ]
492 |   },
493 |   {
494 |    "cell_type": "code",
495 |    "execution_count": 16,
496 |    "metadata": {},
497 |    "outputs": [
498 |     {
499 |      "data": {
500 |       "text/html": [
501 |        "<div>\n",
502 |        "<table border=\"1\" class=\"dataframe\">\n",
503 |        "  <thead>\n",
504 |        "    <tr style=\"text-align: right;\">\n",
505 |        "      <th></th>\n",
506 |        "      <th>Hotel</th>\n",
507 |        "      <th>Endereco</th>\n",
508 |        "      <th>Geom</th>\n",
509 |        "      <th>y</th>\n",
510 |        "      <th>x</th>\n",
511 |        "      <th>yx</th>\n",
512 |        "      <th>RevAddress</th>\n",
513 |        "    </tr>\n",
514 |        "  </thead>\n",
515 |        "  <tbody>\n",
516 |        "    <tr>\n",
517 |        "      <th>0</th>\n",
518 |        "      <td>Copacabana Palace</td>\n",
519 |        "      <td>Avenida Atlântica, 1702, Copacabana - Rio de J...</td>\n",
520 |        "      <td>POINT (-43.1790751 -22.9670027)</td>\n",
521 |        "      <td>-22.967003</td>\n",
522 |        "      <td>-43.179075</td>\n",
523 |        "      <td>-22.9670027, -43.1790751</td>\n",
524 |        "      <td>(Copacabana Palace, 1702, Avenida Atlântica, C...</td>\n",
525 |        "    </tr>\n",
526 |        "    <tr>\n",
527 |        "      <th>1</th>\n",
528 |        "      <td>Hotel Marina</td>\n",
529 |        "      <td>Avenida Delfim Moreira 696, Leblon - Rio de Ja...</td>\n",
530 |        "      <td>POINT (-43.222646 -22.9869118)</td>\n",
531 |        "      <td>-22.986912</td>\n",
532 |        "      <td>-43.222646</td>\n",
533 |        "      <td>-22.9869118, -43.222646</td>\n",
534 |        "      <td>(696, Avenida Delfim Moreira, Leblon, Zona Sul...</td>\n",
535 |        "    </tr>\n",
536 |        "    <tr>\n",
537 |        "      <th>2</th>\n",
538 |        "      <td>Windsor</td>\n",
539 |        "      <td>Avenida Atlantica, 1020, Copacabana – Rio de J...</td>\n",
540 |        "      <td>POINT (-43.189305 -22.984457)</td>\n",
541 |        "      <td>-22.984457</td>\n",
542 |        "      <td>-43.189305</td>\n",
543 |        "      <td>-22.984457, -43.189305</td>\n",
544 |        "      <td>(Petrobras, Avenida Atlântica, Copacabana, Zon...</td>\n",
545 |        "    </tr>\n",
546 |        "  </tbody>\n",
547 |        "</table>\n",
548 |        "</div>"
549 |       ],
550 |       "text/plain": [
551 |        "               Hotel                                           Endereco  \\\n",
552 |        "0  Copacabana Palace  Avenida Atlântica, 1702, Copacabana - Rio de J...   \n",
553 |        "1       Hotel Marina  Avenida Delfim Moreira 696, Leblon - Rio de Ja...   \n",
554 |        "2            Windsor  Avenida Atlantica, 1020, Copacabana – Rio de J...   \n",
555 |        "\n",
556 |        "                              Geom          y          x  \\\n",
557 |        "0  POINT (-43.1790751 -22.9670027) -22.967003 -43.179075   \n",
558 |        "1   POINT (-43.222646 -22.9869118) -22.986912 -43.222646   \n",
559 |        "2    POINT (-43.189305 -22.984457) -22.984457 -43.189305   \n",
560 |        "\n",
561 |        "                         yx                                         RevAddress  \n",
562 |        "0  -22.9670027, -43.1790751  (Copacabana Palace, 1702, Avenida Atlântica, C...  \n",
563 |        "1   -22.9869118, -43.222646  (696, Avenida Delfim Moreira, Leblon, Zona Sul...  \n",
564 |        "2    -22.984457, -43.189305  (Petrobras, Avenida Atlântica, Copacabana, Zon...  "
565 |       ]
566 |      },
567 |      "execution_count": 16,
568 |      "metadata": {},
569 |      "output_type": "execute_result"
570 |     }
571 |    ],
572 |    "source": [
573 |     "end.head()"
574 |    ]
575 |   },
576 |   {
577 |    "cell_type": "markdown",
578 |    "metadata": {},
579 |    "source": [
580 |     "## Geocoders disponíveis"
581 |    ]
582 |   },
583 |   {
584 |    "cell_type": "code",
585 |    "execution_count": 17,
586 |    "metadata": {},
587 |    "outputs": [
588 |     {
589 |      "data": {
590 |       "text/plain": [
591 |        "{'arcgis': geopy.geocoders.arcgis.ArcGIS,\n",
592 |        " 'azure': geopy.geocoders.azure.AzureMaps,\n",
593 |        " 'baidu': geopy.geocoders.baidu.Baidu,\n",
594 |        " 'bing': geopy.geocoders.bing.Bing,\n",
595 |        " 'databc': geopy.geocoders.databc.DataBC,\n",
596 |        " 'geocodeearth': geopy.geocoders.geocodeearth.GeocodeEarth,\n",
597 |        " 'geocodefarm': geopy.geocoders.geocodefarm.GeocodeFarm,\n",
598 |        " 'geonames': geopy.geocoders.geonames.GeoNames,\n",
599 |        " 'google': geopy.geocoders.googlev3.GoogleV3,\n",
600 |        " 'googlev3': geopy.geocoders.googlev3.GoogleV3,\n",
601 |        " 'here': geopy.geocoders.here.Here,\n",
602 |        " 'ignfrance': geopy.geocoders.ignfrance.IGNFrance,\n",
603 |        " 'liveaddress': geopy.geocoders.smartystreets.LiveAddress,\n",
604 |        " 'mapbox': geopy.geocoders.mapbox.MapBox,\n",
605 |        " 'nominatim': geopy.geocoders.osm.Nominatim,\n",
606 |        " 'opencage': geopy.geocoders.opencage.OpenCage,\n",
607 |        " 'openmapquest': geopy.geocoders.openmapquest.OpenMapQuest,\n",
608 |        " 'pelias': geopy.geocoders.pelias.Pelias,\n",
609 |        " 'photon': geopy.geocoders.photon.Photon,\n",
610 |        " 'pickpoint': geopy.geocoders.pickpoint.PickPoint,\n",
611 |        " 'tomtom': geopy.geocoders.tomtom.TomTom,\n",
612 |        " 'what3words': geopy.geocoders.what3words.What3Words,\n",
613 |        " 'yandex': geopy.geocoders.yandex.Yandex}"
614 |       ]
615 |      },
616 |      "execution_count": 17,
617 |      "metadata": {},
618 |      "output_type": "execute_result"
619 |     }
620 |    ],
621 |    "source": [
622 |     "geopy.geocoders.SERVICE_TO_GEOCODER"
623 |    ]
624 |   },
625 |   {
626 |    "cell_type": "code",
627 |    "execution_count": null,
628 |    "metadata": {},
629 |    "outputs": [],
630 |    "source": []
631 |   }
632 |  ],
633 |  "metadata": {
634 |   "kernelspec": {
635 |    "display_name": "Python 3",
636 |    "language": "python",
637 |    "name": "python3"
638 |   },
639 |   "language_info": {
640 |    "codemirror_mode": {
641 |     "name": "ipython",
642 |     "version": 3
643 |    },
644 |    "file_extension": ".py",
645 |    "mimetype": "text/x-python",
646 |    "name": "python",
647 |    "nbconvert_exporter": "python",
648 |    "pygments_lexer": "ipython3",
649 |    "version": "3.5.2"
650 |   }
651 |  },
652 |  "nbformat": 4,
653 |  "nbformat_minor": 2
654 | }
655 | 


--------------------------------------------------------------------------------
/Intro_PyGIS.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Introdução análise espacial com Python\n",
  8 |     "\n",
  9 |     "Neste tutorial vamos usar [geopandas](https://pypi.org/project/geopandas/) para carregar layer espacial, [fiona](https://pypi.org/project/Fiona/) para identificar as layers existentes, [matplotlib](https://matplotlib.org/) para 'plotar' a layer carregada (apresentação visual em forma de \"mapa\").\n",
 10 |     "Os dados são provenientes do mapeamento sistemático do [IBGE - \"BCIM 2016 em formato geopackage\"](https://www.ibge.gov.br/geociencias-novoportal/cartas-e-mapas/bases-cartograficas-continuas/15759-brasil.html?=&t=downloads)\n",
 11 |     "\n",
 12 |     "**Passos a serem executados:**\n",
 13 |     "1. Carregar bibliotecas;\n",
 14 |     "1. Identificar layers do Geopackage (mapeamento do IBGE 1:1000000);\n",
 15 |     "1. Carregar layer;\n",
 16 |     "1. Visualizar layer carregada;\n",
 17 |     "1. Conversão de coordenadas;\n",
 18 |     "1. Gerar um buffer;"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "markdown",
 23 |    "metadata": {},
 24 |    "source": [
 25 |     "## 1. Carregando bibliotecas"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 11,
 31 |    "metadata": {},
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "import geopandas as gpds\n",
 35 |     "%matplotlib inline\n",
 36 |     "import fiona"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "markdown",
 41 |    "metadata": {},
 42 |    "source": [
 43 |     "## 2. Identificando layers do Gepackage"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 12,
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/plain": [
 54 |        "['hid_massa_dagua_a',\n",
 55 |        " 'hid_trecho_massa_dagua_a',\n",
 56 |        " 'hid_barragem_l',\n",
 57 |        " 'hid_barragem_p',\n",
 58 |        " 'hid_trecho_drenagem_l',\n",
 59 |        " 'hid_sumidouro_vertedouro_p',\n",
 60 |        " 'lim_outros_limites_oficiais_l',\n",
 61 |        " 'lim_pais_a',\n",
 62 |        " 'lim_municipio_a',\n",
 63 |        " 'lim_unidade_federacao_a',\n",
 64 |        " 'hid_queda_dagua_l',\n",
 65 |        " 'lim_unidade_uso_sustentavel_a',\n",
 66 |        " 'lim_unidade_conservacao_nao_snuc_a',\n",
 67 |        " 'lim_unidade_protecao_integral_a',\n",
 68 |        " 'lim_area_desenvolvimento_controle_a',\n",
 69 |        " 'lim_terra_indigena_a',\n",
 70 |        " 'lim_terra_indigena_p',\n",
 71 |        " 'hid_corredeira_l',\n",
 72 |        " 'hid_corredeira_p',\n",
 73 |        " 'hid_ilha_a',\n",
 74 |        " 'hid_rocha_em_agua_a',\n",
 75 |        " 'adm_edif_pub_militar_a',\n",
 76 |        " 'adm_edif_pub_militar_p',\n",
 77 |        " 'adm_posto_fiscal_p',\n",
 78 |        " 'hid_banco_areia_a',\n",
 79 |        " 'hid_recife_a',\n",
 80 |        " 'hid_terreno_sujeito_inundacao_a',\n",
 81 |        " 'rel_curva_batimetrica_l',\n",
 82 |        " 'rel_curva_nivel_l',\n",
 83 |        " 'rel_ponto_cotado_batimetrico_p',\n",
 84 |        " 'rel_elemento_fisiografico_natural_l',\n",
 85 |        " 'rel_elemento_fisiografico_natural_p',\n",
 86 |        " 'rel_ponto_cotado_altimetrico_p',\n",
 87 |        " 'rel_duna_a',\n",
 88 |        " 'rel_pico_p',\n",
 89 |        " 'veg_mangue_a',\n",
 90 |        " 'veg_brejo_pantano_a',\n",
 91 |        " 'veg_veg_restinga_a',\n",
 92 |        " 'tra_travessia_l',\n",
 93 |        " 'tra_trecho_rodoviario_l',\n",
 94 |        " 'tra_tunel_l',\n",
 95 |        " 'tra_ponte_l',\n",
 96 |        " 'tra_travessia_p',\n",
 97 |        " 'tra_edif_metro_ferroviaria_p',\n",
 98 |        " 'tra_trecho_ferroviario_l',\n",
 99 |        " 'tra_trecho_duto_l',\n",
100 |        " 'tra_pista_ponto_pouso_p',\n",
101 |        " 'tra_trecho_hidroviario_l',\n",
102 |        " 'tra_eclusa_l',\n",
103 |        " 'tra_edif_constr_aeroportuaria_p',\n",
104 |        " 'tra_edif_const_portuaria_p',\n",
105 |        " 'tra_sinalizacao_p',\n",
106 |        " 'enc_est_gerad_energia_eletrica_p',\n",
107 |        " 'enc_hidreletrica_p',\n",
108 |        " 'enc_termeletrica_p',\n",
109 |        " 'eco_ext_mineral_a',\n",
110 |        " 'eco_ext_mineral_p',\n",
111 |        " 'loc_cidade_p',\n",
112 |        " 'loc_vila_p',\n",
113 |        " 'loc_aglomerado_rural_isolado_p',\n",
114 |        " 'loc_capital_p',\n",
115 |        " 'loc_aldeia_indigena_p',\n",
116 |        " 'loc_area_edificada_a',\n",
117 |        " 'eco_edif_agropec_ext_vegetal_pesca_p']"
118 |       ]
119 |      },
120 |      "execution_count": 12,
121 |      "metadata": {},
122 |      "output_type": "execute_result"
123 |     }
124 |    ],
125 |    "source": [
126 |     "fiona.listlayers(\"./bcim_2016_11_03.gpkg\")"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "markdown",
131 |    "metadata": {},
132 |    "source": [
133 |     "## 3. Carregando layer"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 14,
139 |    "metadata": {},
140 |    "outputs": [
141 |     {
142 |      "data": {
143 |       "text/html": [
144 |        "<div>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>nome</th>\n",
150 |        "      <th>nomeabrev</th>\n",
151 |        "      <th>geometriaaproximada</th>\n",
152 |        "      <th>sigla</th>\n",
153 |        "      <th>geocodigo</th>\n",
154 |        "      <th>geometry</th>\n",
155 |        "    </tr>\n",
156 |        "  </thead>\n",
157 |        "  <tbody>\n",
158 |        "    <tr>\n",
159 |        "      <th>0</th>\n",
160 |        "      <td>Goiás</td>\n",
161 |        "      <td></td>\n",
162 |        "      <td>Sim</td>\n",
163 |        "      <td>GO</td>\n",
164 |        "      <td>52</td>\n",
165 |        "      <td>(POLYGON ((-50.15876477687289 -12.415812044190...</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>1</th>\n",
169 |        "      <td>Mato Grosso</td>\n",
170 |        "      <td></td>\n",
171 |        "      <td>Sim</td>\n",
172 |        "      <td>MT</td>\n",
173 |        "      <td>51</td>\n",
174 |        "      <td>(POLYGON ((-60.10040697817874 -12.912708943845...</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>2</th>\n",
178 |        "      <td>Mato Grosso do Sul</td>\n",
179 |        "      <td></td>\n",
180 |        "      <td>Sim</td>\n",
181 |        "      <td>MS</td>\n",
182 |        "      <td>50</td>\n",
183 |        "      <td>(POLYGON ((-56.09815327835105 -17.172196303916...</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>3</th>\n",
187 |        "      <td>Rio Grande do Sul</td>\n",
188 |        "      <td></td>\n",
189 |        "      <td>Sim</td>\n",
190 |        "      <td>RS</td>\n",
191 |        "      <td>43</td>\n",
192 |        "      <td>(POLYGON ((-53.03425191679247 -27.154553165496...</td>\n",
193 |        "    </tr>\n",
194 |        "    <tr>\n",
195 |        "      <th>4</th>\n",
196 |        "      <td>Paraná</td>\n",
197 |        "      <td></td>\n",
198 |        "      <td>Sim</td>\n",
199 |        "      <td>PR</td>\n",
200 |        "      <td>41</td>\n",
201 |        "      <td>(POLYGON ((-52.0809035547552 -22.5289329222165...</td>\n",
202 |        "    </tr>\n",
203 |        "  </tbody>\n",
204 |        "</table>\n",
205 |        "</div>"
206 |       ],
207 |       "text/plain": [
208 |        "                 nome nomeabrev geometriaaproximada sigla geocodigo  \\\n",
209 |        "0               Goiás                           Sim    GO        52   \n",
210 |        "1         Mato Grosso                           Sim    MT        51   \n",
211 |        "2  Mato Grosso do Sul                           Sim    MS        50   \n",
212 |        "3   Rio Grande do Sul                           Sim    RS        43   \n",
213 |        "4              Paraná                           Sim    PR        41   \n",
214 |        "\n",
215 |        "                                            geometry  \n",
216 |        "0  (POLYGON ((-50.15876477687289 -12.415812044190...  \n",
217 |        "1  (POLYGON ((-60.10040697817874 -12.912708943845...  \n",
218 |        "2  (POLYGON ((-56.09815327835105 -17.172196303916...  \n",
219 |        "3  (POLYGON ((-53.03425191679247 -27.154553165496...  \n",
220 |        "4  (POLYGON ((-52.0809035547552 -22.5289329222165...  "
221 |       ]
222 |      },
223 |      "execution_count": 14,
224 |      "metadata": {},
225 |      "output_type": "execute_result"
226 |     }
227 |    ],
228 |    "source": [
229 |     "Estados = gpds.read_file(\"./bcim_2016_11_03.gpkg\", layer = \"lim_unidade_federacao_a\")\n",
230 |     "Estados.head()"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "markdown",
235 |    "metadata": {},
236 |    "source": [
237 |     "## 4. Visualizando layer carregada"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 15,
243 |    "metadata": {},
244 |    "outputs": [
245 |     {
246 |      "data": {
247 |       "text/plain": [
248 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c81e0dfd0>"
249 |       ]
250 |      },
251 |      "execution_count": 15,
252 |      "metadata": {},
253 |      "output_type": "execute_result"
254 |     },
255 |     {
256 |      "data": {
257 |       "image/png": 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\n",
258 |       "text/plain": [
259 |        "<matplotlib.figure.Figure at 0x7f9c81e159b0>"
260 |       ]
261 |      },
262 |      "metadata": {},
263 |      "output_type": "display_data"
264 |     }
265 |    ],
266 |    "source": [
267 |     "Estados.plot()"
268 |    ]
269 |   },
270 |   {
271 |    "cell_type": "code",
272 |    "execution_count": 16,
273 |    "metadata": {},
274 |    "outputs": [
275 |     {
276 |      "data": {
277 |       "text/plain": [
278 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c81a8e978>"
279 |       ]
280 |      },
281 |      "execution_count": 16,
282 |      "metadata": {},
283 |      "output_type": "execute_result"
284 |     },
285 |     {
286 |      "data": {
287 |       "image/png": 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N3ggPPfgQh/YeYkCL+28zVvbkclIMv2z7H3+MeYxAd/c7tnvgpwVoHN15rMftbmK/71lJVtZpLj9fvuCF8jLnoGDKOgjvPZmaTSNvOZafmcSB+VNY/BAMaUiJo1ZZFiw/DaN+lzCaLffZlKcnMPsbJVOYQumpDkENB4B6kiSFSJKkAx4B/rBxn5XOz4t+ZvDwwfywZT5XkmPtrU4RyVmWoAYn7Y0XmAWHD9J5zrf0WfAjq68Xd0zIzaZVnRbFyujYsD1x2fDrCdvrezMT20hE+Kk4v3MJsslUtF+WZU4sf5fBESqGRkh3nR5QqSQeaixheBvEOxIDIhxYuXo9Z86cQaPRIEkSYWFhtGrVCqPRaOvLUriHsamxFUKYgWeBjcBJYIkQ4rQt+7QHTk5OfPHlF7Ru3YYTsWXPc2ALzidcYOORLXg6OeN53QXsw21b+W7/PlrXa42Phz8zt27mu32WdIppOenFygn0CqSGmxuvbYarWZX7BvJxLxl9bjqX9t9IinNyzecEqlP4bkD5PC2+6K3nkbAkIiIiMJvNAMTExBAdHY1Op0OSJBwdHUlKSrLKNSgo/I3Nw3WFEH8KIRoIIeoLIT60dX/2pFPnThy+FF0l5m+drueAzdUXEpeVBcCGixfp2SySns26M7LLCHQaHfMO7SfIO5C29e+cR2BUt3Ek5ECzbypF9SL61LOMWuMPrSraZ0g4yuf3y3g6le+Nrp6PxMe9TFx8Ds5NhZw34Kv+0Psm7zq9Xk9AQACSZBk5p6enK0ESChVGiSCzIoWFhXh6etKrSXda1W1pb3VIzU5j2e7l5BRks2nc43Sf/wORTbpyX4P2gCXpjCzLpVosupR4mUXbl+DlBBE1VGwZK+Ootf0c7m8nBSOWgk6rwWw2o1UJDkyEJv626ftsqqDhf4s/9u677/Lmm2/apF+Fqo0SrlsF6dKpCz5mL5qFNLG3KgCYZTM/blmIE0YuZ6TzRK9xBHkHlUvW4YvRbD66Cb3JzJ+Pwv31KmfB7GCcYNomicQcwex+N0a8tiSzQFDnS8i4vVpPEfv376dt27Y210XB/ijGtgpSq2YtejToRkiNu/u2Vhap2WnsOrObYzEnCPMPZUy3keWWJcsy36z/ioy8PHrVgWUPg4fjv9OHdekJwdgVEg90HslvUYuL9ktIiOuucHq9Hp1OZy8VFSoJxdhWMYxGI66urrw4cCpajdbe6txGYkYS6bkZNKpV8ZCw1QfWcuTysaLtvx6D7mH/HqNbaBT4fQwmNLw1zpJr4Y25LwLQqUlXdp3YDkCLFi2Ijo62m54KlYNibKsYJ0+epHf33jzZ/TF7q1JpbDuxgx2nd6GRBD8OgYebgEYFx5Mg3IdKmde1BQaTwGEW1Amqx5MDJgOgN+rJLcjBx923yPACGAwGtNqq93BVsB7Vwc/2nuL48eP4efjaW41KpVuTLrwx7BUaBjfi0eXw4BKI+Aqafws7q47LcZlJyAUJaHmTl4aD1gEfd8v3+/6Ez3jqgSkALFiwoKjNmTNnyLru/aGgcDOKsbUi0dHReDp62luNSkelUjGkw2BCawSx+bKas6nwbHsVPevYW7PyM2ixBBK0rHdnl7gQ/zBcHFyYOHGiJWtbl65ERETg6enJoIEPsGbNmiJfXgUFxdhakUMHDuHnXsPeatiNR7uPI9gnlGB3+G9/UW2TvyTmCI4nCZrXbV1UFflOTH3olaLPO3bu4PH+k+jZ6n40WY5Mffq5oig1s9mMLFsn5aVC9USZs7Uigf4BDGszFG9XL3urYje+X/8F70YWMKFN9TS0ANJ0y/34+ugZuDnfOafEzcQmxQBQ2z+0aJ8Qgg0H1rD96Nbb2v/yyy+MHj26qF1iYiLHjh3j0qVLnDt3Dr1ez6effqpUCK4iKAtkVYjMzEwCAwKZ9sDz1XZEV1FOXT3DlugVJEyrvgtjYDG2apWaWU98YhV5QgjSslJwcnTmWspVFv45FwA3Nydycm448uqcHPGoHYTZYCD98jUAvvjiC6ZOnXrP3lNVhWpRFude4ciRI9T0q3lP/yj2nPmLlztLOFbjhfk1Zy0P/uGRo6wmU5IkfD39EEKQm5+DWqVmy7oQfliQzi9LCuj84fPoPFxvu3cS9x/n+eef551ZM/nogw+ZMGGC1XRSqHyUOVsrcfToUXxdqk56xcrmWuo1MnOzmNym+r6lxGULBl2PXWhmxXBrIQSxSTGs2LGU37cv4flnvejWxY1V6/Pxa90IB0+3Yh/SAe2a0v3rN/Fo14inJk2iefPmJCeXvTy9QtVAGdlaiX179uHr4mNvNezGlmMbmNhWhadT9TW2Q/5n+feNR2daVe6OY1v5c/8anB0deP+dAF6f5g9ATpaB/JMXSjxXpVZRd3B3fJrU4+Sc32nZpjXrVq+hWbNm9/RbVHVEGdlaiSPR0fh7+ttbDbuQlpNGSmYy0+6r3qvtB+MhuEZtXB1drSZTbyjkz/1rWDy/FnlpDYsMLcDI4Z6YCw2YjaYSJFjwrFuLjh8+h3uXZrRo0QKVSkVBQQmJGypAampqsfvT0tIYOnSokgGtnCjG1gqYTCYuxVymxj0W0PA3fx5ez0NN1NR0r74jrSlrLQbk4e6jrSp314ntuLs6MXLE7VNMb0yzFPPMuZJQKlmSJFGza2si//s6AO3v62D1hOcGg+GOHhDTpk1j69YtVu3vXkIxtuVg3rx5RblOO7TvwObNm3F3dUenufcSkuQW5pGQfpU3u1Rf5/29VwVf7Ycuzbrj42E9P+mzV0+z+dCfLPk5oNjjTZo4gQRnFq0pk1y1VkPk7NeIz05n6LAHreq/q9PpcHFxKfbY/PnzyczMVqYvyonNjK0kSdMlSbomSdLh6399bdWXLTGZTJw6dYpnn322yMA++eSTAHi6eLBv/z769euHr9u9OV+7MXoj3cLUNPCtnj9AWRY88YdELd8A+rUfZFXZF66dxctTQ78+d/bV7dbZBQe34o1bSah1WiKeHs7+08dp0qwpCQmlGx0r2A9bL5B9JoT4zMZ92IzJkyfz7bff3rZ/QOt+tKprqdmVmJlEgb6A2r61Kls9u2MymbiSdJY5jwosmQSqFzuvCL45IBGfo+H5R563uvxW4e3YdWI7mZkmPD2L/6nt2ldI2APh5ZLv4O5K46kjOfLFL4wY+QjvzZxF586d7xr1pmAfbG1sq98vEPjzzz/p168fAI5aR8b3HIu7kxs67e3TBAH36KIYwOZjfxHuK9Gx6qTuLRVCCL7YK/HmFvB2D+CR3sNtMgUUk3gJrVZzR0MLYDKY0bo6l7sPtU5Ly5fGEbt6G926daNH715s2bip3PIUbIetje0zkiQ9ChwEXhJCVOl0SEIIJkyYwLx58wAY0WkYDWqWb9Txb0eWZc7HHeWnoTLV6Zmali94crWarZclRveZSJ2gejbrKyywLiaTucSRLYA+M6dC/Riyc7m68zAqjZqtW7fi6+1J//79GDN2PMHBwfj5+XHs2DFq1apFvXr1lDlXO1EhYytJ0ibg5qGdBAjgTeAbYKYQQkiS9C7wGfBEcXJmzJhR9DkyMpLIyMiKqFVu/n79CvIO5PGe45SbsgT2nN2Hp4OZAfXtrUnpuZAmaPM9FJrNDOs2DoFACNslzAnwDsTLzZsff8rgxanFL7yNf9SL+T//hQSE9O1Urn5iN+wm2FXFF+PDydWbuZqu59uNq/l50ZKiNvVrepGWo0dGxXPPPcf0d2Yp93cJREVFERUVZVWZlZIbQZKkEGC1EKJZMceqRG6E7OxsPDw8GNNtJGE3JRNRKJ5v133Gp731jG1RPX6wcdmC4E8tn50cnVBJKgr0BbQKb0fPVn3wcLF+aszs/Gy+XPYR77zlycsv+N2xXZ1Gp7mWqqbrp9PK3EdObAL7P5zHJ8Nq0yCg5OkIIQQxaXo+2JDCf7+fz0MPPVTm/u5VqnTycEmSbvZ3eRA4Yau+rMHatWtRSSrF0JaCozHHQTYwsqm9NSk9fi4wux881UZCMhcQ0bAuY8aMISH7Kp8v/ZBCQ6FV+zOZTSz8cw5BQfDcMyX7Xzdu5IDOrezztkIWHPl6CeH+znc1tGAxGGG+jgxp6sLMt5UqwZWNLZctP5Yk6ZgkSUeAbsALNuyrQphMJkaNGkWof4i9VakW7D+7jde7gFZdPUa1YNF1SgeJ7waBlyPExlykU6dOvD39bZAgIyfdqv1l5WaQkBbP7r/C0OlK/pm1aOaEg0vZjW3K0bMYsnKZMbBs1ZJ7NfLk+OlzirtYJWMzYyuEGCuEaCaEaCGEGCKESLJVXxXlgw8+AGBIuwfsrEnV51LiZXIKcpjY2v5TP+WhwCiIy4HmbToW7fNw9yAhLc6q/UiS5aflWIrKwyG1dWRdTSyT/OyYeC6u2EKDIBdcHS1LLxl5JlYdSafQWHKQg06jIsTPne3bt5epT4WKoTjkAatWrKJx7Ua4OJbfBedeYduJjUztoMLVofqMam9mziHLLd+x4w1jiwQ5+dlW7cfLzRtHnSOde16+a4RX4whHzAYjxrzS5TpIPxvDgQ/n4Wgo5J1BwWw6lcWLv8Yw7sfzzNuRxL7LN7wbcvVm9Kbb+28RpGHeD3PLdlEKFUIxtsDI0SM5GXvK3mpUea6lxZGWk84LHapnwplrWYLn18t4eboTFHTj1dvD05OsfOt6JUqSxLNDX+L4qXw2bM4tse197V2QVBJppy6WSvax75YC8FZff+ZsS+S/W+JxctDjrJPwddXQurYlkc6hmFxGzTnH8G/P8tPuZE7F57PjXDbJ2UY613dn0+YtSnHKSuSeT7G4efNmpk0r+yrwvUZmbiZLd/zCs+3V1HCpnsZ2/B8qgmv68+Cw4bfsT0tJpXnz1hWWL8syZtmE9nqAxN+uVb173D2LWL16bmScjcG/TeMSXbKELDAX6AF4YcllAEbep6FLAw2TFxTy+SMhODtYxlDrjmcAEOipZs2xdNYcy0CrkcgtNPO3A9CuXbvo379/+S5YoUzc08Z2wpMTWL5sOZ0jOhJRq6G91anSbDy6hU614YOeZqpTEIMQgiUn4IOdKi6mCyZOfpiIiIii4waDgaycLOpX4PvPyc/mx3XfkZaditlsJrx2Q2Qhk5AaT90wNzSau79APv+0K8+/foLEvcdo88p43GoHFt/XVcuiVs48Nxw1IGOZg91wzJL9a9JPFxGAm6MaAJ0a4r++1dhfTDKz57yZcd8VsGrVKsXYVhL37DTCsWPH+GHeDzzefSzdm3a7p8NuS0OYXyh7r8qcTrG3JmVj7iF4fJVESJv+vPfRZ7cYWoALFy4gIeHm5FYu+UIIft36C1n5KSz5qSYnD4Xj6H6VJi0TeONVFw7vLl0s8+SJNchPaohsMqPPuvO0w/nfNgLgqAGNRoXuuiG/v5mWvHlu9Gis4bGuWnIKzeQUmpl6/+1hyHX91YzprKNhkMTcOXPKcdUK5eGeHdkuXLgQgKXbf6Vtgw40Uka2JdK2fmvi0q7RfcEpzkwR+DhXj9Gtgwb8fDwZNOj2jF6xsbF8+eWXNAppUq5oqtTMZBZtnk9qViqXToVTu5bFsB3ZV7dcum7dlgeAa807B0AE3teCzAtXWR1tYmjbWw2pwSzx8UhHWoaqmNRTR2KmzOA2d875MOcJJzrPzCc/Px9nZ2Vx2Nbck8b27NmzfPaZJRnZQGMeS/b9weYDa/DzrU3Xxl0I8in+Fe5e54F2g/jPilN8c0DirW721ubuyLJg8QkVLh7Fp7/09PREkiROXTlBXmEuLqWo0CCEICsvkw0H1nL0wmF693Bj3YpGpZoqKIn0dBOPP30VgF1vzMYzrCbZVxNx9PGk0WOD8QirCUBA+6ac/nk1szcYbjG2648YGfZlPgUGaFtXw663ndFqbv955xQIftll4I/DMm3qWHRWq9UV0l2hdNyTxvb8+fMAHPEPxFet5jVXN/YZDPyam8TivxbiqNbgoHNAo3PGydENLzdvanj4Eujlj7+H/z2bwi4tJx2jrOKDHTLjmkNtz6o9ut1yGXYb4rgQAAAgAElEQVRdgXc/eqbY4+7u7kyYMIE5c+YwZ/VXvDD8tRLlnYo5zuo9K8jJy6ZmkBOvvFiDj2aVLaDgTqxam821OCP/fcWL0CANB0/m8GAPP+6fmsrBj36kyYRh+LWKIG7HYQA+He2IEAKjGUZ9VcDvB4wMiXTk9CUzBy4aycgT+Hnc+v2YzAL3Jy0ubkMiHXl3hSVqTqe795Le24NKyY1QogJ2yI1QUFDAwN69OXrgAHt9auB00ytkgRCcMRqJN5uIN5uJFYIrZjOxJiOJRhMFQsZJpcJRo0Onc0Sjc8bD1Ys+LXrh/C/30121fzUp6Zdw18k80kTPjO721qhkMgoEEV+Bp38IL77yxh3bzZ8/n7179/JAp2F0aNSJ7PxsHHWOt6RdNBj1zFjwOkMfcOeHr2vh7W3dccqVKwZCG53m85d8eH7UjRG2LMvc/2wKm/fdHk7sqIVCI2hUMP0pDyYNc6VGrzi+e9yRp3o6kJEneHOpgVWH9MRn3PiNvTvZgzef8GT3kQI6PZGs1BQrBdbIjXBPjmydnJyY+dFHdO7cmRiTiQit9sYxSaKlTkdLin/aFwpBotlM3HVjHG/MZWdyBnPXf8sjkWPw96rchTZZlvlm/TfIsgmVJCEQOGgdaVf/PlrUaW7Vvi4mXGJcM1c8HDQsOJLA/3UVaKpwyK6Xk8S3AwUT15Ycljp+/HhSU1P5Y9fvbNi/Br1Rj4eLBy89/CYateUnsu/0HgCW/y/MJrqGhOioGaTlyFk9cMPYqlQqNn3jz5b9BYx4LZ3/POdOsL8GvVGwYHUusgwLZvji7qqizRjLdTaqqabQIPCemI1WDa0b6QgKhJfHujOi942qECYZtFq1TTOfKdzgnjS2YIkgeuKxx5j7+3I+u8nY3g1HSSJUoyH0pvmwqULwWV4u329ZwIAOQ2gY3MAWKt+GLMv8HLWIvMI83u7eBLVKQghYfuoqqw+uo1aNYHysWK4n1D+UvdeusHhYPb47lMj0bRLv9ajao6IzqRIu7nfP6PXyyy/z+Wefk5Obw8SJE5k+fToGo77I2MYmx1C/bvGFEK3BoiXpJCQaGdXXq9jjPds5kfZXzVv2Dexy65vUz7N8aDYigbHf5hOTavleCvfWuuO0164jRgYPGqAY2kri3px8xPJaMHnKFNYb9CzPz6+wrJdc3fjY3ZM1e1aw49QuK2lZMvP/Wkhs6jVe6NiQZgFeNPbzpIm/J692aYRWpeKb9XOITYm1Sl9CCBIzkwjzdMBRo6KRrzMX0qwi2mZkFghm74OWbTvevTEwZeoU4uLimD59OgHegTg5WIxZVm4mp2NOsHBuzbtIKDsmk0zTtucY88RV3n/Gkz733W7Q9QZRqlf9iDAd//ekB2l50Kqhjp0/lry+sG4PDBo8rEL6K5See9bYArRq1Yqp06bxZl4O6eaKV4cd4uzMYm9fDp3aycq9q6yg4Z2RZZn49ET6hQfRKeTWxNQOGjULh3Ug2MOZhVsXkZSZQmp2GgkZ5c/yVGgsJC07jVc6WTw1gtx0JJQchWp3PtylQta60bt371K1Lyy8MS86ddjLRSO+/Wf24OKs5b72ZS/MWBI5OSZCGpzhxKkCPn7Ok1fG3VoYMr9AZvr3WXh0vUr7ccmkZd79Hp3+lCfZO2tzaFEgnZo73rFdcrqZnYcz6NOnT4WvQ6F03LPTCH/z9ttvE713L0P27WOLmwfaCr5StXVwYL2vHw/Hn2PhpvmM7vFo0auotZBlmTkbLaV7BjYofjXc1UHLtE4RvLohmh83/4jpejKUmj5BuDq5IiHx96UKAQKBSpJQSSpkIRftE8Lyl309Ucuacxk80cqf8S1r0H9ROgMXq5gzUCbIvWq9iq44LZi9V/DMcxPRFOMCVRwHDhwAYObjHxftS81KYWv0Jh4dad3k4gaDTIv7LhCfaIn8igjV3vI6n5RmZui0VPadKKTeiKe4sGUJwf3iiAjT0baxlod6OtO7g2UUvHZHPm0bO+DnXbwLlxCCY+eNHDylx2CEzFyZndF6Irt1JCCg+DLrCtbnnvRG+CdCCFo2aUKDhEQ+c7KOR0GGLDM2I42rKi1DOj9EoFfFfHdjU2JZtW8VAkF2fh4CeKJ1XQZHBJd4XlJuIT8diaG2pwvXMnPZFZvCmGa+yLLg5pq4kgRmIZCFZZ9ashhjtUpCJVm2A1x1jG95YxS9LSaLFzZeocBgZtM46FS7ahjc5acEY1dIdOt5P4OHDC31eU899RQA70+4URB6z8md7DixjowE683Dn7+gZ9BDMSSkqWkw+X3OL51DYcwpUrYE4+Ik8fuWfLYf1vP9ijzCx03Ds54l6i3z0lmubllF1oXTAHz9mi/tm2hoM8aSnvHBHq589YoHgTVuPFxkWfD6V5l89ks2apWEs06Ns1pLur6Qd2Z9xMsvv2y16/o3Yw1vhAoZW0mSHgJmABFAWyHE4ZuOvQ48DpiA54QQG+8gw+7GFmDM6NEc+2M16909rCbTIATv5eWwKCcHL1dPOjftXubFs8SMJI7HniAxIwmzPoXedQMI83ZFhUQjPw/UqtJ9/7IQjPltN11ru/LNQOuuqI9cdo5zaXl8/wAMa2Rfg3s6RdD2e+g/ZDi9evUq07nvvfs+sVevMLbPEzQMaQxAWlYKs5f/B0NWo1LJSE01sXpdNiOGeeDicmOkKYQg+kgB+w7m8928dGJSPWj45BvoXC1hwsc+nEpNTz0/z/Km7aOJeNfwwLvbMPzbdLlFvmwykXx4N5f+WIRsNODsKBGsccNZpeWqIZe0Qj3LP6mBRmN5aE58L52EVDMv12lOV1/LA/+PpFjmXjmFLMvK4lgpqQrGtgGWXBjfA9P+NraSJEUAi4G2QDCwGahfnFWtCsZ27dq1DBw4kKbOzkSoNXTXaIjQaKlXgpfCtsJC/igs4FPP4lePbyZDlvkpP485uTlodY40C2/HfeHtb1u8WLXvD2JTr+Kg1VA/sAFxaXFcTr6xwPVKlwg6h9w5lLNEHQoMjPt9D8MbefNJH+tWpLiapeeNLbHsiM0l1EsizEtFoBsEusoEughquFjK0vg4g48TeDuBuwOoSvmgKAu9fpI4pw/g/96aXq7z33jjDdLS0pgw8BnCAutils28Pe8VPvsokOefLb5o49/s2JXLY09dI+aKHn8/LXXrOPLZhwEEBmh4fXoSy1ZkotbqcKwRRP1xL6G5yS/72NezyI69hIuThKNPIBFT3iuxr+zYS5z8dhYNXbz4oGE7JElClmWmntnNldxcXB3V5Baa0UoST9SOYIC/JUdDVGo8cxIv8te2KNq0aVOu/6N7Ebsb25sU2YqlVPnfxvY1QAghPrq+vR6YIYTYV8y5dje2MTExrF69GmdnZ06dOMHh3buJ2r+f7X7+1NHcbnB/zsvl9axMAK4FlfwafzN6IVhRkM8XublkyjLuLh60a9SJpiFN2HVmD9tPbKORnwfN/D355WgMTho1rg4anmhdl6xCI/3CKxat9MSKvWhVgt1PNK6QnDvx09EUTLIgNd9IYq6RlDwj2QaZfJNMocmSxLrQKFNoEphkcNaCu6OEt5OEj7NUZJT9nc34OIHXdcPs5WQpZePtBJ6O3NG312QWeHwAj02YTPPm5fMxzsjI4LXXXqO2fyiTHpgKwC+bfiS8aSwrlpT8RhDS8Aw5Lg2oP/xJLq9dQl78FfITLRUg3L1cqPfEWzjXKN4PO+ngDlK2/o4muCF1Bo0uGvGWxMVVv5C6ZyteOgeerNWQ+7wtsmPyc/DTOeGoUt3yQN+VnsiHF46wdetWu1Wwrq5U5aCGmsCem7bjru+rkoSGhjJlypRb9tWrXZt5Obm8d5OxzZZlpufmsLKwgICAALyyc/4pqkQcJIlHnF0Y4eTMOZOJLQY93x9Yx8aDa8k3ywS5OeGo0/HL0RgACkxmCkxmjqXkMql1xV/9fZ0dic+2nQvB2OYlj/xuJt8gcy1Hz9UsA4m5BpJyjSTlGTmfYuZAoYkCs6DQZKbQJGMwy+hNMnqTQG8CB43AzQE8HFXXDTXUcIFrmWYcHXTlNrQAXl5eeHp4Ep96DVnIliq8hnxq17q7L3bbVs78GXUBjaMTDR6eAEDi/u1onF3xbdKqxHP923S5bcrgbtTqOZiE3VsID43gswvH0Mac4JGguvTzq432H29NiYX5fHjhCO/OmqUYWjtxV2MrSdIm4ObHsQQI4E0hxGpbKWZvpr3xBk8//TTx11eyBbA5NYW+vXpxds4cJj06ls7Hy1cwWCVJNNRqaajVMtHZhYYJcejUKuJzCmjUvhOvPNKK5ORkTp48SWhoKFcO7bbKNTlp1bQoRRXWysBZpyLcx4lwn7IFCphkmYQcI/E5BhJyjSTmGkjJM5GSb2RbbA6DBw+ssG4vTXuJt956i+SMJAK8A8nITqdeHWfy82Wcne/sLTl2tAe/r4qBm8rgBLTrWmF97oTO1Q0HnQPJOensfPd/rD68jU9XzWVZwmXeb9iWYCdLJFqh2cy7V47z/rvv8vqbSlVde3FXYyuEKJ2T4q3EAbVu2g6+vq9YZsyYUfQ5MjKySjx5R44cSXBw8C31o8ZmZTFq1CjUajUOOh2BFciWJITgosnE/+VmoweOHDpc7Ijst99+4919O8rdz81Ex6fzTLvqnbdXo1JRy8OBWh4Ot+yfvS8RT081PXv2rHAffn5+1Amrw7crvwAJjCYjU6dlsnVbPsuX3Hm++4+1OTi5u0ApXc2sQcTkNzn99btEx5zhwXa9eKBVJCM+f47p5w4xPLAOXX0CeSfmGG0ju/Hq669Xml7VnaioKKKioqwq05pzttOEEIeubzcCFgHtsUwfbKIKL5CVh6F9+tD34CEGlsNVLN5sYnhGOlcMBvx9fVmzfv0dFytGj3yEzKN7rDKN8MH2UyTl5vHX2MoJJ65Mei26QGizdgwfMcIq8kwmE/v27SMqKoq4uFhkGVQqWLU0jAF93W9rP+WlBL76LpmmE1/Bo25EMRJtgyE3m/2znuODUS/Qv6Ul76Usy7y26FM2HLNEMg64vy8rVv+Btgxh6Qq3Yo052wpFkEmSNESSpKtAB2DN9YUwhBCngKXAKWAdMLlaWtQSUKvVlKUSlxCCX/JyeS0zgz7paTTo1IlTp06RmJJS4qrw7l27CHVzuOPxstAh2Ic8Q/WsH1YS0Ql5XEzNpVcpI8VKg0ajoUaNGqSlpaFzcqPp5P/DyVnHqdM3osx+XZZBvyExBISd5Kvvkgns2KtSDW127EVyYi8B0KNJh6L9KpWKgW0sKdkk4D9ffK4Y2ipAhd53hBArgZV3OPYB8EFF5FdlVGo1puuPD5MQnDAaidBqSZPNOCDh848phl8L8pmen8fAAQNonZ7On1u2lMrH8YGhD3Jo9VL6hldc58Px6eTojRUXVMVYejIVLy9PvLzu7oZXWoQQfPrpp2icnGn3xn9Q6XS4NGzPW7N2EpdgIifXzI8L03GtWRvXsJaEtvMnuEtfq/V/N0z5+Rz7+l0AwvyD0ahu/JRXH9rK138tYd26dfTt21fxpa0i3PPhuuUlNSuLqZnpnDEZ+Sb3zl4Jg5xdcFJJLM3NZdmyZQwbVrbEH8OGDWP27NmIjuEV/tHkGkzkG820+P5k0T4hBCazzIBwD/rW86RHmPWCOiqLs2mFNGliPZ9RWZb55JNPkCSJFlPfQXU9uXb9hx4nLiCY+Su2Yc7OpM7gMQR1rPgccXlQXZ8XfrrPI0zq/UjR/nPxMXy85kf27NtLo0alC8RQqBwUY1tOfl2+nPXr13Pk0CHmNGnC448/TlxcHLVq1cJkMjF79mxWrlzJ6p07eXDQILZNm0bXrmVfmf7hh7l4OjlYZXTyfMcGpObrLa4VWKJR9CYzS47HsulSLktPpvNOZDDjWpTehasqkG5UU9vb22ryTp8+zaVLl2j35ufo/pGesWbnPtTsbP/kLSqdjuAeg/h24xJ6N+tEXf9aFBj0/N+y2Xz08ceKoa2CKLkRqjiTJ09m+aKFzBnUxqavg7IQjF22h7ZBTswbXL6ChfbAYJJpMuc0r732GjVrWseV+7fffmNrVBQtX/oAR29fq8i0FTtfHY+j1oG5T81k2b6NpGv1RO3Yds+WbrIVdl8gU7A9zz33HEnZ+eyKtW0N8RWnrmEWgpnda929cRWiwCRjNBopKCiwmsz+/fvj7eXFwY9e5uDMKcgmk9VkW5tavYdgMBl59KtX+fPIDt5+Z7piaKsoyrdSxalfvz46nZaPd5zGLNvuDSCtQI+ng0RN9+pV/M/DUYNWqyErK8tqMl1cXOjRowcAskZr8fmqooT0GkzHD+fhGliL7r17lzn5jkLlUXXvIgXA4saTk2MJsR26eDuH49Nt0k/PMH/icozMPZRkE/m2xEGjtqqxBYpGh+61wqr8SNGYn0tuwlW+/fore6uiUAJV+y5SACylprdu3QrAjL+Os/Gi9Q1iXR83etQN4OdjVbzWTTFIWH8uW5ZldA6ONHx0yt0b25mEv1bz8MhRhIaG2lsVhRJQjG01ITIyEpPJxFtvvcWcAxf4YNtJfj8Zy+z9F1hyIpZcK/jPdgutQWyWnszCqjtHWRzOajNpaWnMnj2bWbNmWUWmn58fBn0hl9YssYo8W5F++giFF0/w1ewv7a2Kwl1QXL+qEWq1mpkzZxIUFMR3337DtrRMmjZtQZ4Eo35bS5i3G5GhNRjaqOyLXMm5hUzfcpyGNZzxdKxet0VagczmzZsBrOax0aRJEwAyThyEgY/cpbV9MORkEbvqF5b9bxG+vlXba0JBMbbVkkmTJjFp0qRb9i1atIgXX3yR+YcvoVGrGXSH2mR3Yn9cGg4aFcuH17emqpWC6qbA6VatWpGVlYWHhyU448qVK3z++ee4uLgwcOBA7rvvvrvKk2WZgwcPAuBSq45tlK4A+kzLvP2lRV/zzKSJ3H///XbWSKE0KH62/zK+/fZbJk+eDMCABkH4OjswqGEwOvWdZ4yScwuZvPoAHWu7sWBw1TMud2POwSTe3xlP07bNiDlzmfz8fLr36E6/vv147733SE+/sag4cOBABg4ciBAClUpFeno6sizj6+tLdnY233zzDZcvXy5qL0kqOn04zx6XVSy5cVc4MnsGAOOfeIJ5c+cq4biVQJWp1FAhBRRja3UuXrzI4sWLefvttwHoFOLHq13unCBl7sGLHIhLZq+NKjjYmqUn05i1M5HH33wWgJSEZDYt20BKksU3ecrM51GpVHz33jfoC24kknnggQdYu3Yt5pvK2Ds6OTJozGDUWg1LvllEzW79qNm1LzrX2zN9VTYph3dz9te5vPrqq4wfP54GDf592duqKoqxVbgrx44do3nz5rQM8qZDsA9eTjoOx6ejVakY2zKM9AIDz687xIsd/JnQuvrluhVCMHrFJeI0Hgx5fPgtx04fOUXc5Wv0GmoJrz2yN5qd67fj6etJRmoGEhJmsxmNVoO7lwcdetxH/SaWjD/6wkK+e/cbANq8+jGO3vYPYT76xdv4OGq4fOmSvVW556jKZXEUqgjNmjVj8+bNjHrkYX46GYePhwex8QkArD5ryefe1N+FcWUoaVOV2BGbQ3RCLg9Peei2YxEtGhHR4kaOgBYdWtKiQ8uibVmW2frHFu7r1Qln11vzEjs4OqJSa/Co36hKGFoArZcPakOevdVQKCeK69c9QM+ePUlKSSU3N48rcfEIIcjPz2fM6FEAnErOQ6epnrfC0lMZuPv64OHteffG/0ClUtFzSO/bDO3fyGYTzn4VK7JpTTSF+YweNdLeaiiUk+r5C1OoME5OTvz8yyK+/vprzALyDOa7n1QFydGbcHCxTV01lVqDVIWix8wOzmTnlK3IqELVoULTCJIkPQTMACKAtjeVMg8BTgNnrjfdK4SYXJG+FGyD2WzG28UBF13566nZixy9mW0x2bSNbGgT+V41vLkWtQ4nX3/8296eHtNsNJC4N4rYzSvRubrjFlKPOoNGoSlHqaTSIBXk0L0K1OdTKB8VfWwfB4YC24o5dkEI0er6n2Joqyj9+/cnPU9PTKbe3qqUmSOJebg76ejYq7NN5I95dgxanZbzy+bfdiw5eg97/u8p4jauoJZjDcwZGSQf2kXGufJVXL4bQghSYy4SGBhoE/kKtqeiZXHOAkjFO/opzn/VgLp169K1U0e+O3yBD3tYJx9sZZFZaMYWLqayLLNn8y5ad26DVqdD7X4jMbkxL5e4beu4tm09ga7+fNBnBgAm2cSTK6dgyM4g8/xJUo/sRRaCgPaRuIfUq7BOpnxLMqLWrVtXWJaCfbClN0KoJEmHgGzgLSHEThv2pVABnn52Cl+//YK91SgTBrPMwmNpeAVbfwHr0PYDHNx+gCO7ohEqCa86IZgNepIP7eLiyp9xdHJlYpvH6Fi7fdE5GpWGloHNib6eSyHctz4xGVdIPrSLwA7dqd17CNoK+Or+nVNXr9fj5ORUsQtUsAt3NbaSJG0CbnbAlLAUVnlTCLH6DqfFA7WFEBmSJLUCVkqS1EgIkVthjRWsyrp169i4cSN6c/XwddabZF7/K47lp1JRq2DYkwOtJjsvJ5fFX/6C3qDHQeNAmFcIZ1LPk3H2GCfmfETO1cu0D27D0+2eKPb85+6bhEk2kZCTRC0Py1vC+nMbWXF4PQl7txLQoTu+zdphzMnCo15EmQIlHDy8cPPx5cKFCzRt2tQq16tQudzV2AohylwfWghhBDKufz4sSdJFIBw4XFz7GTNmFH2OjIwkUlkEqDTmfvctK1ev4cPeIfZWpVS8sy2e9Rez6Na/OyH1Q/CqYb3aY5lpmeQX5jOiyVCC3AKp6R7Eyxv+D7O+ELccA+/3/wQ3R9cSZWhUmiJDC9AvvA/9wvuwIHoxew7vIXHvVlQaDRpnV5o+9RpOvqUPJDEZjCQmJirGthKIiooiKirKqjKtEkEmSdJWYJoQ4tD1bV8gXQghS5JUB8sCWlMhRGYx5yoRZHbEaDQyqG9vArIu8E4XP3urUyKyEDT++hgN2jan24Du5ZYTHxtP9M5Dlo3rc75N2zXHw8udBZ/9yNuRr1LHOxSAxcd+Y0jDATjrrONhIMuyJXT44HwOJp+g6aTXcAksXZa2na+Op06dOly8eNEquiiUHrvXIJMkaYgkSVeBDsAaSZLWXz/UFTgmSdJhYCnwVHGGVsH+aLVavpn7IxsvZ9tblbtyPCmfApNMs3bNKyTnr1Wbibkcg6mmhDEAkrJTWLnwd/Jy8tCo1LcY1lHNhlvN0MKNChCT2oyntpM/l9f+SuymVRz65HXOLZmLIafkihOXlFDdaktFvRFWAiuL2b8cWF4R2QqVR2BgIGnZ+QghqmQGqWNJ+Sw/nc7CIykE1w6s8NRBdnY24Q83I7SvJZFL+ulk9r//F+eOn0VSqTieeIKAej2soXqJdAnpwOLTK8mPvYTKLEhO3U1y9G4kjYagTr0J6z/C5jooVB5KbgQFnJyc0Go05Bpk3ByqVnDDgbhcJq6+jMnRmSZtm9G1X2SFZRr1Ri78foKUQ/EApBy35Io4uvcIXUI70tjvzhnSrEn3Ol3pXudGsEShyUBKXgr/O7aMU9vWU5iSQMS454qO1+49FO1l2/jxKtgeJeuXAgA1vD1p56+lppsWnSRQS+CokXBQq3DWqnDSqjibbmBYQ0/qeTtWik5/Xshk0prLhNarzaCxD1qt8OLpI6dIT75Ray0hNoG4mGv8MOQrNKqq8bB5c/NMkuU82r91o9zNzlfHA5YAB4XKRcn6pWA1Fv/6G6dPn0av16PX6zEajeTn5pBZUMDV7Czy83JZun81Db11lWZsX98cC8Dgx27P6FURbs4EBrDkq0V0CulQZQzt/MOLiMtOoMmT04r2GXMtc+rLlyuzc9UVxdgqANC7d296976zl9/evXtZtXYd99VyqxR9kvOMZBSacXaxvQO/Sq1i15W99K/fh5ru9g2Hjbq8k20xO6nVYyCe9W8kc8+6dBaAoUOH2ks1hQpSdVIaKVRpjhw5Qu/wGvi5aG3e1/GkfNrNPYFaJfHEy0/ZvL8RT4/EwcGBDRe22LyvkojLTmBB9CIAQu4fdsuxnNiLRHYvv7ubgv1RjK1CqdBqtey6nFHq9kcS8sqd3GbC6ktIksSzM19AVQl5dmVZRq/Xsz1ml837Kokfon/GqUYA9YY9dtux/KuX6FiKYpUKVRdlGkGhVAwfPpwJEyaw+Hgqo5oWXzb7ufUx7L6aQ0q+qWifViXxVreajC2hEsS+azk8tSaWVzr606m2G4m5RroPsr3r1d98O/MrAN7o+lKl9VkcVzJiCe75AAHtut12rCAlgZEjlcTh1RnF2CqUCnd3d3799VdGjBjBmou5aJH5vE9tvJ0tt9Do38+z66ol9UUz/wCmtO/AyjOnWXvuLG9vvca685l81KsWIZ6OPLc+hiNJhTzewgcvJzWvbIqj0GTiYkYhZiFw0Kpp1r5FpVzXxdMXMZlM9KzTjXDfimfnqggCQdrJg9TqOeg2f+fCvFwyMkr/ZqFQ9VCmERRKzfDhwzl//jxtBo1hW0wW982z+HwuPJLCrqu5TGrTjqjxTzK7/0Dq+/jycqcuRI1/knd79CYhV0O3BaepP/soWy7nUc87iOlR15i6/gp1vLzRqdUEuGrJM8roHB0q7ZrCGoTh6+vLlkvbSMlLrbR+i+OVjlPJi7/Kie8/QsjyLcccXNzYsWOHnTRTsAbKyFahTNSrV48vZ89m9n//i94sCP0iGoDHW7XmkabNij2nc0gInWrX5mJGOg5qDf6urujUagaGNyDA1Y2Ry35FLUG3UHcyCkzk7Iwn8VoCAcG29wxQqVTUbVqPvJ15+DhbL6lNWZBlmbXnNpCan0Ytj5pcvXyWrMvn8KzbECEEyQd3os/LwdnZNhUgFCoHxdgqlIu1a9cyYMAAxrVoycimzXHUlMEMPqYAAB8BSURBVHwrSZJEPW+fW/a1DLTkom3mH8CxpET6/Hym6NieTbsYOt66/rV3IiUxhQJjAYUmPc7ays8V+8WebziWdBKAmq1qojur5dLyH2g49kWyr5zHfGw3mzZtolevXpWum4L1UCLIFMqFEAKVSsWwRk2Y0r5DheWZZZmMwgJcdQ70/2UhDo4ONGgRUaHsXn8THxvP0T3ROLs6k52ZjYubCxEtGuMf7M8vXy4kIy2D3nW7M7p55eci+N+xZWy4sAVHD0c6Te2Ib31fjIVGTi47yYWoi5gNZqKjo2nRonLmsBWKR4kgU7Ab586dw8vVlSda/X979x0eVZU3cPz7SyeFJPSeBJCqEBABRSC00IsIAuIisLBSl13FVYQXWV8UwRUpCiKKr4iKgNJcUUoI0oO0ACFSAiEkhJoC6ZM57x8zwQCBJGQmkwnn8zx5cufcO/ec82TuL3fOPcUyy7Q4OjhQwd0DgDfbtue930KJPHi80MHWaDBiNBo5dyqKA9v3k2XIIvF6IgG+ftxQKSSn3yQq/SzHwsJvv+fVZ8bTpMrjFqlHYR29Ymr37vZeV1y9TG3Vzm7OBL4UiLOnM0mHk3WgLSV0sNUeyo8//EB7/wDcnS0/yKFjQG3e+y0UEWHFB59T76knaBnU6oHvMRgMrF/2I1cvXSUj687+ve90nEotnxp3pMXdjCfqxjla1ngSF0cXi9ehoMY9NYrp297lyskr1Gx517y2IgwaMMg2BdMsTgdb7aF8vHAhgwPqWOXcTg4OfD9wMAfiLpKSmcnirbuJDAun0dOBtGj7VJ7v2bh8HSrRyJgWIwnw8cPBwQFXR1cyszPxcr13dYVqXlWo5lXFKuUvjOnb3gXg2unr9wRbY4YRXx9fWxRLswIdbLVCS0xMJC4+Hqc6j1ktj8qenvSq1wCArnXrsezwITb8upPwXQepWqcmzdu0oHJ105Iyh/ccIvZ8LG+1e4065QLuOI+rk+3uWgtiQKO+rIlYT40W965sLE4QcTLCBqXSrKFID8hEZA7QG8gAzgIjlFLJ5n1TgJGAAZiklNp8n3PoB2R25r2ZM9ny1XLefrZdsU42npCWxg8nT3AgLpY/rl6lnK83NR7zJzzsKD3rd2VAo74lcvLz/Ez86XWyyxrpPffOxSujdkSReTiTIweP2qhkWg6bL4sDbAYaK6UCgdPAFHPBGgEvAA2B7sAiscerQLtHSkoK8+bO5eXHmxR7YPMtU4ZRzVuwpFdfvu4/kLruXoSHHaVvgx4MbNzPLgOt0WjkZuYtqjS6d+FHZVRUrFiy14XTCq5IwVYptVUplTPUZR+Q8xSiD7BSKWVQSp3HFIhbFiUvrWRYOH8+jSpUIMDXtm2JNb298XEzzavbIaAdF5Iukm5It2mZHsa8sMUANOxz5+oQcUfiiFz7B69OetUWxdKswJJttiOB78zb1YG9ufbFmtM0O5eelsapa9e4mJxEjbLeNi3LtdRUHitfmwX7PyU6MQZXZxdcXVwJ8PLHkJ1FltFAk8qNaOv/DJ4u9z4ku5B0kYtJsSRn3KRjQDtcirl9NzE9iRNXTtLspUA8K/1ZvpuXbnLws0Os/WHtA+cY1uxLvsFWRLYAub/jCKCAqUqpjeZjpgJZSqnv8jhFvmbMmHF7OygoiKCgoIc5jVYMnmzZkkszZ7I56iwjA5vbtCwGYzbRyTF0ad2e6Z3/yZWE61xNuM6hk+G4ubqRdCuZ7w+vJfLaGRpWrMeNtAQuJccTce0PHMWBzOys2+faGhXKe52mF0vANRgNXEu5zqoT63B0c8Tvab879v+x8RSDBw0mODjY6mXR8hYaGkpoaKhFz1nkEWQiMhwYDXRUSmWY094ElFJqtvn1L8DbSqn9ebxfPyCzIyJCvfLl+bR3Pxxs1EZqNBqZ+VsoYZcu8WLPAfRp3/W+x/59zlSiLkYD4FetJvX96nAz5RZXblyjYe3HGNL1OdIy0hn9v68xoHFfeta7/7ks5Z+/TiEhJREnNydaj2lF9eZ/fum7eekmP7+xiV27dtGmTRurl0UrGJuPIBORbsDrQLucQGu2AfhGRD7C1HxQFwgrSl5ayfDuu++ycM4HNgu0ANNCQ4i6lcrYQSN4ttmDhwq//vJ4DkWEs3TtCl4fNg7/ajXvOcbbqyyODo6cS4i2VpHvkJCSSOuxrfF7utY9+y6ExVC9RnWeeeaZYilLcRIR1q5dS4sWLcjIyKBOHev00y6pitobYSHgCWwRkUMisghAKRUBrAIigJ+Bcfr2tXR45ZVXiE9K5JfTp2yS/7fhRwi/epX3Jr5F+yefwTGfFXdrVq5G3w7dWP/RV3kG2hxjBgzjYNwRjHdNbWhp6QbTPYmz2733Oed3R3P8h+NsWL/BLntWFMSFCxcoX748lSvf2/uitCvSna1S6r692pVSs4BZRTm/VvKUL2+auev9Xb/R7bF6xZ5/yLlztAlsSaVyea8WcT+Ojg9eOTcy+izermUttlx6Xi4kXWTWjrkAnN58mmrNqt3ed3LdScJ/PMYniz6heXPbtoVby6N+v6UnD9cKbfLkydSvUvyr0H55+CAxN2/SttmD50l4GE3qNiTNaN2uY4v3f06aIY2Kvk7En7jM5YjLZKZkkpmSyenNZ9ixYwfjxo6zahk029HDdbVCM2Rl4WyDb7lboqPp16E7gfUtP0NX6yZPMnfFp7yzfTZVvaowoHFffMv4WDSPa2lXeGt4NerXcmPsB9Hs+mgXhoxsAFq2bkm7du0smp9WsuhgqxVaaEgIbWvc+3DHmubu3cOV5CTaNi/63Ll5cXcrw8AufVi9ZQNRCefZGxPGsuc+sdj5/7h6mqxsRXArH8p6OLJrSSMAriZk0XVSJOPHjrdYXlrJpJsRtEK7fOUKzlZs27yb0Whk67mzBLVo88CHXEX1cu8X+GnBCno828nUm9xCDEYD8/YuAqCsx51txzmvmzZtarkMtRJJ39lqhXbp8mVuVC6+6Qmn79iOp4cXI/oMLpb8ft61jaZFmEw8NTOVUzeiqOPjz5dHvuZUQgQVKzjxYnC1e451dXGgeaNKxMfH64BbyulgqxVKzhPl744f49Dly4xsGkgFDw+qenohInx59DCR16/zQacuOFng7nf/xRjCYi+yaMpsvL3KFvl8+cnp+pWUnlzg96Qb0ln6+1dEJZ8i8VYquZ+5N6vvybwRtWhWz/2O7lxKKU5dSGfH4VtEXbyFm3meB6300sFWKxQRYdmyZezdu5elS5cyefMv9xzj6upKtxVf4VehIq2rVWP0QwzrTU5P582QbZy5cZ2xLwynSoXimf3KkG0AoL3/g0dvHYg9xKZTvxKfGkdqhoFaVVwZ2t2HCj7l8K/qin9VV06eS6NlY497+sx+/fMVPlp5Gb+aVejRszf7D0ymXr3i70anFS+94KNWJEopjEYj0dHRhIeH07lzZ9zd3Tlw4ADR0dEMGjSIZX37U7tcwZcJXxS2j1UnjlPBpxwv9RzAE3Ubsi1sJ5XLVaBF42Z4e3pZtT4jZ/yDxJvJjGsximbVmmAwGlh9fB0+bmVJSEtiT9x+bqWlANCrjQ8tG3vStbU3zk7538n/sjeRtxbH0LhxA44diyi1gxdKG0sM19XBVrOqya++yr4NG3m3XVC+x2YaDCz+fT8bT5/CYMjG070MSikyMrMwZGfj6+1BQlIK5cr6kJaZTnDrIALrN6Z5gyb5DloojIzMTJ6fPBJnByeW9lvIPzdNISEtEYAyrm5MenE0jerU5x9z3uBGsinoju1fidH9/hwVNXZ2FPtPpLBwsj9tmvz5z+Gp4cfJNqpHvoO/vbH53Aialp8KlSphNBjyPe5CUiITfl2Hv58rn31Yg55dfG7f9aWmGrl8NZMAPzciTqURsjMZD3cPvlq5h1/2bMPRwYl6frXxKONBtjEbH8+yTBj814cus6uLC20CW7L7SBjDfxwLmFb/Xf3B5zg7OSMi7A0/iJurK2AKtlv3X+HLjVcY1KUcg7tUICzClH7kVArhZ9LIzBKcnYxkG3WQfVTpO1vNqn766SdeGjSIHwYOfuADs39v34ZzQAJff+JXqK/WYYduMW3WJbw8hes3DIiDcCIyjWYNHmf66NdwfsjVfzOzMgk/fZLMrEzq+dWhXFmfO4byDp8+jmuJ+T9EG/zCc1Sv4U9Zbx+UUsyePZuQkBBat7ZOf2HNOnQzglbiZWRk4OHhwexOXWhRvUaex/x65hQfH9rD3P+tTt/uRV8B4tTZdLq+EMWwnkPo2dbyk2+fvhDFP/8z/Y60pk2e4MO5H1GnTh18fX1xdna+/aPZP92MoJV4rq6u+NeoAfe5W90VHc3csJ2880Z1ene1zPDYenXcaPq4C39En7VosL2ZcoshU8bcfu3q4sK8+fMYM2asxfLQSi8dbDWry1aKHdHnaVHNNEl2alYWTg4OuDg6sj7yBD07l2PEkMLN4pUfF2fhcvzVIp/nj/Nn2HfsIO5uZfhq4yoA3NzcWL16Nb169crn3Zr2Jx1sNat7dfJkZk6dyuoTx4m4dpXtUWcB6PpYPc6l3ODltpbvQ5uWbuTE2T9ISUvFo4x7od//x/kz/Gf5Ii5du0LtgNp39Hbo0rkLzZo1s2RxtUeAbrPVrC4+Pp7xY8aSmZlBzVq1GDlqFG5ubnQIao2Xh4H1X9elSiXLtm1ev2GgYZvjNKnXiPcmvFXwsl67wvdb1rNl7w4AlixZwsiRI3F0dGT16tUMGjSICxcuULOm9eZo0Eoemz8gE5E5QG8gAzgLjFBKJYuIH3ASiDQfuk8pledEnTrYPrqaN2vEyEEpDOpX8AEPhbE5NImXxp5jysi/0yawZb7Hb93/G/O++YwxY8Ywa9YsfHwsO8WiZr8sEWyLOnh9M9BYKRUInAam5Np3RinV3PyjZ0TW8qDwcLfe7GHBQaal1uOuxpORmXn/UijFL7tDmPfNZ0ybNo3FixfrQKtZXFGXxdma6+U+4Plcr/U4RO2+MjMziYuLp8Fj1p897KuNq24/3PKrWpMFb7x7e+2ymPhYvtjwHYlpN9m9e3epXGhRKxkseVsxEtiU67W/iBwUke0i8qwF89FKgZCQEKpWdqSOv6tV89m8+jGWfxLA95/VxrusI9GXYvhpx2aSbiaz/9ghpnwyi7+MHsHxEyd0oNWsKt87WxHZAuReClMABUxVSm00HzMVyFJKfWs+Jg6opZRKEJHmwDoRaaSUupVXHjNmzLi9HRQURFBQ0ENURbMnO3fu5ImGTlafiCXwcY/b2327+bB81XWWrl3Bpj3buHj5kr6b1fIUGhpKaGioRc9Z5N4IIjIcGA10VEpl3OeY7cBrSqlDeezTD8geMdnZ2XQIepoeQZf461DL9q99EKPRyPHIdLb9lsys+fGULVuWpKSkYstfs182f0AmIt2A14E+uQOtiFQQEQfzdm2gLhBVlLy00mP37t3s3HWAvb/folLDIwwYeYa0dKPV83VwcKByBWdmzY+nY8f2JCYmWj1PTctR1K5fpwEX4Lo5aZ9SapyI9AfeATIBIzBdKfXzfc6h72wfMYmJiTRp0piYmLjbaXX8Xdm7qaFV81VK0X1IDN6+j7N12/Y7JpbRtAexeT9bS9DB9tFkMBhuT1ITGBjI0aNHuXIy8KHOlZhsYP6SywQ9W5YvVlzFxUX47EM/HBwciDyTxidfXOFWipFjEalciM0iOztbB1qtUHSw1UqFEydO8OSTgXz/mR9lvRxpVN+tQMHw7Pl0Nocm8/bsuDz316zuQlycAXcXN9rWDmTH2UMYRJGUlISrq3V7QWiliw62WqlRrWpVLsXHA1DJ150K5Z0wZAlNmghPNvWg47NeBPj9uSji8AnR/Lwt4fbrA5P+j/ib13ja7wlupCZTb/ZAAD7q8w+GP2WaMGZL5D5e+GYaqamplClTphhrp9k7HWy1UkNEeKX1c0zp+DKf7FmDh4spGK47sYOo67Ekp6dQs5or/rWc8fRwYNO2ZIY278acnhNwdynYyrQNZg+kSkBNjoQftWZVtFJIz2erlSp1KtTAu4wnb3UafjttUttBAERdj+Wjnd+RkZbF9mO/A9CiRoMCB1qAy7cSCA7U0yJqtqGDrWZzOd9s9kcfZ3SrvnkeU7t8dRb2mwxAhiGT1Mx0fN3LFjqvgwcPPnxBNa0IdDOCZlPx8fG0bNmSmJiYe/b9o+1gOtZtgadrGRpXro2LU9GmYfT9n84AREZGUr9+/SKdS3u06DZbzW5FRkYya9Ysli9fTk2fisTfTKCsqzupWelkG41kZt+7Im+z6vXZ9srHDz3Et++y1/nt3GGCuwTz6+Zfi1oF7RGig61md/bv30+HDh1IS0sDYFyr7rzR7jmcHBzvOTbTYMDJwYGlv2/lq8MhRCf+uczNypdm0rV+4VaojUm8TJMPh9KrR082/venolVEe6ToYKvZlS+//JIxY8ZQ0c2LqUED6NPgqULdpW47G87K8J2ERB0j3ZDF2Kf783bwKFydXAr0/pvpqdR6tw/ffvstQ4YMedhqaI8gHWw1u5CdnU2v7j34Zctmyrt7sX/MbMo4F21QwfJD23k7ZCU1fSqzc/xnBTpfry9eY/f5o0RFRREQEFCk/LVHi80notG0/KxduxYnJyf27NrF4fFzCZ84r8iBFmBY8w5sevl/OHs9ll7LXivQe+JvXWf48OE60Go2obt+aVaz9scf6f/881T29GbPK7NxK2Jvgrs1qFiD6R0G8c727xn23QyaVqtH/Yq1uJGaTHD9VlTxKn/72FsZaZy9dpFFuvlAsxHdjKBZRVhYGK1ataJx5VrM6TqMwKrWu5tcGb6Tf29fRXJ66h3pN97ZkvP1j7+v+5AtFw4RffGCnhdBKzQ9gkwrkXbu3Mnwl/7CxDa9efPZflbPb3CTtgxu0haAs9cv0e7zabg6OZNhyCI2+QrjN84l1Tmb5d98rQOtZjO6zVazqJSUFEb8ZRjdqzYqlkB7txVHfwMgZtpGwmJO0O3LV3lx4l85ciKc4ODgYi+PpuXQd7aaxcTExNCiWXPqeFfizfb9iz3/aynJfHZgMwA7zx1hzPo5rFq7hg4dOhR7WTTtbpZYg+wdoC+mFRkuA8OVUvHmfQuA7kCKOf1IHu/XbbalRP++/XCKvs68biNskv/6iDDGbVxy+/WqVasYOHCgTcqilS4lop+tiHjmrJorIhOBRkqpsSLSAxivlOopIq2A+Uqpe4b86GBr/5RSjB39N7Zu3MT6wf/Ct4ynzcoScjac93atJahvDxZ9uthm5dBKlxLRz/au5ck9MN3hAvQBlpuP2Q94i0hltFIlIiKC53r3Yc+vIWwa+pZNAy3ArcwM3Cp4s/CTj21aDk27m0UekInITBG5ALwITDcnVwdyT+UUa07TSgmDwcCIvwzD91IqS3u9gotj8T0CWHN8D6ev3bkcjlEZmf/7Jt6dMxtHx3vnWtA0WyrQ1SEiW4Dcd6UCKGCqUmqjUmoaME1E3gAmAjMKU4gZM/48PCgoiKCgoMK8XbOByMhIGjY0rYYbBvzfoRAAPuj2Mi82bWf1/Cf99wsAjv99Ph/t3sCB2DO80bY/rt6edOvWzer5a6VbaGgooaGhFj2nRQc1iEhN4L9KqSYi8imwXSn1vXlfJNBeKXX5rvfoNls7dO3aNSpVqoRSCg93d05ERLBgwQLmzp3La8/24dU2eU8CbinVZ//1nrTujZ6i57iXGT9+vFXz1h49JaLNVkTq5nrZD4g0b28AhpmPaQ0k3h1oNftVoUIFjEYjSilupaTg5+fHhx9+yIoVK/hw1wb6fDPLannnnuu2zTNtbm+HxZ1h6NChVstX04rCEm2274tIuIgcAToDkwCUUj8D50TkDLAEGGeBvLQSbujQoRw8eJCDF88wPWQlNzPS2HzmCJb89rIuYj8AS5YsYdmXy6hUzjQHwvyFC/Hx8bFYPppmSXpuBM0qvvjiC0aNGoWvuxcJqTeZ1KY3r7fp+9CrLOSW04Tw8ccfM2HCBABeGjyEr7/7tsjn1rS8lIhmBE3LS5cuXfCvUYuOwZ0JCwtj2ZHtbIg8QNJdk8UURnJGKr2WvwvAsGHDmDBhAn6+lahdy48lX3xuqaJrmlXoO1utWISEhBAcHEx2djYzO7/IM7UaUL9iwXsCxiZfp+XifwGmO9qGDRvSqVMnalWrzuaQbXoBR82qSsQIsqLSwfbRYTAYWL9+Pau/+Y7v1/7Akr5j6dWgRYHeO2rtJ2w9d4z09PQ7+tDGx8dTubIeK6NZl25G0OyKk5MTzz//PCt/XMO8efP4x6ZlbD59hAxD1gPft/TAFjadOkRWVtbtQFuzeg0SEhJ0oNXshr6z1WwmJCSETp06AbCg1yieb/x0nsc9vmASCWl/jgpfs2YN/fv3t8jDNk0rCD15uGbXOnbsSFZWFmvWrGHIkCF4uZQh+LFAAG6k3eKJBZP4V7vnaFCxOnsv/AFAVlYWTk76Y6vZH92MoNmUk5MTgwcP5m+jRrPowC+3Byy8vGY+AElpKWQ6wORXX8NgMOhAq9kt3YyglQjnz5+ncaPGZGdl4eboTFKGqYuYi5MTAbX8iDh9CgcHfW+g2YZ+QKaVGv7+/sTGxfL6m2/cDrQ9unQly5DNCy8O0YFWs3v6E6yVGN7e3tSuXRsAF0cnzp+IxNPdnRf1fAdaKaCDrVZiTJ06lZEjR/Jau35EvfYpzaoE0P+552jQoIGti6ZpRaafNmglxs2kZAY+0YYxTwYzbtPnxDlksG3RelsXS9MsQj8g00oMEaGKly/lvX1o3vZpPlv2BW5ubrYulqbp4bpa6ZIzSGHBvPlM+PtEPWhBKzH0oAatVMnOziY1NRVPT9suGqlp1lCkB2Qi8o6IHBWRwyLyi4hUMae3F5FEETlk/plmmeJqpZmDg4MOtFqpVdTeCHOUUk2VUs2A/wJv59r3m1KquflnZhHzKfEsvTicrZSGepSGOkDpqEdpqIOlFCnYKqVu5XrpARhzvX6kGtxKy4eqNNSjNNQBSkc9SkMdLKXIbbYiMhPTwo6JQIdcu1qLyGEgDnhdKRVR1Lw0TdPsVb53tiKyxbygY87PMfPv3gBKqWlKqVrAN8BE89sOAn7m5oWPgXXWqoCmaZo9sFjXLxGpCfyslHoij33ngCeVUjfy2Kf7fWmaVuLZtOuXiNRVSp0xv+wHnDSnV1ZKXTZvt8QU1O8JtFD0CmiaptmDorbZvi8i9TA9GIsGxpjTB4jIWCALSAMGFTEfTdM0u2bzEWSapmmPApvN+iUiTUVkr3lARJiIPJVr3wIROS0iR0Qk0FZlLAgRWZlr8MY5ETmUa98Ucz1OikiwLcuZHxGZaC7nMRF5P1e6PdXhbRG5mOvv0S3XPrupB4CIvCYiRhEplyvNnq6LPAc8mffZUz3mmD8zR0TkBxEpm2tf4T5TSimb/AC/AsHm7e7AdvN2D+C/5u1WwD5blfEh6vQfYJp5uyFwGFNTjT9wBvM3iZL2AwQBmwEn8+sK9lYHc3nfBl7NI93e6lED+AU4B5Qzp3W3p+sC8My1PRFYbN62q+sb6Aw4mLffB2aZtxsV9jNly/lsjYC3edsHiDVv9wGWAyil9gPeImIv61W/AHxr3u4LrFRKGZRS54HTQEtbFSwfY4H3lVIGAKXUNXO6PdUhR14PXO2tHh8Br9+V1hc7ui7U/Qc82dX1rZTaqpTKKfs+TP8IwVSPQn2mbBls/wn8R0QuAHOAKeb06kBMruNizWklmoi0BeKVUlHmJHuqRz2gnYjsE5HtIvKkOd2e6pBjvPkr3+cikvPP3G7qISJ9gBil1LG7dtlNHXKIyEzz9f0iMN2cbHf1yGUk8LN5u9D1sOqsXyKyBcj9X0sABUzFdHs+SSm1TkQGAMuALtYsz8N6UD2UUhvNaUOA74q7bAX1gDpMw/Q58FVKtTa3na8Gahd/KfOXz2dqEfCOUkqZRzZ+CIwq/lI+WD5/i7coodfB3fK7LpRS04BpIvIGpqaEGcVfyvwV5PoWkalAllLqoa9xqwZbpdR9PzQi8rVSapL5uDUi8rl5VyxQM9ehNfizicEmHlQPABFxBPoDzXMll6h65PO3GAP8aD7ugIhki0h5TOWtlevQEv+3yGUpkPOP0C7+FiLyOKb2v6Nimsy3BnDI3Fe9RNUBCvW3+BbTRFUzsMN6iMhwTG3NHXMlF7oetmxGiBWR9gAi0glTmwfABkxzLSAirYFEZR4gUYJ1AU4qpeJypW0ABouIi4gEAHWBMJuULn/rMH+QzP2mXZRS1zHVYZCd1IHcT7wx/fM7bt62i7+FUuq4UqqKUqq2UioAuAg0U0pdwc6uCxGpm+tlPyDSvG1v9eiGqf28j1IqI9euQn+mbDl5+GhggfmuMB34G4BS6mcR6SEiZ4AUYIQNy1hQg7irCUEpFSEiq4AITIM7xinzY8wS6EtgmYgcAzIwXwx2VgeAOeauREbgPPAK2GU9cijMD/zs8LrIc8CTHdZjIeACbDF92WCfUmrcw3ym9KAGTdO0YqCXMtc0TSsGOthqmqYVAx1sNU3TioEOtpqmacVAB1tN07RioIOtpmlaMdDBVtM0rRjoYKtpmlYM/h9zyeJtt5anuwAAAABJRU5ErkJggg==\n",
288 |       "text/plain": [
289 |        "<matplotlib.figure.Figure at 0x7f9cac5c0860>"
290 |       ]
291 |      },
292 |      "metadata": {},
293 |      "output_type": "display_data"
294 |     }
295 |    ],
296 |    "source": [
297 |     "Estados.plot(column = \"sigla\")"
298 |    ]
299 |   },
300 |   {
301 |    "cell_type": "code",
302 |    "execution_count": 17,
303 |    "metadata": {},
304 |    "outputs": [
305 |     {
306 |      "data": {
307 |       "text/plain": [
308 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c81a40c18>"
309 |       ]
310 |      },
311 |      "execution_count": 17,
312 |      "metadata": {},
313 |      "output_type": "execute_result"
314 |     },
315 |     {
316 |      "data": {
317 |       "image/png": 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\n",
318 |       "text/plain": [
319 |        "<matplotlib.figure.Figure at 0x7f9c81a5f6a0>"
320 |       ]
321 |      },
322 |      "metadata": {},
323 |      "output_type": "display_data"
324 |     }
325 |    ],
326 |    "source": [
327 |     "Estados.plot(column = \"geocodigo\")"
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "markdown",
332 |    "metadata": {},
333 |    "source": [
334 |     "## 5. Conversão de coordenadas"
335 |    ]
336 |   },
337 |   {
338 |    "cell_type": "code",
339 |    "execution_count": 18,
340 |    "metadata": {},
341 |    "outputs": [],
342 |    "source": [
343 |     "EstadosAlbers = Estados.to_crs(\"+proj=aea +lat_1=-5 +lat_2=-42 +lat_0=-32 +lon_0=-60 +x_0=0 +y_0=0 +ellps=aust_SA +units=m +no_defs\")"
344 |    ]
345 |   },
346 |   {
347 |    "cell_type": "code",
348 |    "execution_count": 19,
349 |    "metadata": {},
350 |    "outputs": [
351 |     {
352 |      "data": {
353 |       "text/plain": [
354 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c819252b0>"
355 |       ]
356 |      },
357 |      "execution_count": 19,
358 |      "metadata": {},
359 |      "output_type": "execute_result"
360 |     },
361 |     {
362 |      "data": {
363 |       "image/png": 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\n",
364 |       "text/plain": [
365 |        "<matplotlib.figure.Figure at 0x7f9c81abb710>"
366 |       ]
367 |      },
368 |      "metadata": {},
369 |      "output_type": "display_data"
370 |     }
371 |    ],
372 |    "source": [
373 |     "EstadosAlbers.plot()"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": 20,
379 |    "metadata": {},
380 |    "outputs": [
381 |     {
382 |      "data": {
383 |       "text/plain": [
384 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c8191a0f0>"
385 |       ]
386 |      },
387 |      "execution_count": 20,
388 |      "metadata": {},
389 |      "output_type": "execute_result"
390 |     },
391 |     {
392 |      "data": {
393 |       "image/png": 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\n",
394 |       "text/plain": [
395 |        "<matplotlib.figure.Figure at 0x7f9c81abb278>"
396 |       ]
397 |      },
398 |      "metadata": {},
399 |      "output_type": "display_data"
400 |     }
401 |    ],
402 |    "source": [
403 |     "EstadosAlbers[0:1].plot()"
404 |    ]
405 |   },
406 |   {
407 |    "cell_type": "markdown",
408 |    "metadata": {},
409 |    "source": [
410 |     "## 6. Gerando um buffer"
411 |    ]
412 |   },
413 |   {
414 |    "cell_type": "code",
415 |    "execution_count": 21,
416 |    "metadata": {},
417 |    "outputs": [],
418 |    "source": [
419 |     "GO = EstadosAlbers[0:1]"
420 |    ]
421 |   },
422 |   {
423 |    "cell_type": "code",
424 |    "execution_count": 23,
425 |    "metadata": {},
426 |    "outputs": [
427 |     {
428 |      "data": {
429 |       "text/html": [
430 |        "<div>\n",
431 |        "<table border=\"1\" class=\"dataframe\">\n",
432 |        "  <thead>\n",
433 |        "    <tr style=\"text-align: right;\">\n",
434 |        "      <th></th>\n",
435 |        "      <th>nome</th>\n",
436 |        "      <th>nomeabrev</th>\n",
437 |        "      <th>geometriaaproximada</th>\n",
438 |        "      <th>sigla</th>\n",
439 |        "      <th>geocodigo</th>\n",
440 |        "      <th>geometry</th>\n",
441 |        "    </tr>\n",
442 |        "  </thead>\n",
443 |        "  <tbody>\n",
444 |        "    <tr>\n",
445 |        "      <th>0</th>\n",
446 |        "      <td>Goiás</td>\n",
447 |        "      <td></td>\n",
448 |        "      <td>Sim</td>\n",
449 |        "      <td>GO</td>\n",
450 |        "      <td>52</td>\n",
451 |        "      <td>POLYGON ((485842.7699637936 1675286.84426624, ...</td>\n",
452 |        "    </tr>\n",
453 |        "  </tbody>\n",
454 |        "</table>\n",
455 |        "</div>"
456 |       ],
457 |       "text/plain": [
458 |        "    nome nomeabrev geometriaaproximada sigla geocodigo  \\\n",
459 |        "0  Goiás                           Sim    GO        52   \n",
460 |        "\n",
461 |        "                                            geometry  \n",
462 |        "0  POLYGON ((485842.7699637936 1675286.84426624, ...  "
463 |       ]
464 |      },
465 |      "execution_count": 23,
466 |      "metadata": {},
467 |      "output_type": "execute_result"
468 |     }
469 |    ],
470 |    "source": [
471 |     "GO[:][\"geometry\"] = GO.buffer(100000)\n",
472 |     "GO.head()"
473 |    ]
474 |   },
475 |   {
476 |    "cell_type": "code",
477 |    "execution_count": 24,
478 |    "metadata": {},
479 |    "outputs": [
480 |     {
481 |      "data": {
482 |       "text/plain": [
483 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f9c819304a8>"
484 |       ]
485 |      },
486 |      "execution_count": 24,
487 |      "metadata": {},
488 |      "output_type": "execute_result"
489 |     },
490 |     {
491 |      "data": {
492 |       "image/png": 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\n",
493 |       "text/plain": [
494 |        "<matplotlib.figure.Figure at 0x7f9c818fa780>"
495 |       ]
496 |      },
497 |      "metadata": {},
498 |      "output_type": "display_data"
499 |     }
500 |    ],
501 |    "source": [
502 |     "GO.plot()"
503 |    ]
504 |   },
505 |   {
506 |    "cell_type": "markdown",
507 |    "metadata": {},
508 |    "source": [
509 |     "# Alguns links importantes:\n",
510 |     "\n",
511 |     "* [Como instalar jupyter-notebook](https://goo.gl/4GMSqL)\n",
512 |     "* [Repositório grupo de estudos em python](https://github.com/hugonbg/grupo_de_estudos_python)\n",
513 |     "* [Color maps](https://matplotlib.org/users/colormaps.html)"
514 |    ]
515 |   }
516 |  ],
517 |  "metadata": {
518 |   "kernelspec": {
519 |    "display_name": "Python 3",
520 |    "language": "python",
521 |    "name": "python3"
522 |   },
523 |   "language_info": {
524 |    "codemirror_mode": {
525 |     "name": "ipython",
526 |     "version": 3
527 |    },
528 |    "file_extension": ".py",
529 |    "mimetype": "text/x-python",
530 |    "name": "python",
531 |    "nbconvert_exporter": "python",
532 |    "pygments_lexer": "ipython3",
533 |    "version": "3.5.2"
534 |   }
535 |  },
536 |  "nbformat": 4,
537 |  "nbformat_minor": 2
538 | }
539 | 


--------------------------------------------------------------------------------
/ListaEnderecos.csv:
--------------------------------------------------------------------------------
1 | Hotel,Endereco
2 | Copacabana Palace,"Avenida Atlântica, 1702, Copacabana - Rio de Janeiro"
3 | Hotel Marina,"Avenida Delfim Moreira 696, Leblon - Rio de Janeiro"
4 | Windsor,"Avenida Atlantica, 1020, Copacabana – Rio de Janeiro"
5 | 


--------------------------------------------------------------------------------