ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
edgar_xbrl.ipynb
(387333B)
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 3,
6 "metadata": {
7 "ExecuteTime": {
8 "end_time": "2018-12-25T19:24:34.848734Z",
9 "start_time": "2018-12-25T19:24:34.840640Z"
10 },
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12 },
13 "outputs": [],
14 "source": [
15 "from io import BytesIO\n",
16 "from zipfile import ZipFile, BadZipFile\n",
17 "import requests\n",
18 "from datetime import date, datetime\n",
19 "from pathlib import Path\n",
20 "import pandas_datareader.data as web\n",
21 "import datetime\n",
22 "import pandas as pd\n",
23 "import json\n",
24 "import re\n",
25 "from pprint import pprint\n",
26 "from bs4 import BeautifulSoup\n",
27 "from collections import Counter\n",
28 "import matplotlib.pyplot as plt\n",
29 "import matplotlib.dates as mdates\n",
30 "import matplotlib.ticker as mticker"
31 ]
32 },
33 {
34 "cell_type": "code",
35 "execution_count": 4,
36 "metadata": {
37 "ExecuteTime": {
38 "end_time": "2018-12-25T19:50:48.310725Z",
39 "start_time": "2018-12-25T19:50:48.300551Z"
40 },
41 "scrolled": true
42 },
43 "outputs": [],
44 "source": [
45 "plt.style.use('fivethirtyeight')\n",
46 "data_path = Path('data') # perhaps set to external harddrive to accomodate large amount of data"
47 ]
48 },
49 {
50 "cell_type": "markdown",
51 "metadata": {},
52 "source": [
53 "## Dowload FS & Notes"
54 ]
55 },
56 {
57 "cell_type": "markdown",
58 "metadata": {},
59 "source": [
60 "The following code downloads and extracts all historical filings contained in the [Financial Statement and Notes](https://www.sec.gov/dera/data/financial-statement-and-notes-data-set.html) (FSN) datasets for the given range of quarters:"
61 ]
62 },
63 {
64 "cell_type": "markdown",
65 "metadata": {},
66 "source": [
67 "**Downloads over 40GB of data!**"
68 ]
69 },
70 {
71 "cell_type": "code",
72 "execution_count": 3,
73 "metadata": {
74 "ExecuteTime": {
75 "end_time": "2018-12-25T19:40:50.833183Z",
76 "start_time": "2018-12-25T19:24:34.869085Z"
77 },
78 "scrolled": false
79 },
80 "outputs": [
81 {
82 "name": "stdout",
83 "output_type": "stream",
84 "text": [
85 "2014 1 2014 2 2014 3 2014 4 2015 1 2015 2 2015 3 2015 4 2016 1 2016 2 2016 3 2016 4 2017 1 2017 2 2017 3 2017 4 2018 1 2018 2 2018 3 2018 4 2019 1 "
86 ]
87 }
88 ],
89 "source": [
90 "SEC_URL = 'https://www.sec.gov/files/dera/data/financial-statement-and-notes-data-sets/'\n",
91 "\n",
92 "today = pd.Timestamp(date.today())\n",
93 "this_year = today.year\n",
94 "this_quarter = today.quarter\n",
95 "\n",
96 "past_years = range(2014, this_year)\n",
97 "filing_periods = [(y, q) for y in past_years for q in range(1, 5)]\n",
98 "filing_periods.extend([(this_year, q) for q in range(1, this_quarter + 1)])\n",
99 "for i, (yr, qtr) in enumerate(filing_periods, 1):\n",
100 " print(yr, qtr, end=' ', )\n",
101 " filing = f'{yr}q{qtr}_notes.zip'\n",
102 " path = data_path / f'{yr}_{qtr}' / 'source'\n",
103 " if not path.exists():\n",
104 " path.mkdir(exist_ok=True, parents=True)\n",
105 "\n",
106 " response = requests.get(SEC_URL + filing).content\n",
107 " try:\n",
108 " with ZipFile(BytesIO(response)) as zip_file:\n",
109 " for file in zip_file.namelist():\n",
110 " local_file = path / file\n",
111 " if local_file.exists():\n",
112 " continue\n",
113 " with local_file.open('wb') as output:\n",
114 " for line in zip_file.open(file).readlines():\n",
115 " output.write(line)\n",
116 " except BadZipFile:\n",
117 " continue"
118 ]
119 },
120 {
121 "cell_type": "markdown",
122 "metadata": {},
123 "source": [
124 "## Save to parquet"
125 ]
126 },
127 {
128 "cell_type": "markdown",
129 "metadata": {},
130 "source": [
131 "The data is fairly large and to enable faster access than the original text files permit, it is better to convert the text files to binary, columnar parquet format (see Section 'Efficient data storage with pandas' in chapter 2 for a performance comparison of various data-storage options compatible with pandas DataFrames):"
132 ]
133 },
134 {
135 "cell_type": "code",
136 "execution_count": 4,
137 "metadata": {
138 "ExecuteTime": {
139 "end_time": "2018-12-25T19:49:47.370017Z",
140 "start_time": "2018-12-25T19:40:50.834705Z"
141 },
142 "scrolled": true
143 },
144 "outputs": [],
145 "source": [
146 "for f in data_path.glob('**/*.tsv'):\n",
147 " file_name = f.stem + '.parquet'\n",
148 " path = Path(f.parents[1]) / 'parquet'\n",
149 " if (path / file_name).exists():\n",
150 " continue\n",
151 " if not path.exists():\n",
152 " path.mkdir(exist_ok=True)\n",
153 " try:\n",
154 " df = pd.read_csv(f, sep='\\t', encoding='latin1', low_memory=False)\n",
155 " except:\n",
156 " print(f)\n",
157 " df.to_parquet(path / file_name)"
158 ]
159 },
160 {
161 "cell_type": "markdown",
162 "metadata": {},
163 "source": [
164 "## Metadata json"
165 ]
166 },
167 {
168 "cell_type": "code",
169 "execution_count": 5,
170 "metadata": {
171 "ExecuteTime": {
172 "end_time": "2018-12-25T19:49:47.512610Z",
173 "start_time": "2018-12-25T19:49:47.371067Z"
174 },
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177 "outputs": [
178 {
179 "name": "stdout",
180 "output_type": "stream",
181 "text": [
182 "{'@context': 'http://www.w3.org/ns/csvw',\n",
183 " 'dialect': {'delimiter': '\\t', 'header': True, 'headerRowCount': 1},\n",
184 " 'tables': [{'tableSchema': {'aboutUrl': 'readme.htm',\n",
185 " 'columns': [{'datatype': {'base': 'string',\n",
186 " 'maxLength': 20,\n",
187 " 'minLength': 20},\n",
188 " 'dc:description': 'Accession Number. '\n",
189 " 'The 20-character '\n",
190 " 'string formed '\n",
191 " 'from the 18-digit '\n",
192 " 'number assigned '\n",
193 " 'by the Commission '\n",
194 " 'to each EDGAR '\n",
195 " 'submission.',\n",
196 " 'name': 'adsh',\n",
197 " 'required': 'true',\n",
198 " 'titles': ['Accession Number']},\n",
199 " {'datatype': {'base': 'decimal',\n",
200 " 'maxLength': 10,\n",
201 " 'minInclusive': 0},\n",
202 " 'dc:description': 'Central Index Key '\n",
203 " '(CIK). Ten digit '\n",
204 " 'number assigned '\n",
205 " 'by the Commission '\n",
206 " 'to each '\n",
207 " 'registrant that '\n",
208 " 'submits filings.',\n",
209 " 'name': 'cik',\n",
210 " 'titles': ['Central Index Key']},\n",
211 " {'datatype': {'base': 'string',\n",
212 " 'maxLength': 150},\n",
213 " 'dc:description': 'Name of '\n",
214 " 'registrant. This '\n",
215 " 'corresponds to '\n",
216 " 'the name of the '\n",
217 " 'legal entity as '\n",
218 " 'recorded in EDGAR '\n",
219 " 'as of the filing '\n",
220 " 'date.',\n",
221 " 'name': 'name',\n",
222 " 'titles': ['Registrant']},\n",
223 " {'datatype': {'base': 'string',\n",
224 " 'maxLength': 4},\n",
225 " 'dc:description': 'Standard '\n",
226 " 'Industrial '\n",
227 " 'Classification '\n",
228 " '(SIC). Four digit '\n",
229 " 'code assigned by '\n",
230 " 'the Commission as '\n",
231 " 'of the filing '\n",
232 " 'date, indicating '\n",
233 " \"the registrant's \"\n",
234 " 'type of business.',\n",
235 " 'name': 'sic',\n",
236 " 'titles': ['Standard Industrial '\n",
237 " 'Classification Code']},\n",
238 " {'datatype': {'base': 'string',\n",
239 " 'maxLength': 2,\n",
240 " 'minLength': 2},\n",
241 " 'dc:description': 'The ISO 3166-1 '\n",
242 " 'country of the '\n",
243 " \"registrant's \"\n",
244 " 'business address.',\n",
245 " 'name': 'countryba',\n",
246 " 'titles': ['Business Address Country',\n",
247 " 'Country (B)']},\n",
248 " {'datatype': {'base': 'string',\n",
249 " 'maxLength': 2,\n",
250 " 'minLength': 2},\n",
251 " 'dc:description': 'The state or '\n",
252 " 'province of the '\n",
253 " \"registrant's \"\n",
254 " 'business address, '\n",
255 " 'if field '\n",
256 " 'countryba is US '\n",
257 " 'or CA.',\n",
258 " 'name': 'stprba',\n",
259 " 'titles': ['Business Address State '\n",
260 " 'or Province',\n",
261 " 'State (B)']},\n",
262 " {'datatype': {'base': 'string',\n",
263 " 'maxLength': 30},\n",
264 " 'dc:description': 'The city of the '\n",
265 " \"registrant's \"\n",
266 " 'business address.',\n",
267 " 'name': 'cityba',\n",
268 " 'titles': ['Business Address City',\n",
269 " 'City (B)']},\n",
270 " {'datatype': {'base': 'string',\n",
271 " 'maxLength': 10},\n",
272 " 'dc:description': 'The zip code of '\n",
273 " \"the registrant's \"\n",
274 " 'business address.',\n",
275 " 'name': 'zipba',\n",
276 " 'titles': ['Business Address Zip or '\n",
277 " 'Postal Code',\n",
278 " 'Zip (B)']},\n",
279 " {'datatype': {'base': 'string',\n",
280 " 'maxLength': 40},\n",
281 " 'dc:description': 'The first line of '\n",
282 " 'the street of the '\n",
283 " \"registrant's \"\n",
284 " 'business address.',\n",
285 " 'name': 'bas1',\n",
286 " 'titles': ['Business Address Street '\n",
287 " '1',\n",
288 " 'Street1 (B)']},\n",
289 " {'datatype': {'base': 'string',\n",
290 " 'maxLength': 40},\n",
291 " 'dc:description': 'The second line '\n",
292 " 'of the street of '\n",
293 " \"the registrant's \"\n",
294 " 'business address.',\n",
295 " 'name': 'bas2',\n",
296 " 'titles': ['Business Address Street '\n",
297 " '2',\n",
298 " 'Street2 (B)']},\n",
299 " {'datatype': {'base': 'string',\n",
300 " 'maxLength': 12},\n",
301 " 'dc:description': 'The phone number '\n",
302 " 'of the '\n",
303 " \"registrant's \"\n",
304 " 'business address.',\n",
305 " 'name': 'baph',\n",
306 " 'titles': ['Business Address Phone',\n",
307 " 'Phone (B)']},\n",
308 " {'datatype': {'base': 'string',\n",
309 " 'maxLength': 2,\n",
310 " 'minLength': 2},\n",
311 " 'dc:description': 'The ISO 3166-1 '\n",
312 " 'country of the '\n",
313 " \"registrant's \"\n",
314 " 'mailing address.',\n",
315 " 'name': 'countryma',\n",
316 " 'titles': ['Mailing Address Country',\n",
317 " 'Country (M)']},\n",
318 " {'datatype': {'base': 'string',\n",
319 " 'maxLength': 2,\n",
320 " 'minLength': 2},\n",
321 " 'dc:description': 'The state or '\n",
322 " 'province of the '\n",
323 " \"registrant's \"\n",
324 " 'mailing address, '\n",
325 " 'if field '\n",
326 " 'countryma is US '\n",
327 " 'or CA.',\n",
328 " 'name': 'stprma',\n",
329 " 'titles': ['Mailing Address State or '\n",
330 " 'Province',\n",
331 " 'State (M)']},\n",
332 " {'datatype': {'base': 'string',\n",
333 " 'maxLength': 30},\n",
334 " 'dc:description': 'The city of the '\n",
335 " \"registrant's \"\n",
336 " 'mailing address.',\n",
337 " 'name': 'cityma',\n",
338 " 'titles': ['Mailing Address City',\n",
339 " 'City (M)']},\n",
340 " {'datatype': {'base': 'string',\n",
341 " 'maxLength': 12},\n",
342 " 'dc:description': 'The zip code of '\n",
343 " \"the registrant's \"\n",
344 " 'mailing address.',\n",
345 " 'name': 'zipma',\n",
346 " 'titles': ['Mailing Address Zip or '\n",
347 " 'Postal Code',\n",
348 " 'Zip (M)']},\n",
349 " {'datatype': {'base': 'string',\n",
350 " 'maxLength': 40},\n",
351 " 'dc:description': 'The first line of '\n",
352 " 'the street of the '\n",
353 " \"registrant's \"\n",
354 " 'mailing address.',\n",
355 " 'name': 'mas1',\n",
356 " 'titles': ['Mailing Address Street1',\n",
357 " 'Street1 (M)']},\n",
358 " {'datatype': {'base': 'string',\n",
359 " 'maxLength': 40},\n",
360 " 'dc:description': 'The second line '\n",
361 " 'of the street of '\n",
362 " \"the registrant's \"\n",
363 " 'mailing address.',\n",
364 " 'name': 'mas2',\n",
365 " 'titles': ['Mailing Address Street2',\n",
366 " 'Street1 (M)']},\n",
367 " {'datatype': {'base': 'string',\n",
368 " 'maxLength': 2,\n",
369 " 'minLength': 2},\n",
370 " 'dc:description': 'The country of '\n",
371 " 'incorporation for '\n",
372 " 'the registrant.',\n",
373 " 'name': 'countryinc',\n",
374 " 'titles': ['Country of Incorporation',\n",
375 " 'Incorporation Country']},\n",
376 " {'datatype': {'base': 'string',\n",
377 " 'maxLength': 2,\n",
378 " 'minLength': 2},\n",
379 " 'dc:description': 'The state or '\n",
380 " 'province of '\n",
381 " 'incorporation for '\n",
382 " 'the registrant, '\n",
383 " 'if countryinc is '\n",
384 " 'US or CA, '\n",
385 " 'otherwise NULL.',\n",
386 " 'name': 'stprinc',\n",
387 " 'titles': ['State or Province of '\n",
388 " 'Incorporation',\n",
389 " 'Incorporation State']},\n",
390 " {'datatype': {'base': 'string',\n",
391 " 'maxLength': 9},\n",
392 " 'dc:description': 'Employee '\n",
393 " 'Identification '\n",
394 " 'Number, 9 digit '\n",
395 " 'identification '\n",
396 " 'number assigned '\n",
397 " 'by the Internal '\n",
398 " 'Revenue Service '\n",
399 " 'to business '\n",
400 " 'entities '\n",
401 " 'operating in the '\n",
402 " 'United States.',\n",
403 " 'name': 'ein',\n",
404 " 'titles': ['EIN',\n",
405 " 'Employee Identification '\n",
406 " 'Number']},\n",
407 " {'datatype': {'base': 'string',\n",
408 " 'maxLength': 150},\n",
409 " 'dc:description': 'Most recent '\n",
410 " 'former name of '\n",
411 " 'the registrant, '\n",
412 " 'if any.',\n",
413 " 'name': 'former',\n",
414 " 'titles': ['Former Name']},\n",
415 " {'datatype': {'base': 'string',\n",
416 " 'maxLength': 8,\n",
417 " 'minLength': 8},\n",
418 " 'dc:description': 'Date of change '\n",
419 " 'from the former '\n",
420 " 'name, if any.',\n",
421 " 'name': 'changed',\n",
422 " 'titles': ['Date of Name Change']},\n",
423 " {'datatype': {'base': 'string',\n",
424 " 'maxLength': 5},\n",
425 " 'dc:description': 'Filer status with '\n",
426 " 'the Commission at '\n",
427 " 'the time of '\n",
428 " 'submission: '\n",
429 " '1-LAF=Large '\n",
430 " 'Accelerated, '\n",
431 " '2-ACC=Accelerated, '\n",
432 " '3-SRA=Smaller '\n",
433 " 'Reporting '\n",
434 " 'Accelerated, '\n",
435 " '4-NON=Non-Accelerated, '\n",
436 " '5-SML=Smaller '\n",
437 " 'Reporting Filer, '\n",
438 " 'NULL=not '\n",
439 " 'assigned.',\n",
440 " 'name': 'afs',\n",
441 " 'titles': ['Status',\n",
442 " 'Accelerated Filer '\n",
443 " 'Status']},\n",
444 " {'datatype': {'base': 'decimal',\n",
445 " 'maxInclusive': 1,\n",
446 " 'minInclusive': 0},\n",
447 " 'dc:description': 'Well Known '\n",
448 " 'Seasoned Issuer '\n",
449 " '(WKSI). An issuer '\n",
450 " 'that meets '\n",
451 " 'specific '\n",
452 " 'Commission '\n",
453 " 'requirements at '\n",
454 " 'some point during '\n",
455 " 'a 60-day period '\n",
456 " 'preceding the '\n",
457 " 'date the issuer '\n",
458 " 'satisfies its '\n",
459 " 'obligation to '\n",
460 " 'update its shelf '\n",
461 " 'registration '\n",
462 " 'statement.',\n",
463 " 'name': 'wksi',\n",
464 " 'titles': ['Well-known Seasoned '\n",
465 " 'Issuer']},\n",
466 " {'datatype': {'base': 'string',\n",
467 " 'maxLength': 4},\n",
468 " 'dc:description': 'Fiscal Year End '\n",
469 " 'Date.',\n",
470 " 'name': 'fye',\n",
471 " 'titles': ['FY End Date']},\n",
472 " {'datatype': {'base': 'string',\n",
473 " 'maxLength': 20},\n",
474 " 'dc:description': 'The submission '\n",
475 " 'type of the '\n",
476 " \"registrant's \"\n",
477 " 'filing.',\n",
478 " 'name': 'form',\n",
479 " 'titles': ['Submission Type',\n",
480 " 'Filing Type',\n",
481 " 'EDGAR Form Type']},\n",
482 " {'datatype': {'base': 'string',\n",
483 " 'maxLength': 8,\n",
484 " 'minLength': 8},\n",
485 " 'dc:description': 'Balance Sheet '\n",
486 " 'Date.',\n",
487 " 'name': 'period',\n",
488 " 'titles': ['Report Period',\n",
489 " 'Date of Balance Sheet']},\n",
490 " {'datatype': {'base': 'string',\n",
491 " 'maxLength': 4,\n",
492 " 'minLength': 4},\n",
493 " 'dc:description': 'Fiscal Year Focus '\n",
494 " '(as defined in '\n",
495 " 'EFM Ch. 6).',\n",
496 " 'name': 'fy',\n",
497 " 'titles': ['Fiscal Year']},\n",
498 " {'datatype': {'base': 'string',\n",
499 " 'maxLength': 2,\n",
500 " 'minLength': 2},\n",
501 " 'dc:description': 'Fiscal Period '\n",
502 " 'Focus (as defined '\n",
503 " 'in EFM Ch. 6) '\n",
504 " 'within Fiscal '\n",
505 " 'Year. The 10-Q '\n",
506 " 'for the 1st, 2nd '\n",
507 " 'and 3rd quarters '\n",
508 " 'would have a '\n",
509 " 'fiscal period '\n",
510 " 'focus of Q1, Q2 '\n",
511 " '(or H1), and Q3 '\n",
512 " '(or M9) '\n",
513 " 'respectively, and '\n",
514 " 'a 10-K would have '\n",
515 " 'a fiscal period '\n",
516 " 'focus of FY.',\n",
517 " 'name': 'fp',\n",
518 " 'titles': ['Fiscal Period']},\n",
519 " {'datatype': {'base': 'string',\n",
520 " 'maxLength': 8},\n",
521 " 'dc:description': 'The date of the '\n",
522 " \"registrant's \"\n",
523 " 'filing with the '\n",
524 " 'Commission.',\n",
525 " 'name': 'filed',\n",
526 " 'titles': ['Date Filed']},\n",
527 " {'datatype': {'base': 'date',\n",
528 " 'format': 'YYYYMMDD '\n",
529 " 'HH:MM:SS.S'},\n",
530 " 'dc:description': 'The acceptance '\n",
531 " 'date and time of '\n",
532 " \"the registrant's \"\n",
533 " 'filing with the '\n",
534 " 'Commission. '\n",
535 " 'Filings accepted '\n",
536 " 'after 5:30pm EST '\n",
537 " 'are considered '\n",
538 " 'filed on the '\n",
539 " 'following '\n",
540 " 'business day.',\n",
541 " 'name': 'accepted',\n",
542 " 'titles': ['Acceptance Datetime']},\n",
543 " {'datatype': {'base': 'decimal',\n",
544 " 'maxInclusive': 255,\n",
545 " 'minInclusive': 0},\n",
546 " 'dc:description': 'Previous Report. '\n",
547 " 'TRUE indicates '\n",
548 " 'that the '\n",
549 " 'submission '\n",
550 " 'information was '\n",
551 " 'subsequently '\n",
552 " 'amended prior to '\n",
553 " 'the end cutoff '\n",
554 " 'date of the data '\n",
555 " 'set.',\n",
556 " 'name': 'prevrpt',\n",
557 " 'required': 'true',\n",
558 " 'titles': ['Previous Report Flag',\n",
559 " 'Subsequently Amended '\n",
560 " 'Flag']},\n",
561 " {'datatype': {'base': 'decimal',\n",
562 " 'maxInclusive': 255,\n",
563 " 'minInclusive': 0},\n",
564 " 'dc:description': 'TRUE indicates '\n",
565 " 'that the XBRL '\n",
566 " 'submission '\n",
567 " 'contains '\n",
568 " 'quantitative '\n",
569 " 'disclosures '\n",
570 " 'within the '\n",
571 " 'footnotes and '\n",
572 " 'schedules at the '\n",
573 " 'required detail '\n",
574 " 'level (e.g., each '\n",
575 " 'amount).',\n",
576 " 'name': 'detail',\n",
577 " 'required': 'true',\n",
578 " 'titles': ['Detail Tagged']},\n",
579 " {'datatype': {'base': 'string',\n",
580 " 'maxLength': 32},\n",
581 " 'dc:description': 'The name of the '\n",
582 " 'submitted XBRL '\n",
583 " 'Instance Document '\n",
584 " '(EX-101.INS) type '\n",
585 " 'data file. The '\n",
586 " 'name often begins '\n",
587 " 'with the company '\n",
588 " 'ticker symbol.',\n",
589 " 'name': 'instance',\n",
590 " 'titles': ['Instance Filename']},\n",
591 " {'datatype': {'base': 'decimal',\n",
592 " 'maxInclusive': 32767,\n",
593 " 'minInclusive': 0},\n",
594 " 'dc:description': 'Number of Central '\n",
595 " 'Index Keys (CIK) '\n",
596 " 'of registrants '\n",
597 " '(i.e., business '\n",
598 " 'units) included '\n",
599 " 'in the '\n",
600 " 'consolidating '\n",
601 " \"entity's \"\n",
602 " 'submitted filing.',\n",
603 " 'name': 'nciks',\n",
604 " 'required': 'true',\n",
605 " 'titles': ['Number of '\n",
606 " 'Coregistrants']},\n",
607 " {'datatype': {'base': 'string',\n",
608 " 'maxLength': 120},\n",
609 " 'dc:description': 'Additional CIKs '\n",
610 " 'of co-registrants '\n",
611 " 'included in a '\n",
612 " 'consolidating '\n",
613 " \"entity's EDGAR \"\n",
614 " 'submission, '\n",
615 " 'separated by '\n",
616 " 'spaces. If there '\n",
617 " 'are no other '\n",
618 " 'co-registrants '\n",
619 " '(i.e., nciks = '\n",
620 " '1), the value of '\n",
621 " 'aciks is NULL. '\n",
622 " 'For a very small '\n",
623 " 'number of filers, '\n",
624 " 'the list of '\n",
625 " 'co-registrants is '\n",
626 " 'too long to fit '\n",
627 " 'in the field. '\n",
628 " 'Where this is the '\n",
629 " 'case, PARTIAL '\n",
630 " 'will appear at '\n",
631 " 'the end of the '\n",
632 " 'list indicating '\n",
633 " 'that not all '\n",
634 " \"co-registrants' \"\n",
635 " 'CIKs are included '\n",
636 " 'in the field; '\n",
637 " 'users should '\n",
638 " 'refer to the '\n",
639 " 'complete '\n",
640 " 'submission file '\n",
641 " 'for all CIK '\n",
642 " 'information.',\n",
643 " 'name': 'aciks',\n",
644 " 'titles': ['Additional Coregistrant '\n",
645 " 'CIKs']},\n",
646 " {'datatype': {'base': 'decimal'},\n",
647 " 'dc:description': 'Public float, in '\n",
648 " 'USD, if provided '\n",
649 " 'in this '\n",
650 " 'submission.',\n",
651 " 'name': 'pubfloatusd',\n",
652 " 'titles': ['Public Float']},\n",
653 " {'datatype': {'base': 'string',\n",
654 " 'maxLength': 8},\n",
655 " 'dc:description': 'Date on which the '\n",
656 " 'public float was '\n",
657 " 'measured by the '\n",
658 " 'filer.',\n",
659 " 'name': 'floatdate',\n",
660 " 'titles': ['Public Float Measurement '\n",
661 " 'Date']},\n",
662 " {'datatype': {'base': 'string',\n",
663 " 'maxLength': 255},\n",
664 " 'dc:description': 'If the public '\n",
665 " 'float value was '\n",
666 " 'computed by '\n",
667 " 'summing across '\n",
668 " 'several tagged '\n",
669 " 'values, this '\n",
670 " 'indicates the '\n",
671 " 'nature of the '\n",
672 " 'summation.',\n",
673 " 'name': 'floataxis',\n",
674 " 'titles': ['Public Float Axis']},\n",
675 " {'datatype': {'base': 'decimal',\n",
676 " 'maxInclusive': 255,\n",
677 " 'minInclusive': 0},\n",
678 " 'dc:description': 'If the public '\n",
679 " 'float was '\n",
680 " 'computed, the '\n",
681 " 'number of terms '\n",
682 " 'in the summation.',\n",
683 " 'name': 'floatmems',\n",
684 " 'titles': ['Public Float Members']}],\n",
685 " 'primaryKey': 'adsh'},\n",
686 " 'url': 'sub.tsv'},\n",
687 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
688 " 'columns': [{'datatype': {'base': 'string',\n",
689 " 'maxLength': 256},\n",
690 " 'dc:description': 'The unique '\n",
691 " 'identifier (name) '\n",
692 " 'for a tag in a '\n",
693 " 'specific taxonomy '\n",
694 " 'release.',\n",
695 " 'name': 'tag',\n",
696 " 'required': 'true',\n",
697 " 'titles': ['Localname']},\n",
698 " {'datatype': {'base': 'string',\n",
699 " 'maxLength': 20},\n",
700 " 'dc:description': 'For a standard '\n",
701 " 'tag, an '\n",
702 " 'identifier for '\n",
703 " 'the taxonomy; '\n",
704 " 'otherwise the '\n",
705 " 'accession number '\n",
706 " 'where the tag was '\n",
707 " 'defined.',\n",
708 " 'name': 'version',\n",
709 " 'required': 'true',\n",
710 " 'titles': ['Namespace', 'Taxonomy']},\n",
711 " {'datatype': {'base': 'decimal',\n",
712 " 'maxInclusive': 1,\n",
713 " 'minInclusive': 0},\n",
714 " 'dc:description': '1 if tag is '\n",
715 " 'custom '\n",
716 " '(version=adsh), 0 '\n",
717 " 'if it is '\n",
718 " 'standard. Note: '\n",
719 " 'This flag is '\n",
720 " 'technically '\n",
721 " 'redundant with '\n",
722 " 'the version and '\n",
723 " 'adsh fields.',\n",
724 " 'name': 'custom',\n",
725 " 'required': 'true',\n",
726 " 'titles': []},\n",
727 " {'datatype': {'base': 'decimal',\n",
728 " 'maxInclusive': 1,\n",
729 " 'minInclusive': 0},\n",
730 " 'dc:description': '1 if the tag is '\n",
731 " 'not used to '\n",
732 " 'represent a '\n",
733 " 'numeric fact.',\n",
734 " 'name': 'abstract',\n",
735 " 'required': 'true',\n",
736 " 'titles': []},\n",
737 " {'datatype': {'base': 'string',\n",
738 " 'maxLength': 20},\n",
739 " 'dc:description': 'If abstract=1, '\n",
740 " 'then NULL, '\n",
741 " 'otherwise the '\n",
742 " 'data type (e.g., '\n",
743 " 'monetary) for the '\n",
744 " 'tag.',\n",
745 " 'name': 'datatype',\n",
746 " 'titles': []},\n",
747 " {'datatype': {'base': 'string',\n",
748 " 'maxLength': 1},\n",
749 " 'dc:description': 'If abstract=1, '\n",
750 " 'then NULL; '\n",
751 " 'otherwise, I if '\n",
752 " 'the value is a '\n",
753 " 'point in time, or '\n",
754 " 'D if the value is '\n",
755 " 'a duration.',\n",
756 " 'name': 'iord',\n",
757 " 'titles': ['Instant or Duration']},\n",
758 " {'datatype': {'base': 'string',\n",
759 " 'maxLength': 1},\n",
760 " 'dc:description': 'If datatype = '\n",
761 " 'monetary, then '\n",
762 " \"the tag's natural \"\n",
763 " 'accounting '\n",
764 " 'balance from the '\n",
765 " 'perspective of '\n",
766 " 'the balance sheet '\n",
767 " 'or income '\n",
768 " 'statement (debit '\n",
769 " 'or credit); if '\n",
770 " 'not defined, then '\n",
771 " 'NULL.',\n",
772 " 'name': 'crdr',\n",
773 " 'titles': ['Credit or Debit']},\n",
774 " {'datatype': {'base': 'string',\n",
775 " 'maxLength': 512},\n",
776 " 'dc:description': 'If a standard '\n",
777 " 'tag, then the '\n",
778 " 'label text '\n",
779 " 'provided by the '\n",
780 " 'taxonomy, '\n",
781 " 'otherwise the '\n",
782 " 'text provided by '\n",
783 " 'the filer. A tag '\n",
784 " 'which had neither '\n",
785 " 'would have a NULL '\n",
786 " 'value here.',\n",
787 " 'name': 'tlabel',\n",
788 " 'titles': ['Label']},\n",
789 " {'datatype': {'base': 'string',\n",
790 " 'maxLength': 2048},\n",
791 " 'dc:description': 'The detailed '\n",
792 " 'definition for '\n",
793 " 'the tag, '\n",
794 " 'truncated to 2048 '\n",
795 " 'characters. If a '\n",
796 " 'standard tag, '\n",
797 " 'then the text '\n",
798 " 'provided by the '\n",
799 " 'taxonomy, '\n",
800 " 'otherwise the '\n",
801 " 'text assigned by '\n",
802 " 'the filer. Some '\n",
803 " 'tags have '\n",
804 " 'neither, in which '\n",
805 " 'case this field '\n",
806 " 'is NULL.',\n",
807 " 'name': 'doc',\n",
808 " 'titles': ['Documentation']}],\n",
809 " 'primaryKey': ['tag', 'version']},\n",
810 " 'url': 'tag.tsv'},\n",
811 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
812 " 'columns': [{'datatype': {'base': 'string',\n",
813 " 'maxLength': 34},\n",
814 " 'dc:description': 'MD5 hash of the '\n",
815 " 'segments field '\n",
816 " 'text. Although '\n",
817 " 'MD5 is unsuitable '\n",
818 " 'for cryptographic '\n",
819 " 'use, it is used '\n",
820 " 'here merely to '\n",
821 " 'limit the size of '\n",
822 " 'the primary key.',\n",
823 " 'name': 'dimh',\n",
824 " 'required': 'true',\n",
825 " 'titles': ['Dimension Hash']},\n",
826 " {'datatype': {'base': 'string',\n",
827 " 'maxLength': 1024},\n",
828 " 'dc:description': 'Concatenation of '\n",
829 " 'tag names '\n",
830 " 'representing the '\n",
831 " 'axis and members '\n",
832 " 'appearing in the '\n",
833 " 'XBRL segments. '\n",
834 " 'Tag names have '\n",
835 " 'their first '\n",
836 " 'characters '\n",
837 " '\"Statement\", last '\n",
838 " '4 characters '\n",
839 " '\"Axis\", and last '\n",
840 " '6 characters '\n",
841 " '\"Member\" or '\n",
842 " '\"Domain\" '\n",
843 " 'truncated where '\n",
844 " 'they appear. '\n",
845 " 'Namespaces and '\n",
846 " 'prefixes are '\n",
847 " 'ignored because '\n",
848 " 'EDGAR validation '\n",
849 " 'guarantees that '\n",
850 " 'the local-names '\n",
851 " 'are unique with a '\n",
852 " 'submission. Each '\n",
853 " 'dimension is '\n",
854 " 'represented as '\n",
855 " 'the pair '\n",
856 " '\"{axis}={member};\" '\n",
857 " 'and the axes '\n",
858 " 'concatenated in '\n",
859 " 'lexical order. '\n",
860 " 'Example: '\n",
861 " '\"LegalEntity=Xyz;Scenario=Restated;\" '\n",
862 " 'represents the '\n",
863 " 'XBRL segment with '\n",
864 " 'dimension '\n",
865 " 'LegalEntityAxis '\n",
866 " 'and member '\n",
867 " 'XyzMember, '\n",
868 " 'dimension '\n",
869 " 'StatementScenarioAxis '\n",
870 " 'and member '\n",
871 " 'RestatedMember.',\n",
872 " 'name': 'segments',\n",
873 " 'titles': []},\n",
874 " {'datatype': {'base': 'decimal',\n",
875 " 'maxInclusive': 1,\n",
876 " 'minInclusive': 0},\n",
877 " 'dc:description': 'TRUE if the '\n",
878 " 'segments field '\n",
879 " 'would have been '\n",
880 " 'longer than 1024 '\n",
881 " 'characters had it '\n",
882 " 'not been '\n",
883 " 'truncated, else '\n",
884 " 'FALSE.',\n",
885 " 'name': 'segt',\n",
886 " 'required': 'true',\n",
887 " 'titles': ['Segments Truncated']}],\n",
888 " 'primaryKey': 'dimh'},\n",
889 " 'url': 'dim.tsv'},\n",
890 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
891 " 'columns': [{'datatype': {'base': 'string',\n",
892 " 'maxLength': 20,\n",
893 " 'minLength': 20},\n",
894 " 'dc:description': 'Accession Number. '\n",
895 " 'The 20-character '\n",
896 " 'string formed '\n",
897 " 'from the 18-digit '\n",
898 " 'number assigned '\n",
899 " 'by the Commission '\n",
900 " 'to each EDGAR '\n",
901 " 'submission.',\n",
902 " 'name': 'adsh',\n",
903 " 'required': 'true',\n",
904 " 'titles': ['Accession Number']},\n",
905 " {'datatype': {'base': 'string',\n",
906 " 'maxLength': 255},\n",
907 " 'dc:description': 'The unique '\n",
908 " 'identifier (name) '\n",
909 " 'for a tag in a '\n",
910 " 'specific taxonomy '\n",
911 " 'release.',\n",
912 " 'name': 'tag',\n",
913 " 'required': 'true',\n",
914 " 'titles': ['Localname']},\n",
915 " {'datatype': {'base': 'string',\n",
916 " 'maxLength': 20},\n",
917 " 'dc:description': 'For a standard '\n",
918 " 'tag, an '\n",
919 " 'identifier for '\n",
920 " 'the taxonomy; '\n",
921 " 'otherwise the '\n",
922 " 'accession number '\n",
923 " 'where the tag was '\n",
924 " 'defined.',\n",
925 " 'name': 'version',\n",
926 " 'required': 'true',\n",
927 " 'titles': ['Namespace']},\n",
928 " {'datatype': {'base': 'string',\n",
929 " 'maxLength': 8,\n",
930 " 'minLength': 8},\n",
931 " 'dc:description': 'The end date for '\n",
932 " 'the data value, '\n",
933 " 'rounded to the '\n",
934 " 'nearest month '\n",
935 " 'end.',\n",
936 " 'name': 'ddate',\n",
937 " 'required': 'true',\n",
938 " 'titles': ['Data Date']},\n",
939 " {'datatype': {'base': 'decimal',\n",
940 " 'minInclusive': 0},\n",
941 " 'dc:description': 'The count of the '\n",
942 " 'number of '\n",
943 " 'quarters '\n",
944 " 'represented by '\n",
945 " 'the data value, '\n",
946 " 'rounded to the '\n",
947 " 'nearest whole '\n",
948 " 'number. \"0\" '\n",
949 " 'indicates it is a '\n",
950 " 'point-in-time '\n",
951 " 'value.',\n",
952 " 'name': 'qtrs',\n",
953 " 'required': 'true',\n",
954 " 'titles': ['Quarters']},\n",
955 " {'datatype': {'base': 'string',\n",
956 " 'maxLength': 50},\n",
957 " 'dc:description': 'The unit of '\n",
958 " 'measure for the '\n",
959 " 'value.',\n",
960 " 'name': 'uom',\n",
961 " 'required': 'true',\n",
962 " 'titles': ['Unit of Measure']},\n",
963 " {'datatype': {'base': 'string',\n",
964 " 'maxLength': 34},\n",
965 " 'dc:description': 'The 32-byte '\n",
966 " 'hexadecimal key '\n",
967 " 'for the '\n",
968 " 'dimensional '\n",
969 " 'information in '\n",
970 " 'the DIM data set.',\n",
971 " 'name': 'dimh',\n",
972 " 'titles': ['Dimension Hash']},\n",
973 " {'datatype': {'base': 'decimal',\n",
974 " 'maxInclusive': 32767,\n",
975 " 'minInclusive': 0},\n",
976 " 'dc:description': 'A positive '\n",
977 " 'integer to '\n",
978 " 'distinguish '\n",
979 " 'different '\n",
980 " 'reported facts '\n",
981 " 'that otherwise '\n",
982 " 'would have the '\n",
983 " 'same primary key. '\n",
984 " 'For most '\n",
985 " 'purposes, data '\n",
986 " 'with iprx greater '\n",
987 " 'than 1 are not '\n",
988 " 'needed. The '\n",
989 " 'priority for the '\n",
990 " 'fact based on '\n",
991 " 'higher precision, '\n",
992 " 'closeness of the '\n",
993 " 'end date to a '\n",
994 " 'month end, and '\n",
995 " 'closeness of the '\n",
996 " 'duration to a '\n",
997 " 'multiple of three '\n",
998 " 'months. See '\n",
999 " 'fields dcml, durp '\n",
1000 " 'and datp below.',\n",
1001 " 'name': 'iprx',\n",
1002 " 'titles': ['Fact Preference']},\n",
1003 " {'datatype': {'base': 'decimal'},\n",
1004 " 'dc:description': 'The value. This '\n",
1005 " 'is not scaled, it '\n",
1006 " 'is as found in '\n",
1007 " 'the Interactive '\n",
1008 " 'Data file, but is '\n",
1009 " 'rounded to four '\n",
1010 " 'digits to the '\n",
1011 " 'right of the '\n",
1012 " 'decimal point.',\n",
1013 " 'name': 'value',\n",
1014 " 'titles': []},\n",
1015 " {'datatype': {'base': 'string',\n",
1016 " 'maxLength': 512},\n",
1017 " 'dc:description': 'The plain text of '\n",
1018 " 'any superscripted '\n",
1019 " 'footnotes on the '\n",
1020 " 'value, if any, as '\n",
1021 " 'shown on the '\n",
1022 " 'statement page, '\n",
1023 " 'truncated to 512 '\n",
1024 " 'characters.',\n",
1025 " 'name': 'footnote',\n",
1026 " 'titles': ['Footnote Text']},\n",
1027 " {'datatype': {'base': 'decimal',\n",
1028 " 'minInclusive': 0},\n",
1029 " 'dc:description': 'Number of bytes '\n",
1030 " 'in the plain text '\n",
1031 " 'of the footnote '\n",
1032 " 'prior to '\n",
1033 " 'truncation; zero '\n",
1034 " 'if no footnote.',\n",
1035 " 'name': 'footlen',\n",
1036 " 'required': 'true',\n",
1037 " 'titles': ['Footnote Length']},\n",
1038 " {'datatype': {'base': 'decimal',\n",
1039 " 'minInclusive': 0},\n",
1040 " 'dc:description': 'Small integer '\n",
1041 " 'representing the '\n",
1042 " 'number of '\n",
1043 " 'dimensions. Note '\n",
1044 " 'that this value '\n",
1045 " 'is a function of '\n",
1046 " 'the dimension '\n",
1047 " 'segments.',\n",
1048 " 'name': 'dimn',\n",
1049 " 'required': 'true',\n",
1050 " 'titles': ['Number of Dimensions']},\n",
1051 " {'datatype': {'base': 'string',\n",
1052 " 'maxLength': 256},\n",
1053 " 'dc:description': 'If specified, '\n",
1054 " 'indicates a '\n",
1055 " 'specific '\n",
1056 " 'co-registrant, '\n",
1057 " 'the parent '\n",
1058 " 'company, or other '\n",
1059 " 'entity (e.g., '\n",
1060 " 'guarantor). NULL '\n",
1061 " 'indicates the '\n",
1062 " 'consolidated '\n",
1063 " 'entity. Note that '\n",
1064 " 'this value is a '\n",
1065 " 'function of the '\n",
1066 " 'dimension '\n",
1067 " 'segments.',\n",
1068 " 'name': 'coreg',\n",
1069 " 'titles': ['Coregistrant']},\n",
1070 " {'datatype': {'base': 'decimal'},\n",
1071 " 'dc:description': 'The difference '\n",
1072 " 'between the '\n",
1073 " 'reported fact '\n",
1074 " 'duration and the '\n",
1075 " 'quarter duration '\n",
1076 " '(qtrs), expressed '\n",
1077 " 'as a fraction of '\n",
1078 " '1. For example, a '\n",
1079 " 'fact with '\n",
1080 " 'duration of 120 '\n",
1081 " 'days rounded to a '\n",
1082 " '91-day quarter '\n",
1083 " 'has a durp value '\n",
1084 " 'of 29/91 = '\n",
1085 " '+0.3187.',\n",
1086 " 'name': 'durp',\n",
1087 " 'titles': ['Duration Preference']},\n",
1088 " {'datatype': {'base': 'decimal'},\n",
1089 " 'dc:description': 'The difference '\n",
1090 " 'between the '\n",
1091 " 'reported fact '\n",
1092 " 'date and the '\n",
1093 " 'month-end rounded '\n",
1094 " 'date (ddate), '\n",
1095 " 'expressed as a '\n",
1096 " 'fraction of 1. '\n",
1097 " 'For example, a '\n",
1098 " 'fact reported for '\n",
1099 " '29/Dec, with '\n",
1100 " 'ddate rounded to '\n",
1101 " '31/Dec, has a '\n",
1102 " 'datp value of '\n",
1103 " 'minus 2/31 = '\n",
1104 " '-0.0645.',\n",
1105 " 'name': 'datp',\n",
1106 " 'titles': ['Date Preference']},\n",
1107 " {'datatype': {'base': 'decimal',\n",
1108 " 'maxInclusive': 32767,\n",
1109 " 'minInclusive': -32768},\n",
1110 " 'dc:description': 'The value of the '\n",
1111 " 'fact \"decimals\" '\n",
1112 " 'attribute, with '\n",
1113 " 'INF represented '\n",
1114 " 'by 32767.',\n",
1115 " 'name': 'dcml',\n",
1116 " 'titles': ['Decimals']}],\n",
1117 " 'foreignKeys': [{'columnReference': 'adsh',\n",
1118 " 'reference': {'columnReference': 'adsh',\n",
1119 " 'resource': 'sub.tsv'}},\n",
1120 " {'columnReference': 'dimh',\n",
1121 " 'reference': {'columnReference': 'dimh',\n",
1122 " 'resource': 'https://wwww.sec.gov/files2018q3.zip#path=dim.tsv'}},\n",
1123 " {'columnReference': ['tag',\n",
1124 " 'version'],\n",
1125 " 'reference': {'columnReference': ['tag',\n",
1126 " 'version'],\n",
1127 " 'resource': 'https://wwww.sec.gov/files2018q3.zip#path=tag.tsv'}}],\n",
1128 " 'primaryKey': ['adsh',\n",
1129 " 'tag',\n",
1130 " 'version',\n",
1131 " 'ddate',\n",
1132 " 'qtrs',\n",
1133 " 'uom',\n",
1134 " 'dimh',\n",
1135 " 'iprx']},\n",
1136 " 'url': 'num.tsv'},\n",
1137 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
1138 " 'columns': [{'datatype': {'base': 'string',\n",
1139 " 'maxLength': 20,\n",
1140 " 'minLength': 20},\n",
1141 " 'dc:description': 'Accession Number. '\n",
1142 " 'The 20-character '\n",
1143 " 'string formed '\n",
1144 " 'from the 18-digit '\n",
1145 " 'number assigned '\n",
1146 " 'by the Commission '\n",
1147 " 'to each EDGAR '\n",
1148 " 'submission.',\n",
1149 " 'name': 'adsh',\n",
1150 " 'required': 'true',\n",
1151 " 'titles': ['Accession number']},\n",
1152 " {'datatype': {'base': 'string',\n",
1153 " 'maxLength': 255},\n",
1154 " 'dc:description': 'The unique '\n",
1155 " 'identifier (name) '\n",
1156 " 'for a tag in a '\n",
1157 " 'specific taxonomy '\n",
1158 " 'release.',\n",
1159 " 'name': 'tag',\n",
1160 " 'required': 'true',\n",
1161 " 'titles': ['Localname']},\n",
1162 " {'datatype': {'base': 'string',\n",
1163 " 'maxLength': 20},\n",
1164 " 'dc:description': 'For a standard '\n",
1165 " 'tag, an '\n",
1166 " 'identifier for '\n",
1167 " 'the taxonomy; '\n",
1168 " 'otherwise the '\n",
1169 " 'accession number '\n",
1170 " 'where the tag was '\n",
1171 " 'defined. For '\n",
1172 " 'example, '\n",
1173 " '\"invest/2013\" '\n",
1174 " 'indicates that '\n",
1175 " 'the tag is '\n",
1176 " 'defined in the '\n",
1177 " '2013 INVEST '\n",
1178 " 'taxonomy.',\n",
1179 " 'name': 'version',\n",
1180 " 'required': 'true',\n",
1181 " 'titles': ['Namespace', 'Taxonomy']},\n",
1182 " {'datatype': {'base': 'string',\n",
1183 " 'maxLength': 8,\n",
1184 " 'minLength': 8},\n",
1185 " 'dc:description': 'The end date for '\n",
1186 " 'the data value, '\n",
1187 " 'rounded to the '\n",
1188 " 'nearest month '\n",
1189 " 'end.',\n",
1190 " 'name': 'ddate',\n",
1191 " 'required': 'true',\n",
1192 " 'titles': ['Data Date']},\n",
1193 " {'datatype': {'base': 'decimal',\n",
1194 " 'minInclusive': 0},\n",
1195 " 'dc:description': 'The count of the '\n",
1196 " 'number of '\n",
1197 " 'quarters '\n",
1198 " 'represented by '\n",
1199 " 'the data value, '\n",
1200 " 'rounded to the '\n",
1201 " 'nearest whole '\n",
1202 " 'number. A point '\n",
1203 " 'in time value is '\n",
1204 " 'represented by 0.',\n",
1205 " 'name': 'qtrs',\n",
1206 " 'required': 'true',\n",
1207 " 'titles': ['Quarters']},\n",
1208 " {'datatype': {'base': 'decimal',\n",
1209 " 'maxInclusive': 32767,\n",
1210 " 'minInclusive': -32768},\n",
1211 " 'dc:description': 'A positive '\n",
1212 " 'integer to '\n",
1213 " 'distinguish '\n",
1214 " 'different '\n",
1215 " 'reported facts '\n",
1216 " 'that otherwise '\n",
1217 " 'would have the '\n",
1218 " 'same primary key. '\n",
1219 " 'For most '\n",
1220 " 'purposes, data '\n",
1221 " 'with iprx greater '\n",
1222 " 'than 1 are not '\n",
1223 " 'needed. The '\n",
1224 " 'priority for the '\n",
1225 " 'fact based on '\n",
1226 " 'higher precision, '\n",
1227 " 'closeness of the '\n",
1228 " 'end date to a '\n",
1229 " 'month end, and '\n",
1230 " 'closeness of the '\n",
1231 " 'duration to a '\n",
1232 " 'multiple of three '\n",
1233 " 'months. See '\n",
1234 " 'fields dcml, durp '\n",
1235 " 'and datp below.',\n",
1236 " 'name': 'iprx',\n",
1237 " 'titles': ['Fact Preference',\n",
1238 " 'Preferred Fact Sort '\n",
1239 " 'Key']},\n",
1240 " {'datatype': {'base': 'string',\n",
1241 " 'maxLength': 5},\n",
1242 " 'dc:description': 'The ISO language '\n",
1243 " 'code of the fact '\n",
1244 " 'content.',\n",
1245 " 'name': 'lang',\n",
1246 " 'titles': ['Language']},\n",
1247 " {'datatype': {'base': 'decimal',\n",
1248 " 'maxInclusive': 32767,\n",
1249 " 'minInclusive': -32768},\n",
1250 " 'dc:description': 'The value of the '\n",
1251 " 'fact \"xml:lang\" '\n",
1252 " 'attribute, en-US '\n",
1253 " 'represented by '\n",
1254 " '32767, other \"en\" '\n",
1255 " 'dialects having '\n",
1256 " 'lower values, and '\n",
1257 " 'other languages '\n",
1258 " 'lower still.',\n",
1259 " 'name': 'dcml',\n",
1260 " 'titles': ['Language Preference',\n",
1261 " 'Language Sort Key']},\n",
1262 " {'datatype': {'base': 'decimal'},\n",
1263 " 'dc:description': 'The difference '\n",
1264 " 'between the '\n",
1265 " 'reported fact '\n",
1266 " 'duration and the '\n",
1267 " 'quarter duration '\n",
1268 " '(qtrs), expressed '\n",
1269 " 'as a fraction of '\n",
1270 " '1. For example, a '\n",
1271 " 'fact with '\n",
1272 " 'duration of 120 '\n",
1273 " 'days rounded to a '\n",
1274 " '91-day quarter '\n",
1275 " 'has a durp value '\n",
1276 " 'of 29/91 = '\n",
1277 " '+0.3187.',\n",
1278 " 'name': 'durp',\n",
1279 " 'titles': ['Duration Preference']},\n",
1280 " {'datatype': {'base': 'decimal'},\n",
1281 " 'dc:description': 'The difference '\n",
1282 " 'between the '\n",
1283 " 'reported fact '\n",
1284 " 'date and the '\n",
1285 " 'month-end rounded '\n",
1286 " 'date (ddate), '\n",
1287 " 'expressed as a '\n",
1288 " 'fraction of 1. '\n",
1289 " 'For example, a '\n",
1290 " 'fact reported for '\n",
1291 " '29/Dec, with '\n",
1292 " 'ddate rounded to '\n",
1293 " '31/Dec, has a '\n",
1294 " 'datp value of '\n",
1295 " 'minus 2/31 = '\n",
1296 " '-0.0645.',\n",
1297 " 'name': 'datp',\n",
1298 " 'titles': ['Date Preference']},\n",
1299 " {'datatype': {'base': 'string',\n",
1300 " 'maxLength': 34},\n",
1301 " 'dc:description': 'The 32-byte '\n",
1302 " 'hexadecimal key '\n",
1303 " 'for the '\n",
1304 " 'dimensional '\n",
1305 " 'information in '\n",
1306 " 'the DIM data set.',\n",
1307 " 'name': 'dimh',\n",
1308 " 'titles': ['Dimension Hash']},\n",
1309 " {'datatype': {'base': 'decimal',\n",
1310 " 'minInclusive': 0},\n",
1311 " 'dc:description': 'Small integer '\n",
1312 " 'representing the '\n",
1313 " 'number of '\n",
1314 " 'dimensions, '\n",
1315 " 'useful for '\n",
1316 " 'sorting. Note '\n",
1317 " 'that this value '\n",
1318 " 'is function of '\n",
1319 " 'the dimension '\n",
1320 " 'segments.',\n",
1321 " 'name': 'dimn',\n",
1322 " 'required': 'true',\n",
1323 " 'titles': ['Number of Dimensions']},\n",
1324 " {'datatype': {'base': 'string',\n",
1325 " 'maxLength': 256},\n",
1326 " 'dc:description': 'If specified, '\n",
1327 " 'indicates a '\n",
1328 " 'specific '\n",
1329 " 'co-registrant, '\n",
1330 " 'the parent '\n",
1331 " 'company, or other '\n",
1332 " 'entity (e.g., '\n",
1333 " 'guarantor). NULL '\n",
1334 " 'indicates the '\n",
1335 " 'consolidated '\n",
1336 " 'entity. Note that '\n",
1337 " 'this value is a '\n",
1338 " 'function of the '\n",
1339 " 'dimension '\n",
1340 " 'segments.',\n",
1341 " 'name': 'coreg',\n",
1342 " 'titles': ['Coregistrant']},\n",
1343 " {'datatype': {'base': 'decimal',\n",
1344 " 'maxInclusive': 1,\n",
1345 " 'minInclusive': 0},\n",
1346 " 'dc:description': 'Flag indicating '\n",
1347 " 'whether the value '\n",
1348 " 'has had tags '\n",
1349 " 'removed.',\n",
1350 " 'name': 'escaped',\n",
1351 " 'required': 'true',\n",
1352 " 'titles': []},\n",
1353 " {'datatype': {'base': 'decimal',\n",
1354 " 'minInclusive': 0},\n",
1355 " 'dc:description': 'Number of bytes '\n",
1356 " 'in the original, '\n",
1357 " 'unprocessed '\n",
1358 " 'value. Zero '\n",
1359 " 'indicates a NULL '\n",
1360 " 'value.',\n",
1361 " 'name': 'srclen',\n",
1362 " 'required': 'true',\n",
1363 " 'titles': ['Source Length']},\n",
1364 " {'datatype': {'base': 'decimal',\n",
1365 " 'minInclusive': 0},\n",
1366 " 'dc:description': 'The original '\n",
1367 " 'length of the '\n",
1368 " 'whitespace '\n",
1369 " 'normalized value, '\n",
1370 " 'which may have '\n",
1371 " 'been greater than '\n",
1372 " '8192.',\n",
1373 " 'name': 'txtlen',\n",
1374 " 'required': 'true',\n",
1375 " 'titles': ['Text Length']},\n",
1376 " {'datatype': {'base': 'string',\n",
1377 " 'maxLength': 512},\n",
1378 " 'dc:description': 'The plain text of '\n",
1379 " 'any superscripted '\n",
1380 " 'footnotes on the '\n",
1381 " 'value, as shown '\n",
1382 " 'on the page, '\n",
1383 " 'truncated to 512 '\n",
1384 " 'characters, or if '\n",
1385 " 'there is no '\n",
1386 " 'footnote, then '\n",
1387 " 'this field will '\n",
1388 " 'be blank.',\n",
1389 " 'name': 'footnote',\n",
1390 " 'titles': ['Footnote Text']},\n",
1391 " {'datatype': {'base': 'decimal',\n",
1392 " 'minInclusive': 0},\n",
1393 " 'dc:description': 'Number of bytes '\n",
1394 " 'in the plain text '\n",
1395 " 'of the footnote '\n",
1396 " 'prior to '\n",
1397 " 'truncation.',\n",
1398 " 'name': 'footlen',\n",
1399 " 'required': 'true',\n",
1400 " 'titles': ['Footnote Length']},\n",
1401 " {'datatype': {'base': 'string',\n",
1402 " 'maxLength': 255},\n",
1403 " 'dc:description': 'The value of the '\n",
1404 " 'contextRef '\n",
1405 " 'attribute in the '\n",
1406 " 'source XBRL '\n",
1407 " 'document, which '\n",
1408 " 'can be used to '\n",
1409 " 'recover the '\n",
1410 " 'original HTML '\n",
1411 " 'tagging if '\n",
1412 " 'desired.',\n",
1413 " 'name': 'context',\n",
1414 " 'titles': ['Context Ref']},\n",
1415 " {'datatype': {'base': 'string',\n",
1416 " 'maxLength': 2048},\n",
1417 " 'dc:description': 'The value, with '\n",
1418 " 'all whitespace '\n",
1419 " 'normalized, that '\n",
1420 " 'is, all sequences '\n",
1421 " 'of line feeds, '\n",
1422 " 'carriage returns, '\n",
1423 " 'tabs, '\n",
1424 " 'non-breaking '\n",
1425 " 'spaces, and '\n",
1426 " 'spaces having '\n",
1427 " 'been collapsed to '\n",
1428 " 'a single space, '\n",
1429 " 'and no leading or '\n",
1430 " 'trailing spaces. '\n",
1431 " 'Escaped XML that '\n",
1432 " 'appears in EDGAR '\n",
1433 " '\"Text Block\" tags '\n",
1434 " 'is processed to '\n",
1435 " 'remove all '\n",
1436 " 'mark-up '\n",
1437 " '(comments, '\n",
1438 " 'processing '\n",
1439 " 'instructions, '\n",
1440 " 'elements, '\n",
1441 " 'attributes). The '\n",
1442 " 'value is '\n",
1443 " 'truncated to a '\n",
1444 " 'maximum number of '\n",
1445 " 'bytes. The '\n",
1446 " 'resulting text is '\n",
1447 " 'not intended for '\n",
1448 " 'end user display '\n",
1449 " 'but only for text '\n",
1450 " 'analysis '\n",
1451 " 'applications.',\n",
1452 " 'name': 'value',\n",
1453 " 'titles': []}],\n",
1454 " 'foreignKeys': [{'columnReference': 'adsh',\n",
1455 " 'reference': {'columnReference': 'adsh',\n",
1456 " 'resource': 'sub.tsv'}},\n",
1457 " {'columnReference': 'dimh',\n",
1458 " 'reference': {'columnReference': 'dimh',\n",
1459 " 'resource': 'https://wwww.sec.gov/files2018q3.zip#path=dim.tsv'}},\n",
1460 " {'columnReference': ['tag',\n",
1461 " 'version'],\n",
1462 " 'reference': {'columnReference': ['tag',\n",
1463 " 'version'],\n",
1464 " 'resource': 'https://wwww.sec.gov/files2018q3.zip#path=tag.tsv'}}],\n",
1465 " 'primaryKey': ['adsh',\n",
1466 " 'tag',\n",
1467 " 'version',\n",
1468 " 'ddate',\n",
1469 " 'qtrs',\n",
1470 " 'dimh',\n",
1471 " 'iprx']},\n",
1472 " 'url': 'txt.tsv'},\n",
1473 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
1474 " 'columns': [{'datatype': {'base': 'string',\n",
1475 " 'maxLength': 20,\n",
1476 " 'minLength': 20},\n",
1477 " 'dc:description': 'Accession Number. '\n",
1478 " 'The 20-character '\n",
1479 " 'string formed '\n",
1480 " 'from the 18-digit '\n",
1481 " 'number assigned '\n",
1482 " 'by the Commission '\n",
1483 " 'to each EDGAR '\n",
1484 " 'submission.',\n",
1485 " 'name': 'adsh',\n",
1486 " 'required': 'true',\n",
1487 " 'titles': ['Accession Number']},\n",
1488 " {'datatype': {'base': 'decimal',\n",
1489 " 'minInclusive': 0},\n",
1490 " 'dc:description': 'Represents the '\n",
1491 " 'report grouping. '\n",
1492 " 'The numeric value '\n",
1493 " 'refers to the \"R '\n",
1494 " 'file\" as computed '\n",
1495 " 'by the renderer '\n",
1496 " 'and posted on the '\n",
1497 " 'EDGAR website. '\n",
1498 " 'Note that in some '\n",
1499 " 'situations the '\n",
1500 " 'numbers skip.',\n",
1501 " 'name': 'report',\n",
1502 " 'required': 'true',\n",
1503 " 'titles': ['Report Number']},\n",
1504 " {'datatype': {'base': 'string',\n",
1505 " 'maxLength': 1},\n",
1506 " 'dc:description': 'The type of '\n",
1507 " 'interactive data '\n",
1508 " 'file rendered on '\n",
1509 " 'the EDGAR '\n",
1510 " 'website, H = .htm '\n",
1511 " 'file, X = .xml '\n",
1512 " 'file.',\n",
1513 " 'name': 'rfile',\n",
1514 " 'required': 'true',\n",
1515 " 'titles': ['Report File Type']},\n",
1516 " {'datatype': {'base': 'string',\n",
1517 " 'maxLength': 2},\n",
1518 " 'dc:description': 'If available, one '\n",
1519 " 'of the menu '\n",
1520 " 'categories as '\n",
1521 " 'computed by the '\n",
1522 " 'renderer: '\n",
1523 " 'C=Cover, '\n",
1524 " 'S=Statements, '\n",
1525 " 'N=Notes, '\n",
1526 " 'P=Policies, '\n",
1527 " 'T=Tables, '\n",
1528 " 'D=Details, '\n",
1529 " 'O=Other, and '\n",
1530 " 'U=Uncategorized.',\n",
1531 " 'name': 'menucat',\n",
1532 " 'titles': ['Menu Category']},\n",
1533 " {'datatype': {'base': 'string',\n",
1534 " 'maxLength': 512},\n",
1535 " 'dc:description': 'The portion of '\n",
1536 " 'the long name '\n",
1537 " 'used in the '\n",
1538 " 'renderer menu.',\n",
1539 " 'name': 'shortname',\n",
1540 " 'titles': ['Short Name']},\n",
1541 " {'datatype': {'base': 'string',\n",
1542 " 'maxLength': 512},\n",
1543 " 'dc:description': 'The '\n",
1544 " 'space-normalized '\n",
1545 " 'text of the XBRL '\n",
1546 " 'link \"definition\" '\n",
1547 " 'element content.',\n",
1548 " 'name': 'longname',\n",
1549 " 'titles': ['Long Name']},\n",
1550 " {'datatype': {'base': 'string',\n",
1551 " 'maxLength': 255},\n",
1552 " 'dc:description': 'The XBRL '\n",
1553 " '\"roleuri\" of the '\n",
1554 " 'role.',\n",
1555 " 'name': 'roleuri',\n",
1556 " 'titles': ['Role URI']},\n",
1557 " {'datatype': {'base': 'string',\n",
1558 " 'maxLength': 255},\n",
1559 " 'dc:description': 'The XBRL roleuri '\n",
1560 " 'of a role for '\n",
1561 " 'which this role '\n",
1562 " 'has a matching '\n",
1563 " 'shortname prefix '\n",
1564 " 'and a higher '\n",
1565 " 'level menu '\n",
1566 " 'category, as '\n",
1567 " 'computed by the '\n",
1568 " 'renderer.',\n",
1569 " 'name': 'parentroleuri',\n",
1570 " 'titles': ['Parent Role URI']},\n",
1571 " {'datatype': {'base': 'decimal',\n",
1572 " 'minInclusive': 0},\n",
1573 " 'dc:description': 'The value of the '\n",
1574 " 'report field for '\n",
1575 " 'the role where '\n",
1576 " 'roleuri equals '\n",
1577 " 'this '\n",
1578 " 'parentroleuri.',\n",
1579 " 'name': 'parentreport',\n",
1580 " 'titles': ['Parent Report']},\n",
1581 " {'datatype': {'base': 'decimal',\n",
1582 " 'maxInclusive': 32767,\n",
1583 " 'minInclusive': 0},\n",
1584 " 'dc:description': 'The highest '\n",
1585 " 'ancestor report '\n",
1586 " 'reachable by '\n",
1587 " 'following '\n",
1588 " 'parentreport '\n",
1589 " 'relationships. A '\n",
1590 " 'note (menucat = '\n",
1591 " 'N) is its own '\n",
1592 " 'ultimate parent.',\n",
1593 " 'name': 'ultparentrpt',\n",
1594 " 'titles': ['Ultimate Parent']}],\n",
1595 " 'foreignKeys': [{'columnReference': 'adsh',\n",
1596 " 'reference': {'columnReference': 'adsh',\n",
1597 " 'resource': 'sub.tsv'}}],\n",
1598 " 'primaryKey': ['adsh', 'report']},\n",
1599 " 'url': 'ren.tsv'},\n",
1600 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
1601 " 'columns': [{'datatype': {'base': 'string',\n",
1602 " 'maxLength': 20,\n",
1603 " 'minLength': 20},\n",
1604 " 'dc:description': 'Accession Number. '\n",
1605 " 'The 20-character '\n",
1606 " 'string formed '\n",
1607 " 'from the 18-digit '\n",
1608 " 'number assigned '\n",
1609 " 'by the Commission '\n",
1610 " 'to each EDGAR '\n",
1611 " 'submission.',\n",
1612 " 'name': 'adsh',\n",
1613 " 'required': 'true',\n",
1614 " 'titles': ['Accession Number']},\n",
1615 " {'datatype': {'base': 'decimal',\n",
1616 " 'minInclusive': 0},\n",
1617 " 'dc:description': 'Represents the '\n",
1618 " 'report grouping. '\n",
1619 " 'The numeric value '\n",
1620 " 'refers to the \"R '\n",
1621 " 'file\" as computed '\n",
1622 " 'by the renderer '\n",
1623 " 'and posted on the '\n",
1624 " 'EDGAR website. '\n",
1625 " 'Note that in some '\n",
1626 " 'situations the '\n",
1627 " 'numbers skip.',\n",
1628 " 'name': 'report',\n",
1629 " 'required': 'true',\n",
1630 " 'titles': []},\n",
1631 " {'datatype': {'base': 'decimal',\n",
1632 " 'minInclusive': 0},\n",
1633 " 'dc:description': 'Represents the '\n",
1634 " \"tag's \"\n",
1635 " 'presentation line '\n",
1636 " 'order for a given '\n",
1637 " 'report. Together '\n",
1638 " 'with the '\n",
1639 " 'statement and '\n",
1640 " 'report field, '\n",
1641 " 'presentation '\n",
1642 " 'location, order '\n",
1643 " 'and grouping can '\n",
1644 " 'be derived.',\n",
1645 " 'name': 'line',\n",
1646 " 'required': 'true',\n",
1647 " 'titles': []},\n",
1648 " {'datatype': {'base': 'string',\n",
1649 " 'maxLength': 2},\n",
1650 " 'dc:description': 'The financial '\n",
1651 " 'statement '\n",
1652 " 'location to which '\n",
1653 " 'the value of the '\n",
1654 " '\"report\" field '\n",
1655 " 'pertains.',\n",
1656 " 'name': 'stmt',\n",
1657 " 'titles': ['Statement']},\n",
1658 " {'datatype': {'base': 'decimal',\n",
1659 " 'maxInclusive': 1,\n",
1660 " 'minInclusive': 0},\n",
1661 " 'dc:description': '1 indicates that '\n",
1662 " 'the value was '\n",
1663 " 'presented '\n",
1664 " '\"parenthetically\" '\n",
1665 " 'instead of in '\n",
1666 " 'fields within the '\n",
1667 " 'financial '\n",
1668 " 'statements. For '\n",
1669 " 'example: '\n",
1670 " 'Receivables (net '\n",
1671 " 'of allowance for '\n",
1672 " 'bad debts of USD '\n",
1673 " '200 in 2012) USD '\n",
1674 " '700',\n",
1675 " 'name': 'inpth',\n",
1676 " 'required': 'true',\n",
1677 " 'titles': ['Parenthentical']},\n",
1678 " {'datatype': {'base': 'string',\n",
1679 " 'maxLength': 256},\n",
1680 " 'dc:description': 'The tag chosen by '\n",
1681 " 'the filer for '\n",
1682 " 'this line item.',\n",
1683 " 'name': 'tag',\n",
1684 " 'required': 'true',\n",
1685 " 'titles': ['Localname']},\n",
1686 " {'datatype': {'base': 'string',\n",
1687 " 'maxLength': 20},\n",
1688 " 'dc:description': 'The taxonomy '\n",
1689 " 'identifier if the '\n",
1690 " 'tag is a standard '\n",
1691 " 'tag, otherwise '\n",
1692 " 'adsh.',\n",
1693 " 'name': 'version',\n",
1694 " 'required': 'true',\n",
1695 " 'titles': ['Namespace', 'Taxonomy']},\n",
1696 " {'datatype': {'base': 'string',\n",
1697 " 'maxLength': 50},\n",
1698 " 'dc:description': 'The XBRL link '\n",
1699 " '\"role\" of the '\n",
1700 " 'preferred label, '\n",
1701 " 'using only the '\n",
1702 " 'portion of the '\n",
1703 " 'role URI after '\n",
1704 " 'the last \"/\".',\n",
1705 " 'name': 'prole',\n",
1706 " 'titles': ['Preferred Role']},\n",
1707 " {'datatype': {'base': 'string',\n",
1708 " 'maxLength': 512},\n",
1709 " 'dc:description': 'The text '\n",
1710 " 'presented on the '\n",
1711 " 'line item, also '\n",
1712 " 'known as a '\n",
1713 " '\"preferred\" '\n",
1714 " 'label.',\n",
1715 " 'name': 'plabel',\n",
1716 " 'titles': ['Label']},\n",
1717 " {'datatype': {'base': 'decimal',\n",
1718 " 'maxInclusive': 1,\n",
1719 " 'minInclusive': 0},\n",
1720 " 'dc:description': 'Flag to indicate '\n",
1721 " 'whether the prole '\n",
1722 " 'is treated as '\n",
1723 " 'negating by the '\n",
1724 " 'renderer.',\n",
1725 " 'name': 'negating',\n",
1726 " 'required': 'true',\n",
1727 " 'titles': []}],\n",
1728 " 'foreignKeys': [{'columnReference': ['adsh',\n",
1729 " 'report'],\n",
1730 " 'reference': {'columnReference': ['adsh',\n",
1731 " 'report'],\n",
1732 " 'resource': 'ren.tsv'}},\n",
1733 " {'columnReference': ['tag',\n",
1734 " 'version'],\n",
1735 " 'reference': {'columnReference': ['tag',\n",
1736 " 'version'],\n",
1737 " 'resource': 'tag.tsv'}}],\n",
1738 " 'primaryKey': ['adsh', 'report', 'line']},\n",
1739 " 'url': 'pre.tsv'},\n",
1740 " {'tableSchema': {'aboutUrl': 'readme.htm',\n",
1741 " 'columns': [{'datatype': {'base': 'string',\n",
1742 " 'maxLength': 20,\n",
1743 " 'minLength': 20},\n",
1744 " 'dc:description': 'Accession Number. '\n",
1745 " 'The 20-character '\n",
1746 " 'string formed '\n",
1747 " 'from the 18-digit '\n",
1748 " 'number assigned '\n",
1749 " 'by the Commission '\n",
1750 " 'to each EDGAR '\n",
1751 " 'submission.',\n",
1752 " 'name': 'adsh',\n",
1753 " 'required': 'true',\n",
1754 " 'titles': ['Accession Number']},\n",
1755 " {'datatype': {'base': 'decimal',\n",
1756 " 'maxInclusive': 255,\n",
1757 " 'minInclusive': 0},\n",
1758 " 'dc:description': 'Sequential number '\n",
1759 " 'for grouping arcs '\n",
1760 " 'in a submission.',\n",
1761 " 'name': 'grp',\n",
1762 " 'required': 'true',\n",
1763 " 'titles': ['Group']},\n",
1764 " {'datatype': {'base': 'decimal',\n",
1765 " 'minInclusive': 255},\n",
1766 " 'dc:description': 'Sequential number '\n",
1767 " 'for arcs within a '\n",
1768 " 'group in a '\n",
1769 " 'submission.',\n",
1770 " 'name': 'arc',\n",
1771 " 'required': 'true',\n",
1772 " 'titles': []},\n",
1773 " {'datatype': {'base': 'decimal',\n",
1774 " 'maxInclusive': 1,\n",
1775 " 'minInclusive': 0},\n",
1776 " 'dc:description': 'Indicates a '\n",
1777 " 'weight of -1 '\n",
1778 " '(TRUE if the arc '\n",
1779 " 'is negative), but '\n",
1780 " 'typically +1 '\n",
1781 " '(FALSE).',\n",
1782 " 'name': 'negative',\n",
1783 " 'required': 'true',\n",
1784 " 'titles': ['Negative Weight']},\n",
1785 " {'datatype': {'base': 'string',\n",
1786 " 'maxLength': 256},\n",
1787 " 'dc:description': 'The tag for the '\n",
1788 " 'parent of the arc',\n",
1789 " 'name': 'ptag',\n",
1790 " 'required': 'true',\n",
1791 " 'titles': ['Parent Tag']},\n",
1792 " {'datatype': {'base': 'string',\n",
1793 " 'maxLength': 20},\n",
1794 " 'dc:description': 'The version of '\n",
1795 " 'the tag for the '\n",
1796 " 'parent of the arc',\n",
1797 " 'name': 'pversion',\n",
1798 " 'required': 'true',\n",
1799 " 'titles': ['Parent Namespace']},\n",
1800 " {'datatype': {'base': 'string',\n",
1801 " 'maxLength': 255},\n",
1802 " 'dc:description': 'The tag for the '\n",
1803 " 'child of the arc',\n",
1804 " 'name': 'ctag',\n",
1805 " 'required': 'true',\n",
1806 " 'titles': ['Child Tag']},\n",
1807 " {'datatype': {'base': 'string',\n",
1808 " 'maxLength': 20},\n",
1809 " 'dc:description': 'The version of '\n",
1810 " 'the tag for the '\n",
1811 " 'child of the arc',\n",
1812 " 'name': 'cversion',\n",
1813 " 'required': 'true',\n",
1814 " 'titles': ['Child Namespace']}],\n",
1815 " 'foreignKeys': [{'columnReference': 'adsh',\n",
1816 " 'reference': {'columnReference': 'adsh',\n",
1817 " 'resource': 'sub.tsv'}},\n",
1818 " {'columnReference': ['ptag',\n",
1819 " 'pversion'],\n",
1820 " 'reference': {'columnReference': ['tag',\n",
1821 " 'version'],\n",
1822 " 'resource': 'tag.tsv'}},\n",
1823 " {'columnReference': ['ctag',\n",
1824 " 'cversion'],\n",
1825 " 'reference': {'columnReference': ['tag',\n",
1826 " 'version'],\n",
1827 " 'resource': 'tag.tsv'}}],\n",
1828 " 'primaryKey': ['adsh', 'grp', 'arc']},\n",
1829 " 'url': 'cal.tsv'}]}\n"
1830 ]
1831 }
1832 ],
1833 "source": [
1834 "file = data_path / '2018_3' / 'source' / '2018q3_notes-metadata.json'\n",
1835 "with file.open() as f:\n",
1836 " data = json.load(f)\n",
1837 "\n",
1838 "pprint(data)"
1839 ]
1840 },
1841 {
1842 "cell_type": "markdown",
1843 "metadata": {},
1844 "source": [
1845 "## Data Organization"
1846 ]
1847 },
1848 {
1849 "cell_type": "markdown",
1850 "metadata": {},
1851 "source": [
1852 "For each quarter, the FSN data is organized into eight file sets that contain information about submissions, numbers, taxonomy tags, presentation, and more. Each dataset consists of rows and fields and is provided as a tab-delimited text file:"
1853 ]
1854 },
1855 {
1856 "cell_type": "markdown",
1857 "metadata": {},
1858 "source": [
1859 "| File | Dataset | Description |\n",
1860 "|------|--------------|-------------------------------------------------------------|\n",
1861 "| SUB | Submission | Identifies each XBRL submission by company, form, date, etc |\n",
1862 "| TAG | Tag | Defines and explains each taxonomy tag |\n",
1863 "| DIM | Dimension | Adds detail to numeric and plain text data |\n",
1864 "| NUM | Numeric | One row for each distinct data point in filing |\n",
1865 "| TXT | Plain Text | Contains all non-numeric XBRL fields |\n",
1866 "| REN | Rendering | Information for rendering on SEC website |\n",
1867 "| PRE | Presentation | Detail on tag and number presentation in primary statements |\n",
1868 "| CAL | Calculation | Shows arithmetic relationships among tags |"
1869 ]
1870 },
1871 {
1872 "cell_type": "markdown",
1873 "metadata": {},
1874 "source": [
1875 "## Submission Data"
1876 ]
1877 },
1878 {
1879 "cell_type": "markdown",
1880 "metadata": {},
1881 "source": [
1882 "The latest submission file contains around 6,500 entries."
1883 ]
1884 },
1885 {
1886 "cell_type": "code",
1887 "execution_count": 9,
1888 "metadata": {
1889 "ExecuteTime": {
1890 "end_time": "2018-12-25T19:49:47.533421Z",
1891 "start_time": "2018-12-25T19:49:47.513733Z"
1892 },
1893 "scrolled": true
1894 },
1895 "outputs": [
1896 {
1897 "name": "stdout",
1898 "output_type": "stream",
1899 "text": [
1900 "<class 'pandas.core.frame.DataFrame'>\n",
1901 "Int64Index: 6492 entries, 0 to 6491\n",
1902 "Data columns (total 40 columns):\n",
1903 "adsh 6492 non-null object\n",
1904 "cik 6492 non-null int64\n",
1905 "name 6492 non-null object\n",
1906 "sic 6490 non-null float64\n",
1907 "countryba 6481 non-null object\n",
1908 "stprba 5899 non-null object\n",
1909 "cityba 6481 non-null object\n",
1910 "zipba 6477 non-null object\n",
1911 "bas1 6481 non-null object\n",
1912 "bas2 2804 non-null object\n",
1913 "baph 6481 non-null object\n",
1914 "countryma 6447 non-null object\n",
1915 "stprma 5905 non-null object\n",
1916 "cityma 6447 non-null object\n",
1917 "zipma 6446 non-null object\n",
1918 "mas1 6447 non-null object\n",
1919 "mas2 2761 non-null object\n",
1920 "countryinc 5935 non-null object\n",
1921 "stprinc 5631 non-null object\n",
1922 "ein 6491 non-null float64\n",
1923 "former 3618 non-null object\n",
1924 "changed 3618 non-null float64\n",
1925 "afs 6415 non-null object\n",
1926 "wksi 6492 non-null int64\n",
1927 "fye 6489 non-null float64\n",
1928 "form 6492 non-null object\n",
1929 "period 6492 non-null int64\n",
1930 "fy 6492 non-null int64\n",
1931 "fp 6492 non-null object\n",
1932 "filed 6492 non-null int64\n",
1933 "accepted 6492 non-null object\n",
1934 "prevrpt 6492 non-null int64\n",
1935 "detail 6492 non-null int64\n",
1936 "instance 6492 non-null object\n",
1937 "nciks 6492 non-null int64\n",
1938 "aciks 130 non-null object\n",
1939 "pubfloatusd 639 non-null float64\n",
1940 "floatdate 640 non-null float64\n",
1941 "floataxis 3 non-null object\n",
1942 "floatmems 4 non-null float64\n",
1943 "dtypes: float64(7), int64(8), object(25)\n",
1944 "memory usage: 2.0+ MB\n"
1945 ]
1946 }
1947 ],
1948 "source": [
1949 "sub = pd.read_parquet(data_path / '2018_3' / 'parquet' / 'sub.parquet')\n",
1950 "sub.info()"
1951 ]
1952 },
1953 {
1954 "cell_type": "markdown",
1955 "metadata": {},
1956 "source": [
1957 "### Get AAPL submission"
1958 ]
1959 },
1960 {
1961 "cell_type": "markdown",
1962 "metadata": {},
1963 "source": [
1964 "The submission dataset contains the unique identifiers required to retrieve the filings: the Central Index Key (CIK) and the Accession Number (adsh). The following shows some of the information about Apple's 2018Q1 10-Q filing:"
1965 ]
1966 },
1967 {
1968 "cell_type": "code",
1969 "execution_count": 10,
1970 "metadata": {
1971 "ExecuteTime": {
1972 "end_time": "2018-12-25T19:49:47.544268Z",
1973 "start_time": "2018-12-25T19:49:47.534555Z"
1974 },
1975 "scrolled": true
1976 },
1977 "outputs": [
1978 {
1979 "data": {
1980 "text/plain": [
1981 "name APPLE INC\n",
1982 "adsh 0000320193-18-000100\n",
1983 "cik 320193\n",
1984 "name APPLE INC\n",
1985 "sic 3571\n",
1986 "countryba US\n",
1987 "stprba CA\n",
1988 "cityba CUPERTINO\n",
1989 "zipba 95014\n",
1990 "bas1 ONE APPLE PARK WAY\n",
1991 "form 10-Q\n",
1992 "period 20180630\n",
1993 "fy 2018\n",
1994 "fp Q3\n",
1995 "filed 20180801\n",
1996 "Name: 386, dtype: object"
1997 ]
1998 },
1999 "execution_count": 10,
2000 "metadata": {},
2001 "output_type": "execute_result"
2002 }
2003 ],
2004 "source": [
2005 "name = 'APPLE INC'\n",
2006 "apple = sub[sub.name == name].T.dropna().squeeze()\n",
2007 "key_cols = ['name', 'adsh', 'cik', 'name', 'sic', 'countryba', 'stprba',\n",
2008 " 'cityba', 'zipba', 'bas1', 'form', 'period', 'fy', 'fp', 'filed']\n",
2009 "apple.loc[key_cols]"
2010 ]
2011 },
2012 {
2013 "cell_type": "markdown",
2014 "metadata": {},
2015 "source": [
2016 "## Build AAPL fundamentals dataset"
2017 ]
2018 },
2019 {
2020 "cell_type": "markdown",
2021 "metadata": {},
2022 "source": [
2023 "Using the central index key, we can identify all historical quarterly filings available for Apple, and combine this information to obtain 26 Forms 10-Q and nine annual Forms 10-K."
2024 ]
2025 },
2026 {
2027 "cell_type": "markdown",
2028 "metadata": {},
2029 "source": [
2030 "### Get filings"
2031 ]
2032 },
2033 {
2034 "cell_type": "code",
2035 "execution_count": 9,
2036 "metadata": {
2037 "ExecuteTime": {
2038 "end_time": "2018-12-25T19:49:47.905361Z",
2039 "start_time": "2018-12-25T19:49:47.558060Z"
2040 },
2041 "scrolled": true
2042 },
2043 "outputs": [],
2044 "source": [
2045 "aapl_subs = pd.DataFrame()\n",
2046 "for sub in data_path.glob('**/sub.parquet'):\n",
2047 " sub = pd.read_parquet(sub)\n",
2048 " aapl_sub = sub[(sub.cik.astype(int) == apple.cik) & (sub.form.isin(['10-Q', '10-K']))]\n",
2049 " aapl_subs = pd.concat([aapl_subs, aapl_sub])"
2050 ]
2051 },
2052 {
2053 "cell_type": "markdown",
2054 "metadata": {},
2055 "source": [
2056 "We find 15 quarterly 10-Q and 4 annual 10-K reports:"
2057 ]
2058 },
2059 {
2060 "cell_type": "code",
2061 "execution_count": 10,
2062 "metadata": {
2063 "ExecuteTime": {
2064 "end_time": "2018-12-25T19:49:47.912233Z",
2065 "start_time": "2018-12-25T19:49:47.907690Z"
2066 },
2067 "scrolled": true
2068 },
2069 "outputs": [
2070 {
2071 "data": {
2072 "text/plain": [
2073 "10-Q 15\n",
2074 "10-K 4\n",
2075 "Name: form, dtype: int64"
2076 ]
2077 },
2078 "execution_count": 10,
2079 "metadata": {},
2080 "output_type": "execute_result"
2081 }
2082 ],
2083 "source": [
2084 "aapl_subs.form.value_counts()"
2085 ]
2086 },
2087 {
2088 "cell_type": "markdown",
2089 "metadata": {},
2090 "source": [
2091 "### Get numerical filing data"
2092 ]
2093 },
2094 {
2095 "cell_type": "markdown",
2096 "metadata": {},
2097 "source": [
2098 "With the Accession Number for each filing, we can now rely on the taxonomies to select the appropriate XBRL tags (listed in the TAG file) from the NUM and TXT files to obtain the numerical or textual/footnote data points of interest."
2099 ]
2100 },
2101 {
2102 "cell_type": "markdown",
2103 "metadata": {},
2104 "source": [
2105 "First, let's extract all numerical data available from the 19 Apple filings:"
2106 ]
2107 },
2108 {
2109 "cell_type": "code",
2110 "execution_count": 11,
2111 "metadata": {
2112 "ExecuteTime": {
2113 "end_time": "2018-12-25T19:50:44.738085Z",
2114 "start_time": "2018-12-25T19:49:47.914571Z"
2115 }
2116 },
2117 "outputs": [
2118 {
2119 "name": "stdout",
2120 "output_type": "stream",
2121 "text": [
2122 "738\n",
2123 "1345\n",
2124 "707\n",
2125 "961\n",
2126 "1001\n",
2127 "905\n",
2128 "951\n",
2129 "1277\n",
2130 "937\n",
2131 "751\n",
2132 "923\n",
2133 "793\n",
2134 "1364\n",
2135 "1271\n",
2136 "682\n",
2137 "805\n",
2138 "942\n",
2139 "919\n",
2140 "952\n"
2141 ]
2142 }
2143 ],
2144 "source": [
2145 "aapl_nums = pd.DataFrame()\n",
2146 "for num in data_path.glob('**/num.parquet'):\n",
2147 " num = pd.read_parquet(num).drop('dimh', axis=1)\n",
2148 " aapl_num = num[num.adsh.isin(aapl_subs.adsh)]\n",
2149 " print(len(aapl_num))\n",
2150 " aapl_nums = pd.concat([aapl_nums, aapl_num])\n",
2151 "aapl_nums.ddate = pd.to_datetime(aapl_nums.ddate, format='%Y%m%d') \n",
2152 "aapl_nums.to_parquet(data_path / 'aapl_nums.parquet')"
2153 ]
2154 },
2155 {
2156 "cell_type": "markdown",
2157 "metadata": {},
2158 "source": [
2159 "In total, the nine years of filing history provide us with over 18,000 numerical values for AAPL."
2160 ]
2161 },
2162 {
2163 "cell_type": "code",
2164 "execution_count": 12,
2165 "metadata": {
2166 "ExecuteTime": {
2167 "end_time": "2018-12-25T19:50:44.759350Z",
2168 "start_time": "2018-12-25T19:50:44.739283Z"
2169 },
2170 "scrolled": true
2171 },
2172 "outputs": [
2173 {
2174 "name": "stdout",
2175 "output_type": "stream",
2176 "text": [
2177 "<class 'pandas.core.frame.DataFrame'>\n",
2178 "Int64Index: 18224 entries, 84837 to 5467444\n",
2179 "Data columns (total 15 columns):\n",
2180 "adsh 18224 non-null object\n",
2181 "tag 18224 non-null object\n",
2182 "version 18224 non-null object\n",
2183 "ddate 18224 non-null datetime64[ns]\n",
2184 "qtrs 18224 non-null int64\n",
2185 "uom 18224 non-null object\n",
2186 "iprx 18224 non-null float64\n",
2187 "value 18176 non-null float64\n",
2188 "footnote 68 non-null object\n",
2189 "footlen 18224 non-null int64\n",
2190 "dimn 18224 non-null int64\n",
2191 "coreg 0 non-null object\n",
2192 "durp 18224 non-null float64\n",
2193 "datp 18224 non-null float64\n",
2194 "dcml 18224 non-null float64\n",
2195 "dtypes: datetime64[ns](1), float64(5), int64(3), object(6)\n",
2196 "memory usage: 2.2+ MB\n"
2197 ]
2198 }
2199 ],
2200 "source": [
2201 "aapl_nums.info()"
2202 ]
2203 },
2204 {
2205 "cell_type": "markdown",
2206 "metadata": {},
2207 "source": [
2208 "## Create P/E Ratio from EPS and stock price data"
2209 ]
2210 },
2211 {
2212 "cell_type": "markdown",
2213 "metadata": {},
2214 "source": [
2215 "We can select a useful field, such as Earnings per Diluted Share (EPS), that we can combine with market data to calculate the popular Price/Earnings (P/E) valuation ratio."
2216 ]
2217 },
2218 {
2219 "cell_type": "code",
2220 "execution_count": 15,
2221 "metadata": {
2222 "ExecuteTime": {
2223 "end_time": "2018-12-25T19:50:44.795369Z",
2224 "start_time": "2018-12-25T19:50:44.792012Z"
2225 }
2226 },
2227 "outputs": [
2228 {
2229 "data": {
2230 "text/plain": [
2231 "Timestamp('2014-06-04 00:00:00')"
2232 ]
2233 },
2234 "execution_count": 15,
2235 "metadata": {},
2236 "output_type": "execute_result"
2237 }
2238 ],
2239 "source": [
2240 "stock_split = 7\n",
2241 "split_date = pd.to_datetime('20140604')\n",
2242 "split_date"
2243 ]
2244 },
2245 {
2246 "cell_type": "markdown",
2247 "metadata": {},
2248 "source": [
2249 "We do need to take into account, however, that Apple split its stock 7:1 on June 4, 2014, and Adjusted Earnings per Share before the split to make earnings comparable, as illustrated in the following code block:"
2250 ]
2251 },
2252 {
2253 "cell_type": "code",
2254 "execution_count": 16,
2255 "metadata": {
2256 "ExecuteTime": {
2257 "end_time": "2018-12-25T19:50:44.841306Z",
2258 "start_time": "2018-12-25T19:50:44.796763Z"
2259 }
2260 },
2261 "outputs": [],
2262 "source": [
2263 "# Filter by tag; keep only values measuring 1 quarter\n",
2264 "eps = aapl_nums[(aapl_nums.tag == 'EarningsPerShareDiluted')\n",
2265 " & (aapl_nums.qtrs == 1)].drop('tag', axis=1)\n",
2266 "\n",
2267 "# Keep only most recent data point from each filing\n",
2268 "eps = eps.groupby('adsh').apply(lambda x: x.nlargest(n=1, columns=['ddate']))\n",
2269 "\n",
2270 "# Adjust earnings prior to stock split downward\n",
2271 "eps.loc[eps.ddate < split_date,'value'] = eps.loc[eps.ddate < split_date, 'value'].div(7)\n",
2272 "eps = eps[['ddate', 'value']].set_index('ddate').squeeze().sort_index()\n",
2273 "eps = eps.rolling(4,min_periods=4).sum().dropna()"
2274 ]
2275 },
2276 {
2277 "cell_type": "code",
2278 "execution_count": 17,
2279 "metadata": {
2280 "ExecuteTime": {
2281 "end_time": "2018-12-25T19:50:44.994594Z",
2282 "start_time": "2018-12-25T19:50:44.842251Z"
2283 }
2284 },
2285 "outputs": [
2286 {
2287 "data": {
2288 "image/png": 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m9szMTNxu9yl1XwV4/fXXj3t9PR4PkydPxjCM0tcaSn74aNq0Ke+99x4LFiwod19btmwp7cJ5JvLy8nj66adP2AL72muvkZubi91up3Xr1qd9nMaNGwMls+b+0Z49e8qs11pRY8eOJSgoiEmTJvHbb7+V2V5cXMyKFStOa98iIlWZurCKiJyGY5OLeL1enE4nycnJrFmzBpfLRcuWLXnjjTf+cvHzk7nkkkto0aIFc+bMIS0tje7du3Po0CEWLlzIRRddxBdffFHmMf379+fLL79kwoQJDB8+nNDQUMLDwxk7diwAb7/9NiNGjGDChAm88cYbdOvWjYiICA4cOMCOHTvYunUrH374YemkNFdeeSVz5sxh4cKF9OzZk6FDh+J2u5k/fz6dOnVi165dp3Vuf7WMx6BBg0pbdm677Tb++c9/0rdvX4YPH05AQADr1q3j119/ZciQISxatOi0ju8rEyZM4Ouvv+bLL79k165dDBgwgJycHP7v//6PXr168fXXXx83O+uyZct49NFH6d69O82aNaNevXqkp6ezYMECvF4v//znP0/p+N27d6dPnz7HrQO5fft2OnfufFy35sDAQD788EMuv/xyRo0aRdeuXenYsSMhISGkpaWxdetWkpOTWbFiRemPFqfL7XYzbdo0ZsyYQZcuXWjfvj12u52srCzWrl3L9u3bCQgI4KWXXqrw2pzlGTJkCE2bNmXmzJls376dDh06kJqayjfffENiYmK5Lf0nk5CQwGuvvcbtt99Oz549GTRoEM2aNcPj8ZCWlsa6desoLCz8y1lkRUSqIwVIEZHTcKwVMDAwkNDQUBo2bMiIESMYOnQoQ4YMKTPZx6kym83MnTuXxx57jMWLF7N582aaNWvGtGnT6NevX7kB8oYbbiA1NZXPP/+c1157DbfbTWxsbGmAbNCgAcuWLeOtt95i7ty5fPHFF7jdburVq0fz5s2ZMWPGccs2mEwm3n//fV544QU++ugj3nrrLaKjoxk1ahQPPPAA0dHRp3VufzXeLDw8vDRAjh49mqCgIGbNmsXHH3+MxWKhZ8+ezJw5k3nz5lW7AGm1Wpk/fz7Tpk1j3rx5zJo1i7i4OO65557SAHmsGymULAeSmprKmjVrWLRoEU6nk3r16tG9e3fGjRtH//79T+n4Tz31FPPmzeODDz5g37591KlTh9tuu42HHnqoTDhr06YNq1atYtasWSxYsICPP/4YwzCIjo6mVatW3HnnneWOpzxVNpuNL774gmXLlrF27VoWLlxIRkYGgYGBxMbGMnr0aMaOHXtGrY8AISEhzJs3j8cff5yVK1eyZs0a4uPjuf/++7n99tuZM2fOae33yiuvpF27dsycOZPly5ezbNkyLBYL9evXZ/Dgwac8hlREpDowORyOsv2YRERE5Jx5//33mTBhAi+88ELpLKSVZejQoaxatYotW7YQFxdXqfsWEZHaR2MgRUREzpHy1hrcv38/zzzzDAEBAQwZMsQHVYmIiFScurCKiIicI2PGjMHlctGpUyfCw8PZt28f33zzDfn5+UyePJkGDRr4ukQREZG/pAApIiJyjlx11VV88sknzJs3D6fTSUhICF26dOGWW27ReDkREakWNAZSREREREREKkRjIEVERERERKRCFCBFRERERESkQhQgpdpKTk72dQkiNYauJ5HKpWtKpPLoeqpaFCBFRERERESkQhQgRUREREREpEIUIEVERERERKRCFCBFRERERESkQhQgRUREREREpEIUIEVERERERKRCFCBFRERERESkQhQgRUREREREpEIUIEVERERERKRCFCBFRERERESkQhQgRUREREREpEIUIEVERERERKRCKhQgV61axbXXXkvr1q2x2+3Mnj37uO3z5s3j8ssvp1mzZtjtdpKSks5KsSIiIiIiItXZ9iw3ziKvr8s4bRUKkHl5ebRp04annnoKq9VaZnt+fj7du3fnySefrPQCRUREREREaoIlaQUkfpXB6O8zcXsNX5dzWgIqcqfExEQSExMBuO2228psv/baawE4evRoJZYmIiIiIiJSM3zwWx7/XO3AY4A9yI9qmh8rFiDPhuTkZF8dWmoQvY9EKo+uJ5HKpWtKpPJU5+vJMOD1fYG8uz8QgJtj3IxvmM++lKrZ+JaQkPCX230WIE9WmMjJJCcn630kUkl0PYlULl1TIpWnOl9PhR6DO1dm8el+F/4meK6nnZtbhvi6rDPiswApIiIiIiJSUzkKvfxt6VFWHioiJMDEv/tHMjjG4uuyzpgCpIiIiIiISCXam1PM1d8d5dfsYupb/fhkUBSd6gT5uqxKoQApIiIiIiJSSTYdKeKaxUc57PLS2h7Ap4OjiA2tObGrQmeSm5tLSkoKAF6vl9TUVLZu3UpERASxsbFkZWWxf/9+srOzAdi9ezfh4eFER0cTHR199qoXERERERGpIhbtdzHm+yzyiw36NjDzQf9I7OYKrZxYbVTobDZt2kTfvn3p27cvLpeL6dOn07dvX6ZNmwbAggUL6Nu3L8OGDQPgrrvuom/fvrz77rtnr3IREREREZEq4p0duYxakkl+scG1zax8PjiqxoVHqGALZJ8+fXA4HCfcfv3113P99ddXWlEiIiIiIiLVgdcwmLzBySs/5wLwYKcwJnYKw2Qy+biys6PmdMYVERERERE5hwqKDcYlZfHlHhcBJnixt52/JVTvZTpORgFSRERERETkFGUWeBi1JJO1h4sICzTxnwGRXNiw+i/TcTIKkCIiIiIiIqdgt7OYK787wi6nh0bB/nw6OIq2kYG+LuucUIAUERERERGpoPWHi7huyVGOFHhpFxnIp4OiaBji7+uyzhkFSBERERERkQqYv9fFLcszKfDAwEZm/t0/krDAmjfT6l+pXWcrIiIiIiJyGl7blsuNS0vC440tgvlkUFStC4+gFkgREREREZET8ngNHlmfzevb8wB4tLONezqE1thlOk5GAVJERERERKQc+cVexi7P4qt9BQT6wcwLIri6WbCvy/IpBUgREREREZE/yXB5uG7JUTZkuAkPMvHhgCj6NDD7uiyfU4AUERERERH5g+RsN1d9d5Q9OR5iQ/35bHAUrey1Y5mOk1GAFBERERER+d2a9EJGLTlKVqFBp6hA/jsoiujg2rNMx8koQIqIiIiIiAD/tzufcUlZFHrgolgL7/aLIKQWzrT6V/RsiIiIiIhIrWYYBi/9lMPo70vC4z9ahTB7QKTCYznUAikiIiIiIrVWsdfgwXXZvLOjZJmOKV1t3NGu9i7TcTIKkCIiIiIiUivlur38/ftMvkktxOwPr/eJYGST2r1Mx8koQIqIiIiISK2Tnu/h6sVH2XLUTYTZxMcDozg/Wst0nIwCpIiIiIiI1Co7HCXLdOzP9RAf5s/ng6NoHq5lOipCAVJERERERGqNpIOFXL/0KM4ig251A/l4UBR1LFqmo6IUIEVEREREpFb476587liZhdsLlza28Fa/SKwBmiznVChAioiIiIhIjWYYBs9uyeHJTTkAjG8TwtRu4fj7KTyeKgVIERERERGpsdxeg3tWO/hPcj4mYHqPcMa1CfV1WdWWAqSIiIiIiNRIziIvo7/PZElaIVZ/E2/1i+DSOKuvy6rWFCBFRERERKTGOZDn4arvjrAtq5g6Fj8+GRRF17pBvi6r2lOAFBERERGRGuXnTDfXfHeUtHwPzW0BfDY4iiY2RZ/KoGdRRERERERqjGVpBdy4LJMct0HP6CBmD4gkUst0VBoFSBERERERqRE+TM7j7lUOig24vImV1y6IwKJlOiqVAqSIiIiIiFRrhmEwfXMOT28uWaZjQrtQJne14WdSeKxsCpAiIiIiIlJtFXkM7lqVxSe7XPiZ4Nnz7YxpFeLrsmosBUgREREREamWHIVeblyWyYqDhYQEmHj3wkguirX4uqwaTQFSRERERESqnf25xVz93VF+cRQTbfXjv4Oi6FRHy3ScbQqQIiIiIiJSrWw+UsQ1i4+S7vLSyh7Ap4OjaByqaHMu6FkWEREREZFq49v9BYz+PpO8YoM+9YP4z4Ao7GY/X5dVayhAioiIiIhItfDejjzuW+vAY8DVzay82juCIH/NtHouKUCKiIiIiEiV5jUMpmx08sJPuQDc1zGMR84Lw6RlOs45BUgREREREamyirxwy/Isvtjtwt8EL/ayc0MLLdPhKwqQIiIiIiJSJWUVernjZzObnC7CAk283z+SAY20TIcvKUCKiIiIiEiVs8Ph5rrFR9md40/DYD/+O7gO7SMDfV1WracAKSIiIiIiVcqi/S5uWZ5FjtugZYiXOUPr0yjE39dlCaD5bkVEREREpEowDIMXt+Zw3eJMctwGI+OtvN2hQOGxClGAFBERERERn3MVG4xdkcW/NjoxgEmdbbx7YQQWZccqRV1YRURERETEpw7kebh+6VE2HXETGmDijb4RDI2z+rosKYcCpIiIiIiI+MyGjCKuX3KUdJeXuFB/PhoYRVtNllNlKUCKiIiIiIhPfLwzn7tXZ1HogQvqB/F+/0ii1Ge1SlOAFBERERGRc8rjNZi8wcmr23IB+EerEKb3CCfQz+TjyuRkFCBFREREROSccRR6+cfyTBanFRJggmfOtzO6VYivy5IKUoAUEREREZFzIjnbzXWLM9npLCbK7McHAyLpXd/s67LkFChAioiIiIjIWbckrYDR32fiLDJoGxHARwOjiAtTHKlu9IqJiIiIiMhZYxgGM7fl8tgGJ14DhsVZmNUngtBALUlfHSlAioiIiIjIWVFQbPDPNQ4+3pkPwIOdwniwUxh+Jk2WU10pQIqIiIiISKU7lO/hhqVHWZ/hJjjAxKw+EYyIt/q6LDlDCpAiIiIiIlKpNh0p4volRzmQ7yUmxJ+PBkbSISrI12VJJVCAFBERERGRSvN5Sj53rMyiwAM9o4P4oH8kda3+vi5LKokCpIiIiIiInDGvYTD1RyfPb80F4KYWwTxzvp0gf413rEkUIEVERERE5Iw4i7zcsiKLb/YX4G+Cp3qE849WIZg0WU6NowApIiIiIiKnLcVZzHWLj/JrdjERZhP/vjCKfg3Nvi5LzhIFSBEREREROS3LDxRw07JMHEUGrewBfDwwiiY2RYyaTK+uiIiIiIicEsMwePOXPB7+IRuPAUNiLbzZNwJbkJ+vS5OzTAFSREREREQqrMhjcN9aBx/8lg/AfR3CeLhzGH4a71grVOgnglWrVnHttdfSunVr7HY7s2fPPm67YRhMnz6dVq1aUb9+fYYOHcovv/xyVgoWERERERHfyHB5GL7oCB/8lo/FH97pF8GkLjaFx1qkQgEyLy+PNm3a8NRTT2G1Wstsf+mll5g5cyYzZsxg6dKl1K1bl5EjR5KTk1PpBYuIiIiIyLm39WgR/ednsPZwEQ2D/Vh0SV2uaBrs67LkHKtQgExMTOSxxx5jxIgR+Pkd/xDDMJg1axZ33303I0aMoE2bNsyaNYvc3Fw+//zzs1K0iIiIiIicO1/udjFkwRFS8zx0qxvIsmH16FQnyNdliQ+c8SjXvXv3kp6ezoABA0pvs1qt9OrVi3Xr1p3p7kVERERExEe8hsG0TU5u/j6T/GKDUc2D+eriukQH+/u6NPGRM55EJz09HYC6desed3vdunU5ePDgCR+XnJx8pocW0ftIpBLpehKpXLqmpLrL98Dk34L4/mgAfhhMaOLmuuh89qUcOee16Ho6dxISEv5yu89mYT1ZYSInk5ycrPeRSCXR9SRSuXRNSXW3J6eY8UuOsj2rmPAgE+9dGMWARhaf1KLrqWo54y6s0dHRAGRkZBx3e0ZGBvXq1TvT3YuIiIiIyDmUdLCQAfMz2J5VTEJ4AEsureuz8ChVzxkHyLi4OKKjo1m2bFnpbQUFBaxZs4YePXqc6e5FREREROQceXdHHiO/OUJmoZfBjcwsvrQuzcMDfV2WVCEV6sKam5tLSkoKAF6vl9TUVLZu3UpERASxsbGMHz+e559/noSEBJo3b86zzz5LSEgIV1555VktXkREREREzpzbazBxXTbv7MgDYEK7UB7rYsPfT+s7yvEqFCA3bdrEsGHDSv89ffp0pk+fznXXXcesWbOYMGECLpeL+++/H4cWDeteAAAgAElEQVTDQZcuXZgzZw5hYWFnrXARERERETlzRws83Lgsk1WHijD7w8u9I7immdZ3lPKZHA6H4esiRE6HBlSLVB5dTyKVS9eUVBc/Z7oZteQo+3I91Lf6MXtgFF3qVq31HXU9VS0+m4VVRERERER856u9Lm5dkUVesUHnOoF8OCCKhiFa31H+mgKkiIiIiEgtYhgGz27J4clNOQBc3czKS70isAZovKOcnAKkiIiIiEgtkef2cvtKB1/ucWECHu9q4852oZhMCo9SMQqQIiIiIiK1wP7cYkYtyeSnTDe2QBNv94skMVbrO8qpUYAUEREREanh1qQXcsPSTI4UeGka5s/Hg6Joadf6jnLqFCBFRERERGqwD37L4941Dtxe6N/QzHsXRmI3+/m6LKmmFCBFRERERGqgYq/Bwz9k8+YveQDc1jaEJ7qGE+Cn8Y5y+hQgRURERERqmKxCLzcvy2T5wUKC/OD5Xnb+lhDi67KkBlCAFBERERGpQXY43Fy3+Ci7czzUs/rxn/6R9Ig2+7osqSEUIEVEREREaohF+13csjyLHLdBx6hAZg+IJCZUX/ml8ujdJCIiIiJSzRV7DZ7dksOMzTkYwOVNrLx6gZ3gAE2WI5VLAVJEREREpBrbme3m1hVZbDziBuDRzjbu6RCKyaTJcqTyKUCKiIiIiFRDhmHw9o48HlvvxOUxaBTsz2t97PRraPF1aVKDKUCKiIiIiFQzB/I83L4yi2UHCgG4ppmVGT3sWt9RzjoFSBERERGRauTzlHzuXeMgu8gg0uzHC73sjIi3+rosqSUUIEVEREREqoGsQi/3rnEwZ7cLgItiLbzcy050sL+PK5PaRAFSRERERKSKW5xawB0rszjk8hIaYGJaj3BuSAjWRDlyzilAioiIiIhUUXluL4+ud/Lur3kA9IwOYlafCOLD9DVefEPvPBERERGRKuiHw4XcuiKL3Tkegvzgkc427mgbir+fWh3FdxQgRURERESqkCKPwYzNTl74KRevAW0jAnijbyTtIgN9XZqIAqSIiIiISFWxPcvNrSuy+CnTjQm4u30oD51nw+yvVkepGhQgRURERER8zOM1eG17LlM2OinyQnyYP7P6RNAz2uzr0kSOowApIiIiIuJDe3OKGZ+Uxer0IgBuahHM1O7hhAX6+bgykbIUIEVEREREfMAwDGbvzOehddnkuA3qWf14pXcEF8VafF2ayAkpQIqIiIiInGMZLg8TVjtYsK8AgOFxFl7oZSfK4u/jykT+mgKkiIiIiMg59PVeFxNWOzhS4MUWZOKZ8+1c3dSKyaSJcqTqU4AUERERETkHnEVeJq7L5qOd+QD0a2Bm5gV2YkL1lVyqD71bRURERETOsqSDhdy2Mov9uR4s/vCvruGMbR2Cn1odpZpRgBQREREROUsKig2e+DGb17blAXBenUDe6BNBC3ugjysTOT0KkCIiIiIiZ8HmI0WMS8pih6MYfxPc3zGMezuGEeinVkepvhQgRUREREQqUbHX4IWtOczYnEOxAQnhAbzRJ4LOdYN8XZrIGVOAFBGpII/X4McjbqwBJtpFquuRiIiUtSu7mHFJmazPcANwa+sQJne1ERzg5+PKRCqHAqSIyF847PKwJK2Q71ILWJpWgKPIAGBoYwtPdA2nWbj+jIqICBiGwTs78nhsg5P8YoNGwf7M7GPnwoYWX5cmUqn0zUdE5A88XoONR4r4NrWQxakFbD7qPm57fJg/h11evt5XwDf7C/h7qxAe7BRGpBZ+FhGptQ7kebhzVRZL0goBuLqZlad72LGb1eooNY8CpIjUehl/bGU8UEBWoVG6zewPfeqbGRRjYXAjC83CAziY7+HJH53MTs7njV/y+GRXPvd3DOOW1qGY/TUxgohIbfJFSj73rnHgKDKINPvxQi87I+Ktvi5L5KxRgBSRWufYWMbv0gr4LrWAzUfcGH/YHh/mz+DfA+MFDYLKjFtpEOzPqxdEcGubUB5dn833BwqZtN7J2zvyeLxrOMPjLJi0rpeISI2WVejlvjUOvtjtAiAxxszLvSOoH6weKVKzKUCKSK1wpKCklXFxagFL0grJLPSWbjP7Q+9oc0lojDHTzBZQoQDYPjKQ/0uM4rvUQh5dn82v2cXctCyTntFBTO0WThfNticiUiMtSSvgjpVZHMz3EhJgYlr3cG5sEawfD6VWUIAUkRrJ4zXYdNTNd6klrYyb/tTKGBfqT2KMhUExFi6oH0RI4OmNUzGZTCTGWhjQyMz7v+UxfVMOa9KLGPhVBlc1tfJoFxuNQ/WnVkSkJshze5m8oaTHCcD59YKY1SeCJjb9nZfaQ+92Eakxjv6hlXHxn1oZg/ygd/3/tTI2r2ArY0UF+Jn4e6tQrmwazItbc3htey6fpbiYt9fFbW1C+WeHMGxBmkxBRKS6Wn+4iHFJmexyegj0g0fOs3Fnu1D8/dTqKLWLAqSIVFtew2DTkZJWxsVpBWzMOL6VsXGof2lg7FPffNqtjKciPMiPyV3DubllCFN+dPJ5iosXfsrlP8n5PHyejRtbBBOgLxsiItVGkcfg6c05PP9TDl4D2kQE8EbfSNprPWCppRQgRaRayfy9lfG7tAKWpBZy9E+tjL3qmxnUyExijIWE8MptZTwVcWEBvN0vknFtinjkh2zWHS7injUO3vwllye6hjM4xqyxMiIiVdwvWW5uXZHF1kw3JmBCu1Ae7mzTjNtSqylAikiV5jUMNv8+Y+ri1AI2/KmVMTbUn8GNfm9lbGAm9By0Mp6KrnWDWHRJHebtLWDyhmx2OIq5evFRLmxoZmq3cNrpF2wRkSrHaxi8ti2XKT86KfSUjJuf1SeCXvXNvi5NxOcUIEWkysks8LD0QMm6jEvSCjlS8L9WxkA/6BVtZlBMSStjCx+2MlaUyWRiRLyVIbEW3vwll2e35PD9gUL6zD3M3xKCeaSzTdO+i4hUEftyixmflMWqQ0UA3NgimCe7hxNWxX6gFPEVBUgR8TmvYbDl9xlTF6cWsuFIEd4/NDPGhPgzOMbMoEYW+jY0V9sPcbO/iTvbhXF982BmbM7hnR15/Cc5nzm7XdzVPpQ72oaek3GaIiJSlmEYfLQzn4nrsslxG9Sz+vFybztDYq2+Lk2kSlGAFBGfyCr0sjStoLSVMeNPrYwX1DczuJGZQTEWWtmrfivjqYi0+DPjfDu3tA7hsQ1OFuwrYPqmHN7/NY9JnW1c2zwYvxp0viIiVV2Gy8Pdqx18va8AgGFxFl7oZaeORb1DRP5MAVJEzgmvYbD1WCtjWiHrM8q2Mg76PTD2q8atjKeieXggHw2MIulgIZPWZ7PlqJvbVjp4fXseU7uH07eBxtqIiJxtC/a5mLDKQUaBF1ugiafPt3NNM2uN+uFSpDIpQIrIWeMo9LLsQAHfphayJK2Aw67/tTIGmKB3/SAGx1gYFGOhdQ1rZTwVfRqYWTasLp/ucvHExmy2ZroZvugIF8daeKKbjYRwTbQjIlLZnEVeHv4hmw+T8wHo28DMzAvsxIbq67HIX9EVIiKVzjAMPk1xcf8aB073/5oZGwX7Myjm91bGBmZsQTW/lbGi/Ewmrm0ezPB4CzN/zuXFn3JZuL+ki++YViE82CmMKHWlEhE5Y4Ueg/d/zeO5rTmku7xY/GFyl3BubROi4QMiFaAAKSKVylHo5Z41DubsdgHQvW4QQ+MsDGpkoU1E7W1lrKjgAD/u72TjxhYhTNvk5D/J+bz5Sx6f7Mrn/g5hjG0TqvXHREROQ7HX4JNd+czYnMP+XA8AXesGMvOCCFra1dNDpKIUIEWk0iw/UMD4pCwO5HsJCTDxVI9w/pYQrNB4GqKD/XmpdwRjW4cyaX02yw4U8ugGJ2/vyOPxruGMiLfoeRURqQCvYTB3j4tpm3JIzi4GoLU9gIc727i0sf6WipwqBUgROWOFHoMpG528ui0XgG51A3mzbyRNbPoTc6baRgYyJzGKxWmFPLo+mx2OYm7+PpMe9YKY2i2cbvWCfF2iiEiVZBgG36QWMPXHHH7OdAMQH+bPQ+fZuLKJFX8/BUeR06FvdyJyRrZlurllRSbbs4rxN8EDncK4t0MYAfpgrjQmk4nBMRb6NzTzn9/ymbbJybrDRQz+OoPLm1iZ3MVGXJj+nIuIHLPiYCFTNzr5IaMIgIbBfjzQycb1CcEE6vNJ5IzoG4eInBavYTBrex5PbMym0ANNw/x5s18kXeuqRexsCfAzMbpVCFc0tfLiTzm8ti2XObtdfL3PxbjWodzTMYxwTUwkIrXYhowipmx0svxgIQB1LH78s0MYf28ZgiVAwVGkMihAisgpS8vzcFtSVukH9E0tgnmyezihtWDtxqrAFuTHY13CGd0yhCkbnXya4uKln3P5MDmfieeFcXPLEP3CLiK1ys+Zbqb+6GTR/gIAbEEm7moXxrg2IfpsEqlkCpAickq+3O3i7tVZOIoMosx+vNzbztA4q6/LqpViQwN4s18k49oU8cj6bNakF3H/2mze+iWPJ7rZuChGk0OISM22M9vN9E05zNntwgCCA0yMaxPCXe3CsJsVHEXOBgVIEakQZ5GXB9Y6+GRXyfIciTFmXukdQXSw1ib0tc51g1hwcR3m7y1g8oZsfssu5trFmfRrYGZq93DaR2p6ehGpWfbnFvP05hw+2pmPx4AgPxjdMoR7O4ZRz6rPJZGzSQFSRE5q9aFCxiVlsS/Xg9XfxNTuNsa0DFHrVhViMpkYHm9lSKyFt3fk8fTmkjFAfeceZlRCMJM622igsC8i1Vx6vofntubw71/zKPKCvwlubBHM/R3DiA3V11qRc0FXmoicUJHHYMZmJy/8lIvXgI5RgbzVN4IWWnC5ygryN3Fb21Cuax7M05tL1o2cnZzP/+12cVe7UO5sF0qIxgOJSDWTVejlpZ9yePOXPPKLDUzAVU2tTOxko1m4vs6KnEuV9i0iJyeHiRMn0q5dO+rXr09iYiI//vhjZe1eRM6x3xxuEr/O4LmtuRgG3NshlO+G1lV4rCYizH5M72Fn3chohsVZyC82eGpzDl3npPNhch4er+HrEkVETirH7eXpzU46fnaIF3/KJb/Y4JLGFlaOqMdb/SIVHkV8oNKuurvuuott27Yxa9YsGjVqxH//+18uu+wy1q5dS8OGDSvrMCJylhmGwbu/5jHpBycuj0FsqD9v9ImgV32zr0uT09DUFsB/BkSx6lAhk9Zns+mImztWOnh9ex5PdrPRr6HF1yWKiJThKjZ4Z0cuL2zN5WihF4ALG5qZ1Nmm5aJEfMzkcDjO+Gdol8tFTEwMH3zwAUOHDi29vV+/fgwePJhJkyad6SFEykhOTiYhIcHXZdQoh10e7liZxbepJctzXNvMyozz7VpbsIbwGgafpbiYstFJap4HgItiLUzpasOUsUfXk0gl0mfU6SnyGHyYnM8zW5wczC8Jjj3qBTGps40+DfRDZm2l66lqqZQWyOLiYjweDxbL8b9kW61W1qxZUxmHEJGzbME+F3etcnCkwIs9yMQLveyMbBLs67KkEvmZTFzTLJjhcVZe25bLC1tz+GZ/AYtTCxhZP5CnYj3UsWiiHRE59zxeg09TXDy1ycne3JIfuNpHBvJoZxuDY8yatE2kCqmUFkiAxMRE/P39eeedd4iOjubzzz9n/PjxNG3alA0bNpS5f3JycmUcVkTOUL4HXkgJ4sv0kt+TuoV7mNyiiGizxsjVdEeL4I19gcw9FIAXEyH+BmNi3VzTsBgtnyYi54JhwLKj/ry+L5Dd+SV/eOKtXm6NczMgyoOfcqPIOXey1t5KC5C7d+/m9ttvZ/Xq1fj7+9OxY0eaN2/O5s2b+eGHHyrjECLHUXeGM7cho4ixyzNJyfFg9ofHuoQzvk0Ifvqlt1bZnuXm3uUHWZNV0vrYONSfyV1sXN7Eql/9RU6TPqP+mmEYLEkrZOqPTjYfdQMlf3se7BTGNc2CCVBylD/Q9VS1VNokOk2aNGHBggXk5eWRk5ND/fr1GT16NPHx8ZV1CBGpJMVeg+e25vD05hw8BrSJCOCtvpG01YLztVKbiEBeblvIvuBYHv0hm+2OYv6+PIvXt+fyZPdwutfTuCMRqTyrDpUExzXpRQDUt/pxX8cwbmwRQpC/gqNIVVfpcx+HhIQQEhKCw+FgyZIlPPHEE5V9CBE5A7udxYxdkcn6jJJffO9oG8qkzjYsAfrQru0GNrLQb4SZ2cn5PLnJyfoMN4lfH+GyeCv/6mojPkzT5YvI6dt0pIgpG50sPVAyUVuE2cQ97cP4e+sQggPUb16kuqi0bwNLlizB6/WSkJDA7t27efTRR2nRogXXX399ZR1CRM6AYZTMbDdxXTZ5xQYNg/2Y1SdCyzjIcQL8TNzUMoTLm1p56adcXv05hy/3uFiwz8WtbUK5t0MYdg2QFJFT8EuWmyd/dPLVvgIAwgJN3N42lNvahmLTLN8i1U6lBUin08njjz/OgQMHiIiIYPjw4UyaNInAQHWJE/G1owUeJqxylH54j4y38nwvOxEKAnICYYF+TOps4+YWwUz50cl/d7l45edcZifn82CnMMa0CiFQY5RE5C/sdhYzfbOTz3a5MACrv4lbWodwd/tQIjXjs0i1VWmT6IicaxpQXTFL0gq4LSmLdJcXW6CJZ3raubqpJkeR453setp8pIhH1mez6lDJmKXmtgCe6Gbj4liL3ksi5ajNn1FpeR6e2ezkw+R8ig0I9IObW4Rwb8cw6gcrOMqpq83XU1WkAS0iNZSr2OCxDdm89UseAD2jg3i9TwRxGscmp6FTnSC+GlKHr/cVMHlDNjudxYxakskF9YOY2i2cTnWCfF2iiPhYhsvDCz/l8M6OPAo94GeCUc2DebBTmD57RGoQXc0iNdCWo0WMXZ7Fr9nFBJjgkc427moXir+6HMoZMJlMXBpnJTHGwjs78nh6i5OVh4roPz+Da5sH82hnGw1D1LogUts4Cr28+nMus7bnkldc0rFtZLyVh84Lo4VdQ5lEahoFSJEaxOM1eOXnXJ7c5MTthRbhAbzZN0KtQ1KpgvxNjG8bynXNg3lmSw5v/pLLxzvz+XK3izvahTKhfSihgRpfK1LT5bm9vPFLHi/9lEN2UUlwvCjGzMOdbXSM0ueOSE2lAClSQ+zLLWbciixW/76u1i2tQni8m01To8tZYzf78WT3cP7RKoTJG7KZt7eAZ7bk8MFveTzS2cb1zYPV6i1SAxUUG7z3ax7Pb80ho8ALwAX1g3i0s40e0Vo3VqSmU4AUqQE+3ZXPfWscON0G9ax+zLwggsExWp5Dzo0mtgA+GBDFmvRCJv2QzcYjbu5a5eCN7blM7RZO/0Z6L4rUBG6vwcc783l6cw6peR4AutQJ5NEuNvo1MGtCLZFaQgFSpBpzFHq5d42DL3a7ALiksYWXe9upo+nRxQd6Rpv57tK6zNnt4l8bnGzLKmbkt0dJjDHzRLdwWmkslEi15Cj08tU+F89vySElpyQ4tokIYFJnzcQsUhspQIpUUysOFjJ+RRZp+R5CAkxM7xHODQnB+iAXn/IzmbiyaTBDG1t5fXsuz2/N4dvUQpakHeamFiE8dF4Yda36gUOkqjta4OHrfQXM3+Pi+4OFuEt6qtLM5s/D59kY2cSKnz5vRGolBUiRaqbQYzD1Ryev/pyLAXStG8ibfSNpatPlLFWHNcDEPzuE8beEYJ7anMN7v+bx7q95fJaSzz0dwhjfJhRLgL58ilQl6fkevtrnYt6eAlYeKsTz+0rhfiboUz+Ia5oHc22zYAI0tlmkVtM3TpFqZHuWm1uWZ7Itqxh/E9zfMYz7Oobpw1yqrLpWf57raeeW1iE8tj6bb1MLeXyjk3d25DG5i40rmqoVQ8SXUnOLmb+3gHl7XaxNL+L3zEiACQY2MjM8zsrQOIuGRohIKQVIkWrAaxi8vj2PxzdmU+iBJmH+vNk3km71NE26VA+t7IF8OrgOy9IKeGR9NtuzirllRRavb8/lye7hnK+ZG0XOmT05xczb42LuHhcbj7hLbzf7Q/+GFobHWbiksRW7WbN4i0hZCpAiVdyBPA+3rczi+wOFANzYIphp3cO1zp5US/0bWUhqYGb2znye/NHJxiNuhiw4wvA4C493DaeJumKLnBW/OdzM21vA3D0ufsr8X2i0+psYHGNmeLyVxBgLtiB9tojIX9MntUgVNnePiwmrsnAUGUSZ/Xipt51L46y+LkvkjPj7mbixRQiXN7Hy8s+5vPJTLvP2FrBwfwFjW4dyf8cwtXyInCHDMNiWVcy8vS7m7XGxw1Fcui0s0MRFsRaGx1kZFGPWesEickoUIEWqIGeRl4nrsvloZz4AgxuZefWCCKKDNQZFao7QQD8ePs/GTS1CmPqjk0925jNzWy4f7czjgY42/t4qhCB/jY8UqSjDMNh81M3cPSWh8diSGwD2IBMXN7YyIt7ChQ0smsRKRE6bAqRIFbMmvZBbV2SxL9eDxR+mdgvn761CtDyH1FiNQvyZ1SeCW1uHMGl9NisPFfHQD9m89UsuT3QLZ2hjrTMnciJew2D94SLm7nUxf28B+3P/FxrrWPwY2tjCiHgrfRqYCdSEayJSCRQgRaoIt9dgxqYcnv8pB68BHaMCebNvBC21+LrUEp3qBDF/SB0W7i/gsfVOdjqL+dvSTHpFBzGtezid6mjSKBGAYq/B6vQi5u9x8dU+FwfzvaXb6lv9GBZnZXi8lZ7RQZqlW0QqnQKkSBWQ4izm78sz2XTEjQm4p0MoEzvZ1H1Pah2TycQlja0MjrHw3o48ntqcw+r0Ii6cn8E1zaw82tlGTKg+uqT2cXsNVhwsZN4eF1/vK+BIwf9CY0yIPyPirQyPs9CtXpCWxhGRs0qfwiI+9kVKPnevdpDjNogN9eeNPhH0qq8lDaR2C/QzMbZNKFc3C+a5rTm8sT2X/+4qWXbgjrZhTOgQSphmIpYarqDYYNmBgpJJpva5cBQZpduahv0eGuOtdIoKVDdvETlnFCBFfMRVbPDQOgf//q1kopwR8RZe6hWh2SdF/sBu9mPK7+OA/7XByZd7XDy7NYcPkvN4+Dwbf0sIVhc9qVHyi718l1rI/L0uvtlfQI77f6GxlT2A4fFWhsdZaRsRoNAoIj6hACniA7863Ixelsl2RzFmf5je3c7olsH6MiByAvFhAfy7fyTr0guZtD6b9Rlu7l7t4M3tuUzpHs7ARhZflyhy2pxFXr5NLWDeHhffpRbi8vwvNHaIDPw9NFpooTHxIlIFKECKnGMfJedx39ps8osNmtsCeK9/JO0j9aVApCJ6RJv5dmhd/m+3i8kbnWx3FHPFt0cZ2MjMlG7htInQtSTVg6PQy4J9LubtLWBpWgFF/xvSSNe6gQz/fSKc+DB9VRORqkV/lUTOkVy3l3vXOPjvLhcAVzez8nxPO6EaxyVySkwmE5c3DeaSxlbe+CWX57bksCStkGUHDnNjQjAPd7ZRz6o1U6XqOVLg4eu9Bczb62L5gUKKf29oNAE9o4MYHmdlWJxFE0WJSJWmv1Ai58BPmW7GfJ9JcnYxwQEmnjk/nFHN1WVV5ExYAkxMaB/G9QnBzNiUw7u/5vHv3/L5PMXF3R3CuL1tKFYtli4+djDfw1d7SyaAWp1ehPf30Ohvgn4NzAyPt3BpYyvRwfrRQ0SqBwVIkbPIMAze+zWfh35wUOiBNvYA3u0fSSuNYxGpNHUs/jzT084/Wofw2AYn3+wvYOqPTt7dkctNLUO4rnkwjdWiI+fQ3pxiPkoLYPVvGfxwuIhjIxoD/WBgQzPD4q0MbWwhyqLQKCLVjz5RRc6S7CIvE1Y5+HJPSZfVm1oEM71HOMEB6rIqcja0tAfy30FRLD9QwCPrnfyc6Wb6phye2pRD3wZmRiUEMyzOomtQKt2RAg8rDhSy4mAhyw8WsjvHAwQBRZj9YWAjC8PjrAyJtWimbRGp9hQgRc6CHzOKGLM8kz05HkIDTLzY286VTYN9XZZIrdCvoYUVw818f6CQj3bmM3+vi+W/f7G/L9DEyCZWrm8eTPd6QepGLqfFWeRldXohy38Pjduyio/bbgs00T3czfXt6jI4xqKx7iJSoyhAilQiwzCYtT2PyRuycXtLpl9/78JImoXrUhM5l/xMJgY0sjCgkQVHoZc5u118tPP/27vz+Kire//j75lJZpLJNpkEs8hOwICCCLJIZQmFC1pbFXdQqAtSWnvtLYpolVstFLCIWn9atYCAgkWuu61akLApSwVZFISA7FsgyWSbZJKZ+f7+CEaiooFMZpKZ1/PxyIMk882ZzzfMycx7zvmeU65PT1RrwS63FuxyKysxSiM72nVzB7sy45hKiDOr9BracKJKq454tPJopTadrNZpO20oxiL1TbNpQIZNAzNsujglWnv37FbHdrxxCCD88KoWCJDCSp9+s8al9w9WSpLu7hynP/VKks3CCAcQSg6bWXdkx+mO7Dh96arWojy3Fu9xa3eJV49tLNGUTSXKybRpVFbNyq4xLLwT8bx+Q5sLqmumpB7xaH2+R5W+b263mKTeLawakFkTGnu3sPK4ARAxCJBAAKw77tGdK4p02O1TktWk/3d5sn7eJjbUZQH4lmxHtB7rlaTJPRP10WGPFuaV6/2DlfrosEcfHfYoyerS9e3tGpVl1yWp0UxxjRCGYWiHy1s7JfXjYx6VVBt1jrkwOUoDM20amBGjy9KsSrQyLRVAZCJAAg3gNww9ta1MUzeVyGfUbP48Z6BTbdj4GWjSoswmDWsVo2GtYlRY6dOSryq0aLdbWwqqNefLcs35slydHVEamWXXjR3sbLEQhvaVemtHGFcd9ehEpb/O7e0TLDVTUjNt6p9hUyorpgKAJAIkcM7yK3z61aoiLT/ikSTde1G8Hu6ZqGgzIxZAc+KMsWhcl3iN6xKvzwurtTCvXK/tqdAOl1ePfFqiP24s0dCWMRqZZUO5juYAAB7KSURBVNfwVjGyMi29WTru9mnV0W9WSj1Q5qtze3qsWQMya65hHJBhUyu2fgGA78VfR+AcrDzi0d2rCnW8wq8Um1nPD0jW0JYxoS4LQANd5IzWtD4OPXppkv59qFIL89z696FKfXCw5iPFZtYNHWI1MsuubinWUJeLH+Dy+PXxsZqwuOqoR1+66q6UmmQ1qX+67dS0VJs6JkUxZRkA6oEACZwFn9/QjC2l+svmUhmS+qVZNXugkxUcgTBjtZh0VZtYXdUmVvkVPr22x61FeW5td3n1/PZyPb+9XF2d0aemuMayIXwT4Pb6tf54Ve0I4+aCavlPu4zRHmXSZWnW2pVSuzqjZWHGCACcNQIkUE9Hyn0au6pQHx+rkknSxO4JmnhxgqJ4AQKEtfNiLbrnogT95sJ4bSmo1sLdbi3Z49a2wmo9uKFYkz8t1vBWNVNch7aM4W9CkFT7DW06UVU7wrghv0pVp13GGGWS+pwKjAMybOrVwsr0YwAIAAIkUA9LD1XqV6uKVODxKy3WrBcHODUw0xbqsgAEkclkUvdUq7qnWjWlV5LeP1CpRbvLteywR+/ur9S7+yt1XqxZN3Wwa2SWXZ2To0NdcljxG4a+KPJq5ZFKrTrq0SfHqlTm/WaI0aSavXe/npLaN82q+GhWSgWAQCNAAj+g2m9oysYSPf15mSQpJ9OmFwYk67xYpqsBkcxmMemadrG6pl2sjrp9WrzbrUW73dpV7NUzn5fpmc/L1CM1WqM62nVdO7scNoLM2TIMQ1+V+GpHGFcf9ajAU3el1I5JURqYUbNKav90q5xMJQaARkeABM7gQJlXd64o1H9OVMtikh7ukah7u8bLzCILAE6TYbfod90SdG/XeH16olqLdpfr9a8qtOlktTadLNZDG4r1s9Y1C+/kZNq47u4HHCn31V7DuPqoR4fK666Uer7dUrtSav8Mm87n+nMACDoCJPA93ttfod+sKVJxlaHz7RbNGZSsvmlMWQVwZiaTSb3Os6rXeVb9ubdD7+2v2VtyxRGP3thboTf2VijTbtbNWTVTXLOSIneKq9dv6HiFX0fdPh0o9eqT4zXXMuYV110p1Wkzq3+GVQMzYjQww6b2iRZWSgWAECNAAqfx+AxN/k+xXthRLkka3ipGz13uYFoUgLMSG2XSDR3suqGDXQfLvFq8p0KL8sr1ValPs7aWadbWMvU5z6qRWXZd2y5WidbwmOJqGIZcVYaOun066vbpSLlPx9w+HXX7dcT99ec+5Vf4ZXzPz8dHmdQv3ar+p1ZKvcgZzawPAGhiCJDAKV+VeHX7ikJtKahWtFl69NIkje8Sx7vdABqkVXyU7rs4QRO6xWvt8Sot2u3WW3srtD6/SuvzqzRpfbF+3jZGo7Ls6p9ha7KBqdJr6FhFTQA8Wu47FQhrRhG/DofH3H5V+L4vGtZlknRerFkZdovS7Rb1TI3WgAyberawKpopvgDQpBEgAUmvf+XW7z5xqbTaUJt4i14a5FSPFmwSDiBwTCaT+qXb1C/dpul9kvTOvpoprmuOVem1PRV6bU+FWsVbdMupKa5tE4LzFO03DJ2s9NeMFlb4dLS87mjh10Gx8FsL2JxJQrRJ6XaLMuwWZdjNp/49/cOsNLuFoAgAzRQBEhGtwmvowfUuzdvlliRd3TZGf/1JspLCZDoZgKYpPtqskR3jNLJjnPaVevXqqVVcD5b59PjmUj2+uVQ/SbdqVJZdv2gbe87bUZRVnxohLPefCoe+OuHwqNuvY26fvD8+aKgok5Rutyj9e0PhqbAYZ1ECW2cAQFgjQCJi7XRV6/bcQm13eWWzSNN6O3T7BXamrAIIqrYJUXrwkkQ90D1Bq49WadHucr2zr1IfH6vSx8eqdP+6Yl3TrmYV135pVplMpjqL0HxznWHdaaVH3T6VVtcjGUpKtpmUYbco89SU0m8+rwmLmXEWpcaYm+z0WgBA8BAgEZEW5ZXrvnXFcnsNZSVG6aUcp7o6I3dFRAChZzaZNDDTpoGZNv2lr19v7avQojy31uVXaWGeWwvz3Mq0m+UzdMZFaL4txqLa6wwzTwXDdLu55vO4U1/HWhQTRTAEANQPARIRpazarwlrXVq8p0KSdGOHWM26zHHO08MAoDEkWs0a3SlOozvFaXdxtV7d7daru9064q65DtEkKS3221NJzTVBMe6b7zmsJmZVAAACigCJiLGtsFp3rChUXrFX9iiT/tI3SSOzmLIKoGnLSorWIz2T9NAlidpV7FWi1ay0WLOiWIQGABACBEiEPcMw9NJOtx7c4JLHJ3VxRGlujlPZDqasAmg+LGaTOifzdwsAEFoESIS14iq/7v3Ypbf21UxZ/WUnu6b1cSiW630AAACAs0aARNjadKJKt68o1P4ynxKiTXqqn0PXtbeHuiwAAACg2SJAIuwYhqHntpfrj58Wq9ovXZwSrZcGOdU+kYc7AAAA0BC8okZYKaz06ddrXPrgYKUkaVznOD3WK0k2C1NWAQAAgIYiQCJsrDvu0Z0rinTY7VOS1aRnL0/WVW1iQ10WAAAAEDYIkGj2/Iahp7aVaeqmEvkMqVeLaM0Z5FTreB7eAAAAQCDxChvNWn6FT79aVaTlRzySpHsvitfDPRMVzf5oAAAAQMARINFs/cdl1qMb83W8wq8Um1nPD0jW0JYxoS4LAAAACFsESDQ7Pr+hGVtK9ZfPbTLkV780q2YPdCozzhLq0gAAAICwRoBEs+L2+nXXyiL960ClTJImdk/QxIsTFMWUVQAAAKDRESDRbORX+HTzsgJtOlkth9WkqZ0qNeqSxFCXBQAAAEQMAiSahV2uat2wtED7y3xqHW/R/w1NkenEvlCXBQAAAEQUcyAa8fl8mjJlirp166a0tDR169ZNU6ZMkdfrDUTziHAfH/Pov/55QvvLfOqRGq1lV7VQJ0d0qMsCAAAAIk5ARiCfeuopzZ49W3/729/UpUsXffHFFxo/frysVqsmTpwYiLtAhHr9K7fGry5SlV+6olWMZg9MVlx0QN73AAAAAHCWAhIgN2zYoOHDh+uKK66QJLVp00ZXXHGFNm7cGIjmEYEMw9DT28r0x40lkqS7O8dpWu8kWVgsBwAAAAiZgAzl9O3bV2vWrNGuXbskSV9++aVWr16toUOHBqJ5RBiv39Dv17r0x40lMkma2jtJM/oQHgEAAIBQM7lcLqOhjRiGoSlTpmjWrFmyWCzyer2677779PDDD5/xZ/Ly8hp6twhD5V7poZ02fVJkkc1s6LFOVRqc6gt1WQAAAEBE6Nix4w/eHpAprG+88Yb+8Y9/aPbs2crOzta2bds0adIktW7dWqNHjz6nwhB5jrp9unNpgbYWVctpM+sfQ5zqfZ7tjMfn5eXxOAIChP4EBBZ9Cggc+lPTEpAAOXnyZN1zzz267rrrJEkXXnihDh48qCeffPKMARI43faiat24tECHyn1qn2DRkqGp6pDELjMAAABAUxKQV+hut1sWi6XO9ywWi/x+fyCaR5hbecSj25YXqKTaUO8WVr06xKmUGMuP/yAAAACAoApIgBw+fLieeuoptWnTRtnZ2dq6daueffZZ3XzzzYFoHmHs1d1u/ffHRar2S79oE6MXBjgVG8ViOQAAAEBTFJAA+fjjj2vq1KmaMGGCTp48qbS0NI0ZM4Y9IHFGhmHo8S2lmvZZqSTpngvj9VivRJlNhEcAAACgqQpIgExISND06dM1ffr0QDSHMFftN3Tvxy4t2u2W2SRN752ku7vEh7osAAAAAD+CVUoQVMVVfo3JLdSKIx7FWkyaMyhZV7aODXVZAAAAAOqBAImgOVzu0w1LT2p7kVctYsxaPCRFPVpYQ10WAAAAgHoiQCIothZU6aZlBTrq9qtjUpSWDE1R2wQefgAAAEBzwit4NLqPDldqzPJClXkN9UuzauFPU5RsM4e6LAAAAABniQCJRrVgV7n+5xOXfIZ0fftYPXt5smwWVloFAAAAmiMCJBqFYRiauqlUM7fWbNPx+27xergH23QAAAAAzRkBEgHn8Rn67ZoivfZVhSwm6YnLHPrlBXGhLgsAAABAAxEgEVAuj1+3Li/QmmNViosyaV6OU0NbxoS6LAAAAAABQIBEwOwv9erGpQXaWexVeqxZi4em6OIUtukAAAAAwgUBEgHx2cmabTryK/zq7IjSa0NT1CqehxcAAAAQTniFjwb74GCF7lhRJLfX0MAMmxYMdirJyjYdAAAAQLjhVT4aZPaOMo38qFBur6FbsuxaMjSF8AgAAACEKUYgcU78hqH//bREz3xeJkl6oHuCJnVPkIltOgAAAICwRYDEWav0GvrV6iK9ta9CUSbp6Z84NKoj23QAAAAA4Y4AibNSWOnTyI8KtS6/SonRJi0Y7NSgTLbpAAAAACIBARL1trfEq+uXntSeEp/Ot1v02tAUXeiMDnVZAAAAAIKEAIl6+U9+lW5eVqACj18XOaP12pAUZcZZQl0WAAAAgCAiQOJHvbu/QmNXFqrSJw0536aXcpxKiGalVQAAACDSkALwg577okyjl9eEx9Gd7Hp1SArhEQAAAIhQjEDie/n8hh7aUKwXdpRLkh7pkajfd4tnmw4AAAAgghEg8R1ur19jVxbpnwcqFW2Wnrs8WTd0sIe6LAAAAAAhRoBEHScqfLrlowJ9eqJaSVaTXhmcov4ZtlCXBQAAAKAJIECiVl5xtW5YWqB9pT61jrdoydAUXeBgmw4AAAAANQiQkCStPe7RyI8KVOQx1D0lWouHpCjNzjYdAAAAAL5BgITe+Mqt8WuK5PFJw1vFaM7AZMWx0ioAAACAbyElRDDDMPT0tlLdsbImPN6VHaeFg52ERwAAAADfixHICOX1G5q4rlhzd9Zs0/GnSxN1z0Vs0wEAAADgzAiQEais2q87VxTqw0Me2SzSC/2duqZdbKjLAgAAANDEESAjzDG3TzctK9CWgmo5bWYt+qlTfdPYpgMAAADAjyNARpAdRTXbdBwq96ldgkX/NzRVHZJ4CAAAAACoH9JDhFh11KNblxeopMpQrxbRenVIilJj2KYDAAAAQP0RICPA4j1u3bOmSNV+6edtYvTiAKdio1gsBwAAAMDZYb+GMDdvZ7nGraoJj7++ME7zBhEeAQAAAJwbRiDD2H/yq3T/Opck6c+9k/TrC+NDXBEAAACA5owRyDB1stKnX+YWqtov3d05jvAIAAAAoMEIkGHI5zd054oiHXb71LuFVVN6JYW6JAAAAABhgAAZhqZ+VqKVRz1qEWPWvBynrBaueQQAAADQcATIMPPP/RWatbVMZpM0Z5BTmXFs1QEAAAAgMAiQYeSrEq/Gry6SJP1vz0QNyLCFuCIAAAAA4YQAGSbcXr9uW16gkmpDV7WO0X9fxKI5AAAAAAKLABkGDMPQ/3zi0hdFXnVItOjZ/skymbjuEQAAAEBgESDDwNyd5Vq8p0L2KJNeHpyiJCv/rQAAAAACj6TRzH16okqT1hdLkp7u51CX5OgQVwQAAAAgXBEgm7GTlT6NWV6oar80tnOcbuhgD3VJAAAAAMIYAbKZ8vkN3bWySIfdPvVuYdXUXkmhLgkAAABAmCNANlPTPivViiMepcaY9VKOU1YLi+YAAAAAaFwEyGbo/QMVmrm1VGaTNGegU+fHWUJdEgAAAIAIQIBsZvaWeDVudZEkaXKPRA3MtIW4IgAAAACRggDZjLi9ft2WW6iSKkM/ax2je7vGh7okAAAAABGEANlMGIahCWuL9XlhtTokWvRc/2SZTFz3CAAAACB4CJDNxLydbr26261Yi0kLclKUZOW/DgAAAEBwkUKagU0nqvTAepck6emfOHShMzrEFQEAAACIRATIJq6g0qfRuYWq8ktjs+N0Ywd7qEsCAAAAEKEIkE2Yz2/orpVFOlTuU68W0ZraOynUJQEAAACIYATIJmza5lLlHvEoNcaseTkpslpYNAcAAABA6BAgm6gPDlZo5pZSmU3SnIFOnR9nCXVJAAAAACIcAbIJ2lfq1bhVRZKkR3okamCmLcQVAQAAAAABssmp8Bq6bXmhiqsMXdk6Rr/rGh/qkgAAAABAUoACZNeuXeVwOL7zceONNwai+YhhGIYmrHVpW2G12iVY9NzlyTKZuO4RAAAAQNMQFYhGcnNz5fP5ar8+duyYBg0apGuuuSYQzUeM+bvcWrTbrViLSS8PTpHDxgAxAAAAgKYjIAEyNTW1ztcvv/yyEhISdO211wai+Yiw6USVJq5zSZKe7OfQRc7oEFcEAAAAAHUFfIjLMAy9/PLLuummmxQbGxvo5sNSQaVPo3MLVeWX7syO081Z9lCXBAAAAADfYXK5XEYgG1y+fLlGjBih1atXq2vXrmc8Li8vL5B322z5DOl3X9i0zmXRhfE+vdjNIyszVwEAAACEQMeOHX/w9oBMYT3d/Pnz1aNHjx8Mj9KPFxYppm4q0TpXqVJsZi2+Ik0t4wP+XxK28vLyeBwBAUJ/AgKLPgUEDv2paQnoWNeJEyf0r3/9S2PGjAlks2Hrw4OV+suWUplN0pxByYRHAAAAAE1aQAPkokWLZLPZdN111wWy2bC0r9Sru1cVSpL+cEmiBmXGhLgiAAAAAPhhAQuQhmFowYIFGjFihOLj4wPVbFiq8BoavbxQxVWGhreK0f904/cFAAAAoOkLWIBcvXq19uzZw/TVerh/nUtbC6vVLsGi5/sny2wyhbokAAAAAPhRAbvobsCAAXK5XIFqLmwt2FWuV/LcirWYtGBwihw2llwFAAAA0DyQXoJo88kq3b+uJmTP6udQV2d0iCsCAAAAgPojQAZJYaVPt+UWyuOT7rggTrdk2UNdEgAAAACcFQJkEPj8hu5eVaSDZT71TI3WtD5JoS4JAAAAAM4aATIIHt9SqmWHPXLazJqX45TNwqI5AAAAAJofAmQjW3qoUo9vLpVJ0pyByWoVH7B1iwAAAAAgqAiQjWhfqVdjVxbKkPSHHonKOT8m1CUBAAAAwDkjQDaSSq+hMbmFclUZGt4qRr/vFh/qkgAAAACgQQiQjeT+dS5tKahW2wSLnu+fLLOJ6x4BAAAANG8EyEawYFe5Xs5zK8YiLchxymHj1wwAAACg+SPZBNjmk1W6f51LkjTrMoe6pVhDXBEAAAAABAYBMoCKPH6Nzi2UxyfdfoFdIzvGhbokAAAAAAgYAmSA+A1Dd68s1IEyn3qkRmt6H0eoSwIAAACAgCJABsjjm0u19LBHTptZ83OcsllYNAcAAABAeCFABsCyQ5WasblUJkmzByarVXxUqEsCAAAAgIAjQDbQ/lKv7lpZKEPSQ5ckaPD5MaEuCQAAAAAaBQGyASq9hsbkFspVZWhYqxhNuDgh1CUBAAAAQKMhQDbAxPUubS6oVtsEi17onyyzieseAQAAAIQvAuQ5enlXuRbscivGIi3Iccph41cJAAAAILyRes7B5pNVum+dS5I08zKHuqVYQ1wRAAAAADQ+AuRZKvL4NTq3UB6fNKaTXbd2jAt1SQAAAAAQFATIs+A3DI1bVagDZT51T4nWjD6OUJcEAAAAAEFDgDwLM7eU6t+HPEq2mbRgsFMxUSyaAwAAACByECDr6aPDlZr2WalMkmYPdKp1fFSoSwIAAACAoCJA1sOBMq/uWlkoQ9KkSxL00/NjQl0SAAAAAAQdAfJHVHoNjcktVJHH0H+1tOn+ixNCXRIAAAAAhAQB8kdMWu/SZyer1TreohcGOGU2cd0jAAAAgMhEgPwBC/PKNW+XWzaLtCDHqWQbvy4AAAAAkYtEdAZbC6o0Ya1LkjSzr0PdU60hrggAAAAAQosA+T1cHr9uW16oSp80upNdt3WKC3VJAAAAABByBMhv8RuGxq0q1P4yn7qnROvxPo5QlwQAAAAATQIB8lue2FKqDw95lGwzaX6OUzFRLJoDAAAAABIBso4N+R79+bNSmST9fYBTbRKiQl0SAAAAADQZJKTT9Ei16rcXxSsu2qQhLWNCXQ4AAAAANCkEyNNEmU16rFeSDMMIdSkAAAAA0OQwhfV7mExc9wgAAAAA30aABAAAAADUCwESAAAAAFAvBEgAAAAAQL0QIAEAAAAA9UKABAAAAADUCwESAAAAAFAvBEgAAAAAQL0QIAEAAAAA9UKABAAAAADUCwESAAAAAFAvBEgAAAAAQL0QIAEAAAAA9WJyuVxGqIsAAAAAADR9jEACAAAAAOqFAAkAAAAAqBcCJAAAAACgXgiQAAAAAIB6IUACAAAAAOqFAAkAAAAAqBcCJEJi1qxZysnJUatWrdShQwfddNNN2r59e51jDMPQtGnTlJ2drfT0dP3sZz/Tjh076hwzc+ZMDRs2TJmZmXI4HN97Xw6H4zsfc+fObbRzA0IhmH1KkhYvXqzLL79caWlpat++vcaNG9co5wWEQrD608KFC7/3OcrhcGjTpk2Neo5AMAXzOWrTpk26+uqr1bp1a7Vu3Vq/+MUvtHHjxkY7t0hEgERIrFmzRnfeeac+/PBDvfPOO4qKitI111yjoqKi2mOefvppPfvss5oxY4aWL1+uFi1a6Nprr1VpaWntMR6PR1dddZXGjx//g/f317/+VTt37qz9uOWWWxrt3IBQCGafev755zV58mT99re/1dq1a/Xuu+/qyiuvbNTzA4IpWP1pxIgRdZ6bdu7cqRtvvFFt27bVJZdc0ujnCQRLsPpUWVmZrrvuOqWnp2vZsmVaunSp0tPTNWLEiDrtoGFMLpfLCHURQFlZmVq3bq2FCxfqiiuukGEYys7O1tixY3XfffdJkioqKtSxY0f96U9/0u23317n599++22NGTNGLpfrO207HA7Nnz9fV199dVDOBWgKGqtPuVwudenSRQsXLlROTk7QzgcIpcZ8jjqd2+1Wdna27r33Xk2YMKHRzgcItcbqU5999plycnK0efNmtW3bVpK0b98+de/eXbm5ubwxEyCMQKJJKCsrk9/vr52OsH//fh0/flyDBw+uPSY2Nlb9+vXT+vXrz7r9SZMmqX379srJydHcuXPl9/sDVjvQFDVWn8rNzZXP51N+fr769Omjzp07a9SoUdq3b1+gTwFoMhr7Oeprb775ptxut2699dYG1ww0ZY3Vp7KyspSamqpXXnlFHo9HHo9HCxYsUMuWLZWdnR3w84hUBEg0CZMmTVLXrl3Vu3dvSdLx48clSS1atKhzXIsWLZSfn39WbT/00EOaO3eu3nrrLY0YMUIPP/ywnnjiicAUDjRRjdWn9u3bJ7/fr5kzZ2rq1Kl65ZVX5PV6ddVVV8ntdgfuBIAmpDGfo043f/58DRs2TGlpaedeLNAMNFafSkhI0Hvvvac333xTGRkZysjI0BtvvKG33npLsbGxgTuBCBcV6gKAhx56SOvWrdMHH3wgi8US8PYnTpxY+3m3bt3k9/v1xBNP6P777w/4fQFNQWP2Kb/fr+rqas2YMaP2neIXX3xRF1xwgT744AONGDEioPcHhFpjP0d9bceOHdqwYYNee+21RrsPoClozD5VUVGhe+65R5deeqn+/ve/y+fz6ZlnntHIkSOVm5uruLi4gN5fpGIEEiH14IMP6vXXX9c777xTO1ddUu27rydOnKhz/IkTJ3Teeec16D579uypkpKSBr1LDDRVjd2nvm7nggsuqP1eUlKS0tPTdejQoQZUDjQ9wXyOmjdvnlq2bKkhQ4acc71AU9fYfWrJkiXau3evnnvuOfXo0UO9evXS7NmzdejQIb333nsBOQcQIBFCDzzwQO0fkU6dOtW5rU2bNkpLS1Nubm7t9yorK7V27Vr16dOnQfe7bds2xcTEKCkpqUHtAE1NMPpU3759JUm7d++u/V5ZWZmOHz+uVq1aNfAMgKYjmM9RlZWVWrx4sUaNGiWzmZdmCE/B6FMVFRUymUx1+pHZbJbJZGL9iwBiCitC4r777tPixYv1yiuvyOFw1M59j4uLU3x8vEwmk8aPH69Zs2apY8eOysrK0syZMxUXF6frr7++tp2DBw+qqKhIBw4ckCRt3bpVktS+fXvFx8fr/fffV35+vnr16qXY2FitXr1a06ZN05gxY2Sz2YJ/4kAjCVafysrK0pVXXqlJkybpySeflMPh0LRp05Samqphw4YF/8SBRhCs/vS1t99+WyUlJSyeg7AVrD6Vk5OjyZMna8KECRo3bpz8fr+efPJJWSwWDRgwIPgnHqbYxgMhcabNXx944AE9+OCDkmo2lJ0+fbrmzZsnl8ulnj17aubMmerSpUvt8ePHj9err776nXbeffdd9e/fX8uWLdOjjz6qvXv3yu/3q23btrrttts0duxYRUXx/gnCR7D6lCSVlpbqD3/4g9555x0ZhqG+fftq+vTpateuXSOcGRB8wexPknTllVcqLi5OS5YsCfCZAE1DMPtUbm6uZsyYoe3bt8tkMqlr16565JFHGjyDDd8gQAIAAAAA6oWJ9gAAAACAeiFAAgAAAADqhQAJAAAAAKgXAiQAAAAAoF4IkAAAAACAeiFAAgAAAADqhQAJAAAAAKgXAiQAAAAAoF7+P10PGqUZ4VPdAAAAAElFTkSuQmCC\n",
2289 "text/plain": [
2290 "<Figure size 1008x432 with 1 Axes>"
2291 ]
2292 },
2293 "metadata": {},
2294 "output_type": "display_data"
2295 }
2296 ],
2297 "source": [
2298 "eps.plot(lw=2, figsize=(14, 6), title='Diluted Earnings per Share')\n",
2299 "plt.xlabel('')\n",
2300 "plt.savefig('diluted eps', dps=300);"
2301 ]
2302 },
2303 {
2304 "cell_type": "code",
2305 "execution_count": 18,
2306 "metadata": {
2307 "ExecuteTime": {
2308 "end_time": "2018-12-25T19:50:45.492886Z",
2309 "start_time": "2018-12-25T19:50:44.995908Z"
2310 }
2311 },
2312 "outputs": [
2313 {
2314 "name": "stdout",
2315 "output_type": "stream",
2316 "text": [
2317 "<class 'pandas.core.frame.DataFrame'>\n",
2318 "DatetimeIndex: 1275 entries, 2014-09-30 to 2018-03-27\n",
2319 "Freq: D\n",
2320 "Data columns (total 12 columns):\n",
2321 "Open 877 non-null float64\n",
2322 "High 877 non-null float64\n",
2323 "Low 877 non-null float64\n",
2324 "Close 877 non-null float64\n",
2325 "Volume 877 non-null float64\n",
2326 "ExDividend 877 non-null float64\n",
2327 "SplitRatio 877 non-null float64\n",
2328 "AdjOpen 877 non-null float64\n",
2329 "AdjHigh 877 non-null float64\n",
2330 "AdjLow 877 non-null float64\n",
2331 "AdjClose 877 non-null float64\n",
2332 "AdjVolume 877 non-null float64\n",
2333 "dtypes: float64(12)\n",
2334 "memory usage: 129.5 KB\n"
2335 ]
2336 }
2337 ],
2338 "source": [
2339 "symbol = 'AAPL.US'\n",
2340 "\n",
2341 "aapl_stock = (web.\n",
2342 " DataReader(symbol, 'quandl', start=eps.index.min())\n",
2343 " .resample('D')\n",
2344 " .last()\n",
2345 " .loc['2014':eps.index.max()])\n",
2346 "aapl_stock.info()"
2347 ]
2348 },
2349 {
2350 "cell_type": "code",
2351 "execution_count": 19,
2352 "metadata": {
2353 "ExecuteTime": {
2354 "end_time": "2018-12-25T19:50:45.691513Z",
2355 "start_time": "2018-12-25T19:50:45.494095Z"
2356 },
2357 "scrolled": false
2358 },
2359 "outputs": [
2360 {
2361 "data": {
2362 "image/png": 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\n",
2363 "text/plain": [
2364 "<Figure size 1008x432 with 1 Axes>"
2365 ]
2366 },
2367 "metadata": {},
2368 "output_type": "display_data"
2369 }
2370 ],
2371 "source": [
2372 "pe = aapl_stock.AdjClose.to_frame('price').join(eps.to_frame('eps'))\n",
2373 "pe = pe.fillna(method='ffill').dropna()\n",
2374 "pe['P/E Ratio'] = pe.price.div(pe.eps)\n",
2375 "pe['P/E Ratio'].plot(lw=2, figsize=(14, 6), title='TTM P/E Ratio');"
2376 ]
2377 },
2378 {
2379 "cell_type": "code",
2380 "execution_count": 20,
2381 "metadata": {
2382 "ExecuteTime": {
2383 "end_time": "2018-12-25T19:50:45.697112Z",
2384 "start_time": "2018-12-25T19:50:45.692379Z"
2385 }
2386 },
2387 "outputs": [
2388 {
2389 "name": "stdout",
2390 "output_type": "stream",
2391 "text": [
2392 "<class 'pandas.core.frame.DataFrame'>\n",
2393 "DatetimeIndex: 1275 entries, 2014-09-30 to 2018-03-27\n",
2394 "Freq: D\n",
2395 "Data columns (total 3 columns):\n",
2396 "price 1275 non-null float64\n",
2397 "eps 1275 non-null float64\n",
2398 "P/E Ratio 1275 non-null float64\n",
2399 "dtypes: float64(3)\n",
2400 "memory usage: 39.8 KB\n"
2401 ]
2402 }
2403 ],
2404 "source": [
2405 "pe.info()"
2406 ]
2407 },
2408 {
2409 "cell_type": "code",
2410 "execution_count": 21,
2411 "metadata": {
2412 "ExecuteTime": {
2413 "end_time": "2018-12-25T19:50:46.185052Z",
2414 "start_time": "2018-12-25T19:50:45.698152Z"
2415 }
2416 },
2417 "outputs": [
2418 {
2419 "data": {
2420 "image/png": 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rIr6ZlM4ZafYTer7DWV8V4LSPt5DlDvHWNg9xVhMvb65gUbaP6lnFB6/hNwMTuGdAPLP3eJmx08PHe7wszfVxRdfYk7bO5kwVOCIiIiIiIiIHKfGFmfS//CbZxvrcT/K5Z2kJH+/xnvC5qufNjGnjYECKDQCzCdJjLAA8OjQpeuyibN8JP199cj0h/raunG/zI4OJb+0dB8CC7EhwNO9ATXgDcE5rR63Hm00mJneK4Zd94wH4ZI+XULhltlGpAkdERERERESkysp8PzfNKyLLHWJhjh+Xw8wZqTZGZjrqHGsYBn9YWcbKfD+GAQbQN9nGUz9KwmI+9uqZXE8o+vH9y0p4aaObUn9kVozNbOLBMxI5r72z3sdO31bBy5sqALiqWywXdnDy1T4vZhP8c1QySXYzj6wq5eyDrqOXy8YLZ7u4Y1EJqwpOzqDgJ1aX8frWSJAUbzVxaZdYfv9tJBjrk2xlUKqdbaUBvs2PPP+hrm9Qqh27GQq8Ya78qpAZE9JOynqbMwU4IiIiIiIiIsCiHB8/nVOA/6D5ur+rGvL7zAgXN/eKq3X82sIAz69317pteZ6frklWbq+qGDlaC7N9fHzQLJrcyjC5lf5ax/xrk7vegCMUNnh4ZRn53sjC1xSW8vsVpQQNuLhTDJmxkYqbJ4e76jx2cFXL0qoCf537GsLyvMh5f9Yrjos6xdA61sIZaTb2loeYOSGNjFgLm0sCjP0kn0s6x5Bor79RyGk1cVe/BJ5eV878bB/BsIH1OEKyU5laqERERERERESA59aV4w/Dee0cTB+bwi/7xnFp5xgA/t+3pXV2QPpwVyRwuaxLDJ9dkMbfRkQCksdWlbGzLHjUz7ulJMDkzwuiFTQALruJq7rGsPCiVkwfmwLArkOcc3men3xvmI7xFp4d6SLRZiJoQKsYM08MS6r3MdV6JFmJt5rIcofIqwwd9tjDKfSG2Fhcu4qnzB9mS0kQmxkeH5rE6Kr2qE8vSGPVpRlkVAVLvVw2Nl+ZyXMj6wZMB/v94ETaxVkIhGFPec1aG2sr9KamChwRERERERFp0Wbu8vDejkq+PuDDaoJ/jEom1WnhJx0j4c3G4lw2lQRZVeBnREZNC9LnWZE5NTf3jGNkpoORmQ6W5fl4b0clz35fzv+dlXzI5yzxhbn8ywKGtXIwe08lBnBGmo2zMx38ZkACLkdNvUX3RCsmYK87RCBsYDuo8mR1qZmnvy8BYFLHGG7sGcf1PWLxBA0cFlOtY+tjMZsYlGZjUY6f7wr8nN8+5lhfPgBu+KaIRTl+Rrd20CHeQmZMpNLGAPqn2HBaa9YRazUT+4M04lCVNz/UI8nKvooQW0oDdE2ysjjHx5S5hTw7wsVPu5zew40V4IiIiIiIiEiL9vsVZeyvmj8zqaOTVKel1v1nt3awqSTIgmxfNMAp84fZWhrEboYh6TU7J93UM473dlTyxlYPu8qC/HtMCq1iap8P4Mt9Xr7Nr5n9AvCX4S6Gtqq7E5TTaqJtnIV9FSH2uUN0Toy8lc/xhJi63kHICJLsMHFrn0iLl9lkIt529O1Fg9PsLMrxM3VhMXf1C9I9ycqFHY8tyFmUE2mVWlDPMOSDQ68T1T3JytcHfGwrjVQj3bu0hDK/wc3zi0/7AEctVCIiIiIiItJieYJh9ntCWE3w/vhU/n523aqZ6tafaavL+dHMXLaVBvi+alvsPsk27JaasKR3si368cIcP73fy2Hgf3P4Zn/tXaWqt9UG6JRg4ZEhiQxJt3EonRMiIdDgD3O5ZUERhmHwbb6fkBF57nfHpdI+/vhqNAZXBVDFPoNHVpVx7ddFx9QC5gnWDA16bqSLJ4cn0Ta2JrS6tnvDBSs9XZHXaFNVu9bxDIs+VakCR0RERERERFqsXWWRyptOCVYmHGIHpIN3btpcEmTEzLzo1teDUmuHLkk/aAUKGbDHHeKORSUsvCidlKrqnnVVAc5vByVw78CEIw7k7ZpoZWGOn7AB7++oZGqfeFZXDR7+9YB4hp9Alcuo1g7axlpoE2em1G+wtTTI2kI/XRKPLjI4UBF5DTvGW7ihZ6QKaGwbBxd+XsCwdHutUOtEnZEWOdeyquHISXYFOCIiIiIiIiKntWW5PlZXbZ/dNbFum1O1ZEftUCZ40MzcwwUn7eMt/O+CNM7/rIB9FSG6vJOD2RQJOnZVDeGd0i32qHZTmto3nvKAwQdVg5PvWVJCQdWuU2ek1W27OhbJDjPrr8jAZDLxp1Wl/HWdmw3FQS7pfHSP318V4LSNq3kNe7hsbLwiE0sD5ysDUmwk2U3sLg+xpzyIxVTzBCW+cK3ZQaeb0/fKRERERERERA5hbaGf8z8r4MGqbcI7H6Ha5PIukZkwZ2Xa+eInacw6L5X/nJPMFV3qzop5cVQyiXYTr41JoV28lTfOTaFfSqRyJGwQDW+6JFjoEH/o4OhgPV02/j0mha8uTAdgTWGAfRUhrCaDYeknFuAAmKqCkD5V1TI/3FHqcKoDnHZxta/FajZFz9tQLGZTtCJqUY6PQm/NblTH0vZ1KlIFjoiIiIiIiLQ4c7Jqz6TpeoQA5+kRLgak2LihZ9wRd0ya0i2WKd1q5r6cmW7nm0npPPe9mzSnmbMy7XiCBl0TrccccAxJt/PJ+WnR0MRZlh3djrshVAc4awr8PLaqjNWFfv4zJqVOa9jBqtfSJq7h1nE4Q9LtfLrXy/qiAEW+SBXS1D5xDfo6NEcKcERERERERKTFqd41qdqP29U//6Zakt3Mnf0Tjvv5bGYT9w48/scfbFTrmratbdvChzny2PVIstIpwcLu8hBPrysHYGmu77Dbi2fV00J1MvVIikQZW0uD0TayPwxOIsZ6es/DUQuViIiIiIiItCjBsMGKvJrtrn/RK45OCapvgEiL0rMjXRxccFMdkhzK2sKaHbkaQ09X5Gu1Mt9PIAxxVtNpH96AAhwRERERERFpYXaVB/GGIkOGF1/UiieHJzX1kpqVMW2cbLmqNb/sG9lRqqDy0AGON2iwoSiA2QQDUxsnwOmUYMVmhlJ/ZJp0irNlRBuKGEVEREREROS4GIbB7vIQ5YEwYQMSbGa6JFoafHBtQ9tYHBl228dlpW9K44QOp5pkh5nMmEhLVP5hKnDWFwcIGtDbZSXe1jhBitVsonuilY0lka9jKwU4IiIiIiIiIvXzBg1u+KaQOft8tW5/c2wKkzoeel5Kc7Cpaoel3o3U8nOqSq0KRvIP2unph77ZHxkGfWYD7IR1LP44JInXt1YAcHPPuEZ97qaiAEdERERERESOSYkvzKTPC/i+KEC81UTHBAueoMGu8hDvbfc02wBnf0WIiZ/ls69q6G4vlwKcw0l3RipwCqsqcJ5eW86SHB9vj0vFaTURNgymb/cA8NPOjfs1P6+9k/PaH37w9OmmZdQZiYiIiIiIyBGV+sMEw8YRj3t1SwXfFwVoG2vhiwvTWXxxBrMvSAdg9l4vH+70RI81DAPDOPI5G8PCbB973CFCBiQ7TLV2c5K60qorcKpm4Dz2XRlfH/Dx6d5KILIL1O7yEJkxZsbotTzpFOCIiIiIiIgI+ytC9H0vh7Gf5FPsO/TMk1DY4NUtkdaVv410RXceahtnYWRGpI3m5vnFvLvdgzsQZvAHuVzxZSGVwaYPcQ54IpU3t/WJY9tVrRtt2+tTVVpMJDIo8Ibwh2q+ftWvY07V//Zw2bCYm/fco9OBAhwREREREZEWaNbuSv68pixaHfPeDg/uoMG6ogB/XlNW61hPMExFIBLqzN3vY687RId4C+Pa1q66eGd8Kr/qHw/Ac9+XszzPz87yEF/u9/HQihIMwyAYNggdRZXP8agIhLl/WQnPf19eb9XP/qrWqU4JVqwKHI6ougInrzLMVV8VRm/fWjU8OLeqMqdVjKKFxqAZOCIiIiIiIi3MhqIAN3xTBMCkjjH0SbYxY0dN29OyXH/0439vdvPQilJ8P5hje3PPuDpVF0l2Mw+ekciLG91sKgkyJ8sbve/VLR5m7KykPGBgNcHfRyVzZdfYBr2uqQuL+XhP5DnjbGZu7lV7uG11gNMmVpU3RyPWaqZNrJkDnjBfH6gZVr2hagh0XlUFjgKcxqFXWUREREREpIV5YYM7+vGe8iBZ7iAbS4JU5zEbigP4qlpmXtxQUSe86RBv4boe9YcvDouJwWmRVqrp2yKhkMseOXF5IHLOoAG3LijmlU3ues9xPCqDBp/trQmM/nvQHJ5q1QFOO7VOHbVZ56fxyJDEWrdtKg4SNoxoBU5GjF7PxqAAR0REREREpIVZXxSIfryrPMTc/ZHqivPbO+mZZCUQhjlZXnaUBtleFmmXubFHLE//KInCG9qw9rIMUp2HftN+dtVA24qquTfTx6VG7/vD4EQGpkbm5ty7rJRdVec/UesK/QSrhhMDbC4J1GmjOlBdgaMA56h1T7JxR9/4WrdVhgyyPWHyKqsrcPR6Nga1UImIiIiIiLQgobDB1tKaAOehFaXRj8e0dpDmNLOlNMj1VS1WAOe2cfDsWclH/Ry/7BOP1QQVAYMBqTbOynRwd794vj7g48Yesfy0cwyDZuQCsCTXR+fE439rahgGs/d6mbkrsjPSpI4xfLS7kmKfQV5lmIxYC7vLg7y3w0OhL4zVBOlO1TIci/oGFG8vDWoGTiNTgCMiIiIiInIaK/OHuWVBMdd0j2VSxxh2lQfrtERZTJG2okmdYri4cwz5lWEW5/jwBA2CBlzQ3nlMz+lymLl/UO22m0eGJvFI1ccpTnhsaCK//7aMZbl+rukeV/ckRxA2DKatLuepteW1bh+abmdLSZDleX4eWlHKWZkOXlhfzs7yyEUPTrdrx6Tj8Ma5KUxbXUaq08yiHD8Lsr3RljRV4DQOBTgiIiIiInJaMwyD17d62FUWJMFu5oYesaS3oDecH+6q5PMsL59neWkda46GN4l2E2X+SIvRa+emMKljTPQx74yPtDx5gwabSwIMqGp5akgjMiJtVsvy/Ec4MsIXMnh/hweTKbIr0t/WlUdn6gBc3CmGni4rl3eJ5dt8P8vz/Hywq5IPqipzqk0bltRwF9GCTO4Uw+ROMTy7rpxFOX7+uq5mflGGKnAahQIcERERERE5rS3P8/OrJSXRz59dV849AxL4zcCEoz7Hf3d4eHu7BxPw895xeIIGWe4QP+8dR4Kteb959YZqQo5sTzj68S96xUXfhJ/T2lHncQBOq4lBVQOJG9qAVBuxVhPbSoMUeEOkHWamTo4nxHPfl/PixopatyfYTKQ4zFzYMYbHhiZiMkUqa27uGceusiBt4yw4LSZsFhPXdIvF5TDTKUFvg09E96Sa169HkpXhrexqSWsk+i9XREREREROa59XbWV9TmsHQcNgcY6fp9aWcc+AeMymI7fShMIGDywvpcgXCT8O3k55Vb6ftw4a0NscFVTWhDZ/GprIpZ1jcVgg1WnB5TCT4jCTaG/8N+A2s4kh6XYWZPtYluvnwqoKoGfXlfPMunJSq0KBQl84WikEML6tg1iribFtndzQIzYa2hxsUJqdTy5Ib5wLaWF+3M7J3f3iGdfOyehDBH9ycijAERERERGR05YvZDCnKsC5Z0A8Y9o4afPmATxBg/yqAbdrCvz8YkExmTFmAmHolGChe5INf9ggGDZIcVoo8oVpF2fBYYEdZTUDZBbn+g711E3CEwyzzx2ih6um5SnfG1nvn4Ymcme/2lVHP/y8sf0oIxLgLM7xcWHHGHaWBXl4VRkAZYFQvY95c2wqMVbNsGkqdouJR4aqDa0pHFXMunjxYq666ip69+6Ny+Vi+vTpte6fOnUqLper1r/x48fXOsbn83HffffRpUsX2rRpw1VXXcX+/fsb7kpEREREREQO8vImNxlvHGBTSZAUhzk6c6Vb1Y5HL2xw84v5RVz/TRHbSoMszPGzLM/Puzsq+dN3Zfx5TTl/Xefmd1W7NI1v6+C3gxIxm+CqrjGYgGKfQWXQONQSGlWxL8y4T/IZPjOPjcU1u0zlVVXgdIxvfn+/H9sm8jX5YFcl2Z4Qb2ytaZFKtJn49II03hmXwr0DE+iZZOX+QQkKb6TFOqrv4IqKCvr06cOUKVO47bbb6j1mzJgxvPTSS9HP7fbafZIvnk5KAAAgAElEQVQPPvggn332Gf/+979JTk7md7/7HVdeeSXz58/HYmk5A8REREREROTk2+sOct+ymu2xb+sTh8MSeePfPt7CuqIAz6+vGcLqtMBFnWLYUhJkUKqNFKcZm9nEkhwfC3P8mIAru8UyIsPBxA5O4mxmFufmkOUOkeMJndA22A2hMmhwxZcFbCoJAjD/gI8+yZEqnOoKnOa41fPwVnZ6u6xsKgnS+72c6O3/OSeZc9s6SXZE1nxBhxh+f2bioU4j0iIc1U+ZCRMmMGHCBAB++ctf1nuMw+EgIyOj3vtKS0t58803+fvf/865554LwEsvvUT//v2ZN28e48aNO561i4iIiIiI1GtBdk1r0xVdY5jaNz76efv4mj8gX9k1hgntnPwow0HbuLp/WC71h/nXRjdj2jgZ2iryR+q4qqHFrWMsZLlDHGiiAGd/RYg0pxmHxcR7Ozx8mx/AYoKQASvza3Z2qq7AaY5bPZtMJv7f4ETuX1bKvoqalqmxbZ24HM0vcBJpSg32HbF06VK6devG4MGDueuuu8jPz4/et2bNGgKBAGPHjo3e1q5dO3r27Mny5csbagkiIiIiIiIAbKuqRHnojAT+NTql1k5R6QftdvTY0CQu7RJbb3gDkGQ3c9+gxGh4c7DWcZFz5njqn9VyMi3M9jFoRg6jZuXx3g4Pv10e2WXrtj6RoGrFQQFOgTcS4KQ1052CJnaIYf0Vmfx5eGSuistuUngjUo8GiYnHjx/PpEmT6NixI3v37uWxxx5j8uTJzJs3D4fDQV5eHhaLhdTU2tPZ09PTycvLO+R5t23b1hDLk2ZMX2MRaUz6mSMijUk/c5rW6gN2wEpCZQHbtuXWui9YagUigUzJvp2U1H34UYnx2wAb6/bkMiAUPJHlHpOtbhP3bHQQCJvZWhrk1gXFANhMBpfE5/GmNYYsd4gH5u6indPAE3TgMBvk7N5BbjMeH3OOFe7rYmVAYkjfP8dIr9fpoXv37oe9v0ECnEsvvTT6cd++fRk0aBD9+/dnzpw5TJ48+bjPe6TFy6lt27Zt+hqLSKPRzxwRaUz6mdP0DqzLBYKM7tme7im2Wvfd08Wg1FnKpI4xdM84/m2Q+3jL4UAZMwticDud/GV4EhbzyUlIgmGDOxYVs6kkyLbSIJ6gQfckKz9u5yC/Mkxl0GBiBydDurfjt4abh1aU8tLemqqhYa0c9OjR7qSsrSH9rkdTr+DUo583LcdJadRs3bo1bdq0YefOnQC0atWKUChEYWEhaWlp0ePy8/MZMWLEyViCiIiIiIi0QO9s9/Dg8hJK/AYmoGs9s2nsFhNPDHOd8HP1rQqG9rpD/HtzBT/tHMNZmccfCB3O+zs8vLujMvr5FV1j+L+RyTjr2ZHplt5xFPnC7HUHmb3Hiydo8OAZGgAscqo7KQFOYWEh2dnZ0aHGgwYNwmaz8c0333D55ZcDsH//frZs2cLw4cNPxhJERERERKQF8QTDmDHx8iY3Jf7Itt4T2jvrDTgaytg2DuZNSmfa6jLm7POxqThw0gKc/6vaMevRIYlM7OCka6IVk6n+a7OaTdEdm3aUBsmtDDHyJK1LRBrPUQU4brc7Wk0TDofZt28f69atIzk5meTkZJ588kkmT55MRkYGe/fu5dFHHyU9PZ0LL7wQgKSkJK677jr++Mc/kp6eHt1GvG/fvowZM+akXZyIiIiIiJz+in1hxn6SR7EvTFnAwGqCTVdmnvShvSaTiUFpdka3cTJnn497l5WSHmPhok4xAJT4wny138vFnWLwhgze31FJqT9MT5eVC9o7DxnA/NDWkgCbS4IkO0xM7RuP7RjatLomWema1LRbnItIwziq7+TVq1czadKk6OfTpk1j2rRpTJkyhWeeeYaNGzfy7rvvUlpaSkZGBqNGjeLVV18lISGh1mMsFgs33XQTXq+X0aNH889//hOLpfltZSciIiIiIqeOR1aWsqu8Zieo4Rl20htxy+w+rpq3VTd8U8SB61oTazXz2HdlvLK5gp/PL67zmFGZdqxmE8+McB12C/JSf5hfLYmMWT6vnfOYwhsROb0cVYAzatQoSkoOPZv9ww8/POI5HA4HTz31FE899dTRr05EREREROQI5uzzAvDTzjHMyfIypVtsoz5/7+TaQ5Jf3+Jhat943thaUev2jBgzE9o5eXObh4U5kW2+715Swsfnp1GfvMoQ53+az86qcOrKro17XSLSvKiWTkREREREGs0bWyuYd8DHX0e4SHYce4uTOxBm+jYPF3eKISPWwv6KENmeMIl2E6+ckwyA+ShbkxpKZqyFX/WP54ssLxtLgjy6qowLOjhJdZrJ9oQBuK1PHFP7xNMxwcqqAj8biyPbji/I9vHrJSU4rXB1tzg+z/JS4guzqsBPljvEvooQfZKtPDncxejWmmMj0pIpwBERERERkQZnGAZ/WFnGf3d4GJhqY3QbJ4GQwcOrygDYVR7kbyNc9Em2YTUfXehiGAZ3LS7hw12VPLC8lF8PiKc8EBlYPDjN3ujBzcEeHpLEw0OSuP7rQj7e4+U/myui4c3WqzJpdVBL14gMRzTAAfjPlkilzj821K7YAciMMTPjx2m0idPoCZGWTgGOiIiIiIg0uK8P+Hi+aueknH0+5uzz1bp/dUGAMZ/kA5BoM3HfoATu7JdQ5zwHW5Dt58NdNVtpP7POHf14aCt7Qy39hEzpFsvHe7y8WhXK9Em21gpvAH5/ZiLl/jAWs4kiX5izM+0syq77Gs2ZmEbfFBvxtpM7jFlETg0KcEREREREpMEtz4vMeJnYwck5rR1sLwtiM0OizcxV3WJ5ZVMFb2ytwBM0KAsY/OHbMsa2cdI3JTJPxjAMSv0G5YEwV88tIsFmYl9FZBbMJZ1iaB9vwQSUBcLE28zc0juuqS61ljFtnMRZTdHKoN4uW51jkh1m/nVOSq3b7uyXQEUgzI6yIFd8Wcid/RMYnqGWKRGpoQBHRERERESOW5Y7yJNryumWaMVsglt7x+O0mliVHwlwruwaG91W+2CPDUvisWFJAFw9t5DP9no5a1YefxicyNg2Dp5f72bm7kp6uay12o0S7SaeGXl883MaQ4zVxH/GpPDVPi82C9zc8+iDpTibmQGpdjZf1fokrlBETlUKcERERERE5KiFwgb/t97Nqnw/fxicyJ2LS6LVNgD/2VxBr2Qbc/dH2oEGp9WtQPmh+wYm8NneyE5Sj64q49FVNfdVhzf3DkhgSCsbvVy2ZhveVDuvvZPz2jubehkicppRgCMiIiIi0sIFwgZPfFdGisPMppIgQcOgf7KNQWl2zsqsPRz4n5sqeKRqEPHXB3x4gkatc+1xh9jjjrQ6jciw0/Yohu+ekWYn/4Y2vL3Nw+tbKwgZEGs10ctl5a1tHrokWPndmQmYmnBIsYhIU1OAIyIiIiLSws3eU8nfvnfXuu19IsOCeyRZcVhMlPrDVAYN8r3h6DE/DG/eH5+Kw2KiIhgm1WFmaCv7UYcuNrOJG3rGccMPWo5+PSCBOKtJ4Y2ItHgKcEREREREWriDW6DOa+dgeIaDGTs9bCwOsrU0WOf4bolWll/SKhrmJNhM7HGH6JN85HapY9U+Xm9ZRERAAY6IiIiISIuSXxlie1mQH1VVxwTCBrN2R6pt7h2QwANnJGAzm/j1gASy3EH+b72bRJuJKd1iibOZ2V0epFOCFYvZRGZsTXtUn+TmPZdGRORUpwBHRERERKSFWJzjY8rcQsr8BhkxZtrGWdhdHqLIF8Zsgrv6x2Mz17QqtY+38tSPXLXO0Tr2yDNtRESk4SnAERERERE5DRmGwaqCAKsL/KwpDBAMG8w/4KPMH5lbk1sZJrcy0gLVLs7CVd1iSbSrikZEpLlSgCMiIiIichp6aEUpL26sqHP7gBQbr45JYUdZkFSnObrbk4YEi4g0bwpwREREREROM7vLg9Hwpn+KjfPaO+maaMVsgrFtHKTHWOiapLcCIiKnEv3UFhERERE5zby/wwPA5V1iePmclCZejYiINAQFOCIiIiIip4EF2T6+LwowNN3GK5sj1TdTusU28apERKShKMARERERETnFbS8NcOWXhVSGjOhtQ9JtnNvG0YSrEhGRhqQAR0RERETkFLazLMikzwui4c2AFBsxVhN/+VGSBhOLiJxGFOCIiIiIiJzC/t+3pWR7wgxLt/P+j1NxObQVuIjI6Ug/3UVERERETlGrC/x8utdLjMXEm2NTFN6IiJzGVIEjIkfNMAyKfGEAHBYT8Tb9kigiItLYNpcEmJPlZd4BH1tKAgDc0juOjFhLE69MREROJgU4IoewusDP9d8Ucf/ABK7rEdfUyzkugbDBy5sq+EkHJx0TTuzb3TAMLp5TyPxsHwAWE9zTP4E+yVYmdojBaW2+PfbLc30UeMP8pGNMUy9FRETkhJT5w4z/JB93sGZYcbLDxN3945twVSIi0hgU4IgcwvRtHrLcIe5cXMLgdDt9km1NvaRj9vf1bh5eVcaLG918f3nmCZ1r9l4v87N9WE3gtJhwBw2eXlcOwAXtK3lnfGpDLLnBBcMG531WAMCXP0lnaCt7E69IRETk+B3whKLhzW194pjcMYYuiVZSnKq+ERE53an/QeQQtpcFox9/tc/bhCs5fp9nRdad5Q5RUtX6dKz2V4RYme/nvqUlADwxLImPzk+rdcz/srw8/3059y0t4dl15ewsC2IYRn2na3TfFfijHz9TFTiJiIicqvIrI/9/PjLDzpPDXYzMdJCp1ikRkRZBFTgi9TAMg+8LA9HP/7CyjNzKMI8PS2rCVR29j3ZV8trWCtYU1oQXn+2t5Orux9YKVuoPM3pWHoVV4U+HeAs39qz/HP9vZVn044dXlXF3v3geGdp0r5c/FKkQWnDAF71tWZ7vMI8QERFp/gq8IQDSY/R3WBGRlkYBjsgPvLLJzatbKqKhRbW/b3Bze994in1h3txWwejWDiZ2aH4zVT7aVcmN84rq3D5rj5cJ7Z3kVYaJt5noEF//t3/YMPjN0hIKvGEqAkb0deiWaOWJYUnYLZFZN71dVjaVBHn93BSW5PioCBrkeEIsyvHhDcFz6914gga9kq1c2jmWf2x04w0a3N0/ntRGKPN+fr2bv6ypXXFT7DNwB8IaviwiIqes6gqcdLVMiYi0OApwRKpsLA5w6RcFZHtqgpuJHZx8tremferWBUWsyPfjC8G/N1fw2QVHnqmS7QnxyiY3v+gdf9JLnEt8Ye5dFml1uq1PHBM7xNA61sywD/OYk+Wl2zs50WPPae3ggwmpBMKwvihSbdQ3xcrm4iCvbvHUOu+MH6cyvp2z1m0zz0tjZ1mQkZkOLupUO8h6d7uH2xYW8/LmCgB+s7Q0et+awgAfV7VgZbmDZLlD5HhCXNI5hrABWRUh/rymnB5JVn7VPx6T6diHI5f6w9F2qYfOSKB3so0/flvKzvIQ+ytC9HQpwBERkVNTvjfye0qaU/9fJiLS0ijAEQGKfWGunlsYDW+u7R7L1D7x9HRZuWdJCW9uiwQaC3NqWpICYXj2+3Kmj4sM7zUMo96w4TdLS/hsr5c5+3wsmJyO2WTCHzKilSyHUxEIc8ATonvSkQcor8jzccuCYgq8YX7Uys60YUnR9QxNt7MiP7J2l91Eid9gfraPdYUB/rK2PDor55zWDi7oEAlqTMDTI5JoE2upE94AZMZaDhlIXdUtlmSHmQXZPmbtrmRfRSh634JsH1d8WcD3RYFaYZnTauL1LRXM2VfT5rS/IsT1PWLpkmg9pqqZD3ZWUhE0ODvTzv2DEgF4ZVMFO8tD7KsI0dN16g2kFhERAbVQiYi0ZApwpMVaW+jnlvnF7KsIEQgb+MOQ4jDzp6GJXN4lNhqwPH92Mv93lotZu70U+kK0jbPQL9lGv//m8uleL//a6GZDcYCZuyt5dUwK49rWhB27y4PRCp71RQFm7/GyvSzItNVl3D8wgfuqwgWAUDgy9Ndirgl27l5Swgc7K5kxIZUlOT5e2liBL2zQJcHKqNYO0mPMTO0TT6LdzO2LSthdHvmlbmrf2pUrg9Js0QDnl33j2V0e4u3tHubu9zJ3vxezCWIsJuZn+6LbhD8zwsVNvY5/+/Tz2js5r72T63rEcvfiEsr9YUZkOvj35gq+2Fd3Fs3Vc+u2fb2yuYJXNlfQI8nKpI5OXt5UQbs4CyYTJNjM3N0/nvPbO2tdayhs8MomNwA3HLT9e7v4SNiU5Q4hIiJyKjEMg5AR+eNKdQtVmlqoRERaHAU40iJ5gmEu+6IwWoYMkRkvM89LpX09s2FMJhMXd67dJjQo1caawgD3L69pD5q+zVMrwPlwV2WtxzyxuoytpUHCBjy+upzJnWLo6bJR5A0xbGYeozIdvHpuCgDlgTAzdkYef+kXhbXOs6U0yJbSyC5ZX2R5eXNsKtuqPs+IMXN++9oVM/1TaipOeifbyIy18PZ2D39dV04gDEPTbVzQIYZHV9UMIm6o7bZ7uWzM+Uk6EBksfEF7JxXByJDowek2+qfYuObrItYVBnBaTPysVxwT2jupCIT567pyNhQF2Voa5K/rIqHMxpKa3cGWzS3i6m6x/GNUMgC7yoKMmpWHO2jQLs7CpI41X7P2cZFfdN/b4eH6HrGYj6M1S0REpDF9keXljsWR6tqwAWYTVP29h3S1UImItDgKcKRFmrGzknxvmP4pNj4+Pw2bGWKtpmN6U//gGYk8+305nRKs7CkPsiTXz4JsH2HDiJ5n9p5IAPOPs13ct6yUzQeFDwAr8/30dNmYd8BHgTfMzN2VJCwuZv4BH/5w3W243xybwri2Dl7b4uGJ78pwBw1WFQS4v2ruTS+XldkXpOH4QXvWwQFO32QbgapzV1VhM7atk7v7xRMIG3ye5WVUpoO+yQ3/48FuMUXbsQ6emzN/cqt6j7+gQwx3LS7mja2RFraLO8Xwm4EJhA2Dr/f7eGRVGW9v9/D4sCSSHWamb/PgDkau7eEhiTitNa9Dx4TI9SzN9XP13CIGptq4qFMMfZLVTiUiIs3TCxvc5FVV3FhMEDroV4NWaqESEWlxFOBIi/R21Uyb2/vGk+w4vl+AqluEIFLa3O/9XPZ7QpwxI5ebe8Vxfnsn3xUEiLFEqnd6umwszfVhM5vIcod4YYOb2xeVcEaanXVFNVuWV4cV1X7VP5495SGGtLJHK0p+2TeeX/aN542tFdy1uITZVW1aF3aMqbekuvdBIUXHeAsWs4krusbw/o5K2sVZ+HmvOCxmEw8MSuSBg9q6moNJHWN4Y6uHBJuJZ0e6cFV9vQam2pmT5WVZnp/vCvwMb2Vn7oHI6/C3ES4u6xJb6zwXdnTybV4cb26r4PMsL59nefloVyUfn59GxkkeLi0iIlItFDZYVeBnQIo9+oeGeQe8zNhZSbLDjDsQJhCOVNssyvFhM8OWKzNJcVrIqwzxr00VeIJhuibq13gRkZbGVFJSUvfP/CKNYNu2bXTv3r3Rn7cyaNBh+gGCYdh9TWuS7A3zF6zbFxUzfVtN+NIqxkxeZZibe8bxzEhXrWO/2e/lkqq2qFYxZtIc5lqtQXf3i+emXnE4LabD7lzlCxnctqCY7WVBEu0m/n52Mp0S6v+Fbk95ELOJaIuYL2TwwU4Po1s7aHeILcWbA8Mw+Gh3JUPS7XXa2x5aUcI/NlTUKim3m2HX1a2JO8TQ443FAT7dU8nfN7gp8UcedHW3WJ4Z4apVsSN1ZbmD3LGohB+3c3B73+PbIawpNdXPHBFpmer7mbOpOMDvVpTy9QEffZKtXNElFosJ/rK2nPJA/b+SX9DeyTvjUxtjySJyitLvOC1H833XJnKSrC7wEwhD32Rrg4U3AGNaO2oFOHmVYeKsJu7uH1/n2EFpdqwmCBqR4/Iqw5iADyek4g4aTGzvrDXM+FAcFlN0Zs6RdPxBsOOwmLi6+/EPKW4sJpOJSzrH1nvf4DQ7UBENbxJsJq7rEXvI8AagT7KNPsk2Sv0GL2yIzNV5e7uHYl+Yt8elnHKhRGP685ry6KDrN7Z66JZkjc4tOivT0dTLExFptnwhg51lQc79JC/avryxOMjDB82e65xg4bz2TtKdFtJjzISqqnB+ONdORERaLgU40uIsz4vsxjS8VcO+4RzTpuZ8H52XSprTQmasud6WpmSHmU8vSKPUbzAv24svBIPTbJzbVr+kHYvhBw1aNptg+5TWdeb/HMq9AxNIdZrpk2zj1gVF/C/Ly+TPC+jpsjFteBK2owjQWoLPsyrp5bIRYzHx3g4PZlOkymlraWS4NMCs3ZXMm9yq1qwlEZGjUewL88jKUsa1c9YaPH+q2ecOkmA31/uHoZX5fi78X340uOmbbOW5s5L5vjDAtrJIC3Wsxczt/Y6/rVtERFoGBTjS4nxzILKF9cjMhtllqVp6jIU/Dk6kImBwTmvHESs5hmdEAp8J+svacTu49StscNThDYDLYeaeAQkAPDo0ibsWl7Awx8/CHD/ntXfy43Y1X5diX5j/7a3k4s4xxFpbzi/Xn+yp5Lqvi2gfb2FyxxgCYZjYwcnDgxNZkusn1Wnmb+vK+a4gwDf7vfUGOKGwwatbKthSGiTWYuLO/vEk282HrDDbXR4kyx0iPcZML5cCIZFTlTsQ5tnv3czY6aHQG8ZuNhFnM9El0cpVXWMZ29aBy25m7Cd57CoP8XmWt9kHOJ5gmGmry3lvh4dQGH49MIFWTjMF3jB/WFlKrNXENd1juXdAAilVf7wxDIPHvivDG4psltAh3sIHE9LIjLUwJL1hfw8REZHTnwIcOe2VB8K8u93DpZ1jsFtMLM31YTbB2DYN3/JRHQhI4+mXYmN9UYABJ1D9cV33WLonWXllUwUf7KpkeZ6/VoDzs3lFfH3Ax6aSIH8amtQQy26Wpm+r4OM9XmwmuLt/Ak98Fyntz3KH+HtVu9kNPeLo4bLRoypcKagM811BCRuLA/Wec+qiYt7fURn9/Ln1brokWPj4/DTaxVsJGwYLs30MSrNT7g8z5INcqjYS4/oesfx2UCJt4jRkWuRUEjYMfrO0hPcO+t4Hg0If7HWHmHfAR4zFRE+XlV3lkbKUnMow+ytCtG1m3+/FvjAvbnSzuTjAjrIgG4pr5tX9bkVprWNL/Qb/2FDByrwA49o5WLvPzsLl2ZQHDBJsJr6/PDM6iF9EROR4KMCR01rYMPj5vCLm7PMxa3clQ9PtBMIwNN0W/euYnNreGpvCIyvL+P2Zx797lslkYkSGgyJvOBLg5Ppq3f91VdXW7D2Vp22AEzYMHlhWGt2GPacyxKaqwdpD0m0Ew5HdzMa1rR189qrabn7zQUO4q20qDvD+jkpsZritTzzPr4+EQDvLQ/T7by49kqx4ggb7KkKckWbjzr7xBA1Ic5op9IZ5Y6uH+Qd8rLksQ7OJRE6iQNjgw12VjGntaJBd+W6aV8Ss3ZFdAVvFmPnPmBR6JlkpDxh8ureSj3ZVsqogwJrCSPBbPRNuaa6vzg6CTcUwDJ5cU85LG2sG3kPk59O/Rifz/Ho3Jf4wXRKsmEzQP8VGRoyFe5aUsCLfz4p8P5Ffsw1sZnjh7GSFNyIicsIU4LRwhmEwd7+P9UUBZuyqxGoCu9lERqyZ50a6TrmQ453tHrLcQe4bmEBWRYg/fFvGnH2RN9+LcvwsyonMv7mjnyplThedEqxHPcj5SIZVzdRZVRAgFDbqtPnE/2A48poCP0l2M51Pg61cd5eHouENwMr8yBurASk2vrqw1SEf17uqEmdLSbDOa/balgoArusex5+GJrGhKBANw6wmojN0AFYXBLh7SQkA13aPZXC6neu+LmKPO8S20mC04kdEGt7MXZXcuqAYgEs6xXBOGwc39owj1xOiMmQccnfD+uwuD0bDm5dGJ3Nl15pAJj0G7uyXwB1941mZH6DYFyYj1sw3+308vKqM97Z7mjzAyfWEyPaEyKkM8ec15QB0TbTwwKBEnBYTIzPtpDktjD3EzLpeLiufZXkxDCgvLuTHvVozMNVW7zw8ERGRY3Xqv+uQE/LpXi/Xfl1U731j23i5qVfz36WoWihsMHVh5BfQ4a0cPLC8JFoVcGvvOPa6Q4QNg4s6xXBRp+bdZy9NIz3GQttYC/s9IXaXh+iaZMUTDEfvP/j37zUFfsbOzsdhNvHBhFRGNuAuTMGwwcxdlVSGDK7rHtso1SfriyKBzbltHCzO8eGvuuzOiYd/0+FymOkYb2GPO8SvlpQwbXhSNOhamR8JTC/uHPl+e+3cFHI8IXq4bHiDBhuLA4SBveVBbp5fHN1Ct5fLxqSOMfy0cwwf7qpk2Mw8Fl6kIckiJ8vqAn/045m7K/lodyX9Umxc93Uh5X6DNZdnkOa0EDYilTpnptnp8oPgemW+nw93eVhXVVXz084xtcKbg5lMJoYeNIS+TayFv64r58v9Pl7Z5GZoKzvdk6yNPnNsf0WIoR/m4jkozO7lsvL1pPSjXsugNDuD0iLXtm1bLt21OYGIiDQgBTgt3Ps7ara9PjPNxl9HuHhvh4d/bqxgY0n9My2aq53lNX/Nv2NxMVnuSF/9a2NSom8gRY6kd7KV/Z4QG0sCFPpC/G+vN3pftifMzF0eHlpRSl5lmLABlSGDe5eVsPiiVg0WtNy3rIRXt0S+N1vFmDm//cn/73d91QybQak28r3haKDT5Sj+8v70CBdXzy3kzW0eHBYTT49wEQob0Ras6uAl0W4msWqHFqfVxJlVAzyHpNv5LMvLjJ2ReRm9XJHnPDvTwYe7Irf9Z7Obv41MbqjLFZGDVFfDXdM9llJfmNl7vYyfnR+9f/YeLz/tHMOTa8r4x4YKuidZuX9gAv6wwZlpdubu9/LHlWWEanIPLj6GP5RUbwJw77JS7l0WmSsTazXx7vhURrdu+Hl1hzJjpwdP0CDdaSbfG0mxpw1LalHD60VEpHlTgNMCFXlD/HZ5KWsLA+yqCjHOjZQAACAASURBVD3axVn4+9nJ9E62UeAN88+NFWwqDpBXGSLFYcZ6CmypXP2GE4iGN48PS1J4I8ekT7KNr/b7uK6eyrR9FSFumlcc/bx1bGT3kY3FQRZk+zmnAQZjL8nxRcMbgK/3+zi/fQy/mF/E4hwfjw1N+v/s3Xd40+X6BvD7m52ONN2bDih7yR4iMixLUAT1qEcFVI7oT3ELx4HrgHsDbkXFgTgoCigCQtlDhuzSQqEt3U1HmmZ+f39ktKEtlNI2abk/1+UlzXwymiZ3nvd58ekxPfqEKBColOCaGFWTdKbszLd/A98tSI5svdX1+9SQ5WHXxKjwwbBA3LWxBBvP2pdILT1h/yAU7SNt0La4L/UPwPIMAzQKAZ0cy6Wuj1fhie322Rh7ClpXoEzUmhx1DOZ9pIc/5FJgX5EZWXqr6/hHt+nwyDYdbI6AJq3Ugns2ldS6nPHtVBgbq0KQUoIJ7S6u8+Suzr4oN9u7Dw+XmFFpEbHkmL7RAc4P6ZU4UWbBjYlq7C4w42CxGQopMDRCiYFhilpLYkVRxPeOL7XeGqKFWibgcLEZVzfDhgdERESNxQDnMvPp0Qo8us1914QBoQr8cW2o6+cujm+/N+ea0PX7XCQFyPDu0EDE+EkRoZZ47TBR5wfOUdFK3NrBB75yAdewdZkuUpfA6jBELRUwOUGNUJUE7zgG8AL2b6lfH6SFUgrM31uO1/eXY8UpwyUFOCtOGfDc7lIUGOzf+l4ZocDmXBN+P1OF+7pZ8IOjO2XGRvuHpi2OeU5fHtdj75RLG/K7LL0Sf+UYoZAAo6JVCFRKsMxxfQkNnH1xbZwaMqEEJ0ot2JBdhQe32OfZdNQ27PwRPlLsmxoOs02EWma/LUEqKU7dFol2S8/iUIkZW3ON6BOigErmna9BRK1RqcmG7EorVFIg3l8KqUTAwZsiIIoiSk0ihqXk40yFFRIBGByuwJ4Ck2uJ5cAwBXbkmxDrJ8UdST6Y3cMfCmnjfj8FQcDDPf3xcE9/nCq3oPfyPPx40oDhUXrc0dG+nLvEaMMr+8oQ7SPFXV186+2M2ZZndAVMzjk2Tm8eqECMrxTrJ4YiTF29RHRZhgGHSywIU0swOloFlUzAKL6HICIiL8MA5zJQbrah0GBDWqnFLbz5dHggegTJaw0njPaVQiMXUGYWYRGBIzoLrvnN3kp9U3s1PrqqaQbGNjXnco1/J/lgcoJ37GJBrc+gMAVkgj3I+Sk5GKGON/jOAOfWDj5YeGX1Up6xsSq8vr8c67KrIIqiW5Dy7YlKfJ2mh8kqomewAs/108BfXvsDhyiKeGFPqWs73a5aGb4ZFYzuy3KRWWFF7+V5bqeP9ZPixkQ1Pj+mx6lyK46XWlxdKxdrZ74RMx0fdMa1s4c3o6JV+Gx4ILblmzA4XHGBS7BTSgV0CZTjn2Izntld5jq85nbsF1LXoFQ/uQRdtDIcKrFg/OpCtNdIsXq8+wcval6iKOKDw3ocKjHj7s6+rvke1DYccyyXTgqQuw0hFwQBWqWAfVPCYbSJkAkCFFIB2XorXt1Xhvu6+aFjgAyHSyxor5E1abAa7y9D9yA5Dhab8eAWHboHytEzWI6pfxRiT6G9XhuA2T2qNyQ4UmJGWqkFBVVWpJyqcrs8X5mA6Z18IRGAJcf1yNJb8dg2HR7q4Q+zzT6ked4u+/ujeX01DImJiMhrMcBp4woMVgz4OQ8lRtHt8GERCkypZ6cHQRAwu4c/Xvzb/i3X+HYqrDpdhexKK5alG/BTRjbWTQxFr+CGvYk/XGLG7euLYLQCapmA8bEqPN8MWzFnO9q92/nxaU2Nl6CRYf+NEQhVSdy+SX6mjwY/nzLguX7u25VfESxHoFJAZoUVJ8utrsGem3ONrqHaALCrwIwd+SaMjVHhkV5+rm+Of8s04M/sKqSXWRHlI8Hq8aGI9JFCIRXw2dVBuH9zCfIMNggARAC9guVYPT4EPjIJsvVWfJ9uwB9ZVfUGOFkVFmw8a4QAYES0CpE+9kGkACARBNeMGQB4tk/17+UNiT644SJ3g+kZbA9wnN1wK8eGYFgTzK+Yc4UGiw5V4HS5FellVrywpwzvDdV6bTdga7X6tAGRPlJIHFsiO+/f1FwT5u60f7g9XWFFytgQT5ZJTcw57L9LPd1yUokAnxrBTrSvFO8MrQ6xuzXTcPEPhwXi+t8LUVBlw1O7SnFfNz9XeANUL/sEAJ3RhlG/FrgNHwaA3TeEQSkVoFVKXOH5XZ19MfiXfKRkViEl0z3o6Rsixy0d+AUQERF5L37SbeM+PapHiVFEgEJwfQD8ZHiQa7vk+jzayx8T4lQIUEgQ6SPFa4OBfj/m4USZBRbR3pL8zahgt/NUmG04VGzGgDAFMiuseP9gBW5IUOOHjEqkl1WvpX+ntAKTE9S41P2tNuYY8cHhClRZRRQbbdjv2Pki0offzNOlifat/Rx6tJc/Hu1Ve/t5qUTAFcEKrM8x4kSpxRXgpDpmwYyMUmJSvBoPbdXhoCPcyKm0YtGwQFRZRNxWY9bOTe19EFejC2V0jApHbo6AyQrIJIBEAEQRrm/JR0ar8H26AVtzTXige3VNoijirxwjon2lmJVa4vahx1cmQG8RcVWkEu8O1SLllD3A+fPaULQPuLQ/Cfd09kVmuQVVVhGDw5VNEt4AwMQ4NSbGqXFUZ8agn/PxdVolNp41InVSGLQNmK9D51dUZUXqWROm/VX9XPx0eKAr5P/ocPXywW15RpSbbXV2klHTKTBYEaCoDpF/PlmJ+zfr0M5PivYaGdLLLKi0iOgWKMcjPf3RM1gOZSOXLh11dOB0DvSuXd66Bcmx64Zw9P8pD9vyTNhTYH9+3tnRB0uOV+K301X46HAFlFIBPjLBFd7c0dEHSol9SHqHgNq3Kc5fhuf6avDEjlKEqCRQSQXXvJ83h2ghYTBMRERejAFOG1ZlEfHpUT0A4KuRwbgyQuH24e9COp/zjX47PylOlNm/qduca4TebINvjTfxL+wpw0dH9Lizow8Ol5ixq8CMTxzXDwA/Jwfjt9NV+OSoHu8fqsCTUe7XJ4oilmUYsLfQBKsN6B0ix21Jdcc8+4tMmPJHIc75sg0Swb5rD1FLCneEhnmG6qByn2Nb3n8n+eC6eDUe26ZzPV+/OVGJRcMCcaDY5HY5dc1bkAgCVDVfqWv8+l4RbP8d/cfR8XKo2D6YPCXTgGXp1Z01Col9Sdj+IjP0jiI2nTW6lmZ1CpChT8ilf3jrHaLAr+NCL3zCRuqslcNPJqDCIuJMhRXrsqvq7SSkC/v2RCW+Oq7H1jxTreO+OKbHlEQfnK6wYNWZKsgl9q2eMyus6PxdLkZFK7FkRBC7oJrBa/vKsGBfOboGyjGntz+y9PYvRCotIo7qLK6OGcDeEbX6TBWGhCuwYmwI5Bf4+76v0ITv0yvxZG+NK/x0DjDudIkBbnPQKiV4Y7AW0/4qhskGRKgleLavBkuO24cNP7HDfabfA9398GIDOnxndvXDsEglEvxl0FtseHFPGca2UzW4s5iIiMhTvO+vNTWZ5ScrUVBlQ/cgOYZFKOxvtC/hvfbz/QOQvq4ImRVWlJlE3PBHEdaMD3G9gf/K8YbK+caqpo4BMlwdpUSiRoZPjuqx5nQVHo5wP82WPBP+c86uFsMjlYipY0nUylNVsIj2pWB3d/HDnRvs38wFt5Ids6htCXeEhnmOAcSiKGKvoyOsd4gCUomAGD8pTpW7Bzy7z9lZ6UKdcedqr5HBR2b/9lj7eXa9p+sVLMfvE0LxY4bBbeeYUJUECf4yfDQ8sNV867xgYAAecAxIXp9jZIDTSKfKLW5L/CJ9JNAZRRgc+0Cn5prw0t9l2JBdBZsITE1Q48pIJWZv0UFvEZGSWYWMMusld22RndEq4q0D5ThVbsF3jvD1YLEZ/67RodcpQIaFwwJxsNiMdn5SBCgkuHFtEYqNNmzNM+GG3wvx5chgt13fNuYY8ci2EphsgFSA6zUoyleKB7r7QxRFHHF04HTxsg4cp0nxapy5LRJGqwh/hQRyiYAxMUr8nmXEgFAFdhZUB5D9Qhv+Guq8vSqZFG/XWBJGRETkzfjOq40SRRGLDtnb3u/v5tck35L2CJJj/40R2FNgQvJvBdiRb8KBYjN6BStQabHB6NhfdESUEj4yAXd19kWgUoLdBSaMjFJBEATE1RhMePcBJf5MrO7i+cfxgddfLiBRI8P+IjMe3qrDa4O1tYabbsu3L0+Z1c0P49tVbxNutJ7TkkPUApwDdZ0dOAVVNuQbbNDIBST424/zOWd5w8hfC9x+friH30UvgZBKBPjJBbe5DyOilIjwkeLB7n4Y/Es+AKB3sAISQcCN7X2wu8CELL0VX4wIuuC39d7o9o72IbrDVuRjQ43B0fkGq/1b9FgVhkcpIRGA5RkGLE2rxOdXByGqjmVxl7Ofa8w+ui2pejB3lUVE/5/zcKbCitf323fvUUsFPNDDHz2C5JiaqMa9m0qQklmFDTlVaB/g55H625ovjunxco3dku7q7Iu0UguKqqzoH6qAWibg1iRf9AiSu4UU+28Mx4EiM6ZtKEZqrgndl+XCRyage5Ac8/pqcF9qCbIrrbWub3uefdnlyXIrzlbaEKgUEO/vvb8jvnIJfGvkS+9fGYi0Ugv6hioQ/mWO6/BBFxmCExERtTYMcNoYk1WEVbTP3zhcYkG4WoIpCeoLn/Ei9A1VYEYnX3x8VI9JawrRXiPDIz39YROB7kFy/DzGfcDlFefsWDI5Xo2DxWYcqZDi/s06jIxWYmKcGsdL7QHOf6/QoMoqYn+RGWuzjRj8cz7WXhuK7o5BiSariL8dnQsDz3mzVmZmgEMtz9mBk+8IcJwDteP8Za7wtGa2mBQgQ1qpfdlCsFKCTdeF1Tl3pyHuSPLF6wfKoZAArw7SYlqn6mWH668NxfuHKvBYjdk9rwzSNup6vEn3QBm0CgE5lTZk6a3QKCS46y/7B9iv0mp3AD69qxSfXe2du+e1BJ3RZp+DViPIX+GYfbRkRBAmxVUv3VPJBLw/VItfT1chSGmfDzI5Qe0K0X1kEoyOUSElswpP7CjF8VILnu6jgUbBpauNUW62wVcmYMlx+3LjmV18kRyjwqhoZYO+ePGXSzA0Qom/JoXhxrWFOFxigd4iYkOOERty7CHxFSFyfH51EGyivfPqhj+KsLvA5JqVBQDDI1WtpgsPAELVUtcOgRIBsIn23fvCOQOPiIjaOAY4rdxHhyvw6VE9bungg01njVjveDPmNKurn9tOOk3l9o4++OK4HqUmEX8XmvH4dvuSBudMjvP5v+5+kAjA83vK8MspA345ZZ97c8yxrr+TVgbfGlt4GqwilmdUonuQfV37i3+XwWAV0TVQhmCV/c1aF60MR3QWxPrxzRu1PGcHTr5jCZUzwKnZ9RHhI8UxR2izc3IYTPaTQiY0fC5VXZ7o7Y8bEtXoWsfyhz6hijYZXAiCgP6hCqzNNmJLrgkv7ytzW57mHNTstPp0Fcqcd3gbV1hlRV6l/baabfaZKbO3lqB3sAJLRwUhRCVFcZUV+4vMUEiA5BhVraBgeJQKw6Pq3/792nYqLNgrwdlKGz46oocI4LU2EAy2tN/PVOHmP4tcP4eqJHipf0Cj/mZH+0qx+bowFBttKDHacMufxThRZkHHABm+GhHkWoqc4C9FoFJAnsGGzAor/sqx78J0dVTTDBz3hM+vDsKbB8rxyXAugyIioravQV+ZbdmyBf/617/QpUsXaLVaLF261O14URSxYMECdO7cGREREZgwYQKOHDnidhqdToeZM2eiXbt2aNeuHWbOnAmdTtd0t+Qyc7LMglWnDXhiRymOlVrw3J4yrM8xQiIASsdnxisjFLi/e/O0t/cMVuDELZH4brT9w+FZxweGweEXbl9WSgU83NMfCzobXd1B2/JMOO74cNsxQIYBYQo82dsf/2pvP965XWiVRcSHjh1R3hpc/YHhixFBGB2txFcj2t6HVfJ+rhk4jqUKOY4AJ7rGt8GvDQpA3xA5fkwOhiAIUErt/11KeAMACqlQZ3jT1vVzdN89u7vUFd481MMPuunRyL49CtM6Vs/GMVhF/JlVVefltCVVFhFXpxRg6Ip8DF2Rj6tXFuDe1BIYrcCOfBM6f5eLLt+fReK3uRBhn7mkll388y9IJcUfE0IxwvGh/+MjerT/5iz6/ZiHVacNFzg3WR3LjR/bXv0eSADw6dVBl/SFi0QQEKKSIilAjvUTQ7HlujBsuT7MbY6cIAgYEm5/3P7KMWLTWWcHTusNcK6LV2PjpDAk1bHjFBERUVvToA4cvV6Prl274pZbbsG9995b6/h33nkHCxcuxMKFC5GUlIRXX30VkydPxq5du+Dvb2/dv/vuu5GVlYXly5cDAB588EH85z//wffff9+EN6ftO11hwXsHK/DxEb3b4e38pJjeyRfTOvlCqxBwttKGEJWkWWdcBCgkGBOjQp8QOf52bFM8NKLhbwJHh1gxPSEQKZkG164aGrmAaF8pBEHA3Cs0KKqy4rt0A/YWmmGxidhTaILJBnQNlGFgePV1ddLKsTw5pL6rImpWzrb9zAorJqyunm1TswOno1aOdRPDWry2tuqqSCUW7C13dT19OSIIk+Krl4u+OkiLUTEqpJVa8MKeMjyzqwyBUiXEQ3kYFa3CCw3Yqaa1+S690rUdcnuNFD4yCUTYl5xlVlixLc/kCtsB+33YWLF+Mvw8JgQLD1Vg/t9lKDLaUGS04fb1xfhmVDDGxNbfwXM5++q4Hg9u0SFYJUFhVfVjMSledUmPx7k0Cgm6BdX9Hd3VUUr8droKCw9VQGcS0c5P6tXzb4iIiKhagwKc5ORkJCcnAwDuu+8+t+NEUcTixYvx0EMP4brrrgMALF68GElJSVi+fDmmT5+OY8eO4c8//8SaNWswYMAAAMBbb72FcePGIS0tDUlJSU15m9qszHILrvmtwPWBpbNWhqQAGeZeoan1DXxLDewUBAH3d/PDXRtL0M5Pijj/i1uVp5IJ6BFUHQDd1N7HrZ0/WCVFB40MJ8oseHSbDrmO2z40vPV+W0htj0YuIM7PvsXyltzqHVGifDgXpLkMDldixZgQ5BmsCFVJMOKcLdgVUgET49Q4XWHBy3vLkF1pRTakACw4VFKBB3v4IUTVtj60fnHMHux/MjwQU8/ZnUsUReQbbLCKwN5CE9ZmVWF6jXlJjXV/Nz/c1ckXOpMNbx0ox4dH9JiVWoITt0S0qpkqLcFkFbFgbxlEwBXeRPpIMChMiVcGtVyg6Oyccs7hGh7ZsHk7RERE5HmXPAMnMzMTeXl5GDlypOswtVqNIUOGYMeOHZg+fTp27twJPz8/DBw40HWaQYMGwdfXFzt27GCAcwEWm30w8fsHK1zhzZBwBb4cGeQVH0Cuj1fjTIW11rDihhoVrcLfhWYopfaZPed6qo8/pv9V4rY9+ZWtuN2b2h5BELBxUhg25xrdtv1t7GBiapjhDZjb0c5Phi3XhyFHb0Ph2Sx8lKvBjnwTtuaa3Dp2vF2F2Yand5aioMpmn5UyIAB+8uqAMKvCgn1FZvjIBExoV/t2CYLg6hSL8lVjQlzT3XaVTECETIqXBwbgu/RKFBttKKqyuYbMkt36nCrkVNqQFCBDytgQSACEqSUtHp6018jwWE9/7CowQS0T8H/NtNSaiIiImt4lBzh5eXkAgNDQULfDQ0NDcfbsWQBAfn4+goOD3d6kCIKAkJAQ5Ofn13vZaWlpl1peq2YVgbcy5PgxVwaLWH3fLe1tQEe/SpSc0aHEg/XVNEENQA9c7EOWlpaGKX5Ar94CtHLAln8Saec8JboDeLOrBPvL7B8GguQiOpmyLvq6iJpbRxFQStQw2hw7TxVmIU3PndG8QRSAKA3QS1eBHVDgt6N56GI2e7qsBvsyS4YvTtUIyStLcX+8Gd/lyPB7gRQnKyUABAwMMCPr5AmP1RkkU6HUJMHOoyfR0Y/P/Zp258gAKNBTbUBFdgYAoMxDtdyssf8HACgoRlrBeU9Ol+Byfy9LRC2Hrzdtw4WaW7x6F6rLvTPnx4xKfH/WHtHIJYDZBoyOVmLCFdEerqxp1Fw+1/kCp728nwnUmkzNL8HStEpcF6/CyJ5t43e1rUhLS8P13aLw0elC7NGr0aFDXIO7H8w2+xIkpRQt3vmoN9vw8958AFbM6uqLxYf1+CZHjnbhwXjrZBkcM3GhUQh4enAkkkIb1w3ZFOIyCnGy0ghlaAySYpp2Do7VJmJ7vgkC7EvkfGQCumhlrWb5j7W0FEAFOkcGISlJc8HTU+vHMQFE1FL4enP5uOQAJzw8HABQUFCA2NhY1+EFBQUIC7MP7AwLC0NRURFEUXS90RJFEYWFha7TUG3fnLAvGXqhnwYP9vBHhdnmtr02EXmf//UPwA0JalzNZX5eaUCYAkFKCU6UWXBEZ2nQDl6iKOKqFfk44hi2/tZgLaZ3vvT5MQ2xu8CEWaklyNJb0Vkrw/8GBMBoBT47pscLe+z9G/9O8sG0Tr7oopXBV+7ZuUvOHdnOVlovcMqLY7GJmLq2CH/lGN0OHxurwpIRQVCes3uTzmjDMZ0ZA8IULRrwVFpseO9gBQ4WmxGgkOCZPhrX0jXnfRLJpZVERETUSJcc4MTFxSE8PBwbNmxAnz59AABVVVXYtm0bXnjhBQDAgAEDUFFRgZ07d7rm4OzcuRN6vd5tLg5VW3SoAuuyjZAJwK1J9mGUfh5+Y05EF6ZVSjAqmjvweCuZRMCEdip8lVaJ1aerXAFOvsGKbH3ds7zyDDZXeAMAT+8qrbUTXnN5959ypJVaIAD4amQQJIKA+QMCIJUApSYbugXKMbOLX6O2A28OkY6wIs9gu8ApL85XxyvxV44RWoWATlo5zDYRB4rMWHOmCkuO6THTMT8tt9KKRYcq8NGRClRZgVldfdEnRIHkWBUCFM3zN9RiE7HprBEVZhG/njZgWXr1VuqbzhqxZEQQwtVS1xB+531EREREdLEaFOBUVFQgI8O+XttmsyErKwsHDhxAYGAgYmNjMWvWLLz55ptISkpChw4d8Prrr8PX1xdTp04FAHTq1AmjR4/Gww8/jLfffhsA8PDDD2PMmDGXZauX2SZCJqDebwVFUcSbB8oBAC/0D/CKQcVERG3FoHAFvkqrxOES+wwcURQx5Y8i/FNsRqcAGR7p5Y+b21fv4nS6wh7edNXKcFhngd4iYsyqQmTcEoGgZn59dgZHf0wIRVKAPWxSyQS8NkjbrNfbWOGOwcV5TdyB83WafYetVwZpXY/NLycNmPZXMd48UI5EjQxVVhEPbdW5bc+9+LAegB43Jarx0fCgJq3J6dOjejy5o9T1s1QA/jcgAF+nVeJgsRkjVhZAAOCcCBTB4c5ERETUSA0KcPbu3YuJEye6fl6wYAEWLFiAW265BYsXL8bs2bNhMBjw+OOPQ6fToW/fvvjpp5/g7+/vOs8nn3yCJ554AlOmTAEAjBs3Dq+++moT3xzvUmmxwUfm/o3fS3+X4Z1/yhGikiDBX4Y4fxneHKx1+/b0WKkFhVU2RKglmNW1Zdr0iYguF0kB9j99zm2UN5414p9ie5hzrNSC/2wqwU2JalfInlluDyOStDL0CVXg6zT78tbTFdZGBzg/ZVRiTVYV3hysrbe70mgVkVFmgUQAegRdeKmXN4jwafolVIdLzNhTaIa/XMDEuOrutolxKleoNnVtkevwfqFyXNtODY1Cgh35RvyQYcCyDAMyyvPhL5dgWidfXNcEO5BVWUQsOlzhWso2JFwBrVKCcbEq3N7RF5Pj1Zi9VYeDxWZk6avvj0gfdtMSERFR4zQowBk2bBh0Ol29xwuCgLlz52Lu3Ln1nkar1eKjjz66+ApbIYtNxH2bS7As3YCBYQqMjlZCLRNQYRbx+n57Z83ZShvOVpqwNc+EEJUEL/YPcJ1/S659jf/QCGWrGc5IRNRaODtZ0sssEEXR0aXh7kSZxXW60xX2D99xfjLMvUKDv3KMyNJbcbbSClmxGUariE5aWYOXuerNNszYaB9Qf0WwArO6+aHEaENxlQ3tA2Q4UmJGWqkFh0rMsIpAe40UKi9ZInUhzuVBNQOLS/Xh4QoAwE3tfdy+FJFKBHwzOhg3ri2CTADC1FJ0DJDhuX4a1yygGZ19YbQW45dTBuwusId0GWUWTIpTuf19tdpESCUXvo8NFhHP7CrFoRIzTpZZXMuiAGB5crBbfeE+Unw3OhgVZhuSvs2FwWrvwQlUMsAhIiKixvHqXahaK5lEQG6l/U3djnwTduSb3I4fE6PEk701yCi3YOamErx/sALXtlO55imsz64OcIiIqGkFKiXQKASUmUT0Xp6H0xVW105/TkN/yYdUENA7RI5tefbX8HZ+UqhlAoZHKbE0rRLP7CrDiTJ7F0+EWoK114bixb/LcKbCip+TQ+oNXX7IqJ6RsvBQBdprZHhkmw4FVVaMiVEhJbPK7fSdta2j+wYAugTKIcDeNWO0irWGC1+sbL0V36XbO57uraMjNd5fhl03hJ/3Mt4cHIARUUocKjHjoyN6ZFZYsS3PBB+ZgKd3lWJzrgnhagm2XR/m6qiy2kS8c7ACv2UaYBHty6JuSFBjfbYR688ZpAwAs7v71eq4dfKTS/DFiCD8688i9AmR84sZIiIiajQGOM3kid7+2J5nRKyfFGNi7S3fiw7Zv+WdkuiDPqEK9AlV4FCxGW/9U4FHtumw9tpQnCq3ugIc5/mIiKhpxfvJcKDYjExHd82/k3yQ6C/D5lwjNuQYYbIBgOgKbwAgzt/+JzPCsdOSM7wJUAjINdjQ44c812l35BsxPEoFi02E9JyZZ2uzqgOaLL0VN/1ZvfwnJbMKUgEYHa10XcdtSdXzeLxdgEKCTloZjuos+KfYjH6hCthE+1KwDgENC6IMFhF7C02Y9lcxCgw2iLCHJ0kNlGlY5gAAIABJREFUPP+5glRS3NnJHv7ojDYsyzBg/OpCt9PkGWzYkGPElET7ff3eweqlUU5/F5odt1HA4mGBiPSRolewPbC6UCgzJlaFgzdFwE/O8IaIiIgajwFOM7kyQomc26Mgq9GSfXN7H/yVY8SUhOq190/21uCHDAMOlVgQ9dVZ1+F9QuSI5lajRETNYnYPP3x6VI9pnXwR7y9F72AFFFIBD/Twh8kqwiKKKDDY8FVaJf44U4V8gxV9Q+wBQsQ5uwgtGx2MMavcA4HNuSb8eNKAL49XYkCoAt+NDkKQSgqbKGJrnj2k//iqQKw+U4USoz08cJo/IAD/ceyq1Br1DVXgqM6CHfkm9AtV4I395fjf3nK80E+DB3v413s+i03E+mwj5u7UIb2seglWiEqCp/tomqS2e7r4YV+RGRVmG85W2lyDhQFgW54JUxJ9kK234s1/7MudXx4YgEFhCiw9UYmPj+gxOFyBj64KRKzfxb994t90IiIiulSCTqcTL3wyak5fp+nxf5vtM4YUEqC9RoZn+2owrt2lD1n0ZmlpaZflLmRE5BmX8pojiqKryyLllAF3bCgGAAQpJUi/JQLtlp5Fubn6z6mfTECFxf3Pa88gOWwADhabEe0jxcGbwl2XmW+woucPuVBIBRy9OaLe5TitwffplfjPphLE+EpxU3s13jxQ4Tpu/oAAzOrqi3KziKd3lSLeX4ZHetpDnaVpety/uXreXqyfFKmTwqBRCJA0w7KjEqMNPjIBewpMGL+6EBq5gF7BcuwtNKPCIiI5Roll14QAsD/+/xSb0SVQDnkDZuUQAXyfQ0Qth683lw924HiBf7X3wct7y5Glt+KVgVpM78ydp4iIvEnNJTKRNTpwegfbZ5q8PUSLuzaW4LGe/vj0WAVKjPbw5rGe/vjxZCVOlltxwLHTFQCMa+c+RDdMLcWGiWHwlQutOrwBgCkJarz7TzkOlVjcwhsA+O/OUvx3Z6nbYQn+UkxO8HGbF/feUC2ujVND24wDf53DhPuEKBCikqCwyobUXHsNQUoJFg8LdJ1WEAT0DFY0Wy1EREREDcEAxwvIJAJWjg3B5lxjq5p1QER0OWrnVx3gzO5hX+o0JdEH/UIViPWT4oEefjhbaYVaKiDOX4Z7u/liybFK9AyWI0QlgVQAugbWnufSpY7DWiOZRMDSUcFYll4Jsw3QmWy4rYMPsvRWzN6iQ5HRBokA2BwNSu8frMDkBB/XVu4/JQdjZHTLzYBTyQRsnxyGYzoLJjhm40xOUCO4kVvEExERETUXBjheIkEjQ4KGDwcRkbcL95Hi65FBiPCRol9odVeGc8hxgEJAgKK6cyREJcWjveqf/dIWxfvL8ERv97k1vUOAsbEq2ERALgEqLSISvz2LPYVmfHeiEodL7AFO39CW73QJUUkREiHFwiu1WJpWiccus8eLiIiIWgcmBkRERBfp2ri2PaOsudQc7O8rF3BVhBJrs424N7UEABDvL3ULv1rabUm+uC2Jy5iJiIjIOzHAISIiIo+Y1c0PaWUWxPhKoZQKuKMjwxMiIiKi+jDAISIiIo8YGa3CvqkRni6DiIiIqFVo3VtdEBERERERERFdBhjgEBERERERERF5OQY4REREREREREReTtDpdKKniyAiIiIiIiIiovqxA4eIiIiIiIiIyMsxwCEiIiIiIiIi8nIMcIiIiIiIiIiIvBwDHCIiIiIiIiIiL8cAh4iIiIiIiIjIyzHAISIiIiIiIiLycgxwiIiIiIiIiIi8HAMcIiIiIiIiIiIvxwCHiIiIiIiIiMjLMcAhIiIiIiIiIvJyDHCIiIiIiIiIiLwcAxwiIiIiIiIiIi/HAIeIiIiIiIiIyMsxwCEiIiIiIiIi8nIMcIiIiIiIiIiIvBwDHCIiokZYsGABtFotUlNT3Q7XarWYMGGCh6pqvNZa98WaMGECtFqtp8ugGpyPSWZmpqdLISIi8moyTxdARETkKed+kJfL5fD390dkZCR69OiBCRMmYOzYsZDL5S1e24QJE7Blyxbs378fcXFxLX79F9KQEGTlypUYNmxYC1RD3iQ9PR3vvfceNm3ahJycHEgkEgQHByMhIQEDBw7EHXfcgdjYWE+XSURE1OowwCEiosvek08+CQCwWq0oKytDWloaVqxYge+++w5JSUn46KOPcMUVV7idZ+bMmZgyZQpiYmI8UbLXcN53dWnXrl0LVtIwH3zwAQwGg6fLaLNSU1Nx0003wWAwoH///hg1ahQ0Gg1yc3Oxe/duvPbaa+jcuTMDHCIiokZggENERJe9uXPn1jpMp9PhxRdfxKefforrr78e69atQ4cOHVzHBwcHIzg4uCXL9Ep13XfejMFB83rooYdgMBiwaNEi3HrrrbWOT0tLg1Qq9UBlRERErR9n4BAREdVBq9XijTfewE033YTS0lLMmzfP7fj6ZuDUZdasWfXO+MjMzIRWq8WsWbPcrnvLli0AgF69ekGr1UKr1aJHjx5u5y0tLcX//vc/DB48GJGRkYiJicHYsWPxyy+/1FmHyWTCq6++it69eyMsLAw9e/bESy+9BKPReMHbcKlKS0vx7rvvYuLEiejatStCQ0PRvn173HzzzdixY0ed53He5tLSUsyZMwfdu3dHcHAwFi1aBKD6fk1NTcWKFSswcuRIREZGIj4+HjNmzEBOTk6ty6xrBk5qaqrrMcjMzMSMGTOQmJiI8PBwXH311VizZk29t2nOnDno2rUrwsPD0b9/f7z//vs4depUrccUAAoKCvDss8+if//+iIqKQmxsLPr06YO7774b//zzT4PuR2f9p06dwnvvvYd+/fohPDwc3bp1w1NPPYXy8vI6z5eXl4c5c+agT58+CA8PR1xcHCZPnoyNGzfWOu3SpUuh1WqxYMEC7Nq1C1OnTkVcXBy0Wi10Ol29tRUWFiI9PR0ajabO8AYAkpKSkJiYWO9lfP755xgyZAjCw8ORlJSE2bNno7S0tNbpNm3ahNmzZ2PgwIGIjY1FREQEBg0ahPnz59fZYeX8fV26dCn++OMPjBs3DrGxsbWWJ+7btw8zZsxA586dERoaik6dOmHmzJnIyMiot2YiIqKWwg4cIiKi85g7dy6WLVuGNWvWoKysDBqNptmv88knn8Q333yDM2fO4N5770VAQAAAuP4PADk5OZg4cSLS09MxePBgTJs2DZWVlfjjjz8wbdo0PPnkk27dMaIoYtq0aVi1ahXi4+Nxzz33wGw2Y+nSpTh06FCz36bjx4/jxRdfxJAhQ5CcnAytVousrCysXr0af/75J7799lskJyfXOp/JZMKkSZNQWlqKa665Bmq1GtHR0W6n+fTTT7F69WqMGzcOQ4cOxe7du/HTTz/h4MGDSE1NhVKpbFCNZ86cwahRoxAfH4+bb74ZJSUl+Pnnn3Hrrbfil19+wVVXXeU6bVVVFSZNmoT9+/eje/fuuPHGG1FWVoY33ngD27Ztq3XZlZWVSE5OxsmTJzF8+HCMGTMGAJCdnY2//voLV111Va2A7nzmzJmD7du3Y/LkydBoNFi7di0WLlyI7du3Y9WqVW63+dChQ5g8eTIKCgowcuRIjB8/HsXFxfjtt99w/fXX491338Xtt99e6zp27tyJN998E0OHDsUdd9yBvLy883bP+Pv7QyaTQa/XIzc3FxEREQ2+PQAwb948rF+/HmPHjsWIESOQmpqKJUuWICMjAytXrnQ77TvvvIPjx49j4MCBSE5ORlVVFXbs2IFXX30VqampWLlyJWSy2m9zV6xYgXXr1iE5ORkzZsxAfn6+67hly5bhvvvug0KhwLhx4xAdHY2MjAz8+OOPWLNmDX799Vf07Nnzom4TERFRU2KAQ0REdB4JCQmIjo5GdnY29u3b5/YhvrnMnTsXmzdvxpkzZzBr1qw6hxjPmjULGRkZ+OSTTzB16lTX4WVlZbj22mvx6quv4tprr3WFAsuXL8eqVavQp08f/Pbbb1Cr1QCA//73vxg1alSja12wYMF5b4dTx44dcfTo0VrLzrKzszFq1Cg89dRTdQY4eXl56NKlC1avXg0fH586r2fdunVYv349unXr5jrs7rvvdt3myZMnN+i2bN68GXPmzMGcOXNch914442YMmUK3nvvPbfH/t1338X+/ftx3XXX4fPPP4dEYm9qfvTRRzF8+PBal71x40acPHkS9957L15++WW346xWa72dM/XZuXMnUlNTXUvCnn32Wdx+++1YtWoVFi5ciEceecR12XfeeSdKS0uxcuVKXHnlla7LyM3NxahRo/DEE09g7NixCA0NdbuODRs24O2338a0adMaVJNSqcT48eORkpKCMWPGYMaMGRg8eDC6desGX1/fC55/9+7d2LJli+s2WSwWTJw4EampqdizZw/69u3rOu0bb7yBuLg4CILgdhkvvfQSXn/9daxYsQJTpkypdR1r167FDz/8gNGjR7sdnpGRgQceeAAxMTFYtWoVoqKiXMelpqbi+uuvxwMPPFBnxxIREVFL4RIqIiKiC4iMjAQAFBUVebgSu0OHDmHjxo2YMGGCW3gDABqNBnPmzIEoivjhhx9chy9duhQA8Mwzz7jCG8C+TOmxxx5rdC2vvPJKvf/VFBAQUOfMoOjoaEyaNAlpaWk4c+ZMndfx4osv1hveAMB//vMft/AGAO644w4AwJ49exp8W2JjY/H444+7HTZq1CjExMTUupxvv/0WgiDgueeec4U3ABATE1Nr6VRNNe97J6lUetFbm997771u83ykUimef/55CIKAr7/+2nX4H3/8gRMnTuCuu+5yC28AICIiAg888AAMBgNWrFhR6zp69OjR4PDGyblM7vTp05g3bx6Sk5MRExODQYMG4emnn8bp06frPe8TTzzhdptkMhluu+02ALUfx/j4+FrhDQDcf//9AID169fXeR3jx4+vFd4A9i4uo9GI+fPnu4U3ADBs2DCMGzcO+/fvx9GjR+utn4iIqLmxA4eIiOgCRFEEgDo/MHqCc2ZMeXl5nR0wzqDp2LFjrsP2798PQRAwZMiQWqcfOnRoo2s530yUc23fvh0ffPABdu3ahYKCAphMJrfjz549W2vIsEqlQvfu3c97ub179651mHN3sIupr0ePHnUuEYqJicHOnTtdP5eVleHkyZOIiIhAQkJCrdMPGjSo1mFDhw5FVFQU3n77bezduxfJyckYOHAgevXqVedSnwup6zFLSkpCWFgYMjIyUF5eDn9/f9dzJSsrq87ninO2S83nilPNjpeG0mq1+Oqrr5CZmYn169dj7969+Pvvv3Ho0CEcPXoUn332GZYsWYJrrrmm1nkv5nHU6/X44IMP8OuvvyI9PR3l5eWu31PA/lyqS323yXk/bd26Ffv37691fEFBAQD7/dS5c+c6L4OIiKi5McAhIiK6gNzcXABASEiIhyuxKy4uBmBflnO+JR16vd71b+f8nrrmwYSFhTV9kedYuXIl7rzzTqhUKlx99dVISEiAj48PJBIJNm/ejC1bttQ5TDkkJOSCwVnN2UBOziDGarU2uMa6Lsd5WTabzfWzc7nTuUuOnOq6PzUaDf7880+88sorWL16Nf766y/Xdf773//GU089dd4uo4Zch7OmvLw8V4DjfK6kpKQgJSWl3sur+Vy50HU0RFxcHKZPn47p06cDsC+Ve/TRR7FmzRrcd999OHz4MORyudt5Gvo4ms1mTJo0CXv27EHXrl0xefJkhISEuIKwV155pd7B3PXdJuf99P7775/3dtV1PxEREbUUBjhERETnkZ6ejuzsbMhksjo7BBrCucSmrjChrh12LsQ5SPmll17C//3f/zX4PDqdDkajsVaIU3OQa3OZP38+FAoFNmzYgE6dOrkd99BDD7l23TqXt3Q91eTv7w+guivjXPXdn1FRUXjnnXfw9ttvIy0tDZs3b8Znn32GhQsXQqfTYeHChQ2uIT8/H0lJSbUOd9bkrNH5XPnyyy8xadKkBl8+0LT3fXR0ND777DPEx8ejoKAAhw4davTv06pVq7Bnzx7ceuutrh3JnHJzc2st36upvtvkvJ9OnjyJwMDARtVFRETU3DgDh4iI6DycHwbHjx8PPz+/Rl2Gc75JVlZWreP27t1b53mcnQc1Oz+cBgwYAAB17nZUn169ekEURWzdurXWcfWFJ00pIyMDnTp1qhXe2Gw2bN++vdmvvylpNBrEx8cjLy8PJ0+erHX8hW6PIAjo2LEjZsyYgdWrV0OpVOLXX3+9qBrqeszS0tKQn5+PxMREV4DTv39/ABf3XGkuSqUSCoXiki/Huexr4sSJtY5r7HPZeT/V9ftBRETkLRjgEBER1UGn0+HRRx/FsmXLEBgYiOeee67Rl9WvXz8AwBdffOE2pyMzM7PeboGgoCAAqHOwb+/evTF06FCsWrUKS5YscbtMpxMnTrid1zkM9sUXX4TBYHAdrtPp8PrrrzfiVl2cdu3aISMjw202iSiKWLBgQascDPuvf/0Loiji+eefdwvZsrOzsXjx4lqnP3LkCPLy8modXlxcDLPZfFHLpwDggw8+cHt8rVYr5s2bB1EUXY81YA8eExMT8fnnn2PVqlV1Xtb+/ftdS4guhV6vx6uvvlpvB9KiRYtQUVEBrVaLLl26NPp62rVrB8C+a1hNp06dwrx58xp1mTNnzoRCocDTTz+N48eP1zreYrFg06ZNjbpsIiKipsIlVEREdNlzDne12WwoKytDWloatm3bBoPBgE6dOuHDDz9EYmJioy9//Pjx6NixI3766SdkZ2djwIAByM3NxerVqzFmzBj8+OOPtc4zYsQI/PLLL5g9ezYmTZoEPz8/BAQEYObMmQCATz75BNdddx1mz56NDz/8EP3790dgYCBycnJw9OhRHDhwAF9//bVrKPDUqVPx008/YfXq1Rg8eDAmTJgAs9mMlStXonfv3khPT2/UbTvfNuKjR492dTbcd999ePjhh3HVVVdh0qRJkMlk2LFjB44dO4axY8dizZo1jbp+T5k9ezZ+++03/PLLL0hPT8fIkSNRXl6On3/+GUOGDMFvv/3mtjvVhg0b8Mwzz2DAgAFo3749wsLCkJeXh1WrVsFms+Hhhx++qOsfMGAAhg0bhsmTJ0Oj0WDt2rU4fPgw+vTp47asTi6X4+uvv8YNN9yAW2+9Ff369UOvXr3g6+uL7OxsHDhwAGlpadi0aZMrNGwss9mM+fPn45VXXkHfvn3Ro0cPaLValJSUYPv27Th8+DBkMhneeeedOmcxNdTYsWORmJiIhQsX4vDhw+jZsyeysrLw+++/Izk5uc5OtwtJSkrCokWLcP/992Pw4MEYPXo02rdvD6vViuzsbOzYsQNGo/G8u2gRERE1NwY4RER02XN2wcjlcvj5+SEqKgrXXXcdJkyYgLFjx9YatnqxlEolVqxYgWeffRZ//vkn9u3bh/bt22P+/PkYPnx4nQHO7bffjqysLCxfvhyLFi2C2WxGbGysK8CJjIzEhg0b8PHHH2PFihX48ccfYTabERYWhg4dOuCVV15x2zZaEAQsWbIEb731Fr755ht8/PHHCA8Px6233oonnngC4eHhjbpt55s3EhAQ4Apwpk+fDoVCgcWLF+Pbb7+FSqXC4MGDsXDhQqSkpLS6AEetVmPlypWYP38+UlJSsHjxYsTFxeGRRx5xBTjOZUyAfTvyrKwsbNu2DWvWrEFZWRnCwsIwYMAA3HvvvRgxYsRFXf/LL7+MlJQUfPnllzh9+jRCQkJw3333Ye7cubXCka5du2LLli1YvHgxVq1ahW+//RaiKCI8PBydO3fGAw88UOc8nYul0Wjw448/YsOGDdi+fTtWr16NgoICyOVyxMbGYvr06Zg5c+Yldd8AgK+vL1JSUvD8889j8+bN2LZtG+Lj4/H444/j/vvvx08//dSoy506dSq6d++OhQsXYuPGjdiwYQNUKhUiIiJwzTXXXPQMISIioqYm6HS62n3XRERERNQoS5YswezZs/HWW2+5dmFqKhMmTMCWLVuwf/9+xMXFNellExERkXfjDBwiIiKiRqg5z8fpzJkzeO211yCTyTB27FgPVEVERERtFZdQERERETXCjBkzYDAY0Lt3bwQEBOD06dP4/fffUVlZiXnz5iEyMtLTJRIREVEbwgCHiIiIqBFuvPFGfPfdd0hJSUFZWRl8fX3Rt29f3HPPPZyXQkRERE2OM3CIiIiIiIiIiLwcZ+AQEREREREREXk5BjhERERERERERF6OAQ4RERERERERkZdjgEMek5aW5ukSiOgywtccImpJfM0hopbC15vLBwMcIiIiIiIiIiIvx23EiYiIiIiIiFqbqkpID/8NbVYWpLpsT1fjcbbErhCDwzxdRrNigENERERERETUyiiWfQTFul+Q4OlCvETVffNgYYBDRERERERERN5EUpgLAKiIaQ9VRLSHq/E8W1Cop0todgxwiIiIiIiIiFobYxUA4Ozw6xCZPMnDxVBL4BBjIiIiIiIiolZGcAQ4NrnCw5VQS2GAQ0RERERERNTamJwBjtLDhVBLYYBDRERERERE1MqwA+fywwCHiIiIiIiIqLVxBjgKduBcLhjgEBEREREREbUyApdQXXYY4BARERERERG1JqIImIwAuITqcsIAh4iIiIiIiKg1MRkhiCJEuRyQ8GP95ULm6QKIiIiak/TQbkjOnkFIQQHkmQc9XY5XsSZ1hy0uydNlEBER0cVyLJ+CQu3ZOqhFMcAhIqI2SyjKh+q1xyGIImI9XYwXsgUEovLdnz1dBhEREV0k5w5UolLl4UqoJTHAISKiNkuSnw1BFGELDEFRYncEaLWeLslryNevgKS0BLBaACnfDhAREbUqjgAHSg4wvpzwHRsREbVZgq4IAGBN6oGs5FuhTuJyISf51rWAQQ9UGQBff0+XQ0RERBfBuQOVyCVUlxVOOyIiojbLGeCI2mAPV+J9RJX9DZ9QVenhSoiIiOiiuTpwuITqcsIAh4iI2iwGOOeh9gUACAYGOERERK0NZ+BcnhjgEBFRmyWUFAJggFMXUeVj/wc7cIiIiFofEztwLkcMcIiIqM2SlDo6cAJDPFyJ93EtoWIHDhERUavj6sBRMMC5nHCIMRFRKyfdtxXy1DWeLsMrSTJPAABs2mBAb/JwNV6GHThEROTtDJVQfv8BhHKdpyvxOkLBWfs/2IFzWWGAQ0TUyimWfwrpmXRPl+G1RKUKYnAYoM/ydClexbmEikOMiYjIW8kO7IB8Q4qny/BqtpBwT5dALYgBDhFRKycY9ACAqjsfhuiv9XA13scWHQ8oucXmuUS1M8AxeLgSIiKieujLAACWbv1gHjHRw8V4IYUK1q5XAKcyPV0JtRAGOERErZ1jDbS131UQNYEeLoZaDS6hIiIiL+ec02Zr1x7W/sM9XA2R57XoEOPy8nLMmTMH3bt3R0REBJKTk/H333+3ZAlERG2OYHIOsVN6uBJqTVwdOI4OLiIiIm/j/Bslqn09XAmRd2jRAOfBBx/E+vXrsXjxYmzduhUjRozA9ddfj5ycnJYsg4io7RBFwGS0/5u7ENDFcHXgcAkVERF5KWeXqONLB6LLXYsFOAaDASkpKZg3bx6GDRuGxMREzJ07FwkJCfjss89aqgwiorbFbIIgihDlCkDSopk8tXLcRpyIiLydUMkOHKKaWmwGjsVigdVqhUrl/g2xWq3Gtm3bWqoMIqK2xejonmD3DV0k5y5UkvwcSPfv8HA13sfaoSvg6+/pMoiILmtcQkXkrsUCHH9/fwwYMACvv/46unTpgvDwcCxfvhw7d+5EYmJinedJS0trqfLIQ/gYE10aeWkRugMwS2X8fWoA3kfV/EpKkQRAeuIg1G8+6elyvE5FbAek3cn7hS4NX3OILk2H4kL4A8guKkE5f5/Oi683bUNSUtJ5j2/RXag+/PBD3H///ejatSukUil69eqFqVOnYt++fXWe/kLFU+uWlpbGx5joEgk5CgCAzNeXv08XwNecc8THwZx1DEJxgacr8S4WM2RH9sKnrJjPF7okfM0hunRqQQQARHXoCFt7/j7Vh683l48WDXASEhKwatUq6PV6lJeXIyIiAtOnT0d8fHxLlkFE1GYIRucOVGoPV0KtjlwB44zHPV2F9zHo4XfvBAjcXp2IyOOcc9pEDjEmAtDCu1A5+fr6IiIiAjqdDuvWrcP48eM9UQYRUevnCHCg5BbiRE1C6RjubKwCbDYPF0NEdJlzzMABZ+AQAWjhDpx169bBZrMhKSkJJ0+exDPPPIOOHTvitttua8kyiIjaDMHk6MBRcogxUZOQSCCq1BCqDPYh4fzQQETkMa4hxj58LSYCWjjAKSsrw/PPP4+cnBwEBgZi0qRJePrppyGXy1uyDCKitsPZgcNdqIiajKjygVBlgGDQc+cTIiJPsVggmIwQBQnf5xA5tGiAM3nyZEyePLklr5KIqE0TTEYA7MAhalIqHwBFQJXB05UQ0WVAtu1PSI/u93QZ3sditv9f7QMIgmdrIfISLRrgEBFREzM6PmDymymiJiOqHHNwDJUQPVwLEbVxZhOUHy+AYLV6uhKvZQsK83QJRF6DAQ4RUSvGDhyipudcNsWdqIiouQllOghWK0RffxhvvMfT5Xgla5c+ni6ByGswwCEias1cM3C4CxVRk1E5tqtlgENEzUwoKwEA2ILDYRkxycPVEJG388g24kRE1DQEo3MXKrWHKyFqO2ouoSIiak7OAEcMCPRwJUTUGrADh4i8nlCUB8VPn3M5Qx0kZ9Lt/1CyA4eoyTg6cPiaQ0TNTSh1BDj+DHCI6MIY4BCR15Nt/h3yzWs8XYZXswWHe7oEojZDVHMJFRG1DKGsGAA7cIioYRjgEJHXEwx6AIB5yDWw9Bnq4Wq8kK8G1s69PV0FUZshujpwuI04ETUvoUwHABA1DHCI6MIY4BCR93PstGRL7AJr/6s9WwtsTxQkAAAgAElEQVQRtX2ODhyhKB/C2dMeLsYLKVQQg7mtL1FTqJ6BE+ThSoioNWCAQ0Rej1tlE1FLElX2bcTlW/+AfOsfHq7GO1Xd/SQsw8Z5ugxqReSrvoNs+3pPl+F1JPnZAABRo/VwJUTUGjDAISLvZ+JW2UTUcqzd+8LaoTuEilJPl+J9KisgKSuB5OQxgAEOXQRFyleuJdHkTpTKYIuO93QZRNQKMMAhIq8nGB0dOAp24BBR8xODwmB45n1Pl+GVZNvWQfXBixDKGW7RRTDoIRj0EBVKGP77rqer8TpiYAhEbbCnyyCiVoABDhF5P2cHDrfKJiLyKNFPAwDsTqKLIhQXALCHo7aETh6uhoio9ZJ4ugAiogtxzcBhBw4RkUeJ/gEAGODQxZEU5wMAbEGhHq6EiKh1Y4BDRN6PM3CIiLxCdQdOmYcrodakugOHAQ4R0aVggENEXs81A4e7UBEReZSrA6e8FBBFD1dDrYWzA0cMZIBDRHQpOAOHiLwfO3CIiLyDQgVRLodgNtlfm5VqT1fkdRTLP4H02AEkGQxQq3n/AIDg2CqbS6iIiC4NAxwi8nqcgUNE5CUEAaJfAISSQggVZRAZ4LgR8nOgWPk1AMDPw7V4I1tcR0+XQETUqjHAISLvxw4cIiKvIfoFACWFEMpLIQaHe7ocryL9ZxcAwNKtHzL6jEBMTIyHK/IeokYLMSrO02UQEbVqDHCIyLvZrBDMZoiCAMgVnq6GiOiy55yDo/7fA4BE6uFqvIzZBACwDLga+uiOsCUlebggIiJqSxjgEJF3cyyfglwJCIJnayEiIli794Ps8N+u5a3kzuavhfWKIUB+kadLISKiNoYBDhF5NdcHBCWXTxEReQPzhFthHj0ZsNk8XYp3UigBqYwBDhERNTkGOETk3Yz2+TccYExE5EU4vJiIiKjFSTxdABHReTk7cDjAmIiIiIiILmMMcIjIqwkmduAQERERERG12BIqq9WKBQsWYNmyZcjLy0N4eDhuuukmzJkzBzIZV3IRwaCHIuVrCBWlnq7EqwhlJfZ/cAYOEREREf0/e3ceH1dd73/8dc6ZJfvSNt33faWlLZRNLCrrBUXBy1IFrwsXvMAVFVB+VC8qF72ogIpeBBRFXLgilqVlk7UstrR0pzTdt7TpkjTNMss55/v74ySThqRt0iYzk/b9fDz66Mw533POd2aSSead7/fzFTmOpS05uffee3nooYf41a9+xfjx41m5ciXXXXcdkUiEW265JV3dEMlaoUXzicz9U6a7kbX8kl6Z7oKIiIiIiEjGpC3AWbBgAeeddx7nn38+AEOGDOH8889n0aJF6eqCSFazGuoAcMdPxZ3xsQz3JsvYDt6UUzLdCxERERERkYxJW4Bzyimn8PDDD7NmzRpGjx7N6tWreeONN7jpppvS1QWR7NZYrNcfOhp35oUZ7oyIiIiIiIhkk7QFOF/72teora1lxowZOI6D67p885vf5Mtf/nK6uiCS3ZKJ4P9wJLP9EBERERERkayTtgDnb3/7G3/+85956KGHGDt2LMuXL+db3/oWgwcP5qqrrmrzmPLy8nR1TzJEr3Gzfjt30BfYXbOfnXpeRLqE3nNEJJ30niMi6aL3m2PDqFGjDrk/bQHOd77zHa6//nouueQSACZMmMCWLVu45557DhrgHK7z0r2Vl5frNT5AZEE+AD379qdIz4tIp9N7joikk95zRCRd9H5z/LDTdaH6+nocx2mxzXEcfN9PVxdEspqVDGrgGE2hEhERERERkQ9J2wic8847j3vvvZchQ4YwduxYli1bxv3338/ll1+eri6IZLemGjgRBTgiIiIiIiLSUtoCnP/5n//hzjvv5Bvf+Aa7d++mT58+XH311dxyyy3p6oJIdlMRYxERERERETmItAU4hYWF/PCHP+SHP/xhui4p0q1YiSDAMZFohnsiIiIiIiIi2SZtNXBE5DA0AkdEREREREQOQgGOSJawEk1FjDUCR0RERERERFpSgCOSLTQCR0RERERERA5CAY5ItmhcRhzVwBEREREREZEPUYAjkiWsxhE4RiNwRERERERE5EMU4Ihki8YaOJpCJSIiIiIiIh+mAEckS6RG4EQU4IiIiIiIiEhLCnBEsoWKGIuIiIiIiMhBKMARyQbGQEIBjoiIiIiIiLRNAY5INvA8LONjHAecUKZ7IyIiIiIiIllGAY5INmhaQjysJcRFRERERESkNQU4IllAS4iLiIiIiIjIoWiuhmSG52K5yebCvce7+rrg/4hG4IiIiIiIiEhrCnAk7ZzlC8i573amKLxpTSNwREREREREpA0KcCTtnA+WYSUTGMtSwd4DWeBO+0imeyEiIiIiIiJZSJ+eJf08F4DtZ32a4qtvzHBnRERERERERLKfihhL+rlBgGM0+kZERERERESkXRTgSPp5CnBEREREREREOkIBjqSd5SYBMLaT4Z6IiIiIiIiIdA8KcCT9UiNwFOCIiIiIiIiItIcCHEk/1cARERERERER6RAFOJJ2qSlUCnBERERERERE2kUBjqRf4wgcX1OoRERERERERNpFAY6kX2MNHFTEWERERERERKRd0hbgTJo0iZKSklb//vVf/zVdXZBs4TWNwNEUKhEREREREZH2SNsn6FdeeQXP81L3d+zYwcyZM7n44ovT1QXJEs01cDQCR0RERERERKQ90hbg9OrVq8X9Rx99lMLCQj796U+nqwuSLZpWodIUKhEREREREZF2yUgNHGMMjz76KJdddhm5ubmZ6IJkkqdlxEVEREREREQ6IiMBziuvvMKmTZu46qqrMnF5yTQtIy4iIiIiIiLSIRn5BP273/2OqVOnMmnSpEO2Ky8vT1OPJJ3GNdTjENTA0WssIumk9xwRSSe954hIuuj95tgwatSoQ+5Pe4Cza9cu5s6dy49//OPDtj1c56V7itgWENTA0WssIulSXl6u9xwRSRu954hIuuj95viR9ilUf/zjH4lGo1xyySXpvrRkCzdYjUxTqERERERERETaJ60BjjGG3//+93zmM5+hoKAgnZeWLGJ5qoEjIiIiIiIi0hFpDXDeeOMN1q1bx9VXX53Oy0q2aVxG3He0jLiIiIiIiIhIe6R1CMSZZ55JdXV1Oi8p2Si1CpUCHBEREREREZH2yMgy4nKc84IROJpCJSIiIiIiItI+CnAkvXwPy/cxlgWWvvxERERERERE2kOfoCW9GuvfEAqBZWW2LyIiIiIiIiLdhAIcSS8vWEIcTZ8SERERERERaTcFOJJejUuI44Qz2w8RERERERGRbkQBjqSV1TiFyoQ0AkdERERERESkvRTgSHo1LiFOSCNwRERERERERNpLAY6kV+MS4jhOZvshIiIiIiIi0o0owJH0Sq1CpRE4IiIiIiIiIu2lAEfSymqcQqUaOCIiIiIiIiLtpwBH0qtpBI6WERcRERERERFpNwU4kl6pGjiaQiUiIiIiIiLSXhoG0VXiDdibyjPdi6zjbFoLaAqViIiIiIiISEfoU3QXsSsryLvzxkx3I3uFNQJHREREREREpL0U4HQRE4nijZqY6W5kJ8chefYlme6FiIiIiIiISLehAKeLmD4DaLj9F5nuRnYr1xQzERERERERkfZQEWMRERERERERkSynAEdEREREREREJMspwBERERERERERyXJWdXW1yXQnRERERERERETk4DQCR0REREREREQkyynAERERERERERHJcgpwRERERERERESynAIcEREREREREZEspwBHRERERERERCTLKcAREREREREREclyCnBERERERERERLKcAhwRERERERERkSynAEdEREREREREJMspwBERERERERERyXIKcEREREREREREspwCHBERERERERGRLKcAR0REREREREQkyynAERERERERERHJcgpwRERERERERESynAIcEREROSq1tbWUlJRw2WWXtdh+++23U1JSwnvvvXfYtpLdVq1aRUlJCTfffHOmuyIiInLcUoAjIiLSTZWUlHTo32OPPZbpLmetX//6162er969ezNx4kSuueYali1bdtBjFyxYQElJCT//+c8Peq4P/zv11FPb1a/nnnuu1bG9evVizJgxXH755bz88sud8vibAhoFayIiItkrlOkOiIiIyJG59dZbW2374x//yJYtW7jiiisYPHhwi32TJk3qkn7k5+ezYMEC8vPzO7VtJkydOpWzzz4bgJqaGv75z3/y+OOP8+STT/L4449z1llntTrm6aefBuCiiy466Lk+rKysrEP9GjFiBJdeeikADQ0NrFixgueee47nnnuO++67j6uvvrpD5+uokSNHpoIqERERyQwFOCIiIt3Ut7/97Vbb5s+fz5YtW7jyyiv5yEc+kpZ+WJbF6NGjO71tJkybNq3V8/rtb3+bX/3qV9x+++28+eabrY555plnmDRpEkOHDj3suY7UyJEjW53rt7/9LTfddBPf/e53mTVrFqFQ1/1aF4lEsvp1ExEROR5oCpWIiMhxZubMmZSUlLBz507uu+8+ZsyYQe/evbnmmmsA2Lt3L/fccw8XXHABY8eOpaysjFGjRjFr1iwWL17c6nwdqWtzuHo5c+bM4aWXXuK8885jwIABDB48mCuvvJL169e3eb7333+fyy+/nMGDBzNgwADOP/98Xn311dQ0pjlz5hzBM9TSv//7vwOwcuVKGhoaWuxbsWIFGzZsaDX6Jh2aQpvq6upWz09HXsNf//rXnHbaaQA8//zzLaZrNU0LO1QNnO3bt/P1r3+dSZMmUVZWxogRI7jyyitZuHBhFz1yERGR45NG4IiIiBynbrzxRhYuXMjZZ5/NeeedR8+ePQFYtmwZd911F2eccQYXXHABRUVFbNq0iXnz5vHiiy/yxBNPdNnonr/97W88++yznHvuuXzxi19kxYoVzJ07lyVLlvDOO+9QVFSUart8+XIuuOACamtrOe+88xg3bhxr167l0ksvPejUpSNhjEndtiyrxb6m6VMXXnhhp13vSITD4Rb3O/IaTps2jS996Us8/PDDLaZqAZx88smHvO7atWu54IILqKysZObMmVx66aVs27aNOXPm8MILL/Dggw/y6U9/uvMfsIiIyHFIAY6IiMhxavXq1cyfP5/+/fu32D558mTWrFnTqt7Jhg0b+PjHP87s2bN59dVXu6RP8+bN49lnn2XGjBmpbTfffDMPPvggjz/+OF/+8pdT27/2ta+xf/9+fvGLX/C5z30utX3OnDmdWhPmgQceAGDixInk5OS02Pf0008zYsQIxo8f3+q4RYsWcdddd7V5zmnTpnHOOeccVb9+//vf47puaqTSgTryGk6bNo3c3FwefvjhNqdqHcoNN9xAZWUl3//+97nhhhtS26+99lrOPfdcbrjhBs4888xUOCgiIiJHTgGOiIjIceob3/hGq/AGoLS0tM32w4YN4/zzz+exxx6jqqrqoO2OxqxZs1qENwBf+MIXePDBB1m0aFEqwFmzZg2LFi1i3LhxzJo1q0X7T33qU0yfPp133323w9c/MHRpKmK8ePFiwuEw3//+91u0Xb9+PatWreJrX/tam+davHhxm1POAL7yla90KMBZu3Ztql8NDQ0sX76cV155hdzcXO677z4cx2nRPh2vYXl5OW+//TbDhw/nq1/9aot9U6dOZdasWTzyyCP89a9/TU1DExERkSOnAEdEROQ4NW3atIPue/311/n1r3/N4sWL2bVrF8lkssX+7du3d0mAc+KJJ7baNmDAAACqq6tT25qW9T755JNbTWsCOPXUU48owDkwdAmHw/Tp04fPfvazXH/99UyePLlF22eeeQZovfpUk6985SvcfffdHe5DW9atW8ePfvSjFtsKCgr461//yimnnNLmMV39Gi5duhSA008/vVWABEGtpUceeSTVTkRERI6OAhwREZHjVJ8+fdrc/pe//IVrr72WgoICZs6cyZAhQ8jLy8OyLF555RUWLFhAIpHokj4VFxe32ta0upLv+6ltNTU1wMGX4+7du/cRXb8jocvTTz/NgAEDmDp16hFdqyPOPfdc/vKXvwBQVVXF008/zS233MKsWbN4+eWXGTJkSIv26XgNm16Dg30dNW3ft2/fUV1HREREAgpwREREjlNtjVwB+MEPfkBhYSGvv/56q6Wx169fz4IFC9LQu0MrLCwEYNeuXW3ur6ys7NLrV1RU8O677/LlL3/5oM9jVyktLeWqq67CsixuuOEG/uM//iM1GqhJOl7DpoLSO3fubHN/0/YDC0+LiIjIkdMy4iIiIpLiui5btmxh4sSJrT74J5PJrAhvAE444QQAFixY0GKVqCbvvPNOl17/mWeewRiTkeXDm3zuc59j2rRpzJ8/n2effTa1/Uhew6YpUJ7ntfv6TVPK3nrrrTaPe+211wCYMmVKu88pIiIiB6cAR0RERFJCoRD9+/dn9erV7NmzJ7Xd933uuOMONm3alMHeNRszZgwnnngi77//Po899liLfXPmzGHhwoVdev1nnnmGnj17cvrpp3fpdQ7Fsixmz54NwJ133pmaYnYkr2GPHj0A2Lp1a7uvP2rUKE455RTWrVuXWqmryZIlS3jsscfIz8/nkksu6fBjExERkdY0hUpERERauO6665g9ezann346F110EZZl8eabb7JlyxbOPvtsXnzxxUx3EYB77rmHCy+8kBtuuIG5c+cybtw41q5dy9y5cznvvPN47rnnumR6U1VVFW+++SaXXXZZm8V7mxxqGXGAW2+9Fds+ur+lzZw5kzPOOIP58+fz+OOPc/nllwMdfw3LysqYMGECK1eu5POf/zzjx48nFAoxc+ZMTjrppINe/2c/+xkXXHABt912Gy+99BJTpkxh27ZtzJkzB8/z+OUvf0mvXr2O6jGKiIhIQAGOiIiItHD99ddTUFDAgw8+yB/+8Afy8vI444wzeOSRR/jd736XNQHOlClTeP755/ne977H/Pnzee211zjhhBP4v//7P95++22ee+65Lqm/MnfuXFzXPez0qUMtIw5w8803H3WAAzB79mzOPfdc7rrrLi655BLC4fARvYa//e1vmT17NvPnz09NEcvJyTlkgDN69GheffVVfvKTn/Diiy/yxhtvUFhYyMyZM7nppptaLQkvIiIiR86qrq5uPXFcREREpBu74oormDdvHitXrkwtQ95ZLr/8ct58803Wrl1LNBrt1HOLiIiIHIxq4IiIiEi35Louu3fvbrV93rx5PPfcc0ydOrXTw5u6ujpeffVVzj77bIU3IiIiklYagSMiIiLdUnV1NaNGjeKjH/0oI0eOxLIsVq5cyeuvv05eXh5PPfUU06dPz3Q3RURERDqFAhwRERHplhKJBLfddhuvv/46FRUVNDQ0UFZWxhlnnMHXv/51xo0bl+kuioiIiHQaBTgiIiIiIiIiIllONXBERERERERERLKcAhwRERERERERkSynAEdEREREREREJMspwJGMKS8vz3QXROQ4ovccEUknveeISLro/eb4oQBHRERERERERCTLKcARERERERERyRJ+3Sbcytcz3Q3JQqFMd0BEREREREQkmxgvRnz1z3CKxxMeeGFar92w8Ebw43BCDqFeJ6f12pLdFOCIiIiIiIiINDKJKmIr/hu/ejnezpex8wZiFwzDihS32T5Z8QL+vtVNR2MXDCM88JNHdu1kbRDeAO7WOXh7FmDcWgAsK0Ro0KdwCkcd0bml+1OAIyIiIiIiIgL49duJLf4GJlGV2hZb8i2wo+RM/j5O6Qkt2zfsIPH+T1udx4qWESo7tUPXNm4d7q63Uve9vYtat/HqcSZ9p0PnlWOHauCIiIiIiIiIAMmNf8IkqrByBxAefAl2yWSsvIHgx4mv+h+MMS3ae7vmA2AXTyQy+npCA4LpVok192Pc+nZf1ySqqX/rahKrW4ZBdsEIouNvJTzii0AQMMnxSyNwRERERERE5Ljmx3fjV6/A3R2MgMk54b+w8wcBYIxHw5ufx8R3Y+q3YOUPTh3XNGImPOhiQr3PwBgPv+YD/P3lJDc/QWT45w96TWMM7ta/Y5dMxK9aDm4thAqwc/sRHX8zWCGsaE8sJ4pJ1pJc9xtMQwXGGCzL6sJnQ7KVAhwRERERERE5YsYY8JOAD5aDZYcz3aUOiy//Pn7NBwDYBcNS4Q2AZTnYJZPwKl/Dq16O3RjgGN/D378WAKd0cqpteOiVxJffQXLbM9j5g3F6n9lm4OJXvUei/IEW26JjbiDU56Ot2lrhAggVBCFPshoipZ3zwLOUMT5e5es4pVOwIiWZ7k7WUIAjIiIiIiIiHWaMIbn+dyQ3PwEmGWy0I+RM+W+ckomZ7VwHGOPj164HwOlzFuEBrVedckom4lW+hrv9ebDChPp9HNOwDfwEVk4frHBhqq1dMCy4kdxHfOVdhPYuwi4cTajvWVih/FQ7rzH8aWLlD8XpNeOg/bRz+wUje7Y+g1N6Qio0Oha5254hseaX2MUTyZ3240x3J2sowBEREREREZEOMcaQKH8Ad+vfgw1WCLDAT+BWvNS9Apz4nmAEUbiEnAm3ttnGKZkEgL9/DYnVP8XfvwaMB4BdMLxFWyund4v7bsULUPECfu06omP/M7Xdr93YeIBNzrR7sQtHHXJqlJXbF/aXk9z4GMlNj5N3xh9bBEfHAuPF8KpXkNz8VwD8fSvw43uwoz0z3LPsoCLGIiIiIiIi0iHerjeD8MZyiE64jfyzniFn+j0AuDtfwXiJDPew/UxDBQB2bt+Dtjmw7g0EI0Tc7fOC4z4c4FgtP2aHBn4SsHC3zyOx6XG8ve/h7lmIt/NlAHKm/gSnaPRh69o4xRMO6HQSt7GAckd5+8txd72JaQyg0unDRaA/LPHB/cSX3o6JVaa2uTv+0dXd6jY0AkdEREREREQwxiO5/lHsguGE+px5yLZuxYsAhIdfnWprF4zAivTAJPbS8M9ryJ1+L1akBHfHP7By+mTdqBzjJ0l88HP8xgDHyu130LaWZYPlNI+6KZkMfhyTqG7zubILhuHXbiDU92yio78KXgy34gWS635D8sNt84e2q7+hgZ/C6Xky7s5XSG54lMTq+7CcPKB/u44HcCvnE1/xAyCYLuaUnoDTcwZ2tEe7z3Gk/NguGhZej1M6hejYG1tMJ2viVS8FwC6dghUuxKt8g+S632A5OYQGXHTcF29WgCMiIiIiInIcahoN0fSh2Nu7mOSmPwc7Q3mEek5v+7hEFd7ehWA5hPudk9puWRaRMf9BYs2vMLEdxNf8isiwzxFfdTcAOdPuwSke14WPqGO8XW8F05sa2YcIcACi428hvvIuouNvJdT3rEO3nfRd3IoXCQ/5LACRsf+JXTSOxJr7wY5gF40GP4HTYzpWKLdd/bUsCyuvP+EBF5Lc8GjwGKqWgtP+AMfbu6j59s5X8Ha+gl06hdwTf9jucxwpb+97kNyHV/ka9XsXkzv9Puy85r6bRHUw8sbJIWfKnWCgoXolJrGXxJpfktz8BFakB9FJtx+3U6oU4IiIiIiIyDHP3fEP/IYKrFABoX5nt/nX/+OJMYb4yh/i164l96T7sZwcvMo3U/vd7fNSAY7xXby972LZORDKw93xMhgfp9eMVisEhcpOx84fQsM7X8areg+v50mpfYk1vyQ64dv4dZvADuOUTs7oilVezeoW9+28gYdsH+rzUZyeJ2GF8g57bju3b4slxC3LITzgfJxeJ2E5ee06x8FYkWKi428lvupHGLcOnIO3NW4diTX/i0nWAODt+ScA4WGfw8T34u58Gb9qCbEVdxEdfwuWfYiTHaWmQtEAuPvx9r6LnfdJ/Fglfu0G8BoAGmsBOWBBdMKtJDb+Bb/qPUxsJya2E6/yDexBF3dZP7OZAhwRERERETmmeTVrUqNAAJKbnyAy4ouHHUXR4hzVK3ArXwcg1PdsAEyyOhhB0Q2ndXi738arfA0IPljbRWNxd7+d2n/gh+3EB7/ArXiu1TmanocPs3IHQLi4cbTFG83n3F9OwztfTN0PD76UyMgvH/VjOVJ+1bIW952y0w57zNEELwB2tNdRHZ8SLgj+d/dD9ODN3O3P4e54sfXhgy/FcnLADuFufSpYIr3vWYR6ndI5/WtDaqWvHtPx9r5LYs0v8fevD77ukvtS7ezC0anbTulkcksnY5K1JLc9S3L9b/H2rSJ8nAY4KmIsIiIiIiLHNG/vYgDsorFYeYMw8V3EP/j5YQuqHii+6se4W5/C3foUsfduJbb4m8SXzia57qGu6naXcne8nLpt4nswdZuDD9HhYrBCmIYKjFuPMQZvz8LmA60wTo+phAZciHOQD/uWZeEUjQWaR3zYJSc0748E9VaSm/9KfM3/duh16CzGiwejPgDsMJGxXwsCjW7CCgUBjnHrUtuMMfjxvfjxvann1N0ZhHThYZ8jPPQKAOyicanHGhn+b1iNoZK/r+WIpM7iVa+gYdE38Pe9D0Cof/O0O7fiuRbhDUCo18mtzmGFCwiVBV9vXuXr+HVbuqSv2U4jcERERERE5JjmVQWFUcODPoPT+3TqX/kX8OqDf6F8jBfH3T4PO38whIuDWiP5Q4KlpY2HSdZiYjvAyQPLggM+NLs7XiUy8iuZemhHzK/fmrptYrvwEsGHaKd0CqZ+C37teryq97Dzh2ISeyBUQGTUtdj5g3CKxhz2/E7ppFR4gxUiOuYGGv4ZPE/hoZfj7VmIt2ch7ta/Ex5wfvB8p5Ffvw3wsfIGkHfKw2m9dmdIBTjJ2tS2xOp7UjV97NKphPp8NFju3MklPPizYEdwSiZh5fY/4Dy5REZfR3z59/Fq1nRJX5Nb/oa/b2VwvdwBOD2bA5rQgH/BKZ2CX/NBaulwu7jtYtdW3iAIFYBbS2zZf5F3avd73Y6WAhwRERERETkm+fHdJDf+Gb/qPQCc0hOwLAcrdwCmYRsmvhuMIZF1c7EAACAASURBVLHxMdwtTx72fMGH3z64W59KrUhkklUY47daOjqbGeNhGral7ifW/jp12ymZiB/Kx69dT3z595u3F40l3O8T7b5GaMBFEC4BL4ZdMBQ7fxDhEV/C2/seob4fJ9TnLOrnXwkmiVfzAXaaAxxTvxkAOy+91+0sVrhpBE5t4/8NuDtfCXbaUfyqxSSqgpFnwXSpYJ6V02Nqq3PZjYGcv39Nl3wt+43BUHTSbJzSKcGKUoMuwTRUEBl1LZYdxpROwa/bhFN22kHr8FiWTc7E/0dsybeD718v1q1GTXUGBTgiIiIiInLMMcYQX/Vj/KolADg9pqUK7lrRnpiGbTQsuA6Mf/CT2GHwPSBoY5dMJNzvHKxIKaG+nwiOd2shuR8ixV39kI6Y8V2S63+H0/sMnKIxwUo//ocXswYr0hOn7FRCvT+Cie/Bq1oCfhwAp8eJHbqm5URbBT6RIZ+FxlWZAMLDryK57mH8mg/ggNWs0qFpCo6dPyit1+00TUW43VowJlgVzE9iF48nOu7rJNY/iknsxc7pTXjIvx7yVHa0F1akJyaxB9OwHeswxZwPxqtailezhvCgi1PFqf347iAoDeXj9Do1FQ5FR7UctWaFC8mZ/P1W5/wwp8eJWPmDMXWb8eu34hSOxBgPf99q7KJRWHbkiPreXSjAERERERGRY46/b1UqvAEIHVD01GpagrgpvAkVEup3NuGBF2Filal6LZZl4e54mfiqu7GLxhDufz5WuIBIYy0RK1KKcWsxiSqsLA5w3J2vktz8fyQ3/x+hvh/Hj1cFOyw79RxEx99CqO/HUsfkTL4DCGqs+DXl2CWTOr1fTtEYkhAEOGnix/cQW3IbpmE7QDBtrhuy7AjYUfDjFFU/SXxrMPomVHYGdt5AciZ+u0Pns4vG4O1+C7/mg8OuxnUw8TX3Y+o2k1z3MFakFCvSIzXixy4c3Wkje+y8QXh1mzF1W6BwJO6Wv5NY+yChfucSHXdTp1wjWynAEQCM7wVvYqG8YElFK9ylS8iJiIiIiHSl1Io3PWcQHvSpFlNH7GgvvMbb0Ym3E+p9RvOBuf1anCfU92PYpZODD6Qf+gBqRUox9VswiSpgaBc8ik7i1aduujv+kbrt9DoVb1ewdLhdOrnNQ61QPk6PKV3SLbtwFGDj127AePHUNJ+u5FctxdRtCu44eV0STKWLFcrHJOIU1AbhjV04itCAC4/oXHbRaLzdbxFfdTfJbfOw8wcRGXN9sJx3OxhjgkLYTfcTVZhEFX7tOgBCPacdUb/a7GveIDzArw9GUSW3PQuAW/G8Ahw5thnj41a8hFvxAv6+Fc07nDxyp/0Uu2BoxvomIiIiInKk/IYKAOzi8a3rfjQWgIX2TQ2ym0bsfIgVKQVoDHCy14ErFdnFEwgPuADsME6PacQSVViRHgd9jF3JCuU2TofZiF+7Dqd4PBAUGPb2LMQuHIUV7YH9oVDtaPjxPQCEBlxIZOQ1WE43nnITLoDE3uC2HSZnyn8f8eNpGg0F4O9bgb9vBaH+57WrYDUQTOUCsKPknvob8F0Sa36RWsHsYEvOH4mmaW9Nq4hZoXzSv45ZZijAOc55O18jsfqnzRvsKJgkePV41csV4IiIiIhIt2SaApy81h/+rVDuAbfzj/ga3SbAaVxhysobRM7kH7R4/LnTfnqww9LCKRqDW7cRv2YNTvF4jPGJLfsvTOPoCrtgGLkn/6rd5zPG4FW+HkzfKW09usbEdwfnze3XvcMbgpWomoKL8LCrsMKFR3wuu+QEQv3Px4r2wt+3Cm/vIvzaDe0OcEx8V9Cn3D6pMDA64TYSa+7HLhrdqVMM7eIJAHjVyzHGa64HBN2uoHhHHbuPTNrFq17WfCdUSP7MOYSHzgKavwlFRERERLqbphE4VhujN0L9ziPU/3xyptx1VNdoCnDcPQtxK+cf1bk6i1e1LChga7zUNpMMApzwkMtahDfZwC4aCzQv9e7teisV3kAwysKY9o+vSG78I/GVdxFb8i28quWt9pvGEThWBkYcdTbrwJFkRaOP7ly2Q3TsfxIZNitVA8qv3dju4/1Y8NnRjvY6oH+5RMd/k/DATx5V3z7Mzu2LldMX3NpgqqTvpvaZphFJxygFOMc5v35r6nZk+FUAWDllwb6YAhwRERER6V78+m3Eyx9ILZNt5/Zv1cZyIkTH/meHV1b6MDund3DNqiXEV/ygQx94u4JXs4bY0ttJbnwMb+/i5h2NAU42Flp2ep0M2Hh7FgbFlrc9E+wIH9BXL9aucxnjk9z8ROMdj+Tmx1u3aRyBYx0QNHRXTmkQtLhOaWop8M7QNAvDr9sY1LZpx/Pf/LyWdVo/DqXpsfvVKzDJmuZ+1G9Py/UzRQHOccj4Lsltc4mt/J9UxffoxNsIDfgXAOzGb7qmb0IRERERke4ise63uFueBD+JFS07qilSh+OUnUZ4+L9h5QUrGTXV5MgEk9hHfNkd4CeCvtSUN+9r/IBrhYsy0rdDsaM9cXpOA+MSX/nD1MphOZO+gxUNAjKTrG7XuUz9thYFm5uWCm/R5hgagRMefCl5Zz5BZb/vYjk5nXZeu2A4EKwOFnv3Burf+NfUiLaDMY1//G8aDNDV7IIRAPh1m1MjzHJO/BF28bi0XD9TFOAch9ztc0l88DO8nS+Dn8DKG0Co95mpuYJNabRft4X4B/fj7V1yqNMds4zx8fatwvje4RuLiIiISCve/nUk1v4Gt/L1Dk2DORLunkW4lfPxqoOFOcLDryZn8ve69JqWEyUy9DJCvU4BglVxuvpxHshvqMB4cSBYXcok9oATTJHya9em2qVq4ISzbwQOQGTEl3D6zIQDAia7cGRqxFBT/w/H3x+EVk7Pk8ByMLGdqecHwPiJ1BQbK9L9AxxorOHUzpWi2svOKcPpMQ28Bvz9a8FP4O9fd8hjUlMWG0O3rtZcyHgjNAaUdvEELDuclutniooYH0eMMSTW3I/bOCzRKTuDUNlp2I3V3pukUtNkNe62p3ErniPnhO9hl5xwXC0t7u18jfiqH+H0OpWcE76b6e6IiIiIdCt+ww5ii25KjQiJTr6zU5cSPpC3fx3xpbMBP9gQLiY85HIsy+qS632YlTcQCOqvGLeW6OivAsFS5snNfyM8/GrwGkisfwST3I+dP5TIyC8d1agJr2opsSW3YeX2JTLiyyQ2PApAeNBnSG58LPjgTePyzsnsDnDsgqHkTPgWye0vkFj9U6xoLywnihUpAQ49AsckqvGqlpLY8AdMw47gfMXjg3Crfit+/TacwuEk1j9KcuNjwUHhkm5fwLirRcbcSHzV3amVipu+hg7G378GCIK3dLDyG0e91bwfbAgVYNnHfrxx7D9CSTH1W1PhDUBk9HVtLhdoOTkQKgR3f7DBTxJb8m0AQoM+Q3TUNWnpb6a5exYA4O1+G6+mHKdoVIZ7JCIiItJ9eFVLU+ENBGEGXRTgJDc8Siq8AZziCWkLbwDs/IGp2+7Wp4gM+xxWuIj4+/fi71+Du+OlFu396uW4256GcDE5J9yBUzy2Q9fz43uIr74PjIep30Z8+R3BDitEeNDFJLfOwcQqcXf8I5gO4yfAjkAnTrPpCqF+Z2PZTioEaAqcTKJ1gGN8F7fiRRLrH0nV+AHAySHUawZ+zRq8+q3EFn4VK6cvJrYj1SQ88KIufRzHAju3D7nTfkxi3SMkN/25zdegiUnuD1Z9s6PY+UPS0j8r0hOcvNSUuWwNJzubApzjQHL78/h1m3C3Pdu8MVzcZnjTJNT3LNytT4HlEOp3Dm7FS2CSuNuexikej1N2eod+KPr1WzFuHZaTi5U3AKuTh/l1Cbc2ddPft1IBjoiIiEgH+LXrgaCoqYnvCuqTdAFjDN6+4K/wkXHfxK14sdNXvTkcO29gi/vJbfOIDL0MP1bZsl3hSMLDPk98+ffAeJDcR3z1PeTNeOCw1zDG4G6fi1+3CW/3O5hYJYSLCPU6FZOoxvhxQmWnYYULCQ/6NMkNjxJfdXfztQuGpTXUOhKWZRHq+/Hm+00jcBqnUPl1m4i/fy/YEUysIngOmthRck/6OVZuPyw7jF00Bm/328HxTeGNFSbU9yzCgy9NzwM6BjSPgjr4CByvpmn0zYi0zdiwLAu7cCR+46rKdm6ftFw30xTgHOP82g0kVt/TYptdPJ7IqGsPeVxk1L9jRXpg5/Yn1OdMImNuoP61T4MfJ77iB0TG/CfhAee3qw/u7n8SX9Y8BckunkDO1B93/MGkmd/QXMHc2/MuoT4fTS0VKSIiIiIBv24LibW/Dn5PcnKIjPgilpOTCnCc3h/B3fI3/MZVodxdb+FVLycy/OojmkJkjIe3ZxFO6WQsJxrUNEnug1ABob4fJ9zvE536+NrDChcRGXUtXtUyvN1vkdz0J0J9P4ZlOTRVxIlO+BZOr1OwnBySBcOapzjF27fssb9vJYkPfp66bxeNJWfy99osTBwe9Jlg+e3YjtR1woMuOboHmQFN4YFf8z7J7fNIrPkl+MnmBqECwoMvxel5MpaTg53XvOJYeNCnsQuGByuFOVGwQsHUrCwPsbLNh0dBGS8e1FENF6baeLvfAoKRb+kUHf9NvD0Lg2v3PDmt184UBTjHOHd3MA3ILpkUDGfzYkTG3IDlRA95nGU5RIZefsB9Gzt/UOoHgLdr/iEDHOMn8fetwi4en/qmsnL6YGI78fetxNRvPtqH1qWM76Xm0AJ4e9+lYfE3yZ3xkN70RURE5LhmjMGteBF/3wrCwz5HbMWdmLqNqf3e3sU4RWPwq5cDEOp9ZhDg1G3Bq1kTjD4BTKKK6Nivd7gWSXL9oyQ3/RkrbxDRMTfix3YCwao0mfw9LTzoYsKDLia29Lt4e/6Ju+PloKgwFnln/rXFalhW3kBo/L0adz9+bBf2YVbvSW75GxDUsXR6zSDU+yMHDcCsUC45k24Pjqt4EVO/Daf36Uf/INMtHAQ43u538Ha/07x5xJewC4bjFI1uESQcyHKihHodHx/qu9KHC0nHln0Xv2oJuac/hh3tifHiuDteAWgxeiod7Jze2I0rKR8vFOAc47zGOi7hgZ8i1PuMozxb8w9E4zW0vlbVUpJb/kZk9H/gbn+e5MbHsEunpJbqi477Om7FS7g7XsTd9TbQNXOgj1Zy8xMkt8wJhrUewNRvwzRUYCwbd9vT2CWT9UNBREREjhnunnexoz3watYCBqdkUosRDal2O/5BYvVPAfCqlrWoLQKNdRfrtwLBKBG7aHRQfyVZTezdG1PtvJ2v0rBvdTC1J1JCeNDFh62f4TfsILnpz43X2ULsvZtT++zC4Uf0uDub0/sjQYCzPShfYOX2a7WUeWT4vxGv35r646hfu/6QAY5xG/B2LwSsg9axPJhwv7M7/iCyRKjndNySyfj7PwAvltoeHnyp/qiaLuGWhaSblnn39izA7n8+ft1m8Oqx8gZjFwzNVC+PGwpwjmEmuR9/3/tgOTg9Tjzq89lFY1JL8/k1qzFeosVfTOLv34OJ7SBWU44xQRG5pm9wsLGLxuAk9+HueBF/30rIy64Ax7h1wbLpO19ObXP6nIW385XU/eTWObg7Xg4KPG99BvuUhw771xJjfPzaDdgFw/WDRkRERLKSV72C+NLbW++wo0TGXA8YTMNO8OMkN/81tfvD4U1k5DVYOb0xXj1WuBinx3QsyyY8+FLcna+BHcIK5RPqdw7JLU9i6jbhNZ7D2/1Pck/7fauVZIwxeLvfxgoV4Fa+3ty1gmHB/mQdhHIJ9flYJz0bR6fp9+6m+ix2br9WbezcPuSe9AviH/wCd9szh60P5FUvA5MMArEOhDfdnRUpIXfqjzBuAyaxh+S2Z3FKp+h36jRqHoFTjTkgRGua+te0LLudk57lw493CnCOYd6edwEfu3hyq9T/SERGfBErlE9yy9+DWjgrf0jOCd9J7TeNq1Y1fRMDYEfBj2MXj8VycrBCwRxd48WPuj+dLbH+d6nwxuk1g8jo/8CKlhGL78WvXgqAu3VO8wGNv8BER193yPMmNz9Bct3DhId/ocW0NBEREZFs4TVOdwLAycMuGBb8wc2Pk3j/J60PsKPknvqb1B/3rGhP/Jq1hPqfi2XZrZpHhl9FZPhVLbaF+n6icaWqGPFVP8Ek9hJbchs5J3wXK5SPX789KEIbLiTx/k9bHJsz7ac4xeOP/oF3ATvaE6f3mXiVr4MVwik7+Ch4O28AQKo+UFu8fauIr/hvAJwe2fUH0HSxQrlYoYFER/17prty3Emt7pTcR/1rF6e2+3WbgGAqJKBaoWmiAOcY5jbWnumsaT5WKI/IiH/D6XUqsUVfw9v9NiZZixUuwBiTmnJkF4yAcHEwDLZwBP6+93FKGgta2eHg/wOLj2WQ8WKYZA2moSK1xLqVP5jIqOtSKXLO5Dsw8b0k1v8Ok9yHndsPp+fJxJffkZrbbYzBxCqxcnq3+IuAMYbkuocBSK5/hPCAfwE7HAyTLRrb5i84IiIiIunWVHAYIOfEH2EXjsTUbcLd/Q7JjX8EO0q4/7kQKsDUb8EumYwd7dliNIhT2LEVOy07RKhxWXF/yBaS6x/Br15GbNl/4fSYhrttLiZeCTT/vmQXDMMunoBdNO7oHnAXy5l4G3DbYdtZuY0BzkFG4Hj71xF771bwk1iRnoT6ndOZ3RQ5LMsOYRdPCALdAzS9Z6QCnKgCnHRQgHOMMsbD2/su0PkVuZ3isdhF4/Br3serWU2o53RIVgfzUkMF5J58f4v29oG1d5oCHON2ap+OhLdvFbGl32mxXHho4CeJjv5qi3aWk4OV15+cid9ObTNeHKwQfu16klv+jrfvfbzK14iOv7lF8S6/dm2Lc7mV8/H3r8HdPo/QgAuJjrm++ZzGaDioiIiIdDlv32r8mtWEBlyAZUfw43vwKt8AIOekX+I01pKxCoYSKRga/AHKcrBCeV3Wp/DAi/D3l+PtehO/ennqj2SBYGp+7qmPYOf27bI+ZELT8uN+zWpiK+4k3P983B3/wI9VBrUXk/vBT+L0OYvo2Js6XPBZpDPkTL0bv3Y9sYUHfHZp2I7xkweMwOmRqe4dVxTgHKP8mg8gWYOV0y+oct/JnJLxwXJ+G/6AX7Map3QKwGF/qDbNaTYZHoFjfDcYiurWQrgEyw5j5Q0kMuKL7TrecqLYhaPwa94nUf6/qe3urrdbBDje3iUtjgvq/6wKbm97hvDAT2HnD8K49TQsvB6n5/RWAZKIiIhIZ3F3vU18xffB+CTK/xcrUppaHhg7gp0/uNUxB1vlpzNZoXxyJs3Gr9vUuKKNH0w/KjmB+Op7guWgj7HwBoJVWgkXQ3IfXuUbqSCtRZu8gUTH3KjwRjLGsuxglsWBjI9p2HFAgFOSgZ4dfxTgdHPGeODWY4ULMV48mL/sJ0lu/j8AnF4ndcmoDrt0Cmx+Ar8m+AtO0xA6q40ibS1Y2TGFyq9ejonvxsodQO6MX2PZTofPER50MYmN9dj5QzH1W/Br1+MfsIRmcJ0VAERGfoXE2odS4U1qf90m7PxBuDtfwTRsx936lAIcERER6TR+/Xb8hgr8qqUY4wZ1WRoXm4Cm6Q8WTo+phPqe3aqAcLrZ+UOIjPhCi225p/72mJ12btkOudN/hl+7HrfydbzK11q8Prmn/wErXHpEv6uKdKa2PlP6DdswcdXASScFON1cYu1DuFufJjLySyS3zcXUb2mxPzzgoi65rtNjOtFJs3Er38Db+SrerrcBsBvn8R5UagpVZgMcd9dbAIR6n3HEPxBDfT5KqM9HATB+gvrXLsHUb8W4dVihfIzx8RrnijplZxCx7KCwtB3CJGvx963Eb1xi0zTs7IRHJSIiItIsuX0eidU/A0yL7XbBMKIn3IFJ7sOK9GhcaOLoF7zoKsdqeNPEzu2DnduHUNmpmPHfDKaqLL6F8JDLsKO9Mt09kZTcGQ+S3D4P3FrcihdIrPsNJIOFbDSFKj0U4HRjxndxtzwJQKL8AQCsaC/sgmF4NeWE+n4MO39Ql1zbsixCZadj5/ajYeerNP1i0Naw2xaypIix17iqlNNrRqecz7Ij2AVD8feXU//6JYT6nRNMpXJrsXL6BT+YB32a8KBPA5DcNpfEvpUk1z+CUzQaP17ZKf0QERERATBuHYk1vwQMWGFCfc7ELhgOWDhlpweLNWjZ36xjWQ5O4SjyP/pkprsi0oqdP4joqGtIbn0KKl7A1G1u2oMVVYCTDgpwuqnk5ifw9i46YItNePAlhId9DsuJpq0fVv4wrGgvTHx34/1DBzhWY4Bj/MwVMTZ+AlO/FWhjLudRsIvGpJbSdCtewKtaBkCo71mt2+Y1j1SKLbmt8RcqERERkc7h7VkEfhK7eAK509pYBlxE5AjZhSODG04OoX7n4hSNzepRfMcSBTjdjElUk9jwh9SS1wChgZ8iPOSzGRliaVkWTs+TcbfPBaxUJf2DH9D4JZfBKVR+3VYwPlbegE4Nu+wPLZ1pYjvAcgj1/UTrth8Kug5cutP4bsbnn4uIiEj35u4KiuGGyk7LcE9E5FjjFI8nZ8pd2AXDsSLFme7OcUWfErsR4yeJLb0df3+wNLXT+0xCfT+O0+NELDtzVemdnifhbp+LldMXy8k5dOMsmEJl6jYEXckf2qnndYqaA5zwkH/FLhqLldMXO69/q7ZWpJTImBvx6zbhV69oHBXUWL/Ij0OWBjh+bFewwhlghfKwSyarqJ6IiEiWSG5+Am/f+zglE/F2vQmWjdP7I5nulogcg5weJ2a6C8el7PyUKG1yd76aCm9wcoiM+Dfsw636lAZOz5MJDfoMTsmEwzdOjcDxWlTYTye/tmsCHCtvSOp2eMjlWKG8Q7YPD7igxf36+VdgElUYL9ZpQxCN20Bi/W8J9TsHp2mo45Gey/eILb4FE6tIbXN6TMfpcSKh/udp2KSIiEgGeTXlJNY+GNzeNR8Ap89ZQa0bERE5JijA6Uaaat5ERn+VUL9z01rr5lAs2yE66pr2tbWsYClxkwS8ru3YQXiNI0jsolGHadkxlu2QO+MBjO8dNrxpk904esmLd1qfkhsfw936FO7Wp8j/2HNHdS5v95uYWAVWpBQr2gt/fzne3ncb/y0mZ8qdndRrERER6Qi/YSfx5Xek7of6ng1OlPDQyzPYKxER6WwKcLKcV1NOctOfMW4dftUSAJzSE7MmvDkidhi8JJZJfyFj43v4NWsAcIrGdvr57fwhh290MI2vqfFindQb8JpGbDWe97BT3A4hueXvAISHXoldOJLYopuar7N3EV7NGkysEiunDKdozJF3WkRERDokufGPmPhu7OLx5Ey+EyuUm+kuiYhIF1CAk8X82C5i790MB3ygt3L6Yh2uUHC2s8PgkbYAx92zkPjKHxEdcz1W3iDw41g5/bAiJWm5fntZTk6wGLt/9AGO8eJ4VUsOWNoPvOoVhHpO79h5jCG+/Ht4e98N6haFCoKizJbVqm3s3RtTt63cfkTHfbN90+pERETkiJlENe7OlwGL6LivK7wRETmGKcDJYt7ud8CLYReMIDziC5j4Xpyi0cE0pG7MskJBUJGGAMd4CeJLvwMY4it/iNNjGkB2BgtNo2OOcgSOceuILb65xcpWAN7exe0OcIzbABj8/evwdr+d2h4eeFGrXwydPmfh71uN8eohuS84vqGC+Io7yT35l6mgzBjT7b92RUREsoUxBlO3ieT2eeAncXqdcvjVQEVEpFvrtADnzTff5Oc//zlLly6loqKC+++/n1mzZqX2X3fddfzpT39qccz06dN56aWXOqsLxxyvegUAoQEXEOp5UoZ704kaV6KyuqAGjlv5Bia+m9DAi8FrIL76PgjiIqCxjpAdITz0yk6/9tGy7KYpVEdXAyex/nf4teuxomXYhSOxoj1wtz2Lt3cx7u53MLFdWKE8nF6ntflXOuPWUf/2F8HdH9QrApxepxEeegV24fBUu8i4r+PteovomBtT5zHGx9uzkMTqezGJvTS8exNWbl/Cgy8hUf4r8F2i42/GKZl4VI9RRETkeJbY8BjJTX8BP5HaFh70mQz2SERE0qHTApy6ujrGjx/PFVdcwbXXXttmm5kzZ/LAAw+k7kcimVv6OtsZ4+HvWwmAU5yFo0WORlOA08kjcIwxxFcEhXTt4gkk1v0mVTcoMuo6/Fgl4OP0mN7m0t4Z1wkjcPz4btxt8wCLnMl3YBcMx/gJ3B3/wNRtJL7sv1JtrWgZuac8BMZNLQ1uF43BrylPjaTBxCGUT2TUNdi5fVtcK9zvHML9zmmxzbJsQr1mYE//GbF3b8TEKjCxCuJV76XaxFf/jNwZD2BZFn7dFvz95fgNFYSHXgHGx6/bSHzV3dgFI4iO+4aWKRcRETmAH99NcsOjwR0nBzt/CHbRWOySSZntmIiIdLlOC3DOOecczjkn+DD31a9+tc020WiUPn36dNYlj2nJ9Y9i4ruxor2w8gdnujudyrK7aApVU+gAuBUvpMKb8NBZhAd9qnOv1QWaClMbv3kEjjE++Il2FR/2azcSX30vmCRO2WnYBcFoGcuOYBeNTT0fVt5gTP1mTHwXfu06khv/hLdnIQB26RRCPU8GgqlR0VHXgpOL5XQsbLVzgnDI37+WxNqH8PeXNz+m+s3EV9yJV/UeuHXNxxQMx90+D2/PAgC8uk0kIsVERvwbWGFNvxIREQFMovn3nZypd+MUdu6qmiIikr3SWgPn7bffZuTIkRQXF3P66acze/ZsysrK0tmFbsPd8Q+AoBidZWe4N53M6poROH7D9tRtd9szADi9P0Jk+Oc79TpdpnEK1YEjcBJrH8TdNpecafdg4rtJbv4r+Ans/MHBNDHjBdOkLIv4qrvxa9cBEOr90ZanzhuUCnBCfc7ENOzA3fESfs0HeI3bsaP4VUtwkzVAMPLLihQf8cOxQvk4pZOJTppN/P17IFmDXTwO5S5P9AAAIABJREFUd9szeLvmt2off/8n4Na22OZueRJ3y5M4PaZpmXIRERGA5H4A7JJJCm9ERI4zaQtwPvGJT3DRRRcxZMgQNm/ezA9+8AM++clP8uqrrxKNtr0kdnl5eZvbuzsnWYnvFGDsvDb3W36MfvFdGEKs350He46t56Fn3CUKWLid+hrn1i2m9ID7vhVmtzmRRDf5OiqsaaAQ2F25jdpYORif/lufBKBh4Y2Aj4UPgF+zGrfiBQBqii+ktuAs+jeGNwAbq3tgapofd15dlKY1tyr3Odh+EcVA3canCftJkqG+xHPGUFD7Wqr48bbqMMmGTnruCr4Y/O8nyOlZhu3HiCQ2kIgMJxEdRs/Ke3Eaw5t4dAT1+adjmQRF1XOwTQPe3kWse38hfii7Vg6T7udY/bkiItmpK95zcurX0AOoi9ts1XuaiDTS7zjHhlGjDh3Mpy3AueSSS1K3J0yYwJQpU5g0aRLPP/88n/zkJ9s85nCdz3ZtrboTL38Ad8eT4ORiF47GzutPZPRXsRrrwgB4NR8Q2wZ2/kBGjR6b7m53uYbaoqDmnvE69TVOrH+b5F4ID7mc8LBZYNkUWt2nfkpiQ1+S+6FHoU3vsjgmtpOmyVQWwWil0IALCfX+CImNf0qNqCna9yxlAycS3xa0zTnxbkaWtpwH7+3dT2zJXwHoN3wqeDFi7/2NcDIYtZTb+0SKh19NbMlW/Np1WDn9GDJuZmpaV+dqXdPJjJqCH6vEsiPk5Q2kR+r75gvEls7G27OQIaW1hPoeQ8W8Je3Ky8u7/c8VEek+uuo9J7ltDYk9UFjSj156TxMR9DvO8SRjy4j369eP/v37s379+sM37maMFyO+8n/w95eTM/l7WPlDsSwL49bhbvl70MhrwK9eil+9FLt4POF+Z6eO9+s2A2AfY7VvmlhdVMS4aQqVlde/RSDWXTTVuXG3zsHdOqf1/pzeREZ+BcuJklM8Dr96Bcnt8/Aq3yCx9uH/z96dB2ZRnYsf/56ZebdsJISQQFjCvgiIoIIgKFq9tGK9WuvVq7Zq/al1qfZqXS9V23pp1VK1VerSqrW2at2lLq3gxo4IAoIKsgZCEgIh67vMzPn9MclAACWQ5M32fP4xOe8sZ8Lr8877zDnPAby6NWbWgUUMVUpv/2cj0hOU6RVNrpuuZWYfhwpmEj72d7jVhRipfVBG8sKDCqRjBtIP+pqRORKnbClO+UqsvClJ65MQQgjRFum6qc4qkNHKPRFCCJFsrZbAKSsro6ioqEMWNVZmGLd6IzpWSu2SH6Mi+ahgJm7FWkBjpA8iOORanJ1LSGx6Fnvry1h53/JH6+jqrYBXt6RDUt7brtlXoYrtAsAIdWvW4yaNsc9oFysNI7UvygwRHHgF2o15Ba3rRsQoI4jZdQwoC6fkI3SsFAAzY/BBD61C3TCzjwcjgKpLlISGXE9szW8wu5+E2e2EvcfdZ6nwtsBMH0QCcGsKj/gY2omCm/CvXQghhGiv6hM4yGeaEEJ0Os2WwKmqqvJH07iuS2FhIStXriQrK4usrCx+/etf893vfpfc3Fy2bNnCL37xC3Jycpg2bVpzdaFNMTKG4dQWAaBrt6Frt+19rcswzIwhGGn9sLe/iVu1EXf3p5hdRwPgVK73tkvrl/yOJ4M/OqaZEzj1q1AF2medFCNjCBhBcOMEB15OoOfUQ++TOZLg4KtxawpRZgrWfst611NKET76Fw3arLwpGJlHoULZbXqFp/pCyv4N6xGIfnIzbrSYlHGPooLt8/0hhBBCAH4RYxmBI4QQnU+zJXCWL1/OmWee6f8+Y8YMZsyYwQUXXMDMmTNZs2YNzz33HHv27CE3N5dJkybx5JNPkp7eMZ8eBHr/J07ZEgJ9zsXMHAHaIbr8FgCMVC8xo4wgVv40EhufIVH4GmbX0WitcSu/9LbLGNJq/W9JzTWFyo3t8lbrcqLo2E509WbvuE1YOak1mRmDSZn4LG5t0deOpNmfUopAr4PXkGoMI9z9iPdNmvob1CNM4LjRUv//qcS22QT7XdRcPRNCCCGSQmsNaO9nSeAIIUSn1WwJnEmTJlFeXv61r7/88svNdap2wcwYTOrkFxu0hUb8L3bJh1i5J/ttVs9vk9j4LE7ZUtyqTTiV68CuQgWzUO11KtCh+FOonCM+hLariS6/GX2QaTXt+Ybmm2rBdFbK8v4eOlF50MLgh+KWr/Z/tovflwSOEEKIdsMuW0b889+h4+Wgba+GXd39k0wLFkKIzqfVauB0Rlb3E7G6n9igzQh1xcgajbv7E2qXXLW3PWNIm57W0iT1U6j2G4GjE1VEV/0Sd89q0A5GxhDCY2aijANXkrJLPkTXFKJS+mB2GY5d9Lb3gpXaLgsYi6+nzJBXH8iNgVMLVkqj9nNjZURX3IGum8oIoKPFR5QEEkIIIVqDXfgaOraz7jflJ29AEjhCCNEZSQKnDbDyTia++5O6X9IwM0cSKDi/dTvVkuqnUO1XA8epW5WrnlvxBbpmC+ogtYDcSq/ektXjNAJ9vucncJQRbqlei1akAunoWAydqEA1MoGT2PhXdPUm75e62kK4CbAr907LEkIIIdoA7URxqzejQjno+G5w4+h4OU7ZEgAiE/6CEe6OW7uDxMZn0U41KqVXK/daCCFEskkCpw2wciYSXzsT8JZzDh91Syv3qGUp5SVwUqvmofX/QykDAB0tAcDqcTrarsYpnY9dtpRAal9/m3pu3RdzI61fg9e0G03CFYhkU4EMr85RohIieYfcXsfLsYv+BcogPPYBjNS+1C69Dl2zBR3b2a6n2QkhhGjfnPLPiG94ikD+GVi5J6O1Jvrpz3HLVx50eyOtv1+zzojkERp+YzK7K4QQog2RBE4boKxUrB6nY+94l0Dvs1u7Oy2vbsivZZd611y3cpJbWwyASumNcmM4pfNJfPVnnNKFhEff44+80FrjVm0EwEgraHhsRxI4HVJdwqWxK1HZJfNAO5jZx/sFoVUoG12zBTdWhpHWtpZKF0KIzkprjVP6kZek6OAjSrTWJDY8TWLzcwDEylcR/+pJUMbe6b7KQAUyUeHu3vRh7RDoc04r9loIIURbIgmcNiI45DoC/X+IEcpu7a60uEDPqV7R5vKVxL+cRWLLywQHXu6PwDHCubBP3Ru3Yq2X6KlbbUlXbwa7Cqw0VHC/v1cTCiOLtqt+nn/s0zuwc04EpVBmKoG+38dIyW+wrdYae8e7AFi5J/ntRigbF/apJSCEEKIlabsat3ordvFc0BoVzMJI64fZ7XiU8j7n4+v+iF34GkbGUCLHPtDKPT40t7YIu+jfgCJQcAHKsNCJSqKr78EIdSNQ8N8YKT0P3K96C86eNX7yBgAjhI4W+78Gh/0PVu4pXhJnv5HHQgghBEgCp81QRgDVCZI34H0ZD4/+P/bMu5yAvQNdvQl725v+TYwKd0eFc/YWrgV/xA1A/Ks/AWB1n+QXo7V6fht7+1uY3U9CdGxO6Tz/Z7vkfYKDriTQ89sAaLuW2Opf4lZ87tWT6jbB37Z+VTcdK0tuh4UQohNydn1CdMXtB31NhfOw8qZgZo3GLnwNALfic7QbRxnBZHazUbTW4NTilK8itvpXXj01ILHpWVQkH21XQ6IcF7B3vEtw2E0EenzL398uW0rs0+n+74E+5xIY8CNwY+hoqddohvxpUkIIIcTXkQSOaBXKsCjN/Rn9squIrbwTt2YzOu5Nj1Hh7hihrkTGPYpbuZ7Y6l/hVm8GQDtxnF3LAINg/x/6xwsOuhIzcxRmt+Nb43JEC1NWmv9z6KjbQTvYpR/hlC4g/sUjWHmnoowg9o5/4+zyCoIHep+NsiJ7j1GXIE1s/CtG2gDMrFEoKzW5FyKEEJ2AGy0l9vnv/d/NbhMws0bhRktxit9DR3eQ2PR3Epv+3nC/qo2YGUOS3d1v5OxeRWztTHR074qGKtzdHzWsa7ft3bjuwVN8/eNYOSf4zYktL3n7pfTBSCsg0O8i7wGUGUal9k7OhQghhOgQJIEjWo8RxMw6BjDQNdv8NhXM9H6M5HmrBwFu9Wav9k31ZtAuKqWPvx2AMsNYeVOSfQUiSQIFF4AyCfQ513tfAFbeFGoW/AAdLcGt3oKZPhBnt1cA0sgYRqDveQ2OYWaNBisV7Gpiq+72jpE/jdCQa5N7MUII0YF5BXnv8BIeVhqh4T/D7Hosqm5qtO7/A5ydi4lv+hsk9qBC3VCBLji7PsatWNdmEjhau8RW3uWvAoUywQhh5Z1CcPDVuLtXoO1qjNQCb1pvKAeMINFPbsTds4b4xr+SUmlRu/Qh3Mp1YISIjJ2JCqR943mFEEKIbyIJHNGqlBlERfLQtdsB70v2vvO+VTDLK2CbqEBHi3GrvOXDjYMsLS46LiPc/aCJFiNjGE60BGfnEtw9a3DqVvAIDb8JVbdcvb9tam9SJr1IfP3jXv0CuxJ722yUleaN1gl2Scq1CCFER6ZrCtHVWwCIHPcHP+leT5khrNzJWLmT/bZE4WycXR9jl3xAoNe0pPZ3f9quQcfLcWu3+8kbM3cKoWE3+UkoALPrmIPuH+x3MdEVt2FvfYVMwK1vH3CZJG+EEEI0mSRwRKszUvvg1Cdwuk9u8JpSCjNzFE7pPGKrfoGum3cuqwgJ8BJ5TskHJDb+xW9ToW6oyIEFJMF7P4UGXUFo0BXULr0Ot3Idic3PYRf9m/CY+w5aeFIIIcSh6Xg59s5FOLuWA17SY//kzdex8qYQ3/AUbvkq7LJlWNljW7KrX0vHy6lZ9COwq/02M/dkwkfd0uhjGFmjCfT5Hs6etdRGbdLyjsHIGIyVM7EluiyEEKKTkQSOaHVW3rdwK9ejwt0PeoMTHHAZtWVL/dE3AGbmUcnsomijjPQBDX63ekzFyjvFL279Taz87xD//EEAdLyM6Mq7iBz/CMqQsCiEEIdDx3dTu+RqdHy332Z2bXwSRlmpBPp8n8SGJ4mtnE7MCGFkDCE09IZGJ4Gag71zkZe8MVPAqQHAOszFEZRSBAf+PwAK162j64BBzd5PIYQQnZd8UxGtzup+Ilb3E7/2dSOlJ5HjHsKtG5KtglmYmSOS1T3RhpkZQ/f+okyCQ3/S6KVXrR5TMbsMR4VyqF16LbpmC7VLr8FI7esNlTfb3kooQgjRFiS2vkp8/ROgHUD77SqS7y0RHuiClXvyYR0z0Odc3MovcEoXgFOLu3sFicLXCQ264oj7qbVGKeWtIqVdUPjLlx+Ms9ObMhUc+CNwYjiVX2K20mggIYQQ4mAkgSPaBSO1L0Zq39buhmhjVCB97y/aaXTyBrynpKruPRUceLk3Ra96M071Zpy8U7G6jdt7aLsWp/xTzK5jD6itI4QQnY1dMg+03aBNRXoSHn3PEY+YUYZJeOTP0U4UZ+diYp/NwN294oiOpe1qop/cjFtTiJl9HG5NIbp6EygDs+tYrB7/ccCDIze2C2fXxwCY2cdjhHOQaC+EEKKtafy3HSGEaINUOLfuvz2O+BhWzgQi4x7HyvsWgLdiyD5in88ktvIuEpueO/KOCiFEG+VWb6Zm/kXENz2PjpejtfON2+tYKQChUXdjdp9EaPgtRMY/0SzTnZQZxux2AhhB3KoNRFfcjtZeKWCnYh01Cy6l9pNbcGt3fO0x7NIFuFVfgRvDKZ3nJW9QoF2csqXEVv8KZ8/nDfZJbHkR3DhmtxMwwjlNvg4hhBCiJUgCRwjRroVH3Y2ROZLQyDuadBwjtTdm9rEAuBUNEzhOyUcA2DvmNukcQgjRFtmlC9GxnSQ2PEnNvPOJfnw9Wrskit4lseXlBttq7aBjOwFv5cjwiDuw8qYc1gjIQ1FmEDP7eACcXZ/glC1FOzFiq+5GR4twyz/F3v52g32cPWtIbHuL+IZnsLe+4vWv6xiMLsMxc6eQMukfpJz4d8xu4wGIf/kIzq7l2GVLcSrXYW/7JwCBfv/dbNchhBBCNDeZQiWEaNeMtAIiY+5rnmOlDwbArfzy4BuYoWY5jxBCtCV639EsysKtXE9i03P+Cn9m1zEYaQVou5rYZ7/xat8EMlEtGBNDw28i/kUIe8ccEpufBzfuJ44A3OqNe/ufqCD6yS2gE/tch0Fw2I0Yoez9jvszahZehlv5JdEVtzV4zew2DjNdig4LIYRouySBI4QQdVSkB5gRdHw3OlGBCmR4xS/rXzfD/s/aiRJdcTsqlE1o+M1SG0cI0W65tdsACI/+P5zKr0h89Sc/eQPe6kyWlUr8i4dwypYCtPg0I2WGCQ6+GrvsY9w9a4jVeH20evwHdtE7uBXrvcUNjAC6tshP3gT6/hcYIYyMwQckb8Bb8SrY72LiX/4BFcwCI4yOFgEQ7H9pi16TEEII0VSSwBFCiDpKKYyUXriV67zil12Go+O79m7gxtFa4+5egb1zIe6eNQAkwrkEB17ebP1wqzYR+/wBtBMlcswM70uGEEK0EF1TCHirSAUyhuDuWYuOleBWrgcgseEpEhuearhPoqLF+7VvsoXEHjCCBAouwC56Bx0vo3axt0JVfYy08qcRHHDoJEyg1zSMLsMwUvLRiUpia+7Hyp2MkVbQkpcjhBBCNJkkcIQQYh8qpRdUrsOtXI9T9rH/tBnwCmp+cqOfuKmX2P4WgX4XN8t0Aq1dYmvv97842aULCOSf4RUWjZd70xi026z1JhrdN9fG3v4WZtYoWRVOiA5C29Xo+G4wAqhwDkoZhEf9vO61WmqX3YCu2QpGELPrsTil8wBQ4aYXLG4MK/8MjIzB4ERR4e4YkTxUMMvrc/011P1spA1o9HHNdG9bZYaJjPlN83ZaCCGEaCGSwBFCiH0YKb1w8ApcHoy7Zw0o01upJLUvTtkS3Mp1OKULsPKmNPn89o65fvLGP1/+GURX3IZbtRGr139ib/8nRsZwVDATK/dkrJwTmnzexkhsfYXEV38CM0Kw34WoSL73pS5rJMoIJqUPQojm5dZuB7xlwPdPDCsrQsq4RxtuH9tJYsMz3lSlJFBKYWYMbtAWHHIdTul8An2+R+2Sq/12I71/UvokhBBCtBZJ4AghxD6MlF57f04bQKDvf6FC2UQ/udFvDw79KYEe3pLjiUAa8cp1OLuWNSmBk9j2FvF1j4Dr1XGwep+NvfUVnF2f4FZtwq3yCnbaha8C4JZ/CoCz+1PMbuNRSh3xuRtDuw524eveL04t8fVP+K8Z6QMJH3Mvykpp0T4IIZqfrqstY6TkN2p7I9SN0LCftmSXDsnKmYCVM6FBjTI4vBE4QgghRHskCRwhhNiHkXkUmGHMzFGERt2JUmaD162e3/GTN972IwFwylcdcCy7dD72tjfRbhwjfTDBAT886EgVrTWJLS/4yRsj6xiCA36EvWMuOr6b2iVXNexD3rcwux1P/MtZXsHl6s2oFqjd4MZ2El1+O1a3cRiZI9GxUlQkHytnIs6uj1GhHJxdy3Ar12OXzCPQ8/SDHke7Djg13hSNfQpBCyEaR8f3kCh6ByPSEyN9EG7lOrQTxYj0wMw8qknHdusTOJFeh9iy7VFKQSADEhWY2cejDLmtFUII0bHJJ50QQuzDCHUj5cTnwQg2GNUS6H8pTsmHBPv/oOH2af3ASkVHi3GjJRjh7gA4FV8QW/VLfzu3fBW6dhtW95Mwc0/ypyq4VRtwyleja4tQwSwiE/7ir2gVHnE7sc/uRcfLAANwMdIHERzyE5QZxC5diFP8Hk75ymYpvqldBx0tBmWiwt1JbHoOXbOFxJYtqLq6F1aP0wgWnA9cBkB847MkNj6Drtl80GPW/x285X8NrJ5TMbPHYuVMPPD82vGSUrXbUcEsAv0vafGVboRoa7QTJVH4BmaXYZiZIwCIr38ce8e7B93eSBtAcPDVR5zIcesLGKf0PLIOt7LwyDtJbHuD0KCrDr2xEEII0c5JAkcIIfZzsGLEwYL/goIDaz4oZWKkDcQt/xS3esveBM7ulQAYXY7CyjmB+PoncHYuwtm5iEDtdoL9LkRrp0H9BrP7pAbLkZtZRxOZ+Ay4NigTlAKtUYY3KsjMHFWXwPmMQK/vNvm6oytuxa0bSRToez520b/913RtEagAVt4pDfapL2bsVm894HjatfdJ3gC42NvfxN7+Jk6PqVg9TkM7Ndjb38LK+xYqkIG9bba/v126kOCAywj0mtbkaxOiPdCuTXT5rbgVn5NQAazck9DxXXXxRGFkjsKtXIcR6QGBdNzdK3CrviK68i4ixz9y0ISnTlSCdkAZ2EXvktj6KuGj7/aSz4Cuq4Gz7/TR9sTMPKrJo5CEEEKI9kISOEII0UQqmOn9kNjjt7lVGwCwenwLK/fUBjVjEltf9hI4dVMX6plZRx94bGWAuc+0q31K3dQX7HSrDz765XBoJ4pbvtr/3S56B9wYKphNcOj13s+RfD9B5fchtXddH7YccExn50J0bCcqkk/k2Aep+ejcfY7/NnbR23u3LfuYQJ/ve8fMGoOOFaNrthFf90esHqc1ywpfQrRVOlFJ7PMH0bVFuFVf1Tc2GHVj5pxIeOT/7t3HtYl/8RB20b/AriS67KeEj7kXY5+RNIntbxH//MEDzmcXv08wrR9au37ytbE1cIQQQgjReiSBI4QQTaSCXQCvTkW9+qLDRlp/lBmEQCYkyr0X7WovYVL5VYPjmF0O7yly/egXXVOIdu0m1X/wEjDarydRvyyvSumJ1e34r91PRXqCMtHRYnSiAhXI8F+zSz4CINBrGiqQRnDQVdglH2JmH4+u3e7V8dAuunozuHESm54FwMwaRaD32dR8eA5oG3fPZ5hdxxzxtQnR1iW2v+Uvzw2K0MjpuNVb0PFyb5U3M4LRZXiDfZRhERr2PwQH/Ijoqrtx96yhdtFlgMJIH0hw0FXE1//Z2ziQAdoFuwrYG5909WZwalChHFQwK0lXK4QQQogjJQkcIYRoIhXwRuDougSNdhPomq2A8pMsKpjpvw4QX/8EOr5rn4NYe0fyNPa8ZhgV7oGOFqFrt6HqznUk3OpNAJhZo3F2feJ/0TPCed/cByOA0WU4bvkqaj+5mfDIn/sjANwq75hGXR2PQO//JND7Pw88d20xtYuvADfmbZ/aB2WGsHp+B3vbG0RX3E5o5PSD1s0RyaO1bvHVzjorp/g9AIID/x9m9nEYqX0gZ0Kj9lXBLoSP/hXRVb/C3f0JoHEr1/kr5xmZowgf8xuUUrg126lddBlu5XrvvHvWeNvslxwSQgghRNtktHYHhBCivdt/BI6OloJ2vKfaX7Pqkr1tNk7pAgCs/GlEjp91ROc20upq0NQlS46UW+VNwzJSCzBSevvtKvLNCRyA0PCbUaFu6OpNxL/6EwDajaNrCwGFkdLnG/c3IrkE+u6tL1R//voCrrB3NI9IPu1EqV16LTXvTyOx9dXW7k6H4VR8Sc38H1C7/FZvRIyVhtXrTC95c5iUlULkmP8j9ZS3STnxeVQ412sP5xE66hY/8aYieWCmoOO7cGNlOHXTJqWGjBBCCNE+yAgcIYRoovqRM/4InLqRNSrUde9G2vV/DPS/1K+Ro0LdCA649IinP6mIV7fCjRYf0f713D3eFzkjrR86WoJbsdY7ft0XwW9ihHMIjbqT6NLrcCvWobUm/uUs0C4qkt+o+jWBPt/DLvkA3Lg3LQswcyZiZh+HU7YUt3JDE65OHIp2beLrH0MFMr3RT/nT/H83u/h9f8RGYvvbBx1FJQ5f/Ivfo2Ml6FgJAKHB16CM4CH2OjQV7ELk2Ae9ouoZgxskkZUyMDIGe8WP96zF9UfgSAJHCCGEaA8kgSOEEE3kT6GqH4FzkASOkdoHp2YLmGFvRatmYtSdQ8fKjvgYiS0v4lZ8AWYEs+sxqHA3v8BwYwubGmn9wQiiYyU4JR9ib3/rsPZXZojIsb8HZfirbCnDIjRyOjUfnIOu2Yq9cxFm5iiUlXIEVykORmsXd88anLKPsQtf39vuJuqWiwd7+95i07p6E27VJlRqb5Qyk97f9s6tLSZR+DpGqCtuzd6V21QkHytvSrOdRwUzMb9mSqbZ5Sjc3SuwSz5ER4vBTMFIK2i2cwshhBCi5UgCRwghmuiAKVSxugROcG8CJzj4x8SNAIE+5zTvuUPd6s55ZAkcZ/en/gpZVs4ElBnGTB9EeMz9uBVfYGQMbVw/lImR2he3ch3xjc/47Wb3ExvdF2UeOPpAGUGMtH64leuIrbwLFepO+NjfYYSyG31c8fUSG5/1i0fvyylbAgXn41ZtxK34vO5Lfn/cPaupXXIVRtoAwsf9QWriHKb4+sdwSufv12oQGnJN0vpgZh5FAnBKvKLJZpdhkowTQggh2glJ4AghRBP5I3BiO72lgBOVXvs+q7oYoWzCR93S/OeuS2R8XQJHx3fX1blQmFmjGqwSBWDX1eFBWQT6X+K3m5kjGtSgaYz6RIuuKQQgcvwsjLR+h3WMgwn0vwS78HXcmq3o2u0kNjxNaNj/NPm4nZ0b20liywsAGFmjUcrE2b0CtIO7Zw3x9Y9jly0DwMo7BTP7OOJflKDjO3GrvkLXbGlS4ezOxo2V4excCIDZfRIqmI2VOxkj0iOpK0AZGcPATAGnxutL17FJO7cQQgghmkYSOEII0VRWKgS6QGKPP3UI9quB00K+LoHjVm1CBTOJrvqlX+cCvKKmOlGB2XUMoeE3+V8ow2PuxQjnNKkvVq8zcWuLwY1hZo5oluQNgJU9Fit7LG7VJmqXXIVd9C/cqo2Ej7kXZUWa5RydkbtnLbgJzK5jCI/+P7+9ZsGl6GgRiS0veQ1mCoFeZ2Gk9sbqNo7oZ7/GKX4fZ/en/ipr4tDc3StBu5jZxxEecUer9UNZESLHP4xbvQVlhjG6HF6iVgghhBCtRxI4QgjRREopImPuwy5dQGLDU3vbg0lI4ATrEjjxMnSiCp3Yg71jrjctRlmgba+uTDgPXbsdHd0BgFM6j5oPF3qrZYUBy9YfAAAgAElEQVRzMTKGNLkvZvogImN+0+TjfJ19R3u4letwdi/HauRSy+JAbm0RwAGjaELDbsDeuQgV6IIygpi5kzHqpuoBmJlH4xS/T3zD0+DaWL2+e8RFuDsTHd8NgIr0aOWegBHpgdEG+iGEEEKIwyN3XEII0QyM1D4EIj1IbPyrlzQhSSNwzCBY6WBXUvPRuQ1frOuHkTaQ8NiZJDa/QGLjX/Z53VvqPDTyznZRA0MpRaD/pSQ2PAmAs3uFJHCaQNclcPb/Im9mHY2ZdfTX7md2Ox6+SgW7mvj6x9DaJtj3vBbta0eg494qdcmcLiWEEEKIjsVo7Q4IIURHoYwAKpLn/25EeiXlvPtOY1HBbIz0QYRG3rm3LdIDZVgECi7wap2Ee5Ay+SVSTnqdyISnMdP7J6WfzSHQ9zxCR/8KAGfXCr9duwkS2/6JGy3125yKL4h/9RTaiSe9n+2BW+uNxjrcESFGKJvwMb8GKw2AxIania6eQezLR3CjJc3ez/ZEaxe75EPiG58lUfg62k3sfa1+BM7XrA4lhBBCCHEoMgJHCCGaUbDgQhJb/kFwyLVJq88SGnYD8XWPooJZBIdev3c0zfBbiH/1JwJ1oyOUUoRHz/B/bo+UUt7oECOArtmCTlShAmkktrzkTV9TJkaX4aBM3N11CR5lEOz/g1btd1vkj8AJ5x1iywOZ6YNImfQPYqvvwSmdh1PyAQDOzsWEx87stKuE2dveJP7lH/zfnV3LCI38OUqZ6ISMwBFCCCFE00gCRwghmpGVNwUrb0pSz2mk9CJ89C8b1Zf2mrjZlzICGGkDcCs+x638EnvnYuzC17wXtYNbvqrB9vb2dwj0u6gVetp2aSeGjpUAChXOPaJjKKUIjbgDt3wlbrQUe9sbuBVfEPvs3hathdTWOHvWEFv1K7QTBTcKeKtMOWXLcHYuJrr0OlQ4B7dqIyAJHCGEEEIcOUngCCGEaHeMjMG4FZ+T2PoaTtliry2tH8Eh14GbILrybn+ZZB0vQ3fyqT37c3YtA+1ipA/y6igdofoRUSZgZR9LzfyLcMs/RTtRlBluvg63UVprEoWz0fFdfpuRdQyho27H2bWM2Mq7cKs2QNUG/3UVkClUQgghhDgyksARQgjR7pgZQ7Bhb/Im6xjCI25HBdIBiIydSfyrJ3H2fAZ2Vd10obTW63AbY5d8BHgjRZqLCmaigl3RsRJ0fHebWG2pJWjtgmvjVn5JbO1v/alo4WMfxIjkg5WKUgor+1iME57ELV9JbM19/v4yAkcIIYQQR0qKGAshhGh3zG7jMbPHYaQPwsw+jvCI2/zkDYCRVkD46Luxuo0H9i6Z3dloJ0p847M4uz/d26ZdnLKPAbByJjbr+eoL9NYX7O1o3GgptYuvpOaD7xL95CY/eeMVDx+MCqQ1mKZohHOw8k7FSB/otzVlxJMQQgghOjcZgSOEEKLdUVYq4aPvPvR2daNAdO0OYFAL96r1Jba9iVvxBaBA27g123Ar1pJQBoGC/8ZI64dbvRXsSlQoBxXp2aznrx9dUr9kdnNyawqxi/4NRgBUAGWGsfJOaZC4ay7aiRJf/7g3Usm1QSnM7ONxqzaia7aCMsGMYOWeBK6NmTPhG+tLhUbdTWzlnRjpg5u9r0IIIYToPCSBI4QQosMy6hIUbs1mjOAo3OqtqFBXlJXayj1rfm7NduJf/B7QB76oXRIb/9qgycgc2exFrfcmcJp3BI62q4mumI6ONhxJlSh8lcixD6ICGQ3a3erNOOWrsXqcjjICh30+p3QB9rZ/Nmwrfg/wkoKRsQ+ggl0afTwjlE3kuD8cekMhhBBCiG8gCRwhhBAdVv0IHGfnYvJYTO12UJGeRMY/gVIdaxZxovBV6pM3wcFXgxkGDWbWSNzK9djFH4B2cSvXoWOlWN3GNXsfWmoKVaJwNjpahEotwMo5AdwEdsmH6NoiEoVvEOx3IQBu9VbiG5/BKfkQAGfXJ5hdhmHmnoIR6tro87l1Ra/N3CmEhlxLYvMLJDY/j5E1mvCI/0UFpJ6SEEIIIZJPEjhCCCE6LCN9AEbWaHT1FmzbxnQr0LXb0TVbUal9W7t7zUZrF6euMHH42N9jZjScLmZEemDVFSzWbhy3ejNG2sADjtNULTWFyil5H4DggEv8ukZm17FEV9xGYuurGOkDwI0TW/uAv/oYgFM6H6d0PuaetYRHTm/0+epXlTLTB6GsVAL9L8HsPhkjtQBlmM12XUIIIYQQh0MSOEIIITosZQSIHPNrANatW0fv2Is4JR/glK/GaEcJHLdmO7XLfgqJPRDIJDL2txgp+Witwa7GKV/prfwUymlQMPdglBHETG+ZekB7Ezi7DrFl47lVm3CrNoKVhtl1jN9uZI3GSB+EW7mO2Mq7/Haz6xisvNNQwS44u5aT2PoSTul8apfdCMoikH8GVu7kbzynjnn9V3WjdpRSmOkDmu2ahBBCCCGOhCRwhBBCdBpm5lF+AieQf0Zrd6fREltf9pI3AIly4uv/RGjkdGKr7sbZucjfzuw2vtnr2hwOP4ETK2u2Yya2vwmAlXsSyti7gpNSitCIO4h+egcoy1sJKrUPwQGXoswQ4CVz3NrtOKXzcfd8BkA8VnroBE5dAkoFGz/tSgghhBCipUkCRwghRKdhZh0NgLNrGdp1Gj0dxq0pxK3cAEYAs+vYpC4F7dZsw94xB4Dw6P8juvJunJ0LiH06HWfXx6AMMFMwM0cQ7HdR0vp1MEaaN6rJrdqE1g5KNW26kbar/Wu3en7nwPNF8kgZ/6dvPEZw8DU4XYbjVK7HKX4PXbsdN1qKMsMkNr/g/Q0DXQiPugtlhr3z+iNwspvUfyGEEEKI5iQJHCGEEJ2GSumDivT0vsTvWYOZNfKQ+2jXofbjn4JdCUCg7/kEB1zSsh2tYxd/QGzN/aATmN0mYHYdQ3DQFcS/+L2XeMBLULSV0UQqkIEKd0dHS9DVW1FpBUd0HJ2oJPblI+jqLWBXY2SOPOIpTEaoK0af7xEAok4Nzs7FRD/+yQGFlp3dK7G6HY/WWkbgCCGEEKJNarYlOObPn8/555/PsGHDyMzM5Nlnn23wutaaGTNmMHToUPLy8jjjjDNYu3Ztc51eCCGEOCSlFGa3EwBvFE49t2qDP9JjfzpW6idvABKbnyNR9K+W7Wgde8e7oBOoSA9Cw28EvJEooaNuJTjkOsJjZ7aZ5E09I30wAE7leu+/uz6hZvFVOHvWNGr/+MZnqfno+zjF7+FWfQXQbCOLrO4nAwdfJcutqLsnsavBjYMZQVmRZjmvEEIIIURzaLYETnV1NcOHD+fXv/41kciBNzwPPvggDz/8ML/5zW+YO3cuOTk5nH322VRWVh7kaEIIIUTLMOuK/Lo1WwFvhE300zuJrbmP6rlTiW96rsH2um5JaSNjKChv4Gp87UzcZqzz8nXc6i0AhEf9AmWlAl4Syso9mUD+GZhdhrd4Hw6XUVcg2dm5AO3Gia25D129ieiy/2mwOpVbvbnB39CpWEf13KkkNj7jNSiD0FG3Ej7uYX/qW1NZeVOITPwbkfF/IjTiDiLjHiU04n+985d9jL1zsVdvCDAiPZvlnEIIIYQQzaXZplCdfvrpnH766QBcffXVDV7TWjNr1ixuuOEGzjrrLABmzZrFoEGDePHFF7n00kubqxtCCCHEN1IpvQCvtgyAU7bIG2VTJ7HhKQJ9z0Mp7xmHW5fAUZE8AlmjSGx+AQAd2wlHWCMlsfU17NJ5hEdORwUyDrqNdqLoaDEoExXpcUTnaQ1Wj1NJbPobTukCat7/boPXahZfSaDnt9FuDHvrqxDIIGXco6hgJk7p/L0bKoPQUbdjdT+x2ftn1K0sZaTk13U4DaBuNas7/e2CA3/U7OcWQgghhGiKZhuB8002b95McXExp5xyit8WiUSYMGECixcvTkYXhBBCCACMFG9kha7djtYuicI3DtjGrdqI1o63XbTY2y/cnUD/SzEyR9W1l+KUr8YuW+YneRpD29XE183CLV9FYttb3vliO3Eq16O1xtmzhsT2t4mvfwIAlZKPMtpPyToj1I1g/x+CEfJHLJm5J2N0OQoSe0hsfg576yuAhsQe4nUjbtzqjd7+XYaTMvnVFkneHLy/2QQHXoHZbdzetoyhDZYsF0IIIYRoC5JyR1hc7N385uTkNGjPycmhqKgoGV0QQgghALypSFYa2FXUzLvAW57bCHp1T+pEl14DgJHWH7dqg7dfOBelFEakB275SuLrHkPH6hI3ViqRY39PfP0T6Ggx4bEz/aWs97dvrR278FWM1F7EPn8I7EqMzFG4u1c02N5I6dOcl58UgT7nEOhzDlprcKIoK4J2E9iFb3gFgutWe0ps/CtO6UL04GtxqzYBEBp6Q1JX+dq3vzWLr0RXbybQ68yknl8IIYQQojHa9CO9devWtXYXRAuTf2MhRDLVx5xuqitBqrzkDVCVMgE7kEcwvolwzQqUjqHQfvIGoKjMJlazjvRqSAc/eeMYaZh2FbWLLvO33bLmX8TDQ0G73jLf++ha+iHhup91fDexVb/0X3N3r0BjUZsyGgArUUIlRxPrMLGybtUvG9CaXON1zPgudi38H8LRYjQWX22rAdU612tkXE4wvIFoRW+o7Ch/c9Ga5D5HCJEsEm86hkGDBn3j60lJ4OTm5gJQWlpK7969/fbS0lK6d+/+tfsdqvOifVu3bp38GwshkmbfmGN3+QGJbW8S6HUGRqQHKSl9UYbZYHudqCCx7U2cnQvRsV30HnYKKpBOonAA8Yq926WOuYfoxzcA2m/rkbobnXgbe9s/MbocRXjUXahAOlq71BRtBiA04n9xdi5CJypxdn0COuG1D76StH1GgGS10N+jLYjZx2PveJdw1FsByswYyKDBQ1u5V8e18vlFRyH3OUKIZJF403kkJYHTt29fcnNzee+99xgzxptTHo1GWbhwIb/4xS+S0QUhhBDCZ3U/8ZA1VlQgg2DB+VBwPlprlFJee7DrPgdK95bNNoLgxvzmROHrYFcB4O75jJqPvo+RORK0A3YlKtQNM2ei3wc3tovaBRcBCqvHac17sW1YoOC/QRmocC4YQaxu41u7S0IIIYQQbVazJXCqqqrYsMEbau66LoWFhaxcuZKsrCx69+7Nj3/8Y2bOnMmgQYMYOHAg999/P6mpqZx77rnN1QUhhBCiRdQnbwBUaG8Cx0gfgFKK4JBria/9LVbvs7G3v+Mnb6xeZ+GULkDHSnHLV/n7mV2PbXBMI9SV8LG/R5kRlBmmszBSehIa9j+t3Q0hhBBCiHah2RI4y5cv58wz9w75njFjBjNmzOCCCy5g1qxZXH/99dTW1vKzn/2M8vJyxo4dy8svv0x6enpzdUEIIYRocSq0tyB/oNdZAFh538JMH4BK7Uugz/fR0WKUGUKl9kP3ORe78DWM9IGoQCYoEyNjyAHHNdMHJO0ahBBCCCFE+6PKy8v1oTcTovnJXE0hRDI1Z8xJbHsTFcrG2mfpaSGE2Jfc5wghkkXiTefRplehEkIIIdqiQP53WrsLQgghhBCikzEOvYkQQgghhBBCCCGEaE2SwBFCCCGEEEIIIYRo46QGjhBCCCGEEEIIIUQbJyNwhBBCCCGEEEIIIdo4SeAIIYQQQgghhBBCtHGSwBFCCCGEEEIIIYRo4ySBI4QQQgghhBBCCNHGSQJHCNHuaK0b/FcIIVqSxBwhRLJIvBFCfBNJ4Agh2pWnn36a6dOnt3Y3hBCdhMQcIUSySLwRQhyKJHDEYXNdt7W7IDqp2tpaPv/8c1577TVWr16NUkrejx2c/PuK1iQxp/ORf1/RWiTedE7ybywOlyRwRKPVBxjDaPi2kSGeIhm01kQiEc477zwGDRrE/fffDxz4fhQdg8Qb0dok5nQuEnNEa5J40/lIzBFHyrz11lvvau1OiLbNdV2UUiilAHjmmWf44x//yBdffMHRRx9NMBhs5R6Kjs5xHP8DrkePHuzZs4d//vOf5OfnM3jwYP89Kto/iTeiLZCY03lIzBGtTeJN5yIxRzSVpHXFIdV/qFRXV/Ozn/2M3/zmNziOw/3338/ll1/Ohg0bWrmHoqMzTROAjRs3AnDyySczevRoHn74YbTW8oSqA5F4I9oCiTmdh8Qc0dok3nQuEnNEU0lEEI1y00038bOf/Yx4PM7bb7/NY489xr///W+WLl3Kyy+/TE1NTWt3UXQg+w8fra6u5sYbb+TCCy8EYPDgwXznO99h9+7dPPzww4DMIe5IJN6IZJOY07lJzBHJJPFGSMwRTSEJHNGA4zgNfq//kBk0aBB///vf2bFjB/n5+QCMGDGC888/n1deeYWlS5cmva+i40kkEgAHDBVOTU1l9OjRaK15/PHHAZg0aRKTJ0/m2WefpaioCMMwZN5wOyPxRrQ2iTmdi8Qc0Zok3nQ+EnNES5AEjgC8gOK6rj+Mc9OmTVRVVfkfMldeeSWjRo2ioqKC0tJSf79bbrmFWCzG7Nmz/Xb5gBGHy3VdHnnkEW677TYAbNvm4YcfZsWKFf42//Ef/8H48eN56qmnKCsrIzc3l6lTp5KamsrMmTOBA2+KRNsk8Ua0Nok5nYvEHNGaJN50PhJzREuSIsadXCKRwDRNv5jWZ599xiWXXMKTTz7Js88+SywWY/DgwYTDYQYNGsR9993HmDFjGDJkCEopQqEQwWCQJ554gr59+zJy5Ej5gBGHTSnF3/72N5YvX86QIUOoqalh+vTplJeXM3XqVMB7QmVZFgsWLKCwsJBTTz2VvLw89uzZw+uvv87IkSPp3bs3Wmt5D7ZREm9EWyExp3OQmCPaAok3nYfEHJEMksDppFzXZdasWbz44oucfvrpAPzlL3/hJz/5CZMmTeLmm2+mf//+3HvvvaSlpTFixAgGDBjAihUr+Pe//82pp55KZmYmAKNHj2bu3LmcdNJJDBgwoDUvS7RD9Tcj+fn5LFq0iC+//JLLLruMqqoqPvzwQ/Ly8hg4cCAA3bt3Z+PGjcyePZuTTjqJnj17EggEWLBgAVprJk+eLB90bZDEG9GWSMzp+CTmiLZC4k3nIDFHJJMkcDqpfZ8G9OvXj4KCAhYsWMApp5zCTTfdRH5+PmvWrOGFF16gsrKSvn370q9fPyZOnMg999xDbm4uo0ePxrIsAM477zz/A0iIw1F/M5Kbm0tpaSnvv/8+2dnZTJs2jTlz5vDll19y+umnEwwGCQaDFBYW8sILL7B7927OOussevbsycknn8yZZ57Zylcivo7EG9GWSMzp+CTmiLZC4k3nIDFHJJMkcDqhfZ8GLF68mHXr1jFt2jS6d+/OCSecwKpVqzjvvPOYN28et912G2+99RYAY8aM8T+Afve733HBBReQlZUFeIHLdV15MiCOSP17snfv3ixbtowlS5Zw3nnnYZom7777LoZhMHbsWADmzJmDbdsUFhYyadIksrOz6dKlC4C8B9sgiTeiLZKY03FJzBFtjcSbjk1ijkg2SeB0Qvs/DZg3bx6hUIhJkyaRSCS48cYbGT58OI888ggnnngiy5cvZ+HChfTs2ZMRI0Zw2mmnMWbMGP/DZv/jCnG4lFJorUlPT8e2bT788EPi8Tg//OEPWbNmDY8++ijxeJx//vOfzJ8/n5tvvpk77riDvLy8A44j2haJN6ItkpjTcUnMEW2NxJuOTWKOSDZZhaqTqq9o/r3vfY+CggJeeukliouL+eKLL1i1ahWnnnoqOTk5lJeXU1RURElJCe+//z67du0C4LTTTmvN7osOqP6D6swzz+SYY47h9ddfZ9OmTdx9991cfPHFvPvuuyxdupS7776bKVOmEIlEcF23lXstGkPijWiLJOZ0XBJzRFsj8aZjk5gjkklG4HRS+z4NSCQSfPTRR2itOfHEE5k5cya9evUiMzOTZ555hqysLG699VYuuugif2ifEC3BdV0CgQApKSnMnz+fLVu2MG3aNE455RSmTp3KVVddRa9evfwPSsOQHHR7IPFGtFUSczqmZMWcqqoqgsFgC12F6Ggk3nRccp8jkkkSOJ1Y/dOAAQMGsHr1aj788EPOPfdcMjMz+cc//sGf//xntm3bxi233ML48eMJh8ONmo+5du1annvuOQoKCkhLS0vGpYgOov691bdvXzZt2sTixYvp2bMn/fv3JzU1FQDHcTAMQ4aWtjMtFW/Wr1/PvffeS5cuXcjPz0/GpYgORGJOx9VSMQe8uHPxxRezceNGpkyZIrUqRKNIvOnYWirmbN++nW3btqGUIiUlRZaSFzKFqrNzXZdwOMzZZ5+NaZrcf//9XHfddbz55ps8//zzLFq0iGOOOcbf/pueBsTjca655homTJjAjh07yM7OTsYliA6m/snTeeedh2VZfPrpp34bgGmardU10UTNGW9c1+Xmm29m4sSJlJWVUV5enoxLEB2QxJyOqzljDnj3OVdddRXjx49n2bJlLFiwoFH7CVFP4k3H1pwxJ5FIcP311zNlyhR+9KMfMXnyZNasWSPJGyEJnM6uPnBMnjyZSZMmsXTpUubMmUO3bt047rjjAO9pwKE8+uijDBw4kPXr1/PRRx9xzz33+Evh7fvBJMSh1A9DHTx4MIlEgq1bt/pton1rrngD8MILL7Bq1SpeffVVHn/88Qbzx+W9Ig6HxJyOqzljzm9/+1sKCgrYsmULixYt4qc//Slaa0kei8Mi8aZja66Ys3v3bs4991w2bNjA888/z+9+9zt69+7N3XffDch9TmcnCRzR4GlAIBBgxYoVh/004IEHHiA/P5933nmHESNGsHr1apYsWUJRURG2bTc4jxCHopRi7dq1xONxjjrqKL9NtH/NEW8cx+G5555j8uTJnHDCCSxYsIDf/va3vPrqq5SUlMh7RRw2iTkdV3PEnDlz5jB79mwefvhh3nzzTQYOHMjQoUNZuXKljL4Rh03iTcfWHDHnk08+Ydu2bcyYMYPRo0dz/PHHc/rpp8sUKgFIAkdw5E8DtNZ+cmbWrFls3ryZp59+mosuuoiLLrqIa665hm9/+9vcdddd/nmEaKwXX3yRcePGcemll7Z2V0Qzakq8qVdWVsb27duZOnUq99xzD5dccgkLFy7klltuYdq0af60BiEOh8ScjqkpIx7qVwE64YQTeO+99zj77LP91zIzM8nNzWXhwoUt1nfRcUm86bia4z4nGo2yYcMGunTpAsDOnTt55ZVXyM/P5/XXX2/R/ou2z2rtDoi24XCeBrz33nt+0T7LstBac/LJJzN+/HhuuOEGLrroIv785z+TSCRYtmwZ06dPZ/jw4Vx44YW4ritPq0Sj3HHHHfJe6aCONN7UP7Xq3r07SikeeughgsEgL774IoMHD0ZrzdSpU3niiSfo3bs3vXv3Tto1ifZPYk7HdbgjHurjTv0XqpSUlAO26dmzJxUVFf50CHkqLg6HxJuOran3ORMmTGDo0KGce+65FBQUMGfOHCZOnEhRURE//vGPmTNnDrfddhs9evRI2jWJtkMih/Ad6mnA22+/zdChQznnnHNYu3YtpmniOI7/hGrWrFlcccUV3HHHHYwZM4Zx48ZxxRVXcNlll3HfffcBUuhPNJ68Vzq2I4034BX2O+uss3jzzTfZsGEDAwYMIBgMEolE+PnPf867775LVVVVMi9HdAASczq2xox4+Ka4sy+tNYMGDSIvL48lS5a0ZLdFByXxpuM7kvuc+pkNWVlZzJ49m0cffZQdO3Zw33338dprr/GnP/2J559/nldeeYUtW7Yk83JEG6LKy8ulMIkA+MbRMe+88w4PPfQQQ4cOZc2aNQC89dZbB+xbXV3tL4VY74EHHuDpp5/mtddeo0+fPi13AUKIdqMp8Qbggw8+4NZbbyUzM5O33nrLf/pdXl7OsGHDePrppzn99NNb/DqEEO3DoUYANybu7KuiooIf/vCH5Obm+qMBhRCiXlPvc8C717nxxhuZN28ewWAQwzCoqamhX79+zJw5kwsvvLBFr0G0TZL+Fb6DBZn64cP5+flMnjyZn/zkJ9x0000sXbqUV199FQDbtv1990/eaK1ZvXo148ePl+SNEMJ3pPEmHo8DMH78eC6++GIWLVrEM888Q3V1NQBvvvkmRx99NOPGjUvSlQgh2oOv+yLV2Puc/ffJyMiga9euFBYWEgwG/dHIQggBTfteVc80TXbu3MmOHTv8473++usMGTKEE044IQlXIdoiGYEjDmrFihX069fPL54FXkCxLIuKigpuv/125syZw9q1a4ED536XlJRQW1vL/fffz0cffcQDDzzAySefLHPEhRAHONx44zgOpmlSXV3NzJkzefzxxxk+fDh5eXnMmTOHm2++meuuu07ijRDiazXlPqf+yfpjjz3GPffcw7Jly+jWrVurXIcQou073HhTH2PWrl3L9OnT+fTTT7nsssvYunUrs2fP5vrrr+fGG29srcsRrcy89dZb72rtToi247XXXuN73/ses2fP5vHHH6e8vJwhQ4aQmprq37iEw2F69uzJ3//+d2pqapg0aVKDYYJLlizhscce47bbbsM0TZ566inGjBkDyEpUQoi9mhpvgsEgJ510EkOGDCEzMxOtNQ899BDf/va3AYk3QogDNcd9Tv12CxYsoFu3bkydOlWmUAkhDnCk8ca2bUzTJCcnh/Hjx7Njxw62bduG67o88cQTTJs2rZWvTLQmGYEjfMuXL+fKK6/k0ksv5cQTT2ThwoXMmDGDc845h+nTp5OZmelni6PRKL///e+59957WbduHZmZmUSjUcLhMOXl5cydO5ecnBwmTZrU2pclhGiDmhpvYrEYSin50iSEaLTmiDuWZfkrxdSPBhRCiP01x/cqy7L8FX+j0SiRSKS1L0u0ATICR/jDgt966y3ef/99HnroIfr06cPYsWMJh8PMmTOHiooKJk6c6D99siyL/Px83n//fZYvX86QIUO49oYqaI4AAAi9SURBVNpr6d27N4MGDWLYsGH07du3la9MCNHWNGe8KSgokKXChRCH1Fxx55prrqFv375+3JGVhIQQ+2vO+5z6eKOUIhAItPKVibZCPnmEP4Rv8+bN9OvXr8G0g4svvpijjz6af/3rXw3qTwAUFBRw4YUX8tJLLzFhwgSUUhxzzDHJvwAhRLvRXPEGkHgjhGgUuc8RQiSLxBvR0iSB0wnNnTuXm266iQcffJD58+f77ePGjePjjz+muLgY8Apopaam+vMs586dC3gV0Wtra/njH//InXfeycSJE5k3bx7/+Mc/CIfDyb8gIUSbJfFGCJFsEneEEMki8UYkmyRwOpEdO3Zw/vnnc+WVV1JVVcVLL73Eeeedx9y5c9Fac+qpp9K3b18efPDBBvudeuqpGIbBhg0b/LaSkhIWLFjAH/7wB2bPns2wYcOSfTlCiDZM4o0QItkk7gghkkXijWgtUsS4k6ipqeHGG28kFovx85//nIKCAgC+853vkJOTw9NPP43rurzwwgtcffXVvPHGG0ycONHf//LLL6e4uJg33nijla5ACNFeSLwRQiSbxB0hRLJIvBGtSUbgdBIpKSkEg0EuuOACCgoKiMfjAEydOpV169b5y2OeffbZnHHGGdxwww188MEHaK0pLi5mw4YNfP/732/lqxBCtAcSb4QQySZxRwiRLBJvRGuSETidSCKR8CuY11dIv/baa0kkEjz66KN+WzQa5dxzz2Xt2rWMGjWKzz//nF69evHkk0/Sq1evVr4KIUR7IPFGCJFsEneEEMki8Ua0Fqu1OyCSZ9/l5/atkH722Wf77Y7jEA6H+fOf/8xnn33GJ/+/vbsLabqNwzh++ezBtsreiBmFc44OIkpGYYSzMjuooBcQK0vQsIMgkaQQ5tlOwlIoKiYNio6G1FFWR1may4qFSFAHRm+IRFA5lxuiTdtzEI2keHr/b2zfD3gg3t7+/h5cyMX9v+3v1759+2iJAfwU8gaA0cgdAEYhb5AsFDgZbHBwUAMDA1q5cqWkT+ETi8VkMplktVpltVq1cePGJE8JIB2QNwCMRu4AMAp5A6NwB04Gisc/vTUXDAZlsVhUVFQkSWppadGBAwem3YoOAL+DvAFgNHIHgFHIGxiNEzgZ6PMxv76+Pu3YsUNdXV1qaGjQxMSE2tra5HA4kjwhgHRB3gAwGrkDwCjkDYzGJcYZanx8XMXFxXr58qWys7PV1NSkhoaGZI8FIA2RNwCMRu4AMAp5AyNxAidDmc1m2Ww2lZWV6dixY5oxY0ayRwKQpsgbAEYjdwAYhbyBkTiBk8GmpqZkMpmSPQaADEDeADAauQPAKOQNjMIlxhmMkAFgFPIGgNHIHQBGIW9gFAocAAAAAACAFEeBAwAAAAAAkOIocAAAAAAAAFIcBQ4AAAAAAECKo8ABAAAAAABIcRQ4AAAAAAAAKY4CBwAApAW/36958+YlPnJzc7Vs2TKVl5fr3LlzikQiv7TvwMCAmpubNTg4+IcnBgAA+HH/JnsAAACAP8ntdqugoECxWExv3rxRb2+vmpqa5PV61d7erhUrVvzUfk+ePNGJEydUUlKi/Pz8vzQ1AADA/6PAAQAAaWXTpk0qKipKfH7kyBH19PSosrJSe/fu1YMHD2SxWJI4IQAAwM/jFSoAAJD2NmzYoMbGRg0NDeny5cuSpMePH+vQoUNyOp3Kzc2Vw+FQbW2thoaGEt/n9/tVU1MjSdq+fXvi9Sy/359Y09/fr127dslms2nRokXasmWLAoGAsQ8IAADSHgUOAADICHv27JEkdXV1SZK6u7v17NkzVVZWqqWlRdXV1bp586a2bdumsbExSZLL5dLBgwclSUePHpXP55PP55PL5ZIk9fb2auvWrRoZGVFjY6M8Ho8mJiZUXl6uO3fuJOEpAQBAusoKh8PxZA8BAADwu/x+v+rq6tTZ2TntFaov2Ww22e12BQIBjY2NaebMmdO+HgwGtXnzZvl8vkTh09HRoZqaGl27dk3r1q1LrI3H41qzZo0WL16sK1euKCsrS5L04cMHrV+/XnPmzNGNGzf+0tMCAIBMwwkcAACQMWbPnq1oNCpJ08qbaDSqUCikpUuXau7cuXr48OF393r06JGePn2qiooKhUIhDQ8Pa3h4WJFIRKWlperr60uc5AEAAPhdXGIMAAAyRjQa1cKFCyVJ4XBYHo9HHR0dGhkZmbZudHT0u3s9f/5cklRfX6/6+vpvrgmFQl+d8gEAAPgVFDgAACAjvHr1SqOjo3I4HJKk/fv3KxgMqq6uToWFhcrJyVFWVpZqa2v18ePH7+73eY3H45HT6fzmms9lEQAAwO+iwAEAABnh0qVLkqSysjKFw2Hdvn1bbrdbbrc7sWZ8fFzhcPiH9isoKJD06bWs0tLSPz4vAADAl7gDBwAApL2enh61trYqPz9fu3fv1j//fPoTKB6f/r8c2travjp9M2vWLEn6qthxOp1yOBzyer2KRCJf/cx37979yUcAAAAZjhM4AAAgrdy6dUsvXrzQ5OSk3r59q0AgoO7ubuXl5am9vV1ms1lms1klJSU6c+aMYrGY8vLydP/+fd27d08LFiyYtl9hYaFMJpNOnTql9+/fy2KxaPXq1bLb7Tp79qwqKiq0du1aVVVVacmSJXr9+rXu3r2reDyu69evJ+m3AAAA0g0FDgAASCvHjx+XJGVnZ2v+/Plavny5mpubVVVVpZycnMS68+fPy+126+LFi5qcnFRxcbGuXr2qnTt3TtvParXq9OnTOnnypA4fPqypqSl5vV7Z7Xa5XC51dnaqtbVVFy5cUCQSkdVq1apVq1RdXW3ocwMAgPSWFQ6H499fBgAAAAAAgGThDhwAAAAAAIAUR4EDAAAAAACQ4ihwAAAAAAAAUhwFDgAAAAAAQIqjwAEAAAAAAEhxFDgAAAAAAAApjgIHAAAAAAAgxVHgAAAAAAAApDgKHAAAAAAAgBRHgQMAAAAAAJDi/gOw4XyXQKEQogAAAABJRU5ErkJggg==\n",
2421 "text/plain": [
2422 "<Figure size 1152x576 with 3 Axes>"
2423 ]
2424 },
2425 "metadata": {},
2426 "output_type": "display_data"
2427 }
2428 ],
2429 "source": [
2430 "axes = pe.plot(subplots=True, figsize=(16,8), legend=False, lw=2)\n",
2431 "axes[0].set_title('Adj. Close Price')\n",
2432 "axes[1].set_title('Diluted Earnings per Share')\n",
2433 "axes[2].set_title('Trailing P/E Ratio')\n",
2434 "plt.tight_layout();"
2435 ]
2436 },
2437 {
2438 "cell_type": "markdown",
2439 "metadata": {},
2440 "source": [
2441 "## Explore Additional Fields"
2442 ]
2443 },
2444 {
2445 "cell_type": "markdown",
2446 "metadata": {},
2447 "source": [
2448 "The field `tag` references values defined in the taxonomy:"
2449 ]
2450 },
2451 {
2452 "cell_type": "code",
2453 "execution_count": 22,
2454 "metadata": {
2455 "ExecuteTime": {
2456 "end_time": "2018-12-25T19:50:46.192462Z",
2457 "start_time": "2018-12-25T19:50:46.186117Z"
2458 },
2459 "scrolled": false
2460 },
2461 "outputs": [
2462 {
2463 "data": {
2464 "text/plain": [
2465 "DebtInstrumentInterestRateEffectivePercentage 573\n",
2466 "CashAndCashEquivalentsAtCarryingValue 570\n",
2467 "SalesRevenueNet 544\n",
2468 "AvailableForSaleSecuritiesNoncurrent 532\n",
2469 "AvailableForSaleSecurities 532\n",
2470 "AvailableForSaleSecuritiesCurrent 532\n",
2471 "AvailableForSaleSecuritiesAmortizedCost 532\n",
2472 "AvailableForSaleSecuritiesAccumulatedGrossUnrealizedLossBeforeTax 476\n",
2473 "AvailableForSaleSecuritiesAccumulatedGrossUnrealizedGainBeforeTax 476\n",
2474 "OperatingIncomeLoss 447\n",
2475 "SeniorNotes 374\n",
2476 "DerivativeInstrumentsGainLossRecognizedInOtherComprehensiveIncomeEffectivePortionNet 306\n",
2477 "DebtInstrumentCarryingAmount 295\n",
2478 "DebtInstrumentInterestRateStatedPercentage 287\n",
2479 "StockRepurchasedAndRetiredDuringPeriodShares 255\n",
2480 "AllocatedShareBasedCompensationExpense 231\n",
2481 "DerivativeFairValueOfDerivativeAsset 204\n",
2482 "DerivativeInstrumentsGainLossReclassifiedFromAccumulatedOCIIntoIncomeEffectivePortionNet 201\n",
2483 "DerivativeFairValueOfDerivativeLiability 180\n",
2484 "StockRepurchasedAndRetiredDuringPeriodValue 175\n",
2485 "ConcentrationRiskPercentage1 172\n",
2486 "StockholdersEquity 168\n",
2487 "CommonStockDividendsPerShareDeclared 159\n",
2488 "PropertyPlantAndEquipmentGross 152\n",
2489 "DerivativeNotionalAmount 142\n",
2490 "NonoperatingIncomeExpense 134\n",
2491 "NetIncomeLoss 130\n",
2492 "PaymentsOfDividends 125\n",
2493 "OtherComprehensiveIncomeLossReclassificationAdjustmentFromAOCIOnDerivativesBeforeTax 120\n",
2494 "IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest 118\n",
2495 " ... \n",
2496 "UnrecognizedTaxBenefitsPeriodIncreaseDecrease 2\n",
2497 "RepaymentsOfAssumedDebt 2\n",
2498 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsExercisableWeightedAverageExercisePrice 1\n",
2499 "SharebasedCompensationArrangementBySharebasedPaymentAwardOptionsExercisableIntrinsicValue1 1\n",
2500 "ProceedsFromRepaymentsOfShortTermDebt 1\n",
2501 "StockIssuedDuringPeriodSharesStockOptionsExercised 1\n",
2502 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsExpectedToVestIntrinsicValueAtPeriodEnd 1\n",
2503 "LossContingencySubsidiariesImpactedNumber 1\n",
2504 "UnrecordedUnconditionalPurchaseObligationBalanceOnThirdAnniversary 1\n",
2505 "UnrecordedUnconditionalPurchaseObligationBalanceOnFirstAnniversary 1\n",
2506 "ShareBasedCompensationArrangementsByShareBasedPaymentAwardOptionsExercisesInPeriodWeightedAverageExercisePrice 1\n",
2507 "IncomeTaxReconciliationTaxSettlementsDomestic 1\n",
2508 "ShareBasedCompensationArrangementsByShareBasedPaymentAwardOptionsGrantsInPeriodWeightedAverageExercisePrice 1\n",
2509 "RestrictedInvestmentsIncreaseDecrease 1\n",
2510 "UnrecordedUnconditionalPurchaseObligationBalanceOnFourthAnniversary 1\n",
2511 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsExpectedToVestWeightedAverageExercisePrice 1\n",
2512 "SharebasedCompensationArrangementbySharebasedPaymentAwardOptionsNumberofSharesofCommonSharesAwardedUponSettlement 1\n",
2513 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsGrantsInPeriod 1\n",
2514 "TaxCutsAndJobsActOf2017MeasurementPeriodAdjustmentIncomeTaxExpenseBenefit 1\n",
2515 "PreferredStockSharesAuthorized 1\n",
2516 "SalesRevenueServicesGross 1\n",
2517 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsVestedAndExpectedToVestOutstandingNumber 1\n",
2518 "ResultOfLegalProceedingsAwardUpHeld 1\n",
2519 "UnrecordedUnconditionalPurchaseObligationBalanceOnFifthAnniversary 1\n",
2520 "UnrecordedUnconditionalPurchaseObligationDueAfterFiveYears 1\n",
2521 "UnrecordedUnconditionalPurchaseObligationBalanceOnSecondAnniversary 1\n",
2522 "ShareBasedCompensationArrangementByShareBasedPaymentAwardOptionsExercisableNumber 1\n",
2523 "ResultOfLegalProceedingsAdditionalAmountAwarded 1\n",
2524 "DeferredTaxLiabilityRelatedToAmountsThatMayBeRepatriated 1\n",
2525 "LossContingencyRangeOfPossibleLossMaximum 1\n",
2526 "Name: tag, Length: 427, dtype: int64"
2527 ]
2528 },
2529 "execution_count": 22,
2530 "metadata": {},
2531 "output_type": "execute_result"
2532 }
2533 ],
2534 "source": [
2535 "aapl_nums.tag.value_counts()"
2536 ]
2537 },
2538 {
2539 "cell_type": "markdown",
2540 "metadata": {},
2541 "source": [
2542 "We can select values of interest and track their value or use them as inputs to compute fundamental metrics like the Dividend/Share ratio."
2543 ]
2544 },
2545 {
2546 "cell_type": "markdown",
2547 "metadata": {},
2548 "source": [
2549 "### Dividends per Share"
2550 ]
2551 },
2552 {
2553 "cell_type": "code",
2554 "execution_count": 23,
2555 "metadata": {
2556 "ExecuteTime": {
2557 "end_time": "2018-12-25T19:50:46.202614Z",
2558 "start_time": "2018-12-25T19:50:46.193478Z"
2559 },
2560 "scrolled": true
2561 },
2562 "outputs": [],
2563 "source": [
2564 "fields = ['EarningsPerShareDiluted',\n",
2565 " 'PaymentsOfDividendsCommonStock',\n",
2566 " 'WeightedAverageNumberOfDilutedSharesOutstanding',\n",
2567 " 'OperatingIncomeLoss',\n",
2568 " 'NetIncomeLoss',\n",
2569 " 'GrossProfit']"
2570 ]
2571 },
2572 {
2573 "cell_type": "code",
2574 "execution_count": 24,
2575 "metadata": {
2576 "ExecuteTime": {
2577 "end_time": "2018-12-25T19:50:46.425534Z",
2578 "start_time": "2018-12-25T19:50:46.203780Z"
2579 }
2580 },
2581 "outputs": [
2582 {
2583 "data": {
2584 "image/png": 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\n",
2585 "text/plain": [
2586 "<Figure size 1008x360 with 1 Axes>"
2587 ]
2588 },
2589 "metadata": {},
2590 "output_type": "display_data"
2591 }
2592 ],
2593 "source": [
2594 "dividends = (aapl_nums\n",
2595 " .loc[aapl_nums.tag == 'PaymentsOfDividendsCommonStock', ['ddate', 'value']]\n",
2596 " .groupby('ddate')\n",
2597 " .mean())\n",
2598 "shares = (aapl_nums\n",
2599 " .loc[aapl_nums.tag == 'WeightedAverageNumberOfDilutedSharesOutstanding', ['ddate', 'value']]\n",
2600 " .drop_duplicates()\n",
2601 " .groupby('ddate')\n",
2602 " .mean())\n",
2603 "df = dividends.div(shares).dropna()\n",
2604 "ax = df.plot.bar(figsize=(14, 5), title='Dividends per Share', legend=False)\n",
2605 "ax.xaxis.set_major_formatter(mticker.FixedFormatter(df.index.strftime('%Y-%m')))"
2606 ]
2607 },
2608 {
2609 "cell_type": "markdown",
2610 "metadata": {},
2611 "source": [
2612 "## Bonus: Textual Information"
2613 ]
2614 },
2615 {
2616 "cell_type": "code",
2617 "execution_count": 15,
2618 "metadata": {
2619 "ExecuteTime": {
2620 "end_time": "2018-12-25T19:50:47.288488Z",
2621 "start_time": "2018-12-25T19:50:46.426710Z"
2622 },
2623 "scrolled": true
2624 },
2625 "outputs": [],
2626 "source": [
2627 "txt = pd.read_parquet(data_path / '2016_2' / 'parquet' / 'txt.parquet')"
2628 ]
2629 },
2630 {
2631 "cell_type": "markdown",
2632 "metadata": {},
2633 "source": [
2634 "AAPL's adsh is not avaialble in the txt file but you can obtain notes from the financial statesments here:"
2635 ]
2636 },
2637 {
2638 "cell_type": "code",
2639 "execution_count": 17,
2640 "metadata": {},
2641 "outputs": [
2642 {
2643 "data": {
2644 "text/html": [
2645 "<div>\n",
2646 "<style scoped>\n",
2647 " .dataframe tbody tr th:only-of-type {\n",
2648 " vertical-align: middle;\n",
2649 " }\n",
2650 "\n",
2651 " .dataframe tbody tr th {\n",
2652 " vertical-align: top;\n",
2653 " }\n",
2654 "\n",
2655 " .dataframe thead th {\n",
2656 " text-align: right;\n",
2657 " }\n",
2658 "</style>\n",
2659 "<table border=\"1\" class=\"dataframe\">\n",
2660 " <thead>\n",
2661 " <tr style=\"text-align: right;\">\n",
2662 " <th></th>\n",
2663 " <th>adsh</th>\n",
2664 " <th>tag</th>\n",
2665 " <th>version</th>\n",
2666 " <th>ddate</th>\n",
2667 " <th>qtrs</th>\n",
2668 " <th>iprx</th>\n",
2669 " <th>lang</th>\n",
2670 " <th>dcml</th>\n",
2671 " <th>durp</th>\n",
2672 " <th>datp</th>\n",
2673 " <th>dimh</th>\n",
2674 " <th>dimn</th>\n",
2675 " <th>coreg</th>\n",
2676 " <th>escaped</th>\n",
2677 " <th>srclen</th>\n",
2678 " <th>txtlen</th>\n",
2679 " <th>footnote</th>\n",
2680 " <th>footlen</th>\n",
2681 " <th>context</th>\n",
2682 " <th>value</th>\n",
2683 " </tr>\n",
2684 " </thead>\n",
2685 " <tbody>\n",
2686 " <tr>\n",
2687 " <th>0</th>\n",
2688 " <td>0000014693-16-000160</td>\n",
2689 " <td>AdvertisingCostsPolicyTextBlock</td>\n",
2690 " <td>us-gaap/2015</td>\n",
2691 " <td>20160430</td>\n",
2692 " <td>4</td>\n",
2693 " <td>0</td>\n",
2694 " <td>en-US</td>\n",
2695 " <td>32767</td>\n",
2696 " <td>0.0</td>\n",
2697 " <td>0.0</td>\n",
2698 " <td>0x00000000</td>\n",
2699 " <td>0</td>\n",
2700 " <td>None</td>\n",
2701 " <td>1</td>\n",
2702 " <td>425</td>\n",
2703 " <td>112</td>\n",
2704 " <td>None</td>\n",
2705 " <td>0</td>\n",
2706 " <td>FD2016Q4YTD</td>\n",
2707 " <td>Advertising costs. We expense the costs of adv...</td>\n",
2708 " </tr>\n",
2709 " <tr>\n",
2710 " <th>1</th>\n",
2711 " <td>0000014693-16-000160</td>\n",
2712 " <td>AmendmentFlag</td>\n",
2713 " <td>dei/2014</td>\n",
2714 " <td>20160430</td>\n",
2715 " <td>4</td>\n",
2716 " <td>0</td>\n",
2717 " <td>en-US</td>\n",
2718 " <td>32767</td>\n",
2719 " <td>0.0</td>\n",
2720 " <td>0.0</td>\n",
2721 " <td>0x00000000</td>\n",
2722 " <td>0</td>\n",
2723 " <td>None</td>\n",
2724 " <td>0</td>\n",
2725 " <td>5</td>\n",
2726 " <td>5</td>\n",
2727 " <td>None</td>\n",
2728 " <td>0</td>\n",
2729 " <td>FD2016Q4YTD</td>\n",
2730 " <td>false</td>\n",
2731 " </tr>\n",
2732 " <tr>\n",
2733 " <th>2</th>\n",
2734 " <td>0000014693-16-000160</td>\n",
2735 " <td>ComprehensiveIncomeNoteTextBlock</td>\n",
2736 " <td>us-gaap/2015</td>\n",
2737 " <td>20160430</td>\n",
2738 " <td>4</td>\n",
2739 " <td>0</td>\n",
2740 " <td>en-US</td>\n",
2741 " <td>32767</td>\n",
2742 " <td>0.0</td>\n",
2743 " <td>0.0</td>\n",
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2748 " <td>82857</td>\n",
2749 " <td>2106</td>\n",
2750 " <td>None</td>\n",
2751 " <td>0</td>\n",
2752 " <td>FD2016Q4YTD</td>\n",
2753 " <td>ACCUMULATED OTHER COMPREHENSIVE INCOME The fol...</td>\n",
2754 " </tr>\n",
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2771 " <td>23</td>\n",
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2774 " <td>0</td>\n",
2775 " <td>FD2016Q4YTD</td>\n",
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2781 " <td>ScheduleOfComprehensiveIncomeLossTableTextBlock</td>\n",
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2783 " <td>20160430</td>\n",
2784 " <td>4</td>\n",
2785 " <td>0</td>\n",
2786 " <td>en-US</td>\n",
2787 " <td>32767</td>\n",
2788 " <td>0.0</td>\n",
2789 " <td>0.0</td>\n",
2790 " <td>0x00000000</td>\n",
2791 " <td>0</td>\n",
2792 " <td>None</td>\n",
2793 " <td>1</td>\n",
2794 " <td>67007</td>\n",
2795 " <td>1686</td>\n",
2796 " <td>None</td>\n",
2797 " <td>0</td>\n",
2798 " <td>FD2016Q4YTD</td>\n",
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2816 "2 us-gaap/2015 20160430 4 0 en-US 32767 0.0 0.0 0x00000000 \n",
2817 "3 dei/2014 20160430 4 0 en-US 32767 0.0 0.0 0x00000000 \n",
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2829 "1 false \n",
2830 "2 ACCUMULATED OTHER COMPREHENSIVE INCOME The fol... \n",
2831 "3 Large Accelerated Filer \n",
2832 "4 The following table presents the components of... "
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