File: evals.html

package info (click to toggle)
r-cran-pander 0.6.3%2Bdfsg-2
  • links: PTS, VCS
  • area: main
  • in suites: bullseye
  • size: 1,804 kB
  • sloc: javascript: 301; cpp: 145; lisp: 94; makefile: 21
file content (542 lines) | stat: -rw-r--r-- 165,768 bytes parent folder | download 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
 
<!DOCTYPE html>

<html xmlns="http://www.w3.org/1999/xhtml">

<head>

<meta charset="utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />

<meta name="viewport" content="width=device-width, initial-scale=1">

<meta name="author" content="Roman Tsegelskyi, Gergely Daróczi" />

<meta name="date" content="2018-11-06" />

<title>Capturing evaluation information with evals</title>



<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
a.sourceLine { display: inline-block; line-height: 1.25; }
a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
a.sourceLine:empty { height: 1.2em; position: absolute; }
.sourceCode { overflow: visible; }
code.sourceCode { white-space: pre; position: relative; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
code.sourceCode { white-space: pre-wrap; }
a.sourceLine { text-indent: -1em; padding-left: 1em; }
}
pre.numberSource a.sourceLine
  { position: relative; }
pre.numberSource a.sourceLine:empty
  { position: absolute; }
pre.numberSource a.sourceLine::before
  { content: attr(data-line-number);
    position: absolute; left: -5em; text-align: right; vertical-align: baseline;
    border: none; pointer-events: all;
    -webkit-touch-callout: none; -webkit-user-select: none;
    -khtml-user-select: none; -moz-user-select: none;
    -ms-user-select: none; user-select: none;
    padding: 0 4px; width: 4em;
    color: #aaaaaa;
  }
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
div.sourceCode
  {  }
@media screen {
a.sourceLine::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code span.at { color: #7d9029; } /* Attribute */
code span.bn { color: #40a070; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code span.ch { color: #4070a0; } /* Char */
code span.cn { color: #880000; } /* Constant */
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
code span.dt { color: #902000; } /* DataType */
code span.dv { color: #40a070; } /* DecVal */
code span.er { color: #ff0000; font-weight: bold; } /* Error */
code span.ex { } /* Extension */
code span.fl { color: #40a070; } /* Float */
code span.fu { color: #06287e; } /* Function */
code span.im { } /* Import */
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
code span.op { color: #666666; } /* Operator */
code span.ot { color: #007020; } /* Other */
code span.pp { color: #bc7a00; } /* Preprocessor */
code span.sc { color: #4070a0; } /* SpecialChar */
code span.ss { color: #bb6688; } /* SpecialString */
code span.st { color: #4070a0; } /* String */
code span.va { color: #19177c; } /* Variable */
code span.vs { color: #4070a0; } /* VerbatimString */
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
</style>



<style type="text/css">body {
background-color: #fff;
margin: 1em auto;
max-width: 700px;
overflow: visible;
padding-left: 2em;
padding-right: 2em;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 14px;
line-height: 1.35;
}
#header {
text-align: center;
}
#TOC {
clear: both;
margin: 0 0 10px 10px;
padding: 4px;
width: 400px;
border: 1px solid #CCCCCC;
border-radius: 5px;
background-color: #f6f6f6;
font-size: 13px;
line-height: 1.3;
}
#TOC .toctitle {
font-weight: bold;
font-size: 15px;
margin-left: 5px;
}
#TOC ul {
padding-left: 40px;
margin-left: -1.5em;
margin-top: 5px;
margin-bottom: 5px;
}
#TOC ul ul {
margin-left: -2em;
}
#TOC li {
line-height: 16px;
}
table {
margin: 1em auto;
border-width: 1px;
border-color: #DDDDDD;
border-style: outset;
border-collapse: collapse;
}
table th {
border-width: 2px;
padding: 5px;
border-style: inset;
}
table td {
border-width: 1px;
border-style: inset;
line-height: 18px;
padding: 5px 5px;
}
table, table th, table td {
border-left-style: none;
border-right-style: none;
}
table thead, table tr.even {
background-color: #f7f7f7;
}
p {
margin: 0.5em 0;
}
blockquote {
background-color: #f6f6f6;
padding: 0.25em 0.75em;
}
hr {
border-style: solid;
border: none;
border-top: 1px solid #777;
margin: 28px 0;
}
dl {
margin-left: 0;
}
dl dd {
margin-bottom: 13px;
margin-left: 13px;
}
dl dt {
font-weight: bold;
}
ul {
margin-top: 0;
}
ul li {
list-style: circle outside;
}
ul ul {
margin-bottom: 0;
}
pre, code {
background-color: #f7f7f7;
border-radius: 3px;
color: #333;
white-space: pre-wrap; 
}
pre {
border-radius: 3px;
margin: 5px 0px 10px 0px;
padding: 10px;
}
pre:not([class]) {
background-color: #f7f7f7;
}
code {
font-family: Consolas, Monaco, 'Courier New', monospace;
font-size: 85%;
}
p > code, li > code {
padding: 2px 0px;
}
div.figure {
text-align: center;
}
img {
background-color: #FFFFFF;
padding: 2px;
border: 1px solid #DDDDDD;
border-radius: 3px;
border: 1px solid #CCCCCC;
margin: 0 5px;
}
h1 {
margin-top: 0;
font-size: 35px;
line-height: 40px;
}
h2 {
border-bottom: 4px solid #f7f7f7;
padding-top: 10px;
padding-bottom: 2px;
font-size: 145%;
}
h3 {
border-bottom: 2px solid #f7f7f7;
padding-top: 10px;
font-size: 120%;
}
h4 {
border-bottom: 1px solid #f7f7f7;
margin-left: 8px;
font-size: 105%;
}
h5, h6 {
border-bottom: 1px solid #ccc;
font-size: 105%;
}
a {
color: #0033dd;
text-decoration: none;
}
a:hover {
color: #6666ff; }
a:visited {
color: #800080; }
a:visited:hover {
color: #BB00BB; }
a[href^="http:"] {
text-decoration: underline; }
a[href^="https:"] {
text-decoration: underline; }

code > span.kw { color: #555; font-weight: bold; } 
code > span.dt { color: #902000; } 
code > span.dv { color: #40a070; } 
code > span.bn { color: #d14; } 
code > span.fl { color: #d14; } 
code > span.ch { color: #d14; } 
code > span.st { color: #d14; } 
code > span.co { color: #888888; font-style: italic; } 
code > span.ot { color: #007020; } 
code > span.al { color: #ff0000; font-weight: bold; } 
code > span.fu { color: #900; font-weight: bold; }  code > span.er { color: #a61717; background-color: #e3d2d2; } 
</style>

</head>

<body>




<h1 class="title toc-ignore">Capturing evaluation information with evals</h1>
<h4 class="author"><em>Roman Tsegelskyi, Gergely Daróczi</em></h4>
<h4 class="date"><em>2018-11-06</em></h4>



<p><code>evals</code> is aimed at collecting as much information as possible while evaluating R code. It can evaluate a character vector of R expressions, and it returns a list of information captured while running them:</p>
<ul>
<li><code>src</code> holds the R expression,</li>
<li><code>result</code> contains the raw R object as-is,</li>
<li><code>output</code> represents how the R object is printed to the standard output,</li>
<li><code>type</code> is the class of the returned R object,</li>
<li><code>msg</code> is a list of messages captured while evaluating the R expression. Among other messages, warnings/errors will appear here.</li>
<li><code>stdout</code> contains what, if anything, was written to the standard output.</li>
</ul>
<p>Besides capturing evaluation information, <code>evals</code> is able to automatically identify whether an R expression is returning anything to a graphical device, and can save the resulting image in a variety of file formats.</p>
<p>Another interesting <code>evals</code> feature is caching the results of evaluated expressions. Read the <a href="#result-caching">caching</a> section for more details.</p>
<p><code>evals</code> has a large number of options, which allow users to customize the call exactly as needed. Here we will focus on the most useful features, but the full list of options, with explanations, can be viewed by calling <code>?evalsOptions</code>. Also <code>evals</code> support permanent options that will persist for all calls to <code>evals</code>, this can be achieved by calling <code>evalsOptions</code>.</p>
<p>Let’s start with a basic example by evaluating <code>1:10</code> and collecting all information about it:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'1:10'</span>)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="co">#&gt; [[1]]</span></a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="co">#&gt; $src</span></a>
<a class="sourceLine" id="cb1-4" data-line-number="4"><span class="co">#&gt; [1] &quot;1:10&quot;</span></a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co">#&gt; $result</span></a>
<a class="sourceLine" id="cb1-7" data-line-number="7"><span class="co">#&gt;  [1]  1  2  3  4  5  6  7  8  9 10</span></a>
<a class="sourceLine" id="cb1-8" data-line-number="8"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-9" data-line-number="9"><span class="co">#&gt; $output</span></a>
<a class="sourceLine" id="cb1-10" data-line-number="10"><span class="co">#&gt; [1] &quot; [1]  1  2  3  4  5  6  7  8  9 10&quot;</span></a>
<a class="sourceLine" id="cb1-11" data-line-number="11"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-12" data-line-number="12"><span class="co">#&gt; $type</span></a>
<a class="sourceLine" id="cb1-13" data-line-number="13"><span class="co">#&gt; [1] &quot;integer&quot;</span></a>
<a class="sourceLine" id="cb1-14" data-line-number="14"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-15" data-line-number="15"><span class="co">#&gt; $msg</span></a>
<a class="sourceLine" id="cb1-16" data-line-number="16"><span class="co">#&gt; $msg$messages</span></a>
<a class="sourceLine" id="cb1-17" data-line-number="17"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb1-18" data-line-number="18"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-19" data-line-number="19"><span class="co">#&gt; $msg$warnings</span></a>
<a class="sourceLine" id="cb1-20" data-line-number="20"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb1-21" data-line-number="21"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-22" data-line-number="22"><span class="co">#&gt; $msg$errors</span></a>
<a class="sourceLine" id="cb1-23" data-line-number="23"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb1-24" data-line-number="24"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-25" data-line-number="25"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-26" data-line-number="26"><span class="co">#&gt; $stdout</span></a>
<a class="sourceLine" id="cb1-27" data-line-number="27"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb1-28" data-line-number="28"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-29" data-line-number="29"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb1-30" data-line-number="30"><span class="co">#&gt; [1] &quot;evals&quot;</span></a></code></pre></div>
<p>Not all the information might be useful, so <code>evals</code> makes it is possible to capture only some of the information, by specifying the <code>output</code> parameter:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'1:10'</span>, <span class="dt">output =</span> <span class="kw">c</span>(<span class="st">'result'</span>, <span class="st">'output'</span>))</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="co">#&gt; [[1]]</span></a>
<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="co">#&gt; $result</span></a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co">#&gt;  [1]  1  2  3  4  5  6  7  8  9 10</span></a>
<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="co">#&gt; $output</span></a>
<a class="sourceLine" id="cb2-7" data-line-number="7"><span class="co">#&gt; [1] &quot; [1]  1  2  3  4  5  6  7  8  9 10&quot;</span></a>
<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="co">#&gt; [1] &quot;evals&quot;</span></a></code></pre></div>
<p>One of the neat features of <code>evals</code> that it catches errors/warnings without interrupting the evaluation and saves them.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'x'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>msg</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co">#&gt; $messages</span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co">#&gt; $warnings</span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co">#&gt; $errors</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co">#&gt; [1] &quot;object 'x' not found&quot;</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="kw">evals</span>(<span class="st">'as.numeric(&quot;1.1a&quot;)'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>msg</a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co">#&gt; $messages</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co">#&gt; $warnings</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co">#&gt; [1] &quot;NAs introduced by coercion&quot;</span></a>
<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co">#&gt; $errors</span></a>
<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co">#&gt; NULL</span></a></code></pre></div>
<div id="graphs-and-graphical-options" class="section level2">
<h2>Graphs and Graphical Options</h2>
<p>As mentioned before, <code>evals</code> captures the output to graphical devices and saves it:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'plot(mtcars)'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>result</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co">#&gt; [1] &quot;my_plots/test.jpeg&quot;</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co">#&gt; [1] &quot;image&quot;</span></a></code></pre></div>
<p><img src="data:image/jpeg;base64,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" /></p>
<p>You can specify the output directory using the <code>graph.dir</code> parameter, and the output type using the <code>graph.output</code> parameter. Currently, it could be any of <code>grDevices</code>: <code>png</code>, <code>bmp</code>,<code>jpeg</code>,<code>jpg</code>, <code>tiff</code>, <code>svg</code>, or <code>pdf</code>.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'plot(mtcars)'</span>, <span class="dt">graph.dir =</span> <span class="st">'my_plots'</span>, <span class="dt">graph.output =</span> <span class="st">'jpg'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>result</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">#&gt; [1] &quot;my_plots/test.jpeg&quot;</span></a>
<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co">#&gt; [1] &quot;image&quot;</span></a></code></pre></div>
<p>Moreover, <code>evals</code> provides facilities to:</p>
<ul>
<li>save the environments in which plots were generated</li>
<li>save the plot via <code>recordPlot</code> to distinct files with <code>recodplot</code> extension</li>
<li>save the raw R object returned (usually with <code>lattice</code> or <code>ggplot2</code>) while generating the plot to distinct files with <code>RDS</code> extension</li>
</ul>
<div id="style-unification" class="section level3">
<h3>Style unification</h3>
<p><code>evals</code> provides very powerful facilities to unify the styling of images produced by different packages, like <code>ggplot2</code> and <code>lattice</code>.</p>
<p>Let’s prepare the data for plotting:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">## generating dataset</a>
<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="kw">set.seed</span>(<span class="dv">1</span>)</a>
<a class="sourceLine" id="cb6-3" data-line-number="3">df &lt;-<span class="st"> </span>mtcars[, <span class="kw">c</span>(<span class="st">'hp'</span>, <span class="st">'wt'</span>)]</a>
<a class="sourceLine" id="cb6-4" data-line-number="4">df<span class="op">$</span>factor &lt;-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">'Foo'</span>, <span class="st">'Bar'</span>, <span class="st">'Foo bar'</span>), <span class="dt">size =</span> <span class="kw">nrow</span>(df), <span class="dt">replace =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb6-5" data-line-number="5">df<span class="op">$</span>factor2 &lt;-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">'Foo'</span>, <span class="st">'Bar'</span>, <span class="st">'Foo bar'</span>), <span class="dt">size =</span> <span class="kw">nrow</span>(df), <span class="dt">replace =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb6-6" data-line-number="6">df<span class="op">$</span>time &lt;-<span class="st"> </span><span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(df)</a></code></pre></div>
<p>Now let’s plot the histograms:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">evalsOptions</span>(<span class="st">'graph.unify'</span>, <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="kw">evals</span>(<span class="st">'histogram(df$hp, main = &quot;Histogram with lattice&quot;)'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>result</a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="co">#&gt; [1] &quot;my_plots/test.jpeg&quot;</span></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co">#&gt; [1] &quot;image&quot;</span></a>
<a class="sourceLine" id="cb7-6" data-line-number="6"><span class="kw">evals</span>(<span class="st">'ggplot(df) + geom_histogram(aes(x = hp), binwidth = 50) + ggtitle(&quot;Histogram with ggplot2&quot;)'</span>)[[<span class="dv">1</span>]]<span class="op">$</span>result</a>
<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="co">#&gt; [1] &quot;my_plots/test.jpeg&quot;</span></a>
<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co">#&gt; attr(,&quot;class&quot;)</span></a>
<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="co">#&gt; [1] &quot;image&quot;</span></a>
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="kw">evalsOptions</span>(<span class="st">'graph.unify'</span>, <span class="ot">FALSE</span>)</a></code></pre></div>
<p><img src="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAHgAeADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDH1HxJZ6Z4j0XQ5o52utX8/7O6KCi+UgdtxJyMg8YB/CqjeMbZtT1/TbXTdRvLzRPs/nxQIhMvnLuXy9zjOBknOOnGa5L4kaV/bfxH8A6d9vvrDzv7R/wBJsJvKmTEKN8rYOM4wfYmuA8S6V/Ymj/FvTvt99f8Ak/2P/pN/N5sz5YN8zYGcZwPYCgD6PrP/ALb07/hIf7A+0f8AEz+yfbfI2N/qd+zduxt+9xjOfavCviwLKLx3qMuovdySm2iNhFMJ48MF5+yyRFl3E9fMTAb2roNMsdNg+O2g317bXUN9f+HEmia6Z2ka7ClG3kYXeIVYHgLkZxkigD2Wivmb/R/L/wCYv/wtX+1P+muMed/3x5Wz/O2tn4nGw/4TLxSPEX9oib+yV/4R/wAjzNn+rbzc7eMbs7t3G3d7UAfQFFeIeGNHtNY+IXg0XgkZbTwTY3capIyAyJMpXdg8gHBweMgVyWlXtqnivQtR03+0YNVk8QxW2oPdPM8/lvIRsmfCxfMBwqqTjvxyAfTlFfN1xoltcalcagZbqO6uPiJLpjvFcOmIJf8AWAAHAJ6EjnAFenfCGEWWleJdMiZzaaf4hvLW1R3LeXEuzCgnnufzoA9DqpcXrwahZ2q2N1Mtzv3XEYXy4NoyN5LAjd0GAeeuK+eb23u9H1/U7CNJFt/B+oyazEq9Gillt2VPwjMxrV0bQ47nUfhymqQ7zrw1m7vEyV3i4gHGRgjKFRkc0AeqSeP9C/sfRNWtZJ7yx1nUE0+1lhiK/vGZlywfaQoKMCcZ9Aa6ivmrRrHT7n4R+DrCAzQXMvjGKHUChdHSY7xkE9CIzF93gH3zXpXw402Dw/488daBp5lTTLOSykt4HlZxGZImZ8FiTycfkKAOz07xJZ6n4j1rQ4Y51utI8j7Q7qAjeahddpBycAc5A/GtC/vrfTNOub+8k8u1tYnmmfaTtRQSxwOTgA9K8d1yc22tfF+bZfOFg0zK2EgjmIMWDhirYGCcnB+XNcjpVpp16fFOk6XHI+jXHhWW+jt4/tCxyXMUilJF807mIYYyAASCMdcgH0AninRpJ9EhS8zJrcTTaePKf98ioJCeny/KQfmx+dXbG9e9+077G6tfInaEfaAo80Lj94m1j8hzxnB46CvBNO07wwl78HNTSBhbSRyW8suZcfakIZFPp/pLyH0Of7o4tXLXI8HeL9huRp58dTjVDa58wWeU8zGOf7vTt7UAe+UV4lALIfDLxl/wrka8bY+WLfzt3ldvN+zZ/efczndznGKk+DlvFF4p1CTRLieXQ2sE83FnNBD9q3D/AJ6yuS4G7OOOfpkA9pooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4vxH8MdF8S6tPqM95qlpJdRrHeR2V15cd0q8KJFwc4HHau0ooAhtLWCxs4LS2jEdvBGsUSDoqqMAfkKmoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoopr7io2nByPyzzQA6iiigAooo5z7UAFFFFABRRTRu3tk/LxgUAOooooAKKKBnvQAUUUUAFFNk3GNtpw2Dg+9OoAKKKKACijniigAooooAKKb83mdflx096dQAUUUUAFFHPNFABRRRQAUU2PcI13HLYGT706gAoooOe1ABRRRQAUUU07ty4PHegB1FFFABRRRzn2oAKKKKACiimpuCnccnJ/LPFADqKKKACiiigAooooAKKKKACiiigAooooAKZJtKjccDcv55GKfTZDhR8u75hx+PWgB1FFFABSfxD1paTv0oAWiiigApi7fNfB+bAzT6aD+8YbcdOfWgB1FFFABSDpxS0g6dMUALRRRQAyXaYXDHC7Tkj0p9NkOInO3d8p+X19qdQAUUUUAIeopaQ9RxS0AFFFFADPl84HPzbentT6bn97jb/D97+lOoAKKKKAEHU0tIOp4paACiiigBkW0QoFOV2jBPpT6bGcxIdu35R8vp7U6gApD05paQ9OmaAFooooAKYwXzEyeecflT6ax+dPlz159KAHUUUUAFJ/EfWlpO/SgBaKKKACmR7Qp2nI3N+eTmn02M5U/Lt+Y8fj1oAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTX3FRtODkflnmnUyTaVG44G5fzyMUAPooooAKOc+1FJ/EPWgBaKKKAPPNX1nUoNYu4oryVEWUhVDcAVS/t7Vf+f8Am/76pNc/5Dt7/wBdm/nWfXv06cORaLbsfM1atRTl7z3fV9zR/t7Vf+f+b/vqj+3tV/5/5v8Avqs6ir9lD+VfcR7Wp/M/vZo/29qv/P8Azf8AfVH9var/AM/83/fVZ1FHsofyr7g9rU/mf3s0f7e1X/n/AJv++q0dB1fULnW7WGa8leNmOVJ4PBrna1fDX/Iw2f8AvH/0E1nVpwVOWi2ZpRq1HUinJ7rqelSbjG204bBwfenUyXaYXDHC7Tkj0p9eCfShRRRQAc8UUh6iloAKKKKAG/N5nX5cdPenUz5fOBz823p7U+gAooooAOeaKQdTS0AFFFFADY9wjXcctgZPvTqZFtEKBTldowT6U+gAoOe1FIenNAC0UUUAFNO7cuDx3p1MYL5iZPPOPyoAfRRRQAUc59qKT+I+tAC0UUUAFNTcFO45OT+WeKdTI9oU7Tkbm/PJzQA+iiigAooooAKKKKACiiigAooooAKKKKACmyHCj5d3zDj8etOpr7io2nByPyzzQA6iiigApO/Slo5z7UAFFFFAHl2uf8h29/67N/Os+tDXP+Q7e/8AXZv51n19FT+BeiPlav8AEl6v8woooqyAooooAK1fDX/Iw2f+8f8A0E1lVq+Gv+Rhs/8AeP8A6Cazrfw5ejNaH8WPqj0qQ4ic7d3yn5fX2p1Nk3GNtpw2Dg+9Or54+oCiiigBD1HFLRzxRQAUUUUANz+9xt/h+9/SnU35vM6/Ljp706gAooooAQdTxS0c80UAFFFFADYzmJDt2/KPl9PanU2PcI13HLYGT706gApD06ZpaDntQAUUUUAFNY/Ony568+lOpp3blweO9ADqKKKACk79KWjnPtQAUUUUAFNjOVPy7fmPH49adTU3BTuOTk/lnigB1FFFABRRRQAUUUUAFFZHisqvg7XC101oo0+cm5QEmEeW3zgDkkdeOeK808LTCTxno0X/AAhNj4Ye2nkgnntCGaaU2zOLd9qL/ARLklh8ijOaAPYqKY7upGyPf/wIChGkIO6Paf8AezQA+imbn2Z8vn0zSlm3AbOD1OelADqZJtKjccDcv55GKUM2W+TgdOetMdnEYPlbjuHy59+tAEtFNLNuA2cHqc9KAzZb5OB0560AOpP4h603c+zPl8+maCzb1Gzgjk56UAPopoZst8nA6c9aTc+zPl8+maAPMdc/5Dt7/wBdm/nWfWhrn/Icvf8Arq1Z9fRU/gXoj5Wr/El6v8woooqyAooooAK1fDX/ACMNn/vH/wBBNZVavhv/AJGC0wMncf8A0E1nW/hy9Ga0P4sfVHpMu0wuGOF2nJHpT6ikZxAx8rccH5M9aeWbcBs4PU56V88fUDqKaGbLfJwOnPWk3Psz5fPpmgBx6ilphZtyjZkHqc9KUM2W+TgdOetADqKZufZny+fTNKWbcBs4PU56UAJ8vnA5+bb09qfUe5vOI8vovDZ6+1LufZny+fTNAD6KaWbcBs4PU56UBmy3ycDpz1oAUdTS0zc+0ny+c9M0pZtwGzg9TnpQA6imhmy3ycDpz1pNz7M+Xz6ZoAItohQKcrtGCfSn1HGzbI8R7QVGRn7vtTgzZb5OB0560AOpD05pu59mfL59M0FmyBsyD1OelAD6KaGbLfJwOnPWk3Psz5fPpmgB9MYL5iZPPOPypSzbgNnB6nPSmFm8wDys9cHNAEtFM3Psz5fPpmlLNuA2cHqc9KAHUn8R9aQM2W+TgdOetJufGfL59M0APoppZtwGzg9TnpQGbLfJwOnPWgB1Mj2hTtORub88nNG59mfL59M01GbA/dYGTnn3NAEtFNDNlvk4HTnrQGfaCUwc9M0AOooooAKKKKAIrq2gvbSa1uYllt50aOWNxkOrDBB9iDVRdFsFk06TyWZ9NQx2rSSu5jBUKTyTubaMbjk4Lc/Mc6FFABRRRQAUUUUAFNfcVG04OR+WeadTJNpUbjgbl/PIxQA+iiigAo5z7UUn8Q9aAFooooA8u1z/AJDt7/12b+dZ9aGuf8h29/67N/Os+voqfwL0R8rV/iS9X+YUUUVZAUUUUAFavhr/AJGGz/3j/wCgmsqtXw1/yMNn/vH/ANBNZ1v4cvRmtD+LH1R6VJuMbbThsHB96dTJdphcMcLtOSPSn188fUBRRRQAc8UUh6iloAKKKKAG/N5nX5cdPenUz5fOBz823p7U+gAooooAOeaKQdTS0AFFFFADY9wjXcctgZPvTqZFtEKBTldowT6U+gAoOe1FIenNAC0UUUAFNO7cuDx3p1MYL5iZPPOPyoAfRRRQAUc59qKT+I+tAC0UUUAFNTcFO45OT+WeKdTI9oU7Tkbm/PJzQA+iiigAooooAKKKKACiiigAooooAKKKKACmyHCj5d3zDj8etOpr7io2nByPyzzQA6iiigApO/Slo5z7UAFFFFAHl2uf8h29/wCuzfzrPrQ1z/kO3v8A12b+dZ9fRU/gXoj5Wr/El6v8woooqyAooooAK1fDX/Iw2f8AvH/0E1lVq+Gv+Rhs/wDeP/oJrOt/Dl6M1ofxY+qPSpDiJzt3fKfl9fanU2TcY22nDYOD706vnj6gKKKKAEPUcUtHPFFABRRRQA3P73G3+H739KdTfm8zr8uOnvTqACiiigBB1PFLRzzRQAUUUUANjOYkO3b8o+X09qdTY9wjXcctgZPvTqACkPTpmloOe1ABRRRQAU1j86fLnrz6U6mnduXB470AOooooAKTv0paOc+1ABRRRQAU2M5U/Lt+Y8fj1p1NTcFO45OT+WeKAHUUUUAFFFFABRRRQAUUUUAFFIzKilmYKo6kmkV0ddysGHqDQA6ikyMZyMUuR60AFMk2lRuOBuX88jFOyPWmyMAgOAw3D+dAD6KMj1pMj1oAWk/iHrRkYzkYoyNwFAC0UmR60ZGM5GKAPL9c/wCQ7e/9dm/nWfWhrn/Idvf+uzfzrPr6Kn8C9EfK1f4kvV/mFFFFWQFFFFABWr4a/wCRhs/94/8AoJrKrV8N8eILQnoCxP8A3yazrfw5ejNaH8WPqj0mXaYXDHC7Tkj0p9QyXEIif542+U/LuHPtTvPh/wCesf8A30K+fsz6e6JKKj8+H/nrH/30KPPh/wCesf8A30KLMLoeeopaiNxDx+9j/wC+hS+fD/z1j/76FFmF0SUVH58P/PWP/voU5ZEf7rq30OaLMLoT5fOBz823p7U+mbh5uMD7vX+lOyMZyMUhi0UZHrSZHrQADqaWkDDk8Yz1pcj1oAKKTI9aMjGcjFADYtohQKcrtGCfSn0yNgYk4C5UfL6U7I9aAFpD05oyMZyMUEigBaKTI9aMjGcjFAC0xgvmJk884/Kn5HrTGYb1GAevPpQA+ikyMZyMUuR60AFJ/EfWjI9aNwznjHrQAtFGR60mR60ALTI9oU7Tkbm/PJzTsjGcjFNjYFegX5jx+J5oAfRSZHrRkeooAWiiigAooooAKKKKACiiigAooooAKa+4qNpwcj8s806mSbSo3HA3L+eRigB9FFFABRzn2opP4h60ALRRRQB5drn/ACHb3/rs386z60Nc/wCQ7e/9dm/nWfX0VP4F6I+Vq/xJer/MKKKKsgKKKKACrem/8fT/APXvN/6KaqlW9N/4+n/695v/AEU1TP4WVT+JFSiiiqJCiiigAooooAK7PwL/AKu++qf1rjK7PwL/AKu++qf1rmxn8FnXgP8AeI/P8jrPm8zr8uOnvTqZ8vnA5+bb09qfXhn0QUUUUAHPNFIOppaACiiigBse4RruOWwMn3p1Mi2iFApyu0YJ9KfQAUHPaikPTmgBaKKKACmnduXB4706mMF8xMnnnH5UAPooooAKOc+1FJ/EfWgBaKKKACmpuCnccnJ/LPFOpke0KdpyNzfnk5oAfRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTZDhR8u75hx+PWnU19xUbTg5H5Z5oAdRRRQAUnfpS0c59qACiiigDy7XP8AkO3v/XZv51n1oa5/yHb3/rs386z6+ip/AvRHytX+JL1f5hRRRVkBRRRQAVb03/j6f/r3m/8ARTVUq3pv/H0//XvN/wCimqZ/CyqfxIqUUUVRIUUUUAFFFFABXZ+Bf9XffVP61xldn4F/1d99U/rXNjP4LOvAf7xH5/kdZn97jb/D97+lOpvzeZ1+XHT3p1eGfRBRRRQAg6nilo55ooAKKKKAGxnMSHbt+UfL6e1Opse4RruOWwMn3p1ABSHp0zS0HPagAooooAKax+dPlz159KdTTu3Lg8d6AHUUUUAFJ36UtHOfagAooooAKbGcqfl2/MePx606mpuCnccnJ/LPFADqKKKACiiigDHvvFnhvTLySzv/ABBpVpdR43wz3scbrkAjKk5GQQfxq5purabrNu1xpeoWl9ArlGktZllUNgHBKkjOCDj3FcD8RNctG1i00iPxLBYzJa3LyRJqws2jmCxtCZWDKQpBZducnzAwBCkrrfDa4trrS9WntGd4H1JyrS3n2qQjyovvzBmVyOgKs2FCgncCAAdpRVbUb+30rTLvUbtittawvPKwGSEVSxOO/ANZNr4oeTULKzvdB1XTTesyW8t15BV3CM+0+XK5U7VY8gfdPegDfooooAKKKKACmSbSo3HA3L+eRin02Q4UfLu+Ycfj1oAdRRRQAUn8Q9aWk79KAFooooA8u1z/AJDt7/12b+dZ9aGuf8h29/67N/Os+voqfwL0R8rV/iS9X+YUUUVZAUUUUAFW9N/4+n/695v/AEU1VKt6b/x9P/17zf8Aopqmfwsqn8SKlFFFUSFFFFABRRRQAV2fgX/V331T+tcZXZ+Bf9XffVP61zYz+CzrwH+8R+f5HV/L5wOfm29Pan03P73G3+H739KdXhn0QUUUUAIOppaQdTxS0AFFFFADItohQKcrtGCfSn02M5iQ7dvyj5fT2p1ABSHpzS0h6dM0ALRRRQAUxgvmJk884/Kn01j86fLnrz6UAOooooAKT+I+tLSd+lAC0UUUAFMj2hTtORub88nNPpsZyp+Xb8x4/HrQA6iiigAooooA47xdfxWXiDRludaj0K1kt7rdqJSENuBh2wiSZWVQw3MRjJ8oelXPBupy6na6nnVP7Vtbe+MNpf7UHnx+VGxOYwEbDs6ZUAfL65qpaWHiQeIPEsthe2NlazahG6C802SYyYtLdSysJkBXKleh5Vuew6TTY9Sit2XVLu0uZ95Kva2zQKFwOCrSOSc55z3HHHIBB4kjuJvC2rxWlrHdXL2UyxW8gysrlDhCO4JwPxrg9D0K60nX/DiY1zUE88yx31/JNLst2tZcrIjkrDIJNi5CqSGC8/OK9PooAY8SyEFi4x/dcr/I0LEqAgF+Rjlyf5mn0UAM8pdm3L46/fOfzzSlAWBy2R/tGnUUANEYBY5bnr8x/wAimPFmMKrMPmBzvOevPNRXWo2diyi6uEiLcqGPWqr67pLqAb+LqD19DmrVObV0mQ6kE7Nr7zSKAsDlsj/aNAjALHLc9fmP+RWf/b+k/wDP/D+dH9v6T/z/AMP50eyn/K/uF7an/MvvRf8AKXZty+Ov3zn880FAXU5bgf3jVD+39J/5/wCH86P7f0nOft8P50eyn/K/uD21P+ZfejQEYBY5bnr8x/yKTyl2bcvjr985/PNUP7f0n/n/AIfzo/t/Sf8An/h/Oj2U/wCV/cHtqf8AMvvRwGuf8hy9/wCurVn1f1shtbvCDkGViKoV79P4F6I+Zq/xJer/ADCiiirICiiigAq3pv8Ax9P/ANe83/opqqVb03/j6f8A695v/RTVM/hZVP4kVKKKKokKKKKACiiigArsvAygx3pOeGTocf3q42uz8C/6u++qf1rmxn8FnXgP94j8/wAjqvLxKTubaRyNx6/0pfKXZty+Ov3zn880fL5wOfm29Pan14Z9ENKAsDlsj/aNAjALHLc9fmP+RTqKAGeUNu3LYzn75z+dKUBYHLZH+0aUdTS0ANEYBY5bnr8x/wAik8pdm3L46/fOfzzT6KAI0j+WMuxLqoyQxwTThGAWOW56/Mf8iki2iFApyu0YJ9KfQAzyl2bcvjr985/PNBQEg5bI/wBo0+kPTmgBBGAWOW56/Mf8ik8pdm3L46/fOfzzT6KAGlAWBy2R/tGmmP5wQzYOdw3H9PSpKYwXzEyeecflQAeUuzbl8dfvnP55pSgLA5bI/wBo06igBojALHLc9fmP+RSeUNu3LY/3zn86fSfxH1oAQoCwOWyP9o0CMAsctz1+Y/5FOooAZ5S7NuXx1++c/nmkWPoWZiQTjDHGM8VJTI9oU7Tkbm/PJzQAojALHLc9fmP+RQI1CgZbAOfvGnUUAFFFFAHFeI7/AF3R/EcLw+ILZLG7jkMVi2izXswZREMqsDBmUYYljjaZMHdldm34Y1STVdOmlmv4LuWOdon8qwlszEQAdjxSszhuc84yGHHcx6zp1xc67p13puq2lnqUFvcRrFc2/nrLC7RFzsDo3yskXzA4G7BHIp/h2xaybVWuNSgv764vPNu2gj8tI38qNVQJuYr+7WM4LEnOehFAG3RRRQAUUUUAFFFFAHF+Of8Aj4s/9xv5iuSrrfHP/HxZ/wC438xXJV7uE/gxPnMb/vEv66BRRRXQcoUUUUAFFFFAFzVf+Qrc/wC+ap1c1X/kK3P++ap1MPhRVT436hRRRVEhRRRQAVb03/j6f/r3m/8ARTVUq3pv/H0//XvN/wCimqZ/CyqfxIqUUUVRIUUUUAFFFFABXZ+Bf9XffVP61xldn4F/1d99U/rXNjP4LOvAf7xH5/kdZn97jb/D97+lOpvzeZ1+XHT3p1eGfRBRRRQAg6nilo55ooAKKKKAGxnMSHbt+UfL6e1Opse4RruOWwMn3p1ABSHp0zS0HPagAooooAKax+dPlz159KdTTu3Lg8d6AHUUUUAFJ36UtHOfagAooooAKbGcqfl2/MePx606mpuCnccnJ/LPFADqKKKACiiigDznx5ZeHLfxJpc9/a+FrW5vo5w+oa1arKrbPKwpTem44xh2PyhcD7+D0fgtdPTRZE0280C6txO3zaHbLBAp2rkFVkcb+5ORwRx3OB8T7q7hjtoYJ9VTz7G+W1TTHlEkl8ERoARF85AAlP8Ad4+atzwbqDanHrVykt1JZHVJFtPtQZXVQiB12t8ygTecArAEAAYAxQB01FFFABRRRQAUUUUAcX45/wCPiz/3G/mK5Kut8c/8fFn/ALjfzFclXu4T+DE+cxv+8S/roFFFFdByhRRRQAUUUUAXNV/5Ctz/AL5qnVzVf+Qrc/75qnUw+FFVPjfqFFFFUSFFFFABVvTf+Pp/+veb/wBFNVSrem/8fT/9e83/AKKapn8LKp/EipRRRVEhRRRQAUUUUAFdn4F/1d99U/rXGV2fgX/V331T+tc2M/gs68B/vEfn+R1fy+cDn5tvT2p9Nz+9xt/h+9/SnV4Z9EFFFFACDqaWkHU8UtABRRRQAyLaIUCnK7Rgn0p9NjOYkO3b8o+X09qdQAUh6c0tIenTNAC0UUUAFMYL5iZPPOPyp9NY/Ony568+lADqKKKACk/iPrS0nfpQAtFFFABTI9oU7Tkbm/PJzT6bGcqfl2/MePx60AOooooAKKKKAOM8Yy3dl4i0PULMTJJHDdQmZdKnv0QOYjhkiwVJ2DDbh0IwwJK6Hg6EC01G7aeaa4vb0zztJpstiu/y40ASKX5tu1F5ycndz2HM/E60j1i4g0yK3tNTu5NOvYYNPa6hWVLiRU8mcJIy527ZBuHzDdkDrXUeFLS7t11e4urGSwS91BrmC0kkR2iUxxhs7GZQWkWR8An7/qTQB0NFFFACNu2nbjd2z0pF37fnK59hTqKAE5x2zS80UUAcV45/4+LP/cb+Yrk663xz/wAfFn/uN/MVyVe7hP4MT5zG/wC8S/roFFFFdByhRRRQAUUUUAXNV/5Ctz/vmqdXNV/5Ctz/AL5qnUw+FFVPjfqFFFFUSFFFFABVvTf+Pp/+veb/ANFNVSrem/8AH0//AF7zf+imqZ/CyqfxIqUUUVRIUUUUAFFFFABXZeBf9XffVP8A2auNrs/Av+rvvqn9a5sZ/BZ14D/eI/P8jrMN5nUbcdPel5x2zTfl84HPzbentT68M+iDmjmiigBOeeRS80g6mloAOaTnHbNLRQA1AwjXcQWwMn1NO5pkW0QoFOV2jBPpT6AE5x2zQc9sUtIenNAC80nOO2aWigA5ppDb1wRjvTqYwXzEyeecflQA7nHbNLzRRQAc0nPqKWk/iPrQAvNHNFFACc47ZpEDY+Yg8n8s8U6mR7Qp2nI3N+eTmgB/NJzS0UAFFFFAHC6v4d8QS6lqb2NlpE0V1qlnqMU9xeyRSIIBBmMqIW6mFxkN0kPHUHrtNk1KW3ZtUtLS2n3kKlrctOpXA5LNGhBznjHYc88XKKACiiigAooooAKKKKAOL8c/8fFn/uN/MVyVdb45/wCPiz/3G/mK5Kvdwn8GJ85jf94l/XQKKKK6DlCiiigAooooAuar/wAhW5/3zVOrmq/8hW5/3zVOph8KKqfG/UKKKKokKKKKACrem/8AH0//AF7zf+imqpVvTf8Aj6f/AK95v/RTVM/hZVP4kVKKKKokKKKKACiiigArs/Av+rvvqn9a4yuz8C/6u++qf1rmxn8FnXgP94j8/wAjrM/vcbf4fvf0p1N+bzOvy46e9Orwz6IKKKKAEHU8UtHPNFABRRRQA2M5iQ7dvyj5fT2p1Nj3CNdxy2Bk+9OoAKQ9OmaWg57UAFFFFABTWPzp8uevPpTqad25cHjvQA6iiigApO/Slo5z7UAFFFFABTYzlT8u35jx+PWnU1NwU7jk5P5Z4oAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHF+Of+Piz/wBxv5iuSrrfHP8Ax8Wf+438xXJV7uE/gxPnMb/vEv66BRRRXQcoUUUUAFFFFAFzVf8AkK3P++ap1c1X/kK3P++ap1MPhRVT436hRRRVEhRRRQAVb03/AI+n/wCveb/0U1VKt6b/AMfT/wDXvN/6Kapn8LKp/EipRRRVEhRRRQAUUUUAFdn4F/1d99U/rXGV2fgX/V331T+tc2M/gs68B/vEfn+R1fy+cDn5tvT2p9Nz+9xt/h+9/SnV4Z9EFFFFACDqaWkHU8UtABRRRQAyLaIUCnK7Rgn0p9NjOYkO3b8o+X09qdQAUh6c0tIenTNAC0UUUAFMYL5iZPPOPyp9NY/Ony568+lADqKKKACk/iPrS0nfpQAtFFFABTI9oU7Tkbm/PJzT6bGcqfl2/MePx60AOooooAKKKKACiiigCjrcV9PoOoxaXKItQe1lW1kJwElKkIfwbFclo+napHrulS2+meIrCCMv9vfVNYF1HKnlsAqp58nzeYUbcFXhT64rt54Ibq3lt7iJJYZUKSRuMq6kYII7gisfSvBvhnQ3t5dM0DTbae3TZFOlsvmqNu3/AFmNxJGQSTk5Oc5oA2JJRGQCrnP91SaVJQ4JCuMDPKEU+igBnmDZuw+On3Tn8qUuAwGG5/2TTqKAOK8cHNxadeFYcj3FcnXW+Of+Piz/ANxv5iuSr3cJ/BifOY3/AHiX9dAoooroOUKKKKACiiigC5qv/IVuf981Tq5qv/IVuf8AfNU6mHwoqp8b9QoooqiQooooAKt6b/x9P/17zf8AopqqVb03/j6f/r3m/wDRTVM/hZVP4kVKKKKokKKKKACiiigArsvAzAR3oweWToP96uNrovDmrxaPZ3M00buJJFUBMcYBPeufFRcqTjHc6sHNQrKUttTu95Mp+9tA5G09aXzBs3YfHT7pz+Vcz/wmtj5m/wCzXGcY/h/xpf8AhN7L/n1uP/Hf8a8r6rW/lPa+uUP5jpi4DAYbn/ZNAcEsMNx/smuZ/wCE3sv+fW4/8d/xo/4Tey/59bj/AMd/xo+q1v5Q+uUP5jpfMG3dhsZ6bTn8qUuAwGG5/wBk1zP/AAm9kM/6Lcc/7v8AjR/wm9l/z63H/jv+NH1Wt/KH1yh/MdMHBLDDcf7JpPMGzdh8dPunP5VzX/Cb2X/Prcf+O/41LP4wtLdlVrac7kVxjHQjPr70vq1b+UPrdDfmN9HIWNXyWKjJCnGacHBLDDcf7JrmE8a2KRqgtrjCgD+H/Gl/4Tey/wCfW4/8d/xp/Va38ofXKH8x0vmDZuw+On3Tn8qC4yBhsn/ZNc1/wm9l/wA+tx/47/jQfG9kf+XW4/8AHf8AGj6rW/lD65Q/mOmDglhhuP8AZNJ5g2bsPjp905/Kua/4Tey/59bj/wAd/wAaP+E3sv8An1uP/Hf8aPqtb+UPrlD+Y6YuAwGG5/2TTS5LjG4Bc7vlPP0rAtvGFndXUNutvOGldUBOMAk49a6BgvmJk884/Ksp0503aSsa06sKivB3DzBs3YfHT7pz+VKXAYDDc/7Jp1FQaDQ4JYYbj/ZNJ5g27sNjpjac/lT6T+I+tACFwGAw3P8AsmgOCWGG4/2TTqKAGeYNm7D46fdOfypFcjAbJJJwQpxjPFSUyPaFO05G5vzyc0AKHBLDDcf7JoEgKg4bk4+6adRQAUUUUAFFFFABRRRQAUUUUAFFFFAHF+Of+Piz/wBxv5iuSrrfHP8Ax8Wf+438xXJV7uE/gxPnMb/vEv66BRRRXQcoUUUUAFFFFAFzVf8AkK3P++ap1c1X/kK3P++ap1MPhRVT436hRRRVEhRRRQAVb03/AI+n/wCveb/0U1VKt6b/AMfT/wDXvN/6Kapn8LKp/EipRRRVEhRRRQAUUUUAFWx/yCG/67j/ANBNVKtj/kEN/wBdx/6CamXQqHX0KlFFFUSFFFFABRRRQAVc1L/XQ/8AXvF/6AKp1c1L/XQ/9e8X/oAqX8S+ZS+F/Ip0UUVRIUUUUAFFFFAFzSf+QzY/9fEf/oQr1Nj86fLnrz6V5ZpP/IZsf+viP/0IV6md25cHjvXlZj8UfQ9nK/gl6/oOooorzz1ApO/Slo5z7UAFFFFABTYzlT8u35jx+PWnU1NwU7jk5P5Z4oAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHF+Of+Piz/wBxv5iuSrrfHP8Ax8Wf+438xXJV7uE/gxPnMb/vEv66BRRRXQcoUUUUAFFFFAFzVf8AkK3P++ap1c1X/kK3P++ap1MPhRVT436hRRRVEhRRRQAVb03/AI+n/wCveb/0U1VKt6b/AMfT/wDXvN/6Kapn8LKp/EipRRRVEhRRRQAUUUUAFWx/yCG/67j/ANBNVKtj/kEN/wBdx/6CamXQqHX0KlFFFUSFFFFABRRRQAVc1L/XQ/8AXvF/6AKp1c1L/XQ/9e8X/oAqX8S+ZS+F/Ip0UUVRIUUUUAFFFFAFzSf+QzY/9fEf/oQr1JgvmJk884/KvLdJ/wCQzY/9fEf/AKEK9TY/Ony568+leVmPxR9D2cr+CXr+g6iiivPPUCk/iPrS0nfpQAtFFFABTI9oU7Tkbm/PJzT6bGcqfl2/MePx60AOooooAKKKKACiiigAooooAKKRlV1KsoZT1BGRSKiIu1FVR6AYoAdRSYGMYGKXA9KAOL8c/wDHxZ/7jfzFclXWeOf+Piz/ANxv5iuTr3cJ/BifOY3/AHiX9dAoooroOUKKKKACiiigC5qv/IVuf981Tq5qv/IVuf8AfNU6mHwoqp8b9QoooqiQooooAKt6b/x9P/17zf8AopqqVb03/j6f/r3m/wDRTVM/hZVP4kVKKKKokKKKKACiiigAq2P+QQ3/AF3H/oJqpVsf8ghv+u4/9BNTLoVDr6FSiiiqJCiiigAooooAKual/rof+veL/wBAFU6ual/rof8Ar3i/9AFS/iXzKXwv5FOiiiqJCiiigAooooAuaT/yGbH/AK+I/wD0IV6md25cHjvXlmk/8hmx/wCviP8A9CFepMF8xCevOPfivKzH4o+h7OV/BL1/QfRSYGMYGKXA9K889QKOc+1JgelGFyRgfSgBaKMD0pMD0oAWmpuCnccnJ/LPFLgYxgYpsYXb8vPzN19cnNAD6KTA9KMD0FAC0UUUAFFFFABRRRQAUUUUAFFFFAHF+Of+Piz/ANxv5iuSrrfHP/HxZ/7jfzFclXu4T+DE+cxv+8S/roFFFFdByhRRRQAUUUUAXNV/5Ctz/vmqdXNV/wCQrc/75qnUw+FFVPjfqFFFFUSFFFFABVvTf+Pp/wDr3m/9FNVSrem/8fT/APXvN/6Kapn8LKp/EipRRRVEhRRRQAUUUUAFWx/yCG/67j/0E1Uq2P8AkEN/13H/AKCamXQqHX0KlFFFUSFFFFABRRRQAVc1L/XQ/wDXvF/6AKp1c1L/AF0P/XvF/wCgCpfxL5lL4X8inRRRVEhRRRQAUUUUAXNJ/wCQzY/9fEf/AKEK9TY/Ony568+leVaY23VrNh2nQ/8Ajwroz45lyD9hX/v5/wDWrz8ZRnVkuRdD08BiKdGD53u/0O0ori/+E5l/58E/7+H/AAo/4TmX/nwT/v4f8K5Pqdbsd31/D9/wZ2lJ36Vxn/Ccy/8APgn/AH8P+FH/AAnMv/Pgn/fw/wCFH1Ot2D6/h+/4M7SiuL/4TmX/AJ8E/wC/h/wo/wCE5l/58E/7+H/Cj6nW7B9fw/f8GdpTYzlT8u35jx+PWsLQfET6zdSwtbLEETdkPnPOPSt1NwU7jk5P5Z4rCpTlTlyy3OinUjUjzQ2HUUUVBoFFFFABRRRQAUUUUAFFFFABRRRQBxfjn/j4s/8Acb+Yrkq63xz/AMfFn/uN/MVyVe7hP4MT5zG/7xL+ugUUUV0HKFFFFABRRRQBc1X/AJCtz/vmqdXNV/5Ctz/vmqdTD4UVU+N+oUUUVRIUUUUAFW9N/wCPp/8Ar3m/9FNVSrem/wDH0/8A17zf+imqZ/CyqfxIqUUUVRIUUUUAFFFFABVsf8ghv+u4/wDQTVSrY/5BDf8AXcf+gmpl0Kh19CpRRRVEhRRRQAUUUUAFXNS/10P/AF7xf+gCqdXNS/10P/XvF/6AKl/EvmUvhfyKdFFFUSFFFFABRRRQBZ07/kJ2n/XZP/QhVarOnf8AITtP+uyf+hCq1L7RX2QooopkhRRRQAUUUUAdR4I/5Cdx/wBcf6iu2j2hTtORub88nNcT4I/5Cdx/1x/qK7eM5U/Lt+Y8fj1rxcb/ABn8j6DL/wCAvmOooorkO0KKKKACiiigAoqjrdjNqeg6jYW9wbae6tZYY5xnMTMpAbjngnP4VyWj+D7iz13Sr1PD3h3REsy5ml0uZmkuQY2Ty2Hkx/LuYPyW5QfWgDu6KY6OxG2Vk9gAf5ihUZQcys31A4/SgB9FM2Ntx5jZz1wM/wAqXadwO84HUYHNAHG+Of8Aj4s/9xv5iuSr1HUdFs9UdGulZigIXDYxmqDeEtIUZMcnUD/WHvxXp0MZTp01F3PIxOBq1KrnG1mee0V6J/wiGk/88pP+/ho/4RDSf+eUn/fw1r9fpeZh/ZtbyPO6K9E/4RDSf+eUn/fw0n/CI6RnHlyf9/DR9fpeYf2bW8jzyivRP+EQ0n/nlJ/38NH/AAiGk/8APKT/AL+Gj6/S8w/s2t5HD6r/AMhW5/3zVOvR5vC2mXEzyyRyF3OThzUQ8JaQWK+XJkf9NDSjjqSilqVPLqzk3oee0V6J/wAIhpP/ADyk/wC/ho/4RDSf+eUn/fw0/r9LzJ/s2t5HndFeif8ACIaT/wA8pP8Av4aT/hEdJP8Ayzk/7+Gj6/S8w/s2t5HnlW9N/wCPp/8Ar3m/9FNXc/8ACIaT/wA8pP8Av4afF4W0uFy6RyAlWT756MCD+hNKWOpNW1Kjl1ZST0POKK9CbwlpCIWMcmAMn94ad/wiGk/88pP+/hp/X6XmT/ZtbyPO6K9E/wCEQ0n/AJ5Sf9/DR/wiGk/88pP+/ho+v0vMP7NreR53RXof/CI6Rx+7k/7+Gl/4RDSf+eUn/fw0fX6XmH9m1vI87q2P+QQ3/Xcf+gmu5/4RDSf+eUn/AH8NP/4RbS/IMPlybC27G89cYpPHUn3Kjl1ZX2POKK9C/wCES0jft8uTOM/6w07/AIRDSf8AnlJ/38NP6/S8yf7NreR53RXon/CIaT/zyk/7+Gj/AIRDSf8AnlJ/38NH1+l5h/ZtbyPO6K9D/wCER0n/AJ5yf9/DS/8ACIaT/wA8pP8Av4aPr9LzD+za3ked1c1L/XQ/9e8X/oAruP8AhENJ/wCeUn/fw0+XwtpkzKXjkJVQg+c9AMCk8dSunqUsurcrWh5xRXoS+EtIdVYRyYIyP3hp3/CIaT/zyk/7+Gn9fpeZP9m1vI87or0T/hENJ/55Sf8Afw0h8I6QP+Wcn/fw0fX6XmH9m1vI88or0T/hENJ/55Sf9/DR/wAIhpP/ADyk/wC/ho+v0vMP7NreRwmnf8hO0/67J/6EKrV6NF4V0uGZJUjkDIwYfOeoqM+EtIDAGOTJ6fvDS+vUr31K/s6ta2h57RXon/CIaT/zyk/7+Gj/AIRDSf8AnlJ/38NP6/S8yf7NreR53RXon/CIaT/zyk/7+Gk/4RHSenlyf9/DR9fpeYf2bW8jzyivRP8AhENJ/wCeUn/fw0f8IhpP/PKT/v4aPr9LzD+za3kYfgj/AJCdx/1x/qK7dNwU7jk5P5Z4rP0/Q7LTJXktVdXZdpJbPFXkUdUdgoY5GBycnNediaiqVOaJ6uEpSpUlCW5JRTQrZY7256DA4oCsFA8xic5zgVgdI6iiigAooooAKKKKACiiigAooooAKbIcKPl3fMOPx606mvuKjacHI/LPNADqKKKACk79KWjnPtQAUUUUAFNB/eMNuOnPrTqaN29sn5eMCgB1FFFABSDp0xS0DPegAooooAbIcROdu75T8vr7U6mybjG204bBwfenUAFFFFACHqOKWjniigAooooAbn97jb/D97+lOpvzeZ1+XHT3p1ABRRRQAg6nilo55ooAKKKKAGxnMSHbt+UfL6e1Opse4RruOWwMn3p1ABSHp0zS0HPagAooooAKax+dPlz159KdTTu3Lg8d6AHUUUUAFJ36UtHOfagAooooAKbGcqfl2/MePx606mpuCnccnJ/LPFADqKKKACiiigAooooAKKKKACiiigAooooAKhuTiJcf89E/9CFTViaj4r0fTbya0u5LnfAqvO6WU0kUIPIZ5FQogwM5YjA5OBQBt0VmaR4g03XDMtjLMZIQpkjntpIHUNkq22RVO04ODjBwfStOgApjb/NTA+TByfyxT6KACiiigAqGMk3Mw9Av9ah1HVbHSUt2vrmOAXNxHaw7uskrnCoB3JP5AEngE1j3Xjrw3Y3M0NxfPG8cjQl/ssuySVTgxo4XbI4PGxSWyCMcGgDpKKo6VrFlrVvJPYySMsUhikSWF4njcAEqyOAynBB5HQg96vUAFMi37DvGDuP5ZOP0p9FABRRRQBDdHFnOR2jb+VTViz+KtES2u5TdmeO2u/sEy28Dzt5+ATGFRSWIDDOAccg9DiCHxtoU15HaCe7jmeRYcTWFxEEkb7iOWQBGbjCsQTuUjO4ZAOhooooAY+/cm0ZG75vpg0+iigAooqjqusWWi28c99JIqyyCKNIoXleRyCQqogLMcAngHgE9qALGT9sA7eWf51NXLf8ACw/CmDKdTKxL8sk7W0ojhY/wSSFdsb8fcYhs445Fa2keINN1wzLYyzGSEKZI57aSB1DZKttkVTtODg4wcH0oA06KKKAGJv3Sbhgbvl9xgf1zT6KKACiiudufHXh20uJoJ72VWhd4ywtJijun3kRwm13GDlVJPB44NAG5anNnAT3jX+VTVzT+O/D1v8k0t9b7FDOs2mXMflJnAd90Y2JnI3thflbn5TjpaACmS79nyDLZH8+afRQAUUUUAFQyE/aIR6k/yqPUtStNIsJb6+l8q3ixubaWOSQAAACSSSAAASSQBWC3xA8MBmDXkweH/Xq1jOGthnGZgUzCPd9oI56UAdRRWNpPirR9bujb6fcySSeWZk328kayxggF42dQJFyR8ykjkeorZoAKYN/nNkfJtGD785/pT6KACiiigAqG2OYmz/z0f/0I1k3/AIw0LTNQksru8eOWJlSV/s8jRRswyqtIFKKxBGFJBORxyKqjx3oCIhzqSeYpeNDpF2GkXqzqvlZZRkZYAgbhkjcMgHTUVFa3MF7aQ3VtKstvOiyRSIch1YZBHsQaloAKKKKACiiigAooooAKKKKACiiigArzfxn8PLvxNc6qtta6RbtqBjI1ZpJTdQBUCsgTBUhgu07WQbWOVJGT6RRQBgaHYauNa1DVtYjsYZri2t7ZIbOZ5VAiaVtxZkU5JmPGOAo5Oa36KKACiiigAooooAy9f02bVdOit4GjV0vbS4JckDbFcRysOAedqED3x061xmr+C/EV8tpaQPpS2una5JrdtK80m+ZzLJKsToEwgzKwLhm6A7a9HooAxfD2n39m2q3epi2S61C8+0mK2kaRIgIo4gu9lUscRAk7R1x2raoooAKKKKACiiigDjdX8J3N82oyNZaXqKz6vHfx2t7JIsbItrHCQxUHDZVjgq6kdRk5XI0PwP4g0q1l0xm0r7BdavBqkjxzSbrYRvE4t40KYZAIUQMWXA/h4Ar0migAooooAKKKKACsbxLb6zdabHDojwpK0w8/zLhoCYsHIRwjlWJ2jO3pnBBwa2aKAPLdR+H2u6l4b1rw9FBo+nWOsXMd1K8d3LO9uyCMEAGNfNLGFWLMVOXbhsZPZ6HYauNa1DVtYjsYZri2t7ZIbOZ5VAiaVtxZkU5JmPGOAo5Oa36KACiiigAooooAK8/1rwvr2uXV5/aWn6PfwmctaG51GdUijVsqBCkQw7AYZ97Ebj95fkPoFFAHl0PgHxBap4kis4NEtbXxDbC0e1inkEenIFdcxfuwJM+Y7lcRjcfevUaKKACiiigAooooAq6l9v8AsEp0v7N9sGDGLrd5bcjIJXkZGRnnBIODjB83t/AXiS1i8RxRNpnl+Ibb7LMJr6eZ7NAHXcJGj3TnErH59mMAZwK9SooA47wv4d1nTbrSBqRsBb6Ppb6bbtbSu73AYw/OwZQE4gX5QW5Y88c9jRRQAUUUUAFFFFAHD+NPC+ueKZRaL/ZwsEdXikkuZ1IGBvSSFMLLkjh9ylA2Vwwyc3S/BniDRPEn9vaZp3h+0P2I2X9mRXcohySrGff5Wd5KICNvIUZbNelUUAZ3h/TG0Xw3pelNKJWsrSK2MgGA5RAucds4rRoooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKxfEOoX9m2lWmmG2S61C8+zCW5jaRIgIpJS2xWUscREAbh1z2raqjquj2WtW8cF9HIyxSCWN4pnieNwCAyuhDKcEjg9CR3oA8u1678VzR38C6/a2NwmuSxGYNIkKhNMEyEHzB5ce5CzKdy7mydwDB8p7m50jULr7PH4k03UJ/EFjbQfa9T8+CZ2jtC0M5ErkkqHbcAMrhd2MpXq8fg3QIrZ7caeHied7l1lleTfI8JgZm3Md2YyVOc5yT15qha/Djw5a3pumhvbmQXUd3ELq/mlWGRBGFKhmPI8peTk4yuduFABUk8R+IEmi0xf7MbUf7a/s2ScwusRQ2ZuQ4TeSCMqMbjnB5Gcjd8Pahf3jaraambZ7rT7z7MZbaNo0lBijlDbGZipxKARuPTPequt+ErXVbyykWMLH/af2+9/fOjuwtXgVkKnKsP3XQr90nr11tK0ey0W3kgsY5FWWQyyPLM8ryOQAWZ3JZjgAcnoAO1AF6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/9k=" /> <img src="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAHgAeADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4/wD4WPo//CuP+E4+zX39mf8APLYnnf67yum7b97n73T8q6W3vXn1C8tWsbqFbbZtuJAvlz7hk7CGJO3ocgc9M181/wDCK/8AGOX/AAkP9va5/wBg37Z/of8Ax97P9Vj/AIF1+9zXb60k0mufF9YJb6JzBpnz2EYkmA8nnC7lyMZzyOM0AewX99b6Zp1zf3knl2trE80z7SdqKCWOBycAHpRYX1vqenW1/ZyeZa3USTQvtI3IwBU4PIyCOtfO3hyz0vVNA8aafp1rNPYf2G0+bSW4FqbmM70yko3iXcoPDFSARg1q3X9lj4Y/DzzPtg8G4f8Atr7N5ufO2nO7HzbPO83OOPT+GgD3yivBrA34+G/j7/hEP7Q/sHdH/Y/mb9+zA+0eXu+bbjOPx75rG0z/AIR7+2PE/wDwi/2z+zP+EGu9v2rfnfuG7bv5xnOccbt2KAPpKivn7V9N0PRvh34Ggvo79rHU0W7vHa4lNuZTbxgeaEBc8BVRUKgYNULNH1DwVpGl3Ulz9mh8fx2USMzo8MOzhBuJdMbjjJyM9c0AfSFFfOGm6RBpOoWN7aTXSS2Hj/8Asa2BuHZY7QkkxgE45JOT1NemfFvdpmlaP4qijLvoWpRTyAdTC52Ov45WgD0OsSDxRYtPpNpexXWnahqvnfZbK6i/eHyhl8lCyr8uDy3II78V4x4J0m9HjPQfDF4HkitZB4kmZ/7728YA/CZmrL8K6dpKD4Q6nqEDYlmvbd5QXx5i3DtbL8vT96+ffPOQOAD37QPEln4j/tT7HHOn9m6hLp83nKBukjxuK4JyvIwTg+1bFfNutaHbDwn8QPFay3KarpnimcWciTsqwkzxBiFBxkh+T1+UV7L8TjqY+G2uHSPN+2/Z/l8rO7ZuG/GOfubqAOtor588IHS/+E31U+BftxjHhCb7J9o37vtHmr9zfzjf6cbt2Kp+AjoZ8f8AgD+yjqH27yrz+1ftXmbftX2c78buN3rt7bM80AfR9Y+geJLPxH/an2OOdP7N1CXT5vOUDdJHjcVwTleRgnB9q+ePh79h3eBxo51L/hJv7Qb7Z/rPK+wb334z8m36fxbs84ro7n7Z/wAIZ4x8j7X9h/4Ti4/tP7JnzfsmU8zbjn+7+Ge2aAPdL++t9M065v7yTy7W1ieaZ9pO1FBLHA5OAD0rlta+JWh6PHprRw6jqT6jbC8gi0+1Mj+QQD5jKcFVwe/PXivLbPTtF1XTvG9r4ThvpvB/9hGUJKZthv0JdPL3/McbQSO5GDkYpE8b6D4X8FeEtP0P7FYajfaXsn1m4t3ka0Te3mgAKXYmYSkL90EZ6YoA920bV7LX9HtdV06bzrS5TfG+MZHQgjsQQQfpV6uY+HkWiQeA9Kh8O3T3elpGyxXDoyGQh23sQwBHz7u1dPQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSKSVBIwSOR6UtNQYRQW3cDn1oAdRRRQAUUUUAFA70Ug6nnvQAtFFFABRRRQAUhJ3AY49aWmn76/Njrx60AOooooAKKKKACg8A45opG+6eccdaAKGuaLY+ItFutJ1KIyWl0myRQcHrkEHsQQD+FYvhfwDpnhbUbjUYbzUtQv54hAbnUbnznWMHIQHAwMgflXVUUAFFFFABRRRQAikkcjHJpaagwOWzyefxp1ABWfrmi2PiLRbrSdSiMlpdJskUHB65BB7EEA/hWhRQBx+hfDfRtEkv5pLjUdUnvrY2k02pXJmfyD1jBwML/hUfh34Y6L4b1iDU4bzVLya1iaGzS9uvNS1QjBWMYGBjjvXaUUAY/hbw5aeEfDtroVhJPJa2u/Y87Audzs5yQAOrHtWxSfxHnt0paACiiigAooooAQkjGBnmlprfw/Njn86dQAUUUUAFFFFABQOQM8UUi/dHOeOtAC0UUUAFFFFABSAkk5GMHj3paao5b5s89PTigB1FFFABRRRQAUUUUAFFFFABTY9vlJt+7gY+lOpEyUUsMHHI9KAFooooAKKKKACmGSNGIZ1Unnk4p9cL43/5Ctv/ANcB/wChGtqFL2s+W9jDE1vY0+e1ztvPh/56x/8AfQo8+H/nrH/30K8iort/s5fzfged/aj/AJfxPXfPh/56x/8AfQo8+H/nrH/30K8ioo/s5fzfgH9qP+X8T13z4f8AnrH/AN9CgPG7jawYgHoc4ryKus8Df8fV5/uL/M1nWwSpwc+bY2oZg6tRQ5bX8ztaKKK4D0gooooAKRsbTnpjmlpDnacDJoAWiiigAooooAKKKKAGpt2nb0yfzzzTqRckcjByf50tABRRRQAUUUUAJxuPrgUtJzuPHFLQAUUUUAFFFFADX25XPrx9adSNnjAzzzS0AFFFFABRRRQAUi42jHTHFLSDO0ZGDQAtFFFABRRRQAU1du58dc8/XAp1IM5bIwM8e/FAC0UUUAFFFFABRRRQAhGaMc55paKAExxjJ/OkRf3agsTgDnPWnU2Pb5Sbfu4GPpQAuOc80Y4xk/nS0UAJjpyePejHOeaWigBMcYyfzrhfG3GqW/8A1wH/AKEa7uuE8b/8hWD/AK4j/wBCNdeB/jL5nDmP8B+qOZooor2jwAooooAK6vwOM3N3z/Av8zXKV1ngb/j6u/8AcX+Zrnxf8GR1YL+PE7THOeaMcYyfzpaK8I+jEx05PHvRjnPNLRQAmOMZP50jDK4yRx1zTqRsbTnpjmgAxznmjHGMn86WigBMdOTx70Y5zzS0UAJjjGT+dGOnJ496WigBqDGSWzyf50uOMZP50ibdp29Mn88806gBMdOTx70Y5zzS0UAJjjGT+dGOnJ496WigBMfOTnt0oxxjJ/OjjcfXApaAEx05PHvRjnPNLRQAmOMZP50Y6cnj3paKAGsOQd2OfzpccYyfzpH25XPrx9adQAmOnJ496Mc55paKAExxjJ/OjHTk8e9LRQAmOc80irhQMk++adSLjaMdMcUAGOnJ496Mc55paKAExxjJ/OjHTk8e9LRQAmOc80irgv8AMTk+vTinU1du58dc8/XAoAXHTk8e9GOc80tFABRRRQAUUUUAFFFFABSJkopYYOOR6UtNQYRQW3cDn1oAdRRRQAUUUUAFcL43/wCQrb/9cB/6Ea7quF8b/wDIVt/+uA/9CNdeB/jL5nDmP8B+qOYooor2jwAooooAK6zwN/x9Xn+4v8zXJ11ngb/j6vP9xf5mufF/wZHVgv48TtaKKK8I+jCiiigApDnacDJpaRvunnHHWgBaKKKACiiigAooooARckcjByf50tNQYHLZ5PP406gAooooAKKKKAE53HjilpP4jz26UtABRRRQAUUUUAI2eMDPPNLTW/h+bHP506gAooooAKKKKACkGdoyMGlpF+6Oc8daAFooooAKKKKACkGctkYGePfilpqjlvmzz09OKAHUUUUAFFFFABRRRQAUUUUAFNj2+Um37uBj6U6kTJRSwwccj0oAWiiigAooooAK4Txv/wAhWD/riP8A0I13dcL43/5Ctv8A9cB/6Ea68D/GXzOHMf4D9UcxRRRXtHgBRRRQAVtaJqEul2N9dQKjSAxqN4JGDu9D7Vi1oW3/ACBdQ/34v/ZqzqpSjZ+X5mtGTjK63s/yNT/hNdT/AOeNr/3w3/xVH/Ca6n/zxtf++G/+Krm6Kn6tR/lRf1uv/OzpP+E11P8A542v/fDf/FUf8Jrqf/PG1/74b/4quboo+rUf5UH1uv8Azs6T/hNdT/542v8A3w3/AMVVnT/Fl/eX0VvJFbBHyDtVs9D71yVaGif8hi3+p/8AQTUTw9JRbUUXTxVZzScnuan/AAmup/8APG1/74b/AOKo/wCE11P/AJ42v/fDf/FVzdFX9Wo/yoj63X/nZ0n/AAmup/8APG1/74b/AOKo/wCE11P/AJ42v/fDf/FVzdFH1aj/ACoPrdf+dnSf8Jrqf/PG1/74b/4qj/hNdT/542v/AHw3/wAVXN0UfVqP8qD63X/nZ6zYymewt5mADSRq7AdMkZP86sVU0v8A5BFl/wBcE/8AQRVuvCkrSaPo4O8U2FFFFSUFFFFACcbj64FLSc7jxxS0AFFFFABRRRQA19uVz68fWnUjZ4wM880tABRRRQAUUUUAFIuNox0xxS0gztGRg0ALRRRQAUUUUAFNXbufHXPP1wKdSDOWyMDPHvxQAtFFFABRRRQBh6Z4ik1DWJdNm0PUrCaOATs9y0DIAWwBmKV8E/MRkDIU1W8N+NbHxReTwWFvMEhUs0rT27D72BlElaRc8kblHA/CtKz0yaxF5JHcxvc3d59oklkiJGzKgIAGHIjUKDnr82D92srQfClzpOp293c6hazx2dpJZ2sdtYLbbUd0cl9rFWb92v3VQD5uOeADp9w9R+dLkZxnmm7EH8K/lS4Gc4GfWgAyPUUiHEa5YHgc+tLtXBG0YPbFNjVDEm1RtAG3I6UAPyM4zzSZHqKMDOcDPrRtXBG0YPbFABkeopcjOM80m1ePlHHTijAznAz60AGR6iuF8b/8hS3/AOuA/wDQjXdbVwRtGD2xXC+NgBqluBx+4H/oRrrwP8ZfM4cx/gP1RzNFFFe0eAFFFFABWhbf8gXUP9+L/wBmrPrQtv8AkC6h/vxf+zVE9vmvzLp7v0f5GfRRRVkBRRRQAVoaJ/yGLf6n/wBBNZ9aGif8hi3+p/8AQTUVPgfoXS+OPqZ9FFFWQFFFFABRRRQB6tpZH9k2Qzz5Cf8AoIq1keoqppYH9lWZwM+QnP8AwEVb2rgjaMHtivnJ/Ez6qn8C9F+QZHqKXIzjPNJtXj5Rx04owM5wM+tSWGR6ijI9RRtXBG0YPbFG1ePlHHTigAz8x57dKMj1FJhfMJx82BzS7VwRtGD2xQAZHqKXIzjPNJtXj5Rx04owM5wM+tABkeooyPUUbVwRtGD2xRtXj5Rx04oARuSoDYOenrS5HqKa4TcpI5zwadtXBG0YPbFABkeopcjOM80m1ePlHHTijAznAz60AGR6ijI9RRtXBG0YPbFG1ePlHHTigBcjOM80ikbR8wOO9GBnOBn1pFVNgAUbSOmKAFyPUUuRnGeaTavHyjjpxRgZzgZ9aADI9RRkeoo2rgjaMHtijavHyjjpxQAuRnGeaap5Y7gRn8uKXAznAz601VTLgKPvfNx3wKAHZHqKXIzjPNJtXj5Rx04owM5wM+tAC0UUUAFFFFABRRRQAUiZKKWGDjkelLTUGEUFt3A59aAHUUUUAFFFFABXC+N/+Qrb/wDXAf8AoRruq4Xxv/yFbf8A64D/ANCNdeB/jL5nDmP8B+qOYooor2jwAooooAK0Lb/kC6h/vxf+zVn1oW3/ACBdQ/34v/Zqie3zX5l0936P8jPoooqyAooooAK0NE/5DFv9T/6Caz60NE/5DFv9T/6Caip8D9C6Xxx9TPoooqyAooooAKKKKAPVtL/5BFl/1wT/ANBFW6qaX/yCLL/rgn/oIq3Xzk/iZ9VT+Bei/IKKKKksKKKKAE53HjilpP4jz26UtABRRRQAUUUUAI2eMDPPNLTW/h+bHP506gAooooAKKKKACkGdoyMGlpF+6Oc8daAFooooAKKKKACkGctkYGePfilpqjlvmzz09OKAHUUUUAFFFFABRRRQAUUUUAFNj2+Um37uBj6U6kTJRSwwccj0oAWiiigAooooAK4Txv/AMhWD/riP/QjXd1wvjf/AJCtv/1wH/oRrrwP8ZfM4cx/gP1RzFFFFe0eAFFFFABWhbf8gXUP9+L/ANmrPrQtv+QLqH+/F/7NUT2+a/Munu/R/kZ9FFFWQFFFFABWhon/ACGLf6n/ANBNZ9aGif8AIYt/qf8A0E1FT4H6F0vjj6mfRRRVkBRRRQAUUUUAeraX/wAgiy/64J/6CKt1U0v/AJBFl/1wT/0EVbr5yfxM+qp/AvRfkFFFFSWFFFFACcbj64FLSc7jxxS0AFFFFABRRRQA19uVz68fWnUjZ4wM880tABRRRQAUUUUAFIuNox0xxS0gztGRg0ALRRRQAUUUUAFNXbufHXPP1wKdSDOWyMDPHvxQAtFFFABRRRQAUUUUAFFITijPOOfyoAWmoMIoLbuBz60u4Yzz+VNjK+Um0HaQMcdqAH0Umecc/lRuGM8/lQAtFJkcdefajPOOfyoAWuF8b/8AIVt/+uA/9CNdzuGM8/lXC+NudUt/+uA/9CNdeB/jL5nDmP8AAfqjmaKKK9o8AKKKKACtC2/5Auof78X/ALNWfWhbf8gXUP8Afi/9mqJ7fNfmXT3fo/yM+iiirICiiigArQ0T/kMW/wBT/wCgms+tDRP+Qxb/AFP/AKCaip8D9C6Xxx9TPoooqyAooooAKKKKAPVtL/5BFl/1wT/0EVbqppZ/4lNkOf8AUJ2/2RVrcMZ5/KvnJ/Ez6qn8C9F+QtFJkcdefajPOOfyqSxaKTcMZ5/KjI468+1AB/Eee3SlpuRvI5zgdqXcMZ5/KgBaKTI468+1Gecc/lQAtFJuGM8/lRkcdefagBG/h+bHP506mOV3KDnOeOO9O3DGefyoAWikyOOvPtRnnHP5UALRSbhjPP5UZHHXn2oAWkX7o5zx1ozzjn8qRWGwEZxj0oAdRSZHHXn2ozzjn8qAFopNwxnn8qMjjrz7UALTVHLfNnnp6cUuecc/lTVZcuQD15474FAD6KTI468+1Gecc/lQAtFFFABRRRQAUUUUAFImSilhg45HpS01BhFBbdwOfWgB1FFFABRRRQAVwvjf/kK2/wD1wH/oRruq4Xxv/wAhW3/64D/0I114H+MvmcOY/wAB+qOYooor2jwAooooAK0Lb/kC6h/vxf8As1Z9aFt/yBdQ/wB+L/2aont81+ZdPd+j/Iz6KKKsgKKKKACtDRP+Qxb/AFP/AKCaz60NE/5DFv8AU/8AoJqKnwP0LpfHH1M+iiirICiiigAooooA9W0v/kEWX/XBP/QRVuqml/8AIIsv+uCf+girdfOT+Jn1VP4F6L8goooqSwooooATnceOKWk/iPPbpS0AFFFFABRRRQAjZ4wM880tNb+H5sc/nTqACiiigAooooAKQZ2jIwaWkX7o5zx1oAWiiigAooooAKQZy2RgZ49+KWmqOW+bPPT04oAdRRRQAUUUUAFFFFABRRRQAU2Pb5Sbfu4GPpTqRMlFLDBxyPSgBaKKKACiiigArhPG/wDyFYP+uI/9CNd3XC+N/wDkK2//AFwH/oRrrwP8ZfM4cx/gP1RzFFFFe0eAFFFFABWhbf8AIF1D/fi/9mrPrQtv+QLqH+/F/wCzVE9vmvzLp7v0f5GfRRRVkBRRRQAVoaJ/yGLf6n/0E1n1oaJ/yGLf6n/0E1FT4H6F0vjj6mfRRRVkBRRRQAUUUUAeraX/AMgiy/64J/6CKt1U0v8A5BFl/wBcE/8AQRVuvnJ/Ez6qn8C9F+QUUUVJYUUUUAJxuPrgUtJzuPHFLQAUUUUAFFFFADX25XPrx9adSNnjAzzzS0AFFFFABRRRQAUi42jHTHFLSDO0ZGDQAtFFFABRRRQAU1du58dc8/XAp1IM5bIwM8e/FAC0UUUAFFFFABRRRQAUUUUAFNQYRQW3cDn1p1Nj2+Um37uBj6UAOooooAKKKKACuF8b/wDIVt/+uA/9CNd1XCeN/wDkKwf9cR/6Ea68D/GXzOHMf4D9UczRRRXtHgBRRRQAVoW3/IF1D/fi/wDZqz60Lb/kC6h/vxf+zVE9vmvzLp7v0f5GfRRRVkBRRRQAVoaJ/wAhi3+p/wDQTWfWhon/ACGLf6n/ANBNRU+B+hdL44+pn0UUVZAUUUUAFFFFAHq2l/8AIIsv+uCf+girdVNL/wCQRZf9cE/9BFW6+cn8TPqqfwL0X5BRRRUlhRRRQAn8R57dKWk43H1wKWgAooooAKKKKAGt/D82Ofzp1Nfblc+vH1p1ABRRRQAUUUUAFIv3RznjrS0i42jHTHFAC0UUUAFFFFABTVHLfNnnp6cU6mrt3Pjrnn64FADqKKKACisfxBrF7o8EElloN9qzSSpGy2jRjywXVSTvYHoxPAI+U7ig+YLp2q6lfX/lT+Hr3T7YRMxnu54CS+V2qqxSPnILEkkYwOueADhvAmq+Xrirea/b6k89oz3EiXt0VtJjNGixPHNIVV3L4UbI2Gwrjmp/AWpLd6ppKw6xd311No7zazDNdvMLe7Dw4BRiRC2WnGwBeF6cV6TRQAwGXuif99n/AAp2W3dBt9c8/lS0UANBfacquew3f/WpFMmxCUUMR8wz0/Sn01BhFBbdwOfWgBctu6Db655/KkBfacquew3f/Wp1FADSXwMKue/zdP0pctu6Db655/KlooAaC+05Vc9hu/8ArVw3jbP9qW+QAfIHQ/7Rru64Xxv/AMhW3/64D/0I114H+MvmcOY/wH6o5iiiivaPACiiigArQtv+QLqH+/F/7NWfWhbf8gXUP9+L/wBmqJ7fNfmXT3fo/wAjPoooqyAooooAK0NE/wCQxb/U/wDoJrPrQ0T/AJDFv9T/AOgmoqfA/Qul8cfUz6KKKsgKKKKACiiigDuk8U2em29vaSwzs8cEeSoGDlAfX3pf+E30/wD597n8l/xrktX/AOP/AP7Yw/8Aotao1xxwdKS5n18zvlj60Xypqy8ju/8AhN9P/wCfe5/Jf8aP+E30/wD597n8l/xrhKKf1Gj2/En+0a/dfcd3/wAJvp//AD73P5L/AI0f8Jvp/wDz73P5L/jXCUUfUaPb8Q/tGv3X3HqOlatFq8DzwRuiK2w78A5wD2+tXgX2nKrnsN3/ANaub8Ef8gmf/ruf/QRXTV5VeChUcVsj2sPN1KUZS3Y0l8DCrnv83T9KXLbug2+uefypaKyNhoL7TlVz2G7/AOtQS+BhVz3+bp+lOooAYxfeAFUrnrnmlBfacquew3f/AFqG/h+bHP506gBpL4GFXPf5un6UuW3dBt9c8/lS0UANBfacquew3f8A1qCXwMKue/zdP0p1FACZbd0G31zz+VNUybMlVDdhu/8ArU+kX7o5zx1oAQl8DCrnv83T9KXLbug2+uefypaKAGgvtOVXPYbv/rUEvgYVc9/m6fpTqKAEy27oNvrnn8qapkw+UUEH5fm6/pT6ao5b5s89PTigAJfAwq57/N0/Sly27oNvrnn8qWigDnfHUEtz4PvYoYvNctEfKKSuJAJUJVliR3KkAggKcgnoMkYHgi60xvEM9tbeE9J0a7W0LtJa2lxDIyblGD5lrCNpPP3icr0OCR0/iy9nsPDs0tpJPHdPLBBC0DRq3mSSpGozIjqASwBJU4GcDOKwfD8GrWnjKKPxC8txey6fM1pN9sjmRI1ki81cJbw4JLRHJDZx1GOQDuKKKKACiiigApse3yk2/dwMfSnUiZKKWGDjkelAC0UUUAFFFFABXCeN/wDkKwf9cR/6Ea7uuF8b/wDIVt/+uA/9CNdeB/jL5nDmP8B+qOYooor2jwAooooAK0Lb/kC6h/vxf+zVn1oW3/IF1D/fi/8AZqie3zX5l0936P8AIz6KKKsgKKKKACtDRP8AkMW/1P8A6Caz60NE/wCQxb/U/wDoJqKnwP0LpfHH1M+iiirICiiigAooooAvav8A8f8A/wBsYf8A0WtUavav/wAf/wD2xh/9FrVGpp/Ci6vxsKKKKogKKKKAO78Ef8gmf/ruf/QRXTVzPgj/AJBM/wD13P8A6CK6avBxX8aR9Jg/4EfQKKKKwOkKKKKAGvtyufXj606kbPGBnnmloAKKKKACiiigApFxtGOmOKWkGdoyMGgBaKKKACiiigApq7dz4655+uBTqQZy2RgZ49+KAFooooA5zx3azXvg69t4IZpWd4Q3kRl5UTzULyRqOS6Ludcc7lGAelY/h21tv+E5N3pkOtvZnTXiml1aO7zFJ5qFVja5+b5huLBeP3adD17uigAooooAKKKKACmoMIoLbuBz606mx7fKTb93Ax9KAHUUUUAFFFFABXC+N/8AkK2//XAf+hGu6rhPG/8AyFYP+uI/9CNdeB/jL5nDmP8AAfqjmaKKK9o8AKKKKACtC2/5Auof78X/ALNWfWhbf8gXUP8Afi/9mqJ7fNfmXT3fo/yM+iiirICiiigArQ0T/kMW/wBT/wCgms+tDRP+Qxb/AFP/AKCaip8D9C6Xxx9TPoooqyAooooAKKKKAL2r/wDH/wD9sYf/AEWtUavav/x//wDbGH/0WtUamn8KLq/GwoooqiAooooA7vwR/wAgmf8A67n/ANBFdNXM+CP+QTP/ANdz/wCgiumrwcV/GkfSYP8AgR9AooorA6QooooAa38PzY5/OnU19uVz68fWnUAFFFFABRRRQAUi/dHOeOtLSLjaMdMcUALRRRQAUUUUAFNUct82eenpxTqau3c+OuefrgUAOooooAKKKKACiiigAopCM98UY5zk/SgBaRMlFLDBxyPSjBx940iA7FG8kgDn1oAdRSY5zk/SjBx940ALRSYPHJoxznJ+lAC1wvjf/kK2/wD1wH/oRrucHH3jXC+Nv+Qpb85/cD/0I114H+MvmcOY/wAB+qOZooor2jwAooooAK0Lb/kC6h/vxf8As1Z9aFt/yBdQ/wB+L/2aont81+ZdPd+j/Iz6KKKsgKKKKACtDRP+Qxb/AFP/AKCaz60NE/5DFv8AU/8AoJqKnwP0LpfHH1M+iiirICiiigAooooAvav/AMf/AP2xh/8ARa1Rq9q//H//ANsYf/Ra1Rqafwour8bCiiiqICiiigDu/BH/ACCZ/wDruf8A0EV01cz4I/5BU/8A13P/AKCK6XBx9414OK/jSPpMH/Aj6C0UmDxyaMc5yfpWB0i0UmDj7xoweOTQANnjAzzzS01gcg7sc9PWlwcfeNAC0UmDxyaMc5yfpQAtFJg4+8aMHjk0ALSDO0ZGDRjnOT9KRQdoG4n3oAdRSYPHJoxznJ+lAC0UmDj7xoweOTQAtIM5bIwM8e/FGOc5P0pFBy/zk5P5cUAOopMHjk0Y5zk/SgBaKKKACiiigAooooAKbHt8pNv3cDH0p1ImSilhg45HpQAtFFFABRRRQAVwnjf/AJCsH/XEf+hGu7rhfG//ACFbf/rgP/QjXXgf4y+Zw5j/AAH6o5iiiivaPACiiigArQtv+QLqH+/F/wCzVn1oW3/IF1D/AH4v/Zqie3zX5l0936P8jPoooqyAooooAK0NE/5DFv8AU/8AoJrPrQ0T/kMW/wBT/wCgmoqfA/Qul8cfUz6KKKsgKKKKACiiigC9q/8Ax/8A/bGH/wBFrVGr2r/8f/8A2xh/9FrVGpp/Ci6vxsKKKKogKKKKAO78Ef8AIJn/AOu5/wDQRXTVzPgj/kEz/wDXc/8AoIrpq8HFfxpH0mD/AIEfQKKKKwOkKKKKAGvtyufXj606kbPGBnnmloAKKKKACiiigApFxtGOmOKWkGdoyMGgBaKKKACiiigApq7dz4655+uBTqQZy2RgZ49+KAFooooAKKKKACiiigAooooAKagwigtu4HPrTqbHt8pNv3cDH0oAdRRRQAUUUUAFcL43/wCQrb/9cB/6Ea7quE8b/wDIVg/64j/0I114H+MvmcOY/wAB+qOZooor2jwAooooAK0Lb/kC6h/vxf8As1Z9aFt/yBdQ/wB+L/2aont81+ZdPd+j/Iz6KKKsgKKKKACtDRP+Qxb/AFP/AKCaz60NE/5DFv8AU/8AoJqKnwP0LpfHH1M+iiirICiiigAooooAvav/AMf/AP2xh/8ARa1Rq9q//H//ANsYf/Ra1Rqafwour8bCiiiqICiiigDu/BH/ACCZ/wDruf8A0EV01cz4I/5BM/8A13P/AKCK6avBxX8aR9Jg/wCBH0CiiisDpCiiigBrfw/Njn86dTX25XPrx9adQAUUUUAFFFFABSL90c5460tIuNox0xxQAtFFFABRRRQAU1Ry3zZ56enFOpq7dz4655+uBQA6iiigAooooAKKwtK8URanqg0+TS9SsJpIGuYPtkap50asFLABiy4LLw4U89OuK/hvxrY+KLyeCwt5gkKlmlae3YfewMokrSLnkjco4H4UAdLRRRQAUiZKKWGDjkelLTUGEUFt3A59aAHUUUUAFFFFABXC+N/+Qrb/APXAf+hGu6rhfG//ACFbf/rgP/QjXXgf4y+Zw5j/AAH6o5iiiivaPACiiigArQtv+QLqH+/F/wCzVn1oW3/IF1D/AH4v/Zqie3zX5l0936P8jPoooqyAooooAK0NE/5DFv8AU/8AoJrPrQ0T/kMW/wBT/wCgmoqfA/Qul8cfUz6KKKsgKKKKACiiigC9q/8Ax/8A/bGH/wBFrVGr2r/8f/8A2xh/9FrVGpp/Ci6vxsKKKKogKKKKAO78Ef8AIJn/AOu5/wDQRXTVzPgj/kEz/wDXc/8AoIrpq8HFfxpH0mD/AIEfQKKKKwOkKKKKAEbPGBnnmlprfw/Njn86dQAUUUUAFFFFABSDO0ZGDS0i/dHOeOtAC0UUUAFFFFABSDOWyMDPHvxS01Ry3zZ56enFADqKKKACiiigDl9A8Oaxo0ksk+q6bdyzFTNcf2bIs8pDgnc5nbI27wAAApIIGBtJoPhS50nU7e7udQtZ47O0ks7WO2sFttqO6OS+1irN+7X7qoB83HPHUUUAMEUY6Rr+VO2ru3bRu9cc0tFADfLQKRsXB6jHWmokZijwo2gDbkdKkpEyUUsMHHI9KADau7dtG71xzSeWgUjYuD1GOtOooAbsQgAqvHTjpS7V3bto3euOaWigBvloFI2Lg9RjrXDeNgBqluAMAQDj/gRru64Xxv8A8hW3/wCuA/8AQjXXgf4y+Zw5j/AfqjmKKKK9o8AKKKKACtC2/wCQLqH+/F/7NWfWhbf8gXUP9+L/ANmqJ7fNfmXT3fo/yM+iiirICiiigArQ0T/kMW/1P/oJrPrQ0T/kMW/1P/oJqKnwP0LpfHH1M+iiirICiiigAooooAvav/x//wDbGH/0WtUavav/AMf/AP2xh/8ARa1Rqafwour8bCiiiqICiiigDuvBAH9lTnHPnn/0Fa6Xy0CkbFweox1rm/BH/IJn/wCu5/8AQRXTV4OK/jSPpMH/AAI+g3YhABVeOnHSl2ru3bRu9cc0tFYHSN8tApGxcHqMdaNiEAFV46cdKdRQAxlQOrFRuzwcc0vloFI2Lg9RjrStnjAzzzS0AN2IQAVXjpx0pdq7t20bvXHNLRQA3y0CkbFweox1o2IQAVXjpx0p1FACbV3bto3euOaaqR+XgINp7Yp9IM7RkYNACbEIAKrx046Uu1d27aN3rjmlooAb5aBSNi4PUY60bEIAKrx046U6igBNq7t20bvXHNMRI/3iqgxu+YY4JwKkpBnLZGBnj34oATYhABVeOnHSl2ru3bRu9cc0tFABRRRQAUUUUAFFFFABTUGEUFt3A59adTY9vlJt+7gY+lADqKKKACiiigArhfG//IVt/wDrgP8A0I13VcJ43/5CsH/XEf8AoRrrwP8AGXzOHMf4D9UczRRRXtHgBRRRQAVoW3/IF1D/AH4v/Zqz60Lb/kC6h/vxf+zVE9vmvzLp7v0f5GfRRRVkBRRRQAVoaJ/yGLf6n/0E1n1oaJ/yGLf6n/0E1FT4H6F0vjj6mfRRRVkBRRRQAUUUUAXtX/4//wDtjD/6LWqNXtX/AOP/AP7Yw/8Aotao1NP4UXV+NhRRRVEBRRRQB3fgj/kEz/8AXc/+giumrmfBH/IJn/67n/0EV01eDiv40j6TB/wI+gUUUVgdIUUUUANb+H5sc/nTqa+3K59ePrTqACiiigAooooAKRfujnPHWlpFxtGOmOKAFooooAKKKKACmqOW+bPPT04p1NXbufHXPP1wKAHUUUUAFFFFABRRRQAUUUUAFImSilhg45HpS01BhFBbdwOfWgB1FFFABRRRQAVwvjf/AJCtv/1wH/oRruq4Xxv/AMhW3/64D/0I114H+MvmcOY/wH6o5iiiivaPACiiigArQtv+QLqH+/F/7NWfWhbf8gXUP9+L/wBmqJ7fNfmXT3fo/wAjPoooqyAooooAK0NE/wCQxb/U/wDoJrPrQ0T/AJDFv9T/AOgmoqfA/Qul8cfUz6KKKsgKKKKACiiigC9q/wDx/wD/AGxh/wDRa1Rq9q//AB//APbGH/0WtUamn8KLq/GwoooqiAooooA7vwR/yCZ/+u5/9BFdNXM+CP8AkEz/APXc/wDoIrpq8HFfxpH0mD/gR9AooorA6QooooARs8YGeeaWmt/D82Ofzp1ABRRRQAUUUUAFIM7RkYNLSL90c5460ALRRRQAUUUUAFIM5bIwM8e/FLTVHLfNnnp6cUAOooooAKKKKACiiigBCcev5UZGcc/lS0UAJuGCeePY02Nl8pNoO0gY47U+kTJRSwwccj0oAMjOOfyo3DBPPHsaWigBNw468+1GRnHP5UtFACbhgnnj2NcL42OdUtz/ANMB/wChGu7rhfG//IVt/wDrgP8A0I114H+MvmcOY/wH6o5iiiivaPACiiigArQtv+QLqH+/F/7NWfWhbf8AIF1D/fi/9mqJ7fNfmXT3fo/yM+iiirICiiigArQ0T/kMW/1P/oJrPrQ0T/kMW/1P/oJqKnwP0LpfHH1M+iiirICiiigAooooAvav/wAf/wD2xh/9FrVGr2r/APH/AP8AbGH/ANFrVGpp/Ci6vxsKKKKogKKKKAO78Ef8gqf/AK7n/wBBWul3DBPPHsa5rwR/yCZ/+u5/9BFdNXg4r+NI+kwf8CPoJuHHXn2oyM45/KlorA6RNwwTzx7GjcOOvPtS0UAMcruUEHOeOKduGCeePY0NnjAzzzS0AJuHHXn2oyM45/KlooATcME88exo3Djrz7UtFACZGcc/lSKy7AQDjHoadSDO0ZGDQAbhx159qMjOOfypaKAE3DBPPHsaNw468+1LRQAmRnHP5U1WXLkA8NzweuBT6QZy2RgZ49+KADcOOvPtRkZxz+VLRQAUUUUAFFFFABRRRQAU1BhFBbdwOfWnU2Pb5Sbfu4GPpQA6iiigAooooAK4Xxv/AMhW3/64D/0I13VcJ43/AOQrB/1xH/oRrrwP8ZfM4cx/gP1RzNFFFe0eAFFFFABWhbf8gXUP9+L/ANmrPrQtv+QLqH+/F/7NUT2+a/Munu/R/kZ9FFFWQFFFFABWhon/ACGLf6n/ANBNZ9aGif8AIYt/qf8A0E1FT4H6F0vjj6mfRRRVkBRRRQAUUUUAXtX/AOP/AP7Yw/8Aotao1e1f/j//AO2MP/otao1NP4UXV+NhRRRVEBRRRQBtWmp3mmaFG1nN5ZkuZA3yg5wqY6j3pv8AwlOtf8/n/kJP8KrP/wAgGD/r6k/9BSqFYqlCV20t2byrVIpKMmlZdTY/4SnWv+fz/wAhJ/hR/wAJTrX/AD+f+Qk/wrHoqvY0/wCVfcT9Yq/zP7zY/wCEp1r/AJ/P/ISf4Uf8JTrX/P5/5CT/AArHoo9jT/lX3B9Yq/zP7zpLDxHqsyXhkusmO3Z1/drwcjnp71T/AOEp1r/n8/8AISf4VX0v/V3/AP16N/6EtZ9RGjT5n7q6dDSVeryx959evmbH/CU61/z+f+Qk/wAKP+Ep1r/n8/8AISf4Vj0Vfsaf8q+4z+sVf5n95sf8JTrX/P5/5CT/AAo/4SnWv+fz/wAhJ/hWPRR7Gn/KvuD6xV/mf3m3D4o1lpo1N5kFgD+6T1+lejdq8ig/4+Iv98fzr10dK87HwjFx5VY9XLak5qXM77BRRRXnnphRRRQAU1Ry3zZ56enFOpq7dz4655+uBQA6iiigAooooAKKKKACiiigApEyUUsMHHI9KWmoMIoLbuBz60AOooooAKKKKACuF8b/APIVt/8ArgP/AEI13VcL43/5Ctv/ANcB/wChGuvA/wAZfM4cx/gP1RzFFFFe0eAFFFFABWhbf8gXUP8Afi/9mrPrQtv+QLqH+/F/7NUT2+a/Munu/R/kZ9FFFWQFFFFABWhon/IYt/qf/QTWfWhon/IYt/qf/QTUVPgfoXS+OPqZ9FFFWQFFFFABRRRQBe1f/j//AO2MP/otao1e1f8A4/8A/tjD/wCi1qjU0/hRdX42FFFFUQFFFFAF9/8AkAwf9fUn/oKVQq+//IBg/wCvqT/0FKoVMOvqy6nT0QUUUVRAUUUUAaGl/wCrv/8Ar0b/ANCWs+tDS/8AV3//AF6N/wChLWfUR+J/L8i5fBH5/mFFFFWQFFFFAEkH/HxF/vj+deu15FB/x8Rf74/nXrvavMzHePzPXyvaXyCiiivNPWCiiigApBnLZGBnj34paao5b5s89PTigB1FFFABRRRQAUV5l4I1mBtQ0hjr1xczz6SW1iO5vWkWC9aSBUQqxxC5Z5lCDbnGMcCpPAWpLd6ppKw6xd311No7zazDNdvMLe7Dw4BRiRC2WnGwBeF6cUAek0UwGTuq/wDfX/1qd827oNvrnmgBabHt8pNv3cDH0oy+DlVz2Gf/AK1KudoyOcc0ALRR3pOaAFoo5o70AFcJ43/5CsH/AFxH/oRruuawdd8OvrN3HOtysWxNmCmc8k+vvXThZxhV5pPQ5cbTlUouMFd6HntFdb/wg0v/AD/p/wB+z/jR/wAINL/z/p/37P8AjXqfW6P8x431HEfy/kclRXW/8INL/wA/6f8Afs/40f8ACDS/8/6f9+z/AI0fW6P8wfUcR/L+RyVaFt/yBdQ/34v/AGat3/hBpf8An/T/AL9n/GrEXg+SOyuLb7YpMzId3l9Nuff3qJ4qi1pLsXDBV09Y9GcVRXW/8INL/wA/6f8Afs/40f8ACDS/8/6f9+z/AI1f1uj/ADEfUcR/L+RyVFdb/wAINL/z/p/37P8AjR/wg0v/AD/p/wB+z/jR9bo/zB9RxH8v5HJVoaJ/yGLf6n/0E1u/8INL/wA/6f8Afs/41YsfB8lneR3BvFcJn5RHjPGPWoniqLi0pF08FXU03E4qiut/4QaX/n/T/v2f8aP+EGl/5/0/79n/ABq/rdH+Yj6jiP5fyOSorrf+EGl/5/0/79n/ABo/4QaX/n/T/v2f8aPrdH+YPqOI/l/I5Kiut/4QaX/n/T/v2f8AGj/hBpf+f9P+/Z/xo+t0f5g+o4j+X8jA1f8A4/8A/tjD/wCi1qjXaXng6S7uPNF4q/IiYMf91Qvr7VB/wg0v/P8Ap/37P+NTDFUVFJyLngq7k2o/kclRXW/8INL/AM/6f9+z/jR/wg0v/P8Ap/37P+NV9bo/zEfUcR/L+RyVFdb/AMINL/z/AKf9+z/jR/wg0v8Az/p/37P+NH1uj/MH1HEfy/kYL/8AIBg/6+pP/QUqhXat4OkbT47X7YuUlaTd5fXIUY6/7P61X/4QaX/n/T/v2f8AGojiqKv73UueCru1o9F2OSorrf8AhBpf+f8AT/v2f8aP+EGl/wCf9P8Av2f8av63R/mI+o4j+X8jkqK63/hBpf8An/T/AL9n/Gj/AIQaX/n/AE/79n/Gj63R/mD6jiP5fyMLS/8AV3//AF6N/wChLWfXa2vg+S2W4BvFbzojFxH0yQc9faq//CDS/wDP+n/fs/41CxVHmb5i5YKu4pcvc5Kiut/4QaX/AJ/0/wC/Z/xo/wCEGl/5/wBP+/Z/xq/rdH+Yj6jiP5fyOSorrf8AhBpf+f8AT/v2f8aP+EGl/wCf9P8Av2f8aPrdH+YPqOI/l/I5aD/j4i/3x/OvXR0rjY/BEqSo/wBuQ7SD/qz/AI12IzjpXBjasKjjyO56WX0KlJS51a9haKOaO9cJ6IUUnNLzQAU1du58dc8/XAp3em5b5vlGc8c9aAHUU3L4GFX356fpS/Nu6Db655oAWiiigAooooAKKKKACmoMIoLbuBz606mx48tdvC4GM+lADqKKKACiiigApB1PPelpBjJx680ALRRRQAUUUUAFNP31+bHXj1p1NOPMXI5wcUAOooooAKKKKACkb7p5xx1paRsbTnpigBaKKKACiiigAooooAagwOWzyefxp1NTG07emT+eadQAUUUUAFFFFACfxHnt0paTjcfXFLQAUUUUAFFFFADW/h+bHP506mvjK5z14p1ABRRRQAUUUUAFIv3RznjrS0i42jHTFAC0UUUAFFFFABTVHLfNnnp6cU6mrjc+OuefrgUAOooooAKKKKACiiigAooooAiubq3sraS5up4oLeJS0ksrhVQDuSeAKh02/stSsI7jT723vLc/KJreVZEJHBwQSKz/ABbY3OoeH2itLYXM0d1a3IgLBfNWKeOVkBbjJCEDOBk8kVH4YtbyOTWr28sGsDqGofaI7eR0Z0UQRR5bYSuSY2bgnqO9AG/RRRQAUUUUAFFFFABRRRQAUUUUARzzw2tvJcXEscMMSl5JJGCqijkkk8AVSuNb0i21WOwuNWsYb5lLLayXCLKw65Ck5I4PbtVTxlpTa54K1vTIraO5nuLKVIIn24aXafL+9wCG2kE9CAeMVxfjjwvrmt6d4p0Sx0gSHWbu3uYNQaaNYohHHCpVwTv3ZhYDapHz9RzQB6HpusaXrMLzaXqVnfRI213tZ1lCn0JUnBq7XM+Hre+l8Q6nq11o8mlRT2lrbRwSyRM7GIzFm/dsy7f3qqOc/L0HFdNQAUUUUAFFFFABRRRQAUUUUAFZ8+u6Ra6imnXGq2MN867ltpLhFlYcnIUnJHB/KtCvN/G+g6n4ibWtJXQLg2d1GPJuLY2yJPP5YCSzs0gkwjbRhVzhP4gQtAHWJ4v8MLZG6PiTSDbeaY/O+3R7N5527t2M47VtqyugdGDKwyCDkEV5p9g1z/hYP/CV/wDCHz/Zf7I/sz7D9otfO379/mf6zZsx8n3t3+ziu38MafPpHhPRtNudv2izsYLeXYcjckYU4PpkUAatFFFABRRRQAUUUUAFFFFABUVzdW9lbSXN1PFBbxKWkllcKqAdyTwBUtZ+tK7aVL5elxao6sji0kZVEhVweCw27hjcM4GQOR1ABWPirw462jr4g0orcvi3IvI8TEHBCc/MckDjvVux1vStTuJ7ew1Oyu57c4mjguFkaI5xhgDkcg9fSvK4fC3iWMeMPN0K5un8R2otoZ7ie1E0LAOu+4CMEH3wR5QbiMZGa6fwfo+q2d3oSXujmxTR9GfTpJ2liYXMhaDDIEYnb+5Y/MFPzjjrQB3dFFFABRRRQAUUUUAFFFFABRRRQBFPcwWqI1xPHCrusamRwoZ2OFUZ6kkgAdyayx4q8OILx28QaUFtnAnJvI8Qk/KA/PynII5rE8beGtb1q7sLvTb21KWtxavHaz2pfY63CO8wbzUHCDkYJKhgpBeuZt/DviCDUPBF0fDEhXwvavbXCrcW+67ZoxGHh+fGAVL/ADlD83TNAHqttdW97bR3NrPFPbyqGjlicMrg9wRwRUtYnhKxudP8PrFd2wtppLq6uTAGDeUss8kqoSvGQHAOMjI4JrboAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK43/hKNUs/EGrwzWS3GlwatbWSz+cEaHzorcKqoF+ceZLkksCA/GcYHZVjzeG7Of7buknH2zULfUJMMOJIfJ2gcfdPkJkdeW5HGADIj8Yai/wDpTaHEmlrqraY0/wBt/elvtJt1kWPZgqW25ywI54OMnr64zSPBsxSRtR1DUI4jq9xf/wBnrJEYXP2t5YWJ2lwMeW20MBnqM5FdnQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//Z" /></p>
<p>Options for unification can be set with <code>panderOptions</code>. For example:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1"><span class="kw">panderOptions</span>(<span class="st">'graph.fontfamily'</span>, <span class="st">&quot;Comic Sans MS&quot;</span>)</a>
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="kw">panderOptions</span>(<span class="st">'graph.fontsize'</span>, <span class="dv">18</span>)</a>
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="kw">panderOptions</span>(<span class="st">'graph.fontcolor'</span>, <span class="st">'blue'</span>)</a>
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="kw">panderOptions</span>(<span class="st">'graph.grid.color'</span>, <span class="st">'blue'</span>)</a>
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="kw">panderOptions</span>(<span class="st">'graph.axis.angle'</span>, <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb8-6" data-line-number="6"><span class="kw">panderOptions</span>(<span class="st">'graph.boxes'</span>, T)</a>
<a class="sourceLine" id="cb8-7" data-line-number="7"><span class="kw">panderOptions</span>(<span class="st">'graph.legend.position'</span>, <span class="st">'top'</span>)</a>
<a class="sourceLine" id="cb8-8" data-line-number="8"><span class="kw">panderOptions</span>(<span class="st">'graph.colors'</span>, <span class="kw">rainbow</span>(<span class="dv">5</span>))</a>
<a class="sourceLine" id="cb8-9" data-line-number="9"><span class="kw">panderOptions</span>(<span class="st">'graph.grid'</span>, <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb8-10" data-line-number="10"><span class="kw">panderOptions</span>(<span class="st">'graph.symbol'</span>, <span class="dv">22</span>)</a></code></pre></div>
<p>More information and examples on style unification can be obtained by <code>Pandoc.brew</code>ing the tutorial available <a href="https://github.com/Rapporter/pander/blob/master/inst/examples/graphs.brew">here</a>.</p>
</div>
</div>
<div id="logging" class="section level2">
<h2>Logging</h2>
<p>To make execution and debugging easier to understand, <code>evals</code> provides logging with the <code>log</code> parameter. Logging in <code>evals</code> relies on the <a href="cran.r-project.org/web/packages/futile.logger/futile.logger.pdf"><code>futile.logger</code></a> package, which provides a logging API similar to <code>log4j</code>. Basic example:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">x &lt;-<span class="st"> </span><span class="kw">evals</span>(<span class="st">'1:10'</span>, <span class="dt">log =</span> <span class="st">'foo'</span>)</a>
<a class="sourceLine" id="cb9-2" data-line-number="2"><span class="co">#&gt; INFO [2018-11-06 11:17:40] Command run: 1:10</span></a></code></pre></div>
<p><code>futile.logger</code>’s thresholds range from most verbose to least verbose: <code>TRACE</code>, <code>DEBUG</code>, <code>INFO</code>, <code>WARN</code>, <code>ERROR</code>, <code>FATAL</code>. The threshold defaults to <code>INFO</code>, which will hide some unessential information. To permanently set the threshold for logger use <code>flog.threshold</code>:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1"><span class="kw">evalsOptions</span>(<span class="st">'log'</span>, <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb10-2" data-line-number="2"><span class="kw">flog.threshold</span>(TRACE, <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb10-4" data-line-number="4">x &lt;-<span class="st"> </span><span class="kw">evals</span>(<span class="st">'1:10'</span>, <span class="dt">cache.time =</span> <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb10-5" data-line-number="5"><span class="co">#&gt; INFO [2018-11-06 11:17:40] Command run: 1:10</span></a>
<a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co">#&gt; TRACE [2018-11-06 11:17:40] Cached result</span></a>
<a class="sourceLine" id="cb10-7" data-line-number="7"><span class="co">#&gt; DEBUG [2018-11-06 11:17:40] Returned object: class = integer, length = 10, dim = , size = 96 bytes</span></a></code></pre></div>
<p><code>futile.logger</code> also provides a very useful ability to write logs to files instead of printing them to the prompt:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1">t &lt;-<span class="st"> </span><span class="kw">tempfile</span>()</a>
<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="kw">flog.appender</span>(<span class="kw">appender.file</span>(t), <span class="dt">name =</span> <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb11-3" data-line-number="3"><span class="co">#&gt; NULL</span></a>
<a class="sourceLine" id="cb11-4" data-line-number="4">x &lt;-<span class="st"> </span><span class="kw">evals</span>(<span class="st">'1:10'</span>, <span class="dt">log =</span> <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb11-5" data-line-number="5"><span class="kw">readLines</span>(t)</a>
<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="co">#&gt; [1] &quot;INFO [2018-11-06 11:17:40] Command run: 1:10&quot;          </span></a>
<a class="sourceLine" id="cb11-7" data-line-number="7"><span class="co">#&gt; [2] &quot;TRACE [2018-11-06 11:17:40] Returning cached R object.&quot;</span></a>
<a class="sourceLine" id="cb11-8" data-line-number="8"><span class="co"># revert back to console</span></a>
<a class="sourceLine" id="cb11-9" data-line-number="9"><span class="kw">flog.appender</span>(<span class="kw">appender.console</span>(), <span class="dt">name =</span> <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb11-10" data-line-number="10"><span class="co">#&gt; NULL</span></a></code></pre></div>
</div>
<div id="result-caching" class="section level2">
<h2>Result Caching</h2>
<p><code>evals</code> is uses a custom caching algorithm to cache the results of evaluated R expressions.</p>
<div id="how-it-works" class="section level3">
<h3>How it works</h3>
<ul>
<li>All R code passed to <code>evals</code> is split into single expressions and parsed.</li>
<li>For each R expression (function call, assignment, etc.), <code>evals</code> extracts symbols in a separate list in <code>getCallParts</code>. This list describes the unique structure and the content of the passed R expressions</li>
<li>A hash is computed for each list element and cached in <code>pander</code>’s local environments. This is useful if you are using large data frames; otherwise, the caching algorithm would have to compute the hash for the same data frame each time it’s touched! This way the hash is recomputed only if the R object with the given name is changed.</li>
<li>The list of such R objects is serialized, then an SHA-1 hash is computed, taking into consideration <code>panderOptions</code> and <code>evalsOptions</code>, which all together is unique and there is no real risk of collision.</li>
<li>If <code>evals</code> can find the cached results in the appropriate environment (if <code>cache.mode set</code> to environment) or in a file named to the computed hash (if <code>cache.mode</code> set to <code>disk</code>), then it is returned on the spot. The objects modified/created by the cached code are also updated.</li>
<li>Otherwise the call is evaluated and the results and the modified R objects of the environment are optionally saved to cache (e.g. if <code>cache</code> is active and if the evaluation <code>proc.time()</code> &gt; <code>cache.time</code> parameter). Cached results are saved in <code>cached.results</code> in <code>pander</code>’s namespace. <code>evals</code> also remembers if R expressions change the evaluation environment (for example assignments) and saves such changes in <code>cached.environemnts</code> in <code>pander</code>’s namespace.</li>
</ul>
</div>
<div id="examples" class="section level3">
<h3>Examples</h3>
<p>We will set <code>cache.time</code> to 0, to cache all expressions regardless of time they took to evaluate. We will also use the logging facilites described above to simplify the understanding of how caching works.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw">evalsOptions</span>(<span class="st">'cache.time'</span>, <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb12-2" data-line-number="2"><span class="kw">evalsOptions</span>(<span class="st">'log'</span>, <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb12-3" data-line-number="3"><span class="kw">flog.threshold</span>(TRACE, <span class="st">'evals'</span>)</a>
<a class="sourceLine" id="cb12-4" data-line-number="4"><span class="co">#&gt; NULL</span></a></code></pre></div>
<p>Let’s start with small example.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'1:1e5'</span>))</a>
<a class="sourceLine" id="cb13-2" data-line-number="2"><span class="co">#&gt; INFO [2018-11-06 11:17:40] Command run: 1:1e+05</span></a>
<a class="sourceLine" id="cb13-3" data-line-number="3"><span class="co">#&gt; TRACE [2018-11-06 11:17:40] Cached result</span></a>
<a class="sourceLine" id="cb13-4" data-line-number="4"><span class="co">#&gt; DEBUG [2018-11-06 11:17:40] Returned object: class = integer, length = 100000, dim = , size = 400048 bytes</span></a>
<a class="sourceLine" id="cb13-5" data-line-number="5"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb13-6" data-line-number="6"><span class="co">#&gt;   0.474   0.011   0.485</span></a>
<a class="sourceLine" id="cb13-7" data-line-number="7"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'1:1e5'</span>))</a>
<a class="sourceLine" id="cb13-8" data-line-number="8"><span class="co">#&gt; INFO [2018-11-06 11:17:40] Command run: 1:1e+05</span></a>
<a class="sourceLine" id="cb13-9" data-line-number="9"><span class="co">#&gt; TRACE [2018-11-06 11:17:40] Returning cached R object.</span></a>
<a class="sourceLine" id="cb13-10" data-line-number="10"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb13-11" data-line-number="11"><span class="co">#&gt;   0.006   0.000   0.005</span></a></code></pre></div>
<p>Results cached by <code>evals</code> can be stored in an <em>environment</em> in current <code>R</code> session or permanently on disk by setting the <code>cache.mode</code> parameter appropriately.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1">res &lt;-<span class="st"> </span><span class="kw">evals</span>(<span class="st">'1:1e5'</span>, <span class="dt">cache.mode =</span> <span class="st">'disk'</span>, <span class="dt">cache.dir =</span> <span class="st">'cachedir'</span>)</a>
<a class="sourceLine" id="cb14-2" data-line-number="2"><span class="co">#&gt; INFO [2018-11-06 11:17:40] Command run: 1:1e+05</span></a>
<a class="sourceLine" id="cb14-3" data-line-number="3"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Cached result</span></a>
<a class="sourceLine" id="cb14-4" data-line-number="4"><span class="co">#&gt; DEBUG [2018-11-06 11:17:41] Returned object: class = integer, length = 100000, dim = , size = 400048 bytes</span></a>
<a class="sourceLine" id="cb14-5" data-line-number="5"><span class="kw">list.files</span>(<span class="st">'cachedir'</span>)</a>
<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co">#&gt; [1] &quot;7e8d4836253d5bafc47099163d42856723684ddb&quot;</span></a></code></pre></div>
<p>Since the hash for caching is computed based on the <em>structure</em> and <em>content</em> of the R commands, instead of the variable names or R expressions, <code>evals</code> is able to achieve great results:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">x &lt;-<span class="st"> </span>mtcars<span class="op">$</span>hp</a>
<a class="sourceLine" id="cb15-2" data-line-number="2">y &lt;-<span class="st"> </span><span class="fl">1e3</span></a>
<a class="sourceLine" id="cb15-3" data-line-number="3"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'sapply(rep(x, y), mean)'</span>))</a>
<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: sapply(rep(x, y), mean)</span></a>
<a class="sourceLine" id="cb15-5" data-line-number="5"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Cached result</span></a>
<a class="sourceLine" id="cb15-6" data-line-number="6"><span class="co">#&gt; DEBUG [2018-11-06 11:17:41] Returned object: class = numeric, length = 32000, dim = , size = 256048 bytes</span></a>
<a class="sourceLine" id="cb15-7" data-line-number="7"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb15-8" data-line-number="8"><span class="co">#&gt;   0.224   0.000   0.224</span></a></code></pre></div>
<p>Let us create some custom functions and variables, which are not identical to the above call:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1">f &lt;-<span class="st"> </span>sapply</a>
<a class="sourceLine" id="cb16-2" data-line-number="2">g &lt;-<span class="st"> </span>rep</a>
<a class="sourceLine" id="cb16-3" data-line-number="3">h &lt;-<span class="st"> </span>mean</a>
<a class="sourceLine" id="cb16-4" data-line-number="4">X &lt;-<span class="st"> </span>mtcars<span class="op">$</span>hp <span class="op">*</span><span class="st"> </span><span class="dv">1</span></a>
<a class="sourceLine" id="cb16-5" data-line-number="5">Y &lt;-<span class="st"> </span><span class="dv">1000</span></a>
<a class="sourceLine" id="cb16-6" data-line-number="6"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'f(g(X, Y), h)'</span>))</a>
<a class="sourceLine" id="cb16-7" data-line-number="7"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: f(g(X, Y), h)</span></a>
<a class="sourceLine" id="cb16-8" data-line-number="8"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Returning cached R object.</span></a>
<a class="sourceLine" id="cb16-9" data-line-number="9"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb16-10" data-line-number="10"><span class="co">#&gt;   0.009   0.000   0.009</span></a></code></pre></div>
<p>Another important feature of <code>evals</code> is that it notes changes in the evaluation environment. For example:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">x &lt;-<span class="st"> </span><span class="dv">1</span></a>
<a class="sourceLine" id="cb17-2" data-line-number="2">res &lt;-<span class="st"> </span><span class="kw">evals</span>(<span class="st">'x &lt;- 1:10;'</span>)</a>
<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: x &lt;- 1:10</span></a>
<a class="sourceLine" id="cb17-4" data-line-number="4"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Cached result</span></a></code></pre></div>
<p><code>x &lt;- 1:10</code> will be cached; if the same assignment occurs again we won’t need to evaluate it. But what about the change of <code>x</code> when we get the result from the cache? <code>evals</code> takes care of that.</p>
<p>So in the following example we can see that <code>x &lt;- 1:10</code> is not evaluated, but retrieved from cache with the change to <code>x</code> in the environment.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1"><span class="kw">evals</span>(<span class="st">'x &lt;- 1:10; x[3]'</span>)[[<span class="dv">2</span>]]<span class="op">$</span>result</a>
<a class="sourceLine" id="cb18-2" data-line-number="2"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: x &lt;- 1:10</span></a>
<a class="sourceLine" id="cb18-3" data-line-number="3"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Returning cached R object.</span></a>
<a class="sourceLine" id="cb18-4" data-line-number="4"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: x[3]</span></a>
<a class="sourceLine" id="cb18-5" data-line-number="5"><span class="co">#&gt; TRACE [2018-11-06 11:17:41] Cached result</span></a>
<a class="sourceLine" id="cb18-6" data-line-number="6"><span class="co">#&gt; DEBUG [2018-11-06 11:17:41] Returned object: class = integer, length = 1, dim = , size = 56 bytes</span></a>
<a class="sourceLine" id="cb18-7" data-line-number="7"><span class="co">#&gt; [1] 3</span></a></code></pre></div>
<p>Also <code>evals</code> is able to cache output to graphical devices produced during evaluation:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'plot(mtcars)'</span>))</a>
<a class="sourceLine" id="cb19-2" data-line-number="2"><span class="co">#&gt; INFO [2018-11-06 11:17:41] Command run: plot(mtcars)</span></a>
<a class="sourceLine" id="cb19-3" data-line-number="3"><span class="co">#&gt; TRACE [2018-11-06 11:17:42] Image file written: my_plots/test.jpeg</span></a>
<a class="sourceLine" id="cb19-4" data-line-number="4"><span class="co">#&gt; TRACE [2018-11-06 11:17:42] Cached result</span></a>
<a class="sourceLine" id="cb19-5" data-line-number="5"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb19-6" data-line-number="6"><span class="co">#&gt;   0.131   0.004   0.135</span></a>
<a class="sourceLine" id="cb19-7" data-line-number="7"><span class="kw">system.time</span>(<span class="kw">evals</span>(<span class="st">'plot(mtcars)'</span>))</a>
<a class="sourceLine" id="cb19-8" data-line-number="8"><span class="co">#&gt; INFO [2018-11-06 11:17:42] Command run: plot(mtcars)</span></a>
<a class="sourceLine" id="cb19-9" data-line-number="9"><span class="co">#&gt; TRACE [2018-11-06 11:17:42] Image found in cache: my_plots/test.jpeg</span></a>
<a class="sourceLine" id="cb19-10" data-line-number="10"><span class="co">#&gt;    user  system elapsed </span></a>
<a class="sourceLine" id="cb19-11" data-line-number="11"><span class="co">#&gt;   0.008   0.000   0.007</span></a></code></pre></div>
</div>
</div>



<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>

</body>
</html>