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Instead (re)-allocating memory and parsing the values into R data types (particularly for characters) takes the bulk of the time.</p> <p>Therefore you can obtain very rapid input by first performing a fast indexing step and then using the Altrep framework available in R versions 3.5+ to access the values in a lazy / delayed fashion.</p> <div id="how-it-works" class="section level2"> <h2>How it works</h2> <p>The initial reading of the file simply records the locations of each individual record, the actual values are not read into R. Altrep vectors are created for each column in the data which hold a pointer to the index and the memory mapped file. When these vectors are indexed the value is read from the memory mapping.</p> <p>This means initial reading is extremely fast, in the real world dataset below it is ~ 1/4 the time of the multi-threaded <code>data.table::fread()</code>. Sampling operations are likewise extremely fast, as only the data actually included in the sample is read. This means things like the tibble print method, calling <code>head()</code>, <code>tail()</code> <code>x[sample(), ]</code> etc. have very low overhead. Filtering also can be fast, only the columns included in the filter selection have to be fully read and only the data in the filtered rows needs to be read from the remaining columns. Grouped aggregations likewise only need to read the grouping variables and the variables aggregated.</p> <p>Once a particular vector is fully materialized the speed for all subsequent operations should be identical to a normal R vector.</p> <p>This approach potentially also allows you to work with data that is larger than memory. As long as you are careful to avoid materializing the entire dataset at once it can be efficiently queried and subset.</p> </div> <div id="reading-delimited-files" class="section level1"> <h1>Reading delimited files</h1> <p>The following benchmarks all measure reading delimited files of various sizes and data types. Because vroom delays reading the benchmarks also do some manipulation of the data afterwards to try and provide a more realistic performance comparison.</p> <p>Because the <code>read.delim</code> results are so much slower than the others they are excluded from the plots, but are retained in the tables.</p> <div id="taxi-trip-dataset" class="section level2"> <h2>Taxi Trip Dataset</h2> <p>This real world dataset is from Freedom of Information Law (FOIL) Taxi Trip Data from the NYC Taxi and Limousine Commission 2013, originally posted at <a href="https://chriswhong.com/open-data/foil_nyc_taxi/" class="uri">https://chriswhong.com/open-data/foil_nyc_taxi/</a>. It is also hosted on <a href="https://archive.org/details/nycTaxiTripData2013">archive.org</a>.</p> <p>The first table trip_fare_1.csv is 1.55G in size.</p> <pre><code>#> Observations: 14,776,615 #> Variables: 11 #> $ medallion <chr> "89D227B655E5C82AECF13C3F540D4CF4", "0BD7C8F5B... #> $ hack_license <chr> "BA96DE419E711691B9445D6A6307C170", "9FD8F69F0... #> $ vendor_id <chr> "CMT", "CMT", "CMT", "CMT", "CMT", "CMT", "CMT... #> $ pickup_datetime <chr> "2013-01-01 15:11:48", "2013-01-06 00:18:35", ... #> $ payment_type <chr> "CSH", "CSH", "CSH", "CSH", "CSH", "CSH", "CSH... #> $ fare_amount <dbl> 6.5, 6.0, 5.5, 5.0, 9.5, 9.5, 6.0, 34.0, 5.5, ... #> $ surcharge <dbl> 0.0, 0.5, 1.0, 0.5, 0.5, 0.0, 0.0, 0.0, 1.0, 0... #> $ mta_tax <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0... #> $ tip_amount <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... #> $ tolls_amount <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.8, 0.0, 0... #> $ total_amount <dbl> 7.0, 7.0, 7.0, 6.0, 10.5, 10.0, 6.5, 39.3, 7.0...</code></pre> <div id="taxi-benchmarks" class="section level3"> <h3>Taxi Benchmarks</h3> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/taxi">bench/taxi</a></p> <p>All benchmarks were run on a Amazon EC2 <a href="https://aws.amazon.com/ec2/instance-types/m5/">m5.4xlarge</a> instance with 16 vCPUs and an <a href="https://aws.amazon.com/ebs/">EBS</a> volume type.</p> <p>The benchmarks labeled <code>vroom_base</code> uses <code>vroom</code> with base functions for manipulation. <code>vroom_dplyr</code> uses <code>vroom</code> to read the file and dplyr functions to manipulate. <code>data.table</code> uses <code>fread()</code> to read the file and <code>data.table</code> functions to manipulate and <code>readr</code> uses <code>readr</code> to read the file and <code>dplyr</code> to manipulate. By default vroom only uses Altrep for character vectors, these are labeled <code>vroom(altrep: normal)</code>. The benchmarks labeled <code>vroom(altrep: full)</code> instead use Altrep vectors for all supported types and <code>vroom(altrep: none)</code> disable Altrep entirely.</p> <p>The following operations are performed.</p> <ul> <li>The data is read</li> <li><code>print()</code> - <em>N.B. read.delim uses <code>print(head(x, 10))</code> because printing the whole dataset takes > 10 minutes</em></li> <li><code>head()</code></li> <li><code>tail()</code></li> <li>Sampling 100 random rows</li> <li>Filtering for “UNK” payment, this is 6434 rows (0.0435% of total).</li> <li>Aggregation of mean fare amount per payment type.</li> </ul> <img 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SDs2rVrpvV28eLF9u9//9uGDRvm5+sfBZBaTkFedmCsll8Fkdtss4199dVX9vXXX1fLq5ZRgNqjR49MXrWcgsoWLVrYZ599ZptttplttFHx1xS+/e1vZ/KrVlvlv3fv3n6aWmeVhg8fnimnPl955ZU2ZcoUO++88/Qxr3TJJZdYq1atYpc99thjbcKECXbZZZfFzmciAggggAACCCCAAAIIIJBUgToPgE855RS77777fKAYBIGdddZZdsMNN9iTTz5pH374oW95VLfkXXfd1e666y7r1KmTn3b++efbcccd5x3VYqnuy5qnLsFq+dxqq61s/PjxPghVgKsAuH///j6w/uKLL2z+/Pl2zTXX2IABA3wr6B577JGpE+37gQce8Nt4++23bb/99rMf/ehHmfkvv/yyzZo1y3cB7tmzp02bNq1KXvfZZx+/beXh3XfftYsvvtiOOOIImzlzpi+bAuFevXqZWmpvueUW23LLLTPb1psxY8ZUa11WIK3yl5J0UUEXBZTnuko777yzjRo1ygfa7du3r6vNsh0EEEAAAQQQQAABBBBAoOICdRoAqyVU96o+99xzdtppp5nusVWLqwJHJQVsf/rTn3zQdsIJJ9j1119vCrh036paNo866ihTMPr888+buveqlfLhhx+2Rx55xK644gq/DQV899xzjw+Add+qAlm9/v73v7e//OUvPgBWt96TTjrJL68gXN2c1cVZQaoC5ZNPPtl+8IMf+Pn658ADD7RnnnnGB7W77LKLD4DDvGr973//+zZixAjbbbfdTMG2AuAhQ4b49dVyrOBaQe8TTzzhg3UF8NF06aWX+lbl6LRiW4qVD93/q6BbLdr9+vWzQw89NLrpWt+fc845VVqqdTFCeVRq2rSpb3X+4IMPvGWtG2MBBBBAAAEEEEAAAQQQQCAhAnUaAKvMahlVC6ICYHXNDQNFzdtxxx198KZuzwqWd9ppJ032rcWbb765/fOf/7QZM2bYXnvtlemie9BBB9kZZ5yRCVjVwqsAUAM/tWnTJjPgk7o1q9uzkoI3dbNW0rKXX365vfjiiz6Yfv/9901BrbpR15TCvKqlWYG8uiSrPEorVqzw+dR75XvL/7T4Kt+33XabD/QVSIZJrcYLFy4MP/pX5X3vvfeuMi2fD8ccc4wPXnWRQWXQBQANbqWkwbIUGGensPt5OF0t5fIPk/ISTbIjAI6K8B4BBBBAAAEEEEAAAQTSIFDnAbBGJVbrproa/+1vf7MLLrgg4xQGagpWN2zY4P/CQFH32CpQ08jG6kocJt2Tq7+wxTTcRjg/GshFp4UBru7RVYungk2Njqwu2mp9ri1F96N7l9U6rVcl3Ser+48//vjjKpvRfcbKT1imcKbKo1bpaFL37mICYLXWaqRqdfFWYK+LDaNHj/abVp6yA225KmBXN+kwONa9yrnuAdaG5K1gmoQAAggggAACCCCAAAIIpEmgzgNg4agVWKMYq4U37j5SBcAa3Omll17y9+POmTPH9KfgWX8TJ060JUuW+FZetbxut912mQA4H3xtQ/cbb7311v4eYg0gddFFF/kANmzFVbAdTQpcFSxmJ7U0615jDXI1aNAgP4iXBpG69dZb/aJqzdb9w9qXRlBWl+7sdPzxx2dPqpPP6haulnYZDR482Pr06ePzpy7kepSRWq8nTZpk2267rW8tX758eV77/eijj/zo0nktzEIIIIAAAggggAACCCCAQEIE6iUA1j2pd9xxh7+vN5fDueee6weW0sBWaqEcOXJkZnRmBdBDhw71Izu3a9cu08KZa1vZ0xXwqQuvkroo77///r4VWNtSd2XdC5zdeqsuzzfddFNsEKzRmJU/BeZqudY9xBp1WvcTK5jXPHWr1vY14FW5kvJw4YUX+m7Xat1WsK4W4bFjx/p7kRXQ6x7h6667rkqW4p6NrHqQm1qJ1Yq8xRZbVFmHDwgggAACCCCAAAIIIIBA0gWauMAtqGQhwpbe7DyoC7O6L8e1IGcvm/1Zj1rSY3z0OJ+w27IGz1LXZHUfzpUUMKrrb9jdOnu57Ly+9dZb9stf/tLuvfdeH8QXk9fsfdTVZz2OSmWpqbxx+1Jrsp4TXNNjkHTBQtuvj6T7kc+b/mDJm77/hal+G81PnVHUtuZ+cnpR67FScQJd97zE9Mi0xpTC2xD0O9eYki4Utm3b1g9+2JjKrbLqIqWOI40tbbLJJv4pCzoON6ak45l6m8X1Lkuzg86F9Pump3io7usqqdEhn6TzHt3uRkIAgfoXUKxVStL/V8VnxRwfSvl9aVZKputiXZ0QxCUFb8Xeh6pWWbVCq0uyugYrZQ/0FLfP2oLFXHnVthpS8Kv86CSz0KRrIXpkVHYrtrp5R58zrBG2Tz311EI3n9fyuS4+5LUyCyVWQAFR9L77xBakgIyH3/Vi/q8WsJsGt6jKrcEJ9Tvd2JIO8o2x3Kpnfc/zOQ6n6TsR/h9Xz7HGlFRu/dX1dz3f7en3hYQAAuURyPf/Za7chL+T5T4+VDwAzgVS6nR1U37llVdK3UyN66t7dXSQrxoXTsBM3TN8lntuc4cOHarkVoHJ7rvvnpmmAbU0UFZ9pLDFvj62zTYbroBaScKB6xpuLus2Z+F3PRycrm633nC3Fvayqa/fkIZbcvM9chpjuVXn+p43tu+6/o/rwnL2mCMN+TtaF3nTmCoKQut6QM18/+9kD0RaF2ViGwggEC+Q7//L+LX/7/Gr+r0o5vhQbEOp8pLaAFituQe4gaDqM+kZx3o2cFqSWrijgW5YLl3d0X3OYVIX6HwH1ArXyfe13FeA8s0Xy9WvgLoB0wW6fo0bytbVEqjAoL5+QxpKOePyod/Yxlhu9fDQ//FiurjFOSZlmo5njbkLtL7rqvu6Svn+36mtN19d5YftIICAlXxMK6ULdCm/LxtReckUePbZZxvdyUQya4pcI4AAAggggAACCCCAQEMRIABuKDVRYD40yra6LJMQQAABBBBAAAEEEEAAAQTyEyAAzs+pwS6lAar0nOPs9MUXX/jnE2d3Ofv0009Nz/nN7muvUSr1LObG1g01243PCCCAAAIIIIAAAgggkF6B1N4DnN4q+2/JrrnmGj/Sop55fPrpp/uRmRXYXnXVVf4RI7pHWY800ue9997bRowYYQqANcjVZ599ZjfffLNpQKtXX33VP5N5q622snfffdcuvvhi07OYSQgggAACCCCAAAIIIIBAmgQIgBNcm3vssYedffbZ/ll7ejTRIYcc4p/Pq5vC77vvPl+yiRMn2pQpU2zAgAF+VOynnnrKP59Pr3pesgLgBx54wAfHGtBLLccKgIcMGeJHcUwwD1lHAAEEEEAAAQQQQAABBKoIEABX4UjWh3CU6+7du1vfvn196+1+++3nW4InTZpk77//vr311lu25ZZb+ucUDxw40E488UTbc889Tcttv/32tmTJEnvjjTds8uTJPlCWgEZ5njFjhp+fLBFyiwACCCCAAAIIIIAAAgjkFiAAzm3T4OeED49WRnUP72abbWavvfaaXXvttTZ06FA74YQTbMcdd7SXXnrJl2XUqFE2d+5cmzZtmo0bN84HyMcee6x/JMlRRx3lX7Wgpm266aZ+Hf5BAAEEEEAAAQQQQAABBNIiwCBYCa5JdW3esGGDD2R176/u4VWLr7oyKwDu16+f7/aseerafNppp1mPHj3sjDPOsOOOO84PhqXnUvbv39/fE6xXzb/uuuv8swsTTEPWEUAAAQQQQAABBBBAAIFqArQAVyNJzoQPP/zQTjnlFP8Q6iuuuMJnfNCgQXbllVfaRRddZOvWrbOdd97ZXnzxRevSpYsddthhds455/gH069Zs8a3FGulYcOG2ciRI033CyugPvnkk61r167JgSCnCCCAAAIIIIAAAggggEAeAgTAeSA1xEWeeOIJny3dr9u6detM92W14N57773+0Uga7blJkyZ2wQUX+GXV8qvRopcvX24aITpMO+ywgz322GP+fmC1CJMQQAABBBBAAAEEEEAAgTQKEAAnvFbbt28fW4JogBtdQAFxrnkEv1Ep3iOAAAIIIIAAAggggEDaBLgHOG01SnkQQAABBBBAAAEEEEAAAQRiBQiAY1mYiAACCCCAAAIIIIAAAgggkDYBAuC01SjlQQABBBBAAAEEEEAAAQQQiBUgAI5lYSICCCCAAAIIIIAAAggggEDaBAiA01ajlAcBBBBAAAEEEEAAAQQQQCBWgAA4loWJCCCAAAIIIIAAAggggAACaRMgAE5bjVIeBBBAAAEEEEAAAQQQQACBWAEC4FgWJiKAAAIIIIAAAggggAACCKRNgAA4bTVKeRBAAAEEEEAAAQQQQAABBGIFCIBjWZiIAAIIIIAAAggggAACCCCQNgEC4LTVKOVBAAEEEEAAAQQQQAABBBCIFSAAjmVhIgIIIIAAAggggAACCCCAQNoECIDTVqOUBwEEEEAAAQQQQAABBBBAIFaAADiWhYkIIIAAAggggAACCCCAAAJpEyAATluNUh4EEEAAAQQQQAABBBBAAIFYAQLgWBYmIoAAAggggAACCCCAAAIIpE2AADhtNUp5EEAAAQQQQAABBBBAAAEEYgUIgGNZmIgAAggggAACCCCAAAIIIJA2AQLgtNUo5UEAAQQQQAABBBBAAAEEEIgVIACOZWEiAggggAACCCCAAAIIIIBA2gQIgNNWo5QHAQQQQAABBBBAAAEEEEAgVoAAOJaFiQgggAACCCCAAAIIIIAAAmkTIABOW41SHgQQQAABBBBAAAEEEEAAgVgBAuBYFiYigAACCCCAAAIIIIAAAgikTYAAOG01SnkQQAABBBBAAAEEEEAAAQRiBQiAY1mYiAACCCCAAAIIIIAAAgggkDYBAuC01SjlQQABBBBAAAEEEEAAAQQQiBUgAI5lYSICCCCAAAIIIIAAAggggEDaBAiA01ajlAcBBBBAAAEEEEAAAQQQQCBWgAA4loWJCCCAAAIIIIAAAggggAACa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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 6.18GB </td> <td style="text-align:right;"> 1m 12.3s </td> <td style="text-align:right;"> 6ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 895ms </td> <td style="text-align:right;"> 1m 14.5s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 6.91GB </td> <td style="text-align:right;"> 37.3s </td> <td style="text-align:right;"> 147ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 17ms </td> <td style="text-align:right;"> 249ms </td> <td style="text-align:right;"> 538ms </td> <td style="text-align:right;"> 38.3s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 6.55GB </td> <td style="text-align:right;"> 18.4s </td> <td style="text-align:right;"> 117ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 961ms </td> <td style="text-align:right;"> 1.2s </td> <td style="text-align:right;"> 20.7s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 6.35GB </td> <td style="text-align:right;"> 1.4s </td> <td style="text-align:right;"> 158ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1.1s </td> <td style="text-align:right;"> 7.4s </td> <td style="text-align:right;"> 10s </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 6.38GB </td> <td style="text-align:right;"> 5.8s </td> <td style="text-align:right;"> 12ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 104ms </td> <td style="text-align:right;"> 764ms </td> <td style="text-align:right;"> 6.7s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 6.41GB </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 76ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 11ms </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 4s </td> <td style="text-align:right;"> 6.7s </td> </tr> </tbody> </table> <p>(<em>N.B. Rcpp used in the dplyr implementation fully materializes all the Altrep numeric vectors when using <code>filter()</code> or <code>sample_n()</code>, which is why the first of these cases have additional overhead when using full Altrep.</em>).</p> </div> </div> <div id="all-numeric-data" class="section level2"> <h2>All numeric data</h2> <p>All numeric data is really a worst case scenario for vroom. The index takes about as much memory as the parsed data. Also because parsing doubles can be done quickly in parallel and text representations of doubles are only ~25 characters at most there isn’t a great deal of savings for delayed parsing.</p> <p>For these reasons (and because the data.table implementation is very fast) vroom is a bit slower than fread for pure numeric data.</p> <p>However because vroom is multi-threaded it is a bit quicker than readr and read.delim for this type of data.</p> <div id="long" class="section level3"> <h3>Long</h3> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/all_numeric-long">bench/all_numeric-long</a></p> <img src="data:image/png;base64,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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 4.79GB </td> <td style="text-align:right;"> 1m 51.4s </td> <td style="text-align:right;"> 1.4s </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 4.5s </td> <td style="text-align:right;"> 37ms </td> <td style="text-align:right;"> 1m 57.3s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 2.82GB </td> <td style="text-align:right;"> 13.1s </td> <td style="text-align:right;"> 64ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 18ms </td> <td style="text-align:right;"> 55ms </td> <td style="text-align:right;"> 13.3s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 2.75GB </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 48ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 18ms </td> <td style="text-align:right;"> 46ms </td> <td style="text-align:right;"> 1.5s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 2.69GB </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 48ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 6ms </td> <td style="text-align:right;"> 55ms </td> <td style="text-align:right;"> 1.4s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 3.29GB </td> <td style="text-align:right;"> 604ms </td> <td style="text-align:right;"> 64ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 42ms </td> <td style="text-align:right;"> 235ms </td> <td style="text-align:right;"> 959ms </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 3.28GB </td> <td style="text-align:right;"> 581ms </td> <td style="text-align:right;"> 55ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 29ms </td> <td style="text-align:right;"> 251ms </td> <td style="text-align:right;"> 920ms </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 2.72GB </td> <td style="text-align:right;"> 256ms </td> <td style="text-align:right;"> 13ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 6ms </td> <td style="text-align:right;"> 25ms </td> <td style="text-align:right;"> 302ms </td> </tr> </tbody> </table> </div> <div id="wide" class="section level3"> <h3>Wide</h3> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/all_numeric-wide">bench/all_numeric-wide</a></p> <img src="data:image/png;base64,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AIIIIAAAggggAACCCBQ8QIEwBV/CKkAAggggAACCCCAAAIIIIBAGgEC4DRKrIMAAggggAACCCCAAAIIIFDxAgTAFX8IqQACCCCAAAIIIIAAAggggEAaAQLgNEqsgwACCCCAAAIIIIAAAgggUPECBMAVfwipAAIIIIAAAggggAACCCCAQBqB1mlWYh0EGlNg5NSJjbm7su5rlz4P2tBPdrZVB40qaznYOQIIIIAAAlkRGDZsWFaqQj2aocDkyZObYa0bt8q0ADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjChAAN643e0MAAQQQQAABBBBAAAEEECiTAAFwmeDZLQIIIIAAAggggAACCCCAQOMKEAA3rjd7QwABBBBAAAEEEEAAAQQQKJMAAXCZ4NktAggggAACCCCAAAIIIIBA4woQADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjChAAN643e0MAAQQQQAABBBBAAAEEECiTAAFwmeDZLQIIIIAAAggggAACCCCAQOMKEAA3rjd7QwABBBBAAAEEEEAAAQQQKJMAAXCZ4NktAggggAACCCCAAAIIIIBA4woQADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjChAAN643e0MAAQQQQAABBBBAAAEEECiTAAFwmeDZLQIIIIAAAggggAACCCCAQOMKEAA3rjd7QwABBBBAAAEEEEAAAQQQKJMAAXCZ4NktAggggAACCCCAAAIIIIBA4woQADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjChAAN643e0MAAQQQQAABBBBAAAEEECiTAAFwmeDZLQIIIIAAAggggAACCCCAQOMKEAA3rjd7QwABBBBAAAEEEEAAAQQQKJMAAXCZ4NktAggggAACCCCAAAIIIIBA4woQADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjChAAN643e0MAAQQQQAABBBBAAAEEECiTAAFwmeDZLQIIIIAAAggggAACCCCAQOMKEAA3rjd7QwABBBBAAAEEEEAAAQQQKJMAAXCZ4NktAggggAACCCCAAAIIIIBA4woQADeuN3tDAAEEEEAAAQQQQAABBBAokwABcJng2S0CCCCAAAIIIIAAAggggEDjCrRu3N2VZm+vv/66tW3b1tZdd91qdzBlyhTbeeedrV27dvb999/79atduREWzJkzx15++eUqexoyZIj16tUrN3/atGnWt29fW3/99XPz3n33Xfvmm29siy22yM2LT3z44Yf2wgsv2HfffWfrrbeeKU+lZcuWmRyS0r777muLFi3yZdpll12SVmEeAggggAACCCCAAAIIIFDRAhXfAvzDDz/YLbfcYmuttVaNB2LixIn29ddf24oVK+zoo4+ucd3GWKigffLkyfbOO+/k/Vu8eHFu9x9//LFNmDDBrrzyytw8TcycOdP+9re/5c0Lb5555hk744wzTEHyt99+a9dee61ddNFFfvHSpUtt/Pjx9uabb+btU2VQ6tSpkyng/uqrr/x7/kMAAQQQQAABBBBAAAEEsiRQ5xZgBVMKPldbbbWcx5dffmldunTxLYnt27e3+fPnW+/evXPL1erZtWtX07J40nZqnYy3fKo1UgHZvHnz/D46duzog1e9X3PNNXObP/74474lVC3AIS1cuND0T/uOl0/LFdzNnTvXaiqrWog/+eQTX55Q1iVLllirVq1M9da/eFnDfot9VcvuL3/5y2o3e+yxx+yAAw7wrbazZs2qsYU7ZPLAAw/Y8ccfb2rRVRo5cqQdcsghpmBahkoKkFdddVU/Xfjf/vvv7wPzs846q3AR7xFAAAEEEEAAAQQQQACBihaocwD80ksv2a233mo33HCDB1Cr4tixY+2OO+7wLawDBgywt956y8aNG+dbXrVe586dfcvjSSed5AO7L774wi655BIfMCsw7d+/v19fgeaxxx5rgwYNsgULFvhtjjnmGJs6daoPnhWM3nzzzT4gvfPOO3MtpGrdPf/88+2zzz7zwbPKpPfbb7997iDddtttFkWRL6vKptbgeFmXL1/uW0xVlrfffttOO+0023vvvW3SpEn23nvv2eeff+67T3fr1s2XtXXrfELlqRbaeFKwHJzi82uaXrlypSm4v+KKK0zTDz30UI3BcshrjTXWsKeeeso22WQTW2eddby5uj3LVC3gtaXNN9/cxowZY6NGjbIOHTrUtjrLEUAAAQQQQAABBBBAAIGKEciP3oootu4rvfzyy33LolpkFWTttddeuRx22GEHH7yptVStmJdeeqkpuFJwquBqxIgRpm7J2lZdfBW8nnnmmfbiiy/a1ltv7fNRAHzQQQfZgw8+6LsCKwhUEK0A+tVXX/X3xSpwVtCnpHtf1WKr4Fjprrvu8kFzPAA+9dRT7eGHH7bf//73fh39F8raokULX4YLLrjABg8e7FuRFQAPHz7cr6sAUgG0gkm1kCoflS+eVAfVJZ5atkzuaa4AXa21IemeXu1P6fnnnzcF2QrE27RpYyeccIKdcsopVVq0w7bhVdtfc801dvLJJ/tW3q222sqOOOII69evX1jF5xUv05ZbbunrrRVUN7VMf/DBB/4CRG4jJhBAAAEEEEAAAQQQQACBCheocwCsQGnYsGH2xBNP+FZUtTredNNNOY7NNtvMFFAqKF1llVVM75XUGqr7dTVIk+6D/c1vfuPnqyVVg1TpHtQQAG+66aZ+mboyaxAoBb9K3bt39y2xyrdHjx5+nv5TS66CvXvuucffA/vaa6/lBX65FQsmQlnVLfqVV17xwbxam5U02JTKqbTddttZaPHdZptt/PzCAFitxur6HU/qhh0PwsMyBaXnnHNOeJvXNVz3+KoFVnVRUhdvlUkXE2pKGuRLeSpAV/2nT5/uLxhcd9113k3b6p5g2YVU2E28Z8+eBMABh1cEEEAAAQQQQAABBBDIjECdA2AJqGX017/+tW2wwQb+nwLTkBSIKWme7hXWPwXNSrrHVt16tUxdjkPSfcCaH1LIQ+/j9/iG5QritE1I6pZ98cUX+/teFZgqgNagULWl+H4U4Kp1OgS6uie2T58+VbLQCMtJ99Gqm3QYVCpspMA9KQDWfpNGrlartrpRH3fccb67tvJR8K0W55oCYLnqeKhbufJWi7v+qRX+ueee8/VSXmp1Tyq7linJVK3OJAQQQAABBBBAAAEEEEAgSwL1CoDVVVYDK2k04yOPPDLRRUGu1lMgutNOO9n777/v/ylo3m233XwLsrr+Kuh6yrUix7tRJ2YYm6kWVN0jrCBaAZsyaypFAABAAElEQVRaPNV1WQM/KeC+/fbbq3RHDoFt2CaWnR/Aa+DAgX7graFDh5ruUdaAUVdffbVf7dlnn7WjjjrKT6s+hx12WHxzP33ggQdWmVfsDLWqy0St2SHpvmcF4+r6XV3SRQLVSyM/n3766T7IVWu0XHbffffqNqsy/6OPPjLdc01CAAEEEEAAAQQQQAABBLIkUK8AWBAaIOr6669PbOEMUCeeeKLvdquBpNSlePTo0b71d4899rAZM2b4QFItvzvuuKMP8sJ2tb0qmFUQrIBN3Z8VtJ533nmm+3wVCKr18+mnn87LRq3Qurd4v/328wN25S10b3RPrsqn+4cVRCvI1b24SmpxDkGpumur/KVI6v4cAu2Qv7op77rrrn4wLNVZQfKTTz4ZFtvGG29s6ub8u9/9zg/wpVZs3Q+t+5GVl7qVh0Gw9txzz9x2YULHRt3Mtb6C5rXXXjss4hUBBBBAAAEEEEAAAQQQyIRACzcictRYNQmPHircn4JidcmtS7dbBYt6RJBaakPSI5TUMq17kKtL6hZcUzfgwrKqFViPcDr00EN9YFzTttXtszHn64KCulKH4D3tvjWYmQbnij8GSc8T1r3ZISk41gjTpUi6yHD8PyaXIusmmecufR60oZ/sbF2GnF6y8qlngC5sxG8vKNnOmkHGuoim+/N1QUkXyUj1F9DFTA3MF7+lpf65Nt8c9NunxwjqufK6GEyqv4A+n/ou1e1PpPQCYeyW9FtUv6bOadIkfZ8cfPDBaVZlHQSapEAY/6cuhWsK3/9qtFPP1VKn+ny/1LsFuJjK6RnBSak+j9tRl2l19w3PDVb++uGvLdUWwFZX1vjgUbXto5zLdZJebPCrayGPPPKIf7xTvOwaufvcc8/NzdIo3GpdJzWcQH3+iBuuFORUjEB4rnYx27BuzQKFA/LVvDZLaxMIz7GvbT2WpxdIGo8k/dasWR8Bfifro8e2lSTQEJ/1cn//N/V4qVED4FJ8+HRVVq2Vuhc4TeBb1zJowK+mfjDrWrewna6uHuuev1x4Yq/WXj2eKiS10sybNy+8bdDX+IBkDZpxE8+sVJ6qtoIKtQLREtQwHwL1VNHFJY0RUPjIs4bZQ/PLRd+tumhH61rDHHu1AOiJC/pOV28nUv0F9PnUd6l6rJHSC4THVKbfovo10/5OcpGiekOWVIZA2s96Um2awve/Avi0PTaS6pB2Xn2+Xyo+ABZS0kjKafHSrtcY+0hblsL11HL76KOP+lGe9cGvLql7s+5dri7IVKt3eARVPA9dZFh99dVzs3QCoBGnSQ0nUOo7EZR/qffRcBqVkROmDX+c+Iw2jGn4HeAz2jCeyiV8NsNrw+VMTmkF0tqnXS/tflkPgcYWaIjPsPJoiHzqWvdy7jtNmVumWYl1mraA7kMcP358rR/0iRMn5gbCato1onQIIIAAAggggAACCCCAQMMLEAA3vGmj5aiuyBoBO36VRV0yNWhVWJY0SI9acOPdYTWtedpWg6bofurG6LrQaFDsCAEEEEAAAQQQQAABBBBwApnoAt0cj6QeHzVu3Dg/GnN8dN+33nrLLrvsM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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 14.41GB </td> <td style="text-align:right;"> 8m 41s </td> <td style="text-align:right;"> 131ms </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 9ms </td> <td style="text-align:right;"> 75ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 8m 41.2s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 5.46GB </td> <td style="text-align:right;"> 56.1s </td> <td style="text-align:right;"> 96ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 26ms </td> <td style="text-align:right;"> 18ms </td> <td style="text-align:right;"> 39ms </td> <td style="text-align:right;"> 56.3s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 5.35GB </td> <td style="text-align:right;"> 6.9s </td> <td style="text-align:right;"> 63ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 95ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 31ms </td> <td style="text-align:right;"> 7.1s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 5.34GB </td> <td style="text-align:right;"> 6.9s </td> <td style="text-align:right;"> 61ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 6ms </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 7s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 7.26GB </td> <td style="text-align:right;"> 3s </td> <td style="text-align:right;"> 68ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 23ms </td> <td style="text-align:right;"> 20ms </td> <td style="text-align:right;"> 77ms </td> <td style="text-align:right;"> 3.2s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 7.26GB </td> <td style="text-align:right;"> 3s </td> <td style="text-align:right;"> 68ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 11ms </td> <td style="text-align:right;"> 42ms </td> <td style="text-align:right;"> 3.1s </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 5.48GB </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 100ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 1.4s </td> </tr> </tbody> </table> </div> </div> <div id="all-character-data" class="section level2"> <h2>All character data</h2> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/all_character-long">bench/all_character-long</a></p> <p>All character data is a best case scenario for vroom when using Altrep, as it takes full advantage of the lazy reading.</p> <div id="long-1" class="section level3"> <h3>Long</h3> <img src="data:image/png;base64,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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 4.53GB </td> <td style="text-align:right;"> 1m 43.1s </td> <td style="text-align:right;"> 8ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 28ms </td> <td style="text-align:right;"> 293ms </td> <td style="text-align:right;"> 1m 43.4s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 4.35GB </td> <td style="text-align:right;"> 1m 2.6s </td> <td style="text-align:right;"> 102ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 17ms </td> <td style="text-align:right;"> 20ms </td> <td style="text-align:right;"> 215ms </td> <td style="text-align:right;"> 1m 2.9s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 4.3GB </td> <td style="text-align:right;"> 50.5s </td> <td style="text-align:right;"> 50ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 21ms </td> <td style="text-align:right;"> 150ms </td> <td style="text-align:right;"> 50.7s </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 4.73GB </td> <td style="text-align:right;"> 42.8s </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 149ms </td> <td style="text-align:right;"> 43s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 3.22GB </td> <td style="text-align:right;"> 595ms </td> <td style="text-align:right;"> 46ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 163ms </td> <td style="text-align:right;"> 2.1s </td> <td style="text-align:right;"> 2.9s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 3.21GB </td> <td style="text-align:right;"> 640ms </td> <td style="text-align:right;"> 58ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 185ms </td> <td style="text-align:right;"> 1.2s </td> <td style="text-align:right;"> 2.1s </td> </tr> </tbody> </table> </div> <div id="wide-1" class="section level3"> <h3>Wide</h3> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/all_character-wide">bench/all_character-wide</a></p> <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8AAAAMACAYAAADrPg4vAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAPAoAMABAAAAAEAAAMAAAAAAOv0XOcAAEAASURBVHgB7N0LvFxVfSjgFZIQQgIJgYCAUB5VQSi+UJSHgC8iL9FWoWhFEasg3mpvr7XeAsHy0gCKNBYRpQICghdbbCFReqP0irki0gsioLxReQghhAQIJMzd/1X3dM6cOefMyTnzPN/6/U5mZj/WXvtbk9n7v9faa0+qFClJBAgQIECAAAECBAgQIECgzwXW6/P9s3sECBAgQIAAAQIECBAgQCALCIB9EQgQIECAAAECBAgQIEBgQggIgCdENdtJAgQIECBAgAABAgQIEBAA+w4QIECAAAECBAgQIECAwIQQEABPiGq2kwQIECBAgAABAgQIECAgAPYdIECAAAECBAgQIECAAIEJISAAnhDVbCcJECBAgAABAgQIECBAQADsO0CAAAECBAgQIECAAAECE0JAADwhqtlOEiBAgAABAgQIECBAgIAA2HeAAAECBAgQIECAAAECBCaEgAB4QlSznSRAgAABAgQIECBAgAABAbDvAAECBAgQIECAAAECBAhMCAEB8ISoZjtJgAABAgQIECBAgAABAgJg3wECBAgQIECAAAECBAgQmBACUybEXtrJcRW47LLL0g9+8IMR83zf+96X9tlnn3TyySenOXPmpI9//OMjrtPKBR577LG02WabtXITLc37f/7P/5m23Xbb9JGPfKSl22ll5s18F9auXZuOO+64dOCBB6Z3vOMdrSxONe962/rP1QVH+Wb+/Pn5O3f88cePcs3OLd6J/ydjrfNOlLlzNWTLBCamwIMPPphOOeWUvPNHHnlk2nfffRtC/PM//3O65ppr0tZbb51OPPHEhsus68Sf/exn6Stf+Ur69Kc/nbbffvt1zcZ6BAh0WEALcIcroBc3//Of/zwfXOIAE39XXHFFOv/889M//dM/DZgeB6tIV155ZZ7eyX29+uqr084779zJIox525deemn6/ve/P+Z8OplBM9+FF154IX+fbrzxxrYVtd62/vO6FiT+byxatGhdV2/7ep36fzKWOu9UmdteOTZIYIILxIWuONeIv3POOWdIjVNPPTUvE7+/453uueeenPejjz463lnLjwCBNgpoAW4jdr9sKg4u8VemOBB94hOfSN/5znfSnnvuWU6uvl511VVp6tSp1c+deBMt1suXL+/Epm2zRqAbvgs1xfG2TqAX/5/0Ypnr2H0kQGAUAltssUW69tpr01NPPZU22mijAWvefffdKS6e1k8fsNAYPrz1rW9N/+///b/0kpe8ZAy5WJUAgU4LaAHudA1MgO3PnDkzbbjhhtU9/c1vfpOeeOKJ/Pm+++5L//t//++0cuXK6vxVq1alJUuWpGXLllWn1b6J7pK/+MUvcstarD9Siiu1K1asyIvF8uW2y/Ueeuih3LL6f//v/02x7dGkhx9+OMUJ+L/927+lssW7XD/yiu1VKpW0evXqdMMNN+S/p59+ulxkwGssE63r//qv/5r+4z/+Iw21XKwU+3T//fcPWL/8EOWIK+Wxfmx/qL81a9aUq6TRmsaJR+T7/PPPV/OINw888ED63e9+N2Ba2Mey4VD/XSgXjDq57rrr8n6V0xq9xncivhtLly4d1qd+3dHY1q870ufnnnsu/Z//83/SvffeO+yicWL2ve99Lz3yyCMNlxupjLXfp/ie3HLLLQPyGe67WLvgUOUd7v/JSN+PkcpWu/3yfbN1Ptx+DVfmkTzLcnglQKC3BP74j/84Pfvss+lf/uVfBhX88ssvT3/4h3+Ydtlll0HzYsJQvyfRCyWOU3F+Up/iWPvrX/86T46L+RtvvHGaPHly/lz72xdluv7669Mdd9wxIIs777wz/eQnPxkwLT5EnvXHy5ge5ai9YD/Wc6bIs0xjKW+Zx0jH4bGWN45RN998c77IERb1qXYfymNhM+dE9fmUn5uth1g+6jLO0aJ8zzzzTJnFgNdmjz1PPvlkPu8pzx1/+9vfpscff3xAXvFhJO9BK5jQnEBxUioRGJPAF7/4xUrxbav86Ec/aphPcSCqzJs3rzqv6IpcKe7xrLz97W/P68W6U6ZMqXz3u9+tFF2WKuuvv36eHq+HH354pQjUquvedNNNlZ122inPLw5A+TXyLg5q1WXq3xT3kVa3E9v61Kc+lRcpAsTK0UcfXZk0aVKlzKu4alwpulfVZzHoc/FDVSnuUc35rrfeetX83/nOd1aKg2Be/lvf+laeXrR6ViLf2Hb8zZ07t/LDH/5wQJ7f+MY3KrNnz87zy/yK+5UrRRfz6nLbbbddpTjw589FC3xetgiCqvPjTRFk5elf/vKXK0WX9Py+3G79669+9au87rqYFhctct7/63/9r+r2i4NenvZHf/RH1Wnx5oMf/GAlyh6p/rtQBNCVd73rXdm/rIO//du/zfkU9+HmdeKf4oBYKe65qoRN/EWdFSchlYsuuqi6zFBvRmsb+dRaD5VvTP+rv/qryvTp06vfn9j3IsitrhLf9b333rty2GGHDaiLY445plKccFWXa6aMl1xySc5j4cKF1by+9KUvVZr5LpYbGq68Q/0/aeb7MVTZyu3WvjZb583s11BlbsaztkzeEyDQ/QLF/bf5ty+Ob3Esid/V+rTrrrtW4hjy+te/Pi9Tzm/m9ySO33F8KS5olqvlY0wcO08//fQ8rbiNJ5ehuAibP5fH+SLwrhQX+qu/zcX4J5XiQnTljW98Y3XaDjvsUClarqt5NzrOxLEutvc3f/M31eXGcs5UzeT3b8ZS3maPw2Mpbxw/wyUMynOCt7zlLZUiSKzuSqPjzcEHH5zXGe6cqJpB3Ztm6qEIuitRjtpyzZo1qxKetamZY084xnlPnPeW+3jsscdWXv7yl1c+9KEPVbNr1ru6gjejEohWGYnAmATWJQCO//Tvfve7KxGEReAcB4YIEuOH6Jvf/GalaCXLwUX82MQBJ1JxZayy6aabVnbbbbdKMchFDoqKq62VYqCLypvf/OYh96Fogax89KMfzT82cUCKH7JIf/EXf1EpruZWzjvvvEoEw8UVxMoHPvCB/AMX+Q+XIhDdYIMNKsX9nTm/22+/PQfTUd74AYxUHmgi4D333HNzoBI/7vGjGQfpMsUPdqz3p3/6pzmAjfLF9mNfi0E2ysUGBGVxMIgDdRnMlwsVgy5Vpk2bVimuGOZyFa2SlfKvuJKayxT273//+/Mq62oaQcwmm2xS+fM///Ny05V//Md/zPsRwWlYRoogr+iulq3jc30A/N/+23/LJw0RSEeeP/3pT/N3ITxqA+Bi8Kycd5wUFFfjK0XrYaUYWCpPi+/AUGldbCOvRgfE+m2cdtppuQ4iII3vT3FluBInPfFdLi+CxIlA7EscnIsr1ZVf/vKXlSOOOCJPiwsUkZotY3nQjwsjcQJY3HpQKa5c54siI30XYzsjlbfR/5Nmvx9DlS22W5+arfNm/o81KnOznvXl8pkAge4WqA2AP/vZz+ZjcPwGlCl+Y+P39rbbbhsUADfzexLnB1tuuWXlZS97WaVo3asU9/vm85Ki23P1guVQAXCcvxS3geXj/F//9V/ncsRF7bjIHucHcSEx8o5jYJkaHWeGCoDX5Zyp3E7ta3lesi7lbfY4HMe9dSlvHNvjwvZee+2Vj6dxThDnWJtvvnll9913rxQ9kfKuNDreFC3sI54T1TrUvm+mHmLfI2CNCxhxfI9jeVzcnjFjRqXoEZeza/bY87GPfSyv9+1vfzuf9xRd6nPwG9/d2gC4We/affG+eQEBcPNWlhxCYF0C4PiRi6ChTJ/85CfzAeOkk04qJ1WKrsl5WhzoIpUtg4sXL64uE2/K7de3qtYuFPnHj1eZ4mpwBGrxQ1SborU5Dn61AWrt/HgfP8LRkhaBc22KoLQ2cCsPNJ///OdrF8vBeCxXdJPJ04vu07mVtOgKNWC5+CGM5eIgEKn+R/qAAw6ovPjFL64eFGKZHXfcsfKe97wn3g5KcRCOoLUYObO67bGYFqNw5jKVG4rP++23Xy5zeVU0DkqxD0W35bxYbQAcB42og7CsTUX3ogGORTeh3Mr6hje8oXax7BLB4P777z9geu2HdbWtt67Ns3wfJzexz7UpDmTRM6C4By1PjhOBuOBR+12Pk7MwKUYzzcs0W8byoF+uFys3+12MZZspb/3/k2a/H43KFtusT83W+Wj2q77MzXrWl81nAgS6W6A2AC66Geff0bhgXqb4vYoL5JFqW4BH83sSAVccl+JiawRicQG3tofZUAFw9MoqU1ygjQvU0TuovOAe84onOOSL7lGeSI2OM0MFwOtyzpQ3UvdPeV4y2vKO5jgcx711KW80VMQF/NrW3ij+xRdfnOs6AsZIQx1vRntOlDMr/mmmHqJxJM43as/T4qJ39FosuqvnrJo59kTjRBz/47tam8pzpTIAHo13bT7eNy9gEKzimyi1X6AI1FJxcKhuOO7ZiVS0lFWnFS2n+X15n0U8fqBo6UpFAJHvAS4XjHt6IhVX31LR3aicPOxr5FH8N8mP2qldsLhqmYof0VS06qXiQJSKbti1s/P74sCWFixYkNePESHjfuS4LyTuS40U9wHVple+8pW1H1MRtObPcd9zPB7qTW96U/6L+2ojj7h/KO5riXujI0V+ce9sfSq6FqeiNTEVgX8qgsBUtKSnuM+0aG2uXzTfWxuPFSp+wFMMRFXu11hM4xFFMVpycSU0DwgS9/AWVyzzPVZR9iIQz/doFS3Z+XFY9YW69dZbs2ERNA+YFYOMhHGZwja+A8VV4FRc7Cgn59d4zEXU+1BpXW2Hyq+cXt6jVfQ8KCfl1+LkK98fVDux6LI/4LtedHPK98SX95qNtoyvetWrqtk3+10cTXmrmRdvRvv9qC1bbT7l+2brvNn9KvOtfR2tZ+263hMg0BsCxYXqFMfWGOm5uBCZCx33/xYBxKAdGM3vSRz/45GNRZfnVATCeayRGHRrpFRcNK8uUlxsTHHce93rXjdg/JM4pykuaKcYf6M8BldXGuHNupwzDZflaMs72uPwupQ3jg9xDC3PkcryH3TQQfltDD5WtOSXk1P98WY050TVTJp8U9yOl4pAPN9fXtx2l+IvzqmKnozVHJo59sS9w5Hqzx1e+9rXpvjelGm03uV6XpsXEAA3b2XJcRR40Yte1DC3OGiUKQ4+tSkGKohpF1xwQe3k/D6Cihh4oNkUeUWKoKo+xUGquEKbB86KgLFRiscRxbNq77rrrnwgK654VoPvCKxrU9HqWvuxOnhGuVyUu7j/I/+4xoExDrZFd5/8zN+i+3IOEgdk8PsPxf1PKfIurobmALi4HzYVXazS2972tgGLF62P+cJCDLgQAXYE3WUai2kcAGJAkBjYKbYRg1AU98ikOIiVj2uKQUriokZcWKhP5eAWtXUey0SetWZlXcXAYEWX+fpsUnyX4iJBXBypT+tqW59P/eeyHEN9P2qXb7RMnJCta/1HHdemZr6Loylvbd6j/X7Ul602r3jfbJ3Hss3sVyxXn1pV5/Xb8ZkAgc4KRFASF13j4nFciI3jcVwUbpRG83sSx9ZifIVU9N5Jr3jFKxplN2hao3Oa+mNb/TnNoEyKCeVxoX5eo/xjmdptNJN/mW+j/GrziuVq8xvtcbhR/pFn7TZq8495sY3awDymRYrzgaIH36ABouqPN82eE/1nrsP/W18Pcb4TF/nPOuusFI/eK1rS83nNhz/84fxIrrig0cyxJ87pIsV3qz7Vn5vF/HU576nP1+fGAv/VzNJ4vqkEWiIQAcBoU1wVjB/BeMRBtODW//33//7fm85ym222ycsWXaEHrRMtcxGENgpcYuEI9OKHNn7AotU1Dr7xI/WZz3wm51X/wzloA3UTintdU3H/bDrjjDPySNLRoh2BY7QcRhoqv6KrUCruG84tutFCWnQPSu973/sGBJsxsmVcHY8rp/GYqvpHN4zFtOjilPYrWm+LLun5wLDtttvmq6MRBEfAFaNqx9XOsGqU4qJFpBiFuzbFRYDaETDLq8FxZb++zuNztJY3Cn4jz3W1rS1Po/fR8hyp0YjO4VF04Wq0WsNpoy1j7UlDs9/FdS3vaL8ftWVrtLPN1nmz+9VoG6P1bJSHaQQIdL9A9DKKi5/FAJo5ICluk0nbbbfdoIKP5vckemZFUFMGYsU9vIPyazRhXc5pYp36HmONzklie+uSf6NyltNGm99oj8OjzT/KFedljfY/jrNxXlAMMlkWP7/WH2+aOScakMHvPzRbD3G+E9+1GJU5jvPFQK6puBUuReNDpGaOPXGeFKm8oJA/FP/EuVrtvo/Wu8zHa/MC6zW/qCUJdFYguohEsBmBXG268MILU3S3afSYgXK5aIGMH5j4ixQ/pPFjGd2nalMcjOLqXn3XmtplIrCLFs9iEKb83OOyK1N0RY4UrcejScX9sbm1NwL48kcvuknFY5MiDZdfdPmJYLG4LzT/KBf3qeR1yn/i+czFgFq51bxR9/CxmMY2oht0lD8OBmWXnuiOHd6xPxGY1rdIl2Ur7gfOXYOLAbDKSfk18qrd57gQEF3Ay4NMuXAE/VH+o446qpw06HUstoMyq5kQ37e4ABKPQ6hNxT1AuWtUXBBpNo2ljM1+F5stb/3/k7F+P+oNmq3zZvcr8q8v81g868vrMwEC3StQDDiYjwFx8Tf+4oJwozSa35NicMz8OL/o7nr22Wen4kkM6R/+4R8aZTvmaXERuT4QKm+lGnPm45zBWI7DzRYljjdxS1P9Y6SitTXScOdl5TZGOicql6t9baYeIt84h4oGifK85itf+UrOJnoeRGrm2LPHHnvkFu1o9KhN0X2/9mJIO7xrtz8R3wuAJ2Kt9+g+/+Vf/mXu7hqBVQR88SNZjLicIsiLLshxv81QKYKVCH7PPPPMVIw0nLs+F4Mv5XtYo+U2fsCKURpza2V0FY4reUOlaEWNK49f/epXcwtnXLWLH7NooYyT8QjSR5Ne+tKX5mf6xo9pPL83DoBxX0ncWxxpuPyiq3RcqY57kuPgUbawxXrFSMH5fuC4fybuLYkThGIAj+pf3C88FtPYxiGHHJKfnRxdg6LlN1Js6zWveU1uHY/gt/YZ0HmB3/8TrfnRneiyyy5LxUBn+Tm6cbJRDIQx4Gp3MVplOuGEE3Idxfaiy3UYReAb96hGV/Sh0lhsh8ozpsfFkyhTXCyJ17AsRnXOnnGAi3I2m8ZSxma/i82Wt/7/yVi/H/UGzdZ5s/sV+deXeSye9eX1mQCB7haIVuC4yBvPoK+9H7O21M3+nsTF9a9//eu5N1fcXxxBT3R9jXOFuCdzvFO0KEbvrGLArXwsi9u74v7j2vFRxnub65rfWI7DzW4zHGI70Wsszini+ctxnhXnY8Vjg/KtYSPlNdw50VDrNlMPf/Inf5L+/d//PV/Yj/OOOAcpnsKRzwXLsWuaOfZEsD1//vzccy9650UjTJz/xHlPnFeWrdrt8B7KY8JML65mSATGJFCOwjya5wAXQdmAbZbPNo3HDpSpaAXMo+XFaIxlKgLVPOpv8SOR5xX3meRh44urqOUiDV8j36IVLK8Tz/qLVHSpyY+GKVoX8/QieK0UQWSluIqX5w/3T/GjXCmuRub1ih+LPIR9jAAYzzaO7UQqR1ssRwQu84vnCcY68ciFSFH2GE24OJHP04tgpRKPiinuWcqfYwTESEXXrupzgPOE3/9TBPV5ub//+7+vnVx93E5sq9Ffufy6mpYbK04Ucv4xaneZiosKeVrROl9Oyq+1o0CXM2I0yniUVZQxRs2M0bWL7ucDHoNUXLzIj/0pguu8XNRVPHKoPv8yz/J1XW2Hsi7zjdcoU9iX358of3wn4lEIZYrRMOu/6zEv1okRLyM1W8Zy5MuiW3ler/ynme9iLNtMeRv9P2nm+zFU2coy1r82U+fN7ld9mZv1rC+TzwQIdLdAEXjk3/94DFyZ4tE5cT5Q/yjE2lGgY9mRfk+KC9n5uFPc85sfsVjmXwTWeUTj4qJunj7UKND1x/l4/GH5uMEyr+JiaS5/ca9onhSPmYtH5NWezxQ9oCrFoKCDngNcfxxp9pyp3Hb5OtR5STPlbfY43Oi412x5i9uaKvHEh/KcJcoVx8oYHbtMIx1vhjonKtevf222HorGlkrR469atq222qoSj3As02iOPUWjSX60UxHoVoqL5pUf/OAHOe849ytTs97l8l5HJzApFi++aBKBnhKILshxr26MHl1eMWtmB4ofuhRX4GKgpTIVPzK59TEGv4p5o0nFD16+Wls7sMNo1q9dNkadjiuexbN/873OtfOGe188DzZ9+tOfzveP1A4eNdw6jeatq2mjvNZlWrSixv0xtXXTKJ8wiqujtQNGNFqudtq62tbmMdT78vsTLd31g3IMtU6j6WMtY7PfxWbK2+j/SSu+H83UebP7VV/msXo2qiPTCBDobYFmf0/auZfR6yzuK41jf6+kdTkOj2bfiuc75/FW4vad0ZzjxTbW9ZyomXqIW6+it0GcKw51vB/u2BPz7rvvvhTd96NHVJmip1/kGa3B0ausPrXau357E+GzAHgi1LJ97FuBOEhEl5/iavege2T7dqftGAECBAgQIECgTqDbz4liMK8ZM2bkxznFYyTLFN2p41a26Ga99957l5O9tlBAANxCXFkTaJVAXEWMgbxidMS47zhGxo4rihIBAgQIECBAYCIJ9NI5UdzrHE/9iGdZRwNGjD8TY9qcdtpp+X7wiVRvndxXAXAn9W2bwBgEPvnJT+bnzsUjG5oZHXEMm7IqAQIECBAgQKBrBXrpnCieWx1PvIgRymPw0mJMk/zXtbh9WDABcB9Wql0iQIAAAQIECBAgQIAAgcECHoM02MQUAgQIECBAgAABAgQIEOhDAQFwH1aqXSJAgAABAgQIECBAgACBwQIC4MEmphAgQIAAAQIECBAgQIBAHwoIgPuwUu0SAQIECBAgQIAAAQIECAwWEAAPNjGFAAECBAgQIECAAAECBPpQYPL8IvXhftmlNgrcfffd6ZJLLkl77LHHgK2uXbs2/eAHP0iXX355evbZZ9OOO+44YH58uPPOO9M3vvGNdO+996Y/+IM/SBtssMGgZWonLF++PF1xxRU530022SRtttlmtbPTSPObKdOADNfhQ2wjnue26667punTpw+bw1jL28r9Gc1+lDv5b//2b+knP/lJ3vdyWrzefPPN6eKLL07xXdlqq63yg+Br58f7Rx99NH3ve99LF1xwQfrOd76THnjggfTiF784bbTRRtVF43vypS99Kf3whz/Mf/HQ+Fhu2rRpg74L1ZW8IUCAAAECBAgQIFAKVCQCYxAoArjKLrvsUnnNa14zIJc1a9ZUXvva11aKALXyoQ99qLL55ptXjjvuuAHLFEFiZfLkyZX3vOc9leJh4DmfRx55ZMAytR9+/vOfV4pAJy97+OGHVzbccMPKtddeW11kpPnNlKma2RjeFM+iqxT/vyr33HPPsLmMtbyt3p9m96Pcyfvvv78ya9asyiGHHFJOyq/nnntuZb311svfh7e97W2V4iJHZenSpQOWue6663Ld7rbbbpVjjjmm8ud//ueVnXbaKX9/iqC4umwRYGfbN7zhDZU3velNlb322quyww475O/R+eefX13OGwIECBAgQIAAAQKNBFKjiaYRaEZg0aJFlW233TYHNPUB8IIFCyoveclLKk8++WTO6vbbb89B0E9/+tP8uWj5zQFP0ZKXPz/33HM5sP3rv/7rITf9ute9rvLxj3+88sILL+RlTjnllErRqlz9PNL8kco05IabnFG0RFYOPPDA7NFMADzW8rZqf0a7H8FTtBZX3vjGN1Zmz549IAD+zW9+k+v5M5/5TFXxnHPOqWy55ZbV70bxIPjKzJkzK2eddVa1LmPh559/vvLBD34wf8fifaQyAK6/uPCpT30qB9/lcnlh/xAgQIAAAQIECBCoE3APcNkU7nVUAtF1953vfGc66qij0v/4H/9j0LpXX311OvLII9PGG2+c5xWteWnPPfdMl112Wf68ePHiVLTcpSJoyp+nTp2a3v/+91fn12f48MMP5661H/nIR9KkSZPy7KJlOXepjS63I82PFUYqU+02oztu0QqZu+SW06O7bWw/5jVKUZ4iOE/f/e53G80eMG08yjua/Rmw8RE+jGY/yqyKYDzXS9GaX07KrzfeeGNavXp1+vCHP1yd/qd/+qfZcMmSJXnaZz/72bTPPvukv/zLv6zWbcyYMmVKOuOMM1LR2pseeuih6vqN3uy3337pqaeeyn+N5ptGgAABAgQIECBAIAQEwL4H6yQwY8aMVLTCpQheInitT3GvZgS4tSk+P/jgg3lSzK+/JzjmFy2GOYisXS/e33fffXlS7TovetGL8j22kedI82PlkcqUN/D7f4ou2/l+5AjKly1blh5//PF0xBFH5PtRY16jFPeuFl2y832rjebXThuP8o5mf2q3PdL70exH5PWzn/0snXnmmfle7qKr86DsY1oEs2WKgDguFMT9wJGK7tDprW99azl7wGtYxz3k22yzzYDpUR9xIeK3v/1tiiD79NNPTxF8x33hEgECBAgQIECAAIGhBP7rrHSoJUwn0EAggt4IQBulohtqDkw23XTTAbPnzJmTg6WYWNwvmurnR/ASAy899thjqT7IjICxuOd30CBZsU5x33Aq7ocddn4zZRpQ2OLD5z//+RSDOkXL5MqVK9PWW2+dA6365crPRXfw8u2IryPtz0jlHWn+iAUYZoHR7MczzzyT3ve+9+UAOAYxq09FN++0/vrrp6J7c/aMYHjhwoVx60Vurb3rrrvyxYUYMKw2XXnllSn2sUzFvcEDBtYq7i8vZ+XX+G5ddNFFA6b5QIAAAQIECBAgQKBeQABcL+LzmAWitS8CndoAJjIt7vOtdomOoKjR/FiudtTf+Byp0fIxPfKI5Uea30yZIr/aFCNSf/Ob38yjW8f7GMm4UWt37TrNvh9reddlf772ta+l4p7sahE/+tGP5osG1Qnr8Ca6v++88865K3yj1Yt7fdMXv/jFdPzxx6dvfetbuSW4GDQtrxO9CGIU7+jSHi25tenEE09MTz/9dJ4U8+JzbZBcDJqVW4Xjgkl0j47gN4LkH//4xynylwgQIECAAAECBAg0EhAAN1IxbUwCEdBE63B0Ha5N8Xm77bbLk+JROL/4xS9qZ+flt9hii4aPDorlI9iN+zxrA+TIc/vtt8+B6XDzmynTgML8/kMxOFPOOwKt+BuvNNL+jFTekeY3Kmfcf112QY/50b07WtXXNcXjh6I1d++9906HHnpozuaWW27Jj7yKz1//+tdzgBv3Te+///7pRz/6UXrpS1+aXv/616cIjOOvGDQrvfzlL8/dmON+8jIVg6aVbwd1pY8Z0V0+6j1SBOCRfzxC6dJLL02nnnpqnu4fAgQIECBAgAABAvUCg2/Yq1/CZwLrIBCtdXFvZ20qRvut3vcb84sRoXPX5XKZWL72Ht9yerwWI0rnVt7aPOPezwhKIxgaaX7kMVKZYpnaFN2q3/ve96ZiZOc0b9689Gd/9mcDylu77Gjfj0d5R7s/0Woaz10u/+qfoTzafYiLA3/3d3+XDjjggBRdneMvuq5HUFt2fY5W3FimeDxSKkZ0TsVji9J//Md/5G7PEThHOuigg3JL+0033TSoCNFVOuphpBTLRYpeBhIBAgQIECBAgACBIQXqRoX2kcCoBYqBsAY9B/iaa66pFC21lXjETTy2KJ4FW7R6VuK5wZGKwCg/4zUeZVQEsZVbb701f/6Xf/mXPD+ecVuMAFwpgtz8Of6JR+IUQVPOY9WqVZUi8KoUrYZNzx+pTNWMfv+m6HZbmTt3bqUYbKlSjNpcKe5ZrsS0kVI88qn4DzfoOcDf/va3KxdffHF19ZH2Z6TyjjS/uqF1fDPUfkS9FBciGuZadKse8BikWKhona3E9OJ+4Uo85zme4Vv7uKui5T4/C7q4n7ty9tln5zovegdkq3i8VtFdvHLFFVfk7ZWPQSq6U1euv/76/BffmXe/+92Volt4pRgRvGG5TCRAgAABAgQIECAQAp4D7HswZoFGAXBketJJJ+XgJQLhV77ylZXvf//7A7ZVPAYnPw825hddpvPy5QLPPvtsDiK/8IUvlJNyIPrmN78551m0KFYOPvjgSjEacNPzY8GRylRmVtxLWpk8eXI18Irpxf3AedoNN9xQLtbwdajA8ZBDDqkUj4KqrhOB9XD7EwuOVN6R5lc3tg5vhtqPCO4jCG6UGgXA8eznfffdNz/rNy4iFI9ZqsRzn2tTBMF/+7d/WykGt8qBbGyjGNiq8id/8ieVX/3qV9VFywA45pd/RUt2vjBSXjypLuwNAQIECBAgQIAAgTqBSfG5OJGUCLREIB55E/fpxv2eQ6W4LzVGWK5/hM55552X7yEtgqABq0Z+MQhU+YzhATOLDyPNb6ZM9XmOx+d4dM/HPvax/Fif2vzGWt5O7U/tPjTzPkb3ju7RUXfDpaJ1P8VzpuM7IREgQIAAAQIECBAYTwEB8HhqymvcBOK+z3iua4xc3C/Pdi1aa1PREpre9KY3jZuTjAgQIECAAAECBAgQaF5AANy8lSXbLBCdE2K0435J/bY//VIv9oMAAQIECBAgQGDiCAiAJ05d21MCBAgQIECAAAECBAhMaAGPQZrQ1W/nCRAgQIAAAQIECBAgMHEEBMATp67tKQECBAgQIECAAAECBCa0gAB4Qle/nSdAgAABAgQIECBAgMDEERAAT5y6tqcECBAgQIAAAQIECBCY0AIC4Ald/XaeAAECBAgQIECAAAECE0dAADxx6tqeEiBAgAABAgQIECBAYEILTJnQe2/n10lg5cqVadWqVeu07kgrTZs2Lc2YMSMtW7ZspEV7Yv6UKVPS5MmT0+rVq3uivCMVctasWen5559PTz/99EiL9sT8mTNnpvg+90OaOnVq2mijjdLy5cvTCy+80PO7tN5666X4PXjmmWd6fl9iB6Ju4lng/fJ9i9/p+B2Ifer1FL/R8du2YsWKtGbNml7fnVz+qJ9WHadHA7TFFluMZvFhl33kkUeGnV/O7OXfwl48Jm2wwQYp/uLY00spzs/i79lnn+2lYqf4jkyaNCk99dRTPVXuOJ6vXbt2XH9jx/L7IgDuqa9PdxQ2TnhadYId/6nj4NWq/DshGPvUL/sTB4v4AeuX/YkT337Zl/hux/+dVv7/bOf/nwiA469f6qf8rvXL/kTd9NN3rZ/+78Qxp/y+tfP/bKu3NZr/O71an734mxdl7sXztvj96sXzs/i/3YvljjJHGs3/41b+pugC3UpdeRMgQIAAAQIECBAgQIBA1wgIgLumKhSEAAECBAgQIECAAAECBFopIABupa68CRAgQIAAAQIECBAgQKBrBATAXVMVCkKAAAECBAgQIECAAAECrRQQALdSV94ECBAgQIAAAQIECBAg0DUCAuCuqQoFIUCAAAECBAgQIECAAIFWCgiAW6krbwIECBAgQIAAAQIECBDoGgEBcNdUhYIQIECAAAECBAgQIECAQCsFBMCt1JU3AQIECBAgQIAAAQIECHSNgAC4a6pCQQgQIECAAAECBAgQIECglQIC4FbqypsAAQIECBAgQIAAAQIEukZAANw1VaEgBAgQIECAAAECBAgQINBKAQFwK3XlTYAAAQIECBAgQIAAAQJdIyAA7pqqUBACBAgQIECAAAECBAgQaKWAALiVuvImQIAAAQIECBAgQIAAga4RmNI1JVEQAr8XOGLxQhYECBAgQIBAiwW+sOkOKW39yhZvpf3Zz5s3r/0btUUCBIYV+OY3vzns/HbO1ALcTm3bIkCAAAECBAgQIECAAIGOCQiAO0ZvwwQIECBAgAABAgQIECDQTgEBcDu1bYsAAQIECBAgQIAAAQIEOiYgAO4YvQ0TIECAAAECBAgQIECAQDsFBMDt1LYtAgQIECBAgAABAgQIEOiYgAC4Y/Q2TIAAAQIECBAgQIAAAQLtFBAAt1PbtggQIECAAAECBAgQIECgYwIC4I7R2zABAgQIECBAgAABAgQItFNAANxObdsiQIAAAQIECBAgQIAAgY4JCIA7Rm/DBAgQIECAAAECBAgQINBOAQFwO7VtiwABAgQIECBAgAABAgQ6JiAA7hi9DRMgQIAAAQIECBAgQIBAOwUEwO3Uti0CBAgQIECAAAECBAgQ6JiAALhj9DZMgAABAgQIECBAgAABAu0UEAC3U9u2CBAgQIAAAQIECBAgQKBjAgLgjtHbMAECBAgQIECAAAECBAi0U0AA3E5t2yJAgAABAgQIECBAgACBjgkIgDtGb8MECBAgQIAAAQIECBAg0E4BAXA7tW2LAAECBAgQIECAAAECBDomIADuGL0NEyBAgAABAgQIECBAgEA7BQTA7dS2LQIECBAgQIAAAQIECBDomIAAuGP0NkyAAAECBAgQIECAAAEC7RQQALdT27YIECBAgAABAgQIECBAoGMCAuCO0dswAQIECBAgQIAAAQIECLRTQADcTm3bIkCAAAECBAgQIECAAIGOCQiAO0ZvwwQIECBAgAABAgQIECDQTgEBcDu1bYsAAQIECBAgQIAAAQIEOiYgAO4YvQ0TIECAAAECBAgQIECAQDsFBMDt1LYtAgQIECBAgAABAgQIEOiYgAC4Y/Q2TIAAAQIECBAgQIAAAQLtFBAAt1PbtggQIECAAAECBAgQIECgYwIC4I7R2zABAgQIECBAgAABAgQItFNAANxObdsiQIAAAQIECBAgQIAAgY4JCIA7Rm/DBAgQIECAAAECBAgQINBOAQFwO7VtiwABAgQIECBAgAABAgQ6JiAA7hi9DRMgQIAAAQIECBAgQIBAOwUEwO3Uti0CBAgQIECAAAECBAgQ6JiAALhj9DZMgAABAgQIECBAgAABAu0UEAC3U9u2CBAgQIAAAQIECBAgQKBjAgLgjtHbMAECBAgQIECAAAECBAi0U0AA3E5t2yJAgAABAgQIECBAgACBjgkIgDtG3x0brlQq6eqrr07xKhEgQIAAAQIECBAgQKCfBQTA/Vy7TezbCy+8kBYsWCAAbsLKIgQIECBAgAABAgQI9LaAALjH6u/JJ59Ma9asSb/97W+rJV+9enW655570qpVq6rT4s3atWvTr3/96/TAAw/kdWpnPvfcc3l6bctv5Bt5xDaWL19eu7j3BAgQIECAAAECBAgQ6HmBKT2/BxNsB97//venHXfcMd1xxx3pjDPOSM8//3w6+eST0w477JDuvPPOdPzxx6eDDjoo3XvvvemEE05Im2++eXr88cdTBMnnn39+2njjjdP111+f191+++1zkFwS/uIXv0jnnHNODn6nTJmSLr/88jRp0qRytlcCBAgQIECAAAECBAj0tIAAuAerb++9905nnXVWDk4/8YlPpBNPPDHtvvvuadmyZTkAPvDAA9PNN9+cjjjiiHTwwQfnPTzmmGPSTTfdlPbZZ590yimn5EB35513Ttdcc0267bbbqgrRknzFFVekTTfdVPBbVfGGAAECBAgQIECAAIF+EBAA92AtvvKVr8zB6RNPPJFuueWWtGjRorR48eK8J0899VQOaA877LC0dOnS9NWvfjXddddduSt0tALfd999adq0aWmnnXbKy++5554DBLbaaqs0d+7cAdN8IECAAAECBAgQIECAQD8ICIB7sBanT59eLXV0VT7kkENSvEaKwHfrrbfOLbzRmhvdoaNF+Atf+EKeP3Xq1NztOQa/mjx5cv5bb73/uhW8Nu+8gn8IECBAgAABAgQIECDQJwL/Ffn0yQ5NpN3YZJNN0i677JIefvjh/LrFFluk0047LQe40TIcXaDnzZuXIuiNe4ZjUKxtt902rb/++rl1OKyWLFmSIhiWCBAgQIAAAQIECBAg0O8CWoB7vIaPPvroNH/+/HTppZfmQPbwww/P9+8eeeSRaeHChenKK6/M3aVf/epX527QMajVqaeemk466aR03nnnpQiao0u0RIAAAQIECBAgQIAAgX4XmFQ8BqfS7zs5EfYv7geOFuHaFC27K1euzCM/104v369YsWLIeeUy8RqPTIr7iMsU3aRnzpxZfhzX1w022CB9+IaLxjVPmREgQIAAAQKDBb60+UvSpBe/avCMcZoynmOK/O53v2uqVNHLLRoDJAIEuksgni4TT68ZrzSW3xctwONVCx3Opz74jeLEvb3x2KOh0nDzatd58MEH0zvf+c7qpI9+9KPpk5/8ZPWzNwQIECBAgEDvCczaaKM0rUcGvhzLyW7v1YwSE+g/gdmzZ3fNTgmAu6YqurcgMajWZZddVi1gBM6PPfZY9fN4vokWYIkAAQIECBBovcCKopfYpBYdz6P0m2222bjtRLPnHTHuiUSAQPcJPPnkk+PaAjyW3xcBcPd9P7quRBGUxj3EZYpHLUXX6lYkB65WqMqTAAECBAgMFlizZk0qzkgHz+jCKc12nax9skUX7oYiEZiwAvF70+z/41YjGQW61cJtyD9u47766qvTSLdzx/OCn3nmmTaUyCYIECBAgAABAgQIECDQfQIC4O6rk1GXKAa7WrBgwYgBcIwKHQNfSQQIECBAgAABAgQIEJiIAgLgHq71GJ35gQceGBD4RveC6J5czmv0jN/owlzbBSHex7RYd9WqVSn66C9fvryHZRSdAAECBAgQIECAAAECgwXcAzzYpCemXH/99emMM85I22+/fVq7dm21zHfccUf63Oc+l4PZrbbaKt19993pi1/8Ytpuu+2qy5x//vlp1qxZ6ZhjjsnT4hnCETTvs88+6ZxzzsnB75QpU1IMVx7PDZYIECBAgAABAgQIECDQDwJagHuwFqOl9pRTTklnnXVWim7Nhx566IC9uO+++9Lpp5+e53/gAx/I3aNrFzjwwAPT97///eqkxYsXp5gW6Z577knnnXdeHvVZ8Fsl8oYAAQIECBAgQIAAgT4QEAD3YCVGgDtt2rS000475dLvueeeA/Zim222qbb4xrxoFa5tJd55551TPCj+1ltvTbfddlvacMMNc0tyZBKtxvGsPaMoDiD1gQABAgQIECBAgACBPhDQBboHKzEeFRQBbdzfO3ny5Pw3VMAaoz5HsBvL1aayFTjWe/vb316dNX369Op7bwgQIECAAAECBAgQINBPAlqAe7A2t9122xzULl26NJd+yZIlORgud+XBBx9Md911V/543XXXpVe96lXlrOrrAQcckG644YYUebzlLW+pTveGAAECBAgQIECAAAEC/SqgBbgHazbuzT311FPTSSedlO/X3WKLLXKX6HJXNttsszR//vw8OvTMmTPzYFnlvPJ1zpw5accdd0zRmhwDYkkECBAgQIAAAQIECBDodwEBcI/W8C677JK+/e1v5+f6brzxxgP2IoLbr33ta/nRRhtttFF13ne/+93q+3gTgfTBBx9cnbbbbrulCy64oPrZGwIECBAgQIAAAQIECPSTgAC4x2uzPvit3Z3a4Ld2+u23356uuuqq9Nhjj6U99tijdpb3BAgQIECAAAECBAgQ6FsBAXCfVW2MAH3ssccOu1fRRXrXXXdNxx13nOf8DitlJgECBAgQIECAAAEC/SQgAO6n2iz2Je7n3X333Yfdq3jM0Tve8Y5hlzGTAAECBAgQIECAAAEC/SZgFOh+q1H7Q4AAAQIECBAgQIAAAQINBQTADVlMJECAAAECBAgQIECAAIF+ExAA91uN2h8CBAgQIECAAAECBAgQaCggAG7IYiIBAgQIECBAgAABAgQI9JuAALjfatT+ECBAgAABAgQIECBAgEBDAQFwQxYTCRAgQIAAAQIECBAgQKDfBATA/Vaj9ocAAQIECBAgQIAAAQIEGgoIgBuymEiAAAECBAgQIECAAAEC/SYgAO63GrU/BAgQIECAAAECBAgQINBQQADckMVEAgQIECBAgAABAgQIEOg3AQFwv9Wo/SFAgAABAgQIECBAgACBhgIC4IYsJhIgQIAAAQIECBAgQIBAvwkIgPutRu0PAQIECBAgQIAAAQIECDQUEAA3ZDGRAAECBAgQIECAAAECBPpNQADcbzVqfwgQIECAAAECBAgQIECgoYAAuCGLiQQIECBAgAABAgQIECDQbwIC4H6rUftDgAABAgQIECBAgAABAg0FBMANWUwkQIAAAQIECBAgQIAAgX4TEAD3W43aHwIECBAgQIAAAQIECBBoKCAAbshiIgECBAgQIECAAAECBAj0m4AAuN9q1P4QIECAAAECBAgQIECAQEMBAXBDFhMJECBAgAABAgQIECBAoN8EBMD9VqP2hwABAgQIECBAgAABAgQaCgiAG7KYSIAAAQIECBAgQIAAAQL9JiAA7rcatT8ECBAgQIAAAQIECBAg0FBAANyQxUQCBAgQIECAAAECBAgQ6DcBAXC/1aj9IUCAAAECBAgQIECAAIGGAgLghiwmEiBAgAABAgQIECBAgEC/CQiA+61G7Q8BAgQIECBAgAABAgQINBQQADdkMZEAAQIECBAgQIAAAQIE+k1AANxvNWp/CBAgQIAAAQIECBAgQKChgAC4IYuJBAgQIECAAAECBAgQINBvAgLgfqtR+0OAAAECBAgQIECAAAECDQUEwA1ZTCRAgAABAgQIECBAgACBfhMQAPdbjdofAgQIECBAgAABAgQIEGgoIABuyGIiAQIECBAgQIAAAQIECPSbgAC432rU/hAgQIAAAQIECBAgQIBAQ4FJlSI1nGMigSEEnnrqqbRy5coh5o5t8oYbbphmzZqVHnroobFl1CVrr7/++mnKlCnp6aef7pISja0Yc+fOTatXr04rVqwYW0ZdsvYmm2ySnnjiiS4pzdiKMW3atDRnzpz06KOPprVr144tsy5YO/7fTJ8+PcXvTT+kqJsXXnghLV++vB92J/9OR93EPvV6iu9a/LY99thj6fnnn+/13UmTJk3K9dMN37Utt9xy3DybPS/o5d/C2bNn99xvxMyZM9OMGTPSI488Mm513Y6Mpk6dmuIcbdWqVe3Y3LhtI85b4v/4smXLxi3PdmQU5/dr1qxJzz333Lhtbiy/L1qAx60aZESAAAECBAgQIECAAAEC3SwgAO7m2lE2AgQIECBAgAABAgQIEBg3AQHwuFHKiAABAgQIECBAgAABAgS6WUAA3M21o2wECBAgQIAAAQIECBAgMG4CAuBxo5QRAQIECBAgQIAAAQIECHSzgAC4m2tH2QgQIECAAAECBAgQIEBg3AQEwONGKSMCBAgQIECAAAECBAgQ6GYBAXA3146yESBAgAABAgQIECBAgMC4CQiAx41SRgQIECBAgAABAgQIECDQzQIC4G6uHWUjQIAAAQIECBAgQIAAgXETmDJuOcmIwDgJHLF44TjlJBsCBHpNYL+tv5MOnX1eSlNm9VrRlZcAgS4RmDdvXpeURDEIEAiBb33rW10FoQW4q6pDYQgQIECAAAECBAgQIECgVQIC4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolIABulax8CRAgQIAAAQIECBAgQKCrBATAXVUdCkOAAAECBAgQIECAAAECrRIQALdKVr4ECBAgQIAAAQIECBAg0FUCAuCuqg6FIUCAAAECBAgQIECAAIFWCQiAWyUrXwIECBAgQIAAAQIECBDoKgEBcFdVh8IQIECAAAECBAgQIECAQKsEBMCtkpUvAQIECBAgQIAAAQIECHSVgAC4q6pDYQgQIECAAAECBAgQIECgVQIC4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolIABulax8CRAgQIAAAQIECBAgQKCrBATAXVUdCkOAAAECBAgQIECAAAECrRIQALdKVr4ECBAgQIAAAQIECBAg0FUCAuCuqg6FIUCAAAECBAgQIECAAIFWCQiAWyUrXwIECBAgQIAAAQIECBDoKgEBcFdVh8IQIECAAAECBAgQIECAQKsEBMCtkpUvAQIECBAgQIAAAQIECHSVgAC4q6pDYQgQIECAAAECBAgQIECgVQIC4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolMKVVGbcz39tuuy1NmzYt/eEf/uGQm120aFHad9990/Tp09Pq1avz8kMu3IYZDz74YLr55psHbWmPPfZIW2yxRXX6ddddl7bddtv00pe+tDrtV7/6VXrqqafSq1/96uq02jf3339/+ulPf5qeeeaZ9JKXvCRFnpGee+65FA6N0qGHHpqefPLJXKb99tuv0SKmESBAgAABAgQIECBAoKcFer4F+IUXXkj/+I//mLbZZpthK2LhwoVpxYoVac2aNen973//sMu2Y2YE7RdeeGH65S9/OeBv1apV1c3/+te/Tuecc046++yzq9PizdKlS9O//uu/DphWfvjRj36U/uIv/iJFkLxy5cr093//9+nkk0/Os5999tm0YMGCdPvttw/YZpQh0qxZs1IE3MuXL8+f/UOAAAECBAgQIECAAIF+EljnFuAIpiL43HDDDaseTzzxRNpkk01yS+KMGTPSo48+mrbaaqvq/Gj1nDNnTop5tSnWi9bJ2pbPaI2MgOzhhx/O29h4441z8BqfX/ziF1dXv/baa3NLaLQAl2nZsmUp/mLbteWL+RHcPfTQQ2m4skYL8W9+85tcnrKsTz/9dJo8eXKK/Y6/2rKW2x3ta7Ts/tVf/dWQq11zzTXpne98Z261veuuu4Zt4S4zueqqq9LRRx+dokU30hFHHJHe/e53pwimwzBSBMgbbLBBfl//z2GHHZYD809+8pP1s3wmQIAAAQIECBAgQIBATwuscwD8s5/9LH3jG99IX/nKVzJAtCqeeuqp6ZJLLsktrDvuuGO644470hlnnJFbXmO52bNn55bHj370ozmwe+yxx9Jpp52WA+YITHfYYYe8fASaH/jAB9Juu+2WHn/88bzOUUcdlRYvXpyD5whGv/71r+eA9Jvf/Ga1hTRad0844YT0yCOP5OA5yhSf99prr2olXXTRRalSqeSyRtmiNbi2rM8//3xuMY2y3Hnnnen4449PBx10ULrgggvS3XffnX73u9/l7tObbrppLuuUKQMJI89ooa1NESyXTrXTh3u/du3aFMH9WWedleL9P/3TPw0bLJd5vehFL0o/+MEP0h/90R+l7bffPptHt+cwjRbwkdKrXvWqdMopp6RjjjkmbbTRRiMtbj4BAgQIECBAgAABAgR6RmBg9DaKYsd9pZ///Odzy2K0yEaQ9fa3v72aw957752Dt2gtjVbM008/PUVwFcFpBFeHHHJIim7JsW508Y3g9ROf+ES66aab0ute97qcTwQXBo7DAABAAElEQVTAf/zHf5y+853v5K7AEQRGEB0B9K233prvi43AOYK+SHHva7TYRnAc6dJLL81Bc20A/LGPfSz98z//czrzzDPzMvFPWdZJkyblMpx44olp9913z63IEQAfeOCBedkIICOAjmAyWkgjnyhfbYp9iH2pTeut17ineQTo0VpbprinN7YX6Sc/+UmKIDsC8alTp6YPfehD6bjjjhvUol2uW77G+ueee2469thjcyvva1/72vTe9743bbfdduUiOa/aMr3mNa/J+x0LxL5Fy/S9996bL0BUV/KGAAECBAgQIECAAAECPS6wzgFwBErz5s1L3/ve93IrarQ6fu1rX6tyvPKVr0wRUEZQuv7666f4HClaQ+N+3RikKe6D/Zu/+Zs8PVpSY5CquAe1DIBf8YpX5HnRlTkGgYrgN9Jmm22WW2Ij37lz5+Zp8U+05Eawd/nll+d7YH/+858PCPyqC9a9Kcsa3aJvueWWHMxHa3OkGGwqyhlpzz33TGWL7+tf//o8vT4Ajlbj6Ppdm6Ibdm0QXs6LoPRTn/pU+XFA1/C4xzdaYGNfIkUX7yhTXEwYLsUgX5FnBOix/0uWLMkXDL785S9nt1g37gkOuzLVdxPffPPNBcAljlcCBAgQIECAAAECBPpGYJ0D4BCIltFPf/rT6WUve1n+i8C0TBGIRYppca9w/EXQHCnusY1uvTEvuhyXKe4DjullKvOIz7X3+JbzI4iLdcoU3bI/+9nP5vteIzCNADoGhRop1W4nAtxonS4D3bgnduuttx6URYyw3Og+2ugmXQ4qVa4UgXujADi222jk6mjVjm7UH/zgB3N37cgngu9ocR4uAA7XqI/oVh55R4t7/EUr/I9//OO8X5FXtLo3KnvMixSm0eosESBAgAABAgQIECBAoJ8ExhQAR1fZGFgpRjN+3/ve19AlgtxYLgLRN77xjemee+7JfxE0v+lNb8otyNH1N4KuHxStyLXdqBtmWDMxWlDjHuEIoiNgixbP6LocAz9FwH3xxRcP6o5cBrblOjXZ5QG8dtlllzzw1gEHHJDiHuUYMOpLX/pSXuyGG25If/Znf5bfx/4cfvjhtavn9+9617sGTRvthGhVD5NozS5T3PccwXh0/R4qxUWC2K8Y+fnjH/94DnKjNTpc3vKWtwy12qDpDzzwQIp7riUCBAgQIECAAAECBAj0k8CYAuCAiAGizjvvvIYtnCXUhz/84dztNgaSii7F8+fPz62/b33rW9P111+fA8lo+d1nn31ykFeuN9JrBLMRBEfAFt2fI2j9zGc+k+I+3wgEo/Xzhz/84YBsohU67i1+xzvekQfsGjCz+BD35Eb54v7hCKIjyI17cSNFi3MZlEZ37Sh/K1J0fy4D7TL/6Ka8//7758GwYp8jSP7+979fzk677rprim7Of/d3f5cH+IpW7LgfOu5HjryiW3k5CNbb3va26nrlm6ib6GYey0fQ/Ad/8AflLK8ECBAgQIAAAQIECBDoC4FJxYjIlXbtSfnoofrtRVAcXXLXpdttBIvxiKBoqS1TPEIpWqbjHuShUnQLHq4bcH1ZoxU4HuH0nve8JwfGw6071DbbOT0uKERX6jJ4b3bbMZhZDM5V+xikeKRUDP5VprhnOgbOakWK78AHfvhf95K3YhvyJECgewX22/o76citL06T1984HxPi1o5+SHFbShxu49jTDymOgVE3bTyFaBlbDAoZF4yjp1XtbVgt22CLM45zn+gR1g3ftfF8mkScKzaToqFjPHrjNbMtyxAg0JxADGQcDYvj+Rs7lt+XMbcAN7fb/7lUPCO4URrLDkSX6ejuWz43OPKP5wePlEYKYIcqa+3gUSNto5Pz4wAw2uA3TmSuvvrq/Hin2rLHQeeKK66oTooTuWitb0Ua7qJFK7YnTwIEuk8gfmMmr/+f40iUY0d0XylHV6Jy5P36QQdHl0v3LB37U+5T95RqbCVpNNbI2HLs3NpRN/3yXSsV+21/yv3ySmAiCJTxU7dcNG1rANyKCo4f+WitjHuBmwl817UMMeBXWXnrmke3rxctxh8onr8cree1KbpDx/3PZYqAuH6k63LeWF8d4MYqaH0CvS/wu2L8hanTns+D+TXb6tPtex09iOLqd/zO9kOK423UTexTr6e4nSqeKBE9v+L2qV5PcSE56qcbvmtbbrnluHE2e97RTxcyxg1PRgQ6LBDHi7jNsnbw4rEWaSy/L40fUDvWErV5/RhJOZ6X28oU24jBvLoxlS23I11Vie7NMXr1UClavctHUA21jOkECBAgQIAAAQIECBDoVYG+CIB7FX+8yh1X4BcsWDDivVgLFy6sDoQ1XtuWDwECBAgQIECAAAECBHpFQADcKzXVoJzRjSBGwK5t+Y3uBStXrsxdDGJeo+5p0Q2htptXvC+7JqxatSrfT90NXaca7LJJBAgQIECAAAECBAgQWGeBnr8HeJ33vMdXjMdHnXHGGWn77bcfMKLaHXfckT73uc/lfvZbbbVVuvvuu9MXv/jFtF3x6KQynX/++fn+oGOOOSZPikc+RdAcA1udc845+b6huCfq8ssvH3Yk7TI/rwQIECBAgAABAgQIEOgFAS3AvVBLdWWMVt5TTjklnXXWWSm6NR966KEDlrjvvvvS6aefnufHoFbRPbo2xYBetc8QXrx4cYppke655578XOfLLrtM8FuL5j0BAgQIECBAgAABAj0vIADuwSqMADdGOdxpp51y6ffcc88Be7HNNttUW3xjXrQK1z53a+edd84jWt96663ptttuy49KiJbkSNFqHKNh9tvjLQYA+UCAAAECBAgQIECAwIQU0AW6B6t96tSpOaCN+3vjGZnxN1TAGqM+x+Ob6p+lWbYCx3rxLOUyxfM3JQIECBAgQIAAAQIECPSjgBbgHqzVeBxTBLVLly7NpV+yZMmAwa4efPDBdNddd+V51113XXrVq141aC8POOCA/GzfyOMtb3nLoPkmECBAgAABAgQIECBAoN8EtAD3YI3GQ+5PPfXUdNJJJ+X7dbfYYovcJbrclc022yzNnz8/jw49c+bMPFhWOa98nTNnTtpxxx1TtCbPmjWrnOyVAAECBAgQIECAAAECfSsgAO7Rqt1ll13St7/97fxc34033njAXkRw+7WvfS0/2mijjTaqzvvud79bfR9vIpA++OCDq9N22223dMEFF1Q/e0OAAAECBAgQIECAAIF+EhAA93ht1ge/tbtTG/zWTr/99tvTVVddlR577LG0xx571M7yngABAgQIECBAgAABAn0rIADus6qNEaCPPfbYYfcqukjvuuuu6bjjjvOoo2GlzCRAgAABAgQIECBAoJ8EBMD9VJvFvsT9vLvvvvuwexWPOXrHO94x7DJmEiBAgAABAgQIECBAoN8EjALdbzVqfwgQIECAAAECBAgQIECgoYAAuCGLiQQIECBAgAABAgQIECDQbwIC4H6rUftDgAABAgQIECBAgAABAg0FBMANWUwkQIAAAQIECBAgQIAAgX4TEAD3W43aHwIECBAgQIAAAQIECBBoKCAAbshiIgECBAgQIECAAAECBAj0m4AAuN9q1P4QIECAAAECBAgQIECAQEMBAXBDFhMJECBAgAABAgQIECBAoN8EBMD9VqP2hwABAgQIECBAgAABAgQaCgiAG7KYSIAAAQIECBAgQIAAAQL9JiAA7rcatT8ECBAgQIAAAQIECBAg0FBAANyQxUQCBAgQIECAAAECBAgQ6DcBAXC/1aj9IUCAAAECBAgQIECAAIGGAgLghiwmEiBAgAABAgQIECBAgEC/CQiA+61G7Q8BAgQIECBAgAABAgQINBQQADdkMZEAAQIECBAgQIAAAQIE+k1AANxvNWp/CBAgQIAAAQIECBAgQKChgAC4IYuJBAgQIECAAAECBAgQINBvAgLgfqtR+0OAAAECBAgQIECAAAECDQUEwA1ZTCRAgAABAgQIECBAgACBfhMQAPdbjdofAgQIECBAgAABAgQIEGgoIABuyGIiAQIECBAgQIAAAQIECPSbgAC432rU/hAgQIAAAQIECBAgQIBAQwEBcEMWEwkQIECAAAECBAgQIECg3wQEwP1Wo/aHAAECBAgQIECAAAECBBoKCIAbsphIgAABAgQIECBAgAABAv0mIADutxq1PwQIECBAgAABAgQIECDQUEAA3JDFRAIECBAgQIAAAQIECBDoNwEBcL/VqP0hQIAAAQIECBAgQIAAgYYCAuCGLCYSIECAAAECBAgQIECAQL8JCID7rUbtDwECBAgQIECAAAECBAg0FBAAN2QxkQABAgQIECBAgAABAgT6TUAA3G81an8IECBAgAABAgQIECBAoKGAALghi4kECBAgQIAAAQIECBAg0G8CAuB+q1H7Q4AAAQIECBAgQIAAAQINBQTADVlMJECAAAECBAgQIECAAIF+ExAA91uN2h8CBAgQIECAAAECBAgQaCgwpeFUEwl0UODyAz6WHnrooQ6WYPw2vf7666cpU6akp59+evwy7WBOc+fOTatXr04rVqzoYCnGb9ObbLJJeuKJJ8Yvww7mNG3atDRnzpz06KOPprVr13awJGPd9LvGmoH1CRCY4AKLFi3qyd/C2bNnp+XLl/dU7c2cOTPNmDEjPfLIIz1V7qlTp6Y4R1u1alVPlTvOWyZNmpSWLVvWU+XutsJqAe62GlEeAgQIECBAgAABAgQIEGiJgAC4JawyJUCAAAECBAgQIECAAIFuExAAd1uNKA8BAgQIECBAgAABAgQItERAANwSVpkSIECAAAECBAgQIECAQLcJCIC7rUaUhwABAgQIECBAgAABAgRaIiAAbgmrTAkQIECAAAECBAgQIECg2wQEwN1WI8pDgAABAgQIECBAgAABAi0REAC3hFWmBAgQIECAAAECBAgQINBtAgLgbqsR5SFAgAABAgQIECBAgACBlggIgFvCKlMCBAgQIECAAAECBAgQ6DYBAXC31YjyECBAgAABAgQIECBAgEBLBKa0JFeZEhiDwBGLF45hbasSIHDZ1uekh+YsSZMmucbp20CAwMQTmDdv3sTbaXtMoMcFLrzwwrbtgbOjtlHbEAECBAgQIECAAAECBAh0UkAA3El92yZAgAABAgQIECBAgACBtgkIgNtGbUMECBAgQIAAAQIECBAg0EkBAXAn9W2bAAECBAgQIECAAAECBNomIABuG7UNESBAgAABAgQIECBAgEAnBQTAndS3bQIECBAgQIAAAQIECBBom4AAuG3UNkSAAAECBAgQIECAAAECnRQQAHdS37YJECBAgAABAgQIECBAoG0CAuC2UdsQAQIECBAgQIAAAQIECHRSQADcSX3bJkCAAAECBAgQIECAAIG2CQiA20ZtQwQIECBAgAABAgQIECDQSQEBcCf1bZsAAQIECBAgQIAAAQIE2iYgAG4btQ0RIECAAAECBAgQIECAQCcFBMCd1LdtAgQIECBAgAABAgQIEGibgAC4bdQ2RIAAAQIECBAgQIAAAQKdFBAAd1LftgkQIECAAAECBAgQIECgbQIC4LZR2xABAgQIECBAgAABAgQIdFJAANxJfdsmQIAAAQIECBAgQIAAgbYJCIDbRm1DBAgQIECAAAECBAgQINBJAQFwJ/VtmwABAgQIECBAgAABAgTaJiAAbhu1DREgQIAAAQIECBAgQIBAJwUEwJ3Ut20CBAgQIECAAAECBAgQaJuAALht1DZEgAABAgQIECBAgAABAp0UEAB3Ut+2CRAgQIAAAQIECBAgQKBtAgLgtlHbEAECBAgQIECAAAECBAh0UkAA3El92yZAgAABAgQIECBAgACBtgkIgNtGbUMECBAgQIAAAQIECBAg0EkBAXAn9W2bAAECBAgQIECAAAECBNomIABuG7UNESBAgAABAgQIECBAgEAnBQTAndS3bQIECBAgQIAAAQIECBBom4AAuG3UNkSAAAECBAgQIECAAAECnRQQAHdS37YJECBAgAABAgQIECBAoG0CAuC2UdsQAQIECBAgQIAAAQIECHRSQADcSX3bJkCAAAECBAgQIECAAIG2CQiA20ZtQwQIECBAgAABAgQIECDQSQEBcCf1bZsAAQIECBAgQIAAAQIE2iYgAG4btQ0RIECAAAECBAgQIECAQCcFBMCd1LdtAgQIECBAgAABAgQIEGibgAC4bdQ2RIAAAQIECBAgQIAAAQKdFBAAd1LftgkQIECAAAECBAgQIECgbQIC4BZSVyqVdPXVV6d4HSmtXr16pEUGzR9pnWXLlqXrr79+0Hox4ZZbbkl33313w3kmEiBAgAABAgQIECBAoB8FBMAtrNUXXnghLViwYMQA+JxzzklLly4dVUmaWefXv/51uuiiixrme91116Ubb7yx4TwTCRAgQIAAAQIECBAg0I8CAuAW1Opzzz2XHnjggUGB79q1a1MEpTFvzZo1ecvRinvfffelVatWpVgvUqPl8ozf/zPadSLfe++9t5p/bV7l+8jznnvuyeUop3klQIAAAQIECBAgQIBAPwlM6aed6YZ9iS7HZ5xxRtp+++1zIFuWKQLQE044IW2++ebp8ccfTxFwnn/++emmm25Kd911V3ryySfTi170orTJJps0XG7jjTcus0o33HBDU+vECo899lg66qij0pw5c9KDDz6YPve5z6Wdd965mle8iTKcfPLJaYcddkh33nlnOv7449NBBx00YBkfCBAgQIAAAQIECBAg0OsCAuBxrMFo1T3llFNSdE+OIPOaa65Jt912W97CzTffnI444oh08MEH58/HHHNMDjz333//dO211+aA89WvfnW66qqrhlyuLGqz62y66aY52L744ovTdtttl6688sp07rnnpi9/+ctlVvk15p944olp9913T3HfcATABx54YJo0adKA5XwgQIAAAQIECBAgQIBALwsIgMex9qIr87Rp09JOO+2Uc91zzz2ruR922GH5Pt+vfvWrufU2ukJHK3B9ana52vWGWycC3/iLtNdee+XgN+5NLtMTTzyRB8RatGhRWrx4cZ781FNP5cB91113LRfzSoAAAQIECBAgQIAAgZ4XEACPYxVOnTo1d3uOAHPy5Mn5b731/vM262gVjntso2txtK5+4QtfaLjlZperXXm4dcrtx/IRcG+11VapdlpMnzJlSjrkkEPya3yOgHrrrbeOtxIBAgQIECBAgAABAgT6RsAgWONYldtuu21af/31qyM6L1myJJWtrfHYoegCPW/evBSB8h133FG9RzjWKVuDh1suWpifffbZXOJm17n//vur3bBj5Od99913wB7HPce77LJLevjhh/PrFltskU477bRq2QYs7AMBAgQIECBAgAABAgR6WEAL8DhWXtwze+qpp6aTTjopnXfeeSmCyegSHenII49MCxcuzPfhxnJxv290g470ile8Ip155pk5CB5uuWOPPTYPsBXLN7PONttskyIoj4A2AvEoSwyCVZ+OPvroNH/+/HTppZfm5Q4//PAU9w9LBAgQIECAAAECBAgQ6CeBSZUi9dMOdcu+rFixItWO3BzliiB05cqVg6bHvGgBjpbh6J483HKxbJlGs87y5cvT7Nmzy1Ubvsb9wNEiPFKKe4RjP1qRNtxww/SRpZe0Imt5EpgwApdtfU56aM6SYiC73u3kE7dmTJ8+PcXvTT+kGIk/ftvjt7gf0qxZs3LdxD71eorv2ty5c/NTE55//vle3508gGXUTzd817bccstx83zooYeayisu9kdjgkSAQG8JXHjhhaMq8Fh+X7QAj4q6+YXrg99YM4LbRtNjXtlSPNJyMb9Mo1lnpOA38hwq+I3HJ33kIx8pN5ve8573pPe+973Vz+P5pv7+5PHMW14EJpLAZptt1tMBcPSUid+D2t+5Xq6/GBciUtRLP6TYn7gVp5+uocdxsh/2p/y/0y/ftfL/S7P7E/svESDQewLN/h8fjz0TAI+HYp/nEa0wr3vd66p7Gc8rfu6556qfx/NNXImXCBAYu0D8H+3lFuAIfuP3oFW/NWMXHl0OG2ywQQ6u+mV/IviN1tJ+CBjjuxY9sGJ/+qFFOwLAqJ9u+K6F63ilZvenvNg0XtuVDwEC7RFo9v94WZqx/L6INkpFr0MKxBWZ+fPnV+dHl8To4t2KFF2gJQIExi4Q/0d7OQDuty7QsT8RXLXqt3Ps35jR5dBvXaDjQu+qVatyEDw6ie5bOgLgqJ9u+K7NmDFj3ICa3Z9+6TUybnAyItAjAs3+Hy93Zyy/L717g1i59138GlfGr7766qaukJejQI9md0ZaZ9myZen6669vmGWMNn333Xc3nGciAQIECBAgQIAAAQIE+lFAANzCWo2r/QsWLBgxAI7n+C5dunRUJWlmnRhl+qKLLmqYbzwS6cYbb2w4z0QCBAgQIECAAAECBAj0o4AAuAW1Gn3YH3jggUGB79q1a/Ojj2LemjVr8pajFfe+4vm+0fWq7PveaLnaYo52ncj33nvvreZfm1f5PvK85557cjnKaV4JECBAgAABAgQIECDQTwLuAR7n2owux2eccUbafvvtUwSyZYoA9IQTTkibb755evzxx/Njj84///x00003pbvuuis9+eSTKQaXipGYGy1XO3r0DTfc0NQ6se3HHnssHXXUUSkewRGjOcdzgHfeeeeyWPk1ynDyySenHXbYId15553p+OOPTwcddNCAZXwgQIAAAQIECBAgQIBArwsIgMexBqNV95RTTknRPTmCzGuuuSbddttteQs333xzOuKII9LBBx+cPx9zzDE5+N1///3TtddemwPOV7/61emqq64acrmyqM2us+mmm+Zg++KLL07bbbdduvLKK9O5556bvvzlL5dZ5deYf+KJJ6bdd989xX3DEQAfeOCB+VmCAxb0gQABAgQIECBAgAABAj0sIAAex8qLrswx+uBOO+2Uc91zzz2ruR922GH5Pt+vfvWrufU27s9tNIhVs8tVMy7eDLdOBL7xF2mvvfbKwW/tYx6eeOKJFANiLVq0KC1evDgvF6M8R+C+66675s/+IUCAAAECBAgQIECAQD8ICIDHsRbjeVTR7TkCzHgOXfzF8wUjRatw3GMbXYujdfULX/hCwy03u1ztysOtU24/lo+Ae6uttqqWqcwjHs9xyCGH5GduxrQIqLfeeutytlcCBAgQIECAAAECBAj0hYBBsMaxGrfddtv88PlyROclS5bkYDg2Ea2s0QV63rx5KQLlO+64o3qPcDywvmwNHm65aGF+9tlnc4mbXef++++vdsOOkZ/33XffAXsc9xzvsssu6eGHH86vW2yxRTrttNOqZRuwsA8ECBAgQIAAAQIECBDoYQEtwONYefHw+VNPPTWddNJJ6bzzzksRTJYPZD/yyCPTwoUL8324sVzc7xvdoCO94hWvSGeeeWYOgodb7thjj80DbMXyzayzzTbbpAjKI6CNVukoSwyCVZ+OPvroNH/+/HTppZfm5Q4//PAU9w9LBAgQIECAAAECBAgQ6CeBSZUi9dMOdcu+rFixItWO3BzliiB05cqVg6bHvGgBjpbh6LI83HKxbJlGs87y5cvT7Nmzy1Ubvsb9wNEiPFKKe4RjP1qRNtxww/SRpZe0Imt5EpgwApdtfU56aM6SYiC73u3kE7dmTJ8+PcXvTT+kGIk/ftvjt7gf0qxZs3LdxD71eorv2ty5c/NTE55//vle3508gGXUTzd817bccstx83zooYeayisu9kdjgkSAQG8JXHjhhaMq8Fh+X7QAj4q6+YXrg99YM4LbRtNjXtlSPNJyMb9Mo1lnpOA38mwm+C237ZUAAQIECBAgQIAAAQK9JtC7zQO9Jq28BAgQIECAAAECBAgQINBRAQFwR/ltnAABAgQIECBAgAABAgTaJSAAbpe07RAgQIAAAQIECBAgQIBARwUEwB3lt3ECBAgQIECAAAECBAgQaJeAALhd0rZDgAABAgQIECBAgAABAh0VEAB3lN/GCRAgQIAAAQIECBAgQKBdAgLgdknbDgECBAgQIECAAAECBAh0VEAA3FF+GydAgAABAgQIECBAgACBdgkIgNslbTsECBAgQIAAAQIECBAg0FEBAXBH+W2cAAECBAgQIECAAAECBNolIABul7TtECBAgAABAgQIECBAgEBHBQTAHeW3cQIECBAgQIAAAQIECBBol4AAuF3StkOAAAECBAgQIECAAAECHRUQAHeU38YJECBAgAABAgQIECBAoF0CAuB2SdsOAQIECBAgQIAAAQIECHRUQADcUX4bJ0CAAAECBAgQIECAAIF2CQiA2yVtOwQIECBAgAABAgQIECDQUQEBcEf5bZwAAQIECBAgQIAAAQIE2iUgAG6XtO0QIECAAAECBAgQIECAQEcFBMAd5bdxAgQIECBAgAABAgQIEGiXgAC4XdK2Q4AAAQIECBAgQIAAAQIdFRAAd5TfxgkQIECAAAECBAgQIECgXQIC4HZJ2w4BAgQIECBAgAABAgQIdFRAANxRfhsnQIAAAQIECBAgQIAAgXYJCIDbJW07BAgQIECAAAECBAgQINBRAQFwR/ltnAABAgQIECBAgAABAgTaJSAAbpe07RAgQIAAAQIECBAgQIBARwUEwB3lt3ECBAgQIECAAAECBAgQaJeAALhd0rZDgAABAgQIECBAgAABAh0VEAB3lN/GCRAgQIAAAQIECBAgQKBdAgLgdknbDgECBAgQIECAAAECBAh0VEAA3FF+GydAgAABAgQIECBAgACBdgkIgNslbTsECBAgQIAAAQIECBAg0FEBAXBH+W2cAAECBAgQIECAAAECBNolIABul7TtECBAgAABAgQIECBAgEBHBQTAHeW3cQIECBAgQIAAAQIECBBol4AAuF3StkOAAAECBAgQIECAAAECHRUQAHeU38YJECBAgAABAgQIECBAoF0CAuB2SdsOAQIECBAgQIAAAQIECHRUYEpHt27jBBoIXH7Ax9JDDz3UYE7vTVp//fXTlClT0tNPP917hW9Q4rlz56bVq1enFStWNJjbe5M22WST9MQTT/RewRuUeNq0aWnOnDnp0UcfTQ+vfVea1GAZkwgQIDARBBYtWpR/C9euXdtTuzt79uy0fPnynirzzJkz04wZM9IjjzzSU+WeOnVqinO0VatW9VS547xl0qRJadmyZT1V7g033DCtWbMmPffcc11Rbi3AXVENCkGAAAECBAgQIECAAAECrRYQALdaWP4ECBAgQIAAAQIECBAg0BUCAuCuqAaFIECAAAECBAgQIECAAIFWCwiAWy0sfwIECBAgQIAAAQIECBDoCgEBcFdUg0IQIECAAAECBAgQIECAQKsFBMCtFpY/AQIECBAgQIAAAQIECHSFgAC4K6pBIQgQIECAAAECBAgQIECg1QIC4FYLy58AAQIECBAgQIAAAQIEukJAANwV1aAQBAgQIECAAAECBAgQINBqAQFwq4XlT4AAAQIECBAgQIAAAQJdITClK0qhEARqBI5YvLDmU0pnv3inNGnOywdM84EAAQIECBAg0Ehg3rx5jSab1qcCF154YZ/umd1qlYAW4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolIABulax8CRAgQIAAAQIECBAgQKCrBATAXVUdCkOAAAECBAgQIECAAAECrRIQALdKVr4ECBAgQIAAAQIECBAg0FUCAuCuqg6FIUCAAAECBAgQIECAAIFWCQiAWyUrXwIECBAgQIAAAQIECBDoKgEBcFdVh8IQIECAAAECBAgQIECAQKsEBMCtkpUvAQIECBAgQIAAAQIECHSVgAC4q6pDYQgQIECAAAECBAgQIECgVQIC4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolIABulax8CRAgQIAAAQIECBAgQKCrBATAXVUdCkOAAAECBAgQIECAAAECrRIQALdKVr4ECBAgQIAAAQIECBAg0FUCAuCuqg6FIUCAAAECBAgQIECAAIFWCQiAWyUrXwIECBAgQIAAAQIECBDoKgEBcFdVh8IQIECAAAECBAgQIECAQKsEBMCtkpUvAQIECBAgQIAAAQIECHSVgAC4q6pDYQgQIECAAAECBAgQIECgVQIC4FbJypcAAQIECBAgQIAAAQIEukpAANxV1aEwBAgQIECAAAECBAgQINAqAQFwq2TlS4AAAQIECBAgQIAAAQJdJSAA7qrqUBgCBAgQIECAAAECBAgQaJWAALhVsvIlQIAAAQIECBAgQIAAga4SEAB3VXUoDAECBAgQIECAAAECBAi0SkAA3CpZ+RIgQIAAAQIECBAgQIBAVwkIgLuqOhSGAAECBAgQIECAAAECBFolIABulax8CRAgQIAAAQIECBAgQKCrBKZ0VWnGsTDPPPNM+vd///f0tre9bchcb7nlljRjxoy044475mVWr16dpk2bNuTyrZ7x4x//OP3ud78btJmpU6emN7/5zWnRokXVeZMmTUpz585NL3vZy9Imm2ySp993333pkUceSXvssUd1uXgT673hDW9I06dPH5BH7UKHHnpoevLJJ9PNN9+c9ttvv9pZ3hMgQIAAAQIECBAgQKAvBPq2Bfiyyy5Lm2222bCVdN1116Ubb7wxL3POOeekpUuXDrt8q2f+5je/Sb/85S/z39lnn51+9rOf5fd33313ioB+wYIF6cEHH0y//e1v8+sll1ySjjrqqBSBb6QIXq+66qr8vvafhQsX5sD42WefzXncfvvt1e2U24vlZ82alcJk+fLl/7+9O4G/as7/OP5pQbsUihaJmhItNCpZs4SQlDKWJlkTD7v+GqIZMY2k+GsspXloNNnXQcgSlSUhhbQoIi2SSpa2f+/vzLn/87udc/v97u/eX+ec+/o+HnXv79xzvvf7fX7v9jnf5fgP5z4CCCCAAAIIIIAAAgggkAiBnPYAb9y40dauXesCKU/np59+sooVK5oe0+2KFSts9913d/e1jwK6WrVquZ5Y7xjdrl+/3gV69erVS+27bt06d1+PKUjTY0oKCGvXrp3qvf3+++/t448/tr59+7rH9Z8CSO2nIC89MFbPr4LIJk2a2G+//WYbNmzYqqzaRwFqnTp1UmXVfgoqd9xxR/vuu++sfv36Vr589ucUevTokSqvem1V/oYNG7pt6p1VOv/881P11N8DBw60iRMn2kUXXaQ/i5Uuv/xyq1SpUuC+p556qo0dO9auvPLKwMfZiAACCCCAAAIIIIAAAgjEVSDnAfCZZ55pDz74oAsUN2/ebH369LGhQ4fas88+a4sWLXI9jxqWfNBBB9l9991nNWvWdNsuvvhi69atm3NUj6WGL+sxDQlWz2fjxo1t9OjRLghVgKsAuEWLFi6wXrlypS1ZssQGDx5sLVu2dL2g7du3T7WJnnvcuHEuj08//dQOP/xwu/baa1OPT5061ebNm+eGANetW9cmT55cpKyHHnqoy1tlmDNnjl166aXWpUsX+/zzz13dFAjvueeepp7aESNGWKNGjVJ5685f//rXrXqXFUir/qVJOqmgkwIqc65SmzZt7JZbbnGBdvXq1XOVLfkggAACCCCAAAIIIIAAAttdIKcBsHpCNVf1lVdesbPPPts0x1Y9rgoclRSwPf/88y5o6969u912222mgEvzVtWzefLJJ5uC0ddff900vFe9lI8++qg99thjNmDAAJeHAr4HHnjABcCat6pAVrePPPKITZo0yQXAGtbbs2dPt7+CcA1z1hBnBakKlHv16mVXX321e1z/HXXUUfbiiy+6oPbAAw90AbBXVh1/1VVX2aBBg6xt27amYFsB8IknnuiOV8+xgmsFvU8//bQL1hXA+9MVV1zhepX927LtKVY5NP9XQbd6tJs1a2bHHnusP+tt3j/vvPOK9FTrZITKqFShQgXX6/zll186y21mxg4IIIAAAggggAACCCCAQEwEchoAq87qGVUPogJgDc31AkU91qpVKxe8adizguXWrVtrs+stbtCggU2fPt1mz55thxxySGqIbqdOnax3796pgFU9vAoAtfBTlSpVUgs+aVizhj0rKXjTMGsl7XvdddfZm2++6YLpuXPnmoJaDaPOlLyyqqdZgbyGJKs+SmvWrHHl1H2Vu9F/e3xV7rvvvtsF+gokvaRe42XLlnl/uluVvWPHjkW2FeePrl27uuBVJxlUB50A0OJWSlosS4FxevKGn3vb1VMufy+pLP4kOwJgvwj3EUAAAQQQQAABBBBAIAkCOQ+AtSqxejc11Pjtt9+2fv36pZy8QE3B6qZNm9w/L1DUHFsFalrZWEOJvaQ5ufrn9Zh6eXiP+wM5/zYvwNUcXfV4KtjU6sgaoq3e520l//No7rJ6p3WrpHmymn+8ePHiItlonrHK49XJe1D1Ua+0P2l4dzYBsHprtVK1hngrsNfJhiFDhrisVab0QFuuCtg1TNoLjjVXOWwOsDKSt4JpEgIIIIAAAggggAACCCCQJIGcB8DCUS+wVjFWD2/QPFIFwFrcacqUKW4+7oIFC0z/FDzr3/jx4+2HH35wvbzqeW3evHkqAC4OvvLQfON9993XzSHWAlL9+/d3AazXi6tg258UuCpYTE/qadZcYy1y1blzZ7eIlxaRuuuuu9yu6s3W/GE9l1ZQ1pDu9HTaaaelb8rJ3xoWrp52GR1//PG29957u/JpCLkuZaTe6wkTJljTpk1db/nq1auL9bxfffWVW126WDuzEwIIIIAAAggggAACCCAQE4G8BMCakzpq1Cg3rzfM4YILLnALS2lhK/VQ3nzzzanVmRVAn3HGGW5l52rVqqV6OMPySt+ugE9DeJU0RPmII45wvcDKS8OVNRc4vfdWQ56HDRsWGARrNWaVT4G5eq41h1irTms+sYJ5PaZh1cpfC16VVVIZLrnkEjfsWr3bCtbVI3zHHXe4ucgK6DVH+NZbby1SpKBrI6sd5KZeYvUi77XXXkWO4Q8EEEAAAQQQQAABBBBAIO4C5bYEbpu3ZyW8nt70MmgIs4YvB/Ugp++b/rcutaTL+OhyPt6wZS2epaHJGj4clhQwauivN9w6fb/0ss6aNcvuvPNOGzNmjAvisylr+nPk6m9djkp1yVTfoOdSb7KuE+y/DJKGRKun20vqLS9pvt6x27rV0PMLpj5UZLcRDfaz8ru2KLItLn+oDfQa1PD4JCRdskyvB72+kpC0SJ93ibG410fvS02t0OefTtTFPel9o88ZXUovCUlto3Yp7kicqNdZ33dqm6S81vTZpu94b/pU1P0zlU9rn6h9ovBa89ZjyVTe4j6WPsUr7Dh9FqoThVQ4Ag89VPR3Y3Fqrt9n+qf4IE5Jv1v0Htcozzgl/b5XJ1suP2NL8/mSlx7gkjSIei2DkvfCDHpsW9vUK6teaA1J1tBgpfSFnoLy2FZQF1ZW5RWl4FflUW90SZPOheiSUem92Ap+/YuZ6ZJV/gC5pM9T0v1rbHmzV/nvomYlPTYq+0ft9VEaFwUmxXk/leY5yvLY0nyAlmU5i/tc+vxLUqpatWqSqpNx/YW4VdS/Vkbcyh5U3kzf8UH7R31bprU+ol72oPIl7bM6qI5sy06gNK+NbH4vZ1fK3B5VmjrntiTxzG27B8D5YtMw5WnTpuUre5evhlf7F/nK65OVQeY6m9Rny3Wba9SoUeTZNGRcvele0plyXYs5HynoJMSaNWvt5zw9Xz7q4M9TJ3I08kCjGZKQ1IulHuC4nTENs9eJCU3BSELSa03vXfViJaFXzhuxk5TXmtpG7ZKU0RM6MaGRLUl5remzTaNBvMUi4/yZoN4htU8UXmuaqpWrVNzfHfosJBWWQHFfG34VnczXv7j9PvM6VOL220Un5LT+Ui57gEvz+ZLYAFiBlBaCymfSMARdGzgpSWe/Dz744K2qo7P8usSTl/Smy9cXqz6M0tPGjett05agK65JPesKGpOQVBf94E1KfZJUF/3oVdKXS/oif3F87Xk/TpL0WkvS603fC2qbJATA3veO3ju5/HG2vd53+izw2md7lSEfz1vczwLvszAfZSDPaAoU97XhL71+z+i1ks2x/nzK+r4+c+NYbn3O6gRjVLzLl3XD8Xy5EXj55ZcT0wuXGxFyQQABBBBAAAEEEEAAAQQyCxAAZ/aJ7KNaZTtuE+Aji0nBEEAAAQQQQAABBBBAoCAECIBj3sxaoCpoFduVK1e66xOnz5/79ttvTdf5TZ/npBWwdS3mpKy4GvNmpfgIIIAAAggggAACCCCQB4GtJ1zm4UnIMj8CgwcPdpds0jWPzznnHDvrrLNcYHvjjTfa0qVLTXOUdUkj/d2xY0cbNGiQKQDWYizfffedDR8+3OrWrWsffPCBuyZz48aNbc6cOXbppZearsVMQgABBBBAAAEEEEAAAQSSJEAAHOPWbN++vZ177rm2fPly69mzpx1zzDFucSqt/vjggw+6mo0fP94mTpxoLVu2dKtiP/fcc+4yHLrV9UIVAI8bN84Fx1rQSz3HCoB12SMWkojxi4OiI4AAAggggAACCCCAwFYCBMBbkcRng7fK9W677Wb77LOP6709/PDDXU/whAkTbO7cuTZr1ixr1KiRu05xu3bt7PTTT7cOHTqY9tt///3dJVNmzpxpL730kguUVXut8jx79mz3eHw0KCkCCCCAAAIIIIAAAgggkFmAADizT6QfLV/+/6dwaw5v/fr1bcaMGfbnP//ZzjjjDOvevbu1atXKpkyZ4upxyy232MKFC23y5Mk2cuRIFyCfeuqp7jpoJ598srvVjtpWr169SNedwiGAAAIIIIAAAggggAACJRX4/wiqpEey/3YX0NBmXQ9MPb1a1EpzeNXjq6HMCoCbNWvmhj3rMQ1tPvvss61OnTrWu3dv69atm1sMS9f+bdGihZsTrFs9fuuttybiOqLbvYEoAAIIIIAAAggggAACCERKgB7gSDVHyQqzaNEiO/PMM2316tU2YMAAd3Dnzp1t4MCB1r9/f1u/fr21adPG3nzzTatVq5Ydd9xxdt5555nmCP/yyy+up1gH9e3b126++WbTfGEF1L169bLatWuXrDDsjQACCCCAAAIIIIAAAghEXIAAOOINFFa8p59+2j2k+bqVK1dODV9WD+6YMWPcpZG02rMWsurXr5/bVz2/Wi1aAbNWiPbSAQccYE888YSbD6weYRICCCCAAAIIIIAAAgggkEQBAuCYt2r16tUDa+APcP07KCAOe4zg1y/FfQQQQAABBBBAAAEEEEiaAHOAk9ai1AcBBBBAAAEEEEAAAQQQQCBQgAA4kIWNCCCAAAIIIIAAAggggAACSRMgAE5ai1IfBBBAAAEEEEAAAQQQQACBQAEC4EAWNiKAAAIIIIAAAggggAACCCRNgAA4aS1KfRBAAAEEEEAAAQQQQAABBAIFCIADWdiIAAIIIIAAAggggAACCCCQNAEC4KS1KPVBAAEEEEAAAQQQQAABBBAIFCAADmRhIwIIIIAAAggggAACCCCAQNIECICT1qLUBwEEEEAAAQQQQAABBBBAIFCAADiQhY0IIIAAAggggAACCCCAAAJJEyAATlqLUh8EEEAAAQQQQAABBBBAAIFAAQLgQBY2IoAAAggggAACCCCAAAIIJE2AADhpLUp9EEAAAQQQQAABBBBAAAEEAgUIgANZ2IgAAggggAACCCCAAAIIIJA0AQLgpLUo9UEAAQQQQAABBBBAAAEEEAgUIAAOZGEjAggggAACCCCAAAIIIIBA0gQIgJPWotQHAQQQQAABBBBAAAEEEEAgUIAAOJCFjQgggAACCCCAAAIIIIAAAkkTIABOWotSHwQQQAABBBBAAAEEEEAAgUABAuBAFjYigAACCCCAAAIIIIAAAggkTYAAOGktSn0QQAABBBBAAAEEEEAAAQQCBQiAA1nYiAACCCCAAAIIIIAAAgggkDQBAuCktSj1QQABBBBAAAEEEEAAAQQQCBQgAA5kYSMCCCCAAAIIIIAAAggggEDSBAiAk9ai1AcBBBBAAAEEEEAAAQQQQCBQgAA4kIWNCCCAAAIIIIAAAggggAACSRMgAE5ai1IfBBBAAAEEEEAAAQQQQACBQAEC4EAWNiKAAAIIIIAAAggggAACCCRNgAA4aS1KfRBAAAEEEEAAAQQQQAABBAIFCIADWdiIAAIIIIAAAggggAACCCCQNAEC4KS1KPVBAAEEEEAAAQQQQAABBBAIFCAADmRhIwIIIIAAAggggAACCCCAQNIECICT1qLUBwEEEEAAAQQQQAABBBBAIFCAADiQhY0IIIAAAggggAACCCCAAAJJEyAATlqLUh8EEEAAAQQQQAABBBBAAIFAAQLgQBY2IoAAAggggAACCCCAAAIIJE2AADhpLUp9EEAAAQQQQAABBBBAAAEEAgUIgANZ2IgAAggggAACCCCAAAIIIJA0AQLgpLUo9UEAAQQQQAABBBBAAAEEEAgUqBi4lY0IbEeBCZ3725IlS7ZjCXhqBBBAAAEEEIirwEsvvWTLli2zjRs3xqoKNWvWtFWrVsWqzNWqVbOqVava0qVLY1VuClvYAvQAF3b7U3sEEEAAAQQQQAABBBBAoGAECIALpqmpKAIIIIAAAggggAACCCBQ2AIEwIXd/tQeAQQQQAABBBBAAAEEECgYAQLggmlqKooAAggggAACCCCAAAIIFLYAAXBhtz+1RwABBBBAAAEEEEAAAQQKRoAAuGCamooigAACCCCAAAIIIIAAAoUtQABc2O1P7RFAAAEEEEAAAQQQQACBghEgAC6YpqaiCCCAAAIIIIAAAggggEBhCxAAF3b7U3sEEEAAAQQQQAABBBBAoGAECIALpqmpKAIIIIAAAggggAACCCBQ2AIVC7v61D6KAmdMvKdIsYY3bG7lajYvso0/EEAAAQQQQACBIIHjjz8+aDPbEiowduzYhNaMauVLgB7gfMmSLwIIIIAAAggggAACCCCAQKQECIAj1RwUBgEEEEAAAQQQQAABBBBAIF8CBMD5kiVfBBBAAAEEEEAAAQQQQACBSAkQAEeqOSgMAggggAACCCCAAAIIIIBAvgQIgPMlS74IIIAAAggggAACCCCAAAKREiAAjlRzUBgEEEAAAQQQQAABBBBAAIF8CRAA50uWfBFAAAEEEEAAAQQQQAABBCIlQAAcqeagMAgggAACCCCAAAIIIIAAAvkSIADOlyz5IoAAAggggAACCCCAAAIIREqAADhSzUFhEEAAAQQQQAABBBBAAAEE8iVAAJwvWfJFAAEEEEAAAQQQQAABBBCIlAABcKSag8IggAACCCCAAAIIIIAAAgjkS4AAOF+y5IsAAggggAACCCCAAAIIIBApAQLgSDUHhUEAAQQQQAABBBBAAAEEEMiXAAFwvmTJFwEEEEAAAQQQQAABBBBAIFICBMCRag4KgwACCCCAAAIIIIAAAgggkC8BAuB8yZIvAggggAACCCCAAAIIIIBApAQIgCPVHBQGAQQQQAABBBBAAAEEEEAgXwIEwPmSJV8EEEAAAQQQQAABBBBAAIFICRAAR6o5KAwCCCCAAAIIIIAAAggggEC+BAiA8yVLvggggAACCCCAAAIIIIAAApESIACOVHNQGAQQQAABBBBAAAEEEEAAgXwJEADnS5Z8EUAAAQQQQAABBBBAAAEEIiVAAByp5qAwCCCAAAIIIIAAAggggAAC+RIgAM6XLPkigAACCCCAAAIIIIAAAghESoAAOFLNQWEQQAABBBBAAAEEEEAAAQTyJUAAnC9Z8kUAAQQQQAABBBBAAAEEEIiUAAFwpJqDwiCAAAIIIIAAAggggAACCORLgAA4X7LkiwACCCCAAAIIIIAAAgggECkBAuBINQeFQQABBBBAAAEEEEAAAQQQyJcAAXC+ZMkXAQQQQAABBBBAAAEEEEAgUgIEwJFqDgqDAAIIIIAAAggggAACCCCQLwEC4HzJki8CCCCAAAIIIIAAAggggECkBAiAI9UcFAYBBBBAAAEEEEAAAQQQQCBfAgTA+ZIlXwQQQAABBBBAAAEEEEAAgUgJEABHqjkoDAIIIIAAAggggAACCCCAQL4ECIDzJUu+CCCAAAIIIIAAAggggAACkRIgAI5Uc1AYBBBAAAEEEEAAAQQQQACBfAkQAOdLlnwRQAABBBBAAAEEEEAAAQQiJUAAHKnmoDAIIIAAAggggAACCCCAAAL5EiAAzpcs+SKAAAIIIIAAAggggAACCERKoGKkSpPDwvz888/21ltv2XHHHRea68yZM61q1aq2zz77uH1+/fVX22mnnUL3z/cD06ZNs+XLl2/1NDvssIMdffTR9tJLL6UeK1eunO222272u9/9znbZZRe3feHChbZ06VJr165daj/d0XEdOnSwypUrF8nDv9Mpp5xiP/74o3344Yd25JFH+h/iPgIIIIAAAggggAACCCCQCIHE9gD/61//sl133TVjI7366qv2/vvvu31Gjhxp77zzTsb98/3gN998Y1988YX7N3z4cJsxY4a7P3/+fFNAf/vtt9vXX39t3377rbv95z//aX/84x9Nga+Sgtcnn3zS3ff/d88997jA+JdffnF5fPbZZ6nn8Z5P+++8884mk1WrVvkP5z4CCCCAAAIIIIAAAgggkAiBnPYAb9y40dauXesCKU/np59+sooVK5oe0+2KFSts9913d/e1jwK6WrVquZ5Y7xjdrl+/3gV69erVS+27bt06d1+PKUjTY0oKCGvXrp3qvf3+++/t448/tr59+7rH9Z8CSO2nIC89MFbPr4LIJk2a2G+//WYbNmzYqqzaRwFqnTp1UmXVfgoqd9xxR/vuu++sfv36Vr589ucUevTokSqvem1V/oYNG7pt6p1VOv/881P11N8DBw60iRMn2kUXXaQ/i5Uuv/xyq1SpUuC+p556qo0dO9auvPLKwMfZiAACCCCAAAIIIIAAAgjEVSDnAfCZZ55pDz74oAsUN2/ebH369LGhQ4fas88+a4sWLXI9jxqWfNBBB9l9991nNWvWdNsuvvhi69atm3NUj6WGL+sxDQlWz2fjxo1t9OjRLghVgKsAuEWLFi6wXrlypS1ZssQGDx5sLVu2dL2g7du3T7WJnnvcuHEuj08//dQOP/xwu/baa1OPT5061ebNm+eGANetW9cmT55cpKyHHnqoy1tlmDNnjl166aXWpUsX+/zzz13dFAjvueeepp7aESNGWKNGjVJ5685f//rXrXqXFUir/qVJOqmgkwIqc65SmzZt7JZbbnGBdvXq1XOVLfkggAACCCCAAAIIIIAAAttdIKcBsHpCNVf1lVdesbPPPts0x1Y9rgoclRSwPf/88y5o6969u912222mgEvzVtWzefLJJ5uC0ddff900vFe9lI8++qg99thjNmDAAJeHAr4HHnjABcCat6pAVrePPPKITZo0yQXAGtbbs2dPt7+CcA1z1hBnBakKlHv16mVXX321e1z/HXXUUfbiiy+6oPbAAw90AbBXVh1/1VVX2aBBg6xt27amJK4VPwAAMzZJREFUYFsB8IknnuiOV8+xgmsFvU8//bQL1hXA+9MVV1zhepX927LtKVY5NP9XQbd6tJs1a2bHHnusP+tt3j/vvPOK9FTrZITKqFShQgXX6/zll186y21mxg4IIIAAAggggAACCCCAQEwEchoAq87qGVUPogJgDc31AkU91qpVKxe8adizguXWrVtrs+stbtCggU2fPt1mz55thxxySGqIbqdOnax3796pgFU9vAoAtfBTlSpVUgs+aVizhj0rKXjTMGsl7XvdddfZm2++6YLpuXPnmoJaDaPOlLyyqqdZgbyGJKs+SmvWrHHl1H2Vu9F/e3xV7rvvvtsF+gokvaRe42XLlnl/uluVvWPHjkW2FeePrl27uuBVJxlUB50A0OJWSlosS4FxevKGn3vb1VMufy+pLP4kOwJgvwj3EUAAAQQQQAABBBBAIAkCOQ+AtSqxejc11Pjtt9+2fv36pZy8QE3B6qZNm9w/L1DUHFsFalrZWEOJvaQ5ufrn9Zh6eXiP+wM5/zYvwNUcXfV4KtjU6sgaoq3e520l//No7rJ6p3WrpHmymn+8ePHiItlonrHK49XJe1D1Ua+0P2l4dzYBsHprtVK1hngrsNfJhiFDhrisVab0QFuuCtg1TNoLjjVXOWwOsDKSt4JpEgIIIIAAAggggAACCCCQJIGcB8DCUS+wVjFWD2/QPFIFwFrcacqUKW4+7oIFC0z/FDzr3/jx4+2HH35wvbzqeW3evHkqAC4OvvLQfON9993XzSHWAlL9+/d3AazXi6tg258UuCpYTE/qadZcYy1y1blzZ7eIlxaRuuuuu9yu6s3W/GE9l1ZQ1pDu9HTaaaelb8rJ3xoWrp52GR1//PG29957u/JpCLkuZaTe6wkTJljTpk1db/nq1auL9bxfffWVW126WDuzEwIIIIAAAggggAACCCAQE4G8BMCakzpq1Cg3rzfM4YILLnALS2lhK/VQ3nzzzanVmRVAn3HGGW5l52rVqqV6OMPySt+ugE9DeJU0RPmII45wvcDKS8OVNRc4vfdWQ56HDRsWGARrNWaVT4G5eq41h1irTms+sYJ5PaZh1cpfC16VVVIZLrnkEjfsWr3bCtbVI3zHHXe4ucgK6DVH+NZbby1SpKBrI6sd5KZeYvUi77XXXkWO4Q8EEEAAAQQQQAABBBBAIO4C5bYEbpu3ZyW8nt70MmgIs4YvB/Ugp++b/rcutaTL+OhyPt6wZS2epaHJGj4clhQwauivN9w6fb/0ss6aNcvuvPNOGzNmjAvisylr+nPk6m9djkp1yVTfoOdSb7KuE+y/DJLqrUXGvKQecZ0wyEdSmc+d/GCRrO9p2tp2qH1AkW1x+UOvP72eNKw8Calq1aruJEnQaIk41k9THTR1IQlJrzXN59cJxe38sZ4TTn1eq05Jea2pbdQuSXm9aRqNPtd0UjjuSZ/ROoGtyzamjw6LY9209onaJwqvtRo1auSMsLij2PS5ka+RdzmrDBnlVEAL5pY06TtG/+L2+0y/W/QeV1wTp6SRtvq+8KZj5qLspfl8yUsPcEkqpV7LoKRASP+ySeqVVS+0hiRraLBS+kJPQfluK1gMK6vyilLwq/Loy7ykST/OdMmo9F5sDSG///77U9l5c6pTG/J8p1KlylY5i/rkuVjFzl4fVEFz1YudQYR29OqS7XszQlVxRVF9snmvRK0e/vLoJEVSktonSa81tUtSXm9qGwUaSUr+tT/iXq8kfrYl5b0T99dWFMuf7WtD75O4/T5TmZWyrfP2aj+VO0on55P17eVrVQ1TnjZtmm9L7u9qeLV/ka/cP0PZ5qg5w322XLc5/YxKoy3DxmfMmJEqjHqYdOmqfKSgExU/binXasvP8+WjDv489cGqH4lxO1Pnr4P/vhapU49ccc/E+4+N4n2d1NIIhyQkncCrVauWWwcgCb1Yet8oINHnTRKS2kZnv/U5m4SkSxyqbZLQA6zXmj7bdJlDbwHNOLeRfmiqfaLwWttjjz1yRlnc3x3b6szIWYHIKDICxX1t+Ausk6v6jaaRH3FK+t2i97g+r+KU9Ptevb+57HEvzedL+TjhlaSs+gDUQlD5TPqC0bWBt3fyem63dWZFw5szDYnSm+rggw/e3tXh+RFAAAEEEEAAAQQQQACBvAgkNgDOi1ZEM9UZ+Ntvv32bQwvuueeexPTcRbQpKBYCCCCAAAIIIIAAAghEWIAAOMKNs62iaRiBLlnk7/nV8AItgOU9FjQ8TcPW/MO8dF/bdKyGgmjObxSGTm2r/jyOAAIIIIAAAggggAACCJREILFzgEuCEMd9J0+e7Bar0rV//fP9Pv/8cxs6dKgLZnW5p/nz59uIESNM83i9pAWtNHz7/PPPd5t0eScFzYcddpiNHDnSBb+aE6VrCHuT7b1juUUAAQQQQAABBBBAAAEE4ipAD3AMW049td71fjWs+ZRTTilSi4ULF9ptt93mrgesRa00PNqfTjzxRHvllVdSmyZOnGjaprRgwQK799577V//+hfBb0qIOwgggAACCCCAAAIIIJAEAQLgGLaiAlwt8tWsWTNX+kMOOaRILbQ6tdfjq8fUK+zvJW7evLlb+e6TTz6x2bNnu0tEqSdZSb3GWg0z7FrIRZ6IPxBAAAEEEEAAAQQQQACBGAkwBDpGjeUVVUu3K6DV/F7vQt5hAatWfdYy79rPn7xeYB13wgknpB5K0nUQU5XiDgIIIIAAAggggAACCCCwRYAe4Bi+DBo2bOiC2nfeeceV/vXXXy9yLcavv/7a5s2b5x579dVXrU2bNlvVsnPnzjZ16lRTHsccc8xWj7MBAQQQQAABBBBAAAEEEEiaAD3AMWxRLUw1ZMgQu+mmm9x83Tp16rgh0V5Vdt11V7v55pvd6tDVqlVzi2V5j3m3tWrVsn322cfUm6wFsUgIIIAAAggggAACCCCAQNIFCIBj2sItWrSwxx9/3F3Xt0aNGkVqoeB2zJgx7tJG1atXTz323HPPpe7rjgLpk046KbWtZcuWNnr06NTf3EEAAQQQQAABBBBAAAEEkiRAABzz1kwPfv3V8Qe//u2fffaZPfnkk7ZixQpr166d/yHuI4AAAggggAACCCCAAAKJFSAATljTagXofv36ZayVhkjvv//+dskll3Cpo4xSPIgAAggggAACCCCAAAJJEiAATlJrbqmL5vO2bds2Y610maOuXbtm3IcHEUAAAQQQQAABBBBAAIGkCbAKdNJalPoggAACCCCAAAIIIIAAAggEChAAB7KwEQEEEEAAAQQQQAABBBBAIGkCBMBJa1HqgwACCCCAAAIIIIAAAgggEChAABzIwkYEEEAAAQQQQAABBBBAAIGkCRAAJ61FqQ8CCCCAAAIIIIAAAggggECgAAFwIAsbEUAAAQQQQAABBBBAAAEEkiZAAJy0FqU+CCCAAAIIIIAAAggggAACgQIEwIEsbEQAAQQQQAABBBBAAAEEEEiaAAFw0lqU+iCAAAIIIIAAAggggAACCAQKEAAHsrARAQQQQAABBBBAAAEEEEAgaQIEwElrUeqDAAIIIIAAAggggAACCCAQKEAAHMjCRgQQQAABBBBAAAEEEEAAgaQJEAAnrUWpDwIIIIAAAggggAACCCCAQKAAAXAgCxsRQAABBBBAAAEEEEAAAQSSJkAAnLQWpT4IIIAAAggggAACCCCAAAKBAgTAgSxsRAABBBBAAAEEEEAAAQQQSJoAAXDSWpT6IIAAAggggAACCCCAAAIIBAoQAAeysBEBBBBAAAEEEEAAAQQQQCBpAgTASWtR6oMAAggggAACCCCAAAIIIBAoQAAcyMJGBBBAAAEEEEAAAQQQQACBpAkQACetRakPAggggAACCCCAAAIIIIBAoAABcCALGxFAAAEEEEAAAQQQQAABBJImQACctBalPggggAACCCCAAAIIIIAAAoECBMCBLGxEAAEEEEAAAQQQQAABBBBImgABcNJalPoggAACCCCAAAIIIIAAAggEChAAB7KwEQEEEEAAAQQQQAABBBBAIGkCBMBJa1HqgwACCCCAAAIIIIAAAgggEChAABzIwkYEEEAAAQQQQAABBBBAAIGkCRAAJ61FqQ8CCCCAAAIIIIAAAggggECgAAFwIAsbEUAAAQQQQAABBBBAAAEEkiZAAJy0FqU+CCCAAAIIIIAAAggggAACgQIEwIEsbEQAAQQQQAABBBBAAAEEEEiaAAFw0lqU+iCAAAIIIIAAAggggAACCAQKEAAHsrARAQQQQAABBBBAAAEEEEAgaQIEwElrUeqDAAIIIIAAAggggAACCCAQKEAAHMjCRgQQQAABBBBAAAEEEEAAgaQJEAAnrUWpDwIIIIAAAggggAACCCCAQKBAxcCtbERgOwpM6NzflixZsh1LwFMjgAACCCCAQFwFXnrpJVu2bJlt3LgxVlWoWbOmrVq1KlZlrlatmlWtWtWWLl0aq3JT2MIWoAe4sNuf2iOAAAIIIIAAAggggAACBSNAAFwwTU1FEUAAAQQQQAABBBBAAIHCFiAALuz2p/YIIIAAAggggAACCCCAQMEIEAAXTFNTUQQQQAABBBBAAAEEEECgsAUIgAu7/ak9AggggAACCCCAAAIIIFAwAgTABdPUVBQBBBBAAAEEEEAAAQQQKGwBAuDCbn9qjwACCCCAAAIIIIAAAggUjAABcME0NRVFAAEEEEAAAQQQQAABBApbgAC4sNuf2iOAAAIIIIAAAggggAACBSNAAFwwTU1FEUAAAQQQQAABBBBAAIHCFiAALuz2p/YIIIAAAggggAACCCCAQMEIEAAXTFNTUQQQQAABBBBAAAEEEECgsAUIgAu7/ak9AggggAACCCCAAAIIIFAwAgTABdPUVBQBBBBAAAEEEEAAAQQQKGwBAuDCbn9qjwACCCCAAAIIIIAAAggUjAABcME0NRVFAAEEEEAAAQQQQAABBApboNzmLamwCah9SQV+/vln+/XXX0t6WLH2X7x4sX3++ed29NFHW7ly5Yp1TJR3Uh30b9OmTVEuZrHLNnXqVNt1112tadOmxT4myjtWrFjRNmzYEOUiFrtsy5cvt48//tg6duxolStXLvZxUd1R75vy5cvbxo0bo1rEEpVrxowZptdby5YtS3RcVHeuUKFCYtpmzZo19u6771rbtm2tZs2aUSUvUbmi8tmWS89Vq1YVy2DFihX20UcfxfKzMCrtVizo/+705Zdf2ldffWVHHHFESQ7b7vvG9Ttm5syZ7nfLgQceuN0NS1IAfZ8r5Mxl2Fmaz5eKJSk8+yIgAf24ztcP7BdeeMEGDx5s3bp1M/3AIkVLYMSIEXb44YfbwQcfHK2CURr78MMP7U9/+pO99tprifkRn6Rm/cc//mE1atRw758k1SsJdVmyZIl774wfP94aNWqUhColsg7F/bGr4FefhZMmTeKzsAxeCVOmTLGHHnrIunbtWgbPxlNMmDDB1q1bZ506dQKjFAIMgS4FHocigAACCCCAAAIIIIAAAgjER4AAOD5tRUkRQAABBBBAAAEEEEAAAQRKIcAc4FLgcWjuBb799lv74osv7Mgjj8x95uRYagHNk6tdu7btu+++pc6LDHIr8P3339snn3xi7du3t0qVKuU2c3IrtYDmZ2t+X4sWLUqdFxnkVmDt2rU2ffp0a9Omje288865zZzcylyAz8KyJV+4cKF98803bs512T5zYT7brFmz3PoLrVq1KkyAHNWaADhHkGSDAAIIIIAAAggggAACCCAQbQGGQEe7fSgdAggggAACCCCAAAIIIIBAjgQq3Lwl5SgvskEgK4E5c+aYLhGioWdVqlTJKg8OKr2ALj+loYD+lTZ1OYm33nrLDbfR5Y/8iXbza+Tv/vr16+399983DeurU6dOkcuDhbWBLlOm4eq6rFjdunVZUT1/zWOzZ892Q89r1apVZOh5pjYIa7c8FrOgs5a33j/+z7CwNsjUbgWNGKHKB7Xd5MmT3XuxSZMmRUqqoblvvPGG7b777qZLOOqzdK+99iqyTyH9oe94/d7Sd4P3r379+oEEQc7aMdN7JOiYefPm2dtvv2277LKLVa1aNfVcujzk888/7y7po/Z5+eWXbY899rAddtghtU+c77zyyivutabL/3gp028qbx/dBjlqO/ZSyE0iAM6NI7lkKXDnnXfa008/bb/99pvdc889bg4Jc7CyxCzFYbqO3xVXXGGNGzc27weELqujbQqI77vvPvfjvnnz5u5ZaLdSYJfg0O+++8769u3rgl4FWmqHk046yc0lDWsD/cg799xz7aeffjJdnuL111+3zp07FwmcS1AEds0gcM0117jrlst81KhR1qFDB3epo0xtENZuGZ6Gh0ohoGvH9u/f36pVq2atW7d2OYW1QaZ2K0URODSHAmFt95e//MX9ltC1aHUyykujR4+2MWPGuGvU/vjjj3b//ffbKaec4j1ccLf6TtDlDJctW+Y+u3Ti+9hjj93KIcw503sk7JgXX3zR7rrrLnf5TP+1a3W5qhtuuMEFxbq04tVXX21HH320Va9efavyxG3DE088YUOHDrVzzjnHfV+r/Jl+U/nrF+aIvV+p9Pe5DnDpDckhSwEtnKDexccff9x0hkzXNnv44Yft+uuvdzlqQawNGzbYnnvumfoAyfKpOCyDwDPPPGNjx47davEXfUnecsstpoUWevbsaeeff7516dLF1C6Z2m3NmjW2dOlS12706GeAL8ZDjz32mDNXEKw0aNAg01nl/fffP7QNHnnkEWvXrp07eaFjLrroItcbrMWx9H5atGiRa2t/b5j2I5VMQCeN1Ds/bNgwd6Cuy/jqq69anz59LKwN1BvPe6dkzqXd+29/+1uRgCjT905Yu+m9o8R3Umlbo3THZ2o75bzffvu566Dvs88+7ok2btzoFjdT76I/6YS7eobr1atnO+64o/+hxN+fO3euOwHwxz/+MbSumZzD3iPb+mzz2ka/I7ykz0uvrbxtuv3666/dicQ4doboO1bf0xpxkp7CflP5X4PYp6vl7+//75fP33OQMwKBAgsWLLCWLVu64Fc76Mzgp59+6vbVB4j+6QOjd+/epp4wUn4EKleu7M6Qq+e3XLly7kn0Ia7hUWofJQ29VTCrHw2Z2k3B2XnnnefyO+uss1yw5jLgv6wEFLzqDLKX1IOhs8CZ2kDDzfxn2b33lc74673097//3a666iq76aabvGy5zUJg7733tpEjR7oj1cuoVZ69oZVhbZCp3XjvZNEI2zjkueeec8Muf//736f2zNQGYe2mg/lOShFutzuZ2k6FUu+hRrx46YMPPrADDjigSJCrIagK/nTiqkePHvbZZ595uxfErQJgjYYYP368Gw6+efPmreqdyTnsPZLpGD2BRpdVqFDB5s+f755PvzHUI6qeX38aPHiw3XrrrdarVy/XIeJ/LA73ddJFJ8w0otGfMv2m8u+XyRF7v1Tp79MDXHpDcshSYMmSJUV6HWvUqOHOmqkHcdq0aaYfL7qci271paUzjKTcCxx33HGpTL0vQwVLmqvjBcTaQWdjV65caWHtpn3+/e9/W79+/eyoo45yc1g0bJeUvYD/zPBrr73mTkqccMIJpl57/9lx772jZ9LJIv3tJd3XyYx33nnHXb7qz3/+s+u51HBq9VrSS+9JZXereWsa6tasWbPUZUDC2mCnnXYKbTfeO9n5hx2l1/yTTz7phqZrhIuXMn1+hbUb30me3va9zdR2KlmjRo3c/FEFCrpU36RJk+yYY46xmTNnpgqunrlx48a5fTXC5u6773avkdQOCb+jAFhJQdpDDz3kRqt4o1i8qmdyDnuPZPps8/LVCQp9j6nXV5cd05QEzffVvFYvqVyawrN8+XI38kztpxPwcUlyCBpin+k3lU6megl7TyL/t/QA59+YZwgR0NlAnS3zks6QqTdS8z80hPP00093ZwK1cIKGfJLKTiC9bfTMah+dkEh/zGs37aPgTGdv1cOo4DdobpH2I5VM4Nlnn7V7773Xhg8f7s7eZ2qDsMfUC6Y2Ue+HAgINZyf4LVk7BO2tE0g6SdegQQM3ZUD7hLVB2HYdw3tHCrlJ+kwaMmSIaY62vlP8KVMbhD3Gd5JfcPvdD2sff4k6derkeoE1PUHXRfePhtF+CpL1T6ljx46uB1iLMRVKevDBB90Ju+7du9sdd9zhvhN0ssifMjmHPRa23Z+v1zba5p2c8D+u+0ceeaTbtNtuu7lAWYtBJSGl+6hO3m8qf/3S9/P/vgp7LGy7P99Ctvc7+O8TAPs1uF+mAvqAU4+il3RfKwAqae6pzsxqdUINM/Sfwff25zZ/ArVr13aLKPnPzKp9NB87U7tpsSUt/qDAV6tyavEZUukE1Fvx6KOPuveDN8Q2Uxtobm/6+0rtpveW5m9ddtllph4tDa9WTwkpOwHNc/d+nOlEQrdu3VyvhnILa4NM7cZ7J7t2CDpK8+g0tPXKK680naBQT596u9TTlakNwtpNz8F3UpB02W7L1HZeSbwf+u+9957ppJ+CA3/yr8ir7zd9Nvq3+fdN2n3NfdYaEF59NcJIvavq1fWnTM5h75FMx3h5N2zY0NRDqvemprt5i9J5j+vWK5vuq33CVqjW43FKmX5T+euRyRF7v1Tp7xMAl96QHLIU0JfTrFmz3IIHOsulXhTNB9GP97PPPtt9MGvOon5YfvXVV1k+C4dlI1CxYkXXC6+eRyUFs+qJ17+wdtN+AwcOdGeU1Zs1YMAAN1xabUvKTuCFF15wZ8o1b9c/DCxTGxx22GGmVTd/+eUXN3Vg6tSp1qZNG7dNPcht27Z1q20qmNZwK1J2Aho+/j//8z/OWTlo7qGGXSqFtUGmduO94+hy8p/aQZe+0fB0/dNoIn2XqEc4UxuEtRvfSTlpllJnkqntvMw1EkMjlXTSXMNn05MCQG9qjhZh0qrRhZI03FjfAZoOo6RAVEPC0wPRTM5h75FMx/h9NQxaPc8a5ecPdr19Jk6caOqR11Bt/XbQ3OEkpEy/qfz1y+SIvV+q9PeZA1x6Q3LIUkBzEy+88EK3urAuW6Af5GeeeaZb8Vln7bWYkuah6oe85i2Sylbgkksuseuuu86eeuop90WlRWCUwtpNj51xxhmmJfz140NzeNTbqA9+UnYCGq6mnkYNV/aShq5dfvnlge8d7aMffbrUxR/+8AfXbmoTzTFST4e3SrF6AhQkpC9A4j0Ht9sWkKnaQp9h6mXS3wqIlcLaQI8FfeZpO+8dKeQ/Zfr8ytRufCflv2229QyZ2s5/rIIsfW8FTZ1SL6Sm6SjIUm+k5u8XStKaHrq0oS4FpX/6jtZnVvp3dCbnTO+RsM82v6966LX+hKZJBSWdoNDvwNWrV7uT6EH7xHVb2G8qf32w92vk9365LYvebL0EXH6fk9wRKCKguToa6qKVCf1JL019CPoX+/E/zv2yEdAKt7oWcHoKazftp3ZTewad4U3Ph7+zF8jUBhrmrPmP6T9udEJJ7630uZHZl6Kwj9QPaZkGzacOa4NM7cZ7p2xeT5naIKzd+E4qm7bZ1rNkarttHes9Hva95j2e9FtdUUDBln+hy/Q6Z3IOe49kOiY9/7C/w/IO2z9u24vz2svkGOaT6ZjiGoXlXdzj47QfAXCcWouyIoAAAggggAACCCCAAAIIZC3AHOCs6TgQAQQQQAABBBBAAAEEEEAgTgIEwHFqLcqKAAIIIIAAAggggAACCCCQtQABcNZ0HIgAAggggAACCCCAAAIIIBAnAQLgOLUWZUUAAQQQQAABBBBAAAEEEMhagAA4azoORAABBBBAAAEEEEAAAQQQiJMAAXCcWouyIoAAAggggAACCCCAAAIIZC1AAJw1HQcigAACCCCAAAIIIIAAAgjESYAAOE6tRVkRQAABBBBAAAEEEEAAAQSyFiAAzpqOAxFAAAEEEEAAAQQQQAABBOIkQAAcp9airAgggAACCCCAAAIIIIAAAlkLEABnTceBCCCAAAIIIIAAAggggAACcRIgAI5Ta1FWBBBAAAEEEEAAAQQQQACBrAUIgLOm40AEEEAAAQQQQAABBBBAAIE4CRAAx6m1KCsCCCCAAAIIIIAAAggggEDWAgTAWdNxIAIIIIAAAggggAACCCCAQJwECIDj1FqUFQEEEEAAAQQQQAABBBBAIGsBAuCs6TgQAQQQQAABBBBAAAEEEEAgTgIEwHFqLcqKAAIIIIAAAggggAACCCCQtQABcNZ0HIgAAggggAACCCCAAAIIIBAnAQLgOLUWZUUAAQQQQAABBBBAAAEEEMhagAA4azoORAABBBBAAAEEEEAAAQQQiJMAAXCcWouyIoAAAggggAACCCCAAAIIZC1QMesjORABBApCYNWqVXmrZ82aNfOWd2kyLsQ6+70Kvf5+i+Lcx6s4SuyDAAIIIIBANAQIgKPRDpQCgUgLXPzuwzkv373tzsp5nrnMcNiDH+UyO5fXNX1b5zzPfGU4YXGPnGd9Rv3Hc55nVDL84d27c16UXdpdlvM8yRABBBBAAIFCF2AIdKG/Aqg/AggggAACCCCAAAIIIFAgAgTABdLQVBMBBBBAAAEEEEAAAQQQKHQBAuBCfwVQfwQQQAABBBBAAAEEEECgQAQIgAukoakmAggggAACCCCAAAIIIFDoAgTAhf4KoP4IIIAAAggggAACCCCAQIEIEAAXSENTTQQQMNu8ebPdf//97jaqHtOmTbOPPsr9CtSq7/z58+2VV16JatW3S7mWLFlizzzzzHZ57kJ70p9//rnQqkx9EUAAAQQiKEAAHMFGoUgIIJAfgU2bNtlFF10U6QD40UcftZdeeikvAO+9957de++9eck7rpl+8cUXdtttt8W1+LEp9/r1623//fePTXkpKAIIIIBAcgUIgJPbttQMgdgLfP/996YfzgsWLEjV5ZdffrFZs2bZ6tWrU9u8O0uXLrWZM2famjVrvE3u9tdffzUFOgqA45J+/PFH+/zzz7cq84oVK+zTTz+1DRs2FKnKxo0bbd68ea6e6Y/JRT2dcUlffvmlzZkzZ6s6/vTTT/bJJ5/Yt99+m6rK2rVrTe2r14N6uL2kPPRa8ZJeSxoBICMdE5a0j/L5+uuvw3aJ1PZM7f7bb7+515D2WblyZZETP8uXL3d11Gtl1apVrk4lfb/NnTvXvdfULn7roHbS63bhwoWm5/VS2GvZe5xbBBBAAAEE8iFQMR+ZkicCCCCQCwH1GLVs2dKmT5/uhqnqB/2ZZ57pepI++OADu+OOO6xv374uUOrRo4d99dVXVrt2bXv//fftn//8p5100kn29NNP23nnnWctWrTYKqDKRRnzkYfKrJ5gBScKyN5++23beeed7frrr7exY8dakyZNbPHixa6n+He/+53Nnj3bTj/9dGvQoIELdDXU9N1337VatWrZ+eef74Y9V6tWzapXr2716tXLR5FzlmevXr1cAKp2VMD08ssv21577eWGrqun9oADDnB1O/XUU+2+++6zG2+80QW1CvAVULVv3965KehXEPzII49Yx44drVWrVrbffvuZgsHPPvvMBg4caJdeemmRcv/www92/PHHu5MuCgr12nvqqaesXLlyRfaLyh+Z2l3lvuCCC9x7RScHFi1aZApY9ZrQ++HVV1+1GjVq2G677ebqN2nSJLdvcd5vsj388MNtl112sW+++cb22GMP9z68+OKLQ9tpyJAh7mRO79697bnnnnPtFvRajoot5UAAAQQQSK4APcDJbVtqhkAiBLp27eoCm0MPPdRuvfVWe/jhh92Pd/WODh061AWICmgUIM6YMcMFe3/605/soYcecoGMfnBrSPHkyZPtwgsvjIVJpUqVXNCv3mwlBcDqOVPdFci89dZbdsMNN6SGM7/xxht29dVX28SJE10PeM2aNU0BzfPPP+/2VeCjnlMFO1FOCkD//e9/u7ZSXa699lrX26uTAJq7rDo9++yzLgAePXp0qndcPf466aF/OnHQuXNnV2+ZTJgwIVVlvYaUh+ZYX3PNNS54Sz245Y6ObdOmjXsdyUyjDzQnO6oprN3VI67XvU4eaB8F++oBVtLJFdVJ9dPrK/01UZz321VXXWWnnXaavfPOO+7ki3rrlTK10+23324VKlSwF1980dTOYa9llxH/IYAAAgggkEcBeoDziEvWCCBQegH1NKkHTgGgAkEFtuPGjXMZ64e0foR36NDBrrvuOtcjrOBGP/CbN2/uevoqV65sbdu2dfurRziqvXl+qSOOOMLKl//P+cnWrVu7Xl316O24445uDrP21TBT9fIOHz7c+vXr5wIL9Yaq/gpuNCRVAc6xxx7rjtMxXbp0ifQiWOpRVA9so0aNXFm7devm2lZlf+CBB+yJJ56wESNGuDpqOLtGBCgpsFW7KphTL7cCYKU999zTBdPujy3/KT+l3Xff3QW6GlmgkwVeeuyxx9zdPn36uNtly5bZ448/bocccoi3S6Ruw9pdJzvUg37ggQe68qrddVJFSe8N/a3XklL37t1dT7r7Y8t/xXm/KY/LL7/cHaJe5OOOO87dVxtkaifvOTK9luPw/vTqwS0CCCCAQDwFCIDj2W6UGoGCEdDQXS/pR7uG9O6www5uk4Zc7rvvvvb666/bWWed5Xr1LrvsMjvssMPcMEvtr148BUvqfdI/L7D08ozirYJ2LykgUM+a/qmuqnN6UjCioEfDwRW8yUD7q/7+uZmeW/rxUfpbAad69BUkqV5eb62CuZNPPtkFyDrZoeHeqqOS/zWiv71gT/fDkuYBV6lSZauHFTzr9eMlBeVRTWHtXrduXTcnWsO99ZrXkHjvZIGGK+u14iXNNfcnv2XY+015eD3KOtbLY926dS7oDmsn73kyvZa9fbhFAAEEEEAgXwIMgc6XLPkigEBOBdS7165dOzcEWPM8GzZsaOeee66b76keqWOOOcY0NFO9vS+88IILfDU/VsGQhl0qKbhSUBDHpJ46zXvee++93TxX9fIOGzbM9XyqZ1zDfTXsdaeddnJDgTV/WD3JGv6tYEV/qwc1yklzSzVPV22rYbuXXHKJWwxL80y1QJPqe+KJJ9obW4b1KqlOJUmaD6ykRdQ0lNwbGeDlofnH6lU/6KCDnLGG7WpYdVRTWLvXr1/fnSzRPHgZ/f3vf08NF1cvuF4TWgxMphqKHJQyvd+Uh+bv6iTChx9+mOplz9ROCqaVFIhnei0HlYVtCCCAAAII5FKAHuBcapIXAgjkVWDw4MH2hz/8wf72t7+5H/RXXnmlqbfrnHPOMS2KpOGbmv945JFH2pNPPumCQ/UkKrAZMGCAC6yCev3yWugcZb7rrru6AL9p06amfxUrVjTNg1XSXFnNaR05cqSr81FHHeUCHPUIK4jUYmLqVY76ZWjq1KljZ599tgtANbRWw7w1L1eLfmnOqQJTDVlWkNy4cWNXx5LwTpkyxQ2NV+CmucHpvbs9e/Z0Jwk0BFveWjhNr7eoprB2V3lHjRrlRktonrxeBxpJoBEAWlBMr5VOnTqZThDpNaFgOCiFvd80l16X1FI7qMf897//vcs7UztpbrWGqqv3WKuY62RV0Gs5qBxsQwABBBBAIJcC5bYMRfrPGLJc5kpeCCCQGAGthnvxu8G9RKWp5L3tzioy/7IkeWk+cPriPTpePVpa5TZoHqF6QfVYcZLqPOzBj4qza4n2uaZv66zr7D2RevS06FN68KZh3ip3UB11UkDHVa1a1csm463ymbC4R8Z9snnwjPqPF6v++lpSe2keqz8pcFXgX5whzv7jdF+9om+++abLUwumBb1GvGO0arJ6LIv7PPL64d27vcNzdrtLu8u26RXU7vJTD62Gw2vIvy7ppGBV9dIlxdT7rQBYSXPINSRa+4el9PebFtdS8KxgWklz8AcNGmQnnHCC+ztTO2mYtHcSKuy17DLhPwQQQAABBPIkQA9wnmDJFgEE8icQFPzq2dIDJn8JggJD/+Nxua8AMD34VdkV6ITVUcOi9S8uScFpUFv656dmWxf/oldheaj3OS4pqN3l99prr7lVoNVrrt5urRqu7RoxoRECGi6vpCHQGi2RKaW/3zSn+JRTTnG99R9//LGbZ6w8vZSpnbzgV/uGvZa9fLhFAAEEEEAgHwIEwPlQJU8EEEAAgUgJ6JrBCv4KJf3jH/9wKz5ruPH999/vho+r7hoFoKBV1wHWfFzNIy7ptaF1qST1KGsutoasa655cXvLC8WfeiKAAAIIRFeAADi6bUPJEEAAAQRyJKBL/xRSUu+qVrP2r2jt1V8jCE4//XTvz6xuNd9X/0gIIIAAAgjETYBVoOPWYpQXAQQQQAABBBBAAAEEEEAgKwEC4KzYOAgBBBBAAAEEEEAAAQQQQCBuAqwCHbcWo7wIlLGAVrjNVyrOgkT5eu5M+RZinf0ehV5/v0Vx7uNVHCX2QQABBBBAIBoCzAGORjtQCgQQQACBmApE9UROTDkpNgIIIIAAAnkVIADOKy+ZI5AMgSpDz815RdYNCL/uaM6fLIsMV/1lWhZHZT6k5o0dMu8QoUd3XnxwzkvzY/33cp4nGSKAAAIIIIAAAiURYA5wSbTYFwEEEEAAAQQQQAABBBBAILYCBMCxbToKjgACCCCAAAIIIIAAAgggUBIBAuCSaLEvAggggAACCCCAAAIIIIBAbAUIgGPbdBQcAQQQQAABBBBAAAEEEECgJAIEwCXRYl8EEEAAAQQQQAABBBBAAIHYChAAx7bpKDgCCHgCy5YtsyeffNL7M/T2559/Dn0sSg8Up5zjx4+31atX25IlS+yZZ56JUvFLVRbVfdSoUXbbbbfZO++8Yx999JHLb9y4cfbTTz+5+8XxKVUhOBgBBBBAAAEEEitAAJzYpqViCBSOgALgp556KmOF169fb/vvv3/GfaLw4GuvvWb9+vXbZlEmTJjgAuAvvvjCBYvbPCAmOyjwfeihh6xOnTr27rvvpgLgq6++2lauXGnF9YlJdSkmAggggAACCJSxANcBLmNwng4BBIov8P3331utWrVs/vz5VrduXatWrZo7WMGsegF1u3nzZmvWrJnddddd7jEdU7t2bfv666/dYw0bNnTbV6xYYQsXLrTly5fbbrvtVvxClOGemzZtsk8//dR+/PFHF9zWqFHDNm7caF9++aXpscaNG1vFiv/52B47dqzVrFnT2ZRhEfP6VGvXrnX179mzp+lfuXLl3D/vSYN89JjaVidBmjZtmvJRXjvssIN988031qBBA3ffy4dbBBBAAAEEEChcAXqAC7ftqTkCkRdo1aqVde7c2S666CIX3Pzv//6vK7N6Bo866ihr06aNtW/f3t5//33r1KmTe0zH9OrVy04//XTr0KGDXXHFFW77kCFDXBDZu3dv27BhQyTrruD8/vvvdz2fd955p82ePdsOOOAA69+/v/Xo0cOaN2/uekFV+NatW7vAOJIVybJQGtY9depUU3D/8MMP26BBg2zEiBGp3HRyw++jB66//nrXs6/XSJMmTWzOnDlu/4EDB9rJJ59sBx98sF177bWpPLiDAAIIIIAAAoUtQABc2O1P7RGIvMChhx5qkyZNckNhr7nmGtejp0LPmjXLpk2bZhoCrJ5Cf1KgqPmjU6ZMsXvuuccFvrfffrtVqFDBXnzxxVQvof+YKNzXsF/V8YgjjrCbbrrJ3njjDdPQ34kTJ9rMmTNdj68skpouvPBCd2JDdVZAm57Uc+/30QkDBcqLFi2yt956y2644Qa79957U4fpRIf2GT58eGobdxBAAAEEEECgsAUYAl3Y7U/tEYi8QLdu3VwZd999d9fjO336dDfEWcOB69WrF1j+k046yW1v1KiRGwa9bt06F/wG7hzhjZoLrID9xhtvdCcA5s6da7/88kuES1y2RdO87x133DEVLGuRLI0O8ALeww47bKth1GVbQp4NAQQQQAABBKImQAActRahPAggECqgeZ1VqlRxj3vzgYN23mWXXVKby5cv74Lg1IYY3bn88svtk08+sb59+1qfPn3ssssui21d8sGu+d/77ruvXXzxxYHZZ3qNBB7ARgQQQAABBBBIvABDoBPfxFQQgXgLPPLII64CGvKsoa5t27bNqkLqKVT67bffsjq+rA6qVKmSW+BLz/f222+7IdCat7zTTju5uc5Rnb+8PXy6d+9uH3zwge29995uLrh6yIcNG7bVkPiyKhvPgwACCCCAAALRF6AHOPptRAkRKGgBzePVnF71/urSP/7e3ZLAaP6v5hPvsccebqVhzbeNYtLiVurR7Nq1q1u8SXNeR44c6YI6Lfw1b968KBa7zMrk99H1j6+66iq3QJq3AvTo0aPLrCw8EQIIIIAAAgjET6DcliFkm+NXbEqMAAJlJbBq1SqrMvTcnD/dugH/uYxPpozr169vb775ppvzu/POO+ekZ0/zgb1h1GHPrTqv+su0sIez3l7zxg5uIattZaDL/ainWr3Buq/y6HJQZZX0fDsvPjjnT/dj/feKVf9tPbHfR/uqV3zNmjVZnxzZ1vPxOAIIIIAAAggkR4Ae4OS0JTVBILECut5trtK2gt9cPU9p8tG8ZQW/SrpflsFvacpdVsf6ffScujZytiMDyqrMPA8CCCCAAAIIREOAOcDRaAdKgQACAQL33Xef1a1bN+ARNiGAAAIIIIAAAgggUHIBeoBLbsYRCCBQRgJdunQpo2fiaRBAAAEEEEAAAQQKQYAe4EJoZeqIAAIIIIAAAggggAACCCBgBMC8CBBAAAEEEEAAAQQQQAABBApCgFWgC6KZqSQC2QtoReB8pVwubpXLMhZinf1+hV5/vwX3EUAAAQQQQCBZAgTAyWpPaoMAAggggAACCCCAAAIIIBAiwBDoEBg2I4AAAggggAACCCCAAAIIJEuAADhZ7UltEEAAAQQQQAABBBBAAAEEQgQIgENg2IwAAggggAACCCCAAAIIIJAsAQLgZLUntUEAAQQQQAABBBBAAAEEEAgRIAAOgWEzAggggAACCCCAAAIIIIBAsgQIgJPVntQGAQQQQAABBBBAAAEEEEAgRIAAOASGzQgggAACCCCAAAIIIIAAAskSIABOVntSGwQQQAABBBBAAAEEEEAAgRABAuAQGDYjgAACCCCAAAIIIIAAAggkS4AAOFntSW0QQAABBBBAAAEEEEAAAQRCBAiAQ2DYjAACCCCAAAIIIIAAAgggkCwBAuBktSe1QQABBBBAAAEEEEAAAQQQCBEgAA6BYTMCCCCAAAIIIIAAAggggECyBAiAk9We1AYBBBBAAAEEEEAAAQQQQCBEgAA4BIbNCCCAAAIIIIAAAggggAACyRIgAE5We1IbBBBAAAEEEEAAAQQQQACBEAEC4BAYNiOAAAIIIIAAAggggAACCCRLgAA4We1JbRBAAAEEEEAAAQQQQAABBEIECIBDYNiMAAIIIIAAAggggAACCCCQLAEC4GS1J7VBAAEEEEAAAQQQQAABBBAIESAADoFhMwIIIIAAAggggAACCCCAQLIE/g/68XcnJVF1hQAAAABJRU5ErkJggg==" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 13.09GB </td> <td style="text-align:right;"> 8m 30.4s </td> <td style="text-align:right;"> 149ms </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 8ms </td> <td style="text-align:right;"> 26ms </td> <td style="text-align:right;"> 224ms </td> <td style="text-align:right;"> 59ms </td> <td style="text-align:right;"> 8m 30.9s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 12.21GB </td> <td style="text-align:right;"> 7m 39.4s </td> <td style="text-align:right;"> 217ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 29ms </td> <td style="text-align:right;"> 38ms </td> <td style="text-align:right;"> 57ms </td> <td style="text-align:right;"> 7m 39.8s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 12.14GB </td> <td style="text-align:right;"> 4m 7.3s </td> <td style="text-align:right;"> 67ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 28ms </td> <td style="text-align:right;"> 35ms </td> <td style="text-align:right;"> 37ms </td> <td style="text-align:right;"> 4m 7.5s </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 12.66GB </td> <td style="text-align:right;"> 3m 21.8s </td> <td style="text-align:right;"> 135ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 33ms </td> <td style="text-align:right;"> 168ms </td> <td style="text-align:right;"> 15ms </td> <td style="text-align:right;"> 3m 22.1s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 6.57GB </td> <td style="text-align:right;"> 3.1s </td> <td style="text-align:right;"> 62ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 55ms </td> <td style="text-align:right;"> 252ms </td> <td style="text-align:right;"> 3.5s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 6.57GB </td> <td style="text-align:right;"> 3.1s </td> <td style="text-align:right;"> 64ms </td> <td style="text-align:right;"> 5ms </td> <td style="text-align:right;"> 4ms </td> <td style="text-align:right;"> 27ms </td> <td style="text-align:right;"> 82ms </td> <td style="text-align:right;"> 160ms </td> <td style="text-align:right;"> 3.4s </td> </tr> </tbody> </table> </div> </div> </div> <div id="reading-multiple-delimited-files" class="section level1"> <h1>Reading multiple delimited files</h1> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/taxi_multiple">bench/taxi_multiple</a></p> <p>The benchmark reads all 12 files in the taxi trip fare data, totaling 173,179,759 rows and 11 columns for a total file size of 18.4G.</p> <img src="data:image/png;base64,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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 63.5GB </td> <td style="text-align:right;"> 7m 55s </td> <td style="text-align:right;"> 837ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 15ms </td> <td style="text-align:right;"> 4.2s </td> <td style="text-align:right;"> 13.5s </td> <td style="text-align:right;"> 8m 13.6s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 63.1GB </td> <td style="text-align:right;"> 3m 52.3s </td> <td style="text-align:right;"> 2.2s </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 14ms </td> <td style="text-align:right;"> 10.5s </td> <td style="text-align:right;"> 7.2s </td> <td style="text-align:right;"> 4m 12.2s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 88.3GB </td> <td style="text-align:right;"> 20.3s </td> <td style="text-align:right;"> 3s </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 21.5s </td> <td style="text-align:right;"> 2m 22.6s </td> <td style="text-align:right;"> 3m 7.5s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 88GB </td> <td style="text-align:right;"> 20.4s </td> <td style="text-align:right;"> 2.8s </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 13ms </td> <td style="text-align:right;"> 23.9s </td> <td style="text-align:right;"> 1m 5.6s </td> <td style="text-align:right;"> 1m 52.7s </td> </tr> <tr> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> data.table </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 59.6GB </td> <td style="text-align:right;"> 1m 35.3s </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1.1s </td> <td style="text-align:right;"> 4.7s </td> <td style="text-align:right;"> 1m 41.1s </td> </tr> </tbody> </table> </div> <div id="reading-fixed-width-files" class="section level1"> <h1>Reading fixed width files</h1> <div id="united-states-census-5-percent-public-use-microdata-sample-files" class="section level2"> <h2>United States Census 5-Percent Public Use Microdata Sample files</h2> <p>This fixed width dataset contains individual records of the characteristics of a 5 percent sample of people and housing units from the year 2000 and is freely available at <a href="https://web.archive.org/web/20150908055439/https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/California/all_California.zip" class="uri">https://web.archive.org/web/20150908055439/https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/California/all_California.zip</a>. The data is split into files by state, and the state of California was used in this benchmark.</p> <p>The data totals 2,342,339 rows and 37 columns with a total file size of 677M.</p> </div> <div id="census-data-benchmarks" class="section level2"> <h2>Census data benchmarks</h2> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/fwf">bench/fwf</a></p> <img src="data:image/png;base64,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" /><!-- --> <table> <thead> <tr> <th style="text-align:right;"> reading package </th> <th style="text-align:right;"> manipulating package </th> <th style="text-align:right;"> altrep </th> <th style="text-align:right;"> memory </th> <th style="text-align:right;"> read </th> <th style="text-align:right;"> print </th> <th style="text-align:right;"> head </th> <th style="text-align:right;"> tail </th> <th style="text-align:right;"> sample </th> <th style="text-align:right;"> filter </th> <th style="text-align:right;"> aggregate </th> <th style="text-align:right;"> total </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> read.delim </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 6.17GB </td> <td style="text-align:right;"> 18m 9.6s </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 3ms </td> <td style="text-align:right;"> 492ms </td> <td style="text-align:right;"> 90ms </td> <td style="text-align:right;"> 18m 10.2s </td> </tr> <tr> <td style="text-align:right;"> readr </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> </td> <td style="text-align:right;"> 6.19GB </td> <td style="text-align:right;"> 32.6s </td> <td style="text-align:right;"> 48ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 17ms </td> <td style="text-align:right;"> 95ms </td> <td style="text-align:right;"> 94ms </td> <td style="text-align:right;"> 32.8s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> FALSE </td> <td style="text-align:right;"> 5.96GB </td> <td style="text-align:right;"> 14.7s </td> <td style="text-align:right;"> 44ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 15ms </td> <td style="text-align:right;"> 468ms </td> <td style="text-align:right;"> 91ms </td> <td style="text-align:right;"> 15.3s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> base </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 4.65GB </td> <td style="text-align:right;"> 164ms </td> <td style="text-align:right;"> 56ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 7ms </td> <td style="text-align:right;"> 285ms </td> <td style="text-align:right;"> 1.8s </td> <td style="text-align:right;"> 2.3s </td> </tr> <tr> <td style="text-align:right;"> vroom </td> <td style="text-align:right;"> dplyr </td> <td style="text-align:right;"> TRUE </td> <td style="text-align:right;"> 4.62GB </td> <td style="text-align:right;"> 163ms </td> <td style="text-align:right;"> 48ms </td> <td style="text-align:right;"> 2ms </td> <td style="text-align:right;"> 1ms </td> <td style="text-align:right;"> 16ms </td> <td style="text-align:right;"> 306ms </td> <td style="text-align:right;"> 1.3s </td> <td style="text-align:right;"> 1.8s </td> </tr> </tbody> </table> </div> </div> <div id="writing-delimited-files" class="section level1"> <h1>Writing delimited files</h1> <p>code: <a href="https://github.com/tidyverse/vroom/tree/main/inst/bench/taxi_writing">bench/taxi_writing</a></p> <p>The benchmarks write out the taxi trip dataset in a few different ways.</p> <ul> <li>An uncompressed file</li> <li>A gzip compressed file using <code>gzfile()</code> <em>(readr and vroom do this automatically for files ending in <code>.gz</code>)</em></li> <li>A gzip compressed file compressed with multiple threads (natively for data.table and using a <code>pipe()</code> connection to <a href="https://zlib.net/pigz/">pigz</a> for the rest).</li> <li>A <a href="https://facebook.github.io/zstd/">Zstandard</a> compressed file (data.table does not support this format).</li> </ul> <img src="data:image/png;base64,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/><!-- --> <table> <thead> <tr> <th style="text-align:right;"> compression </th> <th style="text-align:right;"> base </th> <th style="text-align:right;"> data.table </th> <th style="text-align:right;"> readr </th> <th style="text-align:right;"> vroom </th> </tr> </thead> <tbody> <tr> <td style="text-align:right;"> gzip </td> <td style="text-align:right;"> 3m 17.1s </td> <td style="text-align:right;"> 1m 7.8s </td> <td style="text-align:right;"> 2m 0.2s </td> <td style="text-align:right;"> 1m 14.4s </td> </tr> <tr> <td style="text-align:right;"> multithreaded_gzip </td> <td style="text-align:right;"> 1m 37.8s </td> <td style="text-align:right;"> 8.9s </td> <td style="text-align:right;"> 53.4s </td> <td style="text-align:right;"> 8.1s </td> </tr> <tr> <td style="text-align:right;"> zstandard </td> <td style="text-align:right;"> 1m 39.9s </td> <td style="text-align:right;"> NA </td> <td style="text-align:right;"> 54.2s </td> <td style="text-align:right;"> 12.4s </td> </tr> <tr> <td style="text-align:right;"> uncompressed </td> <td style="text-align:right;"> 1m 37.4s </td> <td style="text-align:right;"> 1.5s </td> <td style="text-align:right;"> 52.2s </td> <td style="text-align:right;"> 1.7s </td> </tr> </tbody> </table> <div id="session-and-package-information" class="section level2"> <h2>Session and package information</h2> <table> <thead> <tr class="header"> <th align="left">package</th> <th align="left">version</th> <th align="left">date</th> <th align="left">source</th> </tr> </thead> <tbody> <tr class="odd"> <td align="left">base</td> <td align="left">4.1.0</td> <td align="left">2021-05-18</td> <td align="left">local</td> </tr> <tr class="even"> <td align="left">data.table</td> <td align="left">1.14.0</td> <td align="left">2021-02-21</td> <td align="left">RSPM (R 4.1.0)</td> </tr> <tr class="odd"> <td align="left">dplyr</td> <td align="left">1.0.7</td> <td align="left">2021-06-18</td> <td align="left">RSPM (R 4.1.0)</td> </tr> <tr class="even"> <td align="left">readr</td> <td align="left">1.4.0</td> <td align="left">2020-10-05</td> <td align="left">RSPM (R 4.1.0)</td> </tr> <tr class="odd"> <td align="left">vroom</td> <td align="left">1.5.1</td> <td align="left">2021-06-22</td> <td align="left">local</td> </tr> </tbody> </table> </div> </div> <!-- code folding --> <!-- 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>