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(v1.7.0) unit tests
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@ -226,32 +226,19 @@
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times <span class="op">=</span> <span class="fl">25</span><span class="op">)</span>
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<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
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<span class="co"># Unit: milliseconds</span>
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<span class="co"># expr min lq mean median uq max</span>
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<span class="co"># as.mo("sau") 10.0 11.0 14.0 12.0 13.0 47.0</span>
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<span class="co"># as.mo("stau") 53.0 56.0 78.0 58.0 94.0 220.0</span>
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<span class="co"># as.mo("STAU") 53.0 54.0 69.0 58.0 92.0 99.0</span>
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<span class="co"># as.mo("staaur") 10.0 11.0 15.0 11.0 12.0 47.0</span>
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<span class="co"># as.mo("STAAUR") 9.4 11.0 14.0 11.0 12.0 64.0</span>
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<span class="co"># as.mo("S. aureus") 24.0 26.0 44.0 33.0 63.0 69.0</span>
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<span class="co"># as.mo("S aureus") 24.0 27.0 40.0 30.0 61.0 70.0</span>
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<span class="co"># as.mo("Staphylococcus aureus") 2.6 3.1 3.2 3.2 3.3 3.8</span>
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<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 230.0 250.0 250.0 250.0 250.0 280.0</span>
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<span class="co"># as.mo("Sthafilokkockus aaureuz") 180.0 190.0 200.0 190.0 200.0 330.0</span>
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<span class="co"># as.mo("MRSA") 10.0 11.0 16.0 12.0 13.0 47.0</span>
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<span class="co"># as.mo("VISA") 17.0 19.0 22.0 19.0 22.0 63.0</span>
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<span class="co"># neval</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span>
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<span class="co"># 25</span></code></pre></div>
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<span class="co"># expr min lq mean median uq max neval</span>
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<span class="co"># as.mo("sau") 10.0 11.0 16.0 11.0 13.0 50 25</span>
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<span class="co"># as.mo("stau") 54.0 58.0 72.0 61.0 89.0 99 25</span>
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<span class="co"># as.mo("STAU") 53.0 55.0 67.0 56.0 91.0 95 25</span>
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<span class="co"># as.mo("staaur") 10.0 11.0 16.0 11.0 13.0 47 25</span>
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<span class="co"># as.mo("STAAUR") 10.0 11.0 17.0 12.0 13.0 58 25</span>
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<span class="co"># as.mo("S. aureus") 26.0 27.0 36.0 31.0 33.0 70 25</span>
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<span class="co"># as.mo("S aureus") 26.0 27.0 40.0 29.0 61.0 68 25</span>
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<span class="co"># as.mo("Staphylococcus aureus") 2.6 3.2 6.5 3.5 3.8 42 25</span>
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<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 240.0 250.0 260.0 260.0 270.0 290 25</span>
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<span class="co"># as.mo("Sthafilokkockus aaureuz") 190.0 190.0 200.0 200.0 210.0 300 25</span>
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<span class="co"># as.mo("MRSA") 10.0 11.0 13.0 12.0 13.0 40 25</span>
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<span class="co"># as.mo("VISA") 18.0 19.0 32.0 20.0 24.0 130 25</span></code></pre></div>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="750"></p>
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<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 200 milliseconds, this is only 5 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations are up to 200 times slower to determine.</p>
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<p>To improve performance, we implemented two important algorithms to save unnecessary calculations: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
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@ -273,8 +260,8 @@
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<span class="co"># what do these values look like? They are of class <mo>:</span>
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<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
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<span class="co"># Class <mo></span>
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<span class="co"># [1] B_STRPT_PNMN B_STPHY_CONS B_ESCHR_COLI B_STRPT_PNMN B_ESCHR_COLI</span>
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<span class="co"># [6] B_ENTRC_FCLS</span>
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<span class="co"># [1] B_STPHY_EPDR B_STRPT_GRPA B_STPHY_AURS B_BCTRD_FRGL B_STPHY_HMNS</span>
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<span class="co"># [6] B_STPHY_CONS</span>
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<span class="co"># as the example_isolates data set has 2,000 rows, we should have 2 million items</span>
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<span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
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@ -290,8 +277,8 @@
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<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
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<span class="co"># Unit: milliseconds</span>
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<span class="co"># expr min lq mean median uq max neval</span>
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<span class="co"># mo_name(x) 180 186 215 221 222 281 10</span></code></pre></div>
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<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.221 seconds. That is 110 nanoseconds on average. You only lose time on your unique input values.</p>
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<span class="co"># mo_name(x) 187 223 233 226 229 318 10</span></code></pre></div>
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<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.226 seconds. That is 113 nanoseconds on average. You only lose time on your unique input values.</p>
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</div>
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<div id="precalculated-results" class="section level3">
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<h3 class="hasAnchor">
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@ -305,10 +292,10 @@
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<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
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<span class="co"># Unit: milliseconds</span>
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<span class="co"># expr min lq mean median uq max neval</span>
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<span class="co"># A 7.45 7.68 8.13 8.23 8.39 8.95 10</span>
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<span class="co"># B 22.70 22.90 31.30 23.90 26.50 62.00 10</span>
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<span class="co"># C 1.51 1.58 1.79 1.84 1.97 1.99 10</span></code></pre></div>
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<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0018 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
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<span class="co"># A 7.28 7.59 8.01 8.03 8.45 8.66 10</span>
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<span class="co"># B 23.00 24.10 30.30 25.50 27.30 75.30 10</span>
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<span class="co"># C 1.55 1.74 7.31 1.95 2.01 56.10 10</span></code></pre></div>
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<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0019 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
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<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="va">run_it</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
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B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
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@ -322,14 +309,14 @@
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<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
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<span class="co"># Unit: milliseconds</span>
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<span class="co"># expr min lq mean median uq max neval</span>
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<span class="co"># A 1.44 1.44 1.51 1.46 1.56 1.68 10</span>
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<span class="co"># B 1.37 1.40 1.53 1.48 1.67 1.81 10</span>
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<span class="co"># C 1.42 1.47 1.61 1.59 1.68 1.84 10</span>
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<span class="co"># D 1.39 1.43 1.52 1.46 1.53 1.92 10</span>
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<span class="co"># E 1.41 1.46 1.58 1.50 1.70 1.91 10</span>
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<span class="co"># F 1.36 1.44 1.49 1.46 1.57 1.64 10</span>
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<span class="co"># G 1.37 1.44 1.64 1.48 1.82 2.40 10</span>
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<span class="co"># H 1.41 1.43 1.52 1.48 1.54 1.87 10</span></code></pre></div>
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<span class="co"># A 1.42 1.45 1.56 1.50 1.57 2.00 10</span>
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<span class="co"># B 1.43 1.46 1.49 1.47 1.55 1.59 10</span>
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<span class="co"># C 1.41 1.43 1.58 1.49 1.57 2.19 10</span>
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<span class="co"># D 1.41 1.48 1.61 1.54 1.63 2.33 10</span>
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<span class="co"># E 1.41 1.45 1.64 1.51 1.56 2.68 10</span>
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<span class="co"># F 1.42 1.52 1.63 1.57 1.71 1.99 10</span>
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<span class="co"># G 1.41 1.46 1.65 1.56 1.90 1.98 10</span>
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<span class="co"># H 1.42 1.46 1.59 1.55 1.70 1.88 10</span></code></pre></div>
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<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package contains all phyla of all known bacteria, it can just return the initial value immediately.</p>
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</div>
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<div id="results-in-other-languages" class="section level3">
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@ -357,13 +344,13 @@
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<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
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<span class="co"># Unit: milliseconds</span>
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<span class="co"># expr min lq mean median uq max neval</span>
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<span class="co"># en 17.63 17.96 22.55 18.30 18.78 68.16 100</span>
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<span class="co"># de 28.48 28.93 33.83 29.18 29.85 76.90 100</span>
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<span class="co"># nl 32.10 33.11 43.48 33.70 35.84 202.50 100</span>
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<span class="co"># es 32.17 32.67 43.51 33.33 66.07 78.29 100</span>
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<span class="co"># it 21.95 22.29 26.55 22.59 23.13 70.41 100</span>
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<span class="co"># fr 21.59 21.94 25.36 22.31 22.88 63.46 100</span>
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<span class="co"># pt 21.75 22.15 28.50 22.48 23.01 68.10 100</span></code></pre></div>
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<span class="co"># en 17.81 18.24 21.93 18.78 19.44 60.56 100</span>
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<span class="co"># de 28.82 29.53 37.92 30.33 32.62 81.75 100</span>
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<span class="co"># nl 32.65 33.61 44.22 34.43 37.44 193.90 100</span>
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<span class="co"># es 32.51 32.95 41.88 33.70 36.39 80.63 100</span>
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<span class="co"># it 22.00 22.57 27.98 23.15 25.14 67.04 100</span>
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<span class="co"># fr 21.71 22.22 27.20 22.83 24.41 66.08 100</span>
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<span class="co"># pt 21.71 22.54 26.97 23.06 23.94 67.41 100</span></code></pre></div>
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<p>Currently supported non-English languages are German, Dutch, Spanish, Italian, French and Portuguese.</p>
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</div>
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</div>
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