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(v0.7.1.9079) small fixes
This commit is contained in:
@ -40,7 +40,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9077</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9079</span>
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</span>
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</div>
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@ -185,7 +185,7 @@
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<h1>Benchmarks</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">20 September 2019</h4>
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<h4 class="date">22 September 2019</h4>
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<div class="hidden name"><code>benchmarks.Rmd</code></div>
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@ -219,36 +219,36 @@
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<a class="sourceLine" id="cb2-16" data-line-number="16"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
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<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 8.6 8.8 9.0 9.0 9.2</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 31.0 31.0 32.0 32.0 33.0</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 31.0 32.0 39.0 33.0 38.0</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 8.6 9.1 17.0 9.7 31.0</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 8.7 9.0 14.0 9.3 9.6</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 23.0 23.0 45.0 24.0 25.0</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 23.0 23.0 29.0 24.0 26.0</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 28.0 28.0 32.0 29.0 30.0</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 530.0 560.0 580.0 570.0 580.0</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 270.0 300.0 310.0 310.0 320.0</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 8.6 9.0 9.3 9.2 9.3</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 19.0 20.0 27.0 20.0 43.0</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 19.0 20.0 26.0 21.0 23.0</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 18.0 24.0 19.0 22.0</span></a>
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<a class="sourceLine" id="cb2-34" data-line-number="34"><span class="co"># max neval</span></a>
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<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="co"># 9.5 10</span></a>
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<a class="sourceLine" id="cb2-36" data-line-number="36"><span class="co"># 38.0 10</span></a>
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<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="co"># 61.0 10</span></a>
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<a class="sourceLine" id="cb2-38" data-line-number="38"><span class="co"># 38.0 10</span></a>
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<a class="sourceLine" id="cb2-39" data-line-number="39"><span class="co"># 35.0 10</span></a>
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<a class="sourceLine" id="cb2-40" data-line-number="40"><span class="co"># 210.0 10</span></a>
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<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="co"># 56.0 10</span></a>
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<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># 57.0 10</span></a>
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<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="co"># 660.0 10</span></a>
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<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="co"># 380.0 10</span></a>
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<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="co"># 10.0 10</span></a>
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<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="co"># 46.0 10</span></a>
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<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># 48.0 10</span></a>
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<a class="sourceLine" id="cb2-48" data-line-number="48"><span class="co"># 42.0 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb2-19" data-line-number="19"><span class="co"># expr min lq mean median uq max</span></a>
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<a class="sourceLine" id="cb2-20" data-line-number="20"><span class="co"># as.mo("sau") 8.5 8.6 11 8.7 9.1 34</span></a>
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<a class="sourceLine" id="cb2-21" data-line-number="21"><span class="co"># as.mo("stau") 31.0 31.0 39 31.0 56.0 58</span></a>
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<a class="sourceLine" id="cb2-22" data-line-number="22"><span class="co"># as.mo("STAU") 31.0 34.0 39 34.0 35.0 60</span></a>
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<a class="sourceLine" id="cb2-23" data-line-number="23"><span class="co"># as.mo("staaur") 8.5 8.7 15 8.9 9.6 67</span></a>
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<a class="sourceLine" id="cb2-24" data-line-number="24"><span class="co"># as.mo("STAAUR") 8.6 8.7 14 9.0 9.9 36</span></a>
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<a class="sourceLine" id="cb2-25" data-line-number="25"><span class="co"># as.mo("S. aureus") 23.0 23.0 40 25.0 26.0 180</span></a>
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<a class="sourceLine" id="cb2-26" data-line-number="26"><span class="co"># as.mo("S aureus") 23.0 24.0 30 26.0 30.0 51</span></a>
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<a class="sourceLine" id="cb2-27" data-line-number="27"><span class="co"># as.mo("Staphylococcus aureus") 28.0 28.0 31 29.0 29.0 51</span></a>
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<a class="sourceLine" id="cb2-28" data-line-number="28"><span class="co"># as.mo("Staphylococcus aureus (MRSA)") 570.0 600.0 620 620.0 640.0 710</span></a>
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<a class="sourceLine" id="cb2-29" data-line-number="29"><span class="co"># as.mo("Sthafilokkockus aaureuz") 280.0 310.0 320 320.0 330.0 340</span></a>
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<a class="sourceLine" id="cb2-30" data-line-number="30"><span class="co"># as.mo("MRSA") 8.4 8.6 11 8.8 9.5 35</span></a>
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<a class="sourceLine" id="cb2-31" data-line-number="31"><span class="co"># as.mo("VISA") 19.0 19.0 21 20.0 22.0 24</span></a>
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<a class="sourceLine" id="cb2-32" data-line-number="32"><span class="co"># as.mo("VRSA") 19.0 19.0 27 23.0 41.0 46</span></a>
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<a class="sourceLine" id="cb2-33" data-line-number="33"><span class="co"># as.mo(22242419) 18.0 18.0 22 21.0 22.0 43</span></a>
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<a class="sourceLine" id="cb2-34" data-line-number="34"><span class="co"># neval</span></a>
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<a class="sourceLine" id="cb2-35" data-line-number="35"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-36" data-line-number="36"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-37" data-line-number="37"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-38" data-line-number="38"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-39" data-line-number="39"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-40" data-line-number="40"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-41" data-line-number="41"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-42" data-line-number="42"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-43" data-line-number="43"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-44" data-line-number="44"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-45" data-line-number="45"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-46" data-line-number="46"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-47" data-line-number="47"><span class="co"># 10</span></a>
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<a class="sourceLine" id="cb2-48" data-line-number="48"><span class="co"># 10</span></a></code></pre></div>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-5-1.png" width="562.5"></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 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
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<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
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@ -260,19 +260,19 @@
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<a class="sourceLine" id="cb3-6" data-line-number="6"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(M.semesiae, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
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<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1201.00 1327.00 1331.00 1340.00 1359.00</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1255.00 1298.00 1333.00 1340.00 1363.00</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 1927.00 1943.00 1985.00 1995.00 2014.00</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 1914.00 1953.00 1979.00 1977.00 1987.00</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 27.84 30.71 31.59 31.12 31.44</span></a>
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<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq</span></a>
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<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("metsem") 1310.00 1340.0 1361.00 1358 1387.00</span></a>
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<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("METSEM") 1304.00 1320.0 1350.00 1341 1382.00</span></a>
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<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("M. semesiae") 1839.00 1968.0 1990.00 2006 2032.00</span></a>
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<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("M. semesiae") 1947.00 1978.0 2014.00 2019 2046.00</span></a>
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<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Methanosarcina semesiae") 30.49 31.2 35.04 32 32.81</span></a>
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<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># max neval</span></a>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1371.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1398.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2040.00 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2058.00 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 39.75 10</span></a></code></pre></div>
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<p>That takes 15.7 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
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<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co"># 1401.00 10</span></a>
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<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co"># 1411.00 10</span></a>
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<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co"># 2049.00 10</span></a>
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<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co"># 2088.00 10</span></a>
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<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co"># 63.03 10</span></a></code></pre></div>
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<p>That takes 15.2 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are almost fast - these are the most probable input from most data sets.</p>
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<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
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<p><img src="benchmarks_files/figure-html/unnamed-chunk-9-1.png" width="562.5"></p>
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<p>In reality, the <code><a href="../reference/as.mo.html">as.mo()</a></code> functions <strong>learns from its own output to speed up determinations for next times</strong>. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use <code><a href="../reference/as.mo.html">as.mo()</a></code> yourself:</p>
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@ -309,8 +309,8 @@
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<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
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<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 610 637 653 652 668 718 10</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.65 seconds (652 ms). You only lose time on your unique input values.</p>
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<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># mo_name(x) 598 639 656 657 671 735 10</span></a></code></pre></div>
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<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.66 seconds (657 ms). 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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@ -322,10 +322,10 @@
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<a class="sourceLine" id="cb5-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
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<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
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<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.280 6.560 9.940 6.720 6.860 39.30 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 22.500 22.900 24.300 23.000 24.900 30.90 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.805 0.829 0.871 0.847 0.869 1.09 10</span></a></code></pre></div>
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<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
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<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co"># A 6.150 6.340 9.110 6.370 6.400 33.700 10</span></a>
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<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co"># B 22.000 22.200 22.900 22.300 22.400 28.300 10</span></a>
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<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co"># C 0.691 0.784 0.783 0.795 0.802 0.814 10</span></a></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 0.0008 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="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">run_it <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
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<a class="sourceLine" id="cb6-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
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@ -339,14 +339,14 @@
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||||
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
|
||||
<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
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||||
<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.456 0.457 0.472 0.465 0.493 0.498 10</span></a>
|
||||
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.629 0.640 0.713 0.668 0.752 0.956 10</span></a>
|
||||
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.798 0.811 0.840 0.832 0.840 0.965 10</span></a>
|
||||
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.428 0.453 0.473 0.464 0.503 0.518 10</span></a>
|
||||
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.446 0.477 0.513 0.495 0.525 0.648 10</span></a>
|
||||
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.466 0.473 0.496 0.484 0.521 0.545 10</span></a>
|
||||
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.457 0.461 0.477 0.468 0.486 0.545 10</span></a>
|
||||
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.456 0.467 0.478 0.477 0.482 0.512 10</span></a></code></pre></div>
|
||||
<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co"># A 0.462 0.471 0.480 0.482 0.491 0.498 10</span></a>
|
||||
<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co"># B 0.609 0.627 0.645 0.638 0.657 0.714 10</span></a>
|
||||
<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co"># C 0.651 0.731 0.771 0.772 0.806 0.887 10</span></a>
|
||||
<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co"># D 0.431 0.457 0.488 0.468 0.485 0.675 10</span></a>
|
||||
<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co"># E 0.450 0.452 0.466 0.465 0.473 0.500 10</span></a>
|
||||
<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co"># F 0.461 0.466 0.481 0.474 0.495 0.514 10</span></a>
|
||||
<a class="sourceLine" id="cb6-19" data-line-number="19"><span class="co"># G 0.449 0.453 0.465 0.464 0.471 0.495 10</span></a>
|
||||
<a class="sourceLine" id="cb6-20" data-line-number="20"><span class="co"># H 0.455 0.458 0.481 0.465 0.485 0.594 10</span></a></code></pre></div>
|
||||
<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> too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
|
||||
</div>
|
||||
<div id="results-in-other-languages" class="section level3">
|
||||
@ -373,13 +373,13 @@
|
||||
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
|
||||
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
|
||||
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
|
||||
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 18.04 18.47 18.79 18.54 19.25 19.70 10</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 19.36 19.88 22.78 20.17 20.39 47.73 10</span></a>
|
||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 24.57 25.38 28.46 25.63 26.12 54.82 10</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 19.50 19.89 25.49 20.51 25.79 44.96 10</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 19.52 19.82 20.44 20.11 20.80 23.09 10</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 19.50 19.79 20.42 19.86 20.53 23.35 10</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 19.25 19.55 22.50 19.59 20.04 47.50 10</span></a></code></pre></div>
|
||||
<a class="sourceLine" id="cb7-21" data-line-number="21"><span class="co"># en 17.93 18.18 19.34 18.76 19.02 26.27 10</span></a>
|
||||
<a class="sourceLine" id="cb7-22" data-line-number="22"><span class="co"># de 19.44 19.63 22.03 19.80 20.23 41.83 10</span></a>
|
||||
<a class="sourceLine" id="cb7-23" data-line-number="23"><span class="co"># nl 24.54 24.78 27.37 25.23 25.55 47.06 10</span></a>
|
||||
<a class="sourceLine" id="cb7-24" data-line-number="24"><span class="co"># es 19.51 19.94 20.27 20.20 20.55 21.16 10</span></a>
|
||||
<a class="sourceLine" id="cb7-25" data-line-number="25"><span class="co"># it 19.40 19.67 24.91 19.99 20.90 46.83 10</span></a>
|
||||
<a class="sourceLine" id="cb7-26" data-line-number="26"><span class="co"># fr 19.24 19.45 22.53 19.80 20.17 47.71 10</span></a>
|
||||
<a class="sourceLine" id="cb7-27" data-line-number="27"><span class="co"># pt 19.18 19.33 19.87 19.72 20.62 20.75 10</span></a></code></pre></div>
|
||||
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user