deploy: 9abfc3a

basics/common_datatypes.html

@@ -374,7 +374,7 @@ <h3>A few more typical ways to create sequences</h3>
 standard library provides a <code>rand</code> procedure we can combine with <code>mapIt</code>:</p>
                         <pre><code class="nohighlight hljs nim"><span class="hljs-keyword">import</span> random
 randomize()
-<span class="hljs-keyword">echo</span> toSeq(<span class="hljs-number">0</span> ..&lt; <span class="hljs-number">5</span>).mapIt(rand(<span class="hljs-number">10.0</span>))</code></pre><pre class="nb-output">@[9.579251111264801, 1.578132457619805, 4.889709401360747, 8.848071916580956, 2.386300214710697]</pre>
+<span class="hljs-keyword">echo</span> toSeq(<span class="hljs-number">0</span> ..&lt; <span class="hljs-number">5</span>).mapIt(rand(<span class="hljs-number">10.0</span>))</code></pre><pre class="nb-output">@[0.8769893891051761, 8.003552359258268, 0.1919852990291604, 9.043200846330667, 1.789818960578717]</pre>
                         <p>samples 5 floating point numbers between 0 and 10.</p>
 <h2><code>Tensor[T]</code> - an ND-array type from <a href="https://github.com/mratsim/Arraymancer">Arraymancer</a></h2>
 <p>Arraymancer provides an ND-array type called <code>Tensor[T]</code> that is best compared to a numpy

basics/units_basics.html

@@ -264,7 +264,7 @@ <h2>Motivating example</h2>
 <span class="hljs-keyword">let</span> lbfs = <span class="hljs-number">1.</span>lbf•s
 <span class="hljs-keyword">let</span> <span class="hljs-type">Ns</span> = lbfs.to(<span class="hljs-type">N•s</span>)
 
-<span class="hljs-keyword">echo</span> lbfs, <span class="hljs-string">&quot; equals &quot;</span>, <span class="hljs-type">Ns</span></code></pre><pre class="nb-output">1 lbf•s equals 4.44822 kg•m•s⁻¹</pre>
+<span class="hljs-keyword">echo</span> lbfs, <span class="hljs-string">&quot; equals &quot;</span>, <span class="hljs-type">Ns</span></code></pre><pre class="nb-output">1 lbf•s equals 4.44822 N•s</pre>
                         <p>As you can see, they thought the value they read was 4.4 times smaller than it really was!</p>
 <p>But what does the code do? Let's dissect it block-by-block:</p>
 <ol>

book.json

@@ -1 +1 @@
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+{"initDir":"/home/runner/work/getting-started/getting-started","cfgDir":"/home/runner/work/getting-started/getting-started","rawCfg":"[nimib]\nsrcDir = \"book\"\nhomeDir = \"docs\"\n\n[nimibook]\ntitle = \"SciNim Getting Started\"\ndescription = \"Tutorials for libraries in SciNim ecosystems\"\ngit_repository_url = \"https://github.com/SciNim/getting-started\"\nplausible_analytics_url = \"scinim.github.io/getting-started\"\n","nbCfg":{"srcDir":"book","homeDir":"docs"},"cfg":{"title":"SciNim Getting Started","language":"en-us","description":"Tutorials for libraries in SciNim ecosystems","default_theme":"light","preferred_dark_theme":"navy","git_repository_url":"https://github.com/SciNim/getting-started","git_repository_icon":"fa-github","plausible_analytics_url":"scinim.github.io/getting-started","favicon_escaped":"<link rel=\"icon\" href=\"data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2280%22>🐳</text></svg>\">"},"theme_option":{"navy":"Navy","rust":"Rust","coal":"Coal","ayu":"Ayu","light":"Light (default)"},"toc":{"entries":[{"title":"Introduction","path":"index.md","levels":[1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Ecosystem overview","path":"overview/index.md","levels":[2],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Basic topics","path":"basics/index.md","levels":[3],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Common datatypes","path":"basics/common_datatypes.nim","levels":[3,1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Data wrangling with dataframes","path":"basics/data_wrangling.nim","levels":[3,2],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Plotting","path":"basics/basic_plotting.nim","levels":[3,3],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Units","path":"basics/units_basics.nim","levels":[3,4],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Numerical methods","path":"numerical_methods/index.md","levels":[4],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Curve fitting","path":"numerical_methods/curve_fitting.nim","levels":[4,1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Integration (1D)","path":"numerical_methods/integration1d.nim","levels":[4,2],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"ODEs","path":"numerical_methods/ode.nim","levels":[4,3],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Optimization","path":"numerical_methods/optimization.nim","levels":[4,4],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Interpolation","path":"numerical_methods/interpolation.nim","levels":[4,5],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Data visualization","path":"data_viz/index.md","levels":[5],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Plotting data","path":"data_viz/plotting_data.nim","levels":[5,1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Interfacing with other language","path":"external_language_integration/index.md","levels":[6],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Nimpy - The Nim Python bridge","path":"external_language_integration/nim_with_py.nim","levels":[6,1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Nimjl - The Nim Julia bridge","path":"external_language_integration/julia/basics.nim","levels":[6,2],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Advanced types","path":"external_language_integration/julia/nimjl_conversions.nim","levels":[6,2,1],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Julia Arrays from Nim","path":"external_language_integration/julia/nimjl_arrays.nim","levels":[6,2,2],"isNumbered":true,"isDraft":false,"isActive":false},{"title":"Interfacing with R","path":"external_language_integration/nim_with_R.nim","levels":[6,3],"isNumbered":true,"isDraft":false,"isActive":false}]},"keep":[]}

external_language_integration/julia/nimjl_arrays.html

@@ -436,13 +436,13 @@ <h2>Conversion between JlArray[T] and Arraymancer's Tensor[T] (and dealing with
 <span class="hljs-keyword">echo</span> localTensor
 
 <span class="hljs-keyword">var</span> localTensor2 = localArray.to(<span class="hljs-type">Tensor</span>[<span class="hljs-built_in">int</span>], rowMajor)
-<span class="hljs-keyword">assert</span>(localTensor == localTensor2)</code></pre><pre class="nb-output">[3 5 2 1 2; 5 1 3 4 4; 3 3 5 4 3; 5 2 5 4 2; 5 2 3 4 5]
+<span class="hljs-keyword">assert</span>(localTensor == localTensor2)</code></pre><pre class="nb-output">[4 2 1 1 3; 4 1 5 3 2; 3 2 2 4 5; 5 5 2 1 4; 5 4 1 2 4]
 Tensor[system.int] of shape &quot;[5, 5]&quot; on backend &quot;Cpu&quot;
-|3      5     2     1     2|
-|5      1     3     4     4|
-|3      3     5     4     3|
-|5      2     5     4     2|
-|5      2     3     4     5|</pre>
+|4      2     1     1     3|
+|4      1     5     3     2|
+|3      2     2     4     5|
+|5      5     2     1     4|
+|5      4     1     2     4|</pre>
                         <p>Both Tensors have identical indexed values but the buffer are different according to the memory layout argument.</p>
 <p>When passing Tensor directly as values in a <code>jlCall</code> / <code>Julia.</code> expression, a <code>JlArray[T]</code> will be constructed by buffer; so you should be aware about the memory layout of the buffer.</p>
                         <pre><code class="nohighlight hljs nim"><span class="hljs-keyword">var</span> orderedTensor = newTensor[<span class="hljs-built_in">int</span>]([<span class="hljs-number">3</span>, <span class="hljs-number">2</span>])

numerical_methods/integration1d.html

@@ -331,12 +331,12 @@ <h3>Let the integration begin!</h3>
 
 timeIt <span class="hljs-string">&quot;AdaGauss&quot;</span>:
   keep adaptiveGauss(f, a, b, tol=tol)</code></pre><pre class="nb-output">   min time    avg time  std dv   runs name
-   0.017 ms    0.019 ms  ±0.001  x1000 Trapz
-   0.018 ms    0.019 ms  ±0.005  x1000 Simpson
-   0.066 ms    0.076 ms  ±0.128  x1000 GaussQuad
-   0.036 ms    0.040 ms  ±0.002  x1000 Romberg
-   0.293 ms    0.493 ms  ±1.211  x1000 AdaSimpson
-   0.040 ms    0.045 ms  ±0.003  x1000 AdaGauss</pre>
+   0.015 ms    0.017 ms  ±0.001  x1000 Trapz
+   0.016 ms    0.019 ms  ±0.064  x1000 Simpson
+   0.065 ms    0.073 ms  ±0.093  x1000 GaussQuad
+   0.035 ms    0.038 ms  ±0.002  x1000 Romberg
+   0.293 ms    0.605 ms  ±1.541  x1000 AdaSimpson
+   0.032 ms    0.037 ms  ±0.040  x1000 AdaGauss</pre>
                         <p>As we can see, all methods except AdaSimpson were roughly equally fast. So if I were to choose
 a winner, it would be <code>adaptiveGauss</code> because it was the most accurate while still being among
 the fastest methods.</p>

numerical_methods/ode.html

@@ -332,10 +332,10 @@ <h2>Let's code</h2>
 <code>odeOptions</code> would probably get them on-par with the others. There is one parameter we haven't talked about though,
 the execution time. Let's look at that and see if it bring any further insights:</p>
                         <pre><code class="nohighlight hljs nim"></code></pre><pre class="nb-output">   min time    avg time  std dv   runs name
-  11.259 ms   20.406 ms  ±6.787   x243 heun2
-  14.426 ms   16.404 ms  ±3.768   x295 rk4
-   0.259 ms    0.284 ms  ±0.009  x1000 rk21
-   0.304 ms    0.361 ms  ±0.118  x1000 tsit54</pre>
+  11.078 ms   16.221 ms  ±6.710   x298 heun2
+  10.544 ms   10.629 ms  ±0.268   x460 rk4
+   0.214 ms    0.217 ms  ±0.002  x1000 rk21
+   0.256 ms    0.258 ms  ±0.002  x1000 tsit54</pre>
                         <p>As we can see, the adaptive methods are orders of magnitude faster while achieving roughly the same errors.
 This is because they take fewer and longer steps when the function is flatter and only decrease the
 step-size when the function is changing more rapidly.</p>
@@ -393,10 +393,10 @@ <h2>Vector-valued functions</h2>
                         <ul>
 <li>tsit54: 2.114975e-13</li>
 </ul>
-                        <pre><code class="nohighlight hljs nim"></code></pre><pre class="nb-output">2615.167 ms 2626.198 ms ±15.601     x2 heun2
-5261.133 ms 5261.133 ms  ±0.000     x1 rk4
-  50.778 ms   51.349 ms  ±0.291    x98 rk21
- 224.308 ms  225.149 ms  ±0.704    x23 tsit54</pre>
+                        <pre><code class="nohighlight hljs nim"></code></pre><pre class="nb-output">2733.102 ms 2738.193 ms  ±7.199     x2 heun2
+5430.602 ms 5430.602 ms  ±0.000     x1 rk4
+  52.290 ms   52.462 ms  ±0.123    x96 rk21
+ 232.087 ms  233.602 ms  ±1.008    x22 tsit54</pre>
                         <p>We can once again see that the high-order adaptive methods are both more accurate and faster than the fixed-order ones.</p>
 <h2>Further reading</h2>
 <ul>

numerical_methods/optimization.html

@@ -300,10 +300,10 @@ <h1>Optimize functions in Nim</h1>
 LBFGS: Tensor[system.float] of shape &quot;[2]&quot; on backend &quot;Cpu&quot;
     1     1
    min time    avg time  std dv   runs name
- 214.122 ms  315.523 ms ±49.139    x16 Steepest
-   0.201 ms    0.268 ms  ±0.332  x1000 Newton
-   3.051 ms    9.665 ms  ±7.740   x509 BFGS
-   4.878 ms    9.730 ms  ±5.731   x504 LBFGS</pre>
+ 158.509 ms  325.429 ms ±106.262    x16 Steepest
+   0.207 ms    0.235 ms  ±0.012  x1000 Newton
+   2.216 ms    3.286 ms  ±0.601  x1000 BFGS
+   3.784 ms    4.218 ms  ±0.760  x1000 LBFGS</pre>
                         <p>As we can see, Newton, BFGS and LBFGS found the exact solution and they did it fast.
 Steepest descent didn't find as good a solution and took by far the longest time.
 Steepest descent has an order of convergence of $O(N)$, Newton has $O(N^2)$ and (L)BFGS