Enable mathematical typesetting globally.

christophe0606 · christophe0606 · commit df078ee16fdf · 2025-04-22T13:26:12.000+02:00
and rework the learning path `CMSIS-DSP with Python` to use LaTeX for mathematical typesetting.

It would be better to be able to enable math mode only for specific learning paths.
diff --git a/config.toml b/config.toml
@@ -27,6 +27,12 @@ cloudFrontDistributionID = "E2NEF61QWPFRIH"
   [markup.goldmark]
     [markup.goldmark.renderer]
       unsafe = true
+    [markup.goldmark.extensions]
+      [markup.goldmark.extensions.passthrough]
+        enable = true
+        [markup.goldmark.extensions.passthrough.delimiters]
+          block = [['\[', '\]'], ['$$', '$$']]
+          inline = [['\(', '\)']]
 
 [frontmatter]
 lastmod = ["lastmod", ":git", "date", "publishDate"]
@@ -83,3 +89,5 @@ title = 'Arm Learning Paths'
 description = 'Tutorials with code examples, created by the Arm ecosystem to develop better code faster across all platforms: Servers, phones, laptops, embedded devices, and microcontrollers.'
 social_image = '/img/social-image.png'
 twitter_handle = '@ArmSoftwareDev'
+
+math = true
diff --git a/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-5.md b/content/learning-paths/embedded-and-microcontrollers/cmsisdsp-dev-with-python/how-to-5.md
@@ -61,45 +61,32 @@ Noise filtering in each band uses a simplified Wiener filter.
 
 A gain is applied to the signal, defined as follow:
 
-```
-H(f) = S(f) / (S(f) + N(f))
-
-```
-- `S(f)` is the speech spectrum.
-- `N(f)` is the noise spectrum.
+$$H(f) = \frac{S(f)}{S(f) + N(f)}$$
 
-```
-H(f) = 1 / (1 + N(f)/S(f))
+- \(S(f)\) is the speech spectrum.
+- \(N(f)\) is the noise spectrum.
 
-```
+$$H(f) = \frac{1}{1 + \frac{N(f)}{S(f)}}$$
 
 For this tutorial, we assume a high SNR. The VAD relies on this assumption: the signal energy is sufficient to detect speech.
 With a high signal-to-noise ratio, the transfer function can be approximated as:
 
-```
-H(f) ~ 1 - N(f)/S(f)
+$$H(f) \approx 1 - \frac{N(f)}{S(f)}$$
 
-```
+You don't have access to \(S(f)\), only to the measured \(S(f) + N(f)\) which will be used under the assumption that the noise is small, making the approximation acceptable:
 
-You don't have access to `S(f)`, only to the measured `S(f) + N(f)` which will be used under the assumption that the noise is small, making the approximation acceptable:
+$$H(f) \approx 1 - \frac{N(f)}{S(f) + N(f)}$$
 
-```
-H(f) ~ 1 - N(f) / (S(f) + N(f))
 
-```
+with \(S(f) + N(f) = E(f)\)
 
-with `S(f) + N(f) = E(f)`
+- \(E(f)\) is the observed energy in a frequency band.
 
 It can be rewritten as:
 
-`H(f)` is approximately:
-
-```
-H(f) ~ (E(f) - N(f)) / E(f)
-```
+$$H(f) \approx \frac{E(f) - N(f)}{E(f)}$$
 
-- `N(f)` is estimated when there is no speech.
-- `E(f)` is the observed energy in a frequency band.
+- \(N(f)\) is estimated when there is no speech.
 
 In the Python code below, you’ll see this formula implemented as:
 
diff --git a/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html b/themes/arm-design-system-hugo-theme/layouts/_default/baseof.html
@@ -9,6 +9,10 @@
         {{partial "head/analytics.html" .}}
         {{partial "head/conditional-redirect.html" .}}
         {{partial "head/hotjar-surveys.html" .}}
+
+        {{ if .Param "math" }}
+           {{ partialCached "math.html" . }}
+        {{ end }}
     </head>
     <body>
         {{partial "header/nav-masthead.html" .}}
diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/math.html b/themes/arm-design-system-hugo-theme/layouts/partials/math.html
@@ -0,0 +1,12 @@
+<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
+<script>
+  MathJax = {
+    tex: {
+      displayMath: [['\\[', '\\]'], ['$$', '$$']],  // block
+      inlineMath: [['\\(', '\\)']]                  // inline
+    },
+    loader:{
+      load: ['ui/safe']
+    },
+  };
+</script>
