Merge pull request #2 from roaldarbol/remove-it

Fixed unknown block tag error

.gitignore

@@ -5,3 +5,5 @@ template.pdf
 *.egg-info
 _build
 
+Exports
+

README.md

@@ -6,7 +6,7 @@ I use [pixi](https://pixi.sh) for managing the environment, so if you don't have
 - clone the repository and `cd` into it
 - run `pixi install`, which will install `typst` and `mystmd`.
 - run `pixi shell` to open a pixi shell (or prefix your commands with `pixi run`)
-
+  /Users/roaldarbol/Filen/Thesis/typst-thesis.md
 ### Typst
 To render the `typst` example, run:
 ```sh

_toc.yml

@@ -0,0 +1,32 @@
+# Table of Contents
+#
+# Myst will respect:
+# 1. New pages
+#      - file: relative/path/to/page
+# 2. New sections without an associated page
+#      - title: Folder Title
+#        sections: ...
+# 3. New sections with an associated page
+#      - file: relative/path/to/page
+#        sections: ...
+#
+# Note: Titles defined on pages here are not recognized.
+#
+# This spec is based on the JupyterBook table of contents.
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: examples/example-myst/00-Front-Matter/00-Front-Page.md
+chapters:
+  - title: Front Matter
+    sections:
+      - file: examples/example-myst/00-Front-Matter/01-Declaration.md
+      - file: examples/example-myst/00-Front-Matter/02-Abstract.md
+      - file: examples/example-myst/00-Front-Matter/03-Preface.md
+      - file: examples/example-myst/00-Front-Matter/04-Acknowledgements.md
+      - file: examples/example-myst/00-Front-Matter/05-Dedication.md
+      - file: examples/example-myst/00-Front-Matter/06-Glossary.md
+  - title: Chapters
+    sections:
+      - file: examples/example-myst/01-Chapters/01-Introduction.md
+      - file: examples/example-myst/01-Chapters/02-Chapter.md

examples/example-typst/main.typ

@@ -5,31 +5,7 @@
     title: "vak: a neural network framework for researchers studying animal acoustic communication",
     subtitle: "This is my subtitle.",
     abstract: [
-How is speech like birdsong? What do we mean when we say an animal learns their vocalizations?
-Questions like these are answered by studying how animals communicate with sound.
-As in many other fields, the study of acoustic communication is being revolutionized by deep neural network models.
-These models enable answering questions that were previously impossible to address,
-in part because the models automate analysis of very large datasets. Acoustic communication researchers
-have developed multiple models for similar tasks, often implemented as research code with one of several libraries,
-such as Keras and Pytorch. This situation has created a real need for a framework
-that allows researchers to easily benchmark multiple models,
-and test new models, with their own data. To address this need, we developed vak (#link("https://github.com/vocalpy/vak")[https://github.com/vocalpy/vak]),
-a neural network framework designed for acoustic communication researchers.
-("vak" is pronounced like "talk" or "squawk" and was chosen
-for its similarity to the Latin root _voc_, as in "vocal".)
-Here we describe the design of the vak,
-and explain how the framework makes it easy for researchers to apply neural network models to their own data.
-We highlight enhancements made in version 1.0 that significantly improve user experience with the library.
-To provide researchers without expertise in deep learning access to these models,
-vak can be run via a command-line interface that uses configuration files.
-Vak can also be used directly in scripts by scientist-coders. To achieve this, vak adapts design patterns and
-an API from other domain-specific PyTorch libraries such as torchvision, with modules representing
-neural network operations, models, datasets, and transformations for pre- and post-processing.
-vak also leverages the Lightning library as a backend,
-so that vak developers and users can focus on the domain.
-We provide proof-of-concept results showing how vak can be used to
-test new models and compare existing models from multiple model families.
-In closing we discuss our roadmap for development and vision for the community of users.
+      How is speech like birdsong? What do we mean when we say an animal learns their vocalizations? Questions like these are answered by studying how animals communicate with sound. As in many other fields, the study of acoustic communication is being revolutionized by deep neural network models. These models enable answering questions that were previously impossible to address, in part because the models automate analysis of very large datasets. Acoustic communication researchers have developed multiple models for similar tasks, often implemented as research code with one of several libraries, such as Keras and Pytorch. This situation has created a real need for a framework that allows researchers to easily benchmark multiple models, and test new models, with their own data. To address this need, we developed vak (#link("https://github.com/vocalpy/vak")[https://github.com/vocalpy/vak]), a neural network framework designed for acoustic communication researchers. ("vak" is pronounced like "talk" or "squawk" and was chosen for its similarity to the Latin root _voc_, as in "vocal".) Here we describe the design of the vak, and explain how the framework makes it easy for researchers to apply neural network models to their own data. We highlight enhancements made in version 1.0 that significantly improve user experience with the library. To provide researchers without expertise in deep learning access to these models, vak can be run via a command-line interface that uses configuration files. Vak can also be used directly in scripts by scientist-coders. To achieve this, vak adapts design patterns and an API from other domain-specific PyTorch libraries such as torchvision, with modules representing neural network operations, models, datasets, and transformations for pre- and post-processing. vak also leverages the Lightning library as a backend, so that vak developers and users can focus on the domain. We provide proof-of-concept results showing how vak can be used to test new models and compare existing models from multiple model families. In closing we discuss our roadmap for development and vision for the community of users.
     ],
     date: datetime(
       year: 2023,
@@ -68,9 +44,10 @@ In closing we discuss our roadmap for development and vision for the community o
 )
 
 /* Written by MyST v1.1.37 */
-
 #set page(columns: 1, margin: (x: 1.5cm, y: 2cm),)
+
 // Here it needs to create each of the front matter sections with the 
+
 = vak: a neural network framework for researchers studying animal acoustic communication
 #include "Chapter-1/Introduction.typ"
 #include "Chapter-1/Discussion.typ"

myst.yml

@@ -16,9 +16,19 @@ project:
   github: https://github.com/roaldarbol/thesis-template
   bibliography: []
   exports:
-  - format: typst
-    template: typst-thesis.typ
-    toc: examples/example-myst/_toc.yml
+    - format: typst
+      template: typst-thesis.typ
+      output: Exports/MyST-Thesis.pdf
+      # toc: _toc.yml
+      articles:
+      - file: examples/example-myst/00-Front-Matter/01-Declaration.md
+      - file: examples/example-myst/00-Front-Matter/02-Abstract.md
+      - file: examples/example-myst/00-Front-Matter/03-Preface.md
+      - file: examples/example-myst/00-Front-Matter/04-Acknowledgements.md
+      - file: examples/example-myst/00-Front-Matter/05-Dedication.md
+      - file: examples/example-myst/00-Front-Matter/06-Glossary.md
+      - file: examples/example-myst/01-Chapters/01-Introduction.md
+      - file: examples/example-myst/01-Chapters/02-Chapter.md
 site:
   template: book-theme
   # title:

pixi.toml

@@ -7,7 +7,7 @@ channels = ["conda-forge"]
 platforms = ["osx-64"]
 
 [tasks]
-render-typst = "typst compile examples/examploe-typst/main.typ --root ../.."
+render-typst = "typst compile examples/example-typst/main.typ --root ../.."
 render-myst = "myst build --typst"
 
 [dependencies]

template.typ

@@ -6,67 +6,13 @@
   frontmatter: (
     title: "[-doc.title-]",
     abstract: [
-  [-parts.abstract-]
-    ],
-  [# if doc.subtitle #]
-    subtitle: "[-doc.subtitle-]",
-  [# endif #]
-  [# if doc.short_title #]
-    short-title: "[-doc.short_title-]",
-  [# endif #]
-  [# if doc.open_access !== undefined #]
-    open-access: [-doc.open_access-],
-  [# endif #]
-  [# if doc.github !== undefined #]
-    github: "[-doc.github-]",
-  [# endif #]
-  [# if doc.doi #]
-    doi: "[-doc.doi-]",
-  [# endif #]
-  [# if doc.date #]
-    date: datetime(
-      year: [-doc.date.year-],
-      month: [-doc.date.month-],
-      day: [-doc.date.day-],
-    ),
-  [# endif #]
-  [# if doc.keywords #]
-    keywords: (
-      [#- for keyword in doc.keywords -#]"[-keyword-]",[#- endfor -#]
-    ),
-  [# endif #]
-    authors: (
-  [# for author in doc.authors #]
-      (
-        name: "[-author.name-]",
-  [# if author.orcid #]
-        orcid: "[-author.orcid-]",
-  [# endif #]
-  [# if author.email #]
-        email: "[-author.email-]",
-  [# endif #]
-  [# if author.affiliations #]
-        affiliations: ([#- for aff in author.affiliations -#]"[-aff.index-]"[#- if not loop.last -#],[#- endif -#][#- endfor -#]),
-  [# endif #]
-      ),
-  [# endfor #]
-    ),
-    affiliations: (
-  [# for aff in doc.affiliations #]
-      (
-        id: "[-aff.index-]",
-        name: "[-aff.name-]",
-      ),
-  [# endfor #]
-    ),
-  [# if doc.license.content #]
-    license: (id: "[-doc.license.content.id-]", name: "[-doc.license.content.name-]", url: "[-doc.license.content.url-]"),
-  [# endif #]
+      [-parts.abstract-]
+    ]
   ),
 )
 
 // This may be moved below the first paragraph to start columns later
-#set page(columns: 2, margin: (x: 1.5cm, y: 2cm),)
+#set page(columns: 1, margin: (x: 1.5cm, y: 2cm),)
 
 [-CONTENT-]
 

template.yml

@@ -34,10 +34,6 @@ doc:
   - id: keywords
   - id: doi
   - id: github
-options:
-  - id: conference-year
-    type: number
-    description: What year is the conference, for example, 2024
 parts:
   - id: abstract
     description: >

typst-thesis.typ

@@ -4,7 +4,7 @@
   set text(size: 8pt)
   set align(left)
   set par(justify: true)
-  text(weight: "bold")[#it.supplement #it.counter.display(it.numbering)]
+  text(weight: "bold")[it.supplement it.counter.display(it.numbering)]
   "."
   h(4pt)
   set text(fill: black.lighten(20%), style: "italic")
@@ -45,22 +45,22 @@
       counter(figure.where(kind: image)).update(0)
       counter(figure.where(kind: table)).update(0)
       counter(math.equation).update(0)
-      [#numbering("1.", ..nums)]
+      [numbering("1.", ..nums)]
     } else {
-      [#numbering("1.1.1", ..nums)]
+      [numbering("1.1.1", ..nums)]
     }
   })
 
   // Configure figure numbering
   set figure(numbering: (..args) => {
     let chapter = counter(heading).display((..nums) => nums.pos().at(0))
-    [#chapter.#numbering("1", ..args.pos())]
+    [chapter.numbering("1", ..args.pos())]
   })
 
   // Configure equation numbering and spacing.
   set math.equation(numbering: (..args) => {
     let chapter = counter(heading).display((..nums) => nums.pos().at(0))
-    [(#chapter.#numbering("1)", ..args.pos())]
+    [(chapter.numbering("1)", ..args.pos())]
   })
   show math.equation: set block(spacing: 1em)
   show figure.caption: leftCaption