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+ +
+

Admonition Boxes#

+

Jeg har lavet denne side, så man kan se, hvordan forskellige bokse vises i Jupyter Book.

+
+

Common Classes for Admonitions in Jupyter Book#

+

The standard classes available for admonitions in Jupyter Book include but may not be limited to:

+
    +
  • note

  • +
  • warning

  • +
  • tip

  • +
  • important

  • +
  • caution

  • +
  • danger

  • +
  • error

  • +
  • hint

  • +
  • seealso

  • +
+

These classes typically map to conventional documentation styles, where each type is used to indicate different levels or types of information:

+
    +
  • note: General information that is important for the reader.

  • +
  • warning: A caution about potential problems or pitfalls.

  • +
  • tip: Advice or insights that might not be immediately obvious.

  • +
  • important: Critical information that must not be overlooked.

  • +
  • caution: Similar to warning but often used for less severe issues.

  • +
  • danger: Indicates a very high risk of serious consequences.

  • +
  • error: Used to describe errors or incorrect methods.

  • +
  • hint: Suggestive information that may help in understanding or solving a problem.

  • +
  • seealso: Used to refer to related information elsewhere.

  • +
+
+

Example of an Admonition in Jupyter Book#

+

Here is how you would typically write a note type admonition in Jupyter Book:

+
```{admonition} Take Note
+:class: note
+This is a special note that will help clarify something important.
+
+
+
+
+
+

Her følger først markdown-kode til admonition boxes, derefter den renderede version#

+

Koden er hentet herfra.

+

https://jupyterbook.org/en/stable/reference/cheatsheet.html#admonitions

+
```{admonition} This is a title
+An example of an admonition with a title.
+```
+
+
+
+

This is a title

+

An example of an admonition with a title.

+
+
```{note} Notes require **no** arguments,
+so content can start here.
+```
+
+
+
+

Note

+

Notes require no arguments, +so content can start here.

+
+
```{warning} This is an exmample
+of a warning directive.
+```
+
+
+
+

Warning

+

This is an example +of a warning directive.

+
+
```{tip} This is an example
+of a tip directive.
+```
+
+
+
+

Tip

+

This is an example +of a tip directive.

+
+
```{caution} This is an example
+of a caution directive.
+```
+
+
+
+

Caution

+

This is an example +of a caution directive.

+
+
```{attention} This is an example
+of an attention directive.
+```
+
+
+
+

Attention

+

This is an example +of an attention directive.

+
+
```{danger} This is an example
+of a danger directive.
+```
+
+
+
+

Danger

+

This is an example +of a danger directive.

+
+
```{error} This is an example
+of an error directive.
+```
+
+
+
+

Error

+

This is an example +of an error directive.

+
+
```{hint} This is an example
+of a hint directive.
+```
+
+
+
+

Hint

+

This is an example +of a hint directive.

+
+
```{important} This is an example
+of an important directive.
+```
+
+
+
+

Important

+

This is an example +of an important directive.

+
+
```{admonition} This is a title
+:class: warning
+An example of an admonition with a title and a warning style.
+```
+
+
+
+

This is a title

+

An example of an admonition with a title and a warning style.

+
+
+
+

Alt herunder er tests fra ting fundet andetsteds#

+
:::{seealso}
+For a list of syntax extensions in MyST, see [the MyST documentation](https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html).
+:::
+
+
+
+

See also

+

For a list of syntax extensions in MyST, see the MyST documentation.

+
+
```{admonition} My title
+:class: seealso
+My content
+```
+
+
+
+

My title

+

My content

+
+
```{admonition} My title
+:class: todo
+My content
+```
+
+
+
+

My title

+

My content

+
+
```{todo} This is an example
+of a todo directive.
+```
+
+
+
+

Todo

+

This is an example +of a todo directive.

+
+
```{admonition} My title
+:class: seealso
+My content
+```
+
+
+
+

My title

+

My content

+
+
```{seealso} This is an example
+of a seealso directive.
+```
+
+
+
+

See also

+

This is an example +of a seealso directive.

+
+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + + + + +
+ + + +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/_to_be_deleted_later/intro_old.html b/_build/html/_to_be_deleted_later/intro_old.html new file mode 100644 index 00000000..158d89d9 --- /dev/null +++ b/_build/html/_to_be_deleted_later/intro_old.html @@ -0,0 +1,547 @@ + + + + + + + + + + + Welcome — KUB Datalab - Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+

Welcome

+ +
+
+ +
+

Contents

+
+ +
+
+
+ + + + +
+ +
+

Welcome#

+

Welcome to KUB Datalab’s Python ressources.

+

The material you have in front of you is connected to KUB Datalab at Copenhagen University Library. We currently offer a two-part introduction to Python for absolute beginners as well as an introduction to the library Pandas.

+

You can find and sign up for our courses here. Before you attend the beginners’ class, please make sure to visit the How to… “Install & Run Python” page so we can get the most out of our time together.

+

The materials you find in this Jupyter Book are intended to help students learn how to code in Python. All pages can be downloaded as a Jupyter Notebook as well as pdf. That means that you can work with most of the code locally on your computer.

+

Below you can see the different elements of our courses.

+
+
+

Attribution#

+

CC Attribution

+

Much of the teaching material presented here has been inspired by, borrowed from, and modified based on the work of others.
+We extend our deepest thanks to these giants on whose proverbial shoulders we stand and encourage you to visit their pages for more Python inspiration.

+ +

Should we inadvertently have missed acknowledging anyone, please let us know so we can ensure proper recognition.

+
+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + +
+ + +
+ + + +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/_to_be_deleted_later/markdown-notebooks.html b/_build/html/_to_be_deleted_later/markdown-notebooks.html new file mode 100644 index 00000000..3239cd9b --- /dev/null +++ b/_build/html/_to_be_deleted_later/markdown-notebooks.html @@ -0,0 +1,582 @@ + + + + + + + + + + + Notebooks with MyST Markdown — KUB Datalab - Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+

Notebooks with MyST Markdown

+ +
+ +
+
+ + + + +
+ +
+

Notebooks with MyST Markdown#

+ +

Jupyter Book also lets you write text-based notebooks using MyST Markdown. +See the Notebooks with MyST Markdown documentation for more detailed instructions. +This page shows off a notebook written in MyST Markdown.

+
+

An example cell#

+

With MyST Markdown, you can define code cells with a directive like so:

+
+
+
print(2 + 2)
+
+
+
+
+
4
+
+
+
+
+

When your book is built, the contents of any {code-cell} blocks will be +executed with your default Jupyter kernel, and their outputs will be displayed +in-line with the rest of your content.

+
+

See also

+

Jupyter Book uses Jupytext to convert text-based files to notebooks, and can support many other text-based notebook files.

+
+
+
+

Create a notebook with MyST Markdown#

+

MyST Markdown notebooks are defined by two things:

+
    +
  1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). +See the YAML at the top of this page for example.

  2. +
  3. The presence of {code-cell} directives, which will be executed with your book.

  4. +
+

That’s all that is needed to get started!

+
+
+

Quickly add YAML metadata for MyST Notebooks#

+

If you have a markdown file and you’d like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command:

+
jupyter-book myst init path/to/markdownfile.md
+
+
+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + + + + +
+ + + +
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+ + + + + + + + \ No newline at end of file diff --git a/_build/html/_to_be_deleted_later/markdown.html b/_build/html/_to_be_deleted_later/markdown.html new file mode 100644 index 00000000..54e52db8 --- /dev/null +++ b/_build/html/_to_be_deleted_later/markdown.html @@ -0,0 +1,586 @@ + + + + + + + + + + + Markdown Files — KUB Datalab - Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
+

Markdown Files

+ +
+ +
+
+ + + + +
+ +
+

Markdown Files#

+

Whether you write your book’s content in Jupyter Notebooks (.ipynb) or +in regular markdown files (.md), you’ll write in the same flavor of markdown +called MyST Markdown. +This is a simple file to help you get started and show off some syntax.

+
+

What is MyST?#

+

MyST stands for “Markedly Structured Text”. It +is a slight variation on a flavor of markdown called “CommonMark” markdown, +with small syntax extensions to allow you to write roles and directives +in the Sphinx ecosystem.

+

For more about MyST, see the MyST Markdown Overview.

+
+
+

Sample Roles and Directives#

+

Roles and directives are two of the most powerful tools in Jupyter Book. They +are like functions, but written in a markup language. They both +serve a similar purpose, but roles are written in one line, whereas +directives span many lines. They both accept different kinds of inputs, +and what they do with those inputs depends on the specific role or directive +that is being called.

+

Here is a “note” directive:

+
+

Note

+

Here is a note

+
+

It will be rendered in a special box when you build your book.

+

Here is an inline directive to refer to a document: Notebooks with MyST Markdown.

+
+
+

Citations#

+

You can also cite references that are stored in a bibtex file. For example, +the following syntax: {cite}`holdgraf_evidence_2014` will render like +this: [HdHPK14].

+

Moreover, you can insert a bibliography into your page with this syntax: +The {bibliography} directive must be used for all the {cite} roles to +render properly. +For example, if the references for your book are stored in references.bib, +then the bibliography is inserted with:

+
+
+
+[HdHPK14] +

Christopher Ramsay Holdgraf, Wendy de Heer, Brian N. Pasley, and Robert T. Knight. Evidence for Predictive Coding in Human Auditory Cortex. In International Conference on Cognitive Neuroscience. Brisbane, Australia, Australia, 2014. Frontiers in Neuroscience.

+
+
+
+
+
+

Learn more#

+

This is just a simple starter to get you started. +You can learn a lot more at jupyterbook.org.

+
+
+ + + + +
+ + + + + + +
+ +
+
+
+ +
+ + + +
+ + +
+ + + +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/_to_be_deleted_later/notebooks.html b/_build/html/_to_be_deleted_later/notebooks.html new file mode 100644 index 00000000..3fa4c6cd --- /dev/null +++ b/_build/html/_to_be_deleted_later/notebooks.html @@ -0,0 +1,610 @@ + + + + + + + + + + + Content with notebooks — KUB Datalab - Python + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Content with notebooks

+ +
+ +
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+ + + + +
+ +
+

Content with notebooks#

+

You can also create content with Jupyter Notebooks. This means that you can include +code blocks and their outputs in your book.

+
+

Markdown + notebooks#

+

As it is markdown, you can embed images, HTML, etc into your posts!

+

+

You can also \(add_{math}\) and

+
+\[ +math^{blocks} +\]
+

or

+
+\[\begin{split} +\begin{aligned} +\mbox{mean} la_{tex} \\ \\ +math blocks +\end{aligned} +\end{split}\]
+

But make sure you $Escape $your $dollar signs $you want to keep!

+
+
+

MyST markdown#

+

MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check +out the MyST guide in Jupyter Book, +or see the MyST markdown documentation.

+
+
+

Code blocks and outputs#

+

Jupyter Book will also embed your code blocks and output in your book. +For example, here’s some sample Matplotlib code:

+
+
+
from matplotlib import rcParams, cycler
+import matplotlib.pyplot as plt
+import numpy as np
+plt.ion()
+
+
+
+
+
<contextlib.ExitStack at 0x10e3e7010>
+
+
+
+
+
+
+
# Fixing random state for reproducibility
+np.random.seed(19680801)
+
+N = 10
+data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]
+data = np.array(data).T
+cmap = plt.cm.coolwarm
+rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))
+
+
+from matplotlib.lines import Line2D
+custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),
+                Line2D([0], [0], color=cmap(.5), lw=4),
+                Line2D([0], [0], color=cmap(1.), lw=4)]
+
+fig, ax = plt.subplots(figsize=(10, 5))
+lines = ax.plot(data)
+ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);
+
+
+
+
+../_images/9857ecce4edd55964cf83f4b9160814fc727c33d73686b8d72f05bb394aeb68f.png +
+
+

There is a lot more that you can do with outputs (such as including interactive outputs) +with your book. For more information about this, see the Jupyter Book documentation

+
+
+ + + + +
+ + + + + + +
+ +
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+
+ + + + + + + + \ No newline at end of file diff --git a/_build/html/docs/101/01_data_types.html b/_build/html/docs/101/01_data_types.html index 0d61b3a3..ec948768 100644 --- a/_build/html/docs/101/01_data_types.html +++ b/_build/html/docs/101/01_data_types.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/02_variables_and_assignment.html b/_build/html/docs/101/02_variables_and_assignment.html index 20b6b409..15d7fc71 100644 --- a/_build/html/docs/101/02_variables_and_assignment.html +++ b/_build/html/docs/101/02_variables_and_assignment.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/03_type_conversion.html b/_build/html/docs/101/03_type_conversion.html index 650a27e4..1f84a7bd 100644 --- a/_build/html/docs/101/03_type_conversion.html +++ b/_build/html/docs/101/03_type_conversion.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/04_built-in_functions.html b/_build/html/docs/101/04_built-in_functions.html index 43e837cb..d9c827b5 100644 --- a/_build/html/docs/101/04_built-in_functions.html +++ b/_build/html/docs/101/04_built-in_functions.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/05_lists.html b/_build/html/docs/101/05_lists.html index 3af1ea96..275106f6 100644 --- a/_build/html/docs/101/05_lists.html +++ b/_build/html/docs/101/05_lists.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/06_for_loops.html b/_build/html/docs/101/06_for_loops.html index bb3b0f9a..0efe6ce0 100644 --- a/_build/html/docs/101/06_for_loops.html +++ b/_build/html/docs/101/06_for_loops.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/07_conditionals.html b/_build/html/docs/101/07_conditionals.html index 9609fbdd..1ed9351d 100644 --- a/_build/html/docs/101/07_conditionals.html +++ b/_build/html/docs/101/07_conditionals.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/part1.html b/_build/html/docs/101/part1.html index 0f8d17ed..07262ca1 100644 --- a/_build/html/docs/101/part1.html +++ b/_build/html/docs/101/part1.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/101/part2.html b/_build/html/docs/101/part2.html index 179d8cbe..2ed71d4f 100644 --- a/_build/html/docs/101/part2.html +++ b/_build/html/docs/101/part2.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/howto/dictionaries.html b/_build/html/docs/howto/dictionaries.html index 872924cd..715c4e97 100644 --- a/_build/html/docs/howto/dictionaries.html +++ b/_build/html/docs/howto/dictionaries.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/howto/dictionary_101.html b/_build/html/docs/howto/dictionary_101.html index a786d451..a369e9d1 100644 --- a/_build/html/docs/howto/dictionary_101.html +++ b/_build/html/docs/howto/dictionary_101.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/howto/dictionary_pandas.html b/_build/html/docs/howto/dictionary_pandas.html index 01000411..3d375911 100644 --- a/_build/html/docs/howto/dictionary_pandas.html +++ b/_build/html/docs/howto/dictionary_pandas.html @@ 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a/_build/html/docs/pandas/02_Pandas_tabular_data.html b/_build/html/docs/pandas/02_Pandas_tabular_data.html index 581ad89d..0a4de9a8 100644 --- a/_build/html/docs/pandas/02_Pandas_tabular_data.html +++ b/_build/html/docs/pandas/02_Pandas_tabular_data.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/pandas/03_Pandas_subsets.html b/_build/html/docs/pandas/03_Pandas_subsets.html index ca9eabdd..256b535f 100644 --- a/_build/html/docs/pandas/03_Pandas_subsets.html +++ b/_build/html/docs/pandas/03_Pandas_subsets.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/pandas/04-Variables.html b/_build/html/docs/pandas/04-Variables.html index c88dd22f..46e1c07c 100644 --- a/_build/html/docs/pandas/04-Variables.html +++ b/_build/html/docs/pandas/04-Variables.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/pandas/04_Pandas_loc_iloc.html b/_build/html/docs/pandas/04_Pandas_loc_iloc.html index 44c16360..44d9586c 100644 --- a/_build/html/docs/pandas/04_Pandas_loc_iloc.html +++ b/_build/html/docs/pandas/04_Pandas_loc_iloc.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/pandas/05_pandas_summary_statistics.html b/_build/html/docs/pandas/05_pandas_summary_statistics.html index 4951309d..1df04147 100644 --- a/_build/html/docs/pandas/05_pandas_summary_statistics.html +++ b/_build/html/docs/pandas/05_pandas_summary_statistics.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/pandas/getting_started.html b/_build/html/docs/pandas/getting_started.html index 63388f40..acb271e8 100644 --- a/_build/html/docs/pandas/getting_started.html +++ b/_build/html/docs/pandas/getting_started.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/test/2024_ods.html b/_build/html/docs/test/2024_ods.html index 10ef97ef..e07f5772 100644 --- a/_build/html/docs/test/2024_ods.html +++ b/_build/html/docs/test/2024_ods.html @@ -58,6 +58,7 @@ + diff --git a/_build/html/docs/test/regex_and_frankenstein.html b/_build/html/docs/test/regex_and_frankenstein.html index 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"transpos": [16, 24], "true": [1, 11, 18, 19, 20, 22], "turn": 27, "type": [5, 7, 9, 15], "up": 21, "upgrad": 23, "us": [6, 7, 10, 11, 20, 28], "usag": 25, "v": 27, "valu": [6, 7, 8, 9, 25], "variabl": [6, 7, 10, 15, 27], "version": [0, 20], "w": 32, "w3school": 17, "wa": 20, "wai": 20, "welcom": [1, 22], "well": 32, "what": [2, 7, 8, 9, 11], "when": [7, 8, 11, 28], "whether": 11, "which": 21, "while": 11, "why": [8, 24], "wider": 27, "window": 20, "word": 14, "work": [8, 9, 10, 25], "workspac": 18, "would": 20, "write": 25, "yaml": 3, "you": [5, 6, 20, 21], "your": [20, 27], "z": 32}}) \ No newline at end of file diff --git a/_build/jupyter_execute/_to_be_deleted_later/admonition_boxes.ipynb b/_build/jupyter_execute/_to_be_deleted_later/admonition_boxes.ipynb new file mode 100644 index 00000000..affa2b09 --- /dev/null +++ b/_build/jupyter_execute/_to_be_deleted_later/admonition_boxes.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f84de9e2-aae8-4c1a-8b93-4fac6088acf2", + "metadata": {}, + "source": [ + "# Admonition Boxes" + ] + }, + { + "cell_type": "markdown", + "id": "b17f745e-872a-4c65-a88a-cd41124b065a", + "metadata": {}, + "source": [ + "Jeg har lavet denne side, så man kan se, hvordan forskellige bokse vises i Jupyter Book." + ] + }, + { + "cell_type": "markdown", + "id": "c430cb07-7336-45a3-9596-0a6d09dba339", + "metadata": {}, + "source": [ + "## Common Classes for Admonitions in Jupyter Book\n", + "\n", + "The standard classes available for admonitions in Jupyter Book include but may not be limited to:\n", + "\n", + "- `note`\n", + "- `warning`\n", + "- `tip`\n", + "- `important`\n", + "- `caution`\n", + "- `danger`\n", + "- `error`\n", + "- `hint`\n", + "- `seealso`\n", + "\n", + "These classes typically map to conventional documentation styles, where each type is used to indicate different levels or types of information:\n", + "\n", + "- **note**: General information that is important for the reader.\n", + "- **warning**: A caution about potential problems or pitfalls.\n", + "- **tip**: Advice or insights that might not be immediately obvious.\n", + "- **important**: Critical information that must not be overlooked.\n", + "- **caution**: Similar to warning but often used for less severe issues.\n", + "- **danger**: Indicates a very high risk of serious consequences.\n", + "- **error**: Used to describe errors or incorrect methods.\n", + "- **hint**: Suggestive information that may help in understanding or solving a problem.\n", + "- **seealso**: Used to refer to related information elsewhere.\n", + "\n", + "### Example of an Admonition in Jupyter Book\n", + "\n", + "Here is how you would typically write a `note` type admonition in Jupyter Book:\n", + "\n", + "````\n", + "```{admonition} Take Note\n", + ":class: note\n", + "This is a special note that will help clarify something important.\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "3007742b-6e88-4eb6-9183-a017286caf25", + "metadata": {}, + "source": [ + "## Her følger først markdown-kode til admonition boxes, derefter den renderede version" + ] + }, + { + "cell_type": "markdown", + "id": "154aad4d-ae6a-4416-9f12-bfc9e8a242b2", + "metadata": {}, + "source": [ + "Koden er hentet herfra.\n", + "\n", + "[https://jupyterbook.org/en/stable/reference/cheatsheet.html#admonitions](https://jupyterbook.org/en/stable/reference/cheatsheet.html#admonitions)" + ] + }, + { + "cell_type": "markdown", + "id": "680184d3-d00c-46cb-902e-ba9b9c614402", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} This is a title\n", + "An example of an admonition with a title.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "10bf4879-4216-4c80-9793-aa85199994fe", + "metadata": {}, + "source": [ + "\n", + "```{admonition} This is a title\n", + "An example of an admonition with a title.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "782a2bde-d29a-4ca5-bb0d-150bdd9e8aa3", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "49754f7d-06f2-4697-8451-9649b8e12ef3", + "metadata": {}, + "source": [ + "````\n", + "```{note} Notes require **no** arguments,\n", + "so content can start here.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "66311383-2944-416e-b8c5-a1d0b5fa1da9", + "metadata": {}, + "source": [ + "```{note} Notes require **no** arguments,\n", + "so content can start here.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "28014987-1386-45b9-b32d-7ebf926411ca", + "metadata": {}, + "source": [ + "````\n", + "```{warning} This is an exmample\n", + "of a warning directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "f4ca4290-05ec-4eb5-885e-8d284500f554", + "metadata": {}, + "source": [ + "```{warning} This is an example\n", + "of a warning directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "4d6acb5b-5e17-4610-8ba9-dd76c3b8598f", + "metadata": {}, + "source": [ + "````\n", + "```{tip} This is an example\n", + "of a tip directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "80041c05-6c99-4a1f-afbe-a9b9f4edb130", + "metadata": {}, + "source": [ + "```{tip} This is an example\n", + "of a tip directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a80fdca7-5b2c-4058-89f7-37021409daa8", + "metadata": {}, + "source": [ + "````\n", + "```{caution} This is an example\n", + "of a caution directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "ff2ef0ba-243b-4936-9ca4-3e7689fdc87b", + "metadata": {}, + "source": [ + "```{caution} This is an example\n", + "of a caution directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "2af1f48c-db08-4fcf-a80d-e5e048480a76", + "metadata": {}, + "source": [ + "````\n", + "```{attention} This is an example\n", + "of an attention directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "5e8f2a75-f629-4a32-ba5f-e74002aeb468", + "metadata": {}, + "source": [ + "```{attention} This is an example\n", + "of an attention directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b9782607-740b-4c4a-bb65-bd60cc362119", + "metadata": {}, + "source": [ + "````\n", + "```{danger} This is an example\n", + "of a danger directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "c80153a3-b306-4ff5-afc2-b2d7065fd946", + "metadata": {}, + "source": [ + "```{danger} This is an example\n", + "of a danger directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1079eb56-c31d-4c73-af0f-3589ab241f51", + "metadata": {}, + "source": [ + "````\n", + "```{error} This is an example\n", + "of an error directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "142b8549-aee4-4821-a134-1f5a5c991705", + "metadata": {}, + "source": [ + "```{error} This is an example\n", + "of an error directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e5b625f2-2e5a-4737-9bf7-2abf947ab807", + "metadata": {}, + "source": [ + "````\n", + "```{hint} This is an example\n", + "of a hint directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "7fa11f46-b4be-4100-b412-d0470f0ab39a", + "metadata": {}, + "source": [ + "```{hint} This is an example\n", + "of a hint directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "17f38c8c-fe80-4d21-b761-a793f5878b87", + "metadata": {}, + "source": [ + "````\n", + "```{important} This is an example\n", + "of an important directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "1ecda7df-a97f-495f-bc8c-fb43c2d27882", + "metadata": {}, + "source": [ + "```{important} This is an example\n", + "of an important directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "439e4cee-b391-4a76-bfe3-775f62980087", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} This is a title\n", + ":class: warning\n", + "An example of an admonition with a title and a warning style.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "43390e67-8944-477d-b910-b69cf7ec8ed0", + "metadata": {}, + "source": [ + "```{admonition} This is a title\n", + ":class: warning\n", + "An example of an admonition with a title and a warning style.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e28308a9-2e28-4c48-9522-2a1edeb256c3", + "metadata": {}, + "source": [ + "## Alt herunder er tests fra ting fundet andetsteds" + ] + }, + { + "cell_type": "markdown", + "id": "ab3092d6-636e-4a06-9959-70d4ec709d9f", + "metadata": {}, + "source": [ + "````\n", + ":::{seealso}\n", + "For a list of syntax extensions in MyST, see [the MyST documentation](https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html).\n", + ":::\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "d5e64f68-cf89-4948-8669-b19f1e28b885", + "metadata": {}, + "source": [ + ":::{seealso}\n", + "For a list of syntax extensions in MyST, see [the MyST documentation](https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html).\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "8b021094-ef88-43a7-8b06-a9a79426341d", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "bc20e078-3343-4641-b873-ef101b92329b", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8775a5fd-3b8a-469b-a5be-9c91a55927e7", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: todo\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "95b68d16-6662-434b-b922-fb93b2c55987", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: todo\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6be16355-dbf5-4a8b-adc5-8c860758a971", + "metadata": {}, + "source": [ + "````\n", + "```{todo} This is an example\n", + "of a todo directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "3c75e1a4-34ee-49ba-b131-0aa25e741800", + "metadata": {}, + "source": [ + "```{todo} This is an example\n", + "of a todo directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1f174572-9150-46ef-9fd8-42db2b3440e9", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "2e644b25-107e-4a8f-a8b1-42e78ae98540", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "76a45eb2-01b2-48b1-bdc4-57bdc7078d18", + "metadata": {}, + "source": [ + "````\n", + "```{seealso} This is an example\n", + "of a seealso directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "cf4a3a2a-ec59-4fe7-87b8-289754835278", + "metadata": {}, + "source": [ + "```{seealso} This is an example\n", + "of a seealso directive.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b407312c-65b7-4f51-836d-198a697249ac", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/_build/jupyter_execute/_to_be_deleted_later/markdown-notebooks.ipynb b/_build/jupyter_execute/_to_be_deleted_later/markdown-notebooks.ipynb new file mode 100644 index 00000000..3b637724 --- /dev/null +++ b/_build/jupyter_execute/_to_be_deleted_later/markdown-notebooks.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e48912af", + "metadata": {}, + "source": [ + "# Notebooks with MyST Markdown\n", + "\n", + "```{contents}\n", + ":local:\n", + "```\n", + "\n", + "Jupyter Book also lets you write text-based notebooks using MyST Markdown.\n", + "See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions.\n", + "This page shows off a notebook written in MyST Markdown.\n", + "\n", + "## An example cell\n", + "\n", + "With MyST Markdown, you can define code cells with a directive like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a17f3ea4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], + "source": [ + "print(2 + 2)" + ] + }, + { + "cell_type": "markdown", + "id": "c2cea87a", + "metadata": {}, + "source": [ + "When your book is built, the contents of any `{code-cell}` blocks will be\n", + "executed with your default Jupyter kernel, and their outputs will be displayed\n", + "in-line with the rest of your content.\n", + "\n", + "```{seealso}\n", + "Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html).\n", + "```\n", + "\n", + "## Create a notebook with MyST Markdown\n", + "\n", + "MyST Markdown notebooks are defined by two things:\n", + "\n", + "1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed).\n", + " See the YAML at the top of this page for example.\n", + "2. The presence of `{code-cell}` directives, which will be executed with your book.\n", + "\n", + "That's all that is needed to get started!\n", + "\n", + "## Quickly add YAML metadata for MyST Notebooks\n", + "\n", + "If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command:\n", + "\n", + "```\n", + "jupyter-book myst init path/to/markdownfile.md\n", + "```" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "md:myst", + "text_representation": { + "extension": ".md", + "format_name": "myst", + "format_version": 0.13, + "jupytext_version": "1.11.5" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "source_map": [ + 13, + 29, + 31 + ] + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/_build/jupyter_execute/_to_be_deleted_later/notebooks.ipynb b/_build/jupyter_execute/_to_be_deleted_later/notebooks.ipynb new file mode 100644 index 00000000..4ec4a31d --- /dev/null +++ b/_build/jupyter_execute/_to_be_deleted_later/notebooks.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Content with notebooks\n", + "\n", + "You can also create content with Jupyter Notebooks. This means that you can include\n", + "code blocks and their outputs in your book.\n", + "\n", + "## Markdown + notebooks\n", + "\n", + "As it is markdown, you can embed images, HTML, etc into your posts!\n", + "\n", + "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", + "\n", + "You can also $add_{math}$ and\n", + "\n", + "$$\n", + "math^{blocks}\n", + "$$\n", + "\n", + "or\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mbox{mean} la_{tex} \\\\ \\\\\n", + "math blocks\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", + "\n", + "## MyST markdown\n", + "\n", + "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", + "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", + "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", + "\n", + "## Code blocks and outputs\n", + "\n", + "Jupyter Book will also embed your code blocks and output in your book.\n", + "For example, here's some sample Matplotlib code:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib import rcParams, cycler\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.ion()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fixing random state for reproducibility\n", + "np.random.seed(19680801)\n", + "\n", + "N = 10\n", + "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", + "data = np.array(data).T\n", + "cmap = plt.cm.coolwarm\n", + "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", + "\n", + "\n", + "from matplotlib.lines import Line2D\n", + "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", + " Line2D([0], [0], color=cmap(.5), lw=4),\n", + " Line2D([0], [0], color=cmap(1.), lw=4)]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "lines = ax.plot(data)\n", + "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a lot more that you can do with outputs (such as including interactive outputs)\n", + "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_build/jupyter_execute/docs/pandas/00_pandas_start.ipynb b/_build/jupyter_execute/docs/pandas/00_pandas_start.ipynb index 39b63483..df518dca 100644 --- a/_build/jupyter_execute/docs/pandas/00_pandas_start.ipynb +++ b/_build/jupyter_execute/docs/pandas/00_pandas_start.ipynb @@ -149,12 +149,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2.2.2)\r\n", - "Requirement already satisfied: numpy>=1.26.0 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2.1.0)\r\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\r\n", - "Requirement already satisfied: pytz>=2020.1 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2024.1)\r\n", - "Requirement already satisfied: tzdata>=2022.7 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from pandas) (2024.1)\r\n", - "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\r\n" + "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.2.2)\r\n", + "Requirement already satisfied: numpy>=1.23.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (1.26.4)\r\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2.8.2)\r\n", + "Requirement already satisfied: pytz>=2020.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2023.3.post1)\r\n", + "Requirement already satisfied: tzdata>=2022.7 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2023.3)\r\n", + "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\r\n" ] } ], @@ -203,7 +203,13 @@ "output_type": "stream", "text": [ "Requirement already satisfied: numpy>=1.23.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (1.26.4)\r\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2.8.2)\r\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2.8.2)\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Requirement already satisfied: pytz>=2020.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2023.3.post1)\r\n", "Requirement already satisfied: tzdata>=2022.7 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2023.3)\r\n", "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\r\n" @@ -254,18 +260,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2.2.2)\r\n" + "Requirement already satisfied: pandas in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (2.2.2)\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy>=1.23.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (1.26.4)\r\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pandas) (2.8.2)\r\n", + "Requirement already satisfied: pytz>=2020.1 in 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b/_to_be_deleted_later/admonition_boxes.ipynb new file mode 100644 index 00000000..fce22ffe --- /dev/null +++ b/_to_be_deleted_later/admonition_boxes.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f84de9e2-aae8-4c1a-8b93-4fac6088acf2", + "metadata": {}, + "source": [ + "# Admonition Boxes" + ] + }, + { + "cell_type": "markdown", + "id": "b17f745e-872a-4c65-a88a-cd41124b065a", + "metadata": {}, + "source": [ + "Jeg har lavet denne side, så man kan se, hvordan forskellige bokse vises i Jupyter Book." + ] + }, + { + "cell_type": "markdown", + "id": "c430cb07-7336-45a3-9596-0a6d09dba339", + "metadata": {}, + "source": [ + "## Common Classes for Admonitions in Jupyter Book\n", + "\n", + "The standard classes available for admonitions in Jupyter Book include but may not be limited to:\n", + "\n", + "- `note`\n", + "- `warning`\n", + "- `tip`\n", + "- `important`\n", + "- `caution`\n", + "- `danger`\n", + "- `error`\n", + "- `hint`\n", + "- `seealso`\n", + "\n", + "These classes typically map to conventional documentation styles, where each type is used to indicate different levels or types of information:\n", + "\n", + "- **note**: General information that is important for the reader.\n", + "- **warning**: A caution about potential problems or pitfalls.\n", + "- **tip**: Advice or insights that might not be immediately obvious.\n", + "- **important**: Critical information that must not be overlooked.\n", + "- **caution**: Similar to warning but often used for less severe issues.\n", + "- **danger**: Indicates a very high risk of serious consequences.\n", + "- **error**: Used to describe errors or incorrect methods.\n", + "- **hint**: Suggestive information that may help in understanding or solving a problem.\n", + "- **seealso**: Used to refer to related information elsewhere.\n", + "\n", + "### Example of an Admonition in Jupyter Book\n", + "\n", + "Here is how you would typically write a `note` type admonition in Jupyter Book:\n", + "\n", + "````\n", + "```{admonition} Take Note\n", + ":class: note\n", + "This is a special note that will help clarify something important.\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "3007742b-6e88-4eb6-9183-a017286caf25", + "metadata": {}, + "source": [ + "## Her følger først markdown-kode til admonition boxes, derefter den renderede version" + ] + }, + { + "cell_type": "markdown", + "id": "154aad4d-ae6a-4416-9f12-bfc9e8a242b2", + "metadata": {}, + "source": [ + "Koden er hentet herfra.\n", + "\n", + "[https://jupyterbook.org/en/stable/reference/cheatsheet.html#admonitions](https://jupyterbook.org/en/stable/reference/cheatsheet.html#admonitions)" + ] + }, + { + "cell_type": "markdown", + "id": "680184d3-d00c-46cb-902e-ba9b9c614402", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} This is a title\n", + "An example of an admonition with a title.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "10bf4879-4216-4c80-9793-aa85199994fe", + "metadata": {}, + "source": [ + "\n", + "```{admonition} This is a title\n", + "An example of an admonition with a title.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "782a2bde-d29a-4ca5-bb0d-150bdd9e8aa3", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "49754f7d-06f2-4697-8451-9649b8e12ef3", + "metadata": {}, + "source": [ + "````\n", + "```{note} Notes require **no** arguments,\n", + "so content can start here.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "66311383-2944-416e-b8c5-a1d0b5fa1da9", + "metadata": {}, + "source": [ + "```{note} Notes require **no** arguments,\n", + "so content can start here.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "28014987-1386-45b9-b32d-7ebf926411ca", + "metadata": {}, + "source": [ + "````\n", + "```{warning} This is an exmample\n", + "of a warning directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "f4ca4290-05ec-4eb5-885e-8d284500f554", + "metadata": {}, + "source": [ + "```{warning} This is an example\n", + "of a warning directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "4d6acb5b-5e17-4610-8ba9-dd76c3b8598f", + "metadata": {}, + "source": [ + "````\n", + "```{tip} This is an example\n", + "of a tip directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "80041c05-6c99-4a1f-afbe-a9b9f4edb130", + "metadata": {}, + "source": [ + "```{tip} This is an example\n", + "of a tip directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a80fdca7-5b2c-4058-89f7-37021409daa8", + "metadata": {}, + "source": [ + "````\n", + "```{caution} This is an example\n", + "of a caution directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "ff2ef0ba-243b-4936-9ca4-3e7689fdc87b", + "metadata": {}, + "source": [ + "```{caution} This is an example\n", + "of a caution directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "2af1f48c-db08-4fcf-a80d-e5e048480a76", + "metadata": {}, + "source": [ + "````\n", + "```{attention} This is an example\n", + "of an attention directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "5e8f2a75-f629-4a32-ba5f-e74002aeb468", + "metadata": {}, + "source": [ + "```{attention} This is an example\n", + "of an attention directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "b9782607-740b-4c4a-bb65-bd60cc362119", + "metadata": {}, + "source": [ + "````\n", + "```{danger} This is an example\n", + "of a danger directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "c80153a3-b306-4ff5-afc2-b2d7065fd946", + "metadata": {}, + "source": [ + "```{danger} This is an example\n", + "of a danger directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1079eb56-c31d-4c73-af0f-3589ab241f51", + "metadata": {}, + "source": [ + "````\n", + "```{error} This is an example\n", + "of an error directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "142b8549-aee4-4821-a134-1f5a5c991705", + "metadata": {}, + "source": [ + "```{error} This is an example\n", + "of an error directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e5b625f2-2e5a-4737-9bf7-2abf947ab807", + "metadata": {}, + "source": [ + "````\n", + "```{hint} This is an example\n", + "of a hint directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "7fa11f46-b4be-4100-b412-d0470f0ab39a", + "metadata": {}, + "source": [ + "```{hint} This is an example\n", + "of a hint directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "17f38c8c-fe80-4d21-b761-a793f5878b87", + "metadata": {}, + "source": [ + "````\n", + "```{important} This is an example\n", + "of an important directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "1ecda7df-a97f-495f-bc8c-fb43c2d27882", + "metadata": {}, + "source": [ + "```{important} This is an example\n", + "of an important directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "439e4cee-b391-4a76-bfe3-775f62980087", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} This is a title\n", + ":class: warning\n", + "An example of an admonition with a title and a warning style.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "43390e67-8944-477d-b910-b69cf7ec8ed0", + "metadata": {}, + "source": [ + "```{admonition} This is a title\n", + ":class: warning\n", + "An example of an admonition with a title and a warning style.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e28308a9-2e28-4c48-9522-2a1edeb256c3", + "metadata": {}, + "source": [ + "## Alt herunder er tests fra ting fundet andetsteds" + ] + }, + { + "cell_type": "markdown", + "id": "ab3092d6-636e-4a06-9959-70d4ec709d9f", + "metadata": {}, + "source": [ + "````\n", + ":::{seealso}\n", + "For a list of syntax extensions in MyST, see [the MyST documentation](https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html).\n", + ":::\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "d5e64f68-cf89-4948-8669-b19f1e28b885", + "metadata": {}, + "source": [ + ":::{seealso}\n", + "For a list of syntax extensions in MyST, see [the MyST documentation](https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html).\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "8b021094-ef88-43a7-8b06-a9a79426341d", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "bc20e078-3343-4641-b873-ef101b92329b", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8775a5fd-3b8a-469b-a5be-9c91a55927e7", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: todo\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "95b68d16-6662-434b-b922-fb93b2c55987", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: todo\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6be16355-dbf5-4a8b-adc5-8c860758a971", + "metadata": {}, + "source": [ + "````\n", + "```{todo} This is an example\n", + "of a todo directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "3c75e1a4-34ee-49ba-b131-0aa25e741800", + "metadata": {}, + "source": [ + "```{todo} This is an example\n", + "of a todo directive.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "1f174572-9150-46ef-9fd8-42db2b3440e9", + "metadata": {}, + "source": [ + "````\n", + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "2e644b25-107e-4a8f-a8b1-42e78ae98540", + "metadata": {}, + "source": [ + "```{admonition} My title\n", + ":class: seealso\n", + "My content\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "76a45eb2-01b2-48b1-bdc4-57bdc7078d18", + "metadata": {}, + "source": [ + "````\n", + "```{seealso} This is an example\n", + "of a seealso directive.\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "cf4a3a2a-ec59-4fe7-87b8-289754835278", + "metadata": {}, + "source": [ + "```{seealso} This is an example\n", + "of a seealso directive.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b407312c-65b7-4f51-836d-198a697249ac", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/to_be_deleted_later/datalab_logo.png b/_to_be_deleted_later/datalab_logo.png similarity index 100% rename from to_be_deleted_later/datalab_logo.png rename to _to_be_deleted_later/datalab_logo.png diff --git a/_to_be_deleted_later/intro_old.md b/_to_be_deleted_later/intro_old.md new file mode 100644 index 00000000..fdd76b60 --- /dev/null +++ b/_to_be_deleted_later/intro_old.md @@ -0,0 +1,30 @@ +# Welcome + +Welcome to KUB Datalab's Python ressources. + +The material you have in front of you is connected to [KUB Datalab](https://kub.kb.dk/datalab) at [Copenhagen University Library](https://kub.ku.dk/english/). We currently offer a two-part introduction to Python for absolute beginners as well as an introduction to the library Pandas. + +You can find and sign up for our courses [here](https://kubkalender.kb.dk/calendar/datalab). Before you attend the beginners' class, please make sure to visit the How to... "Install & Run Python" page so we can get the most out of our time together. + +The materials you find in this Jupyter Book are intended to help students learn how to code in Python. All pages can be downloaded as a Jupyter Notebook as well as pdf. That means that you can work with most of the code locally on your computer. + +Below you can see the different elements of our courses. +___ + +## Attribution + +![CC Attribution](https://mirrors.creativecommons.org/presskit/icons/by.png) + +Much of the teaching material presented here has been inspired by, borrowed from, and modified based on the work of others.\ +We extend our deepest thanks to these giants on whose proverbial shoulders we stand and encourage you to visit their pages for more Python inspiration. + +* Library Carpentry's [Python Intro for Libraries ](https://librarycarpentry.org/lc-python-intro/) +* The official [Pandas documentaion](https://pandas.pydata.org/docs/index.html) +* Melanie Walsh's [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/) + +Should we inadvertently have missed acknowledging anyone, please let us know so we can ensure proper recognition. + +___ + +```{tableofcontents} +``` \ No newline at end of file diff --git a/to_be_deleted_later/kub_datalab_logo.jpeg b/_to_be_deleted_later/kub_datalab_logo.jpeg similarity index 100% rename from to_be_deleted_later/kub_datalab_logo.jpeg rename to _to_be_deleted_later/kub_datalab_logo.jpeg diff --git a/to_be_deleted_later/logo.jpg b/_to_be_deleted_later/logo.jpg similarity index 100% rename from to_be_deleted_later/logo.jpg rename to _to_be_deleted_later/logo.jpg diff --git a/_to_be_deleted_later/markdown-notebooks.md b/_to_be_deleted_later/markdown-notebooks.md new file mode 100644 index 00000000..7ae0c7ae --- /dev/null +++ b/_to_be_deleted_later/markdown-notebooks.md @@ -0,0 +1,57 @@ +--- +jupytext: + formats: md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.11.5 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Notebooks with MyST Markdown + +```{contents} +:local: +``` + +Jupyter Book also lets you write text-based notebooks using MyST Markdown. +See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. +This page shows off a notebook written in MyST Markdown. + +## An example cell + +With MyST Markdown, you can define code cells with a directive like so: + +```{code-cell} +print(2 + 2) +``` + +When your book is built, the contents of any `{code-cell}` blocks will be +executed with your default Jupyter kernel, and their outputs will be displayed +in-line with the rest of your content. + +```{seealso} +Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). +``` + +## Create a notebook with MyST Markdown + +MyST Markdown notebooks are defined by two things: + +1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). + See the YAML at the top of this page for example. +2. The presence of `{code-cell}` directives, which will be executed with your book. + +That's all that is needed to get started! + +## Quickly add YAML metadata for MyST Notebooks + +If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: + +``` +jupyter-book myst init path/to/markdownfile.md +``` diff --git a/_to_be_deleted_later/markdown.md b/_to_be_deleted_later/markdown.md new file mode 100644 index 00000000..faeea606 --- /dev/null +++ b/_to_be_deleted_later/markdown.md @@ -0,0 +1,55 @@ +# Markdown Files + +Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or +in regular markdown files (`.md`), you'll write in the same flavor of markdown +called **MyST Markdown**. +This is a simple file to help you get started and show off some syntax. + +## What is MyST? + +MyST stands for "Markedly Structured Text". It +is a slight variation on a flavor of markdown called "CommonMark" markdown, +with small syntax extensions to allow you to write **roles** and **directives** +in the Sphinx ecosystem. + +For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). + +## Sample Roles and Directives + +Roles and directives are two of the most powerful tools in Jupyter Book. They +are like functions, but written in a markup language. They both +serve a similar purpose, but **roles are written in one line**, whereas +**directives span many lines**. They both accept different kinds of inputs, +and what they do with those inputs depends on the specific role or directive +that is being called. + +Here is a "note" directive: + +```{note} +Here is a note +``` + +It will be rendered in a special box when you build your book. + +Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. + + +## Citations + +You can also cite references that are stored in a `bibtex` file. For example, +the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like +this: {cite}`holdgraf_evidence_2014`. + +Moreover, you can insert a bibliography into your page with this syntax: +The `{bibliography}` directive must be used for all the `{cite}` roles to +render properly. +For example, if the references for your book are stored in `references.bib`, +then the bibliography is inserted with: + +```{bibliography} +``` + +## Learn more + +This is just a simple starter to get you started. +You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). diff --git a/_to_be_deleted_later/notebooks.ipynb b/_to_be_deleted_later/notebooks.ipynb new file mode 100644 index 00000000..fdb7176c --- /dev/null +++ b/_to_be_deleted_later/notebooks.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Content with notebooks\n", + "\n", + "You can also create content with Jupyter Notebooks. This means that you can include\n", + "code blocks and their outputs in your book.\n", + "\n", + "## Markdown + notebooks\n", + "\n", + "As it is markdown, you can embed images, HTML, etc into your posts!\n", + "\n", + "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", + "\n", + "You can also $add_{math}$ and\n", + "\n", + "$$\n", + "math^{blocks}\n", + "$$\n", + "\n", + "or\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mbox{mean} la_{tex} \\\\ \\\\\n", + "math blocks\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", + "\n", + "## MyST markdown\n", + "\n", + "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", + "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", + "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", + "\n", + "## Code blocks and outputs\n", + "\n", + "Jupyter Book will also embed your code blocks and output in your book.\n", + "For example, here's some sample Matplotlib code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import rcParams, cycler\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.ion()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fixing random state for reproducibility\n", + "np.random.seed(19680801)\n", + "\n", + "N = 10\n", + "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", + "data = np.array(data).T\n", + "cmap = plt.cm.coolwarm\n", + "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", + "\n", + "\n", + "from matplotlib.lines import Line2D\n", + "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", + " Line2D([0], [0], color=cmap(.5), lw=4),\n", + " Line2D([0], [0], color=cmap(1.), lw=4)]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "lines = ax.plot(data)\n", + "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a lot more that you can do with outputs (such as including interactive outputs)\n", + "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/pandas/titanic.xlsx b/docs/pandas/titanic.xlsx index 1b78a906..e61292ef 100644 Binary files a/docs/pandas/titanic.xlsx and b/docs/pandas/titanic.xlsx differ