Writing with Markdown Grammar

这是主题正文演示的源码，注意部分地方不是标准的 Markdown 语法，可酌情使用。WordPress 原生对 markdown 不是太友好，建议借助 WP Editor.md 插件在 WordPress 上使用 Markdwon 语法写作。

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# h1 Heading
## h2 Heading
### h3 Heading
#### h4 Heading
##### h5 Heading
###### h6 Heading


## Horizontal Rules

___

---

***


## Typographic replacements

Enable typographer option to see result.

(c) (C) (r) (R) (tm) (TM) (p) (P) +-

test.. test... test..... test?..... test!....

!!!!!! ???? ,,  -- ---

"Smartypants, double quotes" and 'single quotes'


## Emphasis

**This is bold text**

__This is bold text__

*This is italic text*

_This is italic text_

~~Strikethrough~~


## Blockquotes


> Blockquotes can also be nested...
>> ...by using additional greater-than signs right next to each other...
> > > ...or with spaces between arrows.


## Lists

Unordered

+ Create a list by starting a line with `+`, `-`, or `*`
+ Sub-lists are made by indenting 2 spaces:
  - Marker character change forces new list start:
    * Ac tristique libero volutpat at
    + Facilisis in pretium nisl aliquet
    - Nulla volutpat aliquam velit
+ Very easy!

Ordered

1. Lorem ipsum dolor sit amet
2. Consectetur adipiscing elit
3. Integer molestie lorem at massa


1. You can use sequential numbers...
1. ...or keep all the numbers as `1.`

Start numbering with offset:

57. foo
1. bar


## Code

Inline `code`

Indented code

    // Some comments
    line 1 of code
    line 2 of code
    line 3 of code


Block code "fences"

Sample text here...


Syntax highlighting

``` js
var foo = function (bar) {
  return bar++;
};

console.log(foo(5));

Tables

Option	Description
data	path to data files to supply the data that will be passed into templates.
engine	engine to be used for processing templates. Handlebars is the default.
ext	extension to be used for dest files.

Right aligned columns

Option	Description
data	path to data files to supply the data that will be passed into templates.
engine	engine to be used for processing templates. Handlebars is the default.
ext	extension to be used for dest files.

Links

link text

link with title

Autoconverted link https://github.com/nodeca/pica (enable linkify to see)

Images

Minion Stormtroopocat

Like links, Images also have a footnote style syntax

Alt text

With a reference later in the document defining the URL location:

Plugins

The killer feature of markdown-it is very effective support of syntax plugins.

Emojies

Classic markup: 😉 :crush: 😢 :tear: 😆 😋

Shortcuts (emoticons): :-) :-( 8-) ;)

see how to change output with twemoji.

Subscript / Superscript

19^th^
H2O

<ins>

++Inserted text++

<mark>

==Marked text==

Footnotes

Footnote 1 link[^first].

Footnote 2 link[^second].

Inline footnote^[Text of inline footnote] definition.

Duplicated footnote reference[^second].

[^first]: Footnote can have markup

and multiple paragraphs.

[^second]: Footnote text.

Definition lists

Term 1

: Definition 1 with lazy continuation.

Term 2 with inline markup

: Definition 2

    { some code, part of Definition 2 }

Third paragraph of definition 2.

Compact style:

Term 1 ~ Definition 1

Term 2 ~ Definition 2a ~ Definition 2b

Abbreviations

This is HTML abbreviation example.

It converts "HTML", but keep intact partial entries like "xxxHTMLyyy" and so on.

*[HTML]: Hyper Text Markup Language

Custom containers

::: warning here be dragons :::

Code

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn

# Load the data
oecd_bli = pd.read_csv("oecd_bli_2015.csv", thousands=',')
gdp_per_capita = pd.read_csv("gdp_per_capita.csv",thousands=',',delimiter='\t',
                            encoding='latin1', na_values="n/a")

# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]

# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()

# Select a linear model
lin_reg_model = sklearn.linear_model.LinearRegression()

# Train the model
lin_reg_model.fit(X, y)

# Make a prediction for Cyprus
X_new = [[22587]]  # Cyprus' GDP per capita
print(lin_reg_model.predict(X_new)) # outputs [[ 5.96242338]]

Image Gallery

Quotation

We are all in the gutter, but some of us are looking at the stars.

Aplayer Support

[hermit autoplay="false" mode="circulation" preload="auto"]netease_playlist#:3319320[/hermit]

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