#!/usr/bin/env python
# coding: utf-8

# # Fastpages Notebook Blog Post
# > A tutorial of fastpages for Jupyter notebooks.
# 
# - toc: true 
# - badges: true
# - comments: true
# - categories: [jupyter]
# - hide: true
# - search_exclude: true
# - image: images/chart-preview.png

# # About
# 
# This notebook is a demonstration of some of capabilities of [fastpages](https://github.com/fastai/fastpages) with notebooks.
# 
# 
# With `fastpages` you can save your jupyter notebooks into the `_notebooks` folder at the root of your repository, and they will be automatically be converted to Jekyll compliant blog posts!
# 

# ## Front Matter
# 
# The first cell in your Jupyter Notebook or markdown blog post contains front matter. Front matter is metadata that can turn on/off options in your Notebook. It is formatted like this:
# 
# ```
# # "My Title"
# > "Awesome summary"
# 
# - toc: true
# - branch: master
# - badges: true
# - comments: true
# - author: Hamel Husain & Jeremy Howard
# - categories: [fastpages, jupyter]
# ```
# 
# - Setting `toc: true` will automatically generate a table of contents
# - Setting `badges: true` will automatically include GitHub and Google Colab links to your notebook.
# - Setting `comments: true` will enable commenting on your blog post, powered by [utterances](https://github.com/utterance/utterances).
# 
# The title and description need to be enclosed in double quotes only if they include special characters such as a colon. More details and options for front matter can be viewed on the [front matter section](https://github.com/fastai/fastpages#front-matter-related-options) of the README.

# ## Markdown Shortcuts

# A `#hide` comment at the top of any code cell will hide **both the input and output** of that cell in your blog post.
# 
# A `#hide_input` comment at the top of any code cell will **only hide the input** of that cell.

# In[1]:


#hide_input
print('The comment #hide_input was used to hide the code that produced this.')


# put a `#collapse-hide` flag at the top of any cell if you want to **hide** that cell by default, but give the reader the option to show it:

# In[2]:


#collapse-hide
import pandas as pd
import altair as alt


# put a `#collapse-show` flag at the top of any cell if you want to **show** that cell by default, but give the reader the option to hide it:

# In[3]:


#collapse-show
cars = 'https://vega.github.io/vega-datasets/data/cars.json'
movies = 'https://vega.github.io/vega-datasets/data/movies.json'
sp500 = 'https://vega.github.io/vega-datasets/data/sp500.csv'
stocks = 'https://vega.github.io/vega-datasets/data/stocks.csv'
flights = 'https://vega.github.io/vega-datasets/data/flights-5k.json'


# place a `#collapse-output` flag at the top of any cell if you want to put the output under a collapsable element that is closed by default, but give the reader the option to open it:

# In[4]:


#collapse-output
print('The comment #collapse-output was used to collapse the output of this cell by default but you can expand it.')


# ## Interactive Charts With Altair
# 
# Charts made with Altair remain interactive.  Example charts taken from [this repo](https://github.com/uwdata/visualization-curriculum), specifically [this notebook](https://github.com/uwdata/visualization-curriculum/blob/master/altair_interaction.ipynb).

# In[5]:


# hide
df = pd.read_json(movies) # load movies data
df.columns = [x.replace(' ', '_') for x in df.columns.values]
genres = df['Major_Genre'].unique() # get unique field values
genres = list(filter(lambda d: d is not None, genres)) # filter out None values
genres.sort() # sort alphabetically


# In[6]:


#hide
mpaa = ['G', 'PG', 'PG-13', 'R', 'NC-17', 'Not Rated']


# ### Example 1: DropDown

# In[7]:


# single-value selection over [Major_Genre, MPAA_Rating] pairs
# use specific hard-wired values as the initial selected values
selection = alt.selection_single(
    name='Select',
    fields=['Major_Genre', 'MPAA_Rating'],
    init={'Major_Genre': 'Drama', 'MPAA_Rating': 'R'},
    bind={'Major_Genre': alt.binding_select(options=genres), 'MPAA_Rating': alt.binding_radio(options=mpaa)}
)
  
# scatter plot, modify opacity based on selection
alt.Chart(df).mark_circle().add_selection(
    selection
).encode(
    x='Rotten_Tomatoes_Rating:Q',
    y='IMDB_Rating:Q',
    tooltip='Title:N',
    opacity=alt.condition(selection, alt.value(0.75), alt.value(0.05))
)


# ### Example 2: Tooltips

# In[8]:


alt.Chart(df).mark_circle().add_selection(
    alt.selection_interval(bind='scales', encodings=['x'])
).encode(
    alt.X('Rotten_Tomatoes_Rating', type='quantitative'),
    alt.Y('IMDB_Rating', type='quantitative', axis=alt.Axis(minExtent=30)),
#     y=alt.Y('IMDB_Rating:Q', ), # use min extent to stabilize axis title placement
    tooltip=['Title:N', 'Release_Date:N', 'IMDB_Rating:Q', 'Rotten_Tomatoes_Rating:Q']
).properties(
    width=500,
    height=400
)


# ### Example 3: More Tooltips

# In[9]:


# select a point for which to provide details-on-demand
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('date:T'),
    alt.Y('price:Q', scale=alt.Scale(type='log')),
    alt.Color('symbol:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='date:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='price:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='price:Q'
    ).transform_filter(label),
    
    data=stocks
).properties(
    width=500,
    height=400
)


# ## Data Tables
# 
# You can display tables per the usual way in your blog:

# In[10]:


# display table with pandas
df[['Title', 'Worldwide_Gross', 
    'Production_Budget', 'Distributor', 'MPAA_Rating', 'IMDB_Rating', 'Rotten_Tomatoes_Rating']].head()


# ## Images
# 
# ### Local Images
# 
# You can reference local images and they will be copied and rendered on your blog automatically.  You can include these with the following markdown syntax:
# 
# `![](my_icons/fastai_logo.png)`

# ![](my_icons/fastai_logo.png)

# ### Remote Images
# 
# Remote images can be included with the following markdown syntax:
# 
# `![](https://image.flaticon.com/icons/svg/36/36686.svg)`

# ![](https://image.flaticon.com/icons/svg/36/36686.svg)

# ### Animated Gifs
# 
# Animated Gifs work, too!
# 
# `![](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif)`

# ![](https://upload.wikimedia.org/wikipedia/commons/7/71/ChessPawnSpecialMoves.gif)

# ### Captions
# 
# You can include captions with markdown images like this:
# 
# ```
# ![](https://www.fast.ai/images/fastai_paper/show_batch.png "Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/")
# ```
# 
# 
# ![](https://www.fast.ai/images/fastai_paper/show_batch.png "Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/")
# 
# 
# 
# 

# # Other Elements

# ## GitHub Flavored Emojis
# 
# Typing `I give this post two :+1:!` will render this:
# 
# I give this post two :+1:!

# ## Tweetcards
# 
# Typing `> twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20` will render this:
# 
# > twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20

# ## Youtube Videos
# 
# Typing `> youtube: https://youtu.be/XfoYk_Z5AkI` will render this:
# 
# 
# > youtube: https://youtu.be/XfoYk_Z5AkI

# ## Boxes / Callouts 
# 
# Typing `> Warning: There will be no second warning!` will render this:
# 
# 
# > Warning: There will be no second warning!
# 
# 
# 
# Typing `> Important: Pay attention! It's important.` will render this:
# 
# > Important: Pay attention! It's important.
# 
# 
# 
# Typing `> Tip: This is my tip.` will render this:
# 
# > Tip: This is my tip.
# 
# 
# 
# Typing `> Note: Take note of this.` will render this:
# 
# > Note: Take note of this.
# 
# 
# 
# Typing `> Note: A doc link to [an example website: fast.ai](https://www.fast.ai/) should also work fine.` will render in the docs:
# 
# > Note: A doc link to [an example website: fast.ai](https://www.fast.ai/) should also work fine.

# ## Footnotes
# 
# You can have footnotes in notebooks, however the syntax is different compared to markdown documents. [This guide provides more detail about this syntax](https://github.com/fastai/fastpages/blob/master/_fastpages_docs/NOTEBOOK_FOOTNOTES.md), which looks like this:
# 
# ```
# {% raw %}For example, here is a footnote {% fn 1 %}.
# And another {% fn 2 %}
# {{ 'This is the footnote.' | fndetail: 1 }}
# {{ 'This is the other footnote. You can even have a [link](www.github.com)!' | fndetail: 2 }}{% endraw %}
# ```
# 
# For example, here is a footnote {% fn 1 %}.
# 
# And another {% fn 2 %}
# 
# {{ 'This is the footnote.' | fndetail: 1 }}
# {{ 'This is the other footnote. You can even have a [link](www.github.com)!' | fndetail: 2 }}