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

# # Fastpages Notebook Blog Post
# > A demonstration of fastpages blog posts with Jupyter notebooks.
# 
# - toc: true 
# - badges: true
# - comments: true

# # About
# 
# This notebook is a demonstration of some of capabilities of `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
# 
# Front Matter is a markdown cell at the beginning of your notebook that allows you to inject metadata into your notebook. For example:
# 
# - 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).
# 
# More details and options for front matter can be viewed on the notebook section of the [README](https://github.com/fastai/fastpages#Writing-Blog-Posts-With-Jupyter).

# ## Markdown Shortcuts

# put a `#hide` flag at the top of any cell you want to completely hide in the docs

# put a `#collapse` flag at the top of any cell if you want to hide that cell by default, but stil have it be visible to the reader:

# In[3]:


#collapse
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[1]:


#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'


# ## 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[3]:


# hide
df = pd.read_json(movies) # load movies data
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[4]:


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


# ### Example 1: DropDown

# In[5]:


# 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(movies).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[7]:


alt.Chart(movies).mark_circle().add_selection(
    alt.selection_interval(bind='scales', encodings=['x'])
).encode(
    x='Rotten_Tomatoes_Rating:Q',
    y=alt.Y('IMDB_Rating:Q', axis=alt.Axis(minExtent=30)), # use min extent to stabilize axis title placement
    tooltip=['Title:N', 'Release_Date:N', 'IMDB_Rating:Q', 'Rotten_Tomatoes_Rating:Q']
).properties(
    width=600,
    height=400
)


# ### Example 3: More Tooltips

# In[8]:


# 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=700,
    height=400
)


# ## Other Elements

# ### 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.

# In[ ]: