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

# #### New to Plotly?
# Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
# <br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).
# <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
# #### Version Check
# Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version.

# In[1]:


import plotly 
plotly.__version__


# #### Plotly's Figure Factory Module
# Plotly's Python API contains a figure factory module which includes many wrapper functions that create unique chart types that are not yet included in [plotly.js](https://github.com/plotly/plotly.js), Plotly's open-source graphing library. The figure factory functions create a full figure, so some Plotly features, such as subplotting, should be implemented slightly differently with these charts.

# In[2]:


import plotly.figure_factory
help(plotly.figure_factory)


# #### Vertical Figure Factory Charts
# First create the figures that you'd like to appear in the subplot:

# In[3]:


import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go

import numpy as np

## Create first plot
x1,y1 = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))
u1 = np.cos(x1)*y1
v1 = np.sin(x1)*y1

fig1 = ff.create_quiver(x1, y1, u1, v1, name='Quiver')


## Create second plot
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
Y, X = np.meshgrid(x, y)
u = -1 - X**2 + Y
v = 1 + X - Y**2

fig2 = ff.create_streamline(x, y, u, v, arrow_scale=.1, name='Steamline')


# Edit the figures' x and y axes attributes to create subplots:

# In[4]:


for i in range(len(fig1.data)):
    fig1.data[i].xaxis='x1'
    fig1.data[i].yaxis='y1'

fig1.layout.xaxis1.update({'anchor': 'y1'})
fig1.layout.yaxis1.update({'anchor': 'x1', 'domain': [.55, 1]})

for i in range(len(fig2.data)):
    fig2.data[i].xaxis='x2'
    fig2.data[i].yaxis='y2'

# initialize xaxis2 and yaxis2
fig2['layout']['xaxis2'] = {}
fig2['layout']['yaxis2'] = {}

fig2.layout.xaxis2.update({'anchor': 'y2'})
fig2.layout.yaxis2.update({'anchor': 'x2', 'domain': [0, .45]})


# Combine the data and layout objects to create a figure

# In[5]:


fig = go.Figure()
fig.add_traces([fig1.data[0], fig2.data[0]])

fig.layout.update(fig1.layout)
fig.layout.update(fig2.layout)

py.iplot(fig, filename='figure_factory_subplot')


# #### Horizontal Table and Chart

# In[6]:


import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff

table_data = [['Team', 'Wins', 'Losses', 'Ties'],
              ['Montréal<br>Canadiens', 18, 4, 0],
              ['Dallas Stars', 18, 5, 0],
              ['NY Rangers', 16, 5, 0], 
              ['Boston<br>Bruins', 13, 8, 0],
              ['Chicago<br>Blackhawks', 13, 8, 0],
              ['LA Kings', 13, 8, 0],
              ['Ottawa<br>Senators', 12, 5, 0]]

figure = ff.create_table(table_data, height_constant=60)

teams = ['Montréal Canadiens', 'Dallas Stars', 'NY Rangers',
         'Boston Bruins', 'Chicago Blackhawks', 'LA Kings', 'Ottawa Senators']
GFPG = [3.54, 3.48, 3.0, 3.27, 2.83, 2.45, 3.18]
GAPG = [2.17, 2.57, 2.0, 2.91, 2.57, 2.14, 2.77]

trace1 = go.Scatter(x=teams, y=GFPG,
                    marker=dict(color='#0099ff'),
                    name='Goals For<br>Per Game',
                    xaxis='x2', yaxis='y2')
trace2 = go.Scatter(x=teams, y=GAPG,
                    marker=dict(color='#404040'),
                    name='Goals Against<br>Per Game',
                    xaxis='x2', yaxis='y2')

figure.add_traces([trace1, trace2])

# initialize xaxis2 and yaxis2
figure['layout']['xaxis2'] = {}
figure['layout']['yaxis2'] = {}

# Edit layout for subplots
figure.layout.xaxis.update({'domain': [0, .5]})
figure.layout.xaxis2.update({'domain': [0.6, 1.]})

# The graph's yaxis MUST BE anchored to the graph's xaxis
figure.layout.yaxis2.update({'anchor': 'x2'})
figure.layout.yaxis2.update({'title': 'Goals'})

# Update the margins to add a title and see graph x-labels. 
figure.layout.margin.update({'t':50, 'b':100})
figure.layout.update({'title': '2016 Hockey Stats'})

py.iplot(figure, filename='subplot_table')


# #### Vertical Table and Chart

# In[7]:


import plotly.plotly as py
import plotly.graph_objs as go
import plotly.figure_factory as ff

# Add table data
table_data = [['Team', 'Wins', 'Losses', 'Ties'],
              ['Montréal<br>Canadiens', 18, 4, 0],
              ['Dallas Stars', 18, 5, 0],
              ['NY Rangers', 16, 5, 0], 
              ['Boston<br>Bruins', 13, 8, 0],
              ['Chicago<br>Blackhawks', 13, 8, 0],
              ['Ottawa<br>Senators', 12, 5, 0]]

# Initialize a figure with ff.create_table(table_data)
figure = ff.create_table(table_data, height_constant=60)

# Add graph data
teams = ['Montréal Canadiens', 'Dallas Stars', 'NY Rangers',
         'Boston Bruins', 'Chicago Blackhawks', 'Ottawa Senators']
GFPG = [3.54, 3.48, 3.0, 3.27, 2.83, 3.18]
GAPG = [2.17, 2.57, 2.0, 2.91, 2.57, 2.77]

# Make traces for graph
trace1 = go.Bar(x=teams, y=GFPG, xaxis='x2', yaxis='y2',
                marker=dict(color='#0099ff'),
                name='Goals For<br>Per Game')
trace2 = go.Bar(x=teams, y=GAPG, xaxis='x2', yaxis='y2',
                marker=dict(color='#404040'),
                name='Goals Against<br>Per Game')

# Add trace data to figure
figure.add_traces([trace1, trace2])

# initialize xaxis2 and yaxis2
figure['layout']['xaxis2'] = {}
figure['layout']['yaxis2'] = {}

# Edit layout for subplots
figure.layout.yaxis.update({'domain': [0, .45]})
figure.layout.yaxis2.update({'domain': [.6, 1]})

# The graph's yaxis2 MUST BE anchored to the graph's xaxis2 and vice versa
figure.layout.yaxis2.update({'anchor': 'x2'})
figure.layout.xaxis2.update({'anchor': 'y2'})
figure.layout.yaxis2.update({'title': 'Goals'})

# Update the margins to add a title and see graph x-labels. 
figure.layout.margin.update({'t':75, 'l':50})
figure.layout.update({'title': '2016 Hockey Stats'})

# Update the height because adding a graph vertically will interact with
# the plot height calculated for the table
figure.layout.update({'height':800})

# Plot!
py.iplot(figure, filename='subplot_table_vertical')


# #### Reference
# See https://plotly.com/python/reference/ for more information regarding chart attributes!

# In[1]:


from IPython.display import display, HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

#!pip install git+https://github.com/plotly/publisher.git --upgrade
    
import publisher
publisher.publish(
    'ff-subplots.ipynb', 'python/figure-factory-subplots/', 'Python Figure Factory Subplots | plotly',
    'Subplots with Plotly Figure Factory Charts',
    title= 'Figure Factory Subplots in Python | plotly',
    name = 'Figure Factory Subplots',
    has_thumbnail='true', thumbnail='thumbnail/ff-subplots.jpg',
    language='python', 
    display_as='multiple_axes', order=10,
    ipynb= '~PythonPlotBot/1828')


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