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

# 
# ***
# ***
# # Graphlab 安装与使用
# ***
# ***
# 
# 王成军
# 
# wangchengjun@nju.edu.cn
# 
# 计算传播网 http://computational-communication.com

# # Problem
# 
# 只有低版本的anaconda才可以安装，强行安装还会破坏掉anaconda的jupyter notebook中的kernel，排除使用anaconda运行graphlab的方案。
# 

# # Register for Academic Use of GraphLab Create
# 
# https://turi.com/download/academic.html

# ## 查邮件
# 
# https://turi.com/download/install-graphlab-create.html?email=wangchengjun%40nju.edu.cn&key=4972-65DF-8E02-816C-AB15-021C-EC1B-0367&utm_medium=email&utm_source=transactional&utm_campaign=beta_registration_confirmation

# # Renew Academic License for GraphLab Create
# 
# https://turi.com/download/renew.html

# ### License Renewal Confirmation
# Your academic license for GraphLab Create has been renewed. Please restart GraphLab Create while connected to the internet.
# 
# 
# Email: wangchengjun@nju.edu.cn
# 
# Expiration Date: 03-13-2019

# # Python 2.7.x
# 
# GraphLab Create installation requires a Python 2.7.x environment and pip version >= 7 and Anaconda2 v4.0.0 (64-bit). IPython Notebook is recommended.

# 
# # To install a different version of Python without overwriting the current version
# 
# https://conda.io/docs/user-guide/tasks/manage-python.html
# 
# Creating a new environment and install the second Python version into it. To create the new environment for Python 2.7, in your Terminal window or an Anaconda Prompt, run:
# > conda create -n py27 python=2.7 anaconda
# 

# 
# ## Activate the new environment ** 切换到新环境**
# 
# - linux/Mac下需要使用: `source activate py27`
# - windows需要使用: `activate py27`
# 
# **退出环境: `source deactivate py27`
# 也可以使用** `activate root`切回root环境
# 
# 3. [Verify that the new environment is your current environment.](https://conda.io/docs/user-guide/tasks/manage-environments.html#determine-current-env)
# 4. To verify that the current environment uses the new Python version, in your Terminal window or an Anaconda Prompt, run: `python --version`

# In[1]:


get_ipython().system(' python --version')


# # Install your licensed copy of GraphLab Create
# pip install --upgrade --no-cache-dir https://get.graphlab.com/GraphLab-Create/2.1/your registered email address here/your product key here/GraphLab-Create-License.tar.gz

# # Open a terminal and input:
# 
# pip install --upgrade --no-cache-dir https://get.graphlab.com/GraphLab-Create/2.1/wangchengjun@nju.edu.cn/4972-65DF-8E02-816C-AB15-021C-EC1B-0367/GraphLab-Create-License.tar.gz
# 

# # Error 
# Could not find a version that satisfies the requirement graphlab-create>=2.1 (from GraphLab-Create-License==2.1) (from versions: )
# No matching distribution found for graphlab-create>=2.1 (from GraphLab-Create-License==2.1)

# 
# # 使用方法
# 
# https://turi.com/learn/userguide/
# 
# GraphLab Create is a Python package that allows programmers to perform end-to-end large-scale data analysis and data product development.
# 
# - Data ingestion and cleaning with SFrames. SFrame is an efficient disk-based tabular data structure that is not limited by RAM. This lets you scale your analysis and data processing to handle terabytes of data, even on your laptop.
# 
# - Data exploration and visualization with GraphLab Canvas. GraphLab Canvas is a browser-based interactive GUI that allows you to explore tabular data, summary plots and statistics.
# 
# - Network analysis with SGraph. SGraph is a disk-based graph data structure that stores vertices and edges in SFrames.
# 
# - Predictive model development with machine learning toolkits. GraphLab Create includes several toolkits for quick prototyping with fast, scalable algorithms.
# 
# - Production automation with data pipelines. Data pipelines allow you to assemble reusable code tasks into jobs and automatically run them on common execution environments (e.g. Amazon Web Services, Hadoop).

# In[2]:


from graphlab import SGraph, Vertex, Edge
g = SGraph()
verts = [Vertex(0, attr={'breed': 'labrador'}),
         Vertex(1, attr={'breed': 'labrador'}),
         Vertex(2, attr={'breed': 'vizsla'})]
g = g.add_vertices(verts)
g = g.add_edges(Edge(1, 2))
print(g)


# In[3]:


from graphlab import SGraph, Vertex, Edge
g = SGraph()
verts = [Vertex(0, attr={'breed': 'labrador'}),
         Vertex(1, attr={'breed': 'labrador'}),
         Vertex(2, attr={'breed': 'vizsla'})]
g = g.add_vertices(verts)
g = g.add_edges(Edge(1, 2))
print g


# In[4]:


g.show()


# In[5]:


from graphlab import SFrame,SGraph
edge_data = SFrame.read_csv('../data/bond_edges.csv')
    #'https://static.turi.com/datasets/bond/bond_edges.csv')

g = SGraph()
g = g.add_edges(edge_data, src_field='src', dst_field='dst')
print(g)


# In[3]:


vertex_data = SFrame.read_csv('https://static.turi.com/datasets/bond/bond_vertices.csv')

g = SGraph(vertices=vertex_data, edges=edge_data, vid_field='name',
           src_field='src', dst_field='dst')


# In[6]:


g.show(vlabel='id', highlight=['James Bond', 'Moneypenny'], \
       arrows=True)


# # 阅读材料
# - https://turi.com/learn/userguide/
#