%matplotlib inline

import json

import numpy as np
import networkx as nx
import requests
from pattern import web
import matplotlib.pyplot as plt

# set some nicer defaults for matplotlib
from matplotlib import rcParams

#these colors come from colorbrewer2.org. Each is an RGB triplet
dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),
                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),
                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),
                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),
                (0.4, 0.6509803921568628, 0.11764705882352941),
                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),
                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),
                (0.4, 0.4, 0.4)]

rcParams['figure.figsize'] = (10, 6)
rcParams['figure.dpi'] = 150
rcParams['axes.color_cycle'] = dark2_colors
rcParams['lines.linewidth'] = 2
rcParams['axes.grid'] = False
rcParams['axes.facecolor'] = 'white'
rcParams['font.size'] = 14
rcParams['patch.edgecolor'] = 'none'

def remove_border(axes=None, top=False, right=False, left=True, bottom=True):
    """
    Minimize chartjunk by stripping out unnecessary plot borders and axis ticks
    
    The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn
    """
    ax = axes or plt.gca()
    ax.spines['top'].set_visible(top)
    ax.spines['right'].set_visible(right)
    ax.spines['left'].set_visible(left)
    ax.spines['bottom'].set_visible(bottom)
    
    #turn off all ticks
    ax.yaxis.set_ticks_position('none')
    ax.xaxis.set_ticks_position('none')
    
    #now re-enable visibles
    if top:
        ax.xaxis.tick_top()
    if bottom:
        ax.xaxis.tick_bottom()
    if left:
        ax.yaxis.tick_left()
    if right:
        ax.yaxis.tick_right()

"""
Function
--------
get_senate_vote

Scrapes a single JSON page for a particular Senate vote, given by the vote number

Parameters
----------
vote : int
   The vote number to fetch
   
Returns
-------
vote : dict
   The JSON-decoded dictionary for that vote
   
Examples
--------
>>> get_senate_vote(11)['bill']
{u'congress': 113,
 u'number': 325,
 u'title': u'A bill to ensure the complete and timely payment of the obligations of the United States Government until May 19, 2013, and for other purposes.',
 u'type': u'hr'}
"""
#your code here


"""
Function
--------
get_all_votes

Scrapes all the Senate votes from http://www.govtrack.us/data/congress/113/votes/2013,
and returns a list of dicts

Parameters
-----------
None

Returns
--------
votes : list of dicts
    List of JSON-parsed dicts for each senate vote
"""
#Your code here


vote_data = get_all_votes()

"""
Function
--------
vote_graph

Parameters
----------
data : list of dicts
    The vote database returned from get_vote_data

Returns
-------
graph : NetworkX Graph object, with the following properties
    1. Each node in the graph is labeled using the `display_name` of a Senator (e.g., 'Lee (R-UT)')
    2. Each node has a `color` attribute set to 'r' for Republicans, 
       'b' for Democrats, and 'k' for Independent/other parties.
    3. The edges between two nodes are weighted by the number of 
       times two senators have cast the same Yea or Nay vote
    4. Each edge also has a `difference` attribute, which is set to `1 / weight`.

Examples
--------
>>> graph = vote_graph(vote_data)
>>> graph.node['Lee (R-UT)']
{'color': 'r'}  # attributes for this senator
>>> len(graph['Lee (R-UT)']) # connections to other senators
101
>>> graph['Lee (R-UT)']['Baldwin (D-WI)']  # edge relationship between Lee and Baldwin
{'difference': 0.02, 'weight': 50}
"""
#Your code here


votes = vote_graph(vote_data)    

#this makes sure draw_spring results are the same at each call
np.random.seed(1)  

color = [votes.node[senator]['color'] for senator in votes.nodes()]

#determine position of each node using a spring layout
pos = nx.spring_layout(votes, iterations=200)

#plot the edges
nx.draw_networkx_edges(votes, pos, alpha = .05)

#plot the nodes
nx.draw_networkx_nodes(votes, pos, node_color=color)

#draw the labels
lbls = nx.draw_networkx_labels(votes, pos, alpha=5, font_size=8)

#coordinate information is meaningless here, so let's remove it
plt.xticks([])
plt.yticks([])
remove_border(left=False, bottom=False)

#Your code here


#Your code here


#your code here


"""
Function
--------
get_senate_bill

Scrape the bill data from a single JSON page, given the bill number

Parameters
-----------
bill : int
   Bill number to fetch
   
Returns
-------
A dict, parsed from the JSON

Examples
--------
>>> bill = get_senate_bill(10)
>>> bill['sponsor']
{u'district': None,
 u'name': u'Reid, Harry',
 u'state': u'NV',
 u'thomas_id': u'00952',
 u'title': u'Sen',
 u'type': u'person'}
>>> bill['short_title']
u'Agriculture Reform, Food, and Jobs Act of 2013'
"""
#your code here


"""
Function
--------
get_all_bills

Scrape all Senate bills at http://www.govtrack.us/data/congress/113/bills/s

Parameters
----------
None

Returns
-------
A list of dicts, one for each bill
"""
#your code here


bill_list = get_all_bills()

"""
Function
--------
bill_graph

Turn the bill graph data into a NetworkX Digraph

Parameters
----------
data : list of dicts
    The data returned from get_all_bills

Returns
-------
graph : A NetworkX DiGraph, with the following properties
    * Each node is a senator. For a label, use the 'name' field 
      from the 'sponsor' and 'cosponsors' dict items
    * Each edge from A to B is assigned a weight equal to how many 
      bills are sponsored by B and co-sponsored by A
"""
#Your code here


bills = bill_graph(bill_list)

#Your code here


#Your code here


nx.write_gexf(votes, 'votes.gexf')

from IPython.display import Image

path = 'name_of_your_screenshot'
Image(path)