In [1]:

    

%matplotlib inline


    

In [2]:

    

import networkx as nx
import pandas as pd
# import projx as px fails in python3
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as clrs
import seaborn as sns


    

In [3]:

    

plt.rcParams['figure.figsize'] = (12, 7)


    

In [4]:

    

def prob_dist(itrbl):
    count = {}
    for i in itrbl:
        count.setdefault(i, 0)
        count[i] += 1
    sr = pd.Series(count)
    prob = sr.apply(lambda x: float(x) / len(itrbl))
    return prob


def basic_graph_stats(g):
    stats = {
        "num_nodes": len(g),
        "num_edges": len(g.edges()),
        "density": nx.density(g),
        "diameter": nx.diameter(g),
        "avg_short_path": nx.average_shortest_path_length(g),
        "avg_clust": nx.average_clustering(g),
        "transitivity": nx.transitivity(g)
    }
    return pd.Series(stats)


    

In [5]:

    

graph = nx.read_gexf("projections/fifty_percent_cut.gexf")


    

In [6]:

    

min(graph.degree().values())


    

    
Out[6]:





2

In [7]:

    

subgraphs = list(nx.connected_component_subgraphs(graph))
print([len(sub) for sub in subgraphs])


    

    




[236]

In [8]:

    

g = subgraphs[0]


    

In [9]:

    

nx.write_gexf(g, "projections/subgraph_fifty_cut.gexf")


    

In [10]:

    

basic_graph_stats(g)


    

    
Out[10]:





avg_clust            0.803597
avg_short_path       2.809701
density              0.044464
diameter             6.000000
num_edges         1233.000000
num_nodes          236.000000
transitivity         0.329093
dtype: float64

In [11]:

    

bc = nx.betweenness_centrality(g, weight="weight")
ec = nx.eigenvector_centrality(g, weight="weight", max_iter=500)
cc = nx.closeness_centrality(g)
deg = nx.degree(g)
pr = nx.pagerank(g, max_iter=500, weight="weight")


    

In [12]:

    

cent_10_df = pd.DataFrame({
    "bc": [(k, g.node[k]["label"], bc[k]) for k in sorted(bc, key=bc.get, reverse=True)[0:10]],
    "ec": [(k, g.node[k]["label"], ec[k]) for k in sorted(ec, key=ec.get, reverse=True)[0:10]],
    "cc": [(k, g.node[k]["label"], cc[k]) for k in sorted(cc, key=cc.get, reverse=True)[0:10]],
    "dc": [(k, g.node[k]["label"], deg[k]) for k in sorted(deg, key=deg.get, reverse=True)[0:10]],
    "pr": [(k, g.node[k]["label"], pr[k]) for k in sorted(pr, key=pr.get, reverse=True)[0:10]]
})


    

In [13]:

    

print(cent_10_df)


    

    




                                                  bc  \
0   (440, Felix Lope de Vega Carpio, 0.197106716329)   
1  (184, Pedro Fernandez de Castro, Conde de Lemo...   
2                (437, Alonso Perez, 0.141436668139)   
3      (153, Sebastian de Cormellas, 0.128245367804)   
4                 (70, Mateo Aleman, 0.102287983172)   
5              (3, Bernardo Grassa, 0.0998645172895)   
6  (208, Miguel de Cervantes Saavedra, 0.09597349...   
7            (207, Jorge Rodriguez, 0.0809602103162)   
8            (267, Juan de Bonilla, 0.0580469176214)   
9  (215, Francisco Murcia de la Llana, 0.05423760...   

                                                  cc  \
0    (440, Felix Lope de Vega Carpio, 0.54524361949)   
1  (215, Francisco Murcia de la Llana, 0.51762114...   
2  (100, Gutierre de Cetina, Vicario General, 0.4...   
3  (208, Miguel de Cervantes Saavedra, 0.47667342...   
4          (209, Ruy Pirez da Veiga, 0.473790322581)   
5      (153, Sebastian de Cormellas, 0.470941883768)   
6             (352, Marcos Teixiera, 0.469061876248)   
7       (147, Tomas Gracian Dantisco, 0.46442687747)   
8                (437, Alonso Perez, 0.461689587426)   
9                (207, Jorge Rodriguez, 0.458984375)   

                                                  dc  \
0               (440, Felix Lope de Vega Carpio, 88)   
1            (215, Francisco Murcia de la Llana, 63)   
2  (184, Pedro Fernandez de Castro, Conde de Lemo...   
3            (208, Miguel de Cervantes Saavedra, 55)   
4     (100, Gutierre de Cetina, Vicario General, 47)   
5                       (212, Juan de la Cuesta, 36)   
6                             (70, Mateo Aleman, 36)   
7                     (331, Jose de Valdivielso, 34)   
8                          (257, Jorge de Tovar, 32)   
9                      (209, Ruy Pirez da Veiga, 32)   

                                                  ec  \
0  (215, Francisco Murcia de la Llana, 0.43885770...   
1   (440, Felix Lope de Vega Carpio, 0.321814008236)   
2  (208, Miguel de Cervantes Saavedra, 0.31870776...   
3  (100, Gutierre de Cetina, Vicario General, 0.2...   
4           (212, Juan de la Cuesta, 0.261972128741)   
5  (184, Pedro Fernandez de Castro, Conde de Lemo...   
6          (331, Jose de Valdivielso, 0.18776568372)   
7         (214, Hernando de Vallejo, 0.144271507257)   
8              (257, Jorge de Tovar, 0.144003233588)   
9    (259, Miguel de Ondarza Zabala, 0.143252046672)   

                                                  pr  
0   (440, Felix Lope de Vega Carpio, 0.044227642188)  
1  (215, Francisco Murcia de la Llana, 0.03293063...  
2  (208, Miguel de Cervantes Saavedra, 0.02790403...  
3  (184, Pedro Fernandez de Castro, Conde de Lemo...  
4  (100, Gutierre de Cetina, Vicario General, 0.0...  
5                (70, Mateo Aleman, 0.0160517314959)  
6           (433, Alonso de Barros, 0.0146365198798)  
7        (331, Jose de Valdivielso, 0.0143919231841)  
8          (212, Juan de la Cuesta, 0.0143389550352)  
9          (209, Ruy Pirez da Veiga, 0.014244069222)  

In [14]:

    

pd.Series(list(deg.values())).hist()


    

    
Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fd722190>






    

In [15]:

    

deg_prob = prob_dist(list(deg.values()))
plt.scatter(deg_prob.index, deg_prob)


    

    
Out[15]:





<matplotlib.collections.PathCollection at 0x7f10fd6b2710>






    

In [16]:

    

pd.Series(list(bc.values())).hist()


    

    
Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fd5fae10>






    

In [17]:

    

pd.Series(list(cc.values())).hist()


    

    
Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fd612450>






    

In [18]:

    

pd.Series(list(ec.values())).hist()


    

    
Out[18]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fd109b90>






    

In [19]:

    

pd.Series(list(pr.values())).hist()


    

    
Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fd072e50>






    

In [20]:

    

nx.degree_assortativity_coefficient(g)


    

    
Out[20]:





-0.13397654720434693

In [21]:

    

r = nx.degree_assortativity_coefficient(g)
print("%3.1f"%r)


    

    




-0.1

In [22]:

    

nodes_by_deg = sorted(deg, key=deg.get, reverse=True)
mtrx = nx.to_numpy_matrix(g, nodelist=nodes_by_deg)


    

In [23]:

    

weight_sr = pd.Series([attrs["weight"] for s, t, attrs in g.edges(data=True)])
weight_sr.describe()


    

    
Out[23]:





count    1233.000000
mean        0.281060
std         0.233581
min         0.142857
25%         0.166667
50%         0.200000
75%         0.309524
max         2.602381
dtype: float64

In [24]:

    

quant = weight_sr.quantile(.75)


    

In [25]:

    

plt.rcParams['figure.figsize'] = (12, 7)
mtrx


    

    
Out[25]:





matrix([[ 0.        ,  1.67604428,  0.        , ...,  0.17763158,
          0.        ,  0.        ],
        [ 1.67604428,  0.        ,  0.77738095, ...,  0.17763158,
          0.        ,  0.19444444],
        [ 0.        ,  0.77738095,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        ..., 
        [ 0.17763158,  0.17763158,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.        ,  0.19444444,  0.        , ...,  0.        ,
          0.        ,  0.        ]])

In [26]:

    

#colors = [('purple')] + [(cm.jet(i)) for i in xrange(1,256)]
#new_map = clrs.LinearSegmentedColormap.from_list('new_map', colors, N=256)
# heatmap = plt.imshow(mtrx, interpolation='nearest')
# heatmap.set_clim(0.0, quant)
# plt.colorbar()
# sns.heatmap(mtrx)
# plt.savefig("img/cutlines_deg_assort.png")


    

In [27]:

    

stripmtrx = mtrx[:100, :50]


    

In [28]:

    

# heatmap = plt.imshow(stripmtrx)
# heatmap.set_clim(0.0, quant)
# plt.colorbar()
# sns.heatmap(stripmtrx)


    

In [29]:

    

zoommtrx = nx.to_numpy_matrix(g, nodelist=nodes_by_deg)[:50, :10]


    

In [30]:

    

zoomquant = pd.Series(zoommtrx.flatten().tolist()[0]).quantile(0.9)


    

In [31]:

    

# heatmap = plt.imshow(zoommtrx)
# heatmap.set_clim(0.0, zoomquant)
# plt.colorbar()
sns.heatmap(zoommtrx)


    

    
Out[31]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fce0d490>






    

In [32]:

    

place = nx.attribute_assortativity_coefficient(g, "top_place")


    

In [33]:

    

genre = nx.attribute_assortativity_coefficient(g, "top_genre")


    

In [34]:

    

role = nx.attribute_assortativity_coefficient(g, "role")


    

In [35]:

    

assort_df = pd.Series({
    "deg": r,
    "role": role,
    "place": place,
    "genre": genre
})
assort_df


    

    
Out[35]:





deg     -0.133977
genre    0.455555
place    0.251043
role    -0.121747
dtype: float64

In [36]:

    

no_auth_patron = g.copy()
# g.nodes(data=True)


    

In [37]:

    

no_auth_patron = g.copy()
for node, attrs in g.nodes(data=True):
    if attrs.get("role", "") == "author" or attrs.get("role", "") == "patron" or not attrs.get("top_place"):
        no_auth_patron.remove_node(node)


    

In [38]:

    

len(no_auth_patron.nodes())
place = nx.attribute_assortativity_coefficient(no_auth_patron, "top_place")
place


    

    
Out[38]:





0.6271698019463778

In [39]:

    

comps = list(nx.connected_component_subgraphs(no_auth_patron))
for x in comps:
    print(len(x.nodes()))


    

    




105
5
22
9
4
5
2
4
1
1

In [40]:

    

def group_by_top_place(g):
    places = {}
    current = 1
    for n, attrs in g.nodes(data=True):
        tp = attrs["top_place"]
        if tp not in places:
            places[tp] = int(current)
            group = int(current)
            current += 1
        else:
            group = places[tp]
        g.node[n]["group"] = group
    return g


    

In [41]:

    

no_auth_patron = group_by_top_place(no_auth_patron)
no_auth_patron.nodes(data=True)


    

    
Out[41]:





[('214',
  {'avg_date': u'1616',
   'doc_type': u"{u'Tasa': 8, u'Privilegio/Licencia': 2}",
   'fecha': u"{'1614-11-17': 1, '1615-10-21': 1, '1616-09-12': 1, '1618-06-25': 1, '1618-01-29': 1, '1617-11-07': 1, '1617-08-18': 1, '1613-08-12': 1, '1618-03-06': 1, '1618-04-09': 1}",
   'genero': u"{'Teatro': 1, 'Ficcion': 5, 'Poesia': 3}",
   'group': 1,
   'inst': u"{u'Consejo de Castilla': 1}",
   'label': 'Hernando de Vallejo',
   'lugar': u"{u'Madrid': 10}",
   'notas': u"{'Escribano de Camara': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Poesia',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Madrid',
   'type': u'Persona'}),
 ('215',
  {'avg_date': u'1612',
   'doc_type': u"{u'Privilegio/Licencia': 1, u'Fe de erratas': 33}",
   'fecha': u"{'': 8, '1612-02-08': 1, '1614-05-15': 1, '1604-12-20': 1, '1618-03-13': 1, '1611-04-09': 1, '1615-10-21': 1, '1614-09-20': 1, '1616-12-15': 1, '1613-08-07': 1, '1612-11-02': 1, '1618-06-23': 1, '1614-11-10': 1, '1607-03-07': 1, '1609-03-28': 1, '1617-09-24': 1, '1607-10-16': 1, '1609-11-18': 1, '1618-03-04': 1, '1615-03-20': 1, '1609-02-04': 1, '1612-12-04': 1, '1615-10-13': 1, '1618-04-04': 1, '1615-05-03': 1, '1608-06-25': 1, '1605-04-01': 1}",
   'genero': u"{'': 1, 'Teatro': 4, 'Ficcion': 18, 'Poesia': 14}",
   'group': 1,
   'inst': u"{u'Colegio de la Madre de Dios de los Teologos': 1}",
   'label': 'Francisco Murcia de la Llana',
   'lugar': u"{u'Alcala': 3, '': 8, u'Bruselas': 1, u'Madrid': 22}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Poesia',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Madrid',
   'type': u'Persona'}),
 ('212',
  {'avg_date': u'1612',
   'doc_type': u"{u'Privilegio/Licencia': 1, u'Impresion': 15, u'Otro documento': 1}",
   'fecha': u"{'': 1, '1607-07-17': 1, '1612-02-09': 1, '1609-02-10': 1, '1605-02-09': 1, '1618-01-01': 1, '1605-01-01': 1, '1615-11-05': 1, '1612-11-06': 1, '1617-08-18': 1, '1618-06-25': 1, '1617-09-24': 1, '1613-08-12': 1, '1617-01-01': 1, '1617-06-25': 1, '1608-06-25': 1, '1605-04-01': 1}",
   'genero': u"{'': 1, 'Teatro': 1, 'Ficcion': 11, 'Poesia': 2}",
   'group': 1,
   'label': 'Juan de la Cuesta',
   'lugar': u"{'': 1, u'Madrid': 16}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'Poesia',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Madrid',
   'type': u'Persona'}),
 ('219',
  {'avg_date': u'1607',
   'doc_type': u"{u'Impresion': 1}",
   'fecha': u"{'1607-02-23': 1}",
   'genero': u"{'Poesia': 1}",
   'group': 2,
   'label': 'Jaime Cendrat',
   'lugar': u"{u'Barcelona': 1}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Poesia',
   'top_place': u'Barcelona',
   'type': u'Persona'}),
 ('133',
  {'avg_date': u'1617',
   'doc_type': u"{u'Aprobacion': 1}",
   'fecha': u"{'1617-09-12': 1}",
   'genero': u"{'Ficcion': 1}",
   'group': 3,
   'inst': u"{u'Consejo de Navarra': 1, u'Convento de San Agustin': 1}",
   'label': 'Geronimo de Parada, Fray',
   'lugar': u"{u'Pamplona': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Pamplona',
   'type': u'Persona'}),
 ('132',
  {'avg_date': u'1615',
   'doc_type': u"{u'Privilegio/Licencia': 2, u'Impresion': 4}",
   'fecha': u"{'1614-01-11': 2, '1617-09-17': 1, '1617-11-17': 1, '1615-01-01': 1, '1617-01-01': 1}",
   'genero': u"{'Ficcion': 4}",
   'group': 3,
   'label': 'Nicolas de Assiayn',
   'lugar': u"{u'Pamplona': 6}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Pamplona',
   'type': u'Persona'}),
 ('131',
  {'avg_date': u'1615',
   'doc_type': u"{u'Privilegio/Licencia': 2}",
   'fecha': u"{'1617-09-17': 1, '1614-01-11': 1}",
   'genero': u"{'Ficcion': 4}",
   'group': 3,
   'inst': u"{u'Consejo de Navarra': 1}",
   'label': 'Pedro de Zunzarren',
   'lugar': u"{u'Pamplona': 2}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Pamplona',
   'type': u'Persona'}),
 ('130',
  {'avg_date': u'1605',
   'doc_type': u"{u'Tasa': 3}",
   'fecha': u"{'1609-07-24': 1, '1602-11-30': 1, '1604-02-27': 1}",
   'genero': u"{'Teatro': 1, 'Ficcion': 1, 'Poesia': 1}",
   'group': 4,
   'inst': u"{u'Consejo de Castilla': 1}",
   'label': 'Francisco Martinez',
   'lugar': u"{u'Madrid': 1, u'Valladolid': 2}",
   'notas': u"{'Escribano de Camara': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Ficcion',
   'second_place': u'Madrid',
   'top_genre': u'Teatro',
   'top_place': u'Valladolid',
   'type': u'Persona'}),
 ('137',
  {'avg_date': u'1610',
   'doc_type': u"{u'Tasa': 4}",
   'fecha': u"{'1609-04-01': 1, '1604-12-04': 1, '1614-09-24': 1, '1613-06-12': 1}",
   'genero': u"{'Teatro': 1, 'Ficcion': 3, 'Poesia': 2}",
   'group': 1,
   'inst': u"{u'Consejo de Castilla': 1}",
   'label': 'Juan Gallo de Andrada',
   'lugar': u"{u'Madrid': 3, u'Valladolid': 1}",
   'notas': u"{'Escribano de la Camara': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Poesia',
   'second_place': u'Valladolid',
   'top_genre': u'Ficcion',
   'top_place': u'Madrid',
   'type': u'Persona'}),
 ('134',
  {'avg_date': u'1617',
   'doc_type': u"{u'Aprobacion': 1}",
   'fecha': u"{'1617-11-17': 1}",
   'genero': u"{'Ficcion': 1}",
   'group': 3,
   'inst': u"{u'Convento de San Francisco de Pamplona': 1, u'Orden de San Francisco': 1, u'Consejo de Navarra': 1}",
   'label': 'Leon de San Pedro, Fray',
   'lugar': u"{u'Pamplona': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Pamplona',
   'type': u'Persona'}),
 ('225',
  {'avg_date': u'1617',
   'doc_type': u"{u'Impresion': 2}",
   'fecha': u"{'1617-06-04': 1, '1617-01-01': 1}",
   'genero': u"{'Ficcion': 2}",
   'group': 2,
   'label': 'Bautista Sorita',
   'lugar': u"{u'Barcelona': 2}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Barcelona',
   'type': u'Persona'}),
 ('20',
  {'avg_date': u'1605',
   'doc_type': u"{u'Privilegio/Licencia': 2}",
   'fecha': u"{'1605-03-27': 1, '1605-03-25': 1}",
   'genero': u"{'Historia': 1, 'Ficcion': 1}",
   'group': 5,
   'inst': u"{u'Consejo de Inquisicion': 1}",
   'label': 'Costa',
   'lugar': u"{u'Lisboa': 2}",
   'notas': u"{'Nombre que aparece en publicaciones de Lisboa': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Ficcion',
   'second_place': u'',
   'top_genre': u'Historia',
   'top_place': u'Lisboa',
   'type': u'Persona'}),
 ('223',
  {'avg_date': u'1617',
   'doc_type': u"{u'Privilegio/Licencia': 1}",
   'fecha': u"{'1617-08-18': 1}",
   'genero': u"{'Ficcion': 1}",
   'group': 6,
   'label': 'T. Berti',
   'lugar': u"{u'Bruselas': 1}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Bruselas',
   'type': u'Persona'}),
 ('29',
  {'avg_date': u'1618',
   'doc_type': u"{u'Impresion': 1}",
   'fecha': u"{'1618-01-01': 1}",
   'genero': u"{'Poesia': 1}",
   'group': 2,
   'label': 'Esteban Liberos',
   'lugar': u"{u'Barcelona': 1}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
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   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Zaragoza',
   'type': u'Persona'}),
 ('263',
  {'avg_date': u'1615',
   'doc_type': u"{u'Impresion': 6}",
   'fecha': u"{'1614-11-17': 1, '1614-09-24': 1, '1615-10-19': 1, '1616-01-01': 1, '1614-05-17': 1, '1618-03-06': 1}",
   'genero': u"{'Ficcion': 2, 'Poesia': 4}",
   'group': 1,
   'label': 'Viuda de Alonso Martin',
   'lugar': u"{u'Madrid': 6}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'Ficcion',
   'second_place': u'',
   'top_genre': u'Poesia',
   'top_place': u'Madrid',
   'type': u'Persona'}),
 ('355',
  {'avg_date': u'1611',
   'doc_type': u"{u'Impresion': 4}",
   'fecha': u"{'1618-04-19': 1, '1607-02-23': 1, '1612-01-01': 1, '1609-01-01': 1}",
   'genero': u"{'Ficcion': 3, 'Poesia': 1}",
   'group': 2,
   'label': 'Geronimo Margarit',
   'lugar': u"{u'Lerida': 1, u'Barcelona': 3}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'Poesia',
   'second_place': u'Lerida',
   'top_genre': u'Ficcion',
   'top_place': u'Barcelona',
   'type': u'Persona'}),
 ('260',
  {'avg_date': u'1602',
   'doc_type': u"{u'Impresion': 1}",
   'fecha': u"{'1602-08': 1}",
   'genero': u"{'Ficcion': 1}",
   'group': 2,
   'label': 'Antich Ribera',
   'lugar': u"{u'Barcelona': 1}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Barcelona',
   'type': u'Persona'}),
 ('353',
  {'avg_date': u'1604',
   'doc_type': u"{u'Impresion': 1}",
   'fecha': u"{'1604-01-01': 1}",
   'genero': u"{'Poesia': 1}",
   'group': 18,
   'label': 'Diego Lopez Davalos',
   'lugar': u"{u'Mexico': 1}",
   'real': u"{'True': 1}",
   'role': u'printer/editor',
   'second_genre': u'',
   'second_place': u'',
   'top_genre': u'Poesia',
   'top_place': u'Mexico',
   'type': u'Persona'}),
 ('352',
  {'avg_date': u'1603',
   'doc_type': u"{u'Privilegio/Licencia': 10}",
   'fecha': u"{'1604-12-04': 1, '1604-09-09': 1, '1605-03-01': 1, '1609-09-23': 1, '1603-02-26': 1, '1604-08-31': 1, '1600-01-04': 1, '1605-03-29': 1, '1600-01-18': 1, '1604-11-23': 1}",
   'genero': u"{'Historia': 2, 'Teatro': 1, 'Ficcion': 6, 'Poesia': 1}",
   'group': 5,
   'inst': u"{u'Consejo de Portugal': 1}",
   'label': 'Marcos Teixiera',
   'lugar': u"{u'Lisboa': 10}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Historia',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Lisboa',
   'type': u'Persona'}),
 ('392',
  {'avg_date': u'1612',
   'doc_type': u"{u'Tasa': 1, u'Privilegio/Licencia': 2}",
   'fecha': u"{'1610-11-19': 1, '1611-01-14': 1, '1617-01-17': 1}",
   'genero': u"{'Ficcion': 1, 'Poesia': 1}",
   'group': 5,
   'label': 'Pinto',
   'lugar': u"{u'Lisboa': 3}",
   'real': u"{'True': 1}",
   'role': u'signatory',
   'second_genre': u'Poesia',
   'second_place': u'',
   'top_genre': u'Ficcion',
   'top_place': u'Lisboa',
   'type': u'Persona'})]

In [42]:

    

nx.write_gexf(no_auth_patron, "projections/no_auth_patron.gexf")


    

In [43]:

    

b = nx.betweenness_centrality(g)


    

In [44]:

    

d = {"author": [], "signatory": [], "patron": [], "printer/editor": []}
for k, v in b.items():
    role = g.node[k].get("role", "")
    if role:
        d[role].append(v)


    

In [45]:

    

import numpy as np
sns.boxplot([d["author"],  d["patron"], d["printer/editor"], d["signatory"]], names=["author", "patron", "printer", "signatory"])
plt.ylim(0.0, 0.08)


    

    
Out[45]:





(0.0, 0.08)






    

In [46]:

    

plt.hist(d["author"])


    

    
Out[46]:





(array([ 17.,   1.,   1.,   0.,   1.,   0.,   0.,   0.,   0.,   1.]),
 array([ 0.        ,  0.03039818,  0.06079637,  0.09119455,  0.12159274,
         0.15199092,  0.18238911,  0.21278729,  0.24318548,  0.27358366,
         0.30398184]),
 <a list of 10 Patch objects>)






    

In [ ]:

    




    

In [ ]:

    




    

In [47]:

    

nx.average_clustering(g)


    

    
Out[47]:





0.8035973273010204

In [48]:

    

pd.Series(list(nx.clustering(g).values())).hist()


    

    
Out[48]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fc14a590>






    

In [49]:

    

nx.transitivity(g)


    

    
Out[49]:





0.3290931989924433

In [50]:

    

nx.diameter(g)


    

    
Out[50]:





6

In [51]:

    

nx.average_shortest_path_length(g)


    

    
Out[51]:





2.809700685178507

In [52]:

    

mod_df = pd.read_csv("data/communities/modularity.csv", encoding="utf-8")
mod_map = {3: 1, 2: 2, 5: 3, 9: 4, 4: 5, 8: 6, 0: 7, 1: 8, 7: 9, 6: 10}
mod_df["Modularity Class"] = mod_df["Modularity Class"].apply(lambda x: mod_map[x])


    

In [53]:

    

mod = mod_df.groupby("Modularity Class").size()


    

In [54]:

    

mod.plot(kind="bar")


    

    
Out[54]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fc0f56d0>






    

In [55]:

    

mod_places = mod_df.groupby(["Modularity Class", "top_place"]).size()


    

In [56]:

    

one = mod_places[1]
one.sort()
one.plot(kind="bar")
# Core group Castile -> Aragon


    

    
Out[56]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6f1a590>






    

In [57]:

    

two = mod_places[2]
two.sort()
two.plot(kind="bar")
# Core madrid


    

    
Out[57]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6e8c050>






    

In [58]:

    

three = mod_places[3]
three.sort()
three.plot(kind="bar")
# Core Zaragoza in between Madrid and Barcelona


    

    
Out[58]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6dd0890>






    

In [59]:

    

four = mod_places[4]
four.sort()
four.plot(kind="bar")
# Aragon


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-59-6c45d5458cc4> in <module>()
      1 four = mod_places[4]
      2 four.sort()
----> 3 four.plot(kind="bar")
      4 # Aragon

AttributeError: 'numpy.int64' object has no attribute 'plot'

In [60]:

    

five = mod_places[5]
five.sort()
five.plot(kind="bar")
# Lisbon


    

    
Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6d72810>






    

In [61]:

    

six = mod_places[6]
six.sort()
six.plot(kind="bar")


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-61-d650e606e2c3> in <module>()
      1 six = mod_places[6]
      2 six.sort()
----> 3 six.plot(kind="bar")

AttributeError: 'numpy.int64' object has no attribute 'plot'

In [62]:

    

seven = mod_places[7]
seven.sort()
seven.plot(kind="bar")


    

    
Out[62]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fcc69ed0>






    

In [63]:

    

eight = mod_places[8]
eight.sort()
eight.plot(kind="bar")


    

    
Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6c2bd90>






    

In [64]:

    

nine = mod_places[9]
nine.sort()
nine.plot(kind="bar")


    

    
Out[64]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10fccbc2d0>






    

In [65]:

    

ten = mod_places[10]
ten.sort()
ten.plot(kind="bar")


    

    
Out[65]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6a4c490>






    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [66]:

    

mod_places = mod_df.groupby(["role", "Modularity Class"]).size()
mod_places


    

    
Out[66]:





role            Modularity Class
author          1                    7
                2                    1
                3                    2
                5                    1
                7                    3
                8                    1
                9                    4
                10                   2
patron          1                    5
                2                    1
                3                    4
                5                    2
                7                    8
                8                    2
                9                    4
                10                   4
printer/editor  1                    9
                2                    1
                3                    4
                5                    2
                7                   14
                8                    8
                9                    7
                10                  14
signatory       1                   26
                2                    2
                3                   23
                5                    3
                7                   24
                8                   12
                9                   17
                10                  16
dtype: int64

In [67]:

    

df = pd.DataFrame({
        "author": mod_places["author"],
        "patron": mod_places["patron"],
        "printer/editor": mod_places["printer/editor"],
        "signatory": mod_places["signatory"]})


    

In [68]:

    

df.plot(kind="bar", stacked=True)


    

    
Out[68]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f6a36490>






    

In [69]:

    

mod_df.groupby([


    

    




  File "<ipython-input-69-bbfed09d89c7>", line 1
    mod_df.groupby([
                    ^
SyntaxError: unexpected EOF while parsing

In [70]:

    

mod_places = mod_df.groupby(["Modularity Class", "role"]).size()


    

In [71]:

    

mod_places = mod_places.reset_index()


    

In [72]:

    

mod_places = mod_df.groupby(["Modularity Class", "role"]).size()


    

In [73]:

    

grouped = mod_places.groupby(level=0)


    

In [74]:

    

grouped.sum()


    

    
Out[74]:





Modularity Class
1                   47
2                    5
3                   33
5                    8
7                   49
8                   23
9                   32
10                  36
dtype: int64

In [75]:

    

divided = mod_places.divide(grouped.sum())
# divided = divided.reset_index()
divided = divided.unstack(level=0).fillna(0)
trans = divided.transpose()
trans.plot(kind="bar", stacked=True)
# trans


    

    
Out[75]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f10f68cdf10>






    

In [76]:

    

trans


    

    
Out[76]:






  

    

      
role
      
author
      
patron
      
printer/editor
      
signatory
    
    

      
Modularity Class
      

      

      

      

    
  
  

    

      
1 
      
 0.148936
      
 0.106383
      
 0.191489
      
 0.553191
    
    

      
2 
      
 0.200000
      
 0.200000
      
 0.200000
      
 0.400000
    
    

      
3 
      
 0.060606
      
 0.121212
      
 0.121212
      
 0.696970
    
    

      
5 
      
 0.125000
      
 0.250000
      
 0.250000
      
 0.375000
    
    

      
7 
      
 0.061224
      
 0.163265
      
 0.285714
      
 0.489796
    
    

      
8 
      
 0.043478
      
 0.086957
      
 0.347826
      
 0.521739
    
    

      
9 
      
 0.125000
      
 0.125000
      
 0.218750
      
 0.531250
    
    

      
10
      
 0.055556
      
 0.111111
      
 0.388889
      
 0.444444
    
  

In [77]:

    

regroup = divided.groupby(["role", "Modularity Class", ])


    

    




---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-77-6e75f0e98a9a> in <module>()
----> 1 regroup = divided.groupby(["role", "Modularity Class", ])

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/generic.pyc in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze)
   2771         axis = self._get_axis_number(axis)
   2772         return groupby(self, by, axis=axis, level=level, as_index=as_index,
-> 2773                        sort=sort, group_keys=group_keys, squeeze=squeeze)
   2774 
   2775     def asfreq(self, freq, method=None, how=None, normalize=False):

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in groupby(obj, by, **kwds)
   1140         raise TypeError('invalid type: %s' % type(obj))
   1141 
-> 1142     return klass(obj, by, **kwds)
   1143 
   1144 

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze)
    386         if grouper is None:
    387             grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis,
--> 388                                                     level=level, sort=sort)
    389 
    390         self.obj = obj

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in _get_grouper(obj, key, axis, level, sort)
   2039             exclusions.append(gpr)
   2040             name = gpr
-> 2041             gpr = obj[gpr]
   2042 
   2043         if isinstance(gpr, Categorical) and len(gpr) != len(obj):

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1676             return self._getitem_multilevel(key)
   1677         else:
-> 1678             return self._getitem_column(key)
   1679 
   1680     def _getitem_column(self, key):

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
   1683         # get column
   1684         if self.columns.is_unique:
-> 1685             return self._get_item_cache(key)
   1686 
   1687         # duplicate columns & possible reduce dimensionaility

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
   1050         res = cache.get(item)
   1051         if res is None:
-> 1052             values = self._data.get(item)
   1053             res = self._box_item_values(item, values)
   1054             cache[item] = res

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item, fastpath)
   2563 
   2564             if not isnull(item):
-> 2565                 loc = self.items.get_loc(item)
   2566             else:
   2567                 indexer = np.arange(len(self.items))[isnull(self.items)]

/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_loc(self, key)
   1179         loc : int if unique index, possibly slice or mask if not
   1180         """
-> 1181         return self._engine.get_loc(_values_from_object(key))
   1182 
   1183     def get_value(self, series, key):

index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3354)()

index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3289)()

KeyError: 'role'

In [78]:

    

len(g.edges())


    

    
Out[78]:





1233

In [79]:

    

def edge_types(g):
    tps = {}
    for s, t, attrs in g.edges(data=True):
        srole = g.node[s].get("role", "")
        trole = g.node[t].get("role", "")
        if srole and trole:
            if srole == 'patron' and trole == 'printer/editor':
                srole = 'printer/editor'
                trole = 'patron'
            if srole == 'author' and trole == 'patron':
                srole = 'patron'
                trole = 'author'
            if srole == 'author' and trole == 'printer/editor':
                srole = 'printer/editor'
                trole = 'author'
            if srole == 'signatory' and trole == 'patron':
                srole = 'patron'
                trole = 'signatory'
            if srole == 'signatory' and trole == 'printer/editor':
                srole = 'printer/editor'
                trole = 'signatory'
            if srole == 'author' and trole == 'signatory':
                srole = 'signatory'
                trole = 'author'
            if (trole, srole) in tps:
                tps[(trole, srole)].append(attrs["weight"])
            else:
                tps.setdefault((srole, trole), [])
                tps[(srole, trole)].append(attrs.get("weight", 0.0))
    return tps

def edge_aggs(tps, e=1233.0):
    aggs = {}
    for k, v in tps.items():
        aggs[k] = (len(v) / e, sum(v) / len(v))
    return aggs


    

In [80]:

    

g = nx.read_gexf('js_viz/graphs/cutlines_gc_from_gephi.gephi')
etps = edge_types(g)
aggs = edge_aggs(etps)
whole = pd.DataFrame(aggs).ix[0]


    

In [81]:

    

g1 = nx.read_gexf('js_viz/graphs/mod0_from_gephi.gexf')
etps = edge_types(g1)
aggs = edge_aggs(etps, e=len(g1.edges()))
mod0 = pd.DataFrame(aggs).ix[0]
mod0s = pd.DataFrame(aggs).ix[1]


    

In [82]:

    

g2 = nx.read_gexf('js_viz/graphs/mod3_from_gephi.gexf')
etps = edge_types(g2)
aggs = edge_aggs(etps, e=len(g2.edges()))
mod3 = pd.DataFrame(aggs).ix[0]
mod3s = pd.DataFrame(aggs).ix[1]


    

In [83]:

    

g3 = nx.read_gexf('js_viz/graphs/mod1_from_gephi.gexf')
etps = edge_types(g3)
aggs = edge_aggs(etps, e=len(g3.edges()))
mod1 = pd.DataFrame(aggs).ix[0]
mod1s = pd.DataFrame(aggs).ix[1]


    

In [84]:

    

g4 = nx.read_gexf('js_viz/graphs/mod5_from_gephi.gexf')
etps = edge_types(g4)
aggs = edge_aggs(etps, e=len(g4.edges()))
mod5 = pd.DataFrame(aggs).ix[0]
mod5s = pd.DataFrame(aggs).ix[1]


    

In [85]:

    

g5 = nx.read_gexf('js_viz/graphs/mod6_from_gephi.gexf')
etps = edge_types(g5)
aggs = edge_aggs(etps, e=len(g5.edges()))
mod6 = pd.DataFrame(aggs).ix[0]
mod6s = pd.DataFrame(aggs).ix[1]


    

In [86]:

    

g6 = nx.read_gexf('js_viz/graphs/mod7_from_gephi.gexf')
etps = edge_types(g6)
aggs = edge_aggs(etps, e=len(g6.edges()))
mod7 = pd.DataFrame(aggs).ix[0]
mod7s = pd.DataFrame(aggs).ix[1]


    

In [87]:

    

g7 = nx.read_gexf('js_viz/graphs/mod4_from_gephi.gexf')
etps = edge_types(g7)
aggs = edge_aggs(etps, e=len(g7.edges()))
mod4 = pd.DataFrame(aggs).ix[0]
mod4s = pd.DataFrame(aggs).ix[1]


    

In [88]:

    

g8 = nx.read_gexf('js_viz/graphs/mod2_from_gephi.gexf')
etps = edge_types(g8)
aggs = edge_aggs(etps, e=len(g8.edges()))
mod2 = pd.DataFrame(aggs).ix[0]
mod2s = pd.DataFrame(aggs).ix[1]


    

In [89]:

    

df = pd.concat([mod0, mod3, mod6, mod5, mod7, mod1, mod4, mod2], axis=1).fillna(0)
df.columns = ['subgroup0', 'subgroup3', 'subgroup6', 'subgroup5', 'subgroup7', 'subgroup1', 'subgroup4', 'subgroup2']
t = df.T
filtered = t[[('patron', 'author'), ('patron', 'signatory'),('printer/editor', 'author'),
              ('printer/editor', 'patron'), ('printer/editor', 'signatory'), ('signatory', 'author'),
              ('printer/editor', 'printer/editor'), ('signatory', 'signatory')]]
filtered
df = filtered.T
df


    

    
Out[89]:






  

    

      

      

      
subgroup0
      
subgroup3
      
subgroup6
      
subgroup5
      
subgroup7
      
subgroup1
      
subgroup4
      
subgroup2
    
  
  

    

      
patron
      
author
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
signatory
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
printer/editor
      
author
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
patron
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
signatory
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
signatory
      
author
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
printer/editor
      
printer/editor
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
    

      
signatory
      
signatory
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
      
 0
    
  

In [90]:

    

desc = {"patron/author": df.ix['patron'].ix['author'],
        "patron/signatory": df.ix['patron'].ix['signatory'],
        "printer/author": df.ix['printer/editor'].ix['author'],
        "printer/patron": df.ix['printer/editor'].ix['patron'],
        "printer/signatory": df.ix['printer/editor'].ix['signatory'],
        "signatory/author": df.ix['signatory'].ix['author'],
        "printer/printer": df.ix['printer/editor'].ix['printer/editor'],
        "signatory/signatory": df.ix['signatory'].ix['signatory']}
edges_df = pd.DataFrame(desc)


    

In [91]:

    

g = nx.read_gexf('js_viz/graphs/cutlines_gc_from_gephi.gephi')
etps = edge_types(g)
aggs = edge_aggs(etps)
whole = pd.DataFrame(aggs).ix[0]
t = whole.T
filtered = t[[('patron', 'author'), ('patron', 'signatory'),('printer/editor', 'author'),
              ('printer/editor', 'patron'), ('printer/editor', 'signatory'), ('signatory', 'author'),
              ('printer/editor', 'printer/editor'), ('signatory', 'signatory')]]

whole = filtered.T
# whole.colums=["patron/author", "patron/signatory","printer/author", "printer/patron", "printer/signatory",
#           "signatory/author","printer/printer", "signatory/signatory" ]

whole = whole.reset_index()
whole


    

    
Out[91]:






  

    

      

      
level_0
      
level_1
      
0
    
  
  

    

      
0
      
         patron
      
         author
      
 0.033252
    
    

      
1
      
         patron
      
      signatory
      
 0.133820
    
    

      
2
      
 printer/editor
      
         author
      
 0.085969
    
    

      
3
      
 printer/editor
      
         patron
      
 0.078670
    
    

      
4
      
 printer/editor
      
      signatory
      
 0.231955
    
    

      
5
      
      signatory
      
         author
      
 0.166261
    
    

      
6
      
 printer/editor
      
 printer/editor
      
 0.024331
    
    

      
7
      
      signatory
      
      signatory
      
 0.205191
    
  

In [92]:

    

sr = whole[0]
sr.index = ["patron/author", "patron/signatory","printer/author", "printer/patron", "printer/signatory",
          "signatory/author","printer/printer", "signatory/signatory" ]


    

In [93]:

    

# fig = plt.figure()
# ax1 = fig.add_subplot(111)
ax1 = sns.boxplot([
    edges_df["patron/author"],
    edges_df["patron/signatory"],
    edges_df["printer/author"],
    edges_df["printer/patron"],
    edges_df["printer/signatory"],
    edges_df["signatory/author"],
    edges_df["printer/printer"],
    edges_df["signatory/signatory"]
], names=["patron/author", "patron/signatory","printer/author", "printer/patron", "printer/signatory",
          "signatory/author","printer/printer", "signatory/signatory" ])
# ax2 = ax1.twinx()
# ax1.plot(ax1.get_xticks(), sr)
# plt.ylim(0, 0.4)


    

In [94]:

    

df2 = pd.concat([mod0s, mod3s, mod6s, mod5s, mod7s, mod1s, mod4s, mod2s], axis=1).fillna(0)
df2.columns = ['subgroup0', 'subgroup3', 'subgroup6', 'subgroup5', 'subgroup7', 'subgroup1', 'subgroup4', 'subgroup2']
t = df2.T
filtered = t[[('patron', 'author'), ('patron', 'signatory'),('printer/editor', 'author'),
              ('printer/editor', 'patron'), ('printer/editor', 'signatory'), ('signatory', 'author'),
              ('printer/editor', 'printer/editor'), ('signatory', 'signatory')]]

df2 = filtered.T
df2


    

    
Out[94]:






  

    

      

      

      
subgroup0
      
subgroup3
      
subgroup6
      
subgroup5
      
subgroup7
      
subgroup1
      
subgroup4
      
subgroup2
    
  
  

    

      
patron
      
author
      
 0.464841
      
 0.463265
      
 0.654365
      
 0.196429
      
 0.396429
      
 2.167857
      
 0.309524
      
 0.2
    
    

      
signatory
      
 0.332603
      
 0.240787
      
 0.354101
      
 0.157143
      
 0.186835
      
 0.337897
      
 0.309524
      
 0.2
    
    

      
printer/editor
      
author
      
 0.444921
      
 0.238716
      
 0.179444
      
 0.239286
      
 0.227381
      
 0.436756
      
 0.154762
      
 0.2
    
    

      
patron
      
 0.201323
      
 0.204762
      
 0.208854
      
 0.164286
      
 0.181151
      
 0.317641
      
 0.154762
      
 0.2
    
    

      
signatory
      
 0.204777
      
 0.309086
      
 0.191008
      
 0.318398
      
 0.240179
      
 0.235197
      
 0.154762
      
 0.2
    
    

      
signatory
      
author
      
 0.439229
      
 0.325832
      
 0.364921
      
 0.231429
      
 0.297059
      
 0.337897
      
 0.309524
      
 0.2
    
    

      
printer/editor
      
printer/editor
      
 0.399206
      
 0.210317
      
 0.181633
      
 0.142857
      
 0.169841
      
 0.189881
      
 0.000000
      
 0.0
    
    

      
signatory
      
signatory
      
 0.290189
      
 0.374504
      
 0.267857
      
 0.249496
      
 0.215675
      
 0.245346
      
 0.309524
      
 0.2
    
  

In [95]:

    

desc2 = {"patron/author": df2.ix['patron'].ix['author'],
        "patron/signatory": df2.ix['patron'].ix['signatory'],
        "printer/author": df2.ix['printer/editor'].ix['author'],
        "printer/patron": df2.ix['printer/editor'].ix['patron'],
        "printer/signatory": df2.ix['printer/editor'].ix['signatory'],
        "signatory/author": df2.ix['signatory'].ix['author'],
        "printer/printer": df2.ix['printer/editor'].ix['printer/editor'],
        "signatory/signatory": df2.ix['signatory'].ix['signatory']}
strength_df = pd.DataFrame(desc2)


    

In [96]:

    

ax1 = sns.boxplot([
    edges_df["patron/author"],
    edges_df["patron/signatory"],
    edges_df["printer/author"],
    edges_df["printer/patron"],
    edges_df["printer/signatory"],
    edges_df["signatory/author"],
    edges_df["printer/printer"],
    edges_df["signatory/signatory"]
], names=["patron/author", "patron/signatory","printer/author", "printer/patron", "printer/signatory",
          "signatory/author","printer/printer", "signatory/signatory" ])
plt.ylim(0, 0.4)
plt.savefig("img/percent_edges_box.png")


    

In [97]:

    

ax1 = sns.boxplot([
    strength_df["patron/author"],
    strength_df["patron/signatory"],
    strength_df["printer/author"],
    strength_df["printer/patron"],
    strength_df["printer/signatory"],
    strength_df["signatory/author"],
    strength_df["printer/printer"],
    strength_df["signatory/signatory"]
], names=["patron/author", "patron/signatory","printer/author", "printer/patron", "printer/signatory",
          "signatory/author","printer/printer", "signatory/signatory" ])
plt.ylim(0, 0.75)
plt.savefig("img/percent_edgestrength_box.png")


    

In [98]:

    

strength_df


    

    
Out[98]:






  

    

      

      
patron/author
      
patron/signatory
      
printer/author
      
printer/patron
      
printer/printer
      
printer/signatory
      
signatory/author
      
signatory/signatory
    
  
  

    

      
subgroup0
      
 0.464841
      
 0.332603
      
 0.444921
      
 0.201323
      
 0.399206
      
 0.204777
      
 0.439229
      
 0.290189
    
    

      
subgroup3
      
 0.463265
      
 0.240787
      
 0.238716
      
 0.204762
      
 0.210317
      
 0.309086
      
 0.325832
      
 0.374504
    
    

      
subgroup6
      
 0.654365
      
 0.354101
      
 0.179444
      
 0.208854
      
 0.181633
      
 0.191008
      
 0.364921
      
 0.267857
    
    

      
subgroup5
      
 0.196429
      
 0.157143
      
 0.239286
      
 0.164286
      
 0.142857
      
 0.318398
      
 0.231429
      
 0.249496
    
    

      
subgroup7
      
 0.396429
      
 0.186835
      
 0.227381
      
 0.181151
      
 0.169841
      
 0.240179
      
 0.297059
      
 0.215675
    
    

      
subgroup1
      
 2.167857
      
 0.337897
      
 0.436756
      
 0.317641
      
 0.189881
      
 0.235197
      
 0.337897
      
 0.245346
    
    

      
subgroup4
      
 0.309524
      
 0.309524
      
 0.154762
      
 0.154762
      
 0.000000
      
 0.154762
      
 0.309524
      
 0.309524
    
    

      
subgroup2
      
 0.200000
      
 0.200000
      
 0.200000
      
 0.200000
      
 0.000000
      
 0.200000
      
 0.200000
      
 0.200000
    
  

In [99]:

    

strength_df.mean()


    

    
Out[99]:





patron/author          0.606589
patron/signatory       0.264861
printer/author         0.265158
printer/patron         0.204097
printer/printer        0.161717
printer/signatory      0.231676
signatory/author       0.313236
signatory/signatory    0.269074
dtype: float64

In [100]:

    

edges_df.mean()


    

    
Out[100]:





patron/author          0
patron/signatory       0
printer/author         0
printer/patron         0
printer/printer        0
printer/signatory      0
signatory/author       0
signatory/signatory    0
dtype: float64

In [101]:

    

edf = pd.concat([strength_df.mean(), edges_df.mean()], axis=1)


    

In [102]:

    

edf.columns = ["weight", "freq"]


    

In [103]:

    

edf


    

    
Out[103]:






  

    

      

      
weight
      
freq
    
  
  

    

      
patron/author
      
 0.606589
      
 0
    
    

      
patron/signatory
      
 0.264861
      
 0
    
    

      
printer/author
      
 0.265158
      
 0
    
    

      
printer/patron
      
 0.204097
      
 0
    
    

      
printer/printer
      
 0.161717
      
 0
    
    

      
printer/signatory
      
 0.231676
      
 0
    
    

      
signatory/author
      
 0.313236
      
 0
    
    

      
signatory/signatory
      
 0.269074
      
 0
    
  

In [104]:

    

sns.lmplot(x="weight", y="freq", data=edf)
plt.xlim(0.0, 0.7)


    

    
Out[104]:





(0.0, 0.7)






    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [105]:

    

tips = sns.load_dataset("tips")


    

In [106]:

    

tips


    

    
Out[106]:






  

    

      

      
total_bill
      
tip
      
sex
      
smoker
      
day
      
time
      
size
    
  
  

    

      
0  
      
 16.99
      
 1.01
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
1  
      
 10.34
      
 1.66
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 3
    
    

      
2  
      
 21.01
      
 3.50
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 3
    
    

      
3  
      
 23.68
      
 3.31
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
4  
      
 24.59
      
 3.61
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 4
    
    

      
5  
      
 25.29
      
 4.71
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 4
    
    

      
6  
      
  8.77
      
 2.00
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
7  
      
 26.88
      
 3.12
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 4
    
    

      
8  
      
 15.04
      
 1.96
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
9  
      
 14.78
      
 3.23
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
10 
      
 10.27
      
 1.71
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
11 
      
 35.26
      
 5.00
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 4
    
    

      
12 
      
 15.42
      
 1.57
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
13 
      
 18.43
      
 3.00
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 4
    
    

      
14 
      
 14.83
      
 3.02
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
15 
      
 21.58
      
 3.92
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 2
    
    

      
16 
      
 10.33
      
 1.67
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 3
    
    

      
17 
      
 16.29
      
 3.71
      
   Male
      
  No
      
  Sun
      
 Dinner
      
 3
    
    

      
18 
      
 16.97
      
 3.50
      
 Female
      
  No
      
  Sun
      
 Dinner
      
 3
    
    

      
19 
      
 20.65
      
 3.35
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 3
    
    

      
20 
      
 17.92
      
 4.08
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
21 
      
 20.29
      
 2.75
      
 Female
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
22 
      
 15.77
      
 2.23
      
 Female
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
23 
      
 39.42
      
 7.58
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 4
    
    

      
24 
      
 19.82
      
 3.18
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
25 
      
 17.81
      
 2.34
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 4
    
    

      
26 
      
 13.37
      
 2.00
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
27 
      
 12.69
      
 2.00
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
28 
      
 21.70
      
 4.30
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
29 
      
 19.65
      
 3.00
      
 Female
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
214
      
 28.17
      
 6.50
      
 Female
      
 Yes
      
  Sat
      
 Dinner
      
 3
    
    

      
215
      
 12.90
      
 1.10
      
 Female
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
216
      
 28.15
      
 3.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 5
    
    

      
217
      
 11.59
      
 1.50
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
218
      
  7.74
      
 1.44
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
219
      
 30.14
      
 3.09
      
 Female
      
 Yes
      
  Sat
      
 Dinner
      
 4
    
    

      
220
      
 12.16
      
 2.20
      
   Male
      
 Yes
      
  Fri
      
  Lunch
      
 2
    
    

      
221
      
 13.42
      
 3.48
      
 Female
      
 Yes
      
  Fri
      
  Lunch
      
 2
    
    

      
222
      
  8.58
      
 1.92
      
   Male
      
 Yes
      
  Fri
      
  Lunch
      
 1
    
    

      
223
      
 15.98
      
 3.00
      
 Female
      
  No
      
  Fri
      
  Lunch
      
 3
    
    

      
224
      
 13.42
      
 1.58
      
   Male
      
 Yes
      
  Fri
      
  Lunch
      
 2
    
    

      
225
      
 16.27
      
 2.50
      
 Female
      
 Yes
      
  Fri
      
  Lunch
      
 2
    
    

      
226
      
 10.09
      
 2.00
      
 Female
      
 Yes
      
  Fri
      
  Lunch
      
 2
    
    

      
227
      
 20.45
      
 3.00
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 4
    
    

      
228
      
 13.28
      
 2.72
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
229
      
 22.12
      
 2.88
      
 Female
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
230
      
 24.01
      
 2.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 4
    
    

      
231
      
 15.69
      
 3.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 3
    
    

      
232
      
 11.61
      
 3.39
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
233
      
 10.77
      
 1.47
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
234
      
 15.53
      
 3.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
235
      
 10.07
      
 1.25
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
236
      
 12.60
      
 1.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
237
      
 32.83
      
 1.17
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
238
      
 35.83
      
 4.67
      
 Female
      
  No
      
  Sat
      
 Dinner
      
 3
    
    

      
239
      
 29.03
      
 5.92
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 3
    
    

      
240
      
 27.18
      
 2.00
      
 Female
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
241
      
 22.67
      
 2.00
      
   Male
      
 Yes
      
  Sat
      
 Dinner
      
 2
    
    

      
242
      
 17.82
      
 1.75
      
   Male
      
  No
      
  Sat
      
 Dinner
      
 2
    
    

      
243
      
 18.78
      
 3.00
      
 Female
      
  No
      
 Thur
      
 Dinner
      
 2
    
  

244 rows × 7 columns

In [ ]:

    




    

In [ ]:

    




    

In [107]:

    

chisquare(df.whole.values, df.mod0.values)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-107-74a3de4be89b> in <module>()
----> 1 chisquare(df.whole.values, df.mod0.values)

NameError: name 'chisquare' is not defined

In [108]:

    

whole


    

    
Out[108]:






  

    

      

      
level_0
      
level_1
      
0
    
  
  

    

      
0
      
         patron
      
         author
      
 0.033252
    
    

      
1
      
         patron
      
      signatory
      
 0.133820
    
    

      
2
      
 printer/editor
      
         author
      
 0.085969
    
    

      
3
      
 printer/editor
      
         patron
      
 0.078670
    
    

      
4
      
 printer/editor
      
      signatory
      
 0.231955
    
    

      
5
      
      signatory
      
         author
      
 0.166261
    
    

      
6
      
 printer/editor
      
 printer/editor
      
 0.024331
    
    

      
7
      
      signatory
      
      signatory
      
 0.205191
    
  

In [109]:

    

whole


    

    
Out[109]:






  

    

      

      
level_0
      
level_1
      
0
    
  
  

    

      
0
      
         patron
      
         author
      
 0.033252
    
    

      
1
      
         patron
      
      signatory
      
 0.133820
    
    

      
2
      
 printer/editor
      
         author
      
 0.085969
    
    

      
3
      
 printer/editor
      
         patron
      
 0.078670
    
    

      
4
      
 printer/editor
      
      signatory
      
 0.231955
    
    

      
5
      
      signatory
      
         author
      
 0.166261
    
    

      
6
      
 printer/editor
      
 printer/editor
      
 0.024331
    
    

      
7
      
      signatory
      
      signatory
      
 0.205191
    
  

In [110]:

    

mod0


    

    
Out[110]:





author          author            0
patron          author            0
                signatory         0
printer/editor  author            0
                patron            0
                printer/editor    0
                signatory         0
signatory       author            0
                signatory         0
Name: 0, dtype: float64

In [111]:

    

mod0['printer/editor']['patron'] = 0.0


    

In [112]:

    

mod0


    

    
Out[112]:





author          author            0
patron          author            0
                signatory         0
printer/editor  author            0
                patron            0
                printer/editor    0
                signatory         0
signatory       author            0
                signatory         0
Name: 0, dtype: float64

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In [119]:

    

from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})


    

In [113]:

    

def count_roles(g):
    d = {}
    for n, a, in g.nodes(data=True):
        role = a.get("role")
        if role:
            d.setdefault(role, 0)
            d[role] += 1
    return d


    

In [120]:

    

pd.Series(count_roles(g)).plot(kind="bar")
plt.ylabel('# individuals', size=18)
plt.xlabel('role', size=18)
plt.xticks(rotation=50)
plt.savefig("img/article/figure15.eps", format='eps', dpi=1000)
# plt.savefig("img/role_dist.png")


    

    




/home/davebshow/.virtualenvs/scientific/local/lib/python2.7/site-packages/matplotlib/figure.py:1644: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "






    

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