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{"directed": true, "multigraph": false, "graph": {}, "nodes": [{"ppr_percentile": 39.937106918239, "weighted_degree": 6.0, "generic_tag": "method", "born_time": "b1", "id": "maximum_likelihood"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "context-aware"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "interactive_machine_translation"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b5", "id": "neural_encoder-decoder"}, {"ppr_percentile": 36.477987421383645, "weighted_degree": 5.0, "generic_tag": "pheno", "born_time": "b0", "id": "lexicalized_tree-adjoining"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "urdu"}, {"ppr_percentile": 44.077568134171905, "weighted_degree": 6.0, "generic_tag": "method", "born_time": "b0", "id": "class_based"}, {"ppr_percentile": 76.10062893081759, "weighted_degree": 28.0, "generic_tag":

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{"directed": true, "multigraph": false, "graph": {}, "nodes": [{"ppr_percentile": 52.43393602225313, "weighted_degree": 8.0, "generic_tag": "method", "born_time": "b1", "id": "maximum_likelihood"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "context-aware"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "interactive_machine_translation"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b5", "id": "neural_encoder-decoder"}, {"ppr_percentile": 36.578581363004176, "weighted_degree": 4.0, "generic_tag": "pheno", "born_time": "b0", "id": "lexicalized_tree-adjoining"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "urdu"}, {"ppr_percentile": 22.670375521557716, "weighted_degree": 2.0, "generic_tag": "method", "born_time": "b0", "id": "class_based"}, {"ppr_percentile": 60.22253129346314, "weighted_degree": 7.0, "generic_tag"

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{"directed": true, "multigraph": false, "graph": {}, "nodes": [{"ppr_percentile": 14.634146341463413, "weighted_degree": 1.0, "generic_tag": "method", "born_time": "b1", "id": "maximum_likelihood"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "context-aware"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "interactive_machine_translation"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b5", "id": "neural_encoder-decoder"}, {"ppr_percentile": 41.707317073170735, "weighted_degree": 5.0, "generic_tag": "pheno", "born_time": "b0", "id": "lexicalized_tree-adjoining"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "urdu"}, {"ppr_percentile": 23.04878048780488, "weighted_degree": 2.0, "generic_tag": "method", "born_time": "b0", "id": "class_based"}, {"ppr_percentile": 8.78048780487805, "weighted_degree": 1.0, "generic_tag":

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{"directed": true, "multigraph": false, "graph": {}, "nodes": [{"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b1", "id": "maximum_likelihood"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "context-aware"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "interactive_machine_translation"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b5", "id": "neural_encoder-decoder"}, {"ppr_percentile": 26.848249027237355, "weighted_degree": 2.0, "generic_tag": "pheno", "born_time": "b0", "id": "lexicalized_tree-adjoining"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "pheno", "born_time": "b4", "id": "urdu"}, {"ppr_percentile": 37.7431906614786, "weighted_degree": 2.0, "generic_tag": "method", "born_time": "b0", "id": "class_based"}, {"ppr_percentile": 0.0, "weighted_degree": 0.0, "generic_tag": "method", "born_time": "b1",

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{"directed": false, "multigraph": false, "graph": {"name": "Zachary's Karate Club"}, "nodes": [{"club": "Mr. Hi", "id": 0}, {"club": "Mr. Hi", "id": 1}, {"club": "Mr. Hi", "id": 2}, {"club": "Mr. Hi", "id": 3}, {"club": "Mr. Hi", "id": 4}, {"club": "Mr. Hi", "id": 5}, {"club": "Mr. Hi", "id": 6}, {"club": "Mr. Hi", "id": 7}, {"club": "Mr. Hi", "id": 8}, {"club": "Officer", "id": 9}, {"club": "Mr. Hi", "id": 10}, {"club": "Mr. Hi", "id": 11}, {"club": "Mr. Hi", "id": 12}, {"club": "Mr. Hi", "id": 13}, {"club": "Officer", "id": 14}, {"club": "Officer", "id": 15}, {"club": "Mr. Hi", "id": 16}, {"club": "Mr. Hi", "id": 17}, {"club": "Officer", "id": 18}, {"club": "Mr. Hi", "id": 19}, {"club": "Officer", "id": 20}, {"club": "Mr. Hi", "id": 21}, {"club": "Officer", "id": 22}, {"club": "Officer", "id": 23}, {"club": "Officer", "id": 24}, {"club": "Officer", "id": 25}, {"club": "Officer", "id": 26}, {"club": "Officer", "id": 27}, {"club": "Officer", "id": 28}, {"club": "Officer", "id": 29}, {"club": "Officer", "id": 

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import pandas as pd
import scipy.sparse as sps

df = pd.DataFrame({'tag1': ['sean', 'udi', 'bogdan'], 'tag2': ['sean', 'udi', 'udi'], 'freq': [1,2,3]})
# tag1 -> rows, tag2 -> columns
df.set_index(['tag1', 'tag2'], inplace=True)

mat = sps.coo_matrix((df.freq, (df.index.labels[0], df.index.labels[1])))
print(mat.todense())

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from itertools import izip
import numpy as np
import scipy as sp
import math

def get_base(unit ='bit'):
    if unit == 'bit':
        log = sp.log2
    elif unit == 'nat':
        log = sp.log 

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def better_table(table, caption, name):
    
    start = r"""
\begin{{table}}[!htb]
\sisetup{{round-mode=places, round-precision=2}}
\caption{{{}}}\label{{table:{}}}
\centering
""".format(caption, name)
    
    end = r"\end{table}"

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