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repo_directory = '/Users/iaincarmichael/Dropbox/Research/law/law-net/'
data_dir = '/Users/iaincarmichael/data/courtlistener/'
import numpy as np
import os
import sys
import matplotlib.pyplot as plt
from scipy.stats import rankdata
import cPickle as pickle
from collections import Counter
import pandas as pd
# graph package
import igraph as ig
# our code
sys.path.append(repo_directory + 'code/')
from setup_data_dir import setup_data_dir, make_subnetwork_directory
from pipeline.download_data import download_bulk_resource, download_master_edgelist, download_scdb
from helpful_functions import case_info
from viz import print_describe
from stats.linear_model import *
sys.path.append(repo_directory + 'vertex_metrics_experiment/code/')
from custom_vertex_metrics import *
# which network to download data for
network_name = 'scotus' # 'federal', 'ca1', etc
# some sub directories that get used
raw_dir = data_dir + 'raw/'
subnet_dir = data_dir + network_name + '/'
text_dir = subnet_dir + 'textfiles/'
# jupyter notebook settings
%load_ext autoreload
%autoreload 2
%matplotlib inline
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G = ig.Graph.Read_GraphML(subnet_dir + network_name +'_network.graphml')
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num_words = np.array(G.vs['num_words'])
outdegrees = np.array(G.outdegree())
indegrees = G.indegree()
years = G.vs['year']
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plt.figure(figsize=[12, 4])
plt.subplot(1,3,1)
plt.scatter(num_words, outdegrees)
plt.xlabel('num words')
plt.ylabel('outdegree')
plt.subplot(1,3,2)
plt.scatter(num_words, indegrees)
plt.xlabel('num words')
plt.ylabel('indegree')
plt.subplot(1,3,3)
plt.scatter(years, num_words)
plt.ylabel('year')
plt.ylabel('num words')
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get_SLR(num_words, outdegrees, xlabel='num words', ylabel='outdegree')
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# remove cases with extremes
out_deg_upper = np.percentile(outdegrees, 99)
out_deg_lower = np.percentile(outdegrees, 0)
num_words_upper = np.percentile(num_words, 99)
num_words_lower = np.percentile(num_words, 0)
od_to_keep = (out_deg_lower <= outdegrees) & (outdegrees <= out_deg_upper)
nw_to_keep = (num_words_lower <= num_words) & (num_words <= num_words_upper)
to_keep = od_to_keep & nw_to_keep
# remove cases that have zero out-degree
get_SLR(num_words[to_keep], outdegrees[to_keep], xlabel='num words', ylabel='outdegree')
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get_SLR(num_words, indegrees)
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plt.scatter(years, num_words)
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get_SLR(years, num_words)
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def get_year_aggregate(years, x, fcn):
by_year = {y: [] for y in set(years)}
for i in range(len(years)):
by_year[years[i]].append(x[i])
year_agg_dict = {y: fcn(by_year[y]) for y in by_year.keys()}
return pd.Series(year_agg_dict)
in_year_median = get_year_aggregate(years, indegrees, np.median)
nw_year_median = get_year_aggregate(years, num_words, np.median)
od_year_median = get_year_aggregate(years, outdegrees, np.median)
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plt.figure(figsize=[8, 4])
plt.subplot(1,2,1)
plt.plot(nw_year_median.index, nw_year_median/1000, label='num words')
plt.plot(od_year_median.index, od_year_median, label='out degree')
plt.ylabel('mean')
plt.xlabel('year')
plt.legend(loc='upper right')
plt.subplot(1,2,2)
plt.plot(nw_year_median.index, nw_year_median/1000, label='num words')
plt.plot(od_year_median.index, od_year_median, label='out degree')
plt.ylabel('median')
plt.xlabel('year')
plt.legend(loc='upper right')
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plt.figure(figsize=[8, 8])
plt.scatter(nw_year_median.index, nw_year_median/1000,
label='num words/1000', color='blue', marker='*')
plt.scatter(od_year_median.index, od_year_median,
label='out degree', color='red', marker='s')
plt.scatter(in_year_median.index, in_year_median,
label='indegree degree', color='green', marker='o')
plt.ylabel('median')
plt.xlabel('year')
plt.legend(loc='upper right')
plt.xlim([1800, 2017])
plt.ylim([0, 30])
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plt.figure(figsize=[8, 8])
plt.plot(nw_year_median.index, nw_year_median/1000,
label='num words/1000', color='black', marker='$n$', alpha=.7, linewidth=1, linestyle=':')
plt.plot(od_year_median.index, od_year_median,
label='out degree', color='black', marker='$o$', alpha=.7, linestyle=':')
plt.plot(in_year_median.index, in_year_median,
label='indegree degree', color='black', marker='$i$', alpha=.7, linestyle=':')
plt.ylabel('median')
plt.xlabel('year')
plt.legend(loc='upper right')
plt.xlim([1800, 2017])
plt.ylim([0, 30])
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plt.figure(figsize=[6, 9])
plt.subplot(3,1,1)
plt.plot(nw_year_median.index, nw_year_median/1000,
color='black', marker='.', linestyle=':')
plt.axvline(1953, color='black', alpha=.5)
plt.axvline(1969, color='black', alpha=.5)
plt.ylabel('median text length')
plt.xlim([1800, 2017])
plt.ylim([0, 30])
plt.subplot(3,1,2)
plt.plot(od_year_median.index, od_year_median,
color='black', marker='.', linestyle=':')
plt.axvline(1953, color='black', alpha=.5)
plt.axvline(1969, color='black', alpha=.5)
plt.ylabel('median outdegree')
plt.xlim([1800, 2017])
plt.ylim([0, 30])
plt.subplot(3,1,3)
plt.plot(in_year_median.index, in_year_median,
color='black', marker='.', linestyle=':')
plt.axvline(1953, color='black', alpha=.5)
plt.axvline(1969, color='black', alpha=.5)
plt.ylabel('median indegree')
plt.xlabel('year')
plt.xlim([1800, 2017])
plt.ylim([0, 30])
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?plt.scatter
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import networkx as nx
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Gnx = nx.read_graphml(subnet_dir + network_name +'_network.graphml')
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%%time
katz = nx.katz_centrality(Gnx)
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