-
Notifications
You must be signed in to change notification settings - Fork 1
/
workbook.py
245 lines (200 loc) · 8.96 KB
/
workbook.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
__author__ = 'yannis'
import Parsers_Decade as PD
import graph_tool as gt
import my_graph_tool_add_ons as mygt
import pandas
import my_tree_tools as mytree
import my_community_tools as mycomms
import my_containers
import cPickle
import Bayesian_Non_Parametrics as BNP
import numpy
import my_stat_tools as mystat
import my_containers as mycons
import BOMP
import matplotlib.pylab as pylab
#AdjList = PD.get_patent_code_incidence_matrix_from_multiple_files('Patent_files/Patents_v2',range(1790,1810,10),True)
#node_label_list = [None]*len(lookup.keys())
#for elem in lookup.keys():
# node_label_list[lookup[elem]] = elem
#B = mycons.convert_list_to_sparse_matrix(AdjList)
#test = PD.get_adjacency_frames_CP_class_groups(range(1790,1820,10))
#G = gt.load_graph('Network_files/Gclasses_1790.xml.gz')
#remove_set = ['423','082']
#mygt.safe_delete_vertices_based_on_label(G,remove_set)
#t = mytree.tree('c')
#t.add_node('d','c')
#t.add_node('g','d')
#t.add_node('h','d')
#t.add_node('b','c')
#t.add_node('i','b')
#t.add_node('a','b')
#t.add_node('e','a')
#t.add_node('f','a')
#
##x = t.depth_first_search('c')
##print [n.name for n in x]
#
#print t.are_ancestor_descendant_pair('i','b')
#t = mytree.parse_Daniel_semicolon_based_tree_format_to_my_tree_class('tree_data/daniel_sample.txt')
#G = gt.load_graph('Gclasses_1830.xml.gz')
#A = mygt.get_adjacency_matrix_from_gt_graph(G)
#Gclasses_merged = gt.load_graph('Gclasses_1790.xml.gz')
#Gcodes_merged = gt.load_graph('Gcodes_1790.xml.gz')
#for d in range(1800,2020,10):
# print '*** Processing decade {0}...'.format(d)
# print '*Creating current graphs...'
# Gclasses_current,Gcodes_current = PD.load_coocurrence_networks_from_patent_code_file_to_graph_tool('Patents_v2_{0}.csv'.format(d))
#
# print 'Saving current graphs...'
# Gclasses_current.save('Gclasses_' + str(d) + '.xml.gz')
# Gcodes_current.save('Gcodes_' + str(d) + '.xml.gz')
#
# print '*Merging decade {0} with previous...'.format(d)
# print 'Classes:'
# Gclasses_merged = mygt.merge_cooccurrence_networks(Gclasses_merged,Gclasses_current,True,True)
# print 'Codes:'
# Gcodes_merged = mygt.merge_cooccurrence_networks(Gcodes_merged,Gcodes_current,True,True)
#
# print 'Saving merged graphs...'
# Gclasses_merged.save('Gclasses_{0}to{1}.xml.gz'.format(1790,d))
# Gcodes_merged.save('Gcodes_{0}to{1}.xml.gz'.format(1790,d))
#B = PD.get_patent_code_incidence_matrix_from_file('Patents_v2_1790.csv')
#B.get_correlation_column_elem_age_vs_degree()
#INNOV = PD.get_innovation_coordinates('Patents_v2_1790.csv',use_codes=True)
#
# aux = mystat.time_series_flatness([numpy.nan,numpy.nan,5,3,4])
#AdjList,lookup = PD.get_patent_code_incidence_matrix_from_file('Patents_v2_1790.csv',True)
#B = mycons.convert_list_to_sparse_matrix(AdjList)
#PD.get_patent_code_incidence_matrix_from_file('Patents_v2_1790.csv',True)
#PD.filter_patents_decade_given_code_set(True,{'D11/079000'},'CD11',decade_range = range(1900,1910,10),primary_only=True)
#Photoelectric_codes = cPickle.load(open('Photoelectric_codes.cpickle','rb'))
#PD.filter_patents_decade_given_code_set(True,Photoelectric_codes,'PV',decade_range = range(1900,2020,10),primary_only=True)
#node_data = pandas.read_csv('PV_CODES_1790to2010.csv')
#PV_nodes = node_data[node_data.Class=='136']
#
#G_PV_COOC = dict()
#G_PV_SR = dict()
#for d in range(1900,2020,10):
# print 'Loading decade {0}...'.format(d)
# G_PV_COOC[d] = mygt.load_graph_from_pajek('G_PE_{0}_COOC.net'.format(d),weight_label='co_oc',weight_type='int')
# G_PV_SR[d] = mygt.load_graph_from_pajek('G_PE_{0}_SR.net'.format(d),weight_label='SR',weight_type='float')
#
#G_PV_merged_COOC = dict()
#
#G_PV_merged_COOC[1900] = G_PV_COOC[1900]
#
#for d in range(1910,2020,10):
# print 'processing decade {0}...'.format(d)
# G_PV_merged_COOC[d] = mygt.merge_cooccurrence_networks(G_PV_merged_COOC[d-10],G_PV_COOC[d],False,True)
# Gcodes = gt.load_graph('Gcodes_1790to{0}.xml.gz'.format(d))
# G = G_PV_merged_COOC[d]
# G.vertex_properties['No_of_occurrences'] = G.new_vertex_property('int')
# for pv_label in PV_nodes.Label:
# if G.graph_properties['index_of'].has_key(pv_label):
# no_of_occurrences = Gcodes.vertex_properties['No_of_occurrences'][Gcodes.vertex(G.graph_properties['index_of'][pv_label])]
# G.vertex_properties['No_of_occurrences'][G.vertex(G.graph_properties['index_of'][pv_label])] = no_of_occurrences
#
#mygt.load_graph_from_pajek('G_PE_{0}_COOC.net'.format(1900),False,weight_label='co_oc',weight_type='int')
#PV_codes = PD.read_PV_codes_per_decade('PV_patents.csv',range(1970,1980,10))
#PV = PD.read_PV_patents('PV_patents.csv')
#comms_full = mycomms.read_bottom_level_community_structure_from_dot_tree_to_community_list('Gcodes_1800_COOC.tree')
#t1 = numpy.array([1,2,3])
#t2 = numpy.array([0,1,numpy.nan])
#
#c = mystat.normalised_cross_correlation(t1,t2)
#Z = BNP.iBT_random_sample(20,2)
#PD.load_coocurrence_networks_from_patent_code_file_to_graph_tool('Patents_v2_1840.csv')
#PD.split_Patent_Codes_to_decades('Patents_v2.csv')
#Z = BNP.iBT_random_sample(20,2,2)
#Gcl,Gco = PD.load_coocurrence_networks_from_file_to_graph_tool('code_pairs_1830.csv')
#G2 = mygt.get_graph_without_singletons(Gco)
#PD = reload(PD)
#for d in range(1790,2020,10):
# print '*** Processing activity of the ' + str(d) + 's...'
# Rclasses = PD.get_patent_to_technology_matrix_from_file('PatentCodes_{0}.csv'.format(d),use_codes=False)
# cPickle.dump(Rclasses['B'],open('Rclasses_B_' + str(d) + '.cpickle','wb'))
# cPickle.dump(Rclasses['Patents'],open('Rclasses_Patents_' + str(d) + '.cpickle','wb'))
# cPickle.dump(Rclasses['Techs'],open('Rclasses_Techs_' + str(d) + '.cpickle','wb'))
#
# Rcodes = PD.get_patent_to_technology_matrix_from_file('PatentCodes_{0}.csv'.format(d),use_codes = True)
# cPickle.dump(Rcodes['B'],open('Rcodes_B_' + str(d) + '.cpickle','wb'))
# cPickle.dump(Rcodes['Patents'],open('Rcodes_Patents_' + str(d) + '.cpickle','wb'))
# cPickle.dump(Rcodes['Techs'],open('Rcodes_Techs_' + str(d) + '.cpickle','wb'))
#Gtest = mygt.merge_cooccurrence_networks(gt.load_graph('Gclasses_1790.xml.gz'),gt.load_graph('Gclasses_1800.xml.gz'),True,True)
#g = mycomm.read_community_structure_from_dot_map_to_community_list('Gclasses_2010_COOC.map')
##mycomm.read_bottom_level_community_structure_from_dot_tree('Gclasses_1810_COOC.tree')
#decade_range = range(1790,2020,10)
#print '*** opening files...'
#COMMs_SR = PD.read_bottom_level_decade_community_structures_from_dot_tree_files('Gcodes_','_SR',decade_range)
#COMMs_COOC = PD.read_bottom_level_decade_community_structures_from_dot_tree_files('Gcodes_','_COOC',decade_range)
#
#NMI_SR = []
#NMI_COOC = []
#for d in decade_range:
# print '*** processing decade ' + str(d) + '...'
# g_sr = COMMs_SR[d]
# g_cooc = COMMs_COOC[d]
#
# nmi_sr,class_hierarchy = mycomm.get_normalised_mutual_information_between_code_communities_and_USPTO_hierarchy(g_sr)
# nmi_cooc,class_hierarchy = mycomm.get_normalised_mutual_information_between_code_communities_and_USPTO_hierarchy(g_cooc)
#
# NMI_COOC.append(nmi_cooc)
# NMI_SR.append(nmi_sr)
#
# print 'NMI_COOC: {0:.2f}, NMI_SR: {1:.2f}'.format(nmi_cooc,nmi_sr)
#print NMI
#
#g1 = [[1,2],3]
#g2 = [[1,2,3],[4,5]]
#
#mycomm.get_community_substructure_from_node_intersect(g1,g2,set(range(1,4)))
#
#print str(mygt.get_normalised_mutual_information(g1,g2))
#G = PD.merge_decade_graphs(1790,2010)
#decade_range = range(1790,2020,10)
#
#for decade in decade_range:
# print '*** Processing the ' + str(decade) + 's...'
# Gclasses,Gcodes = PD.load_coocurrence_networks_from_file_to_graph_tool('code_pairs_'+str(decade)+'.csv')
# print 'saving graphs...'
# Gclasses.save('Gclasses_'+str(decade)+'.xml.gz')
# Gcodes.save('Gcodes_'+str(decade)+'.xml.gz')
#
# print('normalising edge weights...')
# Gclasses = mygt.calculate_SR(Gclasses)
# Gcodes = mygt.calculate_SR(Gcodes)
# print 're-saving graphs...'
# Gclasses.save('Gclasses_'+str(decade)+'.xml.gz')
# Gcodes.save('Gcodes_'+str(decade)+'.xml.gz')
#PD.split_Patent_Codes_to_decades('patents_sample.csv')
#decades = range(1830,2000,10)
#
#for d in decades:
# print '*** processing decade ' + str(d) + '...'
# Gclasses = gt.load_graph('Gclasses_' + str(d) + '.xml.gz')
# Gcodes = gt.load_graph('Gcodes_' + str(d) + '.xml.gz')
#
# for e in Gclasses.edges():
# Gclasses.edge_properties['co_oc'][e] /= 2
# Gclasses.save('Gclasses_'+str(d) + '.xml.gz')
#
# for e in Gcodes.edges():
# Gcodes.edge_properties['co_oc'][e] /=2
# Gcodes.save('Gcodes_'+str(d) + '.xml.gz')
#
#
#
#G1830 = gt.load_graph('Gclasses_1830.xml.gz')
#
#tree_string = convert_dot_tree_to_tree_string('Gclasses_1830.tree')
#Decades = range(1830,2020,10)
#
#for decade in Decades:
# filename = 'code_pairs_'+str(decade)+'.csv'
# print '******* Reading file ' + filename + '...'
# Gclasses,Gcodes = PD.load_coocurrence_networks_from_file_to_graph_tool(filename)
#
# print 'saving to disk...'
# Gclasses.save('Gclasses_'+str(decade)+'.xml.gz')
# Gcodes.save('Gcodes_'+str(decade)+'.xml.gz')