-
Notifications
You must be signed in to change notification settings - Fork 2
/
analyze.py
executable file
·165 lines (131 loc) · 5.72 KB
/
analyze.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
import os
import sys
import numpy as np
import pandas as pd
from collections import OrderedDict
import clean
def analyze_presenters(presenters):
affiliation_names = []
affiliation_name_cnt = {}
affiliation_locations = []
affiliation_location_cnt = {}
affiliation_countries = []
affiliation_country_cnt = {}
for presenter in presenters:
for name in presenter['affiliation_names']:
if name not in affiliation_names:
affiliation_names.append(name)
affiliation_name_cnt[name] = 1
else:
affiliation_name_cnt[name] += 1
for location in presenter['affiliation_locations']:
# count cities
if location not in affiliation_locations:
affiliation_locations.append(location)
affiliation_location_cnt[location] = 1
else:
affiliation_location_cnt[location] += 1
# count countries
country = location.split(',')[-1].strip()
if country not in affiliation_countries:
affiliation_countries.append(country)
affiliation_country_cnt[country] = 1
else:
affiliation_country_cnt[country] += 1
n_presenters = len(presenters)
n_affiliation_names = len(affiliation_names)
n_affiliation_locations = len(affiliation_locations)
n_affiliation_countries = len(affiliation_countries)
print("Presenter Statistics Summary:")
print("Number of presenters:", n_presenters)
print("Number of affiliations:", n_affiliation_names)
print("Number of locations:", n_affiliation_locations)
print("Number of countries:", n_affiliation_countries)
print("\n")
# sort affiliations by count
sorted_affiliation_name_cnt = sorted(affiliation_name_cnt.items(), key=lambda kv: kv[1], reverse=True)
sorted_affiliation_location_cnt = sorted(affiliation_location_cnt.items(), key=lambda kv: kv[1], reverse=True)
sorted_affiliation_country_cnt = sorted(affiliation_country_cnt.items(), key=lambda kv: kv[1], reverse=True)
generate_csv(sorted_affiliation_name_cnt, "data/presenter_names.csv")
generate_csv(sorted_affiliation_location_cnt, "data/presenter_locations.csv")
generate_csv(sorted_affiliation_country_cnt, "data/presenter_countries.csv")
statistics = {}
statistics['presenters'] = n_presenters
statistics['names'] = [i for i in sorted_affiliation_name_cnt if i[0] != 'None']
statistics['locations'] = [i for i in sorted_affiliation_location_cnt if i[0] != 'None']
statistics['countries'] = [i for i in sorted_affiliation_country_cnt if i[0] != 'None']
return statistics
def analyze_papers(papers):
### Authors ###
authors = []
authors_cnt = {}
for paper in papers:
for author in paper['authors']:
if author not in authors:
authors.append(author)
authors_cnt[author] = 1
else:
authors_cnt[author] += 1
sorted_authors_cnt = sorted(authors_cnt.items(), key=lambda kv: kv[1], reverse=True)
#print(sorted_authors_cnt)
### Affiliations ###
affiliations = []
affiliations_cnt = {}
for paper in papers:
paper_affiliations = [] # hold all unique aff. in this paper
for affiliation in paper['affiliation']:
#print(affiliation)
affiliation = affiliation.split(',')[0]
affiliation = clean.clean_affiliation(affiliation)
if affiliation not in paper_affiliations:
paper_affiliations.append(affiliation)
for affiliation in paper_affiliations:
if affiliation not in affiliations:
affiliations.append(affiliation)
affiliations_cnt[affiliation] = 1
else:
affiliations_cnt[affiliation] += 1
sorted_affiliations_cnt = sorted(affiliations_cnt.items(), key=lambda kv: kv[1], reverse=True)
### Subjects ###
subjects = []
subjects_cnt = {}
for paper in papers:
if paper['subject'] not in subjects:
subjects.append(paper['subject'])
subjects_cnt[paper['subject']] = 1
else:
subjects_cnt[paper['subject']] += 1
sorted_subjects_cnt = sorted(subjects_cnt.items(), key=lambda kv: kv[1], reverse=True)
### Title/Abtract ###
words = []
words_cnt = {}
for paper in papers:
abstract_words = paper['abstract'].lower()
filtered_abstract_words = clean.remove_stopwords(abstract_words)
for word in filtered_abstract_words:
if word not in words:
words.append(word)
words_cnt[word] = 1
else:
words_cnt[word] += 1
sorted_words_cnt = sorted(words_cnt.items(), key=lambda kv: kv[1], reverse=True)
n_papers = len(papers)
print("Paper Statistics Summary:")
print("Number of papers:", n_papers)
print("Number of subjects:", len(sorted_subjects_cnt))
print("Number of unique words:", len(sorted_words_cnt))
print("Number of affiliation:", len(sorted_affiliations_cnt))
generate_csv(sorted_words_cnt, "data/abstract_words.csv")
generate_csv(sorted_authors_cnt, "data/paper_authors.csv")
generate_csv(sorted_subjects_cnt, "data/paper_subjects.csv")
generate_csv(sorted_affiliations_cnt, "data/paper_affiliations.csv")
statistics = {}
statistics['papers'] = n_papers
statistics['authors'] = sorted_authors_cnt
statistics['subjects'] = sorted_subjects_cnt
statistics['words'] = sorted_words_cnt
statistics['affiliations'] = sorted_affiliations_cnt
return statistics
def generate_csv(data_list, filename):
dataframe = pd.DataFrame(data_list)
dataframe.to_csv(filename, sep=',')