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visualize_poets_network.py
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visualize_poets_network.py
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import pickle
import argparse
import os
import math
# 如果需要，可以用opencc实现繁体和简体之间的转换
# 需要在电脑上安装opencc
# opencc = 'opencc -i echart_visualize/poets_network_early.html -o echart_visualize/poets_network_early_zhs.html -c zht2zhs.ini'
# 直接获取排名前visulize_range的引用关系
def get_concerned_relations_by_range(reference_relations_counter, visulize_range):
# 获取引用关系
relations = reference_relations_counter.most_common(visulize_range)
max_refer_count = relations[0][1]
min_refer_count = relations[-1][1]
return relations, max_refer_count, min_refer_count
# 获取指定诗人群体之间的引用关系，适合画出某个群体内部的网络
def get_concerned_relations_by_authors(reference_relations_counter, authors):
# 获取指定作者群体内部的引用关系
relations = []
max_refer_count = 0
min_refer_count = 10000
for (refered_by, refered), count in reference_relations_counter.items():
# 不统计自引用的count
if refered_by == refered:
continue
if refered_by in authors and refered in authors:
if count > max_refer_count:
max_refer_count = count
if count < min_refer_count:
min_refer_count = count
relations.append(((refered_by, refered), count))
return relations, max_refer_count, min_refer_count
# 有些时候如果画出所有关系会显得非常拥挤，用count_to_plot_threshold来控制最小显示出来的关系
# 只有引用数大于等于count_to_plot_threshold的关系才会显示出来
def generate_html_page(relations, max_refer_count, min_refer_count, saved_html_file, count_to_plot_threshold = 1):
html_dir = os.path.dirname(saved_html_file)
html_head_path = os.path.join(html_dir, 'html_head.txt')
html_tail_path = os.path.join(html_dir, 'html_tail.txt')
min_link_width = 0.5
max_link_width = 3.0
# 因为引用关系的强弱范围很大，对其开方降低变化范围，画图更直观
max_refer_count = math.sqrt(max_refer_count)
min_refer_count = math.sqrt(min_refer_count)
width_slope = (max_link_width - min_link_width) / (max_refer_count - min_refer_count)
# 格式化links数据
links_text = 'links: [\n'
links_item_format = """{source: '%s', target: '%s',
lineStyle:{normal:{width: %f}}},
"""
filtered_authors = set()
for (refered_by, refered), count in relations:
# 跳过自引用，不然有可能画出孤立节点
if refered_by == refered:
continue
# 小于门限跳过
if count < count_to_plot_threshold:
continue
filtered_authors.add(refered_by)
filtered_authors.add(refered)
count = math.sqrt(count)
line_width = min_link_width + width_slope * (count - min_refer_count)
links_text += links_item_format % (refered_by, refered, line_width)
links_text += '],\n'
# 格式化node数据
data_text = 'data:[\n'
data_item_format = "{name: '%s'},\n"
for author in filtered_authors:
data_text += data_item_format % author
data_text += '],\n'
# 读取html的head和tail部分
with open(html_head_path, 'r', encoding = 'utf-8') as f:
head_text = f.read()
with open(html_tail_path, 'r', encoding = 'utf-8') as f:
tail_text = f.read()
# 合并存储为html
with open(saved_html_file, 'w', encoding = 'utf-8') as f:
f.write(head_text + data_text + links_text + tail_text)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--relations_path', type=str, default='save/reference_relations.pkl',
help='file to load relations data')
parser.add_argument('--data_dir', type=str, default='data',
help='directory to load authors file')
parser.add_argument('--html_dir', type=str, default='html',
help='directory to save html page')
args = parser.parse_args()
with open(args.relations_path, 'rb') as f:
reference_relations_counter, reference_relations_text = pickle.load(f)
# 生成全唐排名前100的关系图
relations, max_refer_count, min_refer_count = get_concerned_relations_by_range(reference_relations_counter, 100)
saved_html = os.path.join(args.html_dir, 'full_tang_poets_net.html')
generate_html_page(relations, max_refer_count, min_refer_count, saved_html)
# 生成初唐、盛唐、中唐、晚唐四个时期的诗人关系图
# 诗人名字文件 社交关系图网页 引用数门限
files_name_array = [('early_tang_poets.txt', 'early_tang_poets_net.html', 1),
('high_tang_poets.txt', 'high_tang_poets_net.html', 2),
('middle_tang_poets.txt','middle_tang_poets_net.html', 2),
('late_tang_poets.txt', 'late_tang_poets_net.html', 1)]
for authors_file_name, html_file_name, threshold in files_name_array:
authors_file_path = os.path.join(args.data_dir, authors_file_name)
with open(authors_file_path, 'r', encoding='utf-8') as f:
text = f.read()
authors = set(text.split())
relations, max_refer_count, min_refer_count = get_concerned_relations_by_authors(reference_relations_counter, authors)
saved_html = os.path.join(args.html_dir, html_file_name)
generate_html_page(relations, max_refer_count, min_refer_count, saved_html, threshold)
if __name__ == '__main__':
main()