ptecharts实现图谱可视化

ptecharts实现图谱可视化

图片样例如下：

import asyncio
from aiohttp import TCPConnector, ClientSession
import pyecharts.options as opts
from pyecharts.charts import Graph
import nest_asyncio
nest_asyncio.apply()
async def get_json_data(url: str) -> dict:
    async with ClientSession(connector=TCPConnector(ssl=False)) as session:
        async with session.get(url=url) as response:
            return await response.json()


# 使用asyncio.run()报错：AttributeError: module 'asyncio' has no attribute 'run'
# 是由于python版本低于3.7，需要改为asyncio.get_event_loop().run_until_complete(),且添加nest_asyncio.apply()

# 获取官方的数据
data = asyncio.get_event_loop().run_until_complete(
    get_json_data(
        url="https://echarts.apache.org/examples/data/asset/data/npmdepgraph.min10.json"
    )
)

nodes = [
    {
        "x": node["x"],
        "y": node["y"],
        "id": node["id"],
        "name": node["label"],
        "symbolSize": node["size"],
        "itemStyle": {"normal": {"color": node["color"]}},
    }
    for node in data["nodes"]
]

edges = [
    {"source": edge["sourceID"], "target": edge["targetID"]} for edge in data["edges"]
]


(
    Graph(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add(
        series_name="",
        nodes=nodes,
        links=edges,
        layout="none",
        is_roam=True,
        is_focusnode=True,
        label_opts=opts.LabelOpts(is_show=False),
        linestyle_opts=opts.LineStyleOpts(width=0.5, curve=0.3, opacity=0.7),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="NPM Dependencies"))
    .render("graph_plot.html")
)

异常处理：

RuntimeError: This event loop is already running”问题

import nest_asyncio
nest_asyncio.apply()

AttributeError: module ‘asyncio’ has no attribute 'run’问题

是由于python版本低于3.7，需要改为asyncio.get_event_loop().run_until_complete(),且添加nest_asyncio.apply()

data = asyncio.get_event_loop().run_until_complete(
    get_json_data(
        url="https://echarts.apache.org/examples/data/asset/data/npmdepgraph.min10.json"
    )
)

查看获取的data数据结构：

print(data['nodes'][1:3])
print(data['edges'][1:3])

[{'color': '#c71969', 'label': 'backbone', 'attributes': {}, 'y': -862.7517, 'x': -134.2215, 'id': 'backbone', 'size': 6.1554675}, {'color': '#c71969', 'label': 'underscore', 'attributes': {}, 'y': -734.4221, 'x': -75.53079, 'id': 'underscore', 'size': 100.0}]
[{'sourceID': 'jquery', 'attributes': {}, 'targetID': 'xmlhttprequest', 'size': 1}, {'sourceID': 'jquery', 'attributes': {}, 'targetID': 'htmlparser', 'size': 1}]

节点信息：

# data中的节点信息
data["nodes"][1:3]

[{'color': '#c71969',
  'label': 'backbone',
  'attributes': {},
  'y': -862.7517,
  'x': -134.2215,
  'id': 'backbone',
  'size': 6.1554675},
 {'color': '#c71969',
  'label': 'underscore',
  'attributes': {},
  'y': -734.4221,
  'x': -75.53079,
  'id': 'underscore',
  'size': 100.0}]

整理为图中的节点：

nodes[1:3]  # 传入绘图的数据结构

[{'x': -134.2215,
  'y': -862.7517,
  'id': 'backbone',
  'name': 'backbone',
  'symbolSize': 6.1554675,
  'itemStyle': {'normal': {'color': '#c71969'}}},
 {'x': -75.53079,
  'y': -734.4221,
  'id': 'underscore',
  'name': 'underscore',
  'symbolSize': 100.0,
  'itemStyle': {'normal': {'color': '#c71969'}}}]

图中连线：

edges[1:3]

[{'source': 'jquery', 'target': 'xmlhttprequest'},
 {'source': 'jquery', 'target': 'htmlparser'}]

后续可参考该结构，更换数据，结合实体消歧、命名实体识别等方法过滤实体传入数据，实现动态调整可视化。