116 lines
3.9 KiB
Python
116 lines
3.9 KiB
Python
from threading import Lock
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import pandas as pd
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import requests
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from flask import Flask, send_file, make_response, session, request
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from matplotlib import rcParams
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from redis import Redis
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from pca import plot_pca_scatter
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from radarref import plot_radar
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from similarity import row_correlations, row_euclidean_distances, calculate_similarity, plot_similarity_heatmap, \
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plot_similarity_network
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# 全局设置中文字体
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rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体为黑体
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rcParams['axes.unicode_minus'] = False # 解决坐标轴负号显示问题
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app = Flask(__name__)
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redis_client = Redis(host='localhost', port=6379, db=0, password="7ZH2jxM1")
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lock = Lock()
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def handleGetData(taskId, ids):
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# 获取键对应的值
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content = redis_client.get(f'{taskId}:{ids}')
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if content:
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print(f"获取到了{{taskId}}数据---------------")
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return content
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if ids.find(",") != -1:
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new_ids = [int(item) for item in ids.split(",")]
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else:
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new_ids = [] if ids == '' else [int(ids)]
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resp = requests.post(f"http://localhost:8069/prod-api/ggpx/TaskData/toEXCEL2",
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{"taskId": taskId,
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'ids': new_ids})
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# 设置键值对
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redis_client.set(f'{taskId}:{ids}', resp.content, 5)
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return resp.content
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@app.route('/')
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def index():
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return 'Hello World'
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@app.route('/statistics/radar/<id>')
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def radar(id):
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ids = request.args.get('ids', '')
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# 一个行名为样本名，列名为指标名的pd.dataframe
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data = pd.read_excel(handleGetData(id, ids), index_col=0)
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# 任务设置的每个指标的最大值
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max_values = [10, 15, 15, 10, 15, 5, 10, 10, 5, 5] # 示例最大值向量
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while True:
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with lock:
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# 使用这个函数
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img_bytes = plot_radar(data, max_values)
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# 创建响应对象
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response = make_response(img_bytes.read())
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response.headers['Content-Type'] = 'image/png'
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return response
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@app.route('/statistics/pca/<id>')
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def pca(id):
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ids = request.args.get('ids', '')
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# 一个行名为样本名，列名为指标名的pd.dataframe
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data = pd.read_excel(handleGetData(id, ids), index_col=0)
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while True:
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with lock:
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# 生成散点图，带上标签
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img_bytes = plot_pca_scatter(data, labels=data.index)
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# 创建响应对象
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response = make_response(img_bytes.read())
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response.headers['Content-Type'] = 'image/png'
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return response
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@app.route('/statistics/similarity/<id>')
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def similarity(id):
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ids = request.args.get('ids', '')
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# 一个行名为样本名，列名为指标名的pd.dataframe
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matrix = pd.read_excel(handleGetData(id, ids), index_col=0)
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# 调用函数
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correlation_result = row_correlations(matrix)
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while True:
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with lock:
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# 绘制相关系数热力图
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img_bytes = plot_similarity_heatmap(correlation_result)
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# 创建响应对象
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response = make_response(img_bytes.read())
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response.headers['Content-Type'] = 'image/png'
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return response
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@app.route('/statistics/similarity2/<id>')
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def similarity2(id):
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ids = request.args.get('ids', '')
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# 示例矩阵,和之前的扇形图要求一样
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matrix = pd.read_excel(handleGetData(id, ids), index_col=0)
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# 调用函数
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correlation_result = row_correlations(matrix)
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distance_result = row_euclidean_distances(matrix)
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similarity_result = calculate_similarity(correlation_result, distance_result)
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while True:
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with lock:
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# 绘制相似度网络图
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img_bytes = plot_similarity_network(similarity_result, threshold=0.5)
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# 创建响应对象
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response = make_response(img_bytes.read())
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response.headers['Content-Type'] = 'image/png'
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return response
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if __name__ == '__main__':
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app.run(threaded=True, host='0.0.0.0')
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