初版
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app/algorithms.py
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29
app/algorithms.py
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import numpy as np
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from typing import List, Tuple
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def bilinear_interpolation(
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X_NUM: int,
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Y_NUM: int,
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X: float,
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Y: float,
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X0: List[float],
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Y0: List[float],
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Z0: np.ndarray
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) -> Tuple[float, float, float, List[float]]:
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"""
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双线性插值计算
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:param X_NUM: X方向点数 (整数)
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:param Y_NUM: Y方向点数 (整数)
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:param X: 目标X坐标 (浮点数)
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:param Y: 目标Y坐标 (浮点数)
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:param X0: X坐标数组 (浮点数列表)
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:param Y0: Y坐标数组 (浮点数列表)
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:param Z0: 二维数据数组 (numpy.ndarray, 形状为 Y_NUM x X_NUM)
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:return: 元组 (Z, ZX, ZY, Z_OUT)
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"""
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X0_arr = np.array(X0) # 转换为NumPy数组
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Z = np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))])
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ZX = (np.interp(Y, Y0, Z0[:, np.argmin(np.abs(X0_arr - X + 0.1))]) - Z) / 0.1
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ZY = (np.interp(Y + 0.1, Y0, Z0[:, np.argmin(np.abs(X0_arr - X))]) - Z) / 0.1
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Z_OUT = Z0.flatten().tolist()
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return Z, ZX, ZY, Z_OUT
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24
app/main.http
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app/main.http
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### 1. 测试矩阵乘法 API
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POST http://127.0.0.1:5000/api/matrix_multiply
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Content-Type: application/json
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[
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[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]
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]
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### 2. 测试时间序列分析 API
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POST http://127.0.0.1:5000/api/time_series_analysis
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Content-Type: application/json
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[1.2, 3.4, 5.6, 7.8, 9.0]
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### 计算API
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POST http://127.0.0.1:5000/PythonCalculate
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Content-Type: application/json
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{
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"text": "{\"FuncName\":\"calculate_main\",\"ClassName\":\"\",\"Par\":[{\"Name\":\"X_NUM\",\"DataType\":\"0\",\"ArrayType\":\"0\",\"IsOut\":\"2\",\"Data\":5},{\"Name\":\"Y_NUM\",\"DataType\":\"0\",\"ArrayType\":\"0\",\"IsOut\":\"2\",\"Data\":5},{\"Name\":\"X\",\"DataType\":\"1\",\"ArrayType\":\"0\",\"IsOut\":\"2\",\"Data\":2.5},{\"Name\":\"Y\",\"DataType\":\"1\",\"ArrayType\":\"0\",\"IsOut\":\"2\",\"Data\":3.5},{\"Name\":\"X0\",\"DataType\":\"1\",\"ArrayType\":\"1\",\"IsOut\":\"2\",\"Data\":[1,2,3,4,5]},{\"Name\":\"Y0\",\"DataType\":\"1\",\"ArrayType\":\"1\",\"IsOut\":\"2\",\"Data\":[1,2,3,4,5]},{\"Name\":\"Z0\",\"DataType\":\"1\",\"ArrayType\":\"1\",\"IsOut\":\"2\",\"Data\":[1,2,3,4,5,2,3,4,5,6,3,4,5,6,7,4,5,6,7,8,5,6,7,8,9]},{\"Name\":\"Z\",\"DataType\":\"1\",\"ArrayType\":\"0\",\"IsOut\":\"1\",\"Data\":0},{\"Name\":\"ZX\",\"DataType\":\"1\",\"ArrayType\":\"0\",\"IsOut\":\"1\",\"Data\":0},{\"Name\":\"ZY\",\"DataType\":\"1\",\"ArrayType\":\"0\",\"IsOut\":\"1\",\"Data\":0},{\"Name\":\"Z_OUT\",\"DataType\":\"1\",\"ArrayType\":\"1\",\"IsOut\":\"1\",\"Data\":0}]}"
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}
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86
app/main.py
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app/main.py
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import numpy as np
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from flask import Flask, request, jsonify
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from flasgger import Swagger
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import json
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from algorithms import bilinear_interpolation
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app = Flask(__name__)
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# 配置 Swagger
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app.config['SWAGGER'] = {
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'title': '插值计算API',
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'description': '提供基于双线性插值的计算服务',
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'version': '1.0.0',
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'uiversion': 3
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}
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swagger = Swagger(app)
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@app.route('/PythonCalculate', methods=['POST'])
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def calculate():
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"""
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插值计算接口
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---
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tags:
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- 计算
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parameters:
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- in: body
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name: body
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required: true
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schema:
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type: object
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properties:
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text:
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type: string
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description: 包含计算参数的JSON字符串
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example: '{"FuncName":"calculate_main","ClassName":"","Par":[{"name":"X_NUM","dataType":"0","arrayType":"0","isOut":"2","data":5},{"name":"Y_NUM","dataType":"0","arrayType":"0","isOut":"2","data":5},{"name":"X","dataType":"1","arrayType":"0","isOut":"2","data":2.5},{"name":"Y","dataType":"1","arrayType":"0","isOut":"2","data":3.5},{"name":"X0","dataType":"1","arrayType":"1","isOut":"2","data":[1,2,3,4,5]},{"name":"Y0","dataType":"1","arrayType":"1","isOut":"2","data":[1,2,3,4,5]},{"name":"Z0","dataType":"1","arrayType":"1","isOut":"2","data":[1,2,3,4,5,2,3,4,5,6,3,4,5,6,7,4,5,6,7,8,5,6,7,8,9]},{"name":"Z","dataType":"1","arrayType":"0","isOut":"1","data":0},{"name":"ZX","dataType":"1","arrayType":"0","isOut":"1","data":0},{"name":"ZY","dataType":"1","arrayType":"0","isOut":"1","data":0},{"name":"Z_OUT","dataType":"1","arrayType":"1","isOut":"1","data":0}]}'
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responses:
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200:
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description: 计算结果
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schema:
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type: object
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properties:
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text:
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type: string
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description: 包含计算结果的JSON字符串
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"""
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# 解析输入JSON
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request_data = request.json
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input_data = json.loads(request_data["text"])
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# 提取输入参数
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X_NUM = input_data["Par"][0]["Data"]
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Y_NUM = input_data["Par"][1]["Data"]
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X = input_data["Par"][2]["Data"]
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Y = input_data["Par"][3]["Data"]
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X0 = input_data["Par"][4]["Data"]
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Y0 = input_data["Par"][5]["Data"]
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Z0 = input_data["Par"][6]["Data"]
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# 调用算法函数
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Z, ZX, ZY, Z_OUT = bilinear_interpolation(
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X_NUM, Y_NUM, X, Y, X0, Y0, np.array(Z0).reshape(Y_NUM, X_NUM)
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)
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# 构建输出JSON
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output_data = {
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"funcName": "calculate_main",
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"className": "",
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"par": [
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{"name": "X_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": X_NUM},
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{"name": "Y_NUM", "dataType": 0, "arrayType": 0, "isOut": 2, "data": Y_NUM},
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{"name": "X", "dataType": 1, "arrayType": 0, "isOut": 2, "data": X},
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{"name": "Y", "dataType": 2, "arrayType": 0, "isOut": 2, "data": Y},
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{"name": "X0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": X0},
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{"name": "Y0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Y0},
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{"name": "Z0", "dataType": 1, "arrayType": 1, "isOut": 2, "data": Z0},
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{"name": "Z", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(Z)},
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{"name": "ZX", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZX)},
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{"name": "ZY", "dataType": 1, "arrayType": 0, "isOut": 1, "data": float(ZY)},
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{"name": "Z_OUT", "dataType": 1, "arrayType": 1, "isOut": 1, "data": Z_OUT}
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]
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}
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return jsonify(output_data)
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if __name__ == '__main__':
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app.run(debug=True)
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