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43 lines
1.0 KiB
Python
43 lines
1.0 KiB
Python
import numpy as np
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from scipy.optimize import curve_fit
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import matplotlib.pyplot as plt
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def q_function1(x, a, a2, c1,):
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return a * np.log( x + a2,)+ c1
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def q_function2(x, b, b2, b3, b4, c2):
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return b * ((x + b2) ** b3) + b4 * (x+b2) + c2
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x_data = np.array([0, 16, 32, 48, 64, 80, 96, 112, 128])
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y_data = np.array([16, 10, 6.75, 4, 2.5, 1.6, 0.8, 0.3, 0])
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p_est1, err_est1 = curve_fit(q_function1, x_data[:5], y_data[:5], maxfev=1000000)
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p_est2, err_est2 = curve_fit(q_function2, x_data[4:], y_data[4:], maxfev=1000000)
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print(q_function1(x_data[:5], *p_est1))
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print(q_function2(x_data[4:], *p_est2))
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print("参数一：",*p_est1)
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print("参数二：",*p_est2)
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# 绘制图像
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plt.plot(
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np.arange(0, 64.1, 0.1), q_function1(np.arange(0, 64.1, 0.1), *p_est1), label=r"FIT1"
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)
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plt.plot(
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np.arange(64, 128.1, 0.1), q_function2(np.arange(64, 128.1, 0.1), *p_est2), label=r"FIT2"
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)
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plt.scatter(x_data, y_data, color="red") # 标记给定的点
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# plt.xlabel('x')
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# plt.ylabel('y')
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plt.title("Function Fit")
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plt.legend()
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# plt.grid(True)
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plt.show()
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