新的转换算法测试，更好的声音适配

resources/test/volumn_function_fit.py

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

resources/test/volumn_function_test.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+# 定义对数函数
+def q_function1(vol):
+    # return -23.65060754864053*((x+508.2130392724084)**0.8433764630986903) + 7.257078620637543 * (x+407.86870598508153) + 1585.6201108739122
+    # return -58.863374003875954 *((x+12.41481943150274 )**0.9973316187745871 ) +57.92341268595151 * (x+ 13.391132186222036) +  -32.92986286030519
+    return  -8.081720684086314 * np.log( vol + 14.579508825070013,)+ 37.65806375944386 
+    
+    
+def q_function2(vol):
+    return 0.2721359356095803 * ((vol + 2592.272889454798) ** 1.358571233418649) + -6.313841334963396 * (vol + 2592.272889454798) + 4558.496367823575
+
+# 生成 x 值
+x_values = np.linspace(0, 128, 1000)
+
+
+x_data = np.array([0,16,32,48,64,80,96,112,128])
+y_data = np.array([16, 10, 6.75, 4, 2.5, 1.6, 0.8, 0.3, 0])
+
+
+print(q_function1(x_data))
+print(q_function2(x_data))
+
+# 绘制图像
+plt.plot(x_values, q_function1(x_values,),label = "fit1")
+plt.plot(x_values, q_function2(x_values,),label = "fit2")
+plt.scatter(x_data, y_data, color='red')  # 标记给定的点
+# plt.scatter(x_data, y_data2, color='green')  # 标记给定的点
+# plt.scatter(x_data, y_data3, color='blue')  # 标记给定的点
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Function')
+plt.legend()
+plt.grid(True)
+plt.show()