我試圖將曲線擬合到某些數據，但生成的曲線看起來像一團亂麻。不知道系數是否準確。使用這個示例數據集，它會打印出類似三角形的東西，而使用我的原始數據集，它看起來更糟。大部分都是教程。我嘗試從備用教程中刪除 sympy 代碼，但這樣做沒有任何效果。import matplotlib.pyplot as pltfrom scipy.optimize import curve_fitimport numpy as npimport sympy as symx = [0.0009425070688029959,0.0009398496240601303,0.0018779342723004293,0.004694835680751241,0.0009425070688029959,0.004734848484848552,0.0018993352326685255,0.0009460737937558928]y = [0.0028301886792453904,0.003762935089369628,0.001881467544684814,0.0009433962264150743,0.0028301886792453904,0.0019029495718363059,0.0038058991436727804,0.0018939393939393534]"""Plot your data"""plt.plot(x, y, 'ro',label="Original Data")"""brutal force to avoid errors""" x = np.array(x, dtype=float) #transform your data in a numpy array of floats y = np.array(y, dtype=float) #so the curve_fit can work"""create a function to fit with your data. a, b, c and d are the coefficientsthat curve_fit will calculate for you. In this part you need to guess and/or use mathematical knowledge to finda function that resembles your data"""def func(x, b, c, d): return b * x * x + c * x + d"""make the curve_fit"""popt, pcov = curve_fit(func, x, y)"""The result is:popt[0] = a , popt[1] = b, popt[2] = c and popt[3] = d of the function,so f(x) = popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3]."""print("b = " + str(popt[0]) + " c = " + str(popt[1]) + " d = " + str(popt[2]))"""tUse sympy to generate the latex sintax of the function"""xs = sym.Symbol('\lambda') tex = sym.latex(func(xs,*popt)).replace('$', '')plt.title(r'$f(\lambda)= %s$' %(tex),fontsize=16)"""Print the coefficients and plot the funcion."""plt.plot(x, func(x, *popt), label="Fitted Curve") #same as line above \/#plt.plot(x, popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3], label="Fitted Curve") plt.legend(loc='upper left')plt.show()
2 回答
白衣染霜花
TA貢獻1796條經驗 獲得超10個贊
這是因為 Matplotlib 只會在原始數據（x 和 y 數組中）的幾個點之間并按照它們定義的順序繪制線條。只有 3 個唯一的 x 值（加上一些噪聲），這就是為什么您看到的看起來像三角形的原因。
解決方法是創建一個新數組，在您感興趣的范圍內均勻分布且有序的 x 值。您可以使用numpy 中的 linspace 函數來完成此操作。
例如，嘗試執行第二個繪圖命令：
x_eval?=?np.linspace(min(x),?max(x),?100) plt.plot(x_eval,?func(x_eval,?*popt),?label="Fitted?Curve")
x_eval上面是原始數據中最小和最大 x 值之間均勻分布的 100 個值的列表。
守候你守候我
TA貢獻1802條經驗 獲得超10個贊
看起來你需要排序xdata。
嘗試插入這個：
x,y = zip(*sorted(zip(x, y)))
這樣
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
import sympy as sym
x = [0.0009425070688029959,
0.0009398496240601303,
0.0018779342723004293,
0.004694835680751241,
0.0009425070688029959,
0.004734848484848552,
0.0018993352326685255,
0.0009460737937558928]
y = [0.0028301886792453904,
0.003762935089369628,
0.001881467544684814,
0.0009433962264150743,
0.0028301886792453904,
0.0019029495718363059,
0.0038058991436727804,
0.0018939393939393534]
"""
Plot your data
"""
plt.plot(x, y, 'ro',label="Original Data")
"""
brutal force to avoid errors
"""
x,y = zip(*sorted(zip(x, y)))
x = np.array(x, dtype=float) #transform your data in a numpy array of floats
y = np.array(y, dtype=float) #so the curve_fit can work
"""
create a function to fit with your data. a, b, c and d are the coefficients
that curve_fit will calculate for you.
In this part you need to guess and/or use mathematical knowledge to find
a function that resembles your data
"""
def func(x, b, c, d):
return b * x * x + c * x + d
"""
make the curve_fit
"""
popt, pcov = curve_fit(func, x, y)
"""
The result is:
popt[0] = a , popt[1] = b, popt[2] = c and popt[3] = d of the function,
so f(x) = popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3].
"""
print("b = " + str(popt[0]) + " c = " + str(popt[1]) + " d = " + str(popt[2]))
"""t
Use sympy to generate the latex sintax of the function
"""
xs = sym.Symbol('\lambda')
tex = sym.latex(func(xs,*popt)).replace('$', '')
plt.title(r'$f(\lambda)= %s$' %(tex),fontsize=16)
"""
Print the coefficients and plot the funcion.
"""
plt.plot(x, func(x, *popt), label="Fitted Curve") #same as line above \/
#plt.plot(x, popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3], label="Fitted Curve")
plt.legend(loc='upper left')
plt.show()
The plotted curve from the data above.
添加回答
舉報
0/150
提交
取消