使用更准确的 x 值数组在 matplotlib 中生成最佳拟合线？

Question

I am currently stuck on a problem on which I am required to generate a curve of best fit which I am required to use a more precise x array from 250 to 100 in steps of 10. Here is my code below so far..

import numpy as np
from numpy import polyfit, polyval
import matplotlib.pyplot as plt

x = [250,300,350,400,450,500,550,600,700,750,800,900,1000]
x = np.array(x)
y = [0.791, 0.846, 0.895, 0.939, 0.978, 1.014, 1.046, 1.075, 1.102, 1.148, 1.169, 1.204, 1.234]
y= np.array(y)

r = polyfit(x,y,3)
fit = polyval(r, x)

plt.plot(x, fit, 'b')
plt.plot(x,y, color = 'r', marker = 'x')
plt.show()

Answer 1

If I understand correctly, you are trying to create an array of numbers from a to b by steps of c.

With pure python you can use:

list(range(a, b, c)) #in your case list(range(250, 1000, 10))

Or, since you are using numpy you can directly make the numpy array:

np.arange(a, b, c)

Answer 2

To create an array in steps you can use numpy.arange([start,] stop[, step]) :

import numpy as np
x = np.arange(250,1000,10)

Answer 3

To generate values from 250-1000, use range(start, stop, step):

x = range(250,1001,10)
x = np.array(x)