There is a data sample. It needs to find the coefficients of the polynomial of the 3rd degree and a piecewise linear function. Polina I found very simple using functions np.polyfit. and np.poly1d. . It was possible to build a schedule and obtain a specific equation. Now I would like to do the same to obtain the coefficients of several linear functions. I managed to find an example of a code that built a schedule, but to remove the equations for three straight lines. Of course, I can count them manually, but I would like to know if there is a regular method for solving this task?

Import NUMPY AS NP
Import Matplotlib.pyPlot AS PLT
from scipy import optimize
np.random.seed (9999)
x= np.random.normal (0, 1, 1000) * 10
y= np.where (x <
 -15, -2 * x + 3, NP.Where (X <
 10, X +
                                          48, -4 * x + 98)) + np.random.normal (0, 3, 1000)
Plt.scatter (x, y, s= 5, color= u'b ', marker='. ', Label=' Scatter Plt ')
Def PieceWise_Linear (X, X0, X1, B, K1, K2, K3):
   CONDLIST= [X <
 x0, (X >
= x0) &
 (X <
 x1), X >
= x1]
   FUNCLIST= [LAMBDA X: K1 * X + B, LAMBDA X: K1 * X + B + K2 *
               (x -x0), lambda x: k1 * x + b + k2 * (x -x0) + k3 * (x -x1)]
   Return NP.PieceWise (X, Condlist, FunClist)
P, E= Optimize.curve_Fit (PieCewise_Linear, X, Y)
xD= np.linspace (-30, 30, 1000)
Plt.plot (X, Y, "O")
PLT.plot (XD, PieCewise_Linear (XD, * P))
Plt.Show ()