def f(x): return _poly.eval(a, x) # evaluate polynomial a as a function xl, xu = -5, 5
#estimate plotting x range
xlo = x0 - (xn - x0) / 50.
xhi = xn + (xn - x0) / 50.
#plot function f(x)
m = 1000 #number of points for smooth plotting effect
xpts = np.linspace(xlo, xhi, m)
plt.plot(xpts, f(xpts), 'b', label='true function')
#draw vertical segments & interpolation points for integral estimate
#TO DO...
#fill area for integral estimate
xpts = np.linspace(x0, xn, m)
parabolic_coefs = _polyreg.curvefit(axpts, aypts, order=2)
ypts = _poly.eval(parabolic_coefs, xpts)
plt.fill_between(xpts,
ypts,
facecolor='g',
alpha=.2,
edgecolor='g',
label='integral estimate')
#fill area for error
#TO DO...
#show plot w/ title, legend, etc.
plt.title("Single application of Simpson's 1/3 integration rule")
plt.legend(loc="upper left", shadow=True)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
def f(x):
return _poly.eval(a, x)
def f(x): return _poly.eval(a, x) # Graphical Solution xl, xu = -5, 5