Plot smooth line with PyPlot

You could use scipy.interpolate.spline to smooth out your data yourself:

from scipy.interpolate import spline# 300 represents number of points to make between T.min and T.maxxnew = np.linspace(T.min(), T.max(), 300)  power_smooth = spline(T, power, xnew)plt.plot(xnew,power_smooth)plt.show()

spline is deprecated in scipy 0.19.0, use BSpline class instead.

Switching from spline to BSpline isn't a straightforward copy/paste and requires a little tweaking:

from scipy.interpolate import make_interp_spline, BSpline# 300 represents number of points to make between T.min and T.maxxnew = np.linspace(T.min(), T.max(), 300) spl = make_interp_spline(T, power, k=3)  # type: BSplinepower_smooth = spl(xnew)plt.plot(xnew, power_smooth)plt.show()

Before:

After:

For this example spline works well, but if the function is not smooth inherently and you want to have smoothed version you can also try:

from scipy.ndimage.filters import gaussian_filter1dysmoothed = gaussian_filter1d(y, sigma=2)plt.plot(x, ysmoothed)plt.show()

if you increase sigma you can get a more smoothed function.

Proceed with caution with this one. It modifies the original values and may not be what you want.

I presume you mean curve-fitting and not anti-aliasing from the context of your question. PyPlot doesn't have any built-in support for this, but you can easily implement some basic curve-fitting yourself, like the code seen here, or if you're using GuiQwt it has a curve fitting module. (You could probably also steal the code from SciPy to do this as well).