Have implemented @Micah 's solution to generate a trendline with a few changes and thought I'd share:
order=2)Rval=True)Code:
def trendline(xd, yd, order=1, c='r', alpha=1, Rval=False):
"""Make a line of best fit"""
#Calculate trendline
coeffs = np.polyfit(xd, yd, order)
intercept = coeffs[-1]
slope = coeffs[-2]
power = coeffs[0] if order == 2 else 0
minxd = np.min(xd)
maxxd = np.max(xd)
xl = np.array([minxd, maxxd])
yl = power * xl ** 2 + slope * xl + intercept
#Plot trendline
plt.plot(xl, yl, c, alpha=alpha)
#Calculate R Squared
p = np.poly1d(coeffs)
ybar = np.sum(yd) / len(yd)
ssreg = np.sum((p(xd) - ybar) ** 2)
sstot = np.sum((yd - ybar) ** 2)
Rsqr = ssreg / sstot
if not Rval:
#Plot R^2 value
plt.text(0.8 * maxxd + 0.2 * minxd, 0.8 * np.max(yd) + 0.2 * np.min(yd),
'$R^2 = %0.2f$' % Rsqr)
else:
#Return the R^2 value:
return Rsqr