from matplotlib.pyplot import *
from numpy import *
from mpl_toolkits.axisartist import *

x,y = [1.,2.,3.,4.], [6., 5., 7., 10.]
A = vstack([x, ones(len(x))]).T
b2,b1 = linalg.lstsq(A,y)[0]
x2 = arange(0,5,0.1)
y2 = lambda xx: b1+b2*xx

fig = figure(figsize=(3,3))
ax = Subplot(fig,"111",axisbg='none')
fig.add_subplot(ax)
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.plot(x,y,'ro',label="Data")
ax.plot(x2,y2(x2),label="curve fit",lw=2)
ymin, ymax = [], []
for i in range(len(x)):
	yy = y2(x[i])
	if yy >= y[i]:
		ymin, ymax = append(ymin,y[i]), append(ymax,yy)
	else:
		ymin, ymax = append(ymax,yy), append(ymin,y[i])
ax.vlines(x,ymin, ymax,color='g',linestyles='solid',lw=1.5)
ax.set_ylabel("y",fontsize=12)
ax.set_xlabel("x",fontsize=12)
ax.grid(True)
ax.minorticks_on()
ax.set_xlim(0,5)
ax.set_ylim(4,10.5)
ax.legend(frameon=True, handletextpad=0,loc='upper left',\
	labelspacing=.05)
setp(ax.get_legend().get_texts(),fontsize=12)

fig.savefig("Linear_least_squares_example2.svg",bbox_inches="tight",\
	pad_inches=.15)