Javascript Gets Border Width

You can remove the white space padding by setting bbox_inches="tight" in savefig:

plt.savefig("test.png",bbox_inches="tight")

You"ll have to put the argument to bbox_inches as a string, perhaps this is why it didn"t work earlier for you.

Possible duplicates:

Matplotlib plots: removing axis, legends and white spaces

How to set the margins for a matplotlib figure?

Reduce left and right margins in matplotlib plot

I cannot claim I know exactly why or how my “solution” works, but this is what I had to do when I wanted to plot the outline of a couple of aerofoil sections — without white margins — to a PDF file. (Note that I used matplotlib inside an IPython notebook, with the -pylab flag.)

plt.gca().set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
plt.margins(0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.savefig("filename.pdf", bbox_inches = "tight",
    pad_inches = 0)

I have tried to deactivate different parts of this, but this always lead to a white margin somewhere. You may even have modify this to keep fat lines near the limits of the figure from being shaved by the lack of margins.

From the documentation for scatter:

Optional kwargs control the Collection properties; in particular:

    edgecolors:
        The string ‘none’ to plot faces with no outlines
    facecolors:
        The string ‘none’ to plot unfilled outlines

Try the following:

import matplotlib.pyplot as plt 
import numpy as np 

x = np.random.randn(60) 
y = np.random.randn(60)

plt.scatter(x, y, s=80, facecolors="none", edgecolors="r")
plt.show()

Note: For other types of plots see this post on the use of markeredgecolor and markerfacecolor.

You need to add it to an axes. A Circle is a subclass of an Patch, and an axes has an add_patch method. (You can also use add_artist but it"s not recommended.)

Here"s an example of doing this:

import matplotlib.pyplot as plt

circle1 = plt.Circle((0, 0), 0.2, color="r")
circle2 = plt.Circle((0.5, 0.5), 0.2, color="blue")
circle3 = plt.Circle((1, 1), 0.2, color="g", clip_on=False)

fig, ax = plt.subplots() # note we must use plt.subplots, not plt.subplot
# (or if you have an existing figure)
# fig = plt.gcf()
# ax = fig.gca()

ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)

fig.savefig("plotcircles.png")

This results in the following figure:

The first circle is at the origin, but by default clip_on is True, so the circle is clipped when ever it extends beyond the axes. The third (green) circle shows what happens when you don"t clip the Artist. It extends beyond the axes (but not beyond the figure, ie the figure size is not automatically adjusted to plot all of your artists).

The units for x, y and radius correspond to data units by default. In this case, I didn"t plot anything on my axes (fig.gca() returns the current axes), and since the limits have never been set, they defaults to an x and y range from 0 to 1.

Here"s a continuation of the example, showing how units matter:

circle1 = plt.Circle((0, 0), 2, color="r")
# now make a circle with no fill, which is good for hi-lighting key results
circle2 = plt.Circle((5, 5), 0.5, color="b", fill=False)
circle3 = plt.Circle((10, 10), 2, color="g", clip_on=False)
    
ax = plt.gca()
ax.cla() # clear things for fresh plot

# change default range so that new circles will work
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
# some data
ax.plot(range(11), "o", color="black")
# key data point that we are encircling
ax.plot((5), (5), "o", color="y")
    
ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)
fig.savefig("plotcircles2.png")

which results in:

You can see how I set the fill of the 2nd circle to False, which is useful for encircling key results (like my yellow data point).