Creating fancy images with Matplotlib

I have to give a short presentation at SOSP next week, and for it, I needed to have some nice pictures representing a distributed array. After trying out several tools for trying to create these, I began to lament and cry over the state of Linux drawing software. But that’s a different story. I ended up writing a simple matplotlib script to generate the pictures I needed, and since it worked out pretty well, I thought I’d share it here.

Here’s the kind of picture I’m referring to:


It turns out this is pretty straightforward using matplotlib. Here’s the basic function:

def draw_array(a, target=None):
    fig = pylab.gcf()
    fig.frameon = False

ax = fig.gca()

ax.set_aspect('equal', 'box')

size = 1.0
z_scale = 1.4
i = 0
for z in reversed(range(a.shape[2])):
    for (x,y),v in np.ndenumerate(a[:, :, z]):
        i += 2
        alpha = a['transparency'][x,y,z]
        color = tuple(a['color'][x,y,z])
        off_x = 0.01 + x + size + z / z_scale
        off_y = y + size + z / z_scale

        rect = pylab.Rectangle([off_x, off_y], size, size,
                               facecolor=color, edgecolor=(0,0,0),
                               zorder = i, alpha = alpha)

        cx = off_x + size/2
        cy = off_y + size/2

        # sigh
        label = str(a['name'][x,y,z])
        w, h = pylab.matplotlib.text.TextPath((0,0), label).get_extents().size / 30

        #print w, h

        text = pylab.Text(cx - w / 2, cy - h / 2, label, zorder=i+1)

if target is not None:
return ax

The first part of this just turns off the various lines for the axes. We then iterate through the elements of the array and create a Rectangle() for each one; each “layer” (z-axis) is shifted off to the right a little bit from the previous, to give our illusion of depth. (We don’t want a normal perspective projection, as it would hide too much of the deeper layers).

The “sigh” comment is where I’m using a hack to determine the size of the text we’re going to put in so I can center it in the array cell. I couldn’t find an easier way to do this, and no, I don’t know why I have to divide the result by 30.

The input array has 3 fields which specify how to render each rectangle:

dtype=([('color', 'f,f,f'), ('name', 'i'), ('transparency', 'f')]))

Now we can construct an arbitrary array and feed it into our function:

shape = (3,3,5)
a = np.ndarray(shape, dtype=([('color', 'f,f,f'), ('name', 'i'), ('transparency', 'f')]))
a['name'] = np.arange(
a['transparency'] = 1.0
a['color'] = (1,1,1)
return a

draw_array(a, target='array.pdf')

Once we have the basics out of the way, we can do some fancy rendering really easily. First, let’s make a little helper class to draw slices:

class draw_slice(object):
    def <strong>init</strong>(self, a, target=None):
        self.a = a = target

def __getitem__(self, slc):
    slice_z = np.copy(self.a)
    slice_z['color'][slc] = (0.9, 0.5, 0.3)
    slice_z['transparency'] = 0.9

We can wrap an array in draw_slice() to make it easy to construct pictures of slices:



We can be fancier if we like too, drawing the results of a filter operation:,

draw_slice(a)[a[‘name’] &lt;= 1]


If you are interested, the full code for creating these figures is here: All you need is matplotlib and numpy.

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