coding like it’s 1979

It’s old news, but I just discovered Cathode and it’s pretty much made my day week.

There’s something so charming about the feel of analog devices and Cathode comes very close to the real thing. Or at least how I remember it. I installed mutt just so I could stay in the console longer.

Computing the optimal stop placement for transit

Like many people, I take the bus to work most days. My commute isn’t actually that far (about 3 miles), but I am incredibly lazy, and the bus lets me catch up on the magazines that would otherwise be accumulating dust on my table. (And if I keep up on my Harper’s, I can at least pretend I’m up to date on what’s going on).

Anyway, here’s my bus route:

http://pugetsound.onebusaway.org/where/standard/trip.action?id=1_28275667&serviceDate=1440572400000

bus route

The main thing to notice here is that it stops an awful lot.  During peak commute hours, I can sometimes walk faster than the bus.  Given that I’m out of shape and my commute involves a big hill, that’s not a good sign.

It’s been pointed out many times that perhaps stops are placed too close together in many locales:

So there are potentially good reasons why you want to have stops closer together than what might be optimal; though I would mostly bucket these into having a customer base that is old, fat, lazy, grumpy or some combination of those 4.  You can see in the humantransit post the outrage expressed at having stops more than 300 meters apart.  The horror of having to walk more than 1.5 blocks to your stop!

But let’s go ahead and assume we live in a world where people are happy to walk longer distances.  Let’s go further and assume they’re willing to walk as far as they need to ensure their overall trip time is minimized.  If we have such a cooperative public, then what’s our optimal stop distance?  I made up a trivial model of what happens in this case in a Ipython notebook here:

https://gist.github.com/rjpower/f78fba35235a5277ed85

Here’s the resulting plot:

This model is incredibly contrived, but still, it’s interesting to toy with the tradeoffs.  Note that even with a very slow walking pace (2 minutes/block, or 50 meters a minute), the optimal distance is over 5 blocks apart.  (Compare that with the spacing on my route at a ~2 blocks between stops.)
If you have ideas on how to improve the model, please let me know!

Transaction Chain Visualization

We had a paper at the last SOSP on transaction chains.  Our original analysis of chains was done by hand, which is quite a silly way to do it.  We then wrote a simple script to do the graph analysis, but it’s still difficult to picture the interaction of chains (a script telling you that you have an S-C cycle is great, but what should you do about it?)

To make this a bit easier, I made up a little webpage that lets  you enter in a list of chains and indicate commutative links.  This page very effectively illustrates 3 things:

  • My ineptness at Javascript
  • My lack of graph theory knowledge
  • That there are some neat Javascript libraries out there (hello Dagre!)

Try it out here: http://rjpower.org/transaction-chain/

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:

filter

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_axis_off()

ax.patch.set_facecolor('white')
ax.set_aspect('equal', 'box')
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_locator(plt.NullLocator())

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)
        ax.add_patch(rect)

        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)
        ax.add_artist(text)

ax.autoscale_view()
if target is not None:
    pylab.savefig(target)
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(np.prod(shape)).reshape(shape)
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
        self.target = 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
    draw_array(slice_z, self.target)

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


draw_slice(a)[:,:,1]

slice-z

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


draw_slice(a)[a[‘name’] &lt;= 1]

filter

If you are interested, the full code for creating these figures is here: https://gist.github.com/rjpower/7249729. All you need is matplotlib and numpy.

Citibike: Bike Flow

I’m a big fan of the Citibike bike share program that started here recently.  One common issue I and others seem to suffer from is the lack of bikes (when starting a trip) or docks (when ending a trip).  Our neighborhood tends to be a very popular destination in the evening, so when I try to ride a bike in, I often end up a few blocks away from my desired station.  Similarly, if I get a late start in the morning I often find there are no bikes left to ride.

I was curious about how the flow of bikes works around the city — where do the bikes go to when they leave the East Village?  I crawled the Citibike web site and created a simple website to visualize the flow of bikes around the city; the results are pretty interesting:

citibike

With some more work on the data, it might be possible to use it for predictions (“will I be able to return this bike to my station”) and to aid in balancing (choosing which stations to move bikes between, and at what time).

The source code for the application is available on Github.

Making a JIT interpreter with LuaJIT

(N.B. The code for all of these experiments is on Github).

I recently read this post by François Perrad regarding interpreters, where he compared interpreter loops written in Lua, LuaJit and Pypy.  (I think the original toy interpreter example comes from PyPy).   After some suggestions, he ended up with a new PyPy version which performed very well — close to what you’d see from a static compiler.

The bytecode ‘program’ being used for all of these examples is simply calculating, in a round-about fashion, the square of an input number:

MOV_A_R,    0,
MOV_A_R,    1,
MOV_R_A,    0, 
DECR_A,
MOV_A_R,    0,
MOV_R_A,    2, 
ADD_R_TO_A, 1,
MOV_A_R,    2,
MOV_R_A,    0, 
JUMP_IF_A,  4,
MOV_R_A,    2,
RETURN_A

I made a slight modification to this interpreter to force PyPy to load the bytecode at runtime (to ensure it doesn’t “cheat” during translation by just statically optimizing for this particular program).    This version runs quickly, but not as fast as the version that has the bytecode baked in.  It evaluates 100M iterations of the bytecode loop in 1.6seconds; still this is roughly a hundred times faster then the CPython equivalent.  This is what you’d expect, after all — it’s what PyPy is designed for.

The lua based interpreter, when run with Luajit, takes 5.5 seconds; not bad, but it’s 4 times slower then PyPy. Can we do better?  What’s causing Luajit to run slowly?  If we turn on jit debugging for luajit, we see the problem immediately:

luajit -jdump toy-jit.lua 100000000

There’s no output!  The JIT compiler never activated.  What’s going on?

It turns out that our interpreter loop (like all interpreter loops), is unpredictable, as the path of the execution is very data dependent (‘data’ here meaning the bytecode we’re interpreting):

while true do
  local opcode = bytecode[pc]
  pc = pc + 1 
  if opcode == JUMP_IF_A then
    local target = bytecode[pc]
    pc = pc + 1 
    if a ~= 0 then
      pc = target
    end
  elseif opcode == MOV_A_R then
    ...
  elseif opcode == MOV_R_A then
    ...
  elseif opcode == ADD_R_TO_A then
    ...
  elseif opcode == DECR_A then
    ...
  elseif opcode == RETURN_A then 

After executing a bytecode, the interpreter goes back up to the top of the while, and jumps to a different place. A tracing JIT never gets a chance to see the pattern, and so you end up running in the interpreter the whole time.  PyPy solves this problem by using magic meta-tracing.

It turns out we can get a similar effect in Luajit, without too much effort, using partial evaluation.  That is, given a chunk of bytecode, we’ll generate a specialized version of our interpreter for that bytecode.  We do this, in time-honored fashion, by copy-pasting. We step through each opcode, and instead of evaluating it, we build up a Lua string to evaluate it (A much cleaner approach would be to write our interpreter in some structured fashion, and generate the JIT interpreter from that):

if opcode == JUMP_IF_A then
      local target = bytecode[pc]
      pc = pc + 1
      f_str = f_str .. string.format([[
if a == 0 then
  goto op_%d
end
goto op_%d
]], pc, target)
    elseif opcode == MOV_R_A then
      local n = bytecode[pc]
      pc = pc + 1
    f_str = f_str .. string.format([[
a = reg_%d
]], n)

For our test program, this creates a Lua string like this:

function _jit(a)
  local reg = {0, 0, 0, 0, 0, 0, 0, 0}
  ::op_1::
reg[1] = a
::op_3::
reg[2] = a
::op_5::
a = reg[1]
::op_7::
a = a - 1
::op_8::
reg[1] = a
::op_10::
a = reg[3]
::op_12::
a = a + reg[2]
::op_14::
reg[3] = a
::op_16::
a = reg[1]
::op_18::
if a == 0 then
  goto op_20
end
goto op_5
::op_20::
a = reg[3]
::op_22::
return a
end

If we eval() this string, we get back an interpreter that’s been specialized for just this bytecode. What does our performance look like now?

time pypy-jit-c /home/power/tmp/bytecode.str 100000000
1.63s user 0.01s system 99% cpu 1.649 total

time luajit toy.lua 100000000       
5.51s user 0.01s system 99% cpu 5.549 total

time luajit toy-jit.lua 100000000
0.12s user 0.00s system 97% cpu 0.128 total

We’re now much faster then PyPy! Obviously this trick is easier to play with such a simple interpreter (we’re also using the native numeric type of our JIT, which isn’t always correct). Amore complex, dynamically typed systems might prove to be more difficult to do partial evaluation on. There also could be extra hints I could give to PyPy to make it work better (if you have any ideas, please tell me!).

Still, it’s somewhat surprising how easy it was to generate our ‘JIT’ interpreter — the code isn’t much bigger then the original version. Perhaps with some more scaffolding/helper libraries, this could be a viable way to create fast interpreters for new languages?

thread profiling in Python

Python has accumulated a lot of… character over the years.  We’ve got no less then 3 profiling libraries for single threaded execution and a multi-threaded profiler with an incompatible interface (Yappi).  Since many applications use more then one thread, this can be a bit annoying.

Yappi works most of the time.  Except it can sometimes cause your application to hang for unknown reasons (I blame signals, personally). The other issue is that Yappi doesn’t have a way of collecting call-stack information. (I don’t necessarily care that memcpy takes all of the time, I want to know who called memcpy). In particular, the lovely gprof2dot can take in pstats dumps and output a very nice profile graph.

To address this for my uses, I glom together cProfile runs from multiple threads. In case it might be useful for other people I wrote a quick gist illustrating how to do it. To make it easy to drop in, I monkey-patch the Thread.run method, but you can use a more maintainable approach if you like (I create a subclass ProfileThread in my applications).

from threading import Thread
 
import cProfile
import pstats
 
def enable_thread_profiling():
  '''Monkey-patch Thread.run to enable global profiling.
  
Each thread creates a local profiler; statistics are pooled
to the global stats object on run completion.'''
  Thread.stats = None
  thread_run = Thread.run
  
  def profile_run(self):
    self._prof = cProfile.Profile()
    self._prof.enable()
    thread_run(self)
    self._prof.disable()
    
    if Thread.stats is None:
      Thread.stats = pstats.Stats(self._prof)
    else:
      Thread.stats.add(self._prof)
  
  Thread.run = profile_run
  
def get_thread_stats():
  stats = getattr(Thread, 'stats', None)
  if stats is None:
    raise ValueError, 'Thread profiling was not enabled,'
                      'or no threads finished running.'
  return stats
 
if __name__ == '__main__':
  enable_thread_profiling()
  import time
  t = Thread(target=time.sleep, args=(1,))
  t.start()
  t.join()
  
  get_thread_stats().print_stats()

setting figure height in ipython

The default size chosen by imshow yields unpleasantly small images. Fortunately, you can easily change them using the rather strangely named gcf() function:

import pylab as P  
...  
f = P.gcf()  
f.set_figheight(16)  
f.set_figwidth(16)

log-spaced values with numpy

I knew this had to exist, since otherwise generated logarithmic plots in matplotlib would be a pain in the butt. Still, it took a bit of searching, although perhaps just the name should have clued me in.

 fig, ax = plt.subplots()
 steps = N.log10(N.logspace(0.9, 1-1e-5))
 ax.set_yscale('log', basex=10)
 ax.plot(steps, f(steps), '-')

Also, a shout-out for the ipython inline graphs (

ipython notebook --pylab inline

). Beautiful, and I can copy-paste them into emails and google docs!

running a pdf crawler with heritrix

I’ve used the Heritrix web crawler quite a few times in the past.  It’s a great piece of software, and has enough features to handle most crawling tasks with ease.
Recently, I wanted to crawl a whole bunch of PDF’s, and since I didn’t know where the PDF’s were going to come from, Heritrix seemed like a natural fit to help me out.  I’ll go over some of the less intuitive steps:

Download the right version of the crawler

That is to say, version 1.*.  Version 2 seems to have been dropped, and version 3 does not yet have all of the features from version 1 implemented (not to mention, the user interface seems to have gone downhill).

For your convenience, here’s a link to the download page.

Make sure you’re rejecting almost everything

You almost certainly don’t want all the web has to offer.  You only want a tiny fraction of it.  For instance, I use a MatchesRegexpDecideRule to drop any media content with the following expression:

.*.(jpg|jpeg|gif|png|mpg|mpeg|txt|css|js|ppt|JPG|tar.gz|flv|MPG|zip|exe|avi|tvd)$

Similarly, you’ll want to drop pesky calendar like applications:

.*(calendar|/api|lecture).*

And any dynamic pages that want to suck up your bandwidth:

.*?.*

Save only what you need

Heritrix has a nice property of allowing for decision rules to be placed almost anywhere, including just before when a file gets written to disk. To avoid writing files you’re uninterested in, you can request that only certain mimetypes are allowed through – add a default reject rule, and then only accept files you want – in my case pdfs or postscript files:

.*(pdf|postscript).*

Regular expressions are full, not partial matches

You need to ensure your regular expression matches the entire item, not just part of it. This means pre and post-pending

.*

to your normal patterns.

If you’re feeling lazy, you can download the crawl order I used and use it as a base for your crawl. Good luck!