Beyond DRY – Why Redundancy Makes Your Code More Robust and Less Fragile

Anti-Fragile by Nassim Nicholas Taleb is a goldmine of practical ideas for software developers, despite it not being a software development book.

Redundancy is one example of such an idea that is explored. Taleb explains how having some redundancy reduces fragility, and means we don’t need to predict the future so well. Think of food stored in your basement, or cash under your mattress.

Taleb notes how nature’s designs frequently employ redundancy (“Nature likes to overinsure itself”):

“Layers of redundancy are the central risk management property of natural systems. We humans have two kidneys […] extra spare parts, and extra capacity in many, many things (say, lungs, neural system, arterial apparatus), while human design tends to be spare and inversely redundant, so to speak – we have a historical track record of engaging in debt, which is the opposite of redundancy”

Software source code is a good example of human design that tends to be “spare” (having no excess fat) and “inversely redundant”. Redundancy in code is traditionally avoided at all costs. In fact, one of the first principles that junior developers are often taught is the DRY principle – Don’t Repeat Yourself. As far as DRY is concerned, redundant code is a blight that should be eliminated wherever it shows up.

There are good reasons for the DRY principle. Duplicate code adds noise to the project, making it harder to understand without adding any obvious value. It makes the project harder to modify because the same code must be maintained separately at each place it is duplicated. Each of these locations is also another opportunity to introduce bugs. Duplicate code feels like waste.

However, as Taleb states:

“Redundancy is ambiguous because it seems like a waste if nothing unusual happens. Except that something unusual happens – usually.” [emphasis added]

What are these “unusual things that usually happen” in software development? And how could duplicate code possibly help protect us against them?

The Wrong Abstraction

Firstly, remember that duplication is eliminated by introducing abstractions, such as a function or class. The problem with abstractions is that it is difficult to know ahead of time whether a chosen abstraction is actually a good fit for your project. And the cost of getting this wrong is high. Poorly-chosen abstractions add friction to making the kinds of changes that are actually needed for the project, while still exacting an ongoing cost in terms of complexity. There’s also the risk that by the time poor abstractions have been recognised as such, they have already spread throughout the project. Rooting them out at this point will likewise impact code all throughout the project, potentially with unintended consequences.

The “unusual things that usually happen” in software development are unexpected, unpredictable (and unavoidable) changes in business requirements. These have the annoying effect of revealing the shortcomings of your abstractions, abstractions that you perhaps added while faithfully following the DRY principle.

Too-eager abstraction and a lack of redundancy mirrors the problems of centralisation, another idea explored in Anti-Fragile. Centralisation, while efficient in the short-term (read: less code), makes systems fragile. When blow-ups happen, they can take down (or at least damage) the entire system. NNT outlines in Anti-Fragile how such fragility and lack of redundancy was the cause of the banking system collapse of 2008.

Redundancy in the form of duplicated code, on the other hand, makes code more robust. It does this by avoiding the worse evil of introducing the wrong abstraction. In this way, it limits the impact of unexpected changes in business requirements. As Sandi Metz states: “Duplication is far cheaper than the wrong abstraction”

The Rule of Three

As it turns out, there is another software development principle (or rule of thumb) which does recognise the risks of poor abstractions, and seeks to mitigate them through some redundancy. It’s called the “Rule of Three”. It states that you should wait until a piece of code appears three times before abstracting it out. (Note that this appears to contradict the DRY principle). This minimises the chances that the abstraction is premature, and increases the chances that it addresses a real, recurring feature of the problem domain that is worth the cost of abstraction.

Introducing an abstraction is in some sense a prediction of the future. Abstractions make a certain class of future changes easier, at the cost of some extra complexity and fragility. They are worth this cost if and only if the types of changes they make easier actually turn out to be reasonably common. Following The Rule of Three means deliberately holding off on making a prediction until more evidence has come in. The assumption built into the Rule of Three is that past changes are the best predictor of future changes.

Back to Nature

Now to return to Taleb’s observation of widespread redundancy in nature’s designs. An interesting implication of this is that despite all of the apparent “waste” involved, evolutionary processes have nonetheless converged onto it as the best strategy for dealing with unpredictability – a permanent feature of the real world (or at least, a better strategy than no redundancy – having one kidney, for instance).

At a high level, our software projects and teams are similar in the sense that they exist in a challenging, competitive environment punctuated by unpredictable changes. If meaningful parallels can be made between complex systems, it’s worth considering the possibility that despite the apparent “waste” involved, some redundancy is likewise the best strategy for dealing with the unpredictability in our environment too.

This is all to say: go forth and fearlessly copy-paste more code 🙂

References and Further Reading

The Wrong Abstraction

Write code that is easy to delete, not easy to extend

Antifragile: Things That Gain from Disorder (Incerto)

10 Tips for Exploring Foreign Cities

 

Ruins of St. Pauls, Macau

Last month I was fortunate enough to spend two weeks traveling around southern China including Hainan, Guangzhou, Macau and Hong Kong. It was an awesome trip; I would particularly recommend stopping by Hong Kong for a few days to check it out if you get the chance. It’s an amazing, vibrant city.

At some point during the trip I started noting down the things I was learning (about travel in general, and travel around cities in particular) into Evernote. Over time the list kept growing. What follows is an edited version of the original list, compiled into a top 10 (in typical web article fashion…)

1. Get the phone number of contacts in foreign country

If you’re meeting friends at the destination airport, make sure you have their mobile number. Just having them on Google Hangouts, WeChat, Facebook messenger or <insert online service here> won’t cut it as you can’t rely on WiFi access at airports. In Shanghai for example, you’ll still need a local mobile number to access the “Free” airport wifi.

Old fashioned and low-tech is sometimes best.

2. Double check that airports of connecting flights match

Cities can have more than one airport, and they may not be close together at all. As a New Zealander, this was surprising to learn…

3. Bring plenty of cash in the local currency

Unlike credit card and bank cards, cash is guaranteed to be accepted everywhere and is a lifesaver in emergencies.

Even if you’re going to a first-world country, don’t assume your card will be widely accepted, even at popular tourist attractions. For instance, you’ll need cash to buy a ticket for the Victoria Peak Tram in Hong Kong.

Another tip: divide your cash up and distribute it amongst your bags. That way if one goes missing, you still have backups. I had three stashes: one in my checked-in luggage, one in my backpack and a small amount in my wallet.

Again, low-tech = good.

4. Pack the night before

It’s easy to be unrealistic about how easy and fast it will be to “throw everything into your bag in the morning”. If checking out of your hotel room in the morning, do all possible packing the night before.

5. Invest in good walking shoes

When you’re out exploring all day every day, decent shoes will really pay dividends. Conversely, bad shoes and feet that are killing you each day can put a damper on your travel experience!

6. Sort out mobile data for your smartphone

Having internet access on your smartphone is absolutely essential when travelling, if only for Maps/GPS, Google Translate and being able to research other places to see while you’re already out.

With that in mind, set up global roaming with your mobile provider before you leave, or check if SIM cards are freely available at destination country. Some countries require you to be a local resident and/or have identification to get a SIM card (Hong Kong isn’t like this; China is).

Remember to pack the SIM card removal tool for your phone, if applicable.

If going to a country with restricted internet access, you may want to sort out VPN access beforehand so you can still access the online services you’re used to (Facebook, YouTube, etc). Record multiple fallback IP addresses for your VPN provider as it’s hard to know which will be blocked.

7. Always have snacks and water with you

Bring water and lots of snack foods such as energy bars and nuts in your day pack to keep up your energy levels throughout the day. You never know where you might end up while exploring; it might be a long time between proper meals.

8. Find out the off-peak hours of the tourist attractions you want to visit…

…and go then to avoid crowds. Crowds are pretty much guaranteed no matter the time of day at remotely popular attractions but you can avoid the worst of it with careful planning. Again, this was a bit of surprise to someone from a country as small as N.Z. where things are pretty much guaranteed to be quiet on weekdays and mornings.

9. Get a Metro map

This is a must if you’re checking out any city with a decent metro (e.g. Guangzhou, Shanghai and Hong Kong) due to the sheer amount of time you’ll spend using it. A paper map is better (no worries about dead batteries) or download a PDF online onto your phone or tablet.

10. Invest in or borrow a decent camera

As good as phone cameras are these days, there’s still no substitute for a standalone camera.

And finally (bonus tip 11), if I’ve learned one thing about travel so far it’s this: the big-name tourist attractions at any given destination can be pretty overrated. They’re often geared towards foreigners so much so that they shield you from the actual local culture. Some of the most enjoyable experiences I’ve had travelling have been while wandering around exploring, taking it all in and spontaneously discovering things. So don’t just tick all the boxes, get out there and experience the authentic whatever-place-it-is.

What percentage of your users use your app daily?

Both the Developer Console and Google Analytics can display your app’s active users the number of users that opened your app at least once on each day. Knowing the number of active users is a good start to getting an idea of user engagement, but the problem with looking at it in isolation is that it doesn’t give you any idea of how many of your users have your app installed and don’t open it at all each day.

What’s needed is a new metric with more context – the number of active daily users as a percentage of total users. This is a more accurate indicator of the actual value your app is offering your users, and can be used to validate that specific changes to your app are actually making it more useful or enjoyable (in Lean Startup terms, it is more a core metric and less of a vanity metric).

How to measure daily active users as a percentage for your Android app

You will need:

  • an Android app with Google Analytics and a reasonable amount of analytics data
  • Excel, LibreOffice Calc or an equivalent spreadsheet program for plotting graphs

Note: the sample screenshots I’ve included here use data from my recently released RadioDrive app.

  1. Go to Google Play Developer Console, select your app, go to Statistics.
  2. Select Current Installs by User (this accounts for users that have your app installed on more than one of their devices, unlike Current Installs by Device).
  3. Select 1 year for the time range so you get everything.
  4. Click Export to CSV. In the dialog make sure only the Users -> Current checkbox is selected.

Now we want to get hold of data for the number of active users. The Play Developer Console does have this statistic, but unfortunately you can’t currently export the data. Onward to Google Analytics…

  1. Login to Google Analytics, select “All Mobile App Data” for your app.
  2. Click Active Users from your App Overview page.

  1. Adjust the date range (drop-down box in the top-right corner) if necessary, then click Export > CSV

  1. The next step is to import and combine both datasets in Excel. Once you have copied both sets of data into the same spreadsheet, you’ll want to sort the Developer Console data by increasing date so it matches the Analytics data. To do this in Calc, box-select all rows for the date and current_user_install columns, then select Data -> Sort -> Sort by ascending date.

  1. Move active user data so the dates correspond, if necessary…

  1. Make a new column for percentage (Formula: =(C6/B6)*100). You can delete the Day Index column now as it’s redundant.

  1. Plot a line graph (date on X axis, percent on Y axis)

So far so good, we have a graph showing the percentage of active users each day.

But there’s a problem. Say you release an update for your app that is a total flop. Users start to uninstall your app in droves, except for a small segment of your dedicated fans. In this case, the percentage of active users may actually go up, as your botched update eliminates all but your most loyal users.

If you keep an eye on your other statistics such as daily uninstalls and number of active users (as well as monitoring actual user feedback), you would (hopefully) pick up this kind of scenario. However it’d be nice to be able to see this situation occurring in the same graph.

To do this, you can simply plot current user installs or number of active users on the same axes. That way, you’ll know something is up if either of them start trending downward.

Here I’ve plotted current user installs on a secondary Y axis:

The final graph (after adjusting the percentage scale to prevent overlap):

(In case you’re wondering, the lack of active user data until the 8th Dec is due to Google Analytics not being in the app until then!)

Extra credit: add a 3 or 5 day moving average trend line to % Active Users to smooth out fluctuations (having a larger sample size helps with this also).

What core metrics do you measure for your app and what tools do you use to measure them?

Google I/O 2013 – Cognitive Science and Design, and how it applies to Android apps

This is an excellent talk by Alex Faaborg at Google I/O 2013 about cognitive science principles and how they apply to interface design. Here’s a summary of some of the main points and how they could be used to improve your apps:

  • We can search for objects of the same colour much faster than searching for objects of the same shape [18:26]
  • We can scan a group of faces for one we recognise in parallel rather than sequentially. This could be taken advantage of in messaging and address book apps, for example [10:13]
  • Objects in our periphery are recognised much faster than in our frontal field (tiger example in the video). You can put a small notification icon in the corner of the screen away from the user’s focal point and it will still be noticed [6:50]
  • Colour-deficiency: you can get away with using green and red as long as the contrast is significantly different. Best approach is to test your interface with filtering tools to see how it would actually look (e.g. Photoshop) [13:50]
  • Our brains are very good at recognising patterns. It’s not necessary to group objects together in a box, just having whitespace between groups will do [3:24]
  • You’ll recognise a silhouette of an object that just shows its basic geometry faster than you will recognise a more photo-realistic depiction of the object. This principle is used in the Holo icon set [9:10]
  • Notifications/interruptions wipe the contents of our working memory and make us lose the state of “creative flow” if we were in it. Takeaway: use notifications carefully [22:22]
  • “Chunking” optimizes for our working memory. Examples are the groups of digits in credit card and phone numbers. Make sure your interface supports these chunks and ignores user-entered whitespace! [21:17]
  • We make trust decisions quickly and once made they are slow to change, even to the point of us explaining away new information that goes against them. First impressions matter – make sure you have a quality application icon [24:16]
  • You don’t *have* to be consistent with existing interfaces and interaction paradigms when designing your app. Combining innovation with teaching the user (e.g. with a quick example video) can work well. Example: collaborating on documents via email attachments vs. using Google Docs [31:21]

Android: 9 patching a family of images the easy way

9 patch images in Android are great but if you happen to have a family of graphics to convert, it can get pretty tedious. I had a collection of button graphics that needed converting to 9 patches using the same stretchable regions.

Rather than do it all by hand with Photoshop or GIMP (and inevitably need to redo them all again later when something needed changing) I wrote a small BASH script to do it.

To use the script, first use the draw9patch tool to create the 9 patch info for one of your graphics – this will become the template. Once you’re done, go:

[code language=”bash” light=”true”]
./9batch.sh template.9.png button2.png button3.png …
[/code]

to copy the 1 pixel border from the template to your remaining graphics and save a .9.png version of each of them.

Note that you’ll need to install ImageMagick to use the 9batch script:

[code language=”bash” light=”true”]
sudo apt-get install imagemagick
[/code]

Apparently WordPress won’t let me upload the script itself so here’s the source code:

[code language=”bash” light=”true” collapse=”true”]
#!/bin/bash

if [ "$#" -lt 2 ]; then
echo "Usage: 9batch.sh template image1 image2 …" >&2
echo
echo "Applies 9 patch info to a family of images using one image as the template" >&2
echo "Template image should be 2 pixels wider and higher than source images" >&2
exit 1
fi

# 9 patch image to use as template
src=$1

for i in ${@:2}
do
# use sed to change extension from .png to .9.png and assign result to ‘out’
out=`echo $i | sed -e ‘s:\(….\)$:.9\1:’`
composite -gravity center $i $src $out
done
[/code]

Android Device Nudge Detection Helper Class

I recently added a feature to StarCraft 2 Build Player to start playing build orders when the users’ phone is nudged. The idea is so you don’t have to waste precious seconds looking down at your phone to tap the “Play” button, instead you can just mindlessly bump your phone on your desk and you’re off.

Anyway, it turned out to be pretty easy to factor this into a reusable class so here it is:

[sourcecode language=”java”]
package com.kiwiandroiddev.sc2buildassistant;

import java.util.ArrayList;

import android.content.Context;
import android.hardware.Sensor;
import android.hardware.SensorEvent;
import android.hardware.SensorEventListener;
import android.hardware.SensorManager;
import android.os.Handler;

/**
* Class for reporting when the device’s acceleration (excluding gravity) exceeds
* a certain value. Compatible with all Android versions as it uses Sensor.TYPE_ACCELEROMETER
* rather than Sensor.TYPE_LINEAR_ACCELERATION.
*
* NudgeDetector objects are initially disabled. To use, implement
* the NudgeDetectorEventListener interface in your class, then register it
* to a new NudgeDetector object with registerListener(). Finally, call
* setEnabled(true) to start detecting device movement. You should add a call
* to stopDetection() in your Activity’s onPause() method to conserve battery
* life.
*
* @author kiwiandroiddev
*
*/
public class NudgeDetector implements SensorEventListener {

private ArrayList<NudgeDetectorEventListener> mListeners;
private Context mContext;
private SensorManager mSensorManager;
private Sensor mAccelerometer;
private boolean mEnabled = false;
private boolean mCurrentlyDetecting = false;
private boolean mCurrentlyChecking = false;
private int mGraceTime = 1000; // milliseconds
private int mSampleRate = SensorManager.SENSOR_DELAY_GAME;
private double mDetectionThreshold = 0.5f; // ms^-2
private float[] mGravity = new float[] { 0.0f, 0.0f, 0.0f };
private float[] mLinearAcceleration = new float[] { 0.0f, 0.0f, 0.0f };

/**
* Client activities should implement this interface and register themselves using
* registerListener() to be alerted when a nudge has been detected
*/
public interface NudgeDetectorEventListener {
public void onNudgeDetected();
}

public NudgeDetector(Context context) {
mContext = context;
mListeners = new ArrayList<NudgeDetectorEventListener>();
mSensorManager = (SensorManager) mContext.getSystemService(Context.SENSOR_SERVICE);
mAccelerometer = mSensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER);
}

// Accessors follow

public void registerListener(NudgeDetectorEventListener newListener) {
mListeners.add(newListener);
}

public void removeListeners() {
mListeners.clear();
}

public void setEnabled(boolean enabled) {
if (!mEnabled && enabled) {
startDetection();
} else if (mEnabled && !enabled) {
stopDetection();
}
mEnabled = enabled;
}

public boolean isEnabled() {
return mEnabled;
}

/**
* Returns whether this detector is currently registered with the sensor manager
* and is receiving accelerometer readings from the device.
*/
public boolean isCurrentlyDetecting() {
return mCurrentlyDetecting;
}

/**
* Sets the the amount of acceleration needed to trigger a "nudge".
* Units are metres per second per second (ms^-2)
*/
public void setDetectionThreshold(double threshold) {
mDetectionThreshold = threshold;
}

public double getDetectionThreshold() {
return mDetectionThreshold;
}

/**
* Sets the minimum amount of time between when startDetection() is called
* and nudges are actually detected. This should be non-zero to avoid
* false positives straight after enabling detection (e.g. at least 500ms)
*
* @param milliseconds_delay
*/
public void setGraceTime(int milliseconds_delay) {
mGraceTime = milliseconds_delay;
}

public int getGraceTime() {
return mGraceTime;
}

/**
* Sets how often accelerometer readings are received. Affects the accuracy of
* nudge detection. A new sample rate won’t take effect until stopDetection()
* then startDetection() is called.
*
* @param rate must be one of SensorManager.SENSOR_DELAY_UI,
* SensorManager.SENSOR_DELAY_NORMAL, SensorManager.SENSOR_DELAY_GAME,
* SensorManager.SENSOR_DELAY_FASTEST
*/
public void setSampleRate(int rate) {
mSampleRate = rate;
}

public int getSampleRate() {
return mSampleRate;
}

/**
* Starts listening for device movement
* after an initial delay specified by grace time attribute –
* change this using setGraceTime().
* Client Activities might want to call this in their onResume() method.
*
* The actual sensor code uses a moving average to remove the
* gravity component from acceleration. This is why readings
* are collected and not checked during the grace time
*/
public void startDetection() {
if (mEnabled && !mCurrentlyDetecting) {
mCurrentlyDetecting = true;
mSensorManager.registerListener(this, mAccelerometer, mSampleRate);

Handler myHandler = new Handler();
myHandler.postDelayed(new Runnable() {
@Override
public void run() {
if (mEnabled && mCurrentlyDetecting) {
mCurrentlyChecking = true;
}
}
}, mGraceTime);
}
}

/**
* Deregisters accelerometer sensor from the sensor manager.
* Does nothing if nudge detector is currently disabled.
* Client Activities should call this in their onPause() method.
*/
public void stopDetection() {
if (mEnabled && mCurrentlyDetecting) {
mSensorManager.unregisterListener(this);
mCurrentlyDetecting = false;
mCurrentlyChecking = false;
}
}

// SensorEventListener callbacks follow

@Override
public void onAccuracyChanged(Sensor sensor, int accuracy) {
}

@Override
public void onSensorChanged(SensorEvent event) {
// alpha is calculated as t / (t + dT)
// with t, the low-pass filter’s time-constant
// and dT, the event delivery rate

final float alpha = 0.8f;

mGravity[0] = alpha * mGravity[0] + (1 – alpha) * event.values[0];
mGravity[1] = alpha * mGravity[1] + (1 – alpha) * event.values[1];
mGravity[2] = alpha * mGravity[2] + (1 – alpha) * event.values[2];

mLinearAcceleration[0] = event.values[0] – mGravity[0];
mLinearAcceleration[1] = event.values[1] – mGravity[1];
mLinearAcceleration[2] = event.values[2] – mGravity[2];

// find length of linear acceleration vector
double scalarAcceleration = mLinearAcceleration[0] * mLinearAcceleration[0]
+ mLinearAcceleration[1] * mLinearAcceleration[1]
+ mLinearAcceleration[2] * mLinearAcceleration[2];
scalarAcceleration = Math.sqrt(scalarAcceleration);

if (mCurrentlyChecking && scalarAcceleration >= mDetectionThreshold) {
for (NudgeDetectorEventListener listener : mListeners)
listener.onNudgeDetected();
}
}
}

[/sourcecode]

The reason I stuck to using Sensor.TYPE_ACCELEROMETER was because I want to support Froyo with my app. If you’re only targeting 2.3 (API level 9) and higher, you could use Sensor.TYPE_LINEAR_ACCELERATION, and simplify this code a fair bit by stripping out the gravity calculation in onSensorChanged(), etc.

Feel free to use this in your projects. Drop me a comment if you spot bugs or have any suggestions.

Data on Android device supported features

I’ve recently been experimenting with OpenGL ES 2.0 on Android for a graphical app (some excellent guides can be found at http://www.learnopengles.com/). So far so good. It turns out that gone are the days of countless fixed function calls like glBegin() glVertex3f() glColor4f() for sending vertex data, nowadays you use shaders for everything and send your vertex data to OpenGL in large chunks.  Supposedly this makes the graphics driver software a lot simpler to write and leads to better performance overall. Keeping track of all of those calls and their corresponding closing calls could end up a bit of a headache so it seems like it provides some benefit to application developers too.

Before diving in and using ES 2.0 exclusively (well, at first anyway – code for ES 1.x support can always be added later) I wanted to get an idea of how widely ES 2.0 is supported across Android devices because it could have a big effect on the market size for my app.

After filtering through some anecdotal evidence on Stackoverflow, not surprisingly the best place to find this data was straight from the horse’s mouth at the Android Dashboards page.

According to the data, ES 2.0 support is over 90% and it seems reasonable to assume it’s only going to increase in time. So that settles it – OpenGL ES 2.0 it is.

The Dashboards page also has data on the installation base for each Android version which may also be very useful to you during the research phase of developing your app.

Linux: Fixing an unreliable network connection with ASUS P8Z 68-V onboard LAN

Recently I got a new ASUS P8Z 68-V motherboard and CPU, and had been having some strange network issues with it when running Gentoo Linux. The problems included connection failures after random periods of time and generally slow download speeds. The only way to get the connection running again after it failed (which was every few minutes at times) was to run:

ifconfig eth0 down
ifconfig eth0 up

On top of that, running ifconfig (with no args) was reporting 100% RX packets dropped for the interface.

This is the info on the network adapter:

$ lspci
...
07:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8111/8168B PCI Express
Gigabit Ethernet controller (rev 06)

At first I suspected there may have been something wrong with my (relatively new) Gentoo setup, so I did some testing running off an Ubuntu 11.10 live CD.  Interestingly this gave the same unreliable behaviour that wasn’t present when running Windows 7.

After further digging it turned out the problem was that the Linux kernel was loading the wrong module for the network adapter. This was confirmed by the presence of “r8169” in the output of lsmod.

The solution was to remove and blacklist the r8169 module and install Realtek’s official r8168 Linux kernel module from their website. On Gentoo I had compiled the kernel with the r8169 module built-in, so this meant first deselecting it and recompiling the kernel. After that, all that was left was to extract Realtek’s driver package and run ./autorun.sh as root.

Solution source: http://askubuntu.com/questions/46942/how-do-i-stop-my-ethernet-network-connection-from-dropping

Update:
In Linux Mint 12, you first need to run sudo apt-get install build-essential linux-headers-3.0.0-12-generic before running autorun.sh.

Nosetests: Capturing log messages written to stderr

This is a tip for using the Python logging module in conjunction with unit-tests.
When using the root logger to write debug messages, e.g.

 import logging
..
logging.debug('x = %s' % x)

to capture the messages and write them to the console when running nosetests, pass ‘root’ to the –log-debug nosetests option. E.g.

nosetests test_module1.py -s --log-debug=root