13 Dec 2018

feedPlanet Python

gamingdirectional: Create an exit button on the main game menu scene

In this article we will continue with our previous pygame project by creating an exit button on the main game menu scene which when the user clicks on it the program will close the game. We will create the score scene in the next chapter as I have promised in the previous article but in this article we will create the exit button which is also very important for this project. This button will only...

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Related posts:

Open a text file then write the game level onto it with python Create the pause scene for the game Create the third level for this pygame project Lets move on to the next game level! Create a pool object for player missile Create an explosion manager and explosion class for pygame project Pygame loads image and background graphic on game scene The game state engine is ready! Playing background music with Pygame Draw lines with Pygame

13 Dec 2018 10:58am GMT

Jeff Knupp: How To Do Just About Anything With Python Lists

Python's list is one of the built-in sequence types (i.e. "it holds a sequence of things") is a wonderfully useful tool. I figured I'd try to determine what people are most often trying to do with lists (by analyzing Google's query data on the topic) and just bang out examples of "How do I do X with a list in Python?"

Reverse/Sort A List In Python

There are two ways to reverse a list in Python, and which one you use depends on what you want to do with the resulting reversed data. If you're only going iterate over the items in the reversed list (say, to print them out), use the Python built-in function reversed(seq). Here's an example of reversed in action:

original_list = [1, 2, 3, 4, 5]
for element in reversed(original_list):
    print(element)

If you need the reversed list itself, use the Python built-in function sorted(iterable, *, key=None, reverse=False). Let's see some examples:

In [10]: original_list = [3, 1, 2, 5, 4]

In [11]: sorted(original_list)
Out[11]: [1, 2, 3, 4, 5]

In [15]: sorted(original_list, reverse=True)
Out[15]: [5, 4, 3, 2, 1]

But what if your list contains more than just simple integers? How does one sort, say, a list of temperature readings over a given time if those daily readings are each stored as a tuples of the form (<date>, <daily high>, <daily low>)? Look at the following example:

readings = [('1202', 45.0, 28.1), ('1201', 44.0, 33.0), ('1130', 45.0, 32.6)]

Calling sorted(readings) will give us a new list with the elements ordered by the <date> portion of the tuple (the 0-th element, since Python compares tuples lexicographically; each item is compared in order, starting with the first elements). But what if we wanted to sort by <daily high> or <daily low>? Simple! Just pass the key parameter a function that takes a single argument and returns the key for sorted() to use for comparisons. For example, I could sort by daily low temperatures like so:

In [25]: sorted(readings, key=lambda reading: reading[2])
Out[25]: [('1202', 45.0, 28.1), ('1130', 45.0, 32.6), ('1201', 44.0, 33.0)]

In that example, we passed the key parameter a lambda function which accepted one argument and returned a value, in our case the third part of our temperature recording tuple (the reading[2] part). If we had wanted to sort by the daily high in reverse order, we would just change the call like so:

In [26]: sorted(readings, key=lambda reading: reading[1], reverse=True)
Out[26]: [('1202', 45.0, 28.1), ('1130', 45.0, 32.6), ('1201', 44.0, 33.0)]

Accessing elements of a tuple or class is such a common task that Python provides a set of convenience functions in the operator built-in module. To access a specific field in the tuple (as we did above for daily high as reading[1] and daily low as reading[2]), use the field's index in the tuple as an argument to operator.itemgetter:

In [29]: sorted(readings, key=itemgetter(1), reverse=True)
Out[29]: [('1202', 45.0, 28.1), ('1130', 45.0, 32.6), ('1201', 44.0, 33.0)]

But notice that the first two entries have the same high temp recordings (45.0). What if we wanted to first sort by high temp and then by low temp? itemgetter allows for multiple levels of sorting by simply passing in multiple index values. So let's sort by high temp first, then low temp:

In [31]: sorted(readings, key=itemgetter(1,2), reverse=True)
Out[31]: [('1130', 45.0, 32.6), ('1202', 45.0, 28.1), ('1201', 44.0, 33.0)]

Notice that the ('1130', 45.0, 32.6) tuple is now first, as it had an equal high temp and a greater low temp than ('1202', 45.0, 28.1).

Split A Python List Into Chunks

Splitting a list into equally sized sub-lists (for processing data in parallel, perhaps) is a common task. It's so common, in fact, that the itertools module (a module practically begging to be used in these kinds of tasks, by the way) gives actual code for how to accomplish this in Python in the itertools recpipes section of the docs. Here is the relevant code:

def grouper(iterable, n, fillvalue=None):
    "Collect data into fixed-length chunks or blocks"
    # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
    args = [iter(iterable)] * n
    return zip_longest(*args, fillvalue=fillvalue)

The code may look confusing at first, but it's simply creating n (or 3 in the example in the comments) separate iterators over the iterable argument and then cleverly zipping them back together using zip_longest from the itertools module, to collect the elements of iterable in a series of n-sized chunks.

Flatten A Python List Of Lists Into One List

itertools recipes FTW again! Straight out of the section of the Python docs that gave us grouper above, the recipe for "flatten" is:

def flatten(list_of_lists):
    "Flatten one level of nesting."
    return chain.from_iterable(list_of_lists)

It simply calls itertools.chain.from_iterable() on the list_of_lists. A call to flatten([[1, 2, 3], [4, 5], [6, 7, 8]]) will give us an iterator that yields each element individually. We can say flattened_list = list(flatten([[1, 2, 3], [4, 5], [6, 7, 8]])) if what we need is an actual list and not just an iterator.

Insert Into A List In Python

The word "insert" here is vague (insert where?), but let's roll with it. Here are some of the flavors of list assignment mentioned in the Python docs about operations on "Mutable Sequence Types":

Operation   Result

s[i] = x    item i of s is replaced by x     
s[i:j] = t  slice of s from i to j is replaced by the contents of the iterable t     
s[i:j:k] = t    the elements of s[i:j:k] are replaced by those of t (as long as t is the same length as the slice it's replacing)
s.append(x) appends x to the end of the sequence (same as s[len(s):len(s)] = [x])
s.extend(t) or s += t   extends s with the contents of t (for the most part the same as s[len(s):len(s)] = t)    
s.insert(i, x)  inserts x into s at the index given by i (same as s[i:i] = [x])

So six different ways to insert into a list in Python. That doesn't seem very Pythonic!? Let's see which we might use on a case by case basis:

Insert Into The Beginning Of A List In Python

s.insert(0, value)

Insert Into The End Of A List In Python

s.append(value)

Insert Into An Existing Index Of A Python List

s[index] = value

Concatenating Two Python Lists

s += t  # Where s and t are both lists

Python "List Index Out Of Range" Error Message

This entry is less a "how-to" and more of a "what to do when things go wrong" type of entry, but it's nonetheless searched for very often in conjunction with Python lists (for obvious reasons). The message "list index out of range" is brought to you by a specific type of built-in Exception, namely the IndexError. It is simply saying that you tried to access a list, perhaps using code like the following:

def my_function(some_list):
    """Return something really interesting..."""
    third_value = some_list[2]
    # ... possibly more code following

2 is not a valid index to use because some_list does not have three values (we don't know how many it does have, just less than 3 in this case).

Want to know how many values some_list has? The Python built-in len(s) function will give you exactly that.

A simple debugging exercise of the "list index out of range" message might look like this:

def my_function(some_list):
    try:
        third_value = some_list[2]
    except IndexError:
        print(f'some_list only has {len(some_list)} entries')
        raise

Here we put our code in a try...except block and catch any IndexError exceptions that are raised. When we see one, we just print out the length of some_list and re-raise the exception (since we can't exactly handle that in any useful way). That gives me the following output:

In [50]: def my_function(some_list):
    ...:         try:
    ...:             third_value = some_list[2]
    ...:         except IndexError:
    ...:             print(f'some_list only has {len(some_list)} entries')
    ...:             raise
    ...:

In [51]: my_function([1,2])
some_list only has 2 entries
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-51-3f735c70ddb9> in <module>
----> 1 my_function([1,2])

<ipython-input-50-137ffeaa7c70> in my_function(some_list)
    1 def my_function(some_list):
    2         try:
----> 3             third_value = some_list[2]
    4         except IndexError:
    5             print(f'some_list only has {len(some_list)} entries')

IndexError: list index out of range

How To Make A List In Python

The section topic is taken directly from search query volume, so rest assured this isn't me just throwing random facts about Python lists at you. There are a few ways to create a list in Python, but the two you'll use the most is using the "square-brace" syntax:

Generally speaking, prefer the use of the first two methods unless you can think of a very good reason not to.

Apply A Function To A List In Python

Remember when I said list comprehensions were out of scope for this article. I lied. Even though they're a little trickier to grasp, they're very powerful and applying a function to each element in a list in Python is basically their bread and butter. Imagine we have a list of the numbers one through five and want to create a second list whose elements exactly each element in the original list multiplied by two. To construct such a list using a for loop, we'd need the following code:

original_list = [1,2,3,4,5]
double_list = []
for element in original_list:
    double_list.append(element * 2)

The need to do this occurs so frequently that (especially for performance-sensitive applications) a highly optimized general form of the above as part of the Python language in list comprehensions. Rather than go over the topic in great detail here, I'll simply link to the Python documentation on list comprehensions and note that we could achieve the same as code above using a list comprehension, like so:

original_list = [1,2,3,4,5]
double_list = [element * 2 for element in original_list]

I've grown to appreciate the precision of the grammar when reading it aloud, but it definitely takes some getting used to (and this is only the simplest use of a list comprehension).

Of course, there's always the Python built-in function map(), which will do much the same thing in a slightly easier to grok way, but there is an important difference between the two: map() yields the values back one-at-a-time (it's a generator) whereas a list comprehension will always create a new list from the existing list given as an argument.

It may seem difficult, but try to get to a place where you're using list comprehensions rather than map() as the former are both more powerful and more Pythonic.

Summary

This article was a quick hit, but it's good to go back to basics occasionally and see if there's anything you might have missed your first time through (I'll wager that very few Python developers know and use the itertools module as well and often as they should). I tentatively plan on adding to this article over time (a change log would be provided). Let me know if you think there's a common "Python list" Google query I missed or with suggestions on the other parts of Python lists that often trip up newcomers.

13 Dec 2018 8:02am GMT

Yasoob Khalid: Research Writeup: Deanonymization and Proximity Detection Using Wi-Fi

Hi everyone! If you have been following my blog for a while you will know that I did research at Colgate University over the summers. My research was on Wi-Fi and how I can do some interesting stuff using it. The university just published its annual catalogue of all the research projects which happened over the summer. My research was done under the mentorship of Aaron Gember-Jacobson. I could not have asked for a better advisor. Here is the writeup of my project:

According to RAINN (Rape, Abuse & Incest National Network), 23.1% of female and 5.4% of male undergraduate students experience rape or sexual assault, with only a minute percentage reporting their assault to law enforcement1. In certain cases, survivors can forget who the perpetrator was due to trauma and/or intoxication. I want to use technology to counter this problem. My hope is to reduce the number of potential culprits when such an incident occurs to make it easier for the survivor to identify the perpetrator.

This can be made possible by using a device that most people carry at all times - a smartphone. The idea is to save the device identifier and the distance between your phone and that of each person who comes near you in a searchable database. This allows you, the user, to search for which device was near you at a particular time. The research is further divided into two parts. The first involved finding a way to effectively calculate the relative distance between two smartphones and the second involved information storage and querying. I focused mainly on the first part, which turned out to be more difficult and involved than I anticipated.

The cornerstone of this idea is Wi-Fi and the information your smartphone emits when the Wi-Fi is turned on, though not necessarily connected to an access point. The formal requirements of this system are as follows: it should be passive so you don't have to actively monitor it; it shouldn't require other people's smartphones to run any specific application; the error in distance estimation should be less than 1 meter so the algorithm can accurately identify a human interaction; the system needs to work in NLOS (Non-line-of-sight) scenarios since people often have their smartphones in their pockets; finally, it should not require more than three devices, including your smartphone, a nearby smartphone, and a Wi-Fi Access Point to which both phones are connected, because the system should be portable.

Previous research in relative distance estimation offers varying levels of precision. One method involves using RSSI (Received Signal Strength Indication) readings from multiple access points (4+ for accuracy) and triangulating smartphone position based on that. We cannot use this method because 4+ devices are required. Another method involves using Time-of-Flight (ToF) measurements. There are multiple variations of this method, but the basic idea is to send data from your device to the device being localized, and recording the time taken for the data to travel from one device to another and for an acknowledgment to be received. Based on this timing measurement and the required time delay (known as SIFS, or Short Interframe Space) between a device receiving data and sending an acknowledgement, we can estimate the distance between two devices. This gives the best accuracy but is not directly applicable to this situation, because it requires a direct connection between the two smartphones.

I sought to develop a modified version of the ToF method, because it offers the best precision and requires the least number of devices to work effectively. The method I developed was to send unsolicited control packets (a special type of data frame) to the target mobile device and force it to send an acknowledgement (see figure). The major research question is: how do we force the target device to send an acknowledgement even if we are not directly connected to it?

I set up a testbed with three desktops equipped with Wi-Fi cards and running Ubuntu Linux. I used Scapy (a Python program for generating network packets) to generate and send control packets from one desktop to another and tcpdump on the third desktop to monitor and analyze the wireless communication taking place. I was able to send the control packets and solicit an acknowledgment from the target mobile (Ubuntu desktop) without being directly connected to it.

However, there was a bug in the networking drivers of Ubuntu that generated acknowledgments even in cases where no acknowledgment was supposed to be sent by the target device. Currently, I am investigating the bug and trying to figure out the most suitable way forward. Through this research, I found that the process of distance estimation is more complicated than it seems. There are several variables and timing issues that need to be taken into account. In the future, I plan on finding a workaround for this bug, with the eventual goal of making this system usable in everyday life.

If you have any questions about my research or anything in general please write them in the comments below. Looking forward to hearing your views! Have a great day/night! 🙂

13 Dec 2018 5:54am GMT

10 Nov 2011

feedPlanetJava

OSDir.com - Java: Oracle Introduces New Java Specification Requests to Evolve Java Community Process

From the Yet Another dept.:

To further its commitment to the Java Community Process (JCP), Oracle has submitted the first of two Java Specification Requests (JSRs) to update and revitalize the JCP.

10 Nov 2011 6:01am GMT

OSDir.com - Java: No copied Java code or weapons of mass destruction found in Android

From the Fact Checking dept.:

ZDNET: Sometimes the sheer wrongness of what is posted on the web leaves us speechless. Especially when it's picked up and repeated as gospel by otherwise reputable sites like Engadget. "Google copied Oracle's Java code, pasted in a new license, and shipped it," they reported this morning.



Sorry, but that just isn't true.

10 Nov 2011 6:01am GMT

OSDir.com - Java: Java SE 7 Released

From the Grande dept.:

Oracle today announced the availability of Java Platform, Standard Edition 7 (Java SE 7), the first release of the Java platform under Oracle stewardship.

10 Nov 2011 6:01am GMT

28 Oct 2011

feedPlanet Ruby

O'Reilly Ruby: MacRuby: The Definitive Guide

Ruby and Cocoa on OS X, the iPhone, and the Device That Shall Not Be Named

28 Oct 2011 8:00pm GMT

14 Oct 2011

feedPlanet Ruby

Charles Oliver Nutter: Why Clojure Doesn't Need Invokedynamic (Unless You Want It to be More Awesome)

This was originally posted as a comment on @fogus's blog post "Why Clojure doesn't need invokedynamic, but it might be nice". I figured it's worth a top-level post here.

Ok, there's some good points here and a few misguided/misinformed positions. I'll try to cover everything.

First, I need to point out a key detail of invokedynamic that may have escaped notice: any case where you must bounce through a generic piece of code to do dispatch -- regardless of how fast that bounce may be -- prevents a whole slew of optimizations from happening. This might affect Java dispatch, if there's any argument-twiddling logic shared between call sites. It would definitely affect multimethods, which are using a hand-implemented PIC. Any case where there's intervening code between the call site and the target would benefit from invokedynamic, since invokedynamic could be used to plumb that logic and let it inline straight through. This is, indeed, the primary benefit of using invokedynamic: arbitrarily complex dispatch logic folds away allowing the dispatch to optimize as if it were direct.

Your point about inference in Java dispatch is a fair one...if Clojure is able to infer all cases, then there's no need to use invokedynamic at all. But unless Clojure is able to infer all cases, then you've got this little performance time bomb just waiting to happen. Tweak some code path and obscure the inference, and kablam, you're back on a slow reflective impl. Invokedynamic would provide a measure of consistency; the only unforeseen perf impact would be when the dispatch turns out to *actually* be polymorphic, in which case even a direct call wouldn't do much better.

For multimethods, the benefit should be clear: the MM selection logic would be mostly implemented using method handles and "leaf" logic, allowing hotspot to inline it everywhere it is used. That means for small-morphic MM call sites, all targets could potentially inline too. That's impossible without invokedynamic unless you generate every MM path immediately around the eventual call.

Now, on to defs and Var lookup. Depending on the cost of Var lookup, using a SwitchPoint-based invalidation plus invokedynamic could be a big win. In Java 7u2, SwitchPoint-based invalidation is essentially free until invalidated, and as you point out that's a rare case. There would essentially be *no* cost in indirecting through a var until that var changes...and then it would settle back into no cost until it changes again. Frequently-changing vars could gracefully degrade to a PIC.

It's also dangerous to understate the impact code size has on JVM optimization. The usual recommendation on the JVM is to move code into many small methods, possibly using call-through logic as in multimethods to reuse the same logic in many places. As I've mentioned, that defeats many optimizations, so the next approach is often to hand-inline logic everywhere it's used, to let the JVM have a more optimizable view of the system. But now we're stepping on our own feet...by adding more bytecode, we're almost certainly impacting the JVM's optimization and inlining budgets.

OpenJDK (and probably the other VMs too) has various limits on how far it will go to optimize code. A large number of these limits are based on the bytecoded size of the target methods. Methods that get too big won't inline, and sometimes won't compile. Methods that inline a lot of code might not get inlined into other methods. Methods that inline one path and eat up too much budget might push out more important calls later on. The only way around this is to reduce bytecode size, which is where invokedynamic comes in.

As of OpenJDK 7u2, MethodHandle logic is not included when calculating inlining budgets. In other words, if you push all the Java dispatch logic or multimethod dispatch logic or var lookup into mostly MethodHandles, you're getting that logic *for free*. That has had a tremendous impact on JRuby performance; I had previous versions of our compiler that did indeed infer static target methods from the interpreter, but they were often *slower* than call site caching solely because the code was considerably larger. With invokedynamic, a call is a call is a call, and the intervening plumbing is not counted against you.

Now, what about negative impacts to Clojure itself...

#0 is a red herring. JRuby supports Java 5, 6, and 7 with only a few hundred lines of changes in the compiler. Basically, the compiler has abstract interfaces for doing things like constant lookup, literal loading, and dispatch that we simply reimplement to use invokedynamic (extending the old non-indy logic for non-indified paths). In order to compile our uses of invokedynamic, we use Rémi Forax's JSR-292 backport, which includes a "mock" jar with all the invokedynamic APIs stubbed out. In our release, we just leave that library out, reflectively load the invokedynamic-based compiler impls, and we're off to the races.

#1 would be fair if the Oracle Java 7u2 early-access drops did not already include the optimizations that gave JRuby those awesome numbers. The biggest of those optimizations was making SwitchPoint free, but also important are the inlining discounting and MutableCallSite improvements. The perf you see for JRuby there can apply to any indirected behavior in Clojure, with the same perf benefits as of 7u2.

For #2, to address the apparent vagueness in my blog post...the big perf gain was largely from using SwitchPoint to invalidate constants rather than pinging a global serial number. Again, indirection folds away if you can shove it into MethodHandles. And it's pretty easy to do it.

#3 is just plain FUD. Oracle has committed to making invokedynamic work well for Java too. The current thinking is that "lambda", the support for closures in Java 7, will use invokedynamic under the covers to implement "function-like" constructs. Oracle has also committed to Nashorn, a fully invokedynamic-based JavaScript implementation, which has many of the same challenges as languages like Ruby or Python. I talked with Adam Messinger at Oracle, who explained to me that Oracle chose JavaScript in part because it's so far away from Java...as I put it (and he agreed) it's going to "keep Oracle honest" about optimizing for non-Java languages. Invokedynamic is driving the future of the JVM, and Oracle knows it all too well.

As for #4...well, all good things take a little effort :) I think the effort required is far lower than you suspect, though.

14 Oct 2011 2:40pm GMT

07 Oct 2011

feedPlanet Ruby

Ruby on Rails: Rails 3.1.1 has been released!

Hi everyone,

Rails 3.1.1 has been released. This release requires at least sass-rails 3.1.4

CHANGES

ActionMailer

ActionPack

ActiveModel

ActiveRecord

ActiveResource

ActiveSupport

Railties

SHA-1

You can find an exhaustive list of changes on github. Along with the closed issues marked for v3.1.1.

Thanks to everyone!

07 Oct 2011 5:26pm GMT

21 Mar 2011

feedPlanet Perl

Perl NOC Log: Planet Perl is going dormant

Planet Perl is going dormant. This will be the last post there for a while.

image from planet.perl.org

Why? There are better ways to get your Perl blog fix these days.

You might enjoy some of the following:

Will Planet Perl awaken again in the future? It might! The universe is a big place, filled with interesting places, people and things. You never know what might happen, so keep your towel handy.

21 Mar 2011 2:04am GMT

Ricardo Signes: improving on my little wooden "miniatures"

A few years ago, I wrote about cheap wooden discs as D&D minis, and I've been using them ever since. They do a great job, and cost nearly nothing. For the most part, we've used a few for the PCs, marked with the characters' initials, and the rest for NPCs and enemies, usually marked with numbers.

With D&D 4E, we've tended to have combats with more and more varied enemies. (Minions are wonderful things.) Numbering has become insufficient. It's too hard to remember what numbers are what monster, and to keep initiative order separate from token numbers. In the past, I've colored a few tokens in with the red or green whiteboard markers, and that has been useful. So, this afternoon I found my old paints and painted six sets of five colors. (The black ones I'd already made with sharpies.)

D&D tokens: now in color

I'm not sure what I'll want next: either I'll want five more of each color or I'll want five more colors. More colors will require that I pick up some white paint, while more of those colors will only require that I re-match the secondary colors when mixing. I think I'll wait to see which I end up wanting during real combats.

These colored tokens should work together well with my previous post about using a whiteboard for combat overview. Like-type monsters will get one color, and will all get grouped to one slot on initiative. Last night, for example, the two halfling warriors were red and acted in the same initiative slot. The three halfling minions were unpainted, and acted in another, later slot. Only PCs get their own initiative.

I think that it did a good amount to speed up combat, and that's even when I totally forgot to bring the combat whiteboard (and the character sheets!) with me. Next time, we'll see how it works when it's all brought together.

21 Mar 2011 12:47am GMT

20 Mar 2011

feedPlanet Perl

Dave Cross: Perl Vogue T-Shirts

Is Plack the new Black?In Pisa I gave a lightning talk about Perl Vogue. People enjoyed it and for a while I thought that it might actually turn into a project.

I won't though. It would just take far too much effort. And, besides, a couple of people have pointed out to be that the real Vogue are rather protective of their brand.

So it's not going to happen, I'm afraid. But as a subtle reminder of the ideas behind Perl Vogue I've created some t-shirts containing the article titles from the talk. You can get them from my Spreadshirt shop.

20 Mar 2011 12:02pm GMT