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• How to use python to create a GUI application which have cool animation/effects under Linux (like 3D

  - by sgon00

  Hi, I am not sure if my question title makes sense to you or not. I am seeing many cool applications which have cool animations/effects. I would like to learn how to use python to create this kind of GUI applications under Linux. "cool animation/effects" like 3D wall in Cooliris which is written in flash and compiz effects with opengl. I also heard of some python GUI library like wxPython and pyQT. Since I am completely new to python GUI programming, can anyone suggest me where to start and what I should learn to achieve and create such application? maybe learn pyQT with openGL feature? pyopengl binding? I have no clue on where to start. thank you very much for your time and suggestion. By the way, in case if someone need to know which kind of application I am going to create, well, just any kind of applications. maybe photo explorer with 3D wall, maybe IM client, maybe facebook client etc...

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• How to find Tomcat's PID and kill it in python?

  - by 4herpsand7derpsago

  Normally, one shuts down Apache Tomcat by running its shutdown.sh script (or batch file). In some cases, such as when Tomcat's web container is hosting a web app that does some crazy things with multi-threading, running shutdown.sh gracefully shuts down some parts of Tomcat (as I can see more available memory returning to the system), but the Tomcat process keeps running. I'm trying to write a simple Python script that: Calls shutdown.sh Runs ps -aef | grep tomcat to find any process with Tomcat referenced If applicable, kills the process with kill -9 <PID> Here's what I've got so far (as a prototype - I'm brand new to Python BTW): #!/usr/bin/python # Imports import sys import subprocess # Load from imported module. if __init__ == "__main__": main() # Main entry point. def main(): # Shutdown Tomcat shutdownCmd = "sh ${TOMCAT_HOME}/bin/shutdown.sh" subprocess.call([shutdownCmd], shell=true) # Check for PID grepCmd = "ps -aef | grep tomcat" grepResults = subprocess.call([grepCmd], shell=true) if(grepResult.length > 1): # Get PID and kill it. pid = ??? killPidCmd = "kill -9 $pid" subprocess.call([killPidCmd], shell=true) # Exit. sys.exit() I'm struggling with the middle part - with obtaining the grep results, checking to see if their size is greater than 1 (since grep always returns a reference to itself, at least 1 result will always be returned, methinks), and then parsing that returned PID and passing it into the killPidCmd. Thanks in advance!

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• In what order should the Python concepts be explained to absolute beginners?

  - by Tomaž Pisanski

  I am teaching Python to undergraduate math majors. I am interested in the optimal order in which students should be introduced to various Python concepts. In my view, at each stage the students should be able to solve a non-trivial programming problem using only the tools available at that time. Each new tool should enable a simpler solution to a familiar problem. A selection of numerous concepts available in Python is essential in order to keep students focused. They should also motivated and should appreciate each newly mastered tool without too much memorization. Here are some specific questions: For instance, my predecessor introduced lists before strings. I think the opposite is a better solution. Should function definitions be introduced at the very beginning or after mastering basic structured programming ideas, such as decisions (if) and loops (while)? Should sets be introduced before dictionaries? Is it better to introduce reading and writing files early in the course or should one use input and print for most of the course? Any suggestions with explanations are most welcome.

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• Problems with Threading in Python 2.5, KeyError: 51, Help debugging?

  - by vignesh-k

  I have a python script which runs a particular script large number of times (for monte carlo purpose) and the way I have scripted it is that, I queue up the script the desired number of times it should be run then I spawn threads and each thread runs the script once and again when its done. Once the script in a particular thread is finished, the output is written to a file by accessing a lock (so my guess was that only one thread accesses the lock at a given time). Once the lock is released by one thread, the next thread accesses it and adds its output to the previously written file and rewrites it. I am not facing a problem when the number of iterations is small like 10 or 20 but when its large like 50 or 150, python returns a KeyError: 51 telling me element doesn't exist and the error it points out to is within the lock which puzzles me since only one thread should access the lock at once and I do not expect an error. This is the class I use: class errorclass(threading.Thread): def __init__(self, queue): self.__queue=queue threading.Thread.__init__(self) def run(self): while 1: item = self.__queue.get() if item is None: break result = myfunction() lock = threading.RLock() lock.acquire() ADD entries from current thread to entries in file and REWRITE FILE lock.release() queue = Queue.Queue() for i in range(threads): errorclass(queue).start() for i in range(desired iterations): queue.put(i) for i in range(threads): queue.put(None) Python returns with KeyError: 51 for large number of desired iterations during the adding/write file operation after lock access, I am wondering if this is the correct way to use the lock since every thread has a lock operation rather than every thread accessing a shared lock? What would be the way to rectify this?

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• How can I kill off a Python web app on GAE early following a redirect?

  - by Mike Hayes

  Hi Disclaimer: completely new to Python from a PHP background Ok I'm using Python on Google App Engine with Google's webapp framework. I have a function which I import as it contains things which need to be processed on each page. def some_function(self): if data['user'].new_user and not self.request.path == '/main/new': self.redirect('/main/new') This works fine when I call it, but how can I make sure the app is killed off after the redirection. I don't want anything else processing. For example I will do this: class Dashboard(webapp.RequestHandler): def get(self): some_function(self) #Continue with normal code here self.response.out.write('Some output here') I want to make sure that once the redirection is made in some_function() (which works fine), that no processing is done in the get() function following the redirection, nor is the "Some output here" outputted. What should I be looking at to make this all work properly? I can't just exit the script because the webapp framework needs to run. I realise that more than likely I'm just doing things in completely the wrong way any way for a Python app, so any guidance would be a great help. Hopefully I have explained myself properly and someone will be able to point me in the right direction. Thanks

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• Python file iterator over a binary file with newer idiom.

  - by drewk

  In Python, for a binary file, I can write this: buf_size=1024*64 # this is an important size... with open(file, "rb") as f: while True: data=f.read(buf_size) if not data: break # deal with the data.... With a text file that I want to read line-by-line, I can write this: with open(file, "r") as file: for line in file: # deal with each line.... Which is shorthand for: with open(file, "r") as file: for line in iter(file.readline, ""): # deal with each line.... This idiom is documented in PEP 234 but I have failed to locate a similar idiom for binary files. I have tried this: >>> with open('dups.txt','rb') as f: ... for chunk in iter(f.read,''): ... i+=1 >>> i 1 # 30 MB file, i==1 means read in one go... I tried putting iter(f.read(buf_size),'') but that is a syntax error because of the parens after the callable in iter(). I know I could write a function, but is there way with the default idiom of for chunk in file: where I can use a buffer size versus a line oriented? Thanks for putting up with the Python newbie trying to write his first non-trivial and idiomatic Python script.

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• Linear Interpolation. How to implement this algorithm in C ? (Python version is given)

  - by psihodelia

  There exists one very good linear interpolation method. It performs linear interpolation requiring at most one multiply per output sample. I found its description in a third edition of Understanding DSP by Lyons. This method involves a special hold buffer. Given a number of samples to be inserted between any two input samples, it produces output points using linear interpolation. Here, I have rewritten this algorithm using Python: temp1, temp2 = 0, 0 iL = 1.0 / L for i in x: hold = [i-temp1] * L temp1 = i for j in hold: temp2 += j y.append(temp2 *iL) where x contains input samples, L is a number of points to be inserted, y will contain output samples. My question is how to implement such algorithm in ANSI C in a most effective way, e.g. is it possible to avoid the second loop? NOTE: presented Python code is just to understand how this algorithm works. UPDATE: here is an example how it works in Python: x=[] y=[] hold=[] num_points=20 points_inbetween = 2 temp1,temp2=0,0 for i in range(num_points): x.append( sin(i*2.0*pi * 0.1) ) L = points_inbetween iL = 1.0/L for i in x: hold = [i-temp1] * L temp1 = i for j in hold: temp2 += j y.append(temp2 * iL) Let's say x=[.... 10, 20, 30 ....]. Then, if L=1, it will produce [... 10, 15, 20, 25, 30 ...]

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• Is False == 0 and True == 1 in Python an implementation detail or guaranteed by the language?

  - by EOL

  Is it guaranteed that False == 0 and True == 1, in Python? For instance, is it in any way guaranteed that the following code will always produce the same results, whatever the version of Python (existing and in the foreseeable future)? 0 == False # True 1 == True # True ['zero', 'one'][False] # is 'zero' Any reference to the official documentation would be much appreciated! Other comments would be appreciated too… :) Edit: As noted in many answers, bool inherits from int. The question can therefore be recast as: "Is this an implementation detail that might change in the future, or does the documentation officially say that programmers can rely on booleans inheriting from integers?". This question is relevant for writing robust code that won't fail because of implementation details! Edit 2: The original question is still open, I believe (even though I accepted what I thought was the closest answer): even though Python 3 officially recognizes booleans as integers, I have not yet seen any official integer values for False and True… It therefore looks to me like it is best to stay clear from the assumption that False==0 and True==1.

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• Python : How do you find the CPU consumption for a piece of code?

  - by Yugal Jindle

  Background: I have a django application, it works and responds pretty well on low load, but on high load like 100 users/sec, it consumes 100% CPU and then due to lack of CPU slows down. Problem : Profiling the application gives me time taken by functions. This time increases on high load. Time consumed may be due to complex calculation or for waiting for CPU. so, how to find the CPU cycles consumed by a piece of code ? Since, reducing the CPU consumption will increase the response time. I might have written extremely efficient code and need to add more CPU power OR I might have some stupid code taking the CPU and causing the slow down ? Any help is appreciated ! Update: I am using Jmeter to profile my webapp, it gives me a throughput of 2 requests/sec. [ 100 users] I get a average time of 36 seconds on 100 request vs 1.25 sec time on 1 request. More Info Configuration Nginx + Uwsgi with 4 workers No database used, using a responses from a REST API On 1st hit the response of REST API gets cached, therefore doesn't makes a difference. Using ujson for json parsing. Curious to Know: Python-Django is used by so many orgs for so many big sites, then there must be some high end Debug / Memory-CPU analysis tools. All those I found were casual snippets of code that perform profiling.

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• Noob-Ready Cython Tutorials

  - by spearfire

  I know a bunch of scripting languages, (python, ruby, lua, php) but I don't know any compiled languages like C/C++ , I wanted to try and speed up some python code using cython, which is essentially a python - C compiler, aimed at creating C extensions for python. Basically you code in a stricter version of python which compiles into C - native code. here's the problem, I don't know C, yet the cython documentation is aimed at people who obviously already know C (nothing is explained, only presented), and is of no help to me, I need to know if there are any good cython tutorials aimed at python programmers, or if I'm gonna have to learn C before I learn Cython. bear in mind I'm a competent python programmer, i would much rather learn cython from the perspective of the language I'm already good at, rather than learn a whole new language in order to learn cython. 1) PLEASE don't recommend psyco edit: ANY information that will help understand the oficial cython docs is useful information

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• Facebook publish HTTP Error 400 : bad request

  - by Abhishek

  Hey I am trying to publish a score to Facebook through python's urllib2 library. import urllib2,urllib url = "https://graph.facebook.com/USER_ID/scores" data = {} data['score']=SCORE data['access_token']='APP_ACCESS_TOKEN' data_encode = urllib.urlencode(data) request = urllib2.Request(url, data_encode) response = urllib2.urlopen(request) responseAsString = response.read() I am getting this error: response = urllib2.urlopen(request) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 124, in urlopen return _opener.open(url, data, timeout) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 389, in open response = meth(req, response) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 502, in http_response 'http', request, response, code, msg, hdrs) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 427, in error return self._call_chain(*args) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 361, in _call_chain result = func(*args) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/urllib2.py", line 510, in http_error_default raise HTTPError(req.get_full_url(), code, msg, hdrs, fp) urllib2.HTTPError: HTTP Error 400: Bad Request Not sure if this is relating to Facebook's Open Graph or improper urllib2 API use.

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• Cython Speed Boost vs. Usability

  - by zubin71

  I just came across Cython, while I was looking out for ways to optimize Python code. I read various posts on stackoverflow, the python wiki and read the article "General Rules for Optimization". Cython is something which grasps my interest the most; instead of writing C-code for yourself, you can choose to have other datatypes in your python code itself. Here is a silly test i tried, #!/usr/bin/python # test.pyx def test(value): for i in xrange(value): i**2 if(i==1000000): print i test(10000001) $ time python test.pyx real 0m16.774s user 0m16.745s sys 0m0.024s $ time cython test.pyx real 0m0.513s user 0m0.196s sys 0m0.052s Now, honestly, i`m dumbfounded. The code which I have used here is pure python code, and all I have changed is the interpreter. In this case, if cython is this good, then why do people still use the traditional Python interpretor? Are there any reliability issues for Cython?

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• WxPython Incompatible With Snow Leopard?

  - by Alex

  Hello all, Recently I upgraded to Snow Leopard, and now I can't run programs built with wxPython. The errors I get are (from Eclipse + PyDev): import wx File "/var/tmp/wxWidgets/wxWidgets-13~231/2.6/DSTROOT/System/Library/Frameworks /Python.framework/Versions/2.6/Extras/lib/ python/wx-2.8-mac-unicode/wx/__init__.py", line 45, in <module> File "/var/tmp/wxWidgets/wxWidgets-13~231/2.6/DSTROOT /System/Library/Frameworks/Python.framework/Versions/2.6/Extras/lib /python/wx-2.8-mac-unicode/wx/_core.py", line 4, in <module> ImportError:/System/Library/Frameworks /Python.framework/Versions/2.6/Extras/lib/python /wx-2.8-mac-unicode/wx/_core_.so: no appropriate 64-bit architecture (see "man python" for running in 32-bit mode) I don't really understand them and would appreciate if you could help me to do so, also, if you do know what's going on, how can I go about fixing them? Maybe this has something to do with the fact that Snow Leopard is 64-bit? Thanks!!

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• Interpreted vs. Compiled vs. Late-Binding

  - by zubin71

  Python is compiled into an intermediate bytecode(pyc) and then executed. So, there is a compilation followed by interpretation. However, long-time Python users say that Python is a "late-binding" language and that it should`nt be referred to as an interpreted language. How would Python be different from another interpreted language? Could you tell me what "late-binding" means, in the Python context? Java is another language which first has source code compiled into bytecode and then interpreted into bytecode. Is Java an interpreted/compiled language? How is it different from Python in terms of compilation/execution? Java is said to not have, "late-binding". Does this have anything to do with Java programs being slighly faster than Python? Itd be great if you could also give me links to places where people have already discussed this; id love to read more on this. Thank you.

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• associating a filetype with a batch script, and getting parameters passed to file of that type.

  - by Carson Myers

  Sorry for the cryptic title. I have associated python scripts with a batch file that looks like this: python %* I did this because on my machine, python is installed at C:\python26 and I prefer not to reinstall it (for some reason, it won't let me add a file association to the python interpreter. I can copy the executable to Program Files and it works -- but nothing out of Program Files seems to work). Anyways, I can do this, so far: C:\py django-admin C:\py python "C:\python26\Lib\site-packages\django\bin\django-admin.py" Type 'django-admin.py help' for usage. C:\py django-admin startproject myProj C:\py python "C:\python26\Lib\site-packages\django\bin\django-admin.py" Type 'django-admin.py help' for usage. but the additional parameters don't get passed along to the batch script. This is getting very annoying, all I want to do is run python scripts :) How can I grab the rest of the parameters in this situation?

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• Using the StopWatch class to calculate the execution time of a block of code

  - by vik20000in

    Many of the times while doing the performance tuning of some, class, webpage, component, control etc. we first measure the current time taken in the execution of that code. This helps in understanding the location in code which is actually causing the performance issue and also help in measuring the amount of improvement by making the changes. This measurement is very important as it helps us understand the problem in code, Helps us to write better code next time (as we have already learnt what kind of improvement can be made with different code) . Normally developers create 2 objects of the DateTime class. The exact time is collected before and after the code where the performance needs to be measured.  Next the difference between the two objects is used to know about the time spent in the code that is measured. Below is an example of the sample code.             DateTime dt1, dt2;             dt1 = DateTime.Now;             for (int i = 0; i < 1000000; i++)             {                 string str = "string";             }             dt2 = DateTime.Now;             TimeSpan ts = dt2.Subtract(dt1);             Console.WriteLine("Time Spent : " + ts.TotalMilliseconds.ToString());   The above code works great. But the dot net framework also provides for another way to capture the time spent on the code without doing much effort (creating 2 datetime object, timespan object etc..). We can use the inbuilt StopWatch class to get the exact time spent. Below is an example of the same work with the help of the StopWatch class.             Stopwatch sw = Stopwatch.StartNew();             for (int i = 0; i < 1000000; i++)             {                 string str = "string";             }             sw.Stop();             Console.WriteLine("Time Spent : " +sw.Elapsed.TotalMilliseconds.ToString());   [Note the StopWatch class resides in the System.Diagnostics namespace] If you use the StopWatch class the time taken for measuring the performance is much better, with very little effort. Vikram

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• How do I upgrade django on ubuntu 9.04?

  - by Lorin Hochstein

  I've got Django 1.0.2 installed on Ubuntu 9.04. I'd like to upgrade Django, because I have an app that needs Django 1.1 or greater. I tried using pip to do the upgrade, but got the following: $ sudo pip install Django==1.1 Downloading/unpacking Django==1.1 Downloading Django-1.1.tar.gz (5.6Mb): 5.6Mb downloaded Running setup.py egg_info for package Django Installing collected packages: Django Found existing installation: Django 1.0.2-final Not uninstalling Django at /var/lib/python-support/python2.6, outside environment /usr Running setup.py install for Django changing mode of build/scripts-2.6/django-admin.py from 644 to 755 changing mode of /usr/local/bin/django-admin.py to 755 Successfully installed Django It seems like it worked, but it refuses to remove the original Django 1.02, and sure enough: $ pip freeze | grep -i django Django==1.0.2-final django-debug-toolbar==0.8.3 django-sphinx==2.2.3 $ /usr/local/bin/django-admin.py --version 1.0.2 final The problem, apparently, is that pip won't uninstall files outside of /usr. I'd like to remove the existing Django files manually, but I have no idea how to do that, because I'm unfamiliar with how Python packages are laid out in Ubuntu. It looks pretty complicated. The site-packages directory is: $ python -c "from distutils.sysconfig import get_python_lib; print get_python_lib()" /usr/lib/python2.6/dist-packages However, that's not where the django files live: $ ls -ld /usr/lib/python2.6/dist-packages/[Dd]jango* ls: cannot access /usr/lib/python2.6/dist-packages/[Dd]jango*: No such file or directory There's a /var/lib/python-support/python2.6/django directory, and the __init__.py file in that directory points to /usr/share/python-support/python-django/django/__init__.py. Clearly, pip is able to figure out where the files live. Is there any way to retrieve the list of files associated with the django package so I can just delete them manually?

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• Database model for keeping track of likes/shares/comments on blog posts over time

  - by gage

  My goal is to keep track of the popular posts on different blog sites based on social network activity at any given time. The goal is not to simply get the most popular now, but instead find posts that are popular compared to other posts on the same blog. For example, I follow a tech blog, a sports blog, and a gossip blog. The tech blog gets waaay more readership than the other two blogs, so in raw numbers every post on the tech blog will always out number views on the other two. So lets say the average tech blog post gets 500 facebook likes and the other two get an average of 50 likes per post. Then when there is a sports blog post that has 200 fb likes and a gossip blog post with 300 while the tech blog posts today have 500 likes I want to highlight the sports and gossip blog posts (more likes than average vs tech blog with more # of likes but just average for the blog) The approach I am thinking of taking is to make an entry in a database for each blog post. Every x minutes (say every 15 minutes) I will check how many likes/shares/comments an entry has received on all the social networks (facebook, twitter, google+, linkeIn). So over time there will be a history of likes for each blog post, i.e post 1234 after 15 min: 10 fb likes, 4 tweets, 6 g+ after 30 min: 15 fb likes, 15 tweets, 10 g+ ... ... after 48 hours: 200 fb likes, 25 tweets, 15 g+ By keeping a history like this for each blog post I can know the average number of likes/shares/tweets at any give time interval. So for example the average number of fb likes for all blog posts 48hrs after posting is 50, and a particular post has 200 I can mark that as a popular post and feature/highlight it. A consideration in the design is to be able to easily query the values (likes/shares) for a specific time-frame, i.e. fb likes after 30min or tweets after 24 hrs in-order to compute averages with which to compare against (or should averages be stored in it's own table?) If this approach is flawed or could use improvement please let me know, but it is not my main question. My main question is what should a database scheme for storing this info look like? Assuming that the above approach is taken I am trying to figure out what a database schema for storing the likes over time would look like. I am brand new to databases, in doing some basic reading I see that it is advisable to make a 3NF database. I have come up with the following possible schema. Schema 1 DB Popular Posts Table: Post post_id ( primary key(pk) ) url title Table: Social Activity activity_id (pk) url (fk) type (i.e. facebook,twitter,g+) value timestamp This was my initial instinct (base on my very limited db knowledge). As far as I under stand this schema would be 3NF? I searched for designs of similar database model, and found this question on stackoverflow, http://stackoverflow.com/questions/11216080/data-structure-for-storing-height-and-weight-etc-over-time-for-multiple-users . The scenario in that question is similar (recording weight/height of users overtime). Taking the accepted answer for that question and applying it to my model results in something like: Schema 2 (same as above, but break down the social activity into 2 tables) DB Popular Posts Table: Post post_id (pk) url title Table: Social Measurement measurement_id (pk) post_id (fk) timestamp Table: Social stat stat_id (pk) measurement_id (fk) type (i.e. facebook,twitter,g+) value The advantage I see in schema 2 is that I will likely want to access all the values for a given time, i.e. when making a measurement at 30min after a post is published I will simultaneous check number of fb likes, fb shares, fb comments, tweets, g+, linkedIn. So with this schema it may be easier get get all stats for a measurement_id corresponding to a certain time, i.e. all social stats for post 1234 at time x. Another thought I had is since it doesn't make sense to compare number of fb likes with number of tweets or g+ shares, maybe it makes sense to separate each social measurement into it's own table? Schema 3 DB Popular Posts Table: Post post_id (pk) url title Table: fb_likes fb_like_id (pk) post_id (fk) timestamp value Table: fb_shares fb_shares_id (pk) post_id (fk) timestamp value Table: tweets tweets__id (pk) post_id (fk) timestamp value Table: google_plus google_plus_id (pk) post_id (fk) timestamp value As you can see I am generally lost/unsure of what approach to take. I'm sure this typical type of database problem (storing measurements overtime, i.e temperature statistic) that must have a common solution. Is there a design pattern/model for this, does it have a name? I tried searching for "database periodic data collection" or "database measurements over time" but didn't find anything specific. What would be an appropriate model to solve the needs of this problem?

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• Probelms Intstalling Trac using apt-get Ubuntu Jaunty

  - by Ben Waine

  Hi, I'm having some issues getting apt to install trac correctly on my Ubuntu Jaunty Box. Using the command 'apt-get install trac' I get the following output: root@myserver:~# apt-get install trac Reading package lists... Done Building dependency tree Reading state information... Done Some packages could not be installed. This may mean that you have requested an impossible situation or if you are using the unstable distribution that some required packages have not yet been created or been moved out of Incoming. Since you only requested a single operation it is extremely likely that the package is simply not installable and a bug report against that package should be filed. The following information may help to resolve the situation: The following packages have unmet dependencies: trac: Depends: python-setuptools (> 0.5) but it is not installable Depends: python-pysqlite2 (>= 2.3.2) but it is not going to be installed Depends: python-subversion but it is not installable Depends: libjs-jquery but it is not installable Recommends: python-pygments (= 0.6) but it is not installable or enscript but it is not installable Recommends: python-tz but it is not installable E: Broken packages I have successfully used the command on my karmic kola desktop machine and am able to create new projects etc. I thought I might be able to solve the problem by installing all python related extensions. This produced a very similar output. I have Main, universe and multi-verse repositories enabled. Its a remote machine and I have no access to the gui. Hope someone can help, googleing failed to solve the issue or find a solution! Thanks, Ben

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• Error installing pygraphviz on OSX

  - by Neil

  I'm trying to get the graph-models to work (from django-command extensions) on Snow Leopard. It requires pygraphviz, which I installed via macports. After successful install I am getting this error: >>> import pygrahphviz Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: No module named pygrahphviz >>> import pygraphviz Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/__init__.py", line 54, in <module> from agraph import AGraph, Node, Edge, Attribute, ItemAttribute File "/Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/agraph.py", line 19, in <module> import graphviz as gv File "/Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/graphviz.py", line 7, in <module> import _graphviz ImportError: dlopen(/Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/_graphviz.so, 2): Symbol not found: _Agdirected Referenced from: /Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/_graphviz.so Expected in: flat namespace in /Library/Python/2.6/site-packages/pygraphviz-1.1-py2.6-macosx-10.6-universal.egg/pygraphviz/_graphviz.so >>> Any suggestions?

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• Google I/O 2010 - Make your app real-time with PubSubHubbub

  Google I/O 2010 - Make your app real-time with PubSubHubbub Google I/O 2010 - Make your application real-time with PubSubHubbub Social Web 201 Brett Slatkin This session will go over how to add support for the PubSubHubbub protocol to your website. You'll learn how to turn Atom and RSS feeds into real-time streams. We'll go over how to consume real-time data streams and how to make your website reactive to what's happening on the web right now. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 5 0 ratings Time: 55:46 More in Science & Technology

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• How to display time in the top panel?

  - by Mörre

  I thought I already had the time up there in the top bar, and it may have been so in previous Ubuntu versions (don't remember, my Ubuntu laptop is just one of three computers I use). Only that I just noticed - me being someone who never wears a watch, has the cellphone turned off 95% of the time and relying on the computer to tell the time - that there is no time being displayed anywhere, and I had expected it in the top bar on the Unity desktop. I searched around but found no obvious solution, but I'm sure someone immediately knows how I can get my time (back?) into the top bar?

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• Why does my MacBook Pro have long ping times over Wi-Fi?

  - by randynov

  I have been having problems connecting with my Wi-Fi. It is weird, the ping times to the router (<30 feet away) seem to surge, often getting over 10 seconds before slowly coming back down. You can see the trend below. I'm on a MacBook Pro and have done the normal stuff (reset the PRAM and SMC, changed wireless channels, etc.). It happens across different routers, so I think it must be my laptop, but I don't know what it could be. The RSSI value hovers around -57, but I've seen the transmit rate flip between 0, 48 and 54. The signal strength is ~60% with 9% noise. Currently, there are 17 other wireless networks in range, but only one in the same channel. 1 - How can I figure out what's going on? 2 - How can I correct the situation? PING 192.168.1.1 (192.168.1.1): 56 data bytes 64 bytes from 192.168.1.1: icmp_seq=0 ttl=254 time=781.107 ms 64 bytes from 192.168.1.1: icmp_seq=1 ttl=254 time=681.551 ms 64 bytes from 192.168.1.1: icmp_seq=2 ttl=254 time=610.001 ms 64 bytes from 192.168.1.1: icmp_seq=3 ttl=254 time=544.915 ms 64 bytes from 192.168.1.1: icmp_seq=4 ttl=254 time=547.622 ms 64 bytes from 192.168.1.1: icmp_seq=5 ttl=254 time=468.914 ms 64 bytes from 192.168.1.1: icmp_seq=6 ttl=254 time=237.368 ms 64 bytes from 192.168.1.1: icmp_seq=7 ttl=254 time=229.902 ms 64 bytes from 192.168.1.1: icmp_seq=8 ttl=254 time=11754.151 ms 64 bytes from 192.168.1.1: icmp_seq=9 ttl=254 time=10753.943 ms 64 bytes from 192.168.1.1: icmp_seq=10 ttl=254 time=9754.428 ms 64 bytes from 192.168.1.1: icmp_seq=11 ttl=254 time=8754.199 ms 64 bytes from 192.168.1.1: icmp_seq=12 ttl=254 time=7754.138 ms 64 bytes from 192.168.1.1: icmp_seq=13 ttl=254 time=6754.159 ms 64 bytes from 192.168.1.1: icmp_seq=14 ttl=254 time=5753.991 ms 64 bytes from 192.168.1.1: icmp_seq=15 ttl=254 time=4754.068 ms 64 bytes from 192.168.1.1: icmp_seq=16 ttl=254 time=3753.930 ms 64 bytes from 192.168.1.1: icmp_seq=17 ttl=254 time=2753.768 ms 64 bytes from 192.168.1.1: icmp_seq=18 ttl=254 time=1753.866 ms 64 bytes from 192.168.1.1: icmp_seq=19 ttl=254 time=753.592 ms 64 bytes from 192.168.1.1: icmp_seq=20 ttl=254 time=517.315 ms 64 bytes from 192.168.1.1: icmp_seq=37 ttl=254 time=1.315 ms 64 bytes from 192.168.1.1: icmp_seq=38 ttl=254 time=1.035 ms 64 bytes from 192.168.1.1: icmp_seq=39 ttl=254 time=4.597 ms 64 bytes from 192.168.1.1: icmp_seq=21 ttl=254 time=18010.681 ms 64 bytes from 192.168.1.1: icmp_seq=22 ttl=254 time=17010.449 ms 64 bytes from 192.168.1.1: icmp_seq=23 ttl=254 time=16010.430 ms 64 bytes from 192.168.1.1: icmp_seq=24 ttl=254 time=15010.540 ms 64 bytes from 192.168.1.1: icmp_seq=25 ttl=254 time=14010.450 ms 64 bytes from 192.168.1.1: icmp_seq=26 ttl=254 time=13010.175 ms 64 bytes from 192.168.1.1: icmp_seq=27 ttl=254 time=12010.282 ms 64 bytes from 192.168.1.1: icmp_seq=28 ttl=254 time=11010.265 ms 64 bytes from 192.168.1.1: icmp_seq=29 ttl=254 time=10010.285 ms 64 bytes from 192.168.1.1: icmp_seq=30 ttl=254 time=9010.235 ms 64 bytes from 192.168.1.1: icmp_seq=31 ttl=254 time=8010.399 ms 64 bytes from 192.168.1.1: icmp_seq=32 ttl=254 time=7010.144 ms 64 bytes from 192.168.1.1: icmp_seq=33 ttl=254 time=6010.113 ms 64 bytes from 192.168.1.1: icmp_seq=34 ttl=254 time=5010.025 ms 64 bytes from 192.168.1.1: icmp_seq=35 ttl=254 time=4009.966 ms 64 bytes from 192.168.1.1: icmp_seq=36 ttl=254 time=3009.825 ms 64 bytes from 192.168.1.1: icmp_seq=40 ttl=254 time=16000.676 ms 64 bytes from 192.168.1.1: icmp_seq=41 ttl=254 time=15000.477 ms 64 bytes from 192.168.1.1: icmp_seq=42 ttl=254 time=14000.388 ms 64 bytes from 192.168.1.1: icmp_seq=43 ttl=254 time=13000.549 ms 64 bytes from 192.168.1.1: icmp_seq=44 ttl=254 time=12000.469 ms 64 bytes from 192.168.1.1: icmp_seq=45 ttl=254 time=11000.332 ms 64 bytes from 192.168.1.1: icmp_seq=46 ttl=254 time=10000.339 ms 64 bytes from 192.168.1.1: icmp_seq=47 ttl=254 time=9000.338 ms 64 bytes from 192.168.1.1: icmp_seq=48 ttl=254 time=8000.198 ms 64 bytes from 192.168.1.1: icmp_seq=49 ttl=254 time=7000.388 ms 64 bytes from 192.168.1.1: icmp_seq=50 ttl=254 time=6000.217 ms 64 bytes from 192.168.1.1: icmp_seq=51 ttl=254 time=5000.084 ms 64 bytes from 192.168.1.1: icmp_seq=52 ttl=254 time=3999.920 ms 64 bytes from 192.168.1.1: icmp_seq=53 ttl=254 time=3000.010 ms 64 bytes from 192.168.1.1: icmp_seq=54 ttl=254 time=1999.832 ms 64 bytes from 192.168.1.1: icmp_seq=55 ttl=254 time=1000.072 ms 64 bytes from 192.168.1.1: icmp_seq=58 ttl=254 time=1.125 ms 64 bytes from 192.168.1.1: icmp_seq=59 ttl=254 time=1.070 ms 64 bytes from 192.168.1.1: icmp_seq=60 ttl=254 time=2.515 ms

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• Why does my macbook pro have long ping times over wifi?

  - by randynov

  I have been having problems connecting with my wifi. It is weird, the ping times to the router (<30 feet away) seem to surge, often getting over 10s before slowly coming back down. You can see the trend below. I'm on a macbook pro and have done the normal stuff (reset the pram and smc, changed wireless channels, etc.). It happens across different routers, so I think it must be my laptop, but I don't know what it could be. The RSSI value hovers around -57, but I've seen the transmit rate flip between 0, 48 & 54. The signal strength is ~60% with 9% noise. Currently, there are 17 other wireless networks in range, but only one in the same channel. 1 - How can I figure out what's going on? 2 - How can I correct the situation? TIA! Randall PING 192.168.1.1 (192.168.1.1): 56 data bytes 64 bytes from 192.168.1.1: icmp_seq=0 ttl=254 time=781.107 ms 64 bytes from 192.168.1.1: icmp_seq=1 ttl=254 time=681.551 ms 64 bytes from 192.168.1.1: icmp_seq=2 ttl=254 time=610.001 ms 64 bytes from 192.168.1.1: icmp_seq=3 ttl=254 time=544.915 ms 64 bytes from 192.168.1.1: icmp_seq=4 ttl=254 time=547.622 ms 64 bytes from 192.168.1.1: icmp_seq=5 ttl=254 time=468.914 ms 64 bytes from 192.168.1.1: icmp_seq=6 ttl=254 time=237.368 ms 64 bytes from 192.168.1.1: icmp_seq=7 ttl=254 time=229.902 ms 64 bytes from 192.168.1.1: icmp_seq=8 ttl=254 time=11754.151 ms 64 bytes from 192.168.1.1: icmp_seq=9 ttl=254 time=10753.943 ms 64 bytes from 192.168.1.1: icmp_seq=10 ttl=254 time=9754.428 ms 64 bytes from 192.168.1.1: icmp_seq=11 ttl=254 time=8754.199 ms 64 bytes from 192.168.1.1: icmp_seq=12 ttl=254 time=7754.138 ms 64 bytes from 192.168.1.1: icmp_seq=13 ttl=254 time=6754.159 ms 64 bytes from 192.168.1.1: icmp_seq=14 ttl=254 time=5753.991 ms 64 bytes from 192.168.1.1: icmp_seq=15 ttl=254 time=4754.068 ms 64 bytes from 192.168.1.1: icmp_seq=16 ttl=254 time=3753.930 ms 64 bytes from 192.168.1.1: icmp_seq=17 ttl=254 time=2753.768 ms 64 bytes from 192.168.1.1: icmp_seq=18 ttl=254 time=1753.866 ms 64 bytes from 192.168.1.1: icmp_seq=19 ttl=254 time=753.592 ms 64 bytes from 192.168.1.1: icmp_seq=20 ttl=254 time=517.315 ms 64 bytes from 192.168.1.1: icmp_seq=37 ttl=254 time=1.315 ms 64 bytes from 192.168.1.1: icmp_seq=38 ttl=254 time=1.035 ms 64 bytes from 192.168.1.1: icmp_seq=39 ttl=254 time=4.597 ms 64 bytes from 192.168.1.1: icmp_seq=21 ttl=254 time=18010.681 ms 64 bytes from 192.168.1.1: icmp_seq=22 ttl=254 time=17010.449 ms 64 bytes from 192.168.1.1: icmp_seq=23 ttl=254 time=16010.430 ms 64 bytes from 192.168.1.1: icmp_seq=24 ttl=254 time=15010.540 ms 64 bytes from 192.168.1.1: icmp_seq=25 ttl=254 time=14010.450 ms 64 bytes from 192.168.1.1: icmp_seq=26 ttl=254 time=13010.175 ms 64 bytes from 192.168.1.1: icmp_seq=27 ttl=254 time=12010.282 ms 64 bytes from 192.168.1.1: icmp_seq=28 ttl=254 time=11010.265 ms 64 bytes from 192.168.1.1: icmp_seq=29 ttl=254 time=10010.285 ms 64 bytes from 192.168.1.1: icmp_seq=30 ttl=254 time=9010.235 ms 64 bytes from 192.168.1.1: icmp_seq=31 ttl=254 time=8010.399 ms 64 bytes from 192.168.1.1: icmp_seq=32 ttl=254 time=7010.144 ms 64 bytes from 192.168.1.1: icmp_seq=33 ttl=254 time=6010.113 ms 64 bytes from 192.168.1.1: icmp_seq=34 ttl=254 time=5010.025 ms 64 bytes from 192.168.1.1: icmp_seq=35 ttl=254 time=4009.966 ms 64 bytes from 192.168.1.1: icmp_seq=36 ttl=254 time=3009.825 ms 64 bytes from 192.168.1.1: icmp_seq=40 ttl=254 time=16000.676 ms 64 bytes from 192.168.1.1: icmp_seq=41 ttl=254 time=15000.477 ms 64 bytes from 192.168.1.1: icmp_seq=42 ttl=254 time=14000.388 ms 64 bytes from 192.168.1.1: icmp_seq=43 ttl=254 time=13000.549 ms 64 bytes from 192.168.1.1: icmp_seq=44 ttl=254 time=12000.469 ms 64 bytes from 192.168.1.1: icmp_seq=45 ttl=254 time=11000.332 ms 64 bytes from 192.168.1.1: icmp_seq=46 ttl=254 time=10000.339 ms 64 bytes from 192.168.1.1: icmp_seq=47 ttl=254 time=9000.338 ms 64 bytes from 192.168.1.1: icmp_seq=48 ttl=254 time=8000.198 ms 64 bytes from 192.168.1.1: icmp_seq=49 ttl=254 time=7000.388 ms 64 bytes from 192.168.1.1: icmp_seq=50 ttl=254 time=6000.217 ms 64 bytes from 192.168.1.1: icmp_seq=51 ttl=254 time=5000.084 ms 64 bytes from 192.168.1.1: icmp_seq=52 ttl=254 time=3999.920 ms 64 bytes from 192.168.1.1: icmp_seq=53 ttl=254 time=3000.010 ms 64 bytes from 192.168.1.1: icmp_seq=54 ttl=254 time=1999.832 ms 64 bytes from 192.168.1.1: icmp_seq=55 ttl=254 time=1000.072 ms 64 bytes from 192.168.1.1: icmp_seq=58 ttl=254 time=1.125 ms 64 bytes from 192.168.1.1: icmp_seq=59 ttl=254 time=1.070 ms 64 bytes from 192.168.1.1: icmp_seq=60 ttl=254 time=2.515 ms

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• Right-Time Retail Part 1

  - by David Dorf

  This is the first in a three-part series. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Right-Time Revolution Technology enables some amazing feats in retail. I can order flowers for my wife while flying 30,000 feet in the air. I can order my groceries in the subway and have them delivered later that day. I can even see how clothes look on me without setting foot in a store. Who knew that a TV, diamond necklace, or even a car would someday be as easy to purchase as a candy bar? Can technology make a mattress an impulse item? Wake-up and your back is hurting, so you rollover and grab your iPad, then a new mattress is delivered the next day. Behind the scenes the many processes are being choreographed to make the sale happen. This includes moving data between systems with the least amount for friction, which in some cases is near real-time. But real-time isn’t appropriate for all the integrations. Think about what a completely real-time retailer would look like. A consumer grabs toothpaste off the shelf, and all systems are immediately notified so that the backroom clerk comes running out and pushes the consumer aside so he can replace the toothpaste on the shelf. Such a system is not only cost prohibitive, but it’s also very inefficient and ineffectual. Retailers must balance the realities of people, processes, and systems to find the right speed of execution. That’ what “right-time retail” means. Retailers used to sell during the day and count the money and restock at night, but global expansion and the Web have complicated that simplistic viewpoint. Our 24hr society demands not only access but also speed, which constantly pushes the boundaries of our IT systems. In the last twenty years, there have been three major technology advancements that have moved us closer to real-time systems. Networking is the first technology that drove the real-time trend. As systems became connected, it became easier to move data between them. In retail we no longer had to mail the daily business report back to corporate each day as the dial-up modem could transfer the data. That was soon replaced with trickle-polling, when sale transactions were occasionally sent from stores to corporate throughout the day, often through VSAT. Then we got terrestrial networks like DSL and Ethernet that allowed the constant stream of data between stores and corporate. When corporate could see the sales transactions coming from stores, it could better plan for replenishment and promotions. That drove the need for speed into the supply chain and merchandising, but for many years those systems were stymied by the huge volumes of data. Nordstrom has 150 million SKU/Store combinations when planning (RPAS); The Gap generates 110 million price changes during end-of-season (RPM); Argos does 1.78 billion calculations executed each day for replenishment planning (AIP). These areas are now being alleviated by the second technology, storage. The typical laptop disk drive runs at 5,400rpm with PCs stepping up to 7,200rpm and servers hitting 15,000rpm. But the platters can only spin so fast, so to squeeze more performance we’ve had to rely on things like disk striping. Then solid state drives (SSDs) were introduced and prices continue to drop. (Augmenting your harddrive with a SSD is the single best PC upgrade these days.) RAM continues to be expensive, but compressing data in memory has allowed more efficient use. So a few years back, Oracle decided to build a box that incorporated all these advancements to move us closer to real-time. This family of products, often categorized as engineered systems, combines the hardware and software so that they work together to provide better performance. How much better? If Exadata powered a 747, you’d go from New York to Paris in 42 minutes, and it would carry 5,000 passengers. If Exadata powered baseball, games would last only 18 minutes and Boston’s Fenway would hold 370,000 fans. The Exa-family enables processing more data in less time. So with faster networks and storage, that brings us to the third and final ingredient. If we continue to process data in traditional ways, we won’t be able to take advantage of the faster networks and storage. Enter what Harvard calls “The Sexiest Job of the 21st Century” – the data scientist. New technologies like the Hadoop-powered Oracle Big Data Appliance, Oracle Advanced Analytics, and Oracle Endeca Information Discovery change the way in which we organize data. These technologies allow us to extract actionable information from raw data at incredible speeds, often ad-hoc. So the foundation to support the real-time enterprise exists, but how does a retailer begin to take advantage? The most visible way is through real-time marketing, but I’ll save that for part 3 and instead begin with improved integrations for the assets you already have in part 2.

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