[Tutor] Movement controls useing pygame
A little stuck and could do with any sudjestions. Aim:- When the character goes past the middle of the screen, the background & level move downwards so the character can get to higher places, think sonic the hedgehog. This is the bit I'm having problems with, I can get the character to get to the center, the background moves up & down but the character won't go back down when the level returns to normal height. if bkGroundRect['rect'].top < 0 and charRect['rect'].top < winCenterRect.top: bkGroundRect['rect'].top += spY platRect['rect'].top += spY jumpRect.top += spY charRect['dir'] = STOP if levelRect['rect'].bottom > winHeight and charRect['rect'].centery == winCenterx: bkGroundRect['rect'].bottom -= spY platRect['rect'].bottom -= spY #jumpRect.bottom -= spY charRect['dir'] = STOP #--this won't execute ? --# if levelRect['rect'].bottom <= winHeight: if charRect['rect'].centery >= winCenterx and charRect['rect'].centerx < jumpRect.centerx: charRect['dir'] = DOWN I've included the whole code as a note pad so the above makes more sense, don't worry about all the comments, that's just for my convenience. Thanks guys Mark 'knight meets wall, wall won't let knight pass, knight says 'neh' ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Movement controls useing pygame
Are you just reposting this exact same e-mail to the list because you didn't see my reply to the other one, or are you ignoring what I said and posting the same message again, hoping someone else will answer? I hope it's not the latter, that's kind of insulting. The least you could do is say that my idea didn't make sense or wouldn't work for you in this case. -Luke On Thu, Mar 18, 2010 at 7:45 PM, wrote: > A little stuck and could do with any sudjestions. > > Aim:- > When the character goes past the middle of the screen, the background & > level move downwards so the character can get to higher places, think sonic > the hedgehog. > > This is the bit I'm having problems with, I can get the character to get to > the center, the background moves up & down but the character won't go back > down when the level returns to normal height. > > if bkGroundRect['rect'].top < 0 and charRect['rect'].top < > winCenterRect.top: > > bkGroundRect['rect'].top += spY > platRect['rect'].top += spY > jumpRect.top += spY > charRect['dir'] = STOP > > > if levelRect['rect'].bottom > winHeight and charRect['rect'].centery == > winCenterx: > bkGroundRect['rect'].bottom -= spY > platRect['rect'].bottom -= spY > #jumpRect.bottom -= spY > charRect['dir'] = STOP > > #--this won't execute ? --# > if levelRect['rect'].bottom <= winHeight: > if charRect['rect'].centery >= winCenterx and > charRect['rect'].centerx < jumpRect.centerx: > charRect['dir'] = DOWN > > I've included the whole code as a note pad so the above makes more sense, > don't worry about all the comments, that's just for my convenience. > > Thanks guys > > Mark > > 'knight meets wall, wall won't let knight pass, knight says 'neh' > > > ___ > Tutor maillist - Tutor@python.org > To unsubscribe or change subscription options: > http://mail.python.org/mailman/listinfo/tutor > > ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] movement controls
On Fri, Mar 19, 2010 at 4:09 AM, wrote: > But yes, I'm not quite sure about what you mean. I guess you mean keep > the character on the x plane <> then for the jumping movements move the > level? what do you mean by 'You should probably decouple the view from the > model' > > > and it's preferable not to use html when sending e-mails, 'cause your font settings don't necessarily look good on other people's computers. You should really be asking this on the Pygame list, and you probably still should. Having said that... I'll try to answer your question. The way you're currently doing it, you have all of your sprites' coordinates just stored in random variables, and you're calculating where they should be on the screen and then drawing them directly, right? In other words, if player.x = 100 and player.y = 200 then he is located at 100, 200 on the screen? Having a view and a model relates to the MVC (model-view-controller) paradigm in programming. Basically think of it like this. Suppose I have a window that I'm looking out, and my dog is outside running around somewhere. Now you walk by the window. Does it make more sense for me to model your interaction with my dog in relation to what I can see out of my window, or should I model your interaction with the dog in a separate space, independent of where I may be looking? Say I want to describe your position to my friend who lives up and to the right of me. Would I say "see that guy by the bench petting my dog?" or would I say "see the guy that is 12 meters forward and 1 meter to the right of the center of my window?" The friend doesn't have any idea what perspective _I_ view the world from, and he really shouldn't have to. SO in the MVC framework, you model your _world_ space and your VIEW is just an outside observer "peeking" at the current state of the world. So, for example, say your world is a grid of 1000 x 8000 units, and your character is located at 200x200. There might be an enemy moving around at 800x2000, who may not be on the screen, but I still want to model that enemy moving around, so that when the character gets over there, the enemy will be in a new location. Or maybe he'll walk over and be on the screen. When you do it this way, you can merely say "render everything that is around my player's current position" and then all your problems are solved. The view will automatically follow the player, and when he jumps up, the view will raise with him. If you set an upper and lower limit, then the view will naturally not move until the player moves past a certain point of the screen. If you continue to refer to the game objects in terms of their graphical position in relation to the screen, it's going to get very convoluted. In order to "decouple your view from your model" you will likely have to rewrite a lot of your code -- the key here is that you should've written it this way (MVC) in the first place. Read up more on MVC, and specifically how it relates to game design. Also, when you reply, use Reply to All otherwise your reply is only seen by me, not by the other members on the list. Good luck, -Luke ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
[Tutor] difflib context to string-object?
With difflib.context_diff it is possible to write the context to files. difflib.context_diff(a, b[, fromfile][, tofile][, fromfiledate][, tofiledate][, n][, lineterm]) Is it also possible to do this to seperate string-objects instead of writing them to files? ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
[Tutor] using pythnn to open a password protected website
Hi, Im trying to use python to open up a password protected website(e.g. facebook / gmail) in Firefox. supplying the login and password automatically at runtime - so that I can interface my code with fingerprint recognition code. So far I have only found urllib, urllib2 and web browser, none of which I have been able to use. urllib2 can log me in, but then I cant pass this authenicated login to the browser. At the very least I need to be able to send the form post information to the browser via my code, and preferably auto-submit the form. Does anybody have any ideas? Many Thanks, Richard. ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] using pythnn to open a password protected website
On Fri, Mar 19, 2010 at 7:33 AM, richard west wrote: > Hi, > > Im trying to use python to open up a password protected website(e.g. > facebook / gmail) in Firefox. supplying the login and password automatically > at runtime - so that I can interface my code with fingerprint recognition > code. So far I have only found urllib, urllib2 and web browser, none of > which I have been able to use. > > urllib2 can log me in, but then I cant pass this authenicated login to the > browser. At the very least I need to be able to send the form post > information to the browser via my code, and preferably auto-submit the form. > > Does anybody have any ideas? Check the tamper data plugin for firefox. I think that's the one. It should help you get the types of parameters, etc. that are actually passed to the website. Then I think you can use urllib2's variety of methods to connect to the site. HTH, Wayne ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] difflib context to string-object?
On 3/19/2010 6:55 AM, Karjer Jdfjdf wrote: With difflib.context_diff it is possible to write the context to files. difflib.context_diff(/a/, /b/[, /fromfile/][, /tofile/][, /fromfiledate/][, /tofiledate/][, /n/][, /lineterm/]) Is it also possible to do this to seperate string-objects instead of writing them to files? Take a look at the stringIO module. -- Bob Gailer 919-636-4239 Chapel Hill NC ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Understanding (Complex) Modules
(I meant to post this to both the list and Steven back a week or so ago. I missed the list, so am posting it here now. Unfortunately, I'm again in a position where I cannot respond for several days, but I will be back early next week to examine some posts that followed this.) === Thanks. I'm on the road for a few days and will be able to read this more carefully when I get back. I had a feeling from my first attempts of looking at MPL and other heavy-duty libs that it was going to require work to dig out what I needed from the module itself. In my personal view, the usage and learning documents are fairly limiting. Perhaps on in MPL, which is less traditional than numpy and the others, which are founded on common and historic implementations of math and science from older languages. From: Steven D'Aprano To: tutor@python.org Sent: Thu, March 4, 2010 6:24:15 PM Subject: Re: [Tutor] Understanding (Complex) Modules On Thu, 4 Mar 2010 12:24:35 pm Wayne Watson wrote: > First a little preamble before my questions. > > Most of my work in Python has required modifying a program that uses > modules that were imported by the original program. I've made some > use of modules on a command line like math, and have used the idea of > a qualifier. On occasion, I've used examples from matplotlib that > required from matplotlib.image import AxesImage. Further, I've > created some simple classes, and produced objects with them. No use > of inheritance though. So far so good. In a few places, it is said > modules are objects. I'm slightly puzzled by that, but in some way it > seems reasonable from the standpoint of period notation. So far I > have not created a module. Yes you have, you just don't know it. In simple language, a file with Python-usable code in it is a module. There are some technicalities and complexities, but in basic terms, that's all it is. Technically, "module" refers only to the object which exists inside the Python environment after you call "import thing". The import statement does a whole lot of tricks, but in a nutshell it: * finds a file containing code called 'thing' * loads it into memory, executing code as needed * creates a 'module' object to store the code and data in the file * and makes it available to your code Python can create module objects from: compiled Windows DLLs .dll compiled Linux object files .so Python source code .py Python byte code .pyc and .pyo (and .pyw on Windows) Zip files containing any of the above and probably other things as well, but they're the main ones. So any Python file you create (any .py file) is a module, or at least it would be a module if you import it. Ignoring all the various compiled files (.dll, .so, etc) what happens when you run a .py file from the command line (or from IDLE or another IDE). E.g. you type something like: python.exe myscript.py [enter] Python reads the file myscript.py and executes it, but no module object is created. It is possible that a module object *is* created, for internal use, then thrown away when the script is finished. But your script doesn't see the module object. However, if you enter the Python interactive environment, and do this: >>> import myscript # no .py Python loads the file myscript.py into a module object, executing any code in it, and makes it available as a module. > Here comes the questions. Recently I began to use matplotlib, scipy, > and pylab, mostly matplotlib. I've ground out a few useful pieces of > code, but I'm fairly baffled by the imports required to get at > various elements, of say, matplotlib (MPL). Some of the MPL examples > use of imports make sense, but how the writer pulled in the necessary > elements is not. How does one go about understanding the > capabilities of such modules? Time, experience, and a lot of hard work. Welcome to programming! If the documentation is good, then read the documentation. If the documentation is lacking, or bad, or even wrong, then read the source code, or search the Internet for a tutorial, or buy a book about it (the numpy people, for example, sell books about numpy). Python makes experimentation easy: there are a lot of powerful introspection facilities in Python. The interactive interpreter is your best friend. You will live with it, eat with it, sleep with it, take it on double-dates, and throw yourself on live grenades to protect it. Whenever I'm programming, I almost always have three or five interactive sessions open for experimentation. The dir() and help() functions are also good friends. In an interactive session: import math dir(math) # prints a list of names in the math module help(math.sin) help(math) Don't feel that you have to understand the entire module before you use it. Many (alas, not all) modules have a reasonably gentle learning curve: you can start using math.sin without needing to know what math.sinh is for. Google and Wikipedia are also you
[Tutor] Efficiency and speed
Hello all: I've still been working towards learning the language, albeit slowly and I've been working on a project that is somewhat intense on the numerical calculation end of things. Running 10,000 trials takes about 1.5 seconds and running 100,000 trials takes 11 seconds. Running a million trials takes about 81 seconds or so. I don't think 1M trials is needed for accuracy, but 100K probably is. I've made a few other optimizations today that I won't be able to test until I get home, but I was wondering if any of you could give some general pointers on how to make python run a little more quickly. There are two major pieces of code that seem slow, so I'll share the first with you. This section takes up about 1/3 of the time used when running all trials, where trials is 10K or larger. This section doesn't actually do any math, all it's doing is returning the "bin" that the randomly generated number falls in. The second section, the one that is taking up most of the time, does the math. The list nx1 is about 220 floating point numbers long. > sample = random.sample(range(int(self.nx1[b])), trials) # a list of sample > values based on nx1 countlist = [] self.nx1.append(0) #puts a zero on the end of nx1. This is for the case > where one of the random values lies past the minimum number (it scales from > 10M down to almost 0, but not exactly 0) Antyhing past this dates yields a > 0. for s in self.mcrange_gen(sample): countlist.append(s-1) # This appends the bin number (the number > returned previously minus one) to make slices of the premium streams. and here is the generator section: def mcrange_gen(self, sample):#, lensample): lensample = len(sample) # this section is just for speed. All of these > are renames from the globals to bring calc times down at the expense of > memory. I haven't tested these yet. nx2 = self.nx1 nx2_append = nx2.append nx2_sort = nx2.sort nx2_reverse = nx2.reverse nx2_index = nx2.index nx2_remove = nx2.remove for s in range(lensample): q = sample[s] #takes the next randomly generated number from the > sample list nx2_append(q) # and appends it to nx list. nx2_sort() # and sorts it in place nx2_reverse() # reverses the list, because this was the original > order i = nx2_index(q) #get the index of that element nx2_remove(q) # and remove the element. yield i # send the position of that element back to the main > program. ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
James Reynolds, 19.03.2010 17:41: I've still been working towards learning the language, albeit slowly and I've been working on a project that is somewhat intense on the numerical calculation end of things. Running 10,000 trials takes about 1.5 seconds and running 100,000 trials takes 11 seconds. Running a million trials takes about 81 seconds or so. I don't think 1M trials is needed for accuracy, but 100K probably is. I've made a few other optimizations today that I won't be able to test until I get home, but I was wondering if any of you could give some general pointers on how to make python run a little more quickly. There are two major pieces of code that seem slow, so I'll share the first with you. This section takes up about 1/3 of the time used when running all trials, where trials is 10K or larger. This section doesn't actually do any math, all it's doing is returning the "bin" that the randomly generated number falls in. The second section, the one that is taking up most of the time, does the math. The list nx1 is about 220 floating point numbers long. sample = random.sample(range(int(self.nx1[b])), trials) # a list of sample values based on nx1 countlist = [] self.nx1.append(0) #puts a zero on the end of nx1. This is for the case where one of the random values lies past the minimum number (it scales from 10M down to almost 0, but not exactly 0) Antyhing past this dates yields a 0. for s in self.mcrange_gen(sample): countlist.append(s-1) # This appends the bin number (the number returned previously minus one) to make slices of the premium streams. and here is the generator section: def mcrange_gen(self, sample):#, lensample): lensample = len(sample) # this section is just for speed. All of these are renames from the globals to bring calc times down at the expense of memory. I haven't tested these yet. nx2 = self.nx1 nx2_append = nx2.append nx2_sort = nx2.sort nx2_reverse = nx2.reverse nx2_index = nx2.index nx2_remove = nx2.remove for s in range(lensample): q = sample[s] #takes the next randomly generated number from the sample list nx2_append(q) # and appends it to nx list. nx2_sort() # and sorts it in place nx2_reverse() # reverses the list, because this was the original order i = nx2_index(q) #get the index of that element nx2_remove(q) # and remove the element. yield i # send the position of that element back to the main program. This certainly falls into the bin of the most inefficient algorithms I've ever seen. Even walking through the samples one by one to find the target bin would be faster than the above. Could you try to describe in a couple of words what this algorithm is supposed to do? That will almost certainly make it clear how you should write it instead. Stefan ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] parsing a "chunked" text file
Hello, Thanks both of you for these useful information. Regards Karim Hugo Arts wrote: On Thu, Mar 18, 2010 at 12:54 PM, Stefan Behnel wrote: Karim Liateni, 04.03.2010 01:23: Yes, a *big* difference in the true sense of the word. Your code (assuming you meant to write "... for line in ..." ) evaluates the entire list comprehension before returning from the call. Steven's code returns a generator that only handles one line (or a couple of empty lines) at a time. So, assuming that this runs against a large file, Steven's code uses only a constant amount of memory, compared to the whole file in your case, and is likely also a lot faster than your code as it involves less looping. Though, if you changed the brackets into parentheses, you'd get a generator expression, which *is* equivalent to Steven's version, except that it calls strip() twice, which is a bit wasteful. If the unnecessary extra call bothers you, you could do one of two things: 1) Learn how the yield keyword works. You should do this. It's an awesome feature, and you'll come across it many more times. 2) go functional and import itertools. ifilter with a generator expression, like so (pure functional programmers can also use imap instead of the generator expr., which might be faster. profile to be sure) def skip_blanks(lines): return ifilter(None, (l.strip() for l in lines)) Very short, has all the memory and speed benefits of the generator. Personally I really like terse functional programming like this, though I believe the general consensus in the python community is that imperative alternatives are usually clearer to read. If you want to know more about the yield keyword: A terse description (assumes that you know how iterators work) is here: http://docs.python.org/tutorial/classes.html#generators A more detailed description of iterators and generators can be found here: http://www.ibm.com/developerworks/library/l-pycon.html Hugo ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
Well, I'm always out to impress! This is a monte-carlo simulation. The simulation measures the expiration of something and those somethings fall into bins that are not evenly dispersed. These bins are stored in the nx list mentioned previously. So let's say you have the bins, a, b,c,d,e,f and you have the value z from the sample list where z >b and <= a. In this case, it should return the index value at position (a). These, in turn, are used to take slices of other lists and only apply those slices from position zero to position (a), in the case above. I previously tried doing something like: if z >b and <= a then append to new list, else pass, but that seemed like a slower way of going about it, because python needs to cycle through the entire list to find the right value. I guess i could add a count to the list, like len(appendedlist) and using a while loop, so it will stop checking once it's been appended and move to the next iteration in the sample list. But before you recommend a better way of writing it, I would greatly appreciate to know why the above is inefficient and some other way is more efficient. I'm trying to learn the concepts behind programming in general and how to program in python at the same time. On Fri, Mar 19, 2010 at 1:56 PM, Stefan Behnel wrote: > James Reynolds, 19.03.2010 17:41: > > I've still been working towards learning the language, albeit slowly and >> I've been working on a project that is somewhat intense on the numerical >> calculation end of things. >> >> Running 10,000 trials takes about 1.5 seconds and running 100,000 trials >> takes 11 seconds. Running a million trials takes about 81 seconds or so. I >> don't think 1M trials is needed for accuracy, but 100K probably is. >> >> I've made a few other optimizations today that I won't be able to test >> until >> I get home, but I was wondering if any of you could give some general >> pointers on how to make python run a little more quickly. >> >> There are two major pieces of code that seem slow, so I'll share the first >> with you. This section takes up about 1/3 of the time used when running >> all >> trials, where trials is 10K or larger. >> >> This section doesn't actually do any math, all it's doing is returning the >> "bin" that the randomly generated number falls in. >> >> The second section, the one that is taking up most of the time, does the >> math. >> >> The list nx1 is about 220 floating point numbers long. >> >> >> >> sample = random.sample(range(int(self.nx1[b])), trials) # a list of >>> sample >>> values based on nx1 >>> >> >> countlist = [] >> >> self.nx1.append(0) #puts a zero on the end of nx1. This is for the case >> >>> where one of the random values lies past the minimum number (it scales >>> from >>> 10M down to almost 0, but not exactly 0) Antyhing past this dates yields >>> a >>> 0. >>> >> >> for s in self.mcrange_gen(sample): >> >> countlist.append(s-1) # This appends the bin number (the number >> >>> returned previously minus one) to make slices of the premium streams. >>> >> >> >> and here is the generator section: >> >> def mcrange_gen(self, sample):#, lensample): >> >> lensample = len(sample) # this section is just for speed. All of these >> >>> are renames from the globals to bring calc times down at the expense of >>> memory. I haven't tested these yet. >>> >> >> nx2 = self.nx1 >> >> nx2_append = nx2.append >> >> nx2_sort = nx2.sort >> >> nx2_reverse = nx2.reverse >> >> nx2_index = nx2.index >> >> nx2_remove = nx2.remove >> >> for s in range(lensample): >> >> q = sample[s] #takes the next randomly generated number from the >> >>> sample list >>> >> >> nx2_append(q) # and appends it to nx list. >> >> nx2_sort() # and sorts it in place >> >> nx2_reverse() # reverses the list, because this was the original >> >>> order >>> >> >> i = nx2_index(q) #get the index of that element >> >> nx2_remove(q) # and remove the element. >> >> yield i # send the position of that element back to the main >> >>> program. >>> >> > This certainly falls into the bin of the most inefficient algorithms I've > ever seen. Even walking through the samples one by one to find the target > bin would be faster than the above. > > Could you try to describe in a couple of words what this algorithm is > supposed to do? That will almost certainly make it clear how you should > write it instead. > > Stefan > > ___ > Tutor maillist - Tutor@python.org > To unsubscribe or change subscription options: > http://mail.python.org/mailman/listinfo/tutor > ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] using pythnn to open a password protected website
"richard west" wrote Im trying to use python to open up a password protected website(e.g. facebook / gmail) in Firefox. So you are trying to control Firefox not the web site? Is that correct? What mechanism are you using to communicate with Firefox? code. So far I have only found urllib, urllib2 and web browser, none of which I have been able to use. urllib is for interacting with the web site not with the browser. webbrowser is for launching a browser for a user to interact with, it does not really control the browser programatically. You may need to use OS dependent tools like COM on Windows or maybe even GUI events/messages to drive the browser remotely. Are you sure that's really what you want to do? Or do you really want to fill in and submit a web form on a page protected by a login? If thats the case you can do it all from Python without going near Firefox. -- Alan Gauld Author of the Learn to Program web site http://www.alan-g.me.uk/ ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
On 3/19/2010 9:41 AM James Reynolds said... OK, so starting here: def mcrange_gen(self, sample): lensample = len(sample) nx2 = self.nx1 nx2_append = nx2.append nx2_sort = nx2.sort nx2_reverse = nx2.reverse nx2_index = nx2.index nx2_remove = nx2.remove for s in range(lensample): q = sample[s] nx2_append(q) nx2_sort() nx2_reverse() i = nx2_index(q) nx2_remove(q) yield i First, the two lines: for s in range(lensample): q = sample[s] variable s is never used again, so instead we'll do: for q in sample: Which renders lensample as unused, so throw out lensample = len(sample) We now have: def mcrange_gen(self, sample): nx2 = self.nx1 nx2_append = nx2.append nx2_sort = nx2.sort nx2_reverse = nx2.reverse nx2_index = nx2.index nx2_remove = nx2.remove for q in sample: nx2_append(q) nx2_sort() nx2_reverse() i = nx2_index(q) nx2_remove(q) yield i Now, let's see what's going on for each q. You append it to self.nx1 (through the nx2 reference), then sort nx1, then reserve nx1, then scan for the new position of q, note the index, remove it from nx1, and yeild the index. OK, so that way you find out between which two elements of nx1 the current q falls, and i becomes the bin. So, how about something like (untested): def mcrange_gen(self, sample): self.nx1.sort() # now the bin boundries are in order for q in sample: ii = -1 for binlimit in self.nx1: if qhttp://mail.python.org/mailman/listinfo/tutor
[Tutor] Tutorial executable from python script.
Hello, I want to do a executable for linux/unix from python scripts and thus o allow to run on machine which doesn't have recent version (2.0) of python. I found the compile() method but how can I use it to make all in one executable which could be run on old system (old python). If you have any links I would love to have it Thanks Karim ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
"James Reynolds" wrote I've made a few other optimizations today that I won't be able to test until I get home, but I was wondering if any of you could give some general pointers on how to make python run a little more quickly. Always, always, get the algorithm efficient before trying to make the code efficient. Then eliminate redundant variable assignments, extra loops, hidden loops (like in, any etc) Then use the profiler to identify the hot spots. Then fine tune the hot spots. This is where you can start to worry about the speedups of using local variables etc. There are two major pieces of code that seem slow, so I'll share the first with you. This section takes up about 1/3 of the time used when running all trials, where trials is 10K or larger. How are you measuring? Is it via the profiler? Is it by inserying print time statements? Is is subjectively timing it by hand? The second section, the one that is taking up most of the time, does the math. Thats probably what you would expect if the math is complex. The list nx1 is about 220 floating point numbers long. So not very big at all... sample = random.sample(range(int(self.nx1[b])), trials) # a list of sample values based on nx1 The use of self suggests there is an object, or at least a class definition involved? for s in self.mcrange_gen(sample): countlist.append(s-1) # This appends the bin number (the number def mcrange_gen(self, sample):#, lensample): lensample = len(sample) # this section is just for speed. All of these are renames from the globals to bring calc times down at the expense of memory. I haven't tested these yet. This is premature optimisation at this stage. Its cluttering up the code for relatively little benefit. for s in range(lensample): q = sample[s] #takes the next randomly generated number from the for q in sample would be more pythonic nx2_append(q) # and appends it to nx list. nx2_sort() # and sorts it in place nx2_reverse() # reverses the list, because this was the original So you sort and reverse the entire list every time round the for loop? Might it be more efficient to keep the list in the right order to start with? i = nx2_index(q) #get the index of that element nx2_remove(q) # and remove the element. Now you find the thing you inserted and remove it. Wow. yield i # send the position of that element back to the main So you really just want to find out where you would like to insert it in an already sorted/reversed list? Back to step one - can you improve the algorithm? -- Alan Gauld Author of the Learn to Program web site http://www.alan-g.me.uk/ ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Tutorial executable from python script.
"Karim Liateni" wrote on machine which doesn't have recent version (2.0) of python. Given that v2 is at least 10 years old now that's not really "recent" I'd be surprised if any current Linux distros had anything that old on them! Even the ones designed for old hardware. In fact, are you sure your code runs on v1 python? There have been a lot of language changes since then. the compile() method but how can I use it to make all in one executable compile() doesn't make an exe, it compiles python script into python byte code - much as the javac compiler compiles java source into java bytecode. run on old system (old python). Exactly how old is old? If you write the code to run on an old Python interpreter it should still work(mostly!) on Python 2.6. The trick is not to compile the code but to write code that is consistent wityh the oldest version of Python you need to run on. HTH, -- Alan Gauld Author of the Learn to Program web site http://www.alan-g.me.uk/ ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
Here's another idea I had. I thought this would be slower than then the previous algorithm because it has another for loop and another while loop. I read that the overhead of such loops is high, so I have been trying to avoid using them where possible. def mcrange_gen(self, sample): nx2 = self.nx1 for q in sample: for a in nx2: while a > q: pass yield a break On Fri, Mar 19, 2010 at 3:15 PM, Alan Gauld wrote: > "James Reynolds" wrote > > > I've made a few other optimizations today that I won't be able to test >> until >> I get home, but I was wondering if any of you could give some general >> pointers on how to make python run a little more quickly. >> > > Always, always, get the algorithm efficient before trying to make > the code efficient. > > Then eliminate redundant variable assignments, extra loops, > hidden loops (like in, any etc) > > Then use the profiler to identify the hot spots. > > Then fine tune the hot spots. > This is where you can start to worry about the speedups of > using local variables etc. > > > There are two major pieces of code that seem slow, so I'll share the first >> with you. This section takes up about 1/3 of the time used when running >> all >> trials, where trials is 10K or larger. >> > > How are you measuring? Is it via the profiler? Is it by inserying print > time statements? Is is subjectively timing it by hand? > > > The second section, the one that is taking up most of the time, does the >> math. >> > > Thats probably what you would expect if the math is complex. > > > The list nx1 is about 220 floating point numbers long. >> > > So not very big at all... > > > sample = random.sample(range(int(self.nx1[b])), trials) # a list of sample >>> values based on nx1 >>> >> > The use of self suggests there is an object, or at least a class definition > involved? > > > for s in self.mcrange_gen(sample): >>countlist.append(s-1) # This appends the bin number (the number >> > > def mcrange_gen(self, sample):#, lensample): >> lensample = len(sample) # this section is just for speed. All of these >> >>> are renames from the globals to bring calc times down at the expense of >>> memory. I haven't tested these yet. >>> >> > This is premature optimisation at this stage. Its cluttering up the code > for relatively little benefit. > > >for s in range(lensample): >> q = sample[s] #takes the next randomly generated number from the >> > > for q in sample > > would be more pythonic > > >nx2_append(q) # and appends it to nx list. >> nx2_sort() # and sorts it in place >> nx2_reverse() # reverses the list, because this was the original >> > > So you sort and reverse the entire list every time round the for loop? > Might it be more efficient to keep the list in the right order to start > with? > > >i = nx2_index(q) #get the index of that element >> nx2_remove(q) # and remove the element. >> > > Now you find the thing you inserted and remove it. Wow. > > >yield i # send the position of that element back to the main >> > > So you really just want to find out where you would like to insert it > in an already sorted/reversed list? > > Back to step one - can you improve the algorithm? > > -- > Alan Gauld > Author of the Learn to Program web site > http://www.alan-g.me.uk/ > > ___ > Tutor maillist - Tutor@python.org > To unsubscribe or change subscription options: > http://mail.python.org/mailman/listinfo/tutor > ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] difflib context to string-object?
On Fri, 19 Mar 2010 09:55:06 pm Karjer Jdfjdf wrote:
> With difflib.context_diff it is possible to write the context to
> files.
>
> difflib.context_diff(a, b[, fromfile][, tofile][, fromfiledate][,
> tofiledate][, n][, lineterm])
>
> Is it also possible to do this to seperate string-objects instead of
> writing them to files?
Use StringIO objects, which are fake files that can be read as strings.
Untested and entirely from memory, use:
from StringIO import StringIO
fromfile = StringIO("some text goes here")
tofile = StringIO("some different text goes here")
--
Steven D'Aprano
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Re: [Tutor] using pythnn to open a password protected website
On Fri, 19 Mar 2010 11:33:36 pm richard west wrote: > Hi, > > Im trying to use python to open up a password protected website(e.g. > facebook / gmail) in Firefox. supplying the login and password > automatically at runtime - so that I can interface my code with > fingerprint recognition code. So far I have only found urllib, > urllib2 and web browser, none of which I have been able to use. > > urllib2 can log me in, but then I cant pass this authenicated login > to the browser. At the very least I need to be able to send the form > post information to the browser via my code, and preferably > auto-submit the form. You might like to look at Mechanize, which is a third-party Python project for dealing with just that sort of problem. -- Steven D'Aprano ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
On Fri, Mar 19, 2010 at 3:17 PM, James Reynolds wrote: > Here's another idea I had. I thought this would be slower than then the > previous algorithm because it has another for loop and another while loop. I > read that the overhead of such loops is high, so I have been trying to avoid > using them where possible. > > def mcrange_gen(self, sample): > nx2 = self.nx1 > for q in sample: > for a in nx2: > while a > q: > pass > yield a > break > > One thing to consider is whether you need to run this simulation online or offline. That is, whether you can reorder your sample space. IFF you can reorder your sample space, then this algorithm can be extremely simplified / sped up, because then you can sort both the sample space and the binning list in numerical order Then you simply do a single pass through each list and only spend O(n+m) time to categorize your entire sample space. Sort of how a "merge" step of "merge sort" works, except rather than emitting the sort order you would just be emitting tuples with the sample value and its corresponding bin index. If this doesn't make sense let me know. If you are able to run the sim offline and reorder your sample space, and I'm not just being dumb & misunderstanding what you're trying to do (which is entirely possible), try it this way. If you have problems with my explanation, let me know and I'll try to explain it better. HTH, -Luke ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
"James Reynolds" wrote Here's another idea I had. I thought this would be slower than then the previous algorithm because it has another for loop and another while loop. I read that the overhead of such loops is high, so I have been trying to avoid using them where possible. Thats often true but nested loops are sometimes unavoidable. Its unnecessary nests that are really bad! But the other important rule when tuning performamce is: don't guess, measure. Put the new design in and test it to see if it runs any faster. Ultimately thats the only thing that matters (assuming its giving the correct results of course!) def mcrange_gen(self, sample): nx2 = self.nx1 for q in sample: for a in nx2: while a > q: pass yield a break You shouldn't need the break after the yield. The yield returns a value and the loop iteration ends naturally. Alan G. ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
Re: [Tutor] Efficiency and speed
James Reynolds, 19.03.2010 21:17: Here's another idea I had. I thought this would be slower than then the previous algorithm because it has another for loop and another while loop. I read that the overhead of such loops is high, so I have been trying to avoid using them where possible. Premature optimisation is the source of all evil. def mcrange_gen(self, sample): nx2 = self.nx1 for q in sample: for a in nx2: while a> q: pass Looks like an infinite loop to me. Stefan ___ Tutor maillist - Tutor@python.org To unsubscribe or change subscription options: http://mail.python.org/mailman/listinfo/tutor
