threads - was:Is there a way to protect a piece of critical code?
"Hendrik van Rooyen" wrote: > Similarly discrete background thread jobs can be used > in a functional style this way: > http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/491280 > ( an alternative for the laborious OO-centric threading. With the various options available, many of which I haven't used (CallQueue for example), I am wondering what the "best" methodology to use is for a 2-task example on a dual core processor for my next project: -One task is an ADC data collection/device driver whose sole job in life is to fill a numpy "circular buffer" with data from an external ADC device at high speed and expose a pointer - programmatically complex but functionally distinct from the analysis task. It is I/O bound and spends time waiting on the ADC. -The main task is analysis of that data in near-real time with FFT, correlations etc, and computationally bound. It needs to have read access to the array and a pointer, and kill the ADC task when desired. Thoughts/opinions are humbly requested, Ray -- http://mail.python.org/mailman/listinfo/python-list
Re: Python 2.5 install on Gentoo Linux: failed dmb and _tkinter
# ldconfig -p | grep "/usr/local/lib" libtk8.4.so (libc6) => /usr/local/lib/libtk8.4.so libtcl8.4.so (libc6) => /usr/local/lib/libtcl8.4.so Sorin Now that's room service! Choose from over 150,000 hotels in 45,000 destinations on Yahoo! Travel to find your fit. http://farechase.yahoo.com/promo-generic-14795097 -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
Gabriel Genellina wrote: > see this article by M. Simoniato > http://www.phyast.pitt.edu/~micheles/python/documentation.html for a better > way using its decorator factory. Actually the name is Simionato ;) I have just released version 2.0, the new thing is an update_wrapper function similar to the one in the standard library, but with the ability to preserve the signature on demand. For instance def traced(func): def wrapper(*args, **kw): print 'calling %s with args %s, %s' % (func, args, kw) return func(*args, **kw) return update_wrapper(wrapper, func, create=False) works exactly as functools.update_wrapper (i.e. copies__doc__, __module__,etc. from func to wrapper without preserving the signature), whereas update_wrapper(wrapper, func, create=True) creates a new wrapper with the right signature before copying the attributes. Michele Simionato -- http://mail.python.org/mailman/listinfo/python-list
Re: Threaded for loop
"John" <[EMAIL PROTECTED]> writes: > Damn! That is bad news. So even if caclulate is independent for > (i,j) and is computable on separate CPUs (parts of it are CPU bound, > parts are IO bound) python cant take advantage of this? Not at the moment, unless you write C extensions that release the global interpreter lock (GIL). One of these days. Meanwhile there are various extension modules that let you use multiple processes, look up POSH and Pyro. -- http://mail.python.org/mailman/listinfo/python-list
Re: Threaded for loop
John wrote:
> Thanks. Does it matter if I call shell commands os.system...etc in
> calculate?
>
> Thanks,
> --j
The os.system command neglects important changes in the environment
(redirected streams) and would not work with current version of ppsmp.
Although there is a very simple workaround:
print os.popen("yourcommand").read()
instead of os.system("yourcommand")
Here is a complete working example of that code:
http://www.parallelpython.com/component/option,com_smf/Itemid,29/topic,13.0
--
http://mail.python.org/mailman/listinfo/python-list
Re: Maths error
"Tim Peters" <[EMAIL PROTECTED]> wrote: > [Nick Maclaren] > >> ... > >> Yes, but that wasn't their point. It was that in (say) iterative > >> algorithms, the error builds up by a factor of the base at every > >> step. If it wasn't for the fact that errors build up, almost all > >> programs could ignore numerical analysis and still get reliable > >> answers! > >> > >> Actually, my (limited) investigations indicated that such an error > >> build-up was extremely rare - I could achieve it only in VERY > >> artificial programs. But I did find that the errors built up faster > >> for higher bases, so that a reasonable rule of thumb is that 28 > >> digits with a decimal base was comparable to (say) 80 bits with a > >> binary base. > > [Hendrik van Rooyen] > > I would have thought that this sort of thing was a natural consequence > > of rounding errors - if I round (or worse truncate) a binary, I can be > > off by at most one, with an expectation of a half of a least > > significant digit, while if I use hex digits, my expectation is around > > eight, and for decimal around five... > > Which, in all cases, is a half ULP at worst (when rounding -- as > everyone does now). > > > So it would seem natural that errors would propagate > > faster on big base systems, AOTBE, but this may be > > a naive view.. > > I don't know of any current support for this view. It the bad old days, > such things were often confused by architectures that mixed non-binary > bases with "creative" rounding rules (like truncation indeed), and it > could be hard to know where to "pin the blame". > > What you will still see stated is variations on Kahan's telegraphic > "binary is better than any other radix for error analysis (but not very > much)", listed as one of two techincal advantages for binary fp in: > > http://www.cs.berkeley.edu/~wkahan/MktgMath.pdf > > It's important to note that he says "error analysis", not "error > propagation" -- regardless of base in use, rounding is good to <= 1/2 > ULP. A fuller elementary explanation of this can be found in David > Goldberg's widely available "What Every Computer Scientist Should Know > About Floating-Point", in its "Relative Error and Ulps" section. The > short course is that rigorous forward error analysis of fp algorithms is > usually framed in terms of relative error: given a computed > approximation x' to the mathematically exact result x, what's the > largest possible absolute value of the mathematical > >r = (x'-x)/x > > (the relative error of x')? This framework gets used because it's more- > or-less tractable, starting by assuming inputs are exact (or not, in > which case you start by bounding the inputs' relative errors), then > successively computing relative errors for each step of the algorithm. > Goldberg's paper, and Knuth volume 2, contain many introductory examples > of rigorous analysis using this approach. > > Analysis of relative error generally goes along independent of FP base. > It's at the end, when you want to transform a statement about relative > error into a statement about error as measured by ULPs (units in the > last place), where the base comes in strongly. As Goldberg explains, > the larger the fp base the sloppier the relative-error-converted-to-ULPs > bound is -- but this is by a constant factor independent of the > algorithm being analyzed, hence Kahan's "... better ... but not very > much". In more words from Goldberg: > > Since epsilon [a measure of relative error] can overestimate the > effect of rounding to the nearest floating-point number by the > wobble factor of B [the FP base, like 2 for binary or 10 for > decimal], error estimates of formulas will be tighter on machines > with a small B. > > When only the order of magnitude of rounding error is of interest, > ulps and epsilon may be used interchangeably, since they differ by > at most a factor of B. > > So that factor of B is irrelevant to most apps most of the time. For a > combination of an fp algorithm + set of inputs near the edge of giving > gibberish results, of course it can be important. Someone using > Python's decimal implementation has an often very effective workaround > then, short of writing a more robust fp algorithm: just boost the > precision. > Thanks Tim, for taking the trouble. - really nice explanation. My basic error of thinking ( ? - more like gut feel ) was that the bigger bases somehow lose "more bits" at every round, forgetting that half a microvolt is still half a microvolt, whether it is rounded in binary, decimal, or hex... - Hendrik -- http://mail.python.org/mailman/listinfo/python-list
Re: Maths error
In article <[EMAIL PROTECTED]>, "Hendrik van Rooyen" <[EMAIL PROTECTED]> writes: |> "Tim Peters" <[EMAIL PROTECTED]> wrote: |> |> > What you will still see stated is variations on Kahan's telegraphic |> > "binary is better than any other radix for error analysis (but not very |> > much)", listed as one of two techincal advantages for binary fp in: |> > |> > http://www.cs.berkeley.edu/~wkahan/MktgMath.pdf Which I believe to be the final statement of the matter. It was a minority view 30 years ago, but I now know of little dissent. He has omitted that mid-point invariant as a third advantage of binary, but I agree that it could be phrased as "one or two extra mathematical invariants hold for binary (but not very important ones)". |> My basic error of thinking ( ? - more like gut feel ) was that the |> bigger bases somehow lose "more bits" at every round, |> forgetting that half a microvolt is still half a microvolt, whether |> it is rounded in binary, decimal, or hex... That is not an error, but only a mistake :-) Yes, you have hit the nail on the head. Some people claimed that some important algorithms did that, and that binary was consequently much better. If it were true, then the precision you would need would be pro rata to the case - so the decimal equivalent of 64-bit binary would need 160 bits. Experience failed to confirm their viewpoint, and the effect was seen in only artificial algorithms (sorry - I can no longer remember the examples and am reluctant to waste time trying to reinvent them). But it was ALSO found that the converse was not QUITE true, either, and the effective numerical precision is not FULLY independent of the base. So, at a wild guesstimate, 64-bit decimal will deliver a precision comparable to about 56-bit binary, and will cause significant numerical problems to a FEW applications. Hence people will have to convert to the much more expensive 128-bit decimal format for such work. Bloatware rules. All your bits are belong to us. Regards, Nick Maclaren. -- http://mail.python.org/mailman/listinfo/python-list
Re: Maths error
In article <[EMAIL PROTECTED]>, "Hendrik van Rooyen" <[EMAIL PROTECTED]> writes: |> > |> I would suspect that this is one of those questions which are simple |> to ask, but horribly difficult to answer - I mean - if the hardware has |> thrown it away, how do you study it - you need somehow two |> different parallel engines doing the same stuff, and comparing the |> results, or you have to write a big simulation, and then you bring |> your simulation errors into the picture - There be Dragons... No. You just emulate floating-point in software and throw a switch selecting between the two rounding rules. Regards, Nick Maclaren. -- http://mail.python.org/mailman/listinfo/python-list
Re: Rational Numbers
In article <[EMAIL PROTECTED]>, "Hendrik van Rooyen" <[EMAIL PROTECTED]> writes: |> |> > Financial calculations need decimal FIXED-point, with a precisely |> > specified precision. It is claimed that decimal FLOATING-point |> > helps with providing that, but that claim is extremely dubious. |> > I can explain the problem in as much detail as you want, but would |> > very much rather not. |> |> Ok I will throw in a skewed ball at this point - use integer arithmetic, |> and work in tenths of cents or pennies or whatever, and don't be too |> lazy to do your own print formatting... That's not a skewed ball - that's the traditional way of doing it on systems that don't have fixed-point hardware (and sometimes even when they do). Yes, it's dead easy in a language (like Python) that allows decent encapsulation. The decimal floating-point brigade grossly exaggerate the difficulty of doing that, in order to persuade people that their solution is better. If they admitted the difficulties of using decimal floating-point, and merely said "but, overall, we think it is a better solution", I would disagree but say nothing. Regards, Nick Maclaren. -- http://mail.python.org/mailman/listinfo/python-list
Fixed-point [was Re: Rational Numbers]
In article <[EMAIL PROTECTED]>, "Hendrik van Rooyen" <[EMAIL PROTECTED]> writes: |> |> Ok I will throw in a skewed ball at this point - use integer arithmetic, |> and work in tenths of cents or pennies or whatever, and don't be too |> lazy to do your own print formatting... If anyone is interested in doing this, here are a few notes on some of the non-trivial aspects. Please feel free if you want to contact me for more information. All numbers are held as (precision, value). Precision is the number of digits after the decimal point (a small, often constant, integer) and the value is held as an integer. It would be possible to have a separate subclass or context for every precision (like Decimal), but that would be an unnecessary complication. +, - and % return a precision that is the maximum of their input precisions, and // returns a precision of 0. * returns a precision that is the sum of its input precisions. / returns a floating-point number! There is a divide function that takes a divisor, dividend and output precision. It also takes a rounding rule as an optional argument (up, down, in, out, conventional nearest or IEEE 754R nearest). There is a precision conversion function that takes a value and output precision, and a rounding rule as an optional argument. There is a conversion function that takes a string or floating-point number and output precision, and a rounding rule as an optional argument. It raises an exception if a 1 ULP change in the floating- point number would give a different answer; this is needed to make certain operations reliable. The default formatting does the obvious thing :-) Er, that's about it Regards, Nick Maclaren. -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
Gabriel Genellina wrote: [snip] > As said in the beginning, there is no use for decorators as methods (perhaps > someone can find a use case?) Except, perhaps to indicate to the script reader that the decorator only applies within the class? Colin W. > If you move the example above inside the class, you get exactly the same > results. > > HTH, > > -- http://mail.python.org/mailman/listinfo/python-list
Announcing: Spiff Guard (Generic Access Lists for Python)
Introduction Spiff Guard is a library for implementing access lists in Python. It provides a clean and simple API and was implemented with performance and security in mind. It was originally inspired by phpGACL (http://phpgacl.sourceforge.net/), but features an API that is significantly cleaner and easier to use. Spiff Guard is the first library published as part of the Spiff platform. The Spiff platform aims to produce a number of generic libraries generally needed in enterprise (web) applications. Spiff Guard is free software and distributed under the GNU GPLv2. Dependencies - sqlalchemy (http://www.sqlalchemy.org/) Download - Please check out the code from SVN: svn checkout http://spiff.googlecode.com/svn/trunk/libs/Guard/ Links: --- Spiff project page: http://code.google.com/p/spiff/ Bug tracker: http://code.google.com/p/spiff/issues/list Documentation: http://spiff.googlecode.com/svn/trunk/libs/Guard/README Browse the source: http://spiff.googlecode.com/svn/trunk/libs/Guard/ Any questions, please ask (or file a bug). -Samuel -- http://mail.python.org/mailman/listinfo/python-list
Re: Tkinter code (with pmw) executing to soon please help
[EMAIL PROTECTED] wrote: > Scott David Daniels wrote: >> Gabriel Genellina wrote: >> >... So `callback` should return a function, like this: >> > >> > def callback(text): >> > def handler(event): >> > print text >> > >> >> Even better than that: >> def callback(text): >> def handler(event): >> print text >> return handler >> >> Otherwise callback returns the spectacularly un-useful value None. > C:\dex_tracker\csdlist.py bay-at-night.csd > Traceback (most recent call last): > File "C:\dex_tracker\csdlist.py", line 58, in > root.mainloop() > File "C:\Python25\lib\lib-tk\Tkinter.py", line 1023, in mainloop > self.tk.mainloop(n) > File "../../..\Pmw\Pmw_1_2\lib\PmwBase.py", line 1751, in __call__ > File "../../..\Pmw\Pmw_1_2\lib\PmwBase.py", line 1777, in _reporterror > TypeError: unsupported operand type(s) for +: 'type' and 'str' > Script terminated. > > It doesn't like the return handler part of it. Probably because a Tkinter.Button command callback doesn't accept any arguments. Try def callback(text): def handler(): print text return handler Note that 'make_callback' would be a better name than 'callback' because the function 'callback' actually creates the callback (called 'handler'). Peter -- http://mail.python.org/mailman/listinfo/python-list
Re: Comparing a matrix (list[][]) ?
jairodsl wrote: > Hi, > > How can I find the minus element greater than zero in a matrix, my > matrix is > > matrix= > [9,8,12,15], > [0,11,15,18], > [0,0,10,13], > [0,0,0,5] > > I dont want to use "min" function because each element in the matrix is > associated to (X,Y) position. > > Thanks a lot. > > jDSL > You might consider numarray or numpy for this sort of thing. Colin W. -- http://mail.python.org/mailman/listinfo/python-list
Re: template engine
> > > $title > > > #if user > hello $user/name > #else > hello guest > #endif > > This example code would work in cheetah with only 2 changes... www.cheetahtemplate.org Pete -- http://mail.python.org/mailman/listinfo/python-list
Re: Threaded for loop
John> Damn! That is bad news. So even if caclulate is independent for John> (i,j) and is computable on separate CPUs (parts of it are CPU John> bound, parts are IO bound) python cant take advantage of this? It will help if parts are I/O bound, presuming the threads which block release the global interpreter lock (GIL). There is a module in development (processing.py) that provides an API like the threading module but that uses processes under the covers: http://mail.python.org/pipermail/python-dev/2006-October/069297.html You might find that an interesting alternative. Skip -- http://mail.python.org/mailman/listinfo/python-list
Re: Threaded for loop
John wrote: > I want to do something like this: > > for i = 1 in range(0,N): > for j = 1 in range(0,N): >D[i][j] = calculate(i,j) > > I would like to now do this using a fixed number of threads, say 10 > threads. Why do you want to run this in 10 threads? Do you have 10 CPUs? If you are concerned about CPU time, you should not be using threads (regardless of language) as they are often implemented with the assumption that they stay idle most of the time (e.g. win32 threads and pthreads). In addition, CPython has a global interpreter lock (GIL) that prevents the interpreter from running on several processors in parallel. It means that python threads are a tool for things like writing non-blocking i/o and maintaining responsiveness in a GUI'. But that is what threads are implemented to do anyway, so it doesn't matter. IronPython and Jython do not have a GIL. In order to speed up computation you should run multiple processes and do some sort of IPC. Take a look at MPI (e.g. mpi4py.scipy.org) or 'parallel python'. MPI is the de facto industry standard for dealing with CPU bound problems on systems with multiple processors, whether the memory is shared or distributed does not matter. Contrary to common belief, this approach is more efficient than running multiple threads, sharing memory and synchronizong with mutexes and event objects - even if you are using a system unimpeded by a GIL. The number of parallel tasks should be equal to the number of available CPU units, not more, as you will get excessive context shifts if the number of busy threads or processes exceed the number of computational units. If you only have two logical CPUs (e.g. one dual-core processor) you should only run two parallel tasks - not ten. If you try to parallelize using additional tasks (e.g. 8 more), you will just waste time doing more context shifts, more cache misses, etc. But if you are a lucky bastard with access to a 10-way server, sure run 10 tasks in parallel. -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
"Colin J. Williams" <[EMAIL PROTECTED]> escribió en el mensaje news:[EMAIL PROTECTED] > Gabriel Genellina wrote: >> As said in the beginning, there is no use for decorators as methods >> (perhaps >> someone can find a use case?) > Except, perhaps to indicate to the script reader that the decorator only > applies within the class? But it looks rather strange - a method without self, that is not a classmethod nor static method, and can't be called on an instance... -- Gabriel Genellina -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
"Michele Simionato" <[EMAIL PROTECTED]> escribió en el mensaje news:[EMAIL PROTECTED] > Gabriel Genellina wrote: >> see this article by M. Simoniato >> http://www.phyast.pitt.edu/~micheles/python/documentation.html for a >> better >> way using its decorator factory. > > Actually the name is Simionato ;) Oh, sorry! I think I've written it wrong in another place too :( -- Gabriel Genellina -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
Thanks so much for your reply. You've definitely helped me a great deal on this. Your comment about the difference between define time and instantiation time cleared things up more than anything, and that also helped clear up the confusion I was having about "self". I think the places I've seen decorators called like fooDecorator() must be using some default arguments in the function signature...so that part makes a lot more sense now too. Thanks again! Gabriel Genellina wrote: > "MR" <[EMAIL PROTECTED]> escribió en el mensaje > news:[EMAIL PROTECTED] > > > I have a question about decorators, and I think an illustration would > > be helpful. Consider the following simple class: > > > > #begin code > > class Foo: > >def fooDecorator(f): > >print "fooDecorator" > > > >def _f(self, *args, **kw): > >return f(self, *args, **kw) > > > >return _f > > > >@fooDecorator > >def fooMethod(self): > >print "fooMethod" > > > > f = Foo() > > f.fooMethod() > > #end of code > > > > This code runs, and actually serves my purpose. However, I'm a little > > confused about three things and wanted to try and work through them > > while I had the time to do so. I believe all of my confusion is related > > to the parameters related to the fooDecorator: > > [I reordered your questions to make the answer a bit more clear] > > > -why does this code even work, because the first argument to > > fooDecorator isn't self > > fooDecorator is called when the class is *defined*, not when it's > instantiated. `self` has no meaning inside it, neither the class to which it > belongs (Foo does not even exist yet). > At this time, fooDecorator is just a simple function, being collected inside > a namespace in order to construct the Foo class at the end. So, you get > *exactly* the same effect if you move fooDecorator outside the class. > > > -how I would pass arguments into the fooDecorator if I wanted to (my > > various attempts have failed) > > Once you move fooDecorator outside the class, and forget about `self` and > such irrelevant stuff, it's just a decorator with arguments. > If you want to use something like this: >@fooDecorator(3) >def fooMethod(self): > that is translated to: >fooMethod = fooDecorator(3)(fooMethod) > That is, fooDecorator will be called with one argument, and the result must > be a normal decorator - a function accepting a function an an argument and > returning another function. > > def outerDecorator(param): > def fooDecorator(f): > print "fooDecorator" > > def _f(self, *args, **kw): > print "decorated self=%s args=%s kw=%s param=%s" % (self, args, kw, > param) > kw['newparam']=param > return f(self, *args, **kw) > > return _f > return fooDecorator > > This is the most direct way of doing this without any help from other > modules - see this article by M. Simoniato > http://www.phyast.pitt.edu/~micheles/python/documentation.html for a better > way using its decorator factory. > > > -what the difference is between decorating with @fooDecorator versus > > @fooDecorator() > Easy: the second way doesn't work :) > (I hope reading the previous item you can answer this yourself) > > > I'm searched the net and read the PEPs that seemed relevant, but I > > didn't see much about decorators inside of a class like this. Can > > anyone comment on any of these three things? > As said in the beginning, there is no use for decorators as methods (perhaps > someone can find a use case?) > If you move the example above inside the class, you get exactly the same > results. > > HTH, > > -- > Gabriel Genellina -- http://mail.python.org/mailman/listinfo/python-list
Re: Decorators inside of class and decorator parameters
Wow. Really neat stuff there. memoize seems especially cool since you can get lots of nice dynamic programming benefits "for free" (sorry if I just stated the obvious, but I thought was was especially cool.) Michele Simionato wrote: > Gabriel Genellina wrote: > > see this article by M. Simoniato > > http://www.phyast.pitt.edu/~micheles/python/documentation.html for a better > > way using its decorator factory. > > Actually the name is Simionato ;) > I have just released version 2.0, the new thing is an update_wrapper > function similar to the one > in the standard library, but with the ability to preserve the signature > on demand. For instance > > def traced(func): >def wrapper(*args, **kw): >print 'calling %s with args %s, %s' % (func, args, kw) >return func(*args, **kw) > return update_wrapper(wrapper, func, create=False) > > works exactly as functools.update_wrapper (i.e. copies__doc__, > __module__,etc. from func to wrapper without > preserving the signature), whereas update_wrapper(wrapper, func, > create=True) creates a new wrapper > with the right signature before copying the attributes. > > Michele Simionato -- http://mail.python.org/mailman/listinfo/python-list
Re: Tkinter code (with pmw) executing to soon please help
<[EMAIL PROTECTED]> escribió en el mensaje news:[EMAIL PROTECTED] > button[num] = Tkinter.Button(frame,text = returnstring, > command=callback(returnstring))# > > I understand this part of it > > def callback(text): > def handler(event): > print text > It stopped calling it automaticaly but will not do anything when I > click on the button. Does something have to change on this line as > well. Sorry, I overlooked your example. For a button, command should be a function with no arguments (and I forget to return the handler, as someone already pointed out): def callback(text): def handler(): print text return handler -- Gabriel Genellina -- http://mail.python.org/mailman/listinfo/python-list
Re: Newbie - converting csv files to arrays in NumPy - Matlab vs. Numpy comparison
Thank you so much. Your solution works! I greatly appreciate your
help.
sturlamolden wrote:
> oyekomova wrote:
>
> > Thanks for your note. I have 1Gig of RAM. Also, Matlab has no problem
> > in reading the file into memory. I am just running Istvan's code that
> > was posted earlier.
>
> You have a CSV file of about 520 MiB, which is read into memory. Then
> you have a list of list of floats, created by list comprehension, which
> is larger than 274 MiB. Additionally you try to allocate a NumPy array
> slightly larger than 274 MiB. Now your process is already exceeding 1
> GiB, and you are probably running other processes too. That is why you
> run out of memory.
>
> So you have three options:
>
> 1. Buy more RAM.
>
> 2. Low-level code a csv-reader in C.
>
> 3. Read the data in chunks. That would mean something like this:
>
>
> import time, csv, random
> import numpy
>
> def make_data(rows=6E6, cols=6):
> fp = open('data.txt', 'wt')
> counter = range(cols)
> for row in xrange( int(rows) ):
> vals = map(str, [ random.random() for x in counter ] )
> fp.write( '%s\n' % ','.join( vals ) )
> fp.close()
>
> def read_test():
> start = time.clock()
> arrlist = None
> r = 0
> CHUNK_SIZE_HINT = 4096 * 4 # seems to be good
> fid = file('data.txt')
> while 1:
> chunk = fid.readlines(CHUNK_SIZE_HINT)
> if not chunk: break
> reader = csv.reader(chunk)
> data = [ map(float, row) for row in reader ]
> arrlist = [ numpy.array(data,dtype=float), arrlist ]
> r += arrlist[0].shape[0]
> del data
> del reader
> del chunk
> print 'Created list of chunks, elapsed time so far: ', time.clock()
> - start
> print 'Joining list...'
> data = numpy.empty((r,arrlist[0].shape[1]),dtype=float)
> r1 = r
> while arrlist:
> r0 = r1 - arrlist[0].shape[0]
> data[r0:r1,:] = arrlist[0]
> r1 = r0
> del arrlist[0]
> arrlist = arrlist[0]
> print 'Elapsed time:', time.clock() - start
>
> make_data()
> read_test()
>
> This can process a CSV file of 6 million rows in about 150 seconds on
> my laptop. A CSV file of 1 million rows takes about 25 seconds.
>
> Just reading the 6 million row CSV file ( using fid.readlines() ) takes
> about 40 seconds on my laptop. Python lists are not particularly
> efficient. You can probably reduce the time to ~60 seconds by writing a
> new CSV reader for NumPy arrays in a C extension.
--
http://mail.python.org/mailman/listinfo/python-list
Re: Threaded for loop
[EMAIL PROTECTED] wrote: > > There is a module in development (processing.py) that provides an API like > the threading module but that uses processes under the covers: > > http://mail.python.org/pipermail/python-dev/2006-October/069297.html > > You might find that an interesting alternative. See the promised parallel processing overview on the python.org Wiki for a selection of different solutions: http://wiki.python.org/moin/ParallelProcessing Paul -- http://mail.python.org/mailman/listinfo/python-list
help with recursion on GP project
I'm trying to create a recursive function to evaluate the expressions
within a list. The function uses eval() to evaluate the list. Like a
lisp interpreter but very limited.
What I'm looking for is a function to recursively traverse the list and
provide answers in place of lists, so that ...
Example = ['add', ['sub', 5, 4], ['mul', 3, 2]]
Becomes:Example = ['add', 1, 6]
Becomes:Example = 7
*Functions are defined in the script
The code I currently have, which isn't pretty (bottom), doesn't work
because it doesn't return the value of the evaluated list. But I can't
figure out how to do that. Any help would be greatly appreciated.
Jack Trades
def recursive(tree):
if type(tree[1]) != type([]) and type(tree[2]) != type([]):
eval(a[0]+'('+str(tree[1])+','+str(tree[2])+')')
if type(tree[2]) == type([]):
recursive(tree[2])
if type(tree[1]) == type([]):
recursive(tree[1])
--
http://mail.python.org/mailman/listinfo/python-list
Re: Is it possible to get whole commandline include redirection.., etc
[EMAIL PROTECTED] wrote: > Can I get whole commandline not only argument list. > > 1. When I command like this > $ a.py > filename > 2. sys.argv is returns only argument list > ['a.py'] > > Is there a way to find out 'redirection' information. It's not possible to find the exact command line redirections. However, you can tell whether a standard I/O stream has been redirected or not (kind of) using isattr attribute. For instance, sys.stdin.isatty() returns 1 when it's not being redirected. It's not exact, though. It's possible to redirect to a device that is a tty, and sometimes standard I/O streams will not be ttys even without redirection, such as when run by a script with redirection. (It shouldn't be a problem, since the main use case is to check whether the program should run in interactive mode or not.) Carl Banks -- http://mail.python.org/mailman/listinfo/python-list
Re: help with recursion on GP project
First possible raw solution: from operator import add, sub, mul, div, neg def evaluate(expr): if isinstance(expr, list): fun, ops = expr[0], expr[1:] return fun(*map(evaluate, ops)) else: return expr example = [add, [add, [sub, 5, 4], [mul, 3, 2]], [neg, 5]] print evaluate(example) But it's rather slow... Bye, bearophile -- http://mail.python.org/mailman/listinfo/python-list
Conflicting needs for __init__ method
Here's an example of a problem that I've recently come up against for the umpteenth time. It's not difficult to solve, but my previous solutions have never seemed quite right, so I'm writing to ask whether others have encountered this problem, and if so what solutions they've come up with. Suppose you're writing a class "Rational" for rational numbers. The __init__ function of such a class has two quite different roles to play. First, it's supposed to allow users of the class to create Rational instances; in this role, __init__ is quite a complex beast. It needs to allow arguments of various types---a pair of integers, a single integer, another Rational instance, and perhaps floats, Decimal instances, and suitably formatted strings. It has to validate the input and/or make sure that suitable exceptions are raised on invalid input. And when initializing from a pair of integers---a numerator and denominator---it makes sense to normalize: divide both the numerator and denominator by their greatest common divisor and make sure that the denominator is positive. But __init__ also plays another role: it's going to be used by the other Rational arithmetic methods, like __add__ and __mul__, to return new Rational instances. For this use, there's essentially no need for any of the above complications: it's easy and natural to arrange that the input to __init__ is always a valid, normalized pair of integers. (You could include the normalization in __init__, but that's wasteful when gcd computations are relatively expensive and some operations, like negation or raising to a positive integer power, aren't going to require it.) So for this use __init__ can be as simple as: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator So the question is: (how) do people reconcile these two quite different needs in one function? I have two possible solutions, but neither seems particularly satisfactory, and I wonder whether I'm missing an obvious third way. The first solution is to add an optional keyword argument "internal = False" to the __init__ routine, and have all internal uses specify "internal = True"; then the __init__ function can do the all the complicated stuff when internal is False, and just the quick initialization otherwise. But this seems rather messy. The other solution is to ask the users of the class not to use Rational() to instantiate, but to use some other function (createRational(), say) instead. Then __init__ is just the simple method above, and createRational does all the complicated stuff to figure out what the numerator and denominator should be and eventually calls Rational(numerator, denomiator) to create the instance. But asking users not to call Rational() seems unnatural. Perhaps with some metaclass magic one can ensure that "external" calls to Rational() actually go through createRational() instead? Of course, none of this really has anything to do with rational numbers. There must be many examples of classes for which internal calls to __init__, from other methods of the same class, require minimal argument processing, while external calls require heavier and possibly computationally expensive processing. What's the usual way to solve this sort of problem? Mark -- http://mail.python.org/mailman/listinfo/python-list
python - process id
hi... is there a way to have a test python app, get its' own processID. i'm creating a test python script under linux, and was wondering if this is possible.. also, i've tried using an irc client to join the irc #python channel, and for some reason i keep getting the err msg saying that the 'address is banned' i've never been to the python channel, but i'm using dsl, so i'm getting a dynamic address.. and yeah, i've tried changing the address a number of times.. thanks -- http://mail.python.org/mailman/listinfo/python-list
Re: python - process id
On Sun, 14 Jan 2007 16:36:58 -0800, bruce <[EMAIL PROTECTED]> wrote: >hi... > >is there a way to have a test python app, get its' own processID. i'm >creating a test python script under linux, and was wondering if this is >possible.. See the os module, the getpid function. > >also, i've tried using an irc client to join the irc #python channel, and >for some reason i keep getting the err msg saying that the 'address is >banned' i've never been to the python channel, but i'm using dsl, so i'm >getting a dynamic address.. and yeah, i've tried changing the address a >number of times.. IRC bans can cover a wide range of addresses. Someone else on your ISP may have abused the channel badly enough to get a large range of addresses, maybe every address you could possibly get, banned from the channel. Jean-Paul -- http://mail.python.org/mailman/listinfo/python-list
Re: Conflicting needs for __init__ method
Mark wrote: [a lot of valid, but long concerns about types that return an object of their own type from some of their methods] I think that the best solution is to use an alternative constructor in your arithmetic methods. That way users don't have to learn about two different factories for the same type of objects. It also helps with subclassing, because users have to override only a single method if they want the results of arithmetic operations to be of their own type. For example, if your current implementation looks something like this: class Rational(object): # a long __init__ or __new__ method def __add__(self, other): # compute new numerator and denominator return Rational(numerator, denominator) # other simmilar arithmetic methods then you could use something like this instead: class Rational(object): # a long __init__ or __new__ method def __add__(self, other): # compute new numerator and denominator return self.result(numerator, denominator) # other simmilar arithmetic methods @staticmethod def result(numerator, denominator): """ we don't use a classmethod, because users should explicitly override this method if they want to change the return type of arithmetic operations. """ result = object.__new__(Rational) result.numerator = numerator result.denominator = denominator return result Hope this helps, Ziga -- http://mail.python.org/mailman/listinfo/python-list
Re: Maths error
"Hendrik van Rooyen" <[EMAIL PROTECTED]> wrote: >"Nick Maclaren" <[EMAIL PROTECTED]> wrote: > >> What I don't know is how much precision this approximation loses when >> used in real applications, and I have never found anyone else who has >> much of a clue, either. >> >I would suspect that this is one of those questions which are simple >to ask, but horribly difficult to answer - I mean - if the hardware has >thrown it away, how do you study it - you need somehow two >different parallel engines doing the same stuff, and comparing the >results, or you have to write a big simulation, and then you bring >your simulation errors into the picture - There be Dragons... Actually, this is a very well studied part of computer science called "interval arithmetic". As you say, you do every computation twice, once to compute the minimum, once to compute the maximum. When you're done, you can be confident that the true answer lies within the interval. For people just getting into it, it can be shocking to realize just how wide the interval can become after some computations. -- Tim Roberts, [EMAIL PROTECTED] Providenza & Boekelheide, Inc. -- http://mail.python.org/mailman/listinfo/python-list
Python web app. (advice sought)
Hello, I find myself in the, for me, unusual (and at the moment unique) position of having to write a web application. I have quite a lot of existing Python code that will form part of the business logic. This relies on 3rd party libraries (such as numpy) which would make porting to e.g. IronPython difficult (I would imagine). I was thinking LAMP (the P standing for Python, of course), particularly as I was originally encouraged to go for open source solutions. The application will provide some basic statistical analyses of data contained in database tables and associated plots (R / matplotlib / graphviz). There will also be some heavier duty Monte Carlo simulation and graphical modelling / MCMC. The user(s) will need to be able to set model parameters; maybe even tinker with model structure, so it will be very interactive (AJAX?). I've had a look at Django, Turbogears and Plone, and at the moment I am torn between Turbogears and Plone. I get the impression that Turbogears will require me to write more non-Python code, but maybe Plone is more than I need (steeper learning curve?). Maybe Turbogears will lead to a more loosely coupled app. than Plone? The disconcerting thing is that others on the project (who won't be developing) have started to talk about a LAMP back end with an IIS front end, .NET, and the benefits of sharepoint. The emphasis is supposed to be on rapid development, and these technologies are supposed to help. But I have no real familiarity with them at all; just Python, C and SQL to any realistic level of competence. Any advice would be greatly appreciated. I have to do much of the statistical work too, so I need to make good choices (and hopefully be able to justify them so nobody else on the project makes inappropriate choices for me). e.g. I don't mind learning Javascript if it doesn't take too long. The physical server will initially be a multiprocessor machine with several GB of RAM. But we also have a cluster (I have no details, I only started the project a week ago). So any advice regarding parallelisation would also be appreciated (or, in fact, any useful advice / pointers at all). Thanks. Duncan -- http://mail.python.org/mailman/listinfo/python-list
Re: Learning Python book, new edition?
Robert Hicks wrote: > I would get "Core Python Programming" by Wesley Chun. It covers just > about everything under the sun and includes version 2.5. Robert, thanks for the plug. if the OP wants to learn more about my book and its philosophy, feel free to check out my comments on the Amazon product page and/or the book's website at http://corepython.com to see if it's right for you. more on topic, here's a summary of Python books which are rev'd up to 2.5, categorized but not in any particular order: Python learning: - Python for Dummies, Maruch, Sep 2006 - Core Python Programming, Chun, Sep 2006 Python pure reference: - Python Essential Reference, Beazley, Feb 2006 - Python in a Nutshell, Martelli, Jul 2006 Python case study reference: - Programming Python, Lutz, Aug 2006 Enjoy! -wesley - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - "Core Python Programming", Prentice Hall, (c)2007,2001 http://corepython.com wesley.j.chun :: wescpy-at-gmail.com python training and technical consulting cyberweb.consulting : silicon valley, ca http://cyberwebconsulting.com -- http://mail.python.org/mailman/listinfo/python-list
Re: Conflicting needs for __init__ method
At Sunday 14/1/2007 20:32, [EMAIL PROTECTED] wrote: Of course, none of this really has anything to do with rational numbers. There must be many examples of classes for which internal calls to __init__, from other methods of the same class, require minimal argument processing, while external calls require heavier and possibly computationally expensive processing. What's the usual way to solve this sort of problem? In some cases you can differentiate by the type or number of arguments, so __init__ is the only constructor used. In other cases this can't be done, then you can provide different constructors (usually class methods or static methods) with different names, of course. See the datetime class, by example. It has many constructors (today(), fromtimestamp(), fromordinal()...) all of them class methods; it is a C module. For a slightly different approach, see the TarFile class (this is a Python module). It has many constructors (classmethods) like taropen, gzopen, etc. but there is a single public constructor, the open() classmethod. open() is a factory, dispatching to other constructors depending on the combination of arguments used. -- Gabriel Genellina Softlab SRL __ Preguntá. Respondé. Descubrí. Todo lo que querías saber, y lo que ni imaginabas, está en Yahoo! Respuestas (Beta). ¡Probalo ya! http://www.yahoo.com.ar/respuestas -- http://mail.python.org/mailman/listinfo/python-list
How naive is Python?
How naive (in the sense that compiler people use the term) is the current Python system? For example: def foo() : s = "This is a test" return(s) s2 = foo() How many times does the string get copied? Or, for example: s1 = "Test1" s2 = "Test2" s3 = "Test3" s = s1 + s2 + s3 Any redundant copies performed, or is that case optimized? How about this? kcount = 1000 s = '' for i in range(kcount) : s += str(i) + ' ' Is this O(N) or O(N^2) because of recopying of "s"? I just want a sense of what's unusually inefficient in the current implementation. Thanks. John Nagle -- http://mail.python.org/mailman/listinfo/python-list
Re: Conflicting needs for __init__ method
On Sun, 14 Jan 2007 15:32:35 -0800, dickinsm wrote: > Suppose you're writing a class "Rational" for rational numbers. The > __init__ function of such a class has two quite different roles to > play. First, it's supposed to allow users of the class to create > Rational instances; in this role, __init__ is quite a complex beast. > It needs to allow arguments of various types---a pair of integers, a > single integer, another Rational instance, and perhaps floats, Decimal > instances, and suitably formatted strings. It has to validate the > input and/or make sure that suitable exceptions are raised on invalid > input. And when initializing from a pair of integers---a numerator > and denominator---it makes sense to normalize: divide both the > numerator and denominator by their greatest common divisor and make > sure that the denominator is positive. > > But __init__ also plays another role: it's going to be used by the > other Rational arithmetic methods, like __add__ and __mul__, to return > new Rational instances. For this use, there's essentially no need for > any of the above complications: it's easy and natural to arrange that > the input to __init__ is always a valid, normalized pair of integers. > (You could include the normalization in __init__, but that's wasteful Is it really? Have you measured it or are you guessing? Is it more or less wasteful than any other solution? > when gcd computations are relatively expensive and some operations, > like negation or raising to a positive integer power, aren't going to > require it.) So for this use __init__ can be as simple as: > > def __init__(self, numerator, denominator): > self.numerator = numerator > self.denominator = denominator > > So the question is: (how) do people reconcile these two quite > different needs in one function? I have two possible solutions, but > neither seems particularly satisfactory, and I wonder whether I'm > missing an obvious third way. The first solution is to add an > optional keyword argument "internal = False" to the __init__ routine, > and have all internal uses specify "internal = True"; then the > __init__ function can do the all the complicated stuff when internal > is False, and just the quick initialization otherwise. But this seems > rather messy. Worse than messy. I guarantee you that your class' users will, deliberately or accidentally, end up calling Rational(10,30,internal=True) and you'll spent time debugging mysterious cases of instances not being normalised when they should be. > The other solution is to ask the users of the class not to use > Rational() to instantiate, but to use some other function > (createRational(), say) instead. That's ugly! And they won't listen. > Of course, none of this really has anything to do with rational > numbers. There must be many examples of classes for which internal > calls to __init__, from other methods of the same class, require > minimal argument processing, while external calls require heavier and > possibly computationally expensive processing. What's the usual way > to solve this sort of problem? class Rational(object): def __init__(self, numerator, denominator): print "lots of heavy processing here..." # processing ints, floats, strings, special case arguments, # blah blah blah... self.numerator = numerator self.denominator = denominator def __copy__(self): cls = self.__class__ obj = cls.__new__(cls) obj.numerator = self.numerator obj.denominator = self.denominator return obj def __neg__(self): obj = self.__copy__() obj.numerator *= -1 return obj I use __copy__ rather than copy for the method name, so that the copy module will do the right thing. -- Steven D'Aprano -- http://mail.python.org/mailman/listinfo/python-list
on which site or Usenet group should this question be posted?
Operating system: Win XP Version of Python: 2.4 I recognize that this question is not about Python...it has only a tangential Python connection. I'm using a Python package to run a process; that process is issuing an error, and I'm hoping that someone on this site can point me to the site that has the appropriate expertise. Situation is this: 1) using win32com.client.Dispatch to work with the MSXML2.XMLHTTP COM object 2) I use the open() method of the COM object and specify a "POST", then I use the send() method of the COM object 3) when the url is an http url, able to send and then check the responseText property without problems 4) when the url is an https url, the open() method works, but when the send() method is invoked, it raises an exception with the error message: "the download of the specified resource has failed" 5) when I use Google with searches such as "XMLHTTP", "https", "download of the specified resource", I see that other people are experiencing the issue, but I didn't see any solutions, nor did I see whether there was a site (perhaps a Usenet group) on which it would make sense to post this issue Eventually, I want to learn whether the XMLHTTP COM object can work with https url's, but what I'm hoping to learn from this post is advice as to which site I should post this question so that it might be read by folks with the appropriate subject-matter expertise. Thank you. -- http://mail.python.org/mailman/listinfo/python-list
Re: Conflicting needs for __init__ method
On Mon, 15 Jan 2007 14:43:55 +1100, Steven D'Aprano wrote: >> Of course, none of this really has anything to do with rational >> numbers. There must be many examples of classes for which internal >> calls to __init__, from other methods of the same class, require >> minimal argument processing, while external calls require heavier and >> possibly computationally expensive processing. What's the usual way >> to solve this sort of problem? > > class Rational(object): > def __init__(self, numerator, denominator): > print "lots of heavy processing here..." > # processing ints, floats, strings, special case arguments, > # blah blah blah... > self.numerator = numerator > self.denominator = denominator > def __copy__(self): > cls = self.__class__ > obj = cls.__new__(cls) > obj.numerator = self.numerator > obj.denominator = self.denominator > return obj > def __neg__(self): > obj = self.__copy__() > obj.numerator *= -1 > return obj Here's a variation on that which is perhaps better suited for objects with lots of attributes: def __copy__(self): cls = self.__class__ obj = cls.__new__(cls) obj.__dict__.update(self.__dict__) # copy everything quickly return obj -- Steven D'Aprano -- http://mail.python.org/mailman/listinfo/python-list
Re: How naive is Python?
John> How naive (in the sense that compiler people use the term) is the
John> current Python system? For example:
John> def foo() :
John> s = "This is a test"
John> return(s)
John> s2 = foo()
John> How many times does the string get copied?
Never. s and s2 just refer to the same object (strings are immutable).
Executing this:
def foo() :
print id("This is a test")
s = "This is a test"
print id(s)
return(s)
s2 = foo()
print id(s2)
prints the same value three times.
John> Or, for example:
John> s1 = "Test1"
John> s2 = "Test2"
John> s3 = "Test3"
John> s = s1 + s2 + s3
John> Any redundant copies performed, or is that case optimized?
Not optimized. You can see that using the dis module:
4 0 LOAD_CONST 1 ('Test1')
3 STORE_FAST 0 (s1)
5 6 LOAD_CONST 2 ('Test2')
9 STORE_FAST 1 (s2)
6 12 LOAD_CONST 3 ('Test3')
15 STORE_FAST 2 (s3)
7 18 LOAD_FAST0 (s1)
21 LOAD_FAST1 (s2)
24 BINARY_ADD
25 LOAD_FAST2 (s3)
28 BINARY_ADD
29 STORE_FAST 3 (s)
32 LOAD_CONST 0 (None)
35 RETURN_VALUE
The BINARY_ADD opcode creates a new string.
John> How about this?
John> kcount = 1000
John> s = ''
John> for i in range(kcount) :
John> s += str(i) + ' '
John> Is this O(N) or O(N^2) because of recopying of "s"?
O(N). Here's a demonstration of that:
#!/usr/bin/env python
from __future__ import division
def foo(kcount):
s = ''
for i in xrange(kcount) :
s += str(i) + ' '
import time
for i in xrange(5,25):
t = time.time()
foo(2**i)
t = time.time() - t
print 2**i, t, t/2**i
On my laptop t roughly doubles for each iteration and prints around 5e-06
for t/2**i in all cases.
Skip
--
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Re: How naive is Python?
In article <[EMAIL PROTECTED]>, John Nagle <[EMAIL PROTECTED]> wrote: > How naive (in the sense that compiler people use the term) > is the current Python system? For example: > > def foo() : > s = "This is a test" > return(s) > > s2 = foo() > > How many times does the string get copied? All of those just move around pointers to the same (interned) string. > How about this? > > kcount = 1000 > s = '' > for i in range(kcount) : > s += str(i) + ' ' > > Is this O(N) or O(N^2) because of recopying of "s"? This is a well-known (indeed, the canonical) example of quadratic behavior in Python. The standard solution is to store all the strings (again, really just pointers to the strings) in a list, then join all the elements: temp = [] for i in range (1000): temp.append (str(i)) s = "".join (temp) That ends up copying each string once (during the join operation), and is O(N) overall. -- http://mail.python.org/mailman/listinfo/python-list
Re: How naive is Python?
On Mon, 15 Jan 2007 03:25:01 +, John Nagle wrote:
> How naive (in the sense that compiler people use the term)
> is the current Python system? For example:
>
> def foo() :
> s = "This is a test"
> return(s)
>
> s2 = foo()
>
> How many times does the string get copied?
Let's find out. Results below for Python 2.3 -- other versions may vary.
>>> def foo():
... s = "This is a test"
... return s
...
>>> def foo2():
... return "This is a test"
...
>>> import dis
>>> dis.dis(foo)
2 0 LOAD_CONST 1 ('This is a test')
3 STORE_FAST 0 (s)
3 6 LOAD_FAST0 (s)
9 RETURN_VALUE
10 LOAD_CONST 0 (None)
13 RETURN_VALUE
>>> dis.dis(foo2)
2 0 LOAD_CONST 1 ('This is a test')
3 RETURN_VALUE
4 LOAD_CONST 0 (None)
7 RETURN_VALUE
foo and foo2 functions compile to different byte-code. foo does a little
more work than foo2, but it is likely to be a trivial difference.
>>> s1 = foo()
>>> s2 = foo()
>>> s1 == s2, s1 is s2
(True, True)
So the string "This is a test" within foo is not copied each time the
function is called. However, the string "This is a test" is duplicated
between foo and foo2 (the two functions don't share the same string
instance):
>>> s3 = foo2()
>>> s3 == s1, s3 is s1
(True, False)
> Or, for example:
>
> s1 = "Test1"
> s2 = "Test2"
> s3 = "Test3"
> s = s1 + s2 + s3
>
> Any redundant copies performed, or is that case optimized?
I don't believe it is optimized. I believe that in Python 2.5 simple
numeric optimizations are performed, so that "x = 1 + 3" would compile to
"x = 4", but I don't think that holds for strings. If you are running 2.5,
you could find out with dis.dis.
> How about this?
>
> kcount = 1000
> s = ''
> for i in range(kcount) :
> s += str(i) + ' '
>
> Is this O(N) or O(N^2) because of recopying of "s"?
That will be O(N**2), except that CPython version 2.4 (or is it 2.5?) can,
sometimes, optimize it. Note that this is an implementation detail, and
doesn't hold for other Pythons like Jython, IronPython or PyPy.
--
Steven D'Aprano
--
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Re: How to write code to get focuse the application which is open from server
Hi Paul,
Since your reply I try to use pywinauto. I was able to get the control
of a window that is a good news but it is not working for sub window
this main window create.(popup windows)
eg) File -> printwill open print window but the code is not
clicking print button. I try several combination
app,Print.OK.CloseClick() or app,Print.OK.Click() is not working
my code goes like that
app=application.Application()
qi = app.window_(title_re = ".*arcMap.*")
qi.TypeKeys("%FP")
app,Print.OK.Click()
the last line of code is not working same thing with file open to
qi.TypeKeys("%FO") create popup window "open" but
app,Open.Filename.SetEditText("test1,txt") is not working any help to
overcome to this problem (Here the "Print" , "Open" are windows title I
am using without any undestanding why I am using. Any help?
Paul McGuire wrote:
> "vinthan" <[EMAIL PROTECTED]> wrote in message
> news:[EMAIL PROTECTED]
> > hi,
> > I am new to python. I have to write test cases in python. An
> > application is open in the desk top ( application writen in .Net) I
> > have to write code to get focuse the application and click on the link
> > which in the one side and it will load the map on the other and I have
> > to check map is loaded. Any one tell me how do I use Dispatch or any
> > other method to write a code.
> >
> If you are running on Windows, look into pywinauto
> (http://www.openqa.org/pywinauto/).
>
> I have successfully used it to interact with a Flash animation running
> within an IE browser.
>
> I also had to inspect the graphics displayed by the Flash animation, for
> this I used PIL (http://www.pythonware.com/products/pil/).
>
> Good luck,
> -- Paul
--
http://mail.python.org/mailman/listinfo/python-list
Re: How to write code to get focuse the application which is open from server
Hi Paul,
Since your reply I try to use pywinauto. I was able to get the control
of a window that is a good news but it is not working for sub window
this main window create.(popup windows)
eg) File -> printwill open print window but the code is not
clicking print button. I try several combination
app,Print.OK.CloseClick() or app,Print.OK.Click() is not working
my code goes like that
app=application.Application()
qi = app.window_(title_re = ".*arcMap.*")
qi.TypeKeys("%FP")
app,Print.OK.Click()
the last line of code is not working same thing with file open to
qi.TypeKeys("%FO") create popup window "open" but
app,Open.Filename.SetEditText("test1,txt") is not working any help to
overcome to this problem (Here the "Print" , "Open" are windows title I
am using without any undestanding why I am using. Any help?
Paul McGuire wrote:
> "vinthan" <[EMAIL PROTECTED]> wrote in message
> news:[EMAIL PROTECTED]
> > hi,
> > I am new to python. I have to write test cases in python. An
> > application is open in the desk top ( application writen in .Net) I
> > have to write code to get focuse the application and click on the link
> > which in the one side and it will load the map on the other and I have
> > to check map is loaded. Any one tell me how do I use Dispatch or any
> > other method to write a code.
> >
> If you are running on Windows, look into pywinauto
> (http://www.openqa.org/pywinauto/).
>
> I have successfully used it to interact with a Flash animation running
> within an IE browser.
>
> I also had to inspect the graphics displayed by the Flash animation, for
> this I used PIL (http://www.pythonware.com/products/pil/).
>
> Good luck,
> -- Paul
--
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Re: Maths error
"Dennis Lee Bieber" <[EMAIL PROTECTED]>wrote: > On Sun, 14 Jan 2007 07:18:11 +0200, "Hendrik van Rooyen" > <[EMAIL PROTECTED]> declaimed the following in comp.lang.python: > > > > > I recall an SF character known as "Slipstick Libby", > > who was supposed to be a Genius - but I forget > > the setting and the author. > > > Robert Heinlein. Appears a few of the Lazarus Long books. > > > It is something that has become quietly extinct, and > > we did not even notice. > > > And get collector prices -- > http://www.sphere.bc.ca/test/sruniverse.html Thanks Dennis - Fascinating site ! - Hendrik -- http://mail.python.org/mailman/listinfo/python-list
Class list of a module
Hi, I want to get all classes of a module in a list. I wrote this code but I wonder if there's not a simpler solution import inspect def getClassList(aModule): return [getattr(aModule, attName) \ for attName in aModule.__dict__ \ if inspect.isclass(getattr(aModule, attName))] Regards, Laurent Laffont -- http://mail.python.org/mailman/listinfo/python-list
