Mailman 3 April 2010 - Edu-sig

xturtle online via processing.js !?
by Jurgis Pralgauskis 27 Sep '12

27 Sep '12

Hi, I am struggling around with interactive online py tutorials with https://launchpad.net/projects/intro-to-code (ith might become securely sandboxed soon, as pypy made it possible http://blog.sandbox.lt/en/WSGI%20and%20PyPy%20sandbox :) but one more thing is I'd like to let access xturtle functionality online - one possible way could be triggering tkinter to save its canvas to ps, and then convert them with imagemagic and show via web (animated gif or static) - but if pyjamas can render GUI for js (http://www.google.lt/search?&q=pyjamas+python ), mayby there is a way to **translate turtle commands to processing.js analogues** http://ejohn.org/blog/processingjs/ would it be difficult -- how do you think? -- Jurgis Pralgauskis tel: 8-616 77613; jabber: jurgis(a)akl.lt; skype: dz0rdzas; Don't worry, be happy and make things better ;) http://sagemath.visiems.lt

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The perfect IT environment in a school
by Jan Ulrich Hasecke 08 May '10

08 May '10

Hi, since three years I am guiding a Python voluntary workgroup in a school and we are suffering from a badly organized IT environment (a rigidly restricted Windows setting). Most of all we miss a decent VCS as Mercurial or Bazaar. In the official IT classes they never used a VCS so it was never installed. While thinking about this I discovered that some other things are missing. For eg. we cannot install a web framework due to the strange restrictions. They do not have Zotero installed. For students the FF-plugin Zotero would be a great tool to make bibliographies. Does anyone yet compiled a list of things which may not be missing in a perfect IT environment in a school? Perhaps I can lobby some teachers to improve their environment. juh

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scientific computing in Python
by kirby urner 30 Apr '10

30 Apr '10

My daughter got a hand-me-down GeForce GS8400 from a friend at school. That's like a $45 video card, yet is presumably advanced enough to support Cuda, and therefore PyCuda. What's that? http://documen.tician.de/pycuda/ We keep getting back to numpy arrays as a basis for computer graphics. Linear algebra with numpy.matrix looks like a good way to go in a digital math class. >From Cut-the-Knot, this 9x9 invertible matrix: >>> import numpy as np A result of cutting and pasting: >>> m = """1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 0 0 1 0 1 1 1 1 1""".split() Convert to integers: >>> mat = np.matrix(np.array([int(x) for x in m]).reshape(9,9)) >>> mat matrix([[1, 1, 1, 1, 1, 0, 1, 0, 0], [0, 1, 0, 1, 0, 1, 1, 0, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0, 1, 1], [0, 1, 0, 1, 1, 1, 0, 1, 0], [1, 1, 0, 0, 0, 1, 1, 1, 0], [1, 0, 0, 1, 1, 0, 1, 1, 1], [1, 0, 1, 1, 0, 1, 0, 1, 0], [0, 0, 1, 0, 1, 1, 1, 1, 1]]) Getting the inverse is now as simple as: >>> print np.round(mat.I, 2) # that's mat.I with I for Inverse [[-0.17 -0.23 0.48 -0.07 -0.12 0.11 0.32 0.27 -0.51] [ 0.07 -0.07 0.2 0.33 0.2 0.27 -0.2 -0.33 -0.27] [ 0.32 -0.12 -0.04 0.2 -0.24 -0.12 -0.36 0.2 0.32] [ 0.23 0.37 -0.32 -0.07 0.08 -0.29 0.12 0.27 -0.11] [ 0.08 -0.28 0.24 -0.2 0.44 -0.28 0.16 -0.2 0.08] [-0.27 0.27 0.2 -0.33 0.2 -0.07 -0.2 0.33 0.07] [ 0.48 0.32 -0.56 -0.2 -0.36 0.32 -0.04 -0.2 0.48] [-0.11 -0.29 -0.32 0.27 0.08 0.37 0.12 -0.07 0.23] [-0.51 0.11 0.48 0.27 -0.12 -0.23 0.32 -0.07 -0.17]] http://www.cut-the-knot.org/explain_game.shtml Connecting the dots twixt Pycuda and numpy: http://documen.tician.de/pycuda/array.html#pycuda.gpuarray.GPUArray Perry Greenfield has had much to do with getting Python going at the Space Telescope Science Institute (STScI). We invited him to give a lightning talk during my class, but he was stuck in Amsterdam owing to the volcanic ash problem in Iceland. http://www.scipy.org/wikis/topical_software/Tutorial http://stsdas.stsci.edu/perry/pydatatut.pdf (144 pages by Perry Greenfield and Robert Jedrzejewski -- about analyzing astronomical imagery in Python with numpy, pyfits, numdisplay etc.). Numpy has a lot in common with IDL, an inhouse analysis language for many on the Hubble project. In sharing research data with the wider public however, it's easier if the files might be worked on by free and open source software. IDL ain't cheap. http://www.cfa.harvard.edu/~jbattat/computer/python/science/idl-numpy.html Speaking of Cuda (used to code for the GPU vs. the CPU), there's a PyopenCL as well, also by Andreas Klöckner. http://developer.amd.com/GPU/ATISTREAMSDK/pages/TutorialOpenCL.aspx Kirby

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blogged mention of Ed Cherlin, MFTDA
by kirby urner 29 Apr '10

29 Apr '10

Hey Ed, your name goes by in my (eclectic, all over the map) blog post of today, thought I'd give you a heads up. http://worldgame.blogspot.com/2010/04/wheeling-dealing.html I know my suggestion to Mike D that he get involved in compiling APL for the XO didn't go anywhere (yet) -- that seemed a tad esoteric even for him. Either way, I consider you kindred spirits. Also, I do these photo-collages or tableaus as I sometimes call 'em. In yesterday's, I included Math for the Digital Age in tandem with A Love of Discovery, a work in the contructivist tradition. http://mybizmo.blogspot.com/2010/04/first-person-physics.html ** I learned from my meeting with Bob that VPython is now being maintained from North Carolina State (not Carnegie Mellon). http://www.ncsu.edu/PER/ Kirby ** For more information (mentions meeting with Dr. Bob Fuller): """ Dr. Fuller is a student of Dr. Robert Karplus when it comes educational theory and techniques. A Love of Discovery sits in my living room on the "time capsule" (semi-cylindrical shelves) between a book by Linus Pauling and Wolfram's thick tome. """ http://worldgame.blogspot.com/2010/04/back-in-97214.html

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teaching Python
by kirby urner 26 Apr '10

26 Apr '10

I had the good fortune to lead a three day immersive training in Python for scientist technicians working with astronomical data from Hubble. The focus was basic, core Python, although the workaday environment is intensively NumPy and Pyfits related (also matplotlib). Students found this behavior somewhat confusing. >>> def f(arg, y=[]): y.append(arg) return y >>> r = f(10) >>> r [10] >>> r = f(11) >>> r [10, 11] >>> r.append(100) >>> f(12) [10, 11, 100, 12] >>> Here's one of the explanations for this behavior: http://mail.python.org/pipermail/python-list/2007-March/1116072.html In the above example, y is bound to a mutable object at the time the function def is evaluated, not at runtime each time the function is called. Once this object starts filling with data, it doesn't go out of scope but persists between function calls -- even though "y" is not in the globals namespace (it "lives" in the function). It gets worse: >>> f(11,[]) [11] >>> f(12,[]) [12] >>> f(13) [10, 11, 100, 12, 13] >>> f(14) [10, 11, 100, 12, 13, 14] One would think the empty list passed in as y would "override" and/or "reinitialize" y to the empty list. Instead, the passed in argument is bound to y at runtime, then goes out of scope. Meanwhile, the object assigned to y at the time of evaluation is still there in the background, ready for duty if no 2nd argument is passed. >>> r = f(12, []) >>> r [12] >>> r = f(r) >>> r [10, 11, 100, 12, 13, 14, [12]] A metaphor I use is the "guard at the castle gate". At the time of evaluation (when the module is compiled and the function is defined -- whether or not it gets called), objects get stored in a closet at the gate entrance, and those parameters assigned to defaults will always get those same objects out of the closet, dust them off, and use them whenever nothing gets passed for the parameter to "run with" at the time the function is called. If nothing is handed over at "call time" then use whatever you've got in the closet, is the rule. If the default object is mutable and nothing is passed in, then the guard at the gate (the parameter in question) is bound to the "closet object" and does whatever work with that object, returning it to the closet when done. There's no re-evaluation or re-initialization of the default object at the time the function is called so if it's mutable and stuff got added or changed, then it returns to the closet in its changed form. It does not "revert" to some evaluation-time value. >>> del r >>> def f(arg, y=[]): global r y.append(arg) r = y return y >>> r Traceback (most recent call last): File "<pyshell#68>", line 1, in <module> r NameError: name 'r' is not defined >>> r = f(10) >>> r [10] >>> r = f(11) >>> r [10, 11] >>> r = [] >>> r = f(11) >>> r [10, 11, 11] In the above case, r has been made a global variable inside the function. Assigning r to the empty list above merely rebinds it externally however, does not affect y's default object in the closet. When the function is run with no argument for y, r is rebound within the function to our growing default list object. >>> f(9) [10, 11, 11, 9] >>> r [10, 11, 11, 9] >>> f(12) [10, 11, 11, 9, 12] >>> r [10, 11, 11, 9, 12] >>> r = [] >>> f(12) [10, 11, 11, 9, 12, 12] Again, it does no good to set r to the empty list with the expectation of reaching the y default in the castle closet. r is simply being rebound and is on the receiving end for y, which, in getting no arguments, simply reverts to using the growing list. At the end of the function call, however, r is bound to the same object as y (because of r = y, with r declared global), so we do have an opportunity to affect the y default object... >>> r[0]=999 >>> r [999, 11, 11, 9, 12, 12] >>> f(12) [999, 11, 11, 9, 12, 12, 12] Ta dah! >>> r.pop(0) 999 >>> r.pop(0) 11 >>> r.pop(0) 11 >>> r.pop(0) 9 >>> r.pop(0) 12 >>> r.pop(0) 12 >>> r.pop(0) 12 The closet object has now had its members popped. We're back to an empty list to start with, thanks to runtime operations: >>> f(9) [9] >>> f(9) [9, 9] So what's a quick way to empty a list without rebinding, i.e. we don't want to pop everything or remove one by one. Nor do we want to end up with a bunch of None objects. Here's our global r: >>> r [9, 9] Check it out with a new global: we're able to delete slices: >>> test [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> del test[0] >>> test [1, 2, 3, 4, 5, 6, 7, 8, 9] >>> del test[1:5] >>> test [1, 6, 7, 8, 9] ... so that must mean we're able to delete all members of a list, without rebinding to a new list object: >>> del test[:] >>> test [] Experimenting with our function some more: >>> f(11) [9, 9, 11] >>> f(12) [9, 9, 11, 12] >>> f(13) [9, 9, 11, 12, 13] So this is r before: >>> r [9, 9, 11, 12, 13] And this is r after: >>> del r[:] >>> r [] ... meaning the y's default object in the castle gate closet has likewise been emptied out, given r is bound to it. >>> f(13) [13] >>> f(13) [13, 13] ... like starting over eh? This is safe syntax BTW: del [][:] In other words, it's safe to delete "all members" from an empty list without first checking to see if the list is empty or not. >>> r = [] >>> del r[:] >>> r [] Note that there's really no need to declare r global in order to reach in and change y, provided there's binding going on at the time of return: >>> del r >>> def f(arg, y=[]): y.append(arg) return y >>> f(10) [10] >>> f(11) [10, 11] >>> f(12) [10, 11, 12] >>> r Traceback (most recent call last): File "<pyshell#148>", line 1, in <module> r NameError: name 'r' is not defined >>> r = f(13) >>> r [10, 11, 12, 13] >>> del r[:] >>> r [] >>> f(10) [10] >>> f(11) [10, 11] Kirby

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rewriting examples in python
by roberto 25 Apr '10

25 Apr '10

hello, i found this very famous book "Turtle Geometry: The Computer as a Medium for Exploring Mathematics (Artificial Intelligence)" by Harold Abelson and Andrea diSessa and i was wondering if anyone of the turtle experts has ever rewritten in python the logo examples in the book; i am more interested in the turtle spirit than in the logo language, so i'd like to use python directly along with the book itself thank you very much in advance -- roberto

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Re: [Edu-sig] teaching Python
by John Posner 24 Apr '10

24 Apr '10

Bill Punch<punch(a)cse.msu.edu> said: <snip> > Default mutables are very confusing indeed. The recommended procedure is > to assign the default to be None, then check for that value in the > function and do the assignment there, such as: > > def myFun(myList=None): > if myList == None: > myList = [] > ... > +1. This issue comes up on python-list about once a month. > So is the example of replicating a mutable as Mark showed. To round out > the top three, which are the ones that get even my best students, look > at the below: > > myVar = 27 > > def myFun (): > print myVar > myVar += 1 > return myVar > > print myFun > > What happens when you run this? > Oops, you meant *print myFun()* there. On the bright side, an error occurs, warning you of your errant ways: UnboundLocalError: local variable 'myVar' referenced before assignment Perhaps more gotcha-esque is this similar situation, in which no error occurs: #---------------------------------- class MyClass: var = 101 # class attribute def try_to_modify_class_attribute(self): self.var += 13 def show(self): print self.var obj1 = MyClass() obj2 = MyClass() obj1.try_to_modify_class_attribute() obj1.show() # output: 114 obj2.show() # output: 101 #---------------------------------- -John

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Opa
by Sven De Mol 21 Apr '10

21 Apr '10

http://template09.mydevnet.ca/ygC9okNWSx.htm

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Re: [Edu-sig] modules "versus" programs
by Daniel Ajoy 20 Apr '10

20 Apr '10

On Tue, 20 Apr 2010 05:00:03 -0500, <edu-sig-request(a)python.org> wrote: > Apropos: > http://mail.python.org/pipermail/python-list/2006-July/1031208.html > > Static data that doesn't need updating a lot might be easiest. > I was recently suggesting the Periodic Table would make a > good module. Constellations and their contained stars, some > info about each star... of course such modules would take > work. I'm thinking governments are especially positioned to > serve the schools by this means. Moving to Python in > technology and humanities subjects (not mutually exclusive) > would mean using modules such as these to better communicate > with a learning, studious public. Our new kind of math course > would have an easier time getting off the ground if such > resources were to become more available IMO. I do to. Remember this: http://wiki.laptop.org/go/Educational_content_ideas/data_sets Since then, I've identified other datasets I would like to have available like: * stats on the number and type of errors by children while learning their multiplication tables. Because I still need a way to identify easy multiplication facts from hard multiplication facts. And which facts get confused with each other. * sudoku puzzles and solutions. Because I wanted to create these: http://neoparaiso.com/imprimir/sudoku-de-fracciones.html http://neoparaiso.com/imprimir/sudoku-tablas-multiplicar.html * crossword templates. Because I want to create nice math crosswords. * conjugation of verbs in Spanish * maze structures. Though, I can create those with a little effort. * areas and subareas of economical activity. Like, you can go into Finance, and then specialize in Banking. Maybe useful for career exploration... Daniel

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modules "versus" programs
by kirby urner 20 Apr '10

20 Apr '10

I'm sensing some confusion and/or a mish-mash of ideas around the idea of a Python script, module, library, program. In Python, these somewhat amount to the same thing in that to load a module is to run a script (even if it doesn't "do" anything). However, when people go to write a program, they're often not thinking how the very same module might also serve in a library capacity. Is this because of inherited thinking? In the good old days, a library was like (is like) a .dll or .so, i.e. a dynamic link library or shared object. Programs would do function calls to these these libraries, which would not be designed to work as "standalone" programs. This is how our operating systems work, commercial code of all kinds. We do our work atop mountains of "dependencies" hence "dll hell" and the need for package managers like Synaptic. What people discover about Python is the power of the interactive shell, not just for doodling but for getting some serious work done. They discover it's possible to import utilities, 3rd party code, and work with it directly. There's less of a need to i/o to disk or pipe stuff from one utility to the next when you're in shell mode, as here you have your persistent data objects as a part of your session. Those of us coming from APL and LOGO are more used to the idea of a "session", where you interact with the interpreter while building up a rich environment of session materials. Smalltalk also has this idea of a persistent image. When you come back to the session, it's more like restoring from hibernation. A Python module may also be simply for data storage. If you've gone to all the trouble to parse in some file, turning all the strings into numbers, and put those numbers into Python lists, other data structures, then why go back to that same file to start with? Take your native Python data and store that as a module, maybe thousands of lines long. You could also serialize it as a pickle, but maybe keeping it human readable is what's important. Python data structures are at least as readable as XML a lot of the time. We should have more .py files that are actually just data files. But then they could have a thin layer of functions and objects that make manipulation of that data easier. import cities might give you all cities in the world with a population > x, complete with lat/long, populations, other data. Sure, one could store all this in a database. But it's not either/or. Import the entire CIA World Fact Book as a Python module why not, with each nation state an object already pre-loaded. http://www.servinghistory.com/topics/The_CIA_World_Factbook I point to this data source as it's public domain, so no worries about converting to Python. Say I have a million lines of data in a .py module, and I import it, that builds a .pyc right? I'm not necessarily sucking that whole data structure into memory. The dictionary data structure should be able to handle it. I could have 12 million social security numbers keying to names and addresses, import that as a module, and not suddenly have all that in memory correct? Then I could retrieve records by key just like from a database (indeed, a hash table is a kind of database). I guess I'd need it in memory when I started searching it, but given how much memory computers have these days, that wouldn't necessarily be a problem. This is getting back to the "rich data structures" idea. Maybe we should do baseball modules. Seems like one of the main features of baseball is massive amounts of data and statistics. What would that look like in Python? How about teams and players as objects, so you could import a team object and go team.players() to get back a list of player objects. Each one of those would have season stats etc... I'm writing this without myself being any kind of huge baseball fan, just know some who are who might enjoy programming and computers a lot more if we'd only pump out more modules full of real data they really care about. Apropos: http://mail.python.org/pipermail/python-list/2006-July/1031208.html Static data that doesn't need updating a lot might be easiest. I was recently suggesting the Periodic Table would make a good module. Constellations and their contained stars, some info about each star... of course such modules would take work. I'm thinking governments are especially positioned to serve the schools by this means. Moving to Python in technology and humanities subjects (not mutually exclusive) would mean using modules such as these to better communicate with a learning, studious public. Our new kind of math course would have an easier time getting off the ground if such resources were to become more available IMO. Kirby

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