Grok vs. Django for RESTful API?
Just hoping to get some opinions: Grok vs Django for REST? I've started evaluating TastyPie with Django. Is there something similar for Grok? I'm working on a project that will mostly be mobile-app based. Though there will be a web interface, the mobile part is more important to us. Our data model is pretty straight-forward so far (it's a social networking type app), but may become a bit more involved if we begin to add information/event streams. So finding a framework that works well with a dynamically changing data model is somewhat important (hopefully we won't change it too often). Having a good security/authentication/authorization framework is important too. I consider myself pretty strong with Python and other languages, but web programming is new to me. I'm comfortable though with HTTP/SQL/etc. -- http://mail.python.org/mailman/listinfo/python-list
numpy : efficient sum computations
Hi there. I want to do some intensive computations with numpy, and I'm struggling a bit to find my wayy. Here is the problem : m and d are two matrices : m.shape = (x,y,a,b) d.shape = (a,b) I want to return i.shape = (x,y) with i[x,y] = sum(m[x,y] * d) I already found that m[:,:] * d will give me a matrix of shape (x,y,a,b) containing the products. Now I want to sum up on axis 2 and 3. If I do : (m[:,:] * d).sum(axis=3).sum(axis=2) it seems like I get my result. I'm wondering : is this syntax leading to efficient computation, or is there something better ? thanks Thomas -- http://mail.python.org/mailman/listinfo/python-list
Re: numpy : efficient sum computations
Okay, another one which I don't have answer for. it is the reverse case, sort of : phi.shape (x,y) d.shape (a,b) I want to return m : m.shape = (x,y,a,b) with m[x,y] = d * phi[x,y] currently, my code is : m = empty(phi.shape + d.shape) m[:,:] = d this repeats the matrix d x*y times, which is what I want. But now, if I do : m[:,:] = d * phi[:,:] in order to multiply each d by a specific value of phi, it doesn't work. type 'exceptions.ValueError': shape mismatch: objects cannot be broadcast to a single shape Right now I'm stuck here. If anyone can help, I would be glad. -- http://mail.python.org/mailman/listinfo/python-list
Re: Boost python : get the shape of a numpy ndarray in C++ code.
What I'm trying to say here : a numpy array is supposed to have it's
shape stored as a tuple. What I want to do is to access this
information from my C++ code, in order to do some validity check.
So, by looking around in the doc of boost/python/numeric.hpp I was
able to do this :
void
Layer::set_potentials (numeric::array array)
{
for (int h=0; hmap-height; h++){
for (int w=0; wmap-width; w++){
units[w+h*map-width]-potential =
extractfloat(array[make_tuple(w,h)]);
}
}
}
which is fairly simple and actually works. Now, if I look further, I
see there is a method called getshape() in array class, which gives
back an object - I guess this object is a tuple, because the
documentation is quite poor.
So my idea is to get this object and use extract in order to get the
actual dimensions as integers.
but when I add this :
void
Layer::set_potentials (numeric::array array)
{
object shape = array.getshape();
[...]
}
It compiles, and then on execution I get this error :
AttributeError: 'numpy.ndarray' object has no attribute 'getshape'
Does it still have nothing to do with Boost.Python ?
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Boost python : get the shape of a numpy ndarray in C++ code.
Hi there.
I'm strugling here with some boost python code (damn I hate C++) :
All I want to do is to initialize the content of an array with a numpy
ndarray parameter. I have this, which actually works. But I want to
add some kind of data check such as :
* is array two dimensional ?
* are the dimensions corresponding to map's width / height ?
* is array field with floats or ints ?
void
Layer::set_potentials (numeric::array array)
{
for (int h=0; hmap-height; h++){
for (int w=0; wmap-width; w++){
units[w+h*map-width]-potential =
extractfloat(array[make_tuple(w,h)]);
}
}
}
Some help is very welcome here ... thanks.
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numpy performance and list comprehension
Hi there. Reading the page on python performance ( http://scipy.org/PerformancePython ) made me realize that I can achieve tremendous code acceleration with numpy just by using u[:,:] kind of syntax the clever way. Here is a little problem (Oja's rule of synaptic plasticity) * W is a matrix containing the weights of connections between elements i and j * V is an array containing the values of elements I want to make W evolve with this rule : dW[i,j] / dt = alpha * (V[i] * V[j] - W[i,j] * V[i]^2) (don't pay attention to the derivate and stuff) So, how would you write it in this nifty clever way ? As a begining I wrote this : W += V.flatten().reshape((V.size,1)) * V.flatten().reshape((1,V.size)) But it is not complete and, I guess, not efficient. -- http://mail.python.org/mailman/listinfo/python-list
pasting numpy array into bigger array
hi. let's say I have : from numpy import * x = identity(5) y = zeros((7,7)) I want to paste x into y, starting at coordinates (1,1) in order to change y to something like this : 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 how would you do that ? -- http://mail.python.org/mailman/listinfo/python-list
Re: pasting numpy array into bigger array
Thanks, that's exactly what I needed. Tim Heaney wrote: You can use Python slice notation for each dimension y[1:6,1:6] = x -- http://mail.python.org/mailman/listinfo/python-list
Re: access to submodules
okay, thanks everyone. this is much clearer now. -- http://mail.python.org/mailman/listinfo/python-list
access to submodules
hi. This is my first try on modules. I've got : tom/ __init__.py core.py ui.py data.py then, when I'm in my ipython shell : ? from tom import * this works, it loads core, ui and data but when I do this : ? import tom ? tom.core AttributeError: 'module' object has no attribute 'core' Well, i guess I missed something, but I don't see what ... -- http://mail.python.org/mailman/listinfo/python-list
Re: access to submodules
I've just found this : If I add : import core, data, ui inside my tom/__init__.py file, it will work. But this line does not seems to exist in other files (after having a look at several files inside /usr/lib/python2.4). -- http://mail.python.org/mailman/listinfo/python-list
Re: access to submodules
I know this is a bad habit ... I was just doing it to show what is disturbing me. Obviously the star syntax finds the submodules because they are loaded, but when I properly load the module alone with import tom, the dot syntax does not find tom.core. BartlebyScrivener wrote: from tom import * You CAN do this, but it's a bad habit. Try: import tom Then call by tom.function() rd -- http://mail.python.org/mailman/listinfo/python-list
Re: access to submodules
BartlebyScrivener wrote: then you no longer need tom, you imported all of his FUNCTIONS (never heard of submodule). my mistake, I was using the wrong name tom/ -- package __init__.py core.py data.py these are modules contained in tom/ ui.py if I import tom, it is supposed to load functions defined in tom/__init__.py and make all the modules inside accessible through the dot syntax. Therefore, this is supposed to work : ? import tom ? help(tom.core) AttributeError: 'module' object has no attribute 'core' But if I use the bad star syntax ? from tom import * ? help(core) this will work. So that means star loaded functions of __init__.py AND modules contained, whereas dot syntax does not give me access to modules inside. The issue is not about using or not using the import * ... -- http://mail.python.org/mailman/listinfo/python-list
Re: access to submodules
okay, so only when I have inside __init__.py __all__ = [core] this works ? from tom import * ? help(core) but (in a brand new interpretor) ? import tom ? help(tom.core) AttributeError: 'module' object has no attribute 'core' got it. But ... ? import numpy ? help(numpy.core) this will work, even if core is a subpackage of numpy. and i don't have to explicitly import numpy.core. I must be missing something ... :-/ -- http://mail.python.org/mailman/listinfo/python-list
solving equation system
Hi there. Anyone knows how to use numpy / scipy in order to solve this ? * A is an array of shape (n,) * X is a positive float number * B is an array of shape (n,) * O is an array of shape (n,) containing only zeros. A.X - B = O min(X) thanks. -- http://mail.python.org/mailman/listinfo/python-list
Re: solving equation system
Ben C wrote: On 2006-07-17, TG [EMAIL PROTECTED] wrote: Hi there. Anyone knows how to use numpy / scipy in order to solve this ? * A is an array of shape (n,) * X is a positive float number * B is an array of shape (n,) * O is an array of shape (n,) containing only zeros. A.X - B = O min(X) Are we solving for A, B or X? And what do you mean by min(X)? If we're solving for X there will be many combinations of A and B for which there is no solution. Sorry for the poor explanation. I'm trying to put it clear now. i've got A and B. I'm looking for X. I made a mistake in my equation :-/ It's more like : A.X - B = O Well, maybe it will be much more simple if I explain the underlying problem : I have an array of N dimensions (generally 2). - A first calculation gives me a set of integer coordinates inside this array, which I will call the point W. - After several other calculations, I've got a set of coordinates in this N dimensional space that are floating values, and not bound to the limits of my original N-array. This is the point L. What I want to do is to translate the point L along the vector LW in order to get a point L' which coordinates are inside the original N-dimensional array. Then it will be easy to get the closest integer coordinates from L'. I'm not sure this is clear ... pretty hard to talk about maths in english. -- http://mail.python.org/mailman/listinfo/python-list
multinormal distribution
hi there. I'm struggling with a function of numpy. Here it is : import numpy as NP mean = NP.array([0,0]) cov = NP.array([[1,0.25],[0.25,1]]) v = NP.random.multivariate_normal(mean,cov) Quite simple code : it is supposed to generate an array of two random values taken from a multinormal distribution, with respect to means and covariance matrix given as parameters. What happens on my computer is simple : it freezes ! I can't even stop the process with ctrl-C in my python interpreter i have to kill it from outside. I'm running : - python 2.4.1 - numpy 0.9.8 -- http://mail.python.org/mailman/listinfo/python-list
numpy : argmin in multidimensional arrays
Hi there. I am working with multi-dimensional arrays and I need to get coordinates of the min value in it. using myarray.argmin() returns the index in the flatten array, which is a first step, but I wonder if it is possible to get the coordinates directly as an array, rather than calculating them myself by using this flat index and the shape of the array. well, in fact i'm not sure to understand how argmin(myarray) works, when myarray is multidimensional. Thanks -- http://mail.python.org/mailman/listinfo/python-list
Re: numpy : argmin in multidimensional arrays
thanks. unravel_index do the trick. Travis E. Oliphant wrote: TG wrote: Hi there. I am working with multi-dimensional arrays and I need to get coordinates of the min value in it. using myarray.argmin() returns the index in the flatten array, which is a first step, but I wonder if it is possible to get the coordinates directly as an array, rather than calculating them myself by using this flat index and the shape of the array. well, in fact i'm not sure to understand how argmin(myarray) works, when myarray is multidimensional. By default, the argmin method flattens the array and returns the flat index. You can get the corresponding element using myarray.flat[index] Alternatively, you can use the function unravel_index unravel_index(flat_index, myarray.shape) to return an N-dimensional index. If you give an axis argument, then the minimum is found along the specified dimension and you get an N-1 dimensional array of indices that will all be between 1 and myarray.shape[axis] -Travis -- http://mail.python.org/mailman/listinfo/python-list
Numeric N-dimensional array initialization
Hi there ! I'm just starting to use Numeric here, and I'm wondering : how can I efficiently initialize every values of a N-dimensional array, given I don't know the number of dimensions ? I'm looking for something like a map function, or a way to conveniently iterate through the whole N-array, but I didn't find anything ... yet. If anyone has a clue, I'm listening. Thanks -- http://mail.python.org/mailman/listinfo/python-list
Re: Numeric N-dimensional array initialization
I tried to use Numeric.fromfunction, but there seems to be a problem : the function called must have the right number of args (hint : the number of dimensions, which I don't know). So i tried to use a function like : def myfunc(*args, **kw): return 0 and then i get : Numeric.fromfunction(myfunc,(5,5)) 0 I'm a bit puzzled here -- http://mail.python.org/mailman/listinfo/python-list
Re: Numeric N-dimensional array initialization
Thanks for your precious advices. The flat iterator is definitely what i need. -- http://mail.python.org/mailman/listinfo/python-list
Re: Subclassing array
That's great, thanks ! To put it short, when I create a Stimulus object, it first seek __new__() method. But if I don't define it, it looks for the one defined in Vector. This raises a problem because the parameters passed to Stimulus(params) aren't fitting with Vector parameters, raising an exception. That's why I have to use this *arg **kw syntax in order to allow my subclasses having any arguments without causing troubles. Am I right ? -- http://mail.python.org/mailman/listinfo/python-list
Subclassing array
Hi. i've already something about inheriting from array a few weeks ago and had my answer. But again, there is something that I don't understand. Here is my vector class, which works quite well : class Vector(array): def __new__(cls,length,data=None): return super(Vector,cls).__new__(cls,'f') def __init__(self,length,data=None): if data == None: for _ in xrange(length): self.append(0.0) else: for i in xrange(length): self.append(data[i]) Now, i want to inherit from this vector class : class Stimulus(Vector): def __init__(self,width,height,label,data=None): Vector.__init__(self,width*height,data) self.width = width self.height = height self.label = label This doesn't seem to work : s = Stimulus(10,10,data) TypeError: __new__() takes at most 3 arguments (4 given) In order to make it work, it seems that I have to redefine __new__ again, like this. def __new__(cls,width,height,label,data=None): return super(Stimulus,cls).__new__(cls,width*height) Why is that ? When I call Vector.__init__() in Stimulus, doesn't it also call __new__ ? I don't understand the detail of callings to __new__ and __init__ in python inheritance ... -- http://mail.python.org/mailman/listinfo/python-list
Re: Inherit from array
Hmm ... I'm definitely not a python wizard, but it seems to be quite a special case that breaks the rules ... unpythonic, isn't it ? Has anyone seen a PEP on this subject ? Just in case a troll reads this message : i'm not saying python sucks or has huge design flaws here ... -- http://mail.python.org/mailman/listinfo/python-list
Inherit from array
Hi there. I'm trying to create a simple class called Vector which inherit from array. class Vector(array): def __init__(self,length): initialize a vector of random floats of size length. floats are in interval [0;1] array.__init__(self,'f') for _ in xrange(length): self.apprend(random()) but then : v = Vector(10) TypeError: array() argument 1 must be char, not int Well, I guess it means array's __init__ method is not called with proper arguments ... It seems there is a problem with __init__ overloading, like when I call Vector(x), it directly calls __init__ method from array rather than the one defined in Vector class. Anyone got an idea on this ? -- http://mail.python.org/mailman/listinfo/python-list
Re: Inherit from array
from array import array
class Vector(array):
def __init__(self,size):
print pouet
array.__init__('f')
print pouet
v = Vector('c')
print repr(v)
will output :
pouet
pouet
array('c')
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Re: Inherit from array
Obviously, there is something I didn't catch in python's inheritance.
from array import array
class Vector(array):
def __init__(self,size):
print self.typecode
array.__init__(self,'f')
v = Vector('c')
c
Here, it says the typecode is 'c' - I thought such an information was
initalized during the array.__init__(self,'f') but obviously I was
wrong.
Maybe the typecode is defined before, during the call to __new__ method
... But here i'm getting lost.
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