Actually in my traversal of the never-ending maze of understanding arrays in
python, I stumbled that the data in the wrong sequence.
Let's get back to this transpose() deal, say we have these values as integers
(representing rgba again): [[[0,1,2,3][4,5,6,7][8,9,10,11][12,13,14,15]]
[[16,17,18,19][20,21,22,23][24,25,26,27][28,29,30,31]]] Now if I do this:
transpose((2,0,1)), I get this: [[[0,4,8,12] [16,20,24,28]][[1,5,9,13]
[17,21,25,29]][[2,6,10,14][18,22,26,30]][[3,7,11,15][19,23,27,31]]] This is NOT
what I want. I want the new array to be:
[0,4,8,12][1,5,9,13][2,6,10,14][3,7,11,15][16,20,24,28][17,21,25,29][18,22,26,30][19,23,27,31]
How do I do this? -M
> Date: Sat, 17 May 2008 08:58:08 -0700> From: [EMAIL PROTECTED]> To: [EMAIL
> PROTECTED]> CC: python-list@python.org> Subject: Re: numpy.frombuffer !=
> unpack() ??> > Marlin Rowley wrote:> > > > Very cool.> > > > > > a =
> (['rrrrggggbbbbaaaa'],['rrrrggggbbbbaaaa'])> > a represents a tile with
> height of 2 and width of 4 with 4 bits/pixel > > for each color.> > > > > >>>
> b = numpy.frombuffer(''.join(sum(a,[])),dtype='S1')> > this seperates the
> stream into individual values - Check> > > > > >>> b.shape=(2,4,4)> >> > This
> reshapes the array so that b.shape=(height,width,#bits/pixel) - Check> > > >
> >>> c = b.transpose((2,0,1))> > > > What does the (2,0,1) represent in terms
> of width and height and > > number of bytes?> > The (2,0,1) tells how to
> exchange the axes. For instance in a 2D array, > a normal transpose exchanges
> rows and columns. It will change a (a by > b) sized array into a (b by a)
> sized array. This would be equivalent to > the more saying interchange axes
> 0,1 to the new order of 1,0. > > In numpy with higher dimension arrays, the
> default transpose just > exchanges the first two axes, and the full transpose
> allows you to > specify exactly the new ordering of the exes. > > So
> transpose((2,0,1)) means take axes (0,1,2) to the new order > (2,1,0). In
> terms of sizes, an (a by b by c) sized array will end being > of size (c by a
> by b) in size. > > In terms of implementation, there may not be *any* data
> re-arrangement > in a transpose. The only thing that needs changing is how
> the indices > are converted to an actual machine address of an indexed item.
> The > documentation notes this by saying transpose returns a "new view" of
> the > array. This explains why I copied the array before extracting bytes >
> out of it -- you really do need the elements in the new order for the > next
> operation.> > Gary Herron> > >> >> >> >> > > >
> ------------------------------------------------------------------------> >>
> > > Date: Fri, 16 May 2008 17:08:20 -0700> > > From: [EMAIL PROTECTED]> > >
> To: [EMAIL PROTECTED]; python-list@python.org> > > Subject: Re:
> numpy.frombuffer != unpack() ??> > >> > > Marlin Rowley wrote:> > > > All:> >
> > >> > > > Say I have an array:> > > >> > > > a =
> (['rrrrggggbbbbaaaa'],['rrrrggggbbbbaaaa'])> > > >> > > > How do I make it so
> that I now have:> > > >> > > > starting with first element (a[0])> > > >
> new_arr[0] = 'r'> > > > new_arr[1] = 'g'> > > > new_arr[2] = 'b'> > > >
> new_arr[3] = 'a'> > > > new_arr[4] = 'r'> > > > .....> > > >> > > >
> continuing "through" a[1] with the same new_arr> > > > new_arr[N] = 'r'> > >
> > new_arr[N+1] = 'g'> > > > ....> > > >> > > > -M> > >> > > Numpy can do this
> for you. First, do you really mean the array to> > > contain lists of one
> string each? If so:> > >> > > >>> import numpy> > > >>> a =
> (['rrrrggggbbbbaaaa'],['rrrrggggbbbbaaaa'])> > > >>> b =
> numpy.frombuffer(''.join(sum(a,[])),dtype='S1') # Kind of a> > > kludge here>
> > > >>> b> > > array(['r', 'r', 'r', 'r', 'g', 'g', 'g', 'g', 'b', 'b', 'b',
> 'b', 'a',> > > 'a', 'a', 'a', 'r', 'r', 'r', 'r', 'g', 'g', 'g', 'g', 'b',
> 'b',> > > 'b', 'b', 'a', 'a', 'a', 'a'],> > > dtype='|S1')> > > >>>
> b.shape=(2,4,4)> > > >>> b> > > array([[['r', 'r', 'r', 'r'],> > > ['g', 'g',
> 'g', 'g'],> > > ['b', 'b', 'b', 'b'],> > > ['a', 'a', 'a', 'a']],> > >> > >
> [['r', 'r', 'r', 'r'],> > > ['g', 'g', 'g', 'g'],> > > ['b', 'b', 'b', 'b'],>
> > > ['a', 'a', 'a', 'a']]],> > > dtype='|S1')> > > >>> c =
> b.transpose((2,0,1))> > > >>> c> > > array([[['r', 'g', 'b', 'a'],> > > ['r',
> 'g', 'b', 'a']],> > >> > > [['r', 'g', 'b', 'a'],> > > ['r', 'g', 'b',
> 'a']],> > >> > > [['r', 'g', 'b', 'a'],> > > ['r', 'g', 'b', 'a']],> > >> > >
> [['r', 'g', 'b', 'a'],> > > ['r', 'g', 'b', 'a']]],> > > dtype='|S1')> > >
> >>> d=c.copy() # To make it contiguous> > > >>> d.shape = (32,)> > > >>> d> >
> > array(['r', 'g', 'b', 'a', 'r', 'g', 'b', 'a', 'r', 'g', 'b', 'a', 'r',> >
> > 'g', 'b', 'a', 'r', 'g', 'b', 'a', 'r', 'g', 'b', 'a', 'r', 'g',> > > 'b',
> 'a', 'r', 'g', 'b', 'a'],> > > dtype='|S1')> > >> > > Done. Cool no?> > >> >
> > Gary Herron> > >> > > >> > > >> > > >> > > >> > > > > >
> ------------------------------------------------------------------------> > >
> > From: [EMAIL PROTECTED]> > > > To: [EMAIL PROTECTED];
> python-list@python.org> > > > Subject: RE: numpy.frombuffer != unpack() ??> >
> > > Date: Fri, 16 May 2008 17:31:30 -0500> > > >> > > > Thank you! That
> solved it!> > > >> > > > -M> > > >> > > >> > > > > >
> ------------------------------------------------------------------------> > >
> >> > > > > To: python-list@python.org> > > > > From: [EMAIL PROTECTED]> > > >
> > Subject: Re: numpy.frombuffer != unpack() ??> > > > > Date: Fri, 16 May
> 2008 17:25:00 -0500> > > > >> > > > > Marlin Rowley wrote:> > > > > > All:> >
> > > > >> > > > > > I'm getting different floating point values when I use
> numpy> > > > vs. unpack().> > > > > >> > > > > > frgba =
> numpy.frombuffer(<string of bytes>, dtype=float32)> > > > > > buffer =
> unpack("!f", byte)> > > > > >> > > > > > frgba[0] != buffer[0]> > > > > >> >
> > > > > why? This is forcing me use the unpack() function since it's> > > >
> giving me> > > > > > the correct values. What am I doing wrong?> > > > >> > >
> > > Endianness, perhaps? '!' specifies big-endian data (an alias for> > > >
> '>'). Most> > > > > likely, you are on a little-endian platform. All of the
> dtypes> > > > in numpy default> > > > > to the native-endianness unless
> specified. If you want to read> > > > big-endian data> > > > > using numpy,
> do this:> > > > >> > > > > frgba = numpy.frombuffer(<string of bytes>,
> dtype='>f')> > > > >> > > > > If you have any more problems with numpy,
> please join us on the> > > > numpy mailing> > > > > list. When reporting
> problems, please try to provide a small but> > > > complete> > > > > snippet
> of self-contained code, the output that you got, and> > > > explain the
> output> > > > > that you expected to get. Thank you.> > > > >> > > > >
> http://www.scipy.org/Mailing_Lists> > > > >> > > > > --> > > > > Robert Kern>
> > > > >> > > > > "I have come to believe that the whole world is an enigma,
> a> > > > harmless enigma> > > > > that is made terrible by our own mad
> attempt to interpret it as> > > > though it had> > > > > an underlying
> truth."> > > > > -- Umberto Eco> > > > >> > > > > --> > > > >
> http://mail.python.org/mailman/listinfo/python-list> > > >> > > >> > > > > >
> ------------------------------------------------------------------------> > >
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