On Tue, Jan 17, 2012 at 05:11, Andreas Kloeckner
li...@informa.tiker.net wrote:
Hi Robert,
On Fri, 30 Dec 2011 20:05:14 +, Robert Kern robert.k...@gmail.com wrote:
On Fri, Dec 30, 2011 at 18:57, Andreas Kloeckner
li...@informa.tiker.net wrote:
Hi Robert,
On Tue, 27 Dec 2011 10:17:41
Hello,
I get a segfault here:
In [1]: x = np.array([1,2,3], dtype='M')
In [2]: x.searchsorted(2, side='left')
But it's fine here:
In [1]: x = np.array([1,2,3], dtype='M')
In [2]: x.view('i8').searchsorted(2, side='left')
Out[2]: 1
This segfaults again:
On Tue, Jan 17, 2012 at 8:57 AM, Adam Klein a...@lambdafoundry.com wrote:
Hello,
I get a segfault here:
In [1]: x = np.array([1,2,3], dtype='M')
In [2]: x.searchsorted(2, side='left')
But it's fine here:
In [1]: x = np.array([1,2,3], dtype='M')
In [2]: x.view('i8').searchsorted(2,
Here's a thought:
Too bad numpy doesn't have a 24 bit integer, but you could tack a 0
on, making your image 32 bit, then use histogram2d to count the
colors.
something like (untested):
# create the 32 bit image
32bit_im = np.zeros((w, h), dtype = np.uint32)
view = 32bit_im.view(dtype =
While playing with a point-in-polygon test, I have discovered some a
failure mode that I cannot make sence of.
The algorithm is vectorized for NumPy from a C and Python implementation
I found on the net (see links below). It is written to process a large
dataset in chunks. I'm rather happy
Never mind this, it was my own mistake as I expected :-)
def __chunk(n,size):
x = range(0,n,size)
x.append(n)
return zip(x[:-1],x[1:])
makes it a lot better :)
Sturla
Den 18.01.2012 06:26, skrev Sturla Molden:
While playing with a point-in-polygon test, I have discovered
