On Fri, Jul 3, 2009 at 4:44 AM, Pauli Virtanen wrote:
> I think we tried this already (my c99-umath-funcs branch had
> TestC99 special case tests that were in Numpy trunk for a while).
>
> The outcome was that the current implementations of the complex
> functions don't have essentially any specia
Thanks Pierre,
For some reason
'formats':[eval(b) for b in event_format]
didn't work, but as you said should it fail try
dtype([(x,eval(b)) for (x,b) in zip(event_fields, event_format)])
which seems to be working.
Interestingly before when I had typed this out by hand, both using
tuples and a
On Jul 2, 2009, at 6:42 PM, Peter Kelley wrote:
>
> Hey Everyone,
>
> I am reading in a file of columns with mixed data types, and the
> number of columns can vary and their format is inputted by the user.
> So I came up with this:
>
> dt=dtype({'names': [x for x in event_fields], 'formats':
Hi Peter
2009/7/3 Peter Kelley :
> I get TypeError: data type not understood, and I think it is because the
> event format is a list of strings not data types. Does anyone have know how
> to convert the list of strings into the data types for dtype.
In your example the problem actually comes in w
Hey Everyone,
I am reading in a file of columns with mixed data types, and the number of
columns can vary and their format is inputted by the user. So I came up with
this:
dt=dtype({'names': [x for x in event_fields], 'formats': [b for b in
event_format]})
eventArray = loadtxt(behEventFile,dt)
On 2009-07-02, David Cournapeau wrote:
> I think I will merge the complex_umath_tests branch soon
> (platform-specific failures on build bot will be interesting), unless
> someone sees a problem with it.
I think we tried this already (my c99-umath-funcs branch had
TestC99 special case tests that
Elaine Angelino wrote:
> Hi there --
>
> Is there a fast way to make a numpy ndarray from column data?
>
> For example, suppose I want to make an ndarray with 2 rows and 3 columns
> of different data types based on the following column data:
>
> C0 = [1,2]
> C1 = ['a','b']
> C2 = [3.3,4.4]
>
>
> What's wrong with recarrays? In any case, if you need a true ndarray
> object
> you can always do:
>
> ndarr = recarr.view(np.ndarray)
>
> and you are done.
>
I have a question about this though. The object "ndarr" will consist of
"records", e.g.:
In [96]: type(ndarr[0])
Out[96]:
If
I'm relatively certain its possible, but then you have to deal with
locks, semaphores, synchronization, etc...
On Thu, Jul 2, 2009 at 12:04 PM, Sebastian Haase wrote:
> On Thu, Jul 2, 2009 at 5:38 PM, Chris Colbert wrote:
>> Who are quoting Sebastian?
>>
>> Multiprocessing is a python package tha
On Thu, Jul 2, 2009 at 5:38 PM, Chris Colbert wrote:
> Who are quoting Sebastian?
>
> Multiprocessing is a python package that spawns multiple python
> processes, effectively side-stepping the GIL, and provides easy
> mechanisms for IPC. Hence the need for serialization
>
I was replying to the
Who are quoting Sebastian?
Multiprocessing is a python package that spawns multiple python
processes, effectively side-stepping the GIL, and provides easy
mechanisms for IPC. Hence the need for serialization
On Thu, Jul 2, 2009 at 11:30 AM, Sebastian Haase wrote:
> On Thu, Jul 2, 2009 at 5:1
On Thu, Jul 2, 2009 at 5:14 PM, Chris Colbert wrote:
> can you hold the entire file in memory as single array with room to spare?
> If so, you could use multiprocessing and load a bunch of smaller
> arrays, then join them all together.
>
> It wont be super fast, because serializing a numpy array is
can you hold the entire file in memory as single array with room to spare?
If so, you could use multiprocessing and load a bunch of smaller
arrays, then join them all together.
It wont be super fast, because serializing a numpy array is somewhat
slow when using multiprocessing. That said, its stil
On Thu, Jul 2, 2009 at 9:02 AM, David Cournapeau wrote:
>
> True, but we can deal with this once we have tests: we can force to
> use our own, fixed implementations on broken platforms. The glibc
> complex functions are indeed not great, I have noticed quite a few
> problems for special value hand
hi there,
at last scipy'08 sprint, somebody (apologies for my brain fade) was working on
being able to parse the extended struct format string so one could do:
Nested structure
::
struct {
int ival;
struct {
unsigned short sval;
A Thursday 02 July 2009 03:02:53 Elaine Angelino escrigué:
> Hi there --
>
> Is there a fast way to make a numpy ndarray from column data?
>
> For example, suppose I want to make an ndarray with 2 rows and 3 columns of
> different data types based on the following column data:
>
> C0 = [1,2]
> C1 =
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