On Sun, Mar 27, 2011 at 4:09 AM, Paul Anton Letnes <
paul.anton.let...@gmail.com> wrote:
>
> On 26. mars 2011, at 21.44, Derek Homeier wrote:
>
> > Hi Paul,
> >
> > having had a look at the other tickets you dug up,
> >
> >> My opinions are my own, and in detail, they are:
> >> 1752:
> >> I atta
On Fri, Mar 25, 2011 at 10:00 AM, Eli Stevens (Gmail)
wrote:
> Can anyone please give me some suggestions on how to go about writing
> a unit test for this? Or should I just submit a pull request?
I've gotten a bit of positive feedback to adding the 'e' type to the
struct module on the python-id
On Wed, Mar 30, 2011 at 16:03, Ralf Gommers wrote:
> On Thu, Mar 24, 2011 at 5:25 PM, Ralf Gommers
> wrote:
>> On Thu, Mar 24, 2011 at 5:11 PM, Robert Kern wrote:
>>> We really should change the default to 'warn' for numpy 2.0. Maybe
>>> even for numpy 1.6. We've talked about it before, and I t
Hi,
On 30 Mar 2011, at 21:37, Bruce Southey wrote:
> Ticket 1071: 'loadtxt fails if the last column contains empty value'
> http://projects.scipy.org/numpy/ticket/1071
> Invalid mainly because loadtxt states that 'Each row in the text file
> must have the same number of values.' So of cause loadt
On 30 Mar 2011, at 23:26, Benjamin Root wrote:
> Ticket 301: 'Make power and divide return floats from int inputs (like
> true_divide)'
> http://projects.scipy.org/numpy/ticket/301
> Invalid because the output dtype is the same as the input dtype unless
> you override using the dtype argument:
>
On Wed, Mar 30, 2011 at 2:37 PM, Bruce Southey wrote:
> Hi,
> This followup on tickets that I had previously indicated. So I want to
> thank Mark, Ralph and any people for going over those!
>
> For those that I followed I generally agreed with the outcome.
>
> Ticket 301: 'Make power and divide r
On Thu, Mar 24, 2011 at 5:25 PM, Ralf Gommers
wrote:
> On Thu, Mar 24, 2011 at 5:11 PM, Robert Kern wrote:
>> 2011/3/24 Dmitrey :
>> from numpy import inf, array
>> inf*0
>>> nan
>>>
>>> (ok)
>>>
>> array(inf) * 0.0
>>> StdErr: Warning: invalid value encountered in multiply
>>> nan
>>
Hi,
On Wed, Mar 30, 2011 at 11:32 AM, Pauli Virtanen wrote:
> On Wed, 30 Mar 2011 10:37:45 -0700, Matthew Brett wrote:
> [clip]
>> imagine I'm working with a non-latin default encoding, and I've opened a
>> file:
>>
>> fobj = open('my_nonlatin.txt', 'rt')
>>
>> in python 3.2. That might contain
Hi,
This followup on tickets that I had previously indicated. So I want to
thank Mark, Ralph and any people for going over those!
For those that I followed I generally agreed with the outcome.
Ticket 301: 'Make power and divide return floats from int inputs (like
true_divide)'
http://projects.s
On Wed, 30 Mar 2011 10:37:45 -0700, Matthew Brett wrote:
[clip]
> imagine I'm working with a non-latin default encoding, and I've opened a
> file:
>
> fobj = open('my_nonlatin.txt', 'rt')
>
> in python 3.2. That might contain numbers and non-latin text. I can't
> pass that into 'genfromtxt' be
On Wed, Mar 30, 2011 at 7:37 PM, Matthew Brett wrote:
> Hi,
>
> On Wed, Mar 30, 2011 at 10:02 AM, Ralf Gommers
> wrote:
>> On Wed, Mar 30, 2011 at 3:39 AM, Matthew Brett
>> wrote:
>>> Hi,
>>>
>>> On Mon, Mar 28, 2011 at 11:29 PM, wrote:
numpy/lib/test_io.py only uses StringIO in the t
Hi,
On Wed, Mar 30, 2011 at 10:02 AM, Ralf Gommers
wrote:
> On Wed, Mar 30, 2011 at 3:39 AM, Matthew Brett
> wrote:
>> Hi,
>>
>> On Mon, Mar 28, 2011 at 11:29 PM, wrote:
>>> numpy/lib/test_io.py only uses StringIO in the test, no actual csv file
>>>
>>> If I give the filename than I get a
On Wed, Mar 30, 2011 at 12:12, Joon Ro wrote:
> Hi,
> After numpy upgrade, I started to get "Warning: invalid value encountered in
> true_divide," when I run a code which did now show any warning previously.
> What does it mean and where should I look to fix this?
It means that a NaN popped up in
Hi,After numpy upgrade, I started to get "Warning: invalid value encountered in true_divide," when I run a code which did now show any warning previously.What does it mean and where should I look to fix this? It does not stop my debugger so I could not identify where the message was from.Thank you,
On Wed, Mar 30, 2011 at 3:39 AM, Matthew Brett wrote:
> Hi,
>
> On Mon, Mar 28, 2011 at 11:29 PM, wrote:
>> numpy/lib/test_io.py only uses StringIO in the test, no actual csv file
>>
>> If I give the filename than I get a TypeError: Can't convert 'bytes'
>> object to str implicitly
>>
>>
>>
On Wed, Mar 30, 2011 at 1:42 PM, gary ruben wrote:
> You're right, they are not equivalent. vstack will happily create an
> array of higher rank than the parts it is stacking, whereas
> concatenate requires the arrays it is working with to already be at
> least 2d, so the equivalent is
> np.concat
You're right, they are not equivalent. vstack will happily create an
array of higher rank than the parts it is stacking, whereas
concatenate requires the arrays it is working with to already be at
least 2d, so the equivalent is
np.concatenate((np.arange(5.)[newaxis],np.arange(5.)[newaxis]), axis=0)
>
> Well I guess, for a slight performance improvement, you could create your
> own streamlined histogrammer.
>
> But, in order to better grasp your situation it would be beneficial to know
> how the counts and bounds are used later on. Just wondering if this kind
> massive histogramming could be
Dear List,
I have a quick question regarding vstack and concatenate.
In the docs for vstack it says that:
np.concatenate(tup, axis=0)
should be equivalent to:
np.vstack(tup)
However, I tried this out and it doesn't seem to be case, i.e.
>>> np.vstack((np.arange(5.), np.arange(5.)))
array([[ 0.
Hi,
On Wed, Mar 30, 2011 at 10:04 AM, Éric Depagne wrote:
> Hi.
>
> Sorry for not having been clearer. I'll explain a little bit.
>
> I have 4k x 4k images that I want to analyse. I turn them into numpy arrays
> so
> I have 4k x 4k np.array.
>
> My analysis starts with determining the bias level
How about something like this:
# numpy 1.6
def rowhist(A, bins=100):
assert (bins > 0)
assert isinstance(bins, int)
rownum = np.arange(A.shape[0]).reshape((-1, 1)).astype(int) * bins
intA = (bins * (A - A.min()) / float(A.max() - A.min())).astype(int)
intA[intA == bins] = bins
Hi.
Sorry for not having been clearer. I'll explain a little bit.
I have 4k x 4k images that I want to analyse. I turn them into numpy arrays so
I have 4k x 4k np.array.
My analysis starts with determining the bias level. To do that, I compute for
each line, and then for each row, an histogram
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