no one gets to it first, I can try and work on it later in
the year.
Cheers,
Tom
>
> Cheers,
>
> Sylvain
>
>
> On Wed, Sep 23, 2015 at 12:39 PM, Thomas Robitaille
> mailto:thomas.robitai...@gmail.com>> wrote:
>
> Hi everyone,
>
> We have released
Hi everyone,
We have released a small experimental package called numtraits that
builds on top of the traitlets package and provides a NumericalTrait
class that can be used to validate properties such as:
* number of dimension (for arrays)
* shape (for arrays)
* domain (e.g. positive, negative, r
Just to follow-on to my previous email, our labeling convention is
described in more detail here:
https://github.com/astropy/astropy/wiki/Issue-labeling-convention
Cheers,
Tom
Thomas Robitaille wrote:
> The issue with 'low hanging fruit' is that who is it low-hanging fruit
>
The issue with 'low hanging fruit' is that who is it low-hanging fruit
for? Low hanging fruit for a core dev may be days of work for a
newcomer. Also, 'newcomer' doesn't give a good idea of how long it will
take.
I would therefore like to second Tom Aldcroft's suggestion of following
something lik
Hi,
The behavior for ``np.median`` and array sub-classes has changed in
1.8.0rc, which breaks unit-handling code (such as the ``quantities``
package, or ``astropy.units``):
https://github.com/numpy/numpy/issues/3846
This previously worked from Numpy 1.5 (at least) to Numpy 1.7. Is
there a new (a
Hi everyone,
The following example:
import numpy as np
class SimpleArray(np.ndarray):
__array_priority__ = 1
def __new__(cls, input_array, info=None):
return np.asarray(input_array).view(cls)
def __eq__(self, other):
return False
sn't NPY_OBJECT when you implement
> __array__.
>
> dtypes is set with those line:
>
> retval = ufunc->type_resolver(ufunc, casting,
> op, type_tup, dtypes);
Thanks for looking into this - should this be considered a bug?
Tom
>
>
> HTH
&g
Hi everyone,
(this was posted as part of another topic, but since it was unrelated,
I'm reposting as a separate thread)
I've also been having issues with __array_priority__ - the following
code behaves differently for __mul__ and __rmul__:
"""
import numpy as np
class TestClass(object):
d
I've also been having issues with __array_priority__ - the following
code behaves differently for __mul__ and __rmul__:
"""
import numpy as np
class TestClass(object):
def __init__(self, input_array):
self.array = input_array
def __mul__(self, other):
print "Called __mu
Hi everyone,
I am currently trying to write a sub-class of Numpy ndarray, but am
running into issues for functions that return scalar results rather
than array results. For example, in the following case:
import numpy as np
class TestClass(np.ndarray):
def __new__(cls, input_arr
Hi everyone,
I'm currently having issues with installing Numpy 1.6.2 with Python
3.1 and 3.2 using pip in Travis builds - see for example:
https://travis-ci.org/astropy/astropy/jobs/3379866
The build aborts with a cryptic message:
ValueError: underlying buffer has been detached
Has anyone seen
I've recently opened a couple of pull requests that fix bugs with
MaskedArray - these are pretty straightforward, so would it be
possible to consider them in time for 1.7?
https://github.com/numpy/numpy/pull/2703
https://github.com/numpy/numpy/pull/2733
Thanks!
Tom
__
Hello,
Is the following behavior normal?
In [1]: import numpy as np
In [2]: np.dtype([('a','http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi,
I'm trying to extract sub-sections of a multidimensional array while keeping
the number of dimensions the same. If I just select a specific element along a
given direction, then the number of dimensions goes down by one:
>>> import numpy as np
>>> a = np.zeros((10,10,10))
>>> a.shape
(10, 1
josef.pktd wrote:
>
> are you sure this is not just a print precision problem?
>
Thanks for pointing this out, it does seem to be just to do with the
printing precision. I didn't notice this before, because for the last few
elements of the array, print still gives just -1:
In [19]: for x in a
Hi,
I am running into a precision issue with np.loadtxt. I have a data file with
the following contents:
$ cat data.txt
-9.61922814E-01
-9.96192290E-01
-9.99619227E-01
-9.99961919E-01
-9.6192E-01
-9.9611E-01
-1.E+00
If I tr
Thomas Robitaille wrote:
>
> I seem to remember that this used not to be the case, and that even for
> vector columns, one could access array.dtype[0].type to get the numerical
> type. Is this a bug, or deliberate?
>
I submitted a bug report:
http://projects.scipy.org/nu
Pauli Virtanen-3 wrote:
>
> That's a bug. It apparently implicitly encodes the Unicode string you
> pass in to UTF-8, instead of trying to encode in ASCII and fail, like it
> does on Python 2:
>
Thanks! Should I file a bug report?
Cheers,
Tom
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View this message in context:
http://old.n
Hi,
The following example illustrates a problem I'm encountering a problem with the
np.fromstring function in Python 3:
Python 3.1.2 (r312:79360M, Mar 24 2010, 01:33:18)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> impo
Hi,
If I create a structured array with vector columns:
>>> array = np.array(zip([[1,2],[1,2],[1,3]]),dtype=[('a',float,2)])
then examine the type of the column, I get:
>>> array.dtype[0]
dtype(('float64',(2,)))
Then, if I try and view the numerical type, I see:
>>> array.dtype[0].type
I ha
Hello,
I'm trying to understand how array broadcasting can be used for indexing. In
the following, I use the term 'row' to refer to the first dimension of a 2D
array, and 'column' to the second, just because that's how numpy prints them
out.
If I consider the following example:
>>> a = np.ran
Warren Weckesser-3 wrote:
>
> Looks like 'sort' is not handling endianess of the column data
> correctly. If you change the type of the floating point data to ' the sort works.
>
Thanks for identifying the issue - should I submit a bug report?
Thomas
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http:
I am having trouble sorting a structured array - in the example below, sorting
by the first column (col1) seems to work, but not sorting by the second column
(col2). Is this a bug?
I am using numpy svn r8071 on MacOS 10.6.
Thanks for any help,
Thomas
Python 2.6.1 (r261:67515, Jul 7 2009, 23:
Pierre GM-2 wrote:
>
> Well, that's a problem indeed, and I'd put that as a bug.
> However, you can use that syntax instead:
t.fill_value['a']=10
> or set all the fields at once:
t.fill_value=(10,99)
>
Thanks for your reply - should I submit a bug report on the numpy trac site?
T
Hi,
The following code doesn't seem to work:
import numpy.ma as ma
t = ma.array(zip([1,2,3],[4,5,6]),dtype=[('a',int),('b',int)])
print repr(t['a'])
t['a'].set_fill_value(10)
print repr(t['a'])
As the output is
masked_array(data = [1 2 3],
mask = [False False False],
fill
Pierre GM-2 wrote:
>
> Mmh. With a recent (1.3) version of numpy, you should already be able
> to mask individual fields of a structured array without problems. If
> you need fields to be accessed as attributes the np.recarray way, you
> can give numpy.ma.mrecords.MaskedRecords a try. It's
Pierre GM-2 wrote:
>
> Confirmed, it's a bug all right. Would you mind opening a ticket ?
> I'll try to take care of that in the next few days.
>
Done - http://projects.scipy.org/numpy/ticket/1283
Thanks!
Thomas
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Pierre GM-2 wrote:
>
> As a workwaround, perhaps you could use np.object instead of np.str
> while defining your array. You can then get the maximum string length
> by looping, as David suggested, and then use .astype to transform your
> array...
>
I tried this:
np.rec.fromrecords([(1,'
Hi,
I'm having trouble with creating np.string_ fields in recarrays. If I
create a recarray using
np.rec.fromrecords([(1,'hello'),(2,'world')],names=['a','b'])
the result looks fine:
rec.array([(1, 'hello'), (2, 'world')], dtype=[('a', 'http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hello,
I have a question concerning uint64 numbers - let's say I want to
format a uint64 number that is > 2**31, at the moment it's necessary
to wrap the numpy number inside long before formatting
In [3]: "%40i" % np.uint64(2**64-1)
Out[3]: ' -1'
In [4]:
Hi,
I'm trying to generate random 64-bit integer values for integers and
floats using Numpy, within the entire range of valid values for that
type. To generate random 32-bit floats, I can use:
np.random.uniform(low=np.finfo(np.float32).min,high=np.finfo
(np.float32).max,size=10)
which gives
Hi,
I'm interested in constructing a recarray with fields that have two or
more dimensions. This can be done from scratch like this:
r = np.recarray((10,),dtype=[('c1',float,(3,))])
However, I am interested in appending a field to an existing recarray.
Rather than repeating existing code I w
Hi,
To convert some bytes to e.g. a 32-bit int, I can do
bytes = f.read(4)
i = struct.unpack('>i', bytes)[0]
and the convert it to np.int32 with
i = np.int32(i)
However, is there a more direct way of directly transforming bytes
into a np.int32 type without the intermediate 'struct.unpack' st
Nathan Bell-4 wrote:
>
> image = np.histogram2d(x, y, bins=bins, weights=z)[0]
>
This works great - thanks!
Thomas
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Sent from the Numpy-discussion mailing list archive at Nabble.c
Hi,
I have a set of n points with real coordinates between 0 and 1, given
by two numpy arrays x and y, with a value at each point represented by
a third array z. I am trying to then rasterize the points onto a grid
of size npix*npix. So I can start by converting x and y to integer
pixel co
Pauli Virtanen-3 wrote:
>
> I applied the patch from the ticket; I think password resets should work
> now, so you can try using your old accounts again.
>
That worked, thanks! Now I think of it, the problem started occurring after
I had forgotten my password and had to reset it.
Thomas
--
Could it be linked to specific users, since the problem occurs when loading
the account page? I had the same problem on two different computers with two
different browsers.
Thomas
--
View this message in context:
http://www.nabble.com/Numpy-Trac-site-redirecting-in-a-loop--tp23067410p23417595.h
Hi,
I'm having the exact same problem, trying to log in to the trac website for
numpy, and getting stuck in a redirect loop. I tried different browsers, and
no luck. The browser gets stuck on
http://projects.scipy.org/numpy/prefs/account
and stops loading after a while because of too many red
>> import numpy as np
>> arr1 = np.array(['a','b','c'])
>> arr2 = np.array(['d','e','f'])
>>
>> I would like to produce a third array that would contain
>> ['ad','be','cf']. Is there an efficient way to do this? I could do
>> this element by element, but I need a faster method, as I need to do
>> t
Hello,
I am trying to find an efficient way to concatenate the elements of
two same-length numpy str arrays. For example if I define the
following arrays:
import numpy as np
arr1 = np.array(['a','b','c'])
arr2 = np.array(['d','e','f'])
I would like to produce a third array that would contain
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