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
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
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,
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 __mul__
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):
def
, type_tup, dtypes);
Thanks for looking into this - should this be considered a bug?
Tom
HTH
Fred
On Thu, May 16, 2013 at 9:19 AM, Thomas Robitaille
thomas.robitai...@gmail.com wrote:
Hi everyone,
(this was posted as part of another topic, but since it was unrelated,
I'm reposting
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
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
Hello,
Is the following behavior normal?
In [1]: import numpy as np
In [2]: np.dtype([('a','f4',2)])
Out[2]: dtype([('a', 'f4', (2,))])
In [3]: np.dtype([('a','f4',1)])
Out[3]: dtype([('a', 'f4')])
I.e. in the second case, the second dimension of the dtype (1) is
being ignored? Is there a way
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', 'i8'), ('b', '|
S5')])
But if I want to specify the
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:
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
--
View this message in context:
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
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],
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?
Thomas
--
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,
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 'f8',
the sort works.
Thanks for identifying the issue - should I submit a bug report?
Thomas
--
View this message in context:
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 =
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.
import numpy
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
--
View this message in context:
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/numpy/ticket/1557
Cheers
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
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'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, 10, 10)
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
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
this on arrays
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
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:
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
--
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
Nathan Bell-4 wrote:
image = np.histogram2d(x, y, bins=bins, weights=z)[0]
This works great - thanks!
Thomas
--
View this message in context:
http://www.nabble.com/Rasterizing-points-onto-an-array-tp23808494p23820216.html
Sent from the Numpy-discussion mailing list archive at
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'
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
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
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]:
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
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
for? Low hanging
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,
irst, 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
> <thomas.robitai...@gmail.com <mailto:thomas.robitai...@gmail.com>> wrote:
>
> Hi everyone,
>
> We
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