Thank you,
But I was looking for a format statement likw
write(*,(A,5F8.3))
with best regards,
Sudheer
***
Sudheer Joseph
Indian National Centre for Ocean Information Services
Ministry of Earth Sciences, Govt. of India
On 10/05/2013 11:14, Sudheer Joseph wrote:
However writing a formatted out put looks to be bit tricky with
python relative to other programing languages.
...
I was looking for a format statement likw write(*,(A,5F8.3))
Before denigrating a programming language I would make sure to have a
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,
Hi,
I am trying to learn Python after feeling its utility in coding and also
reading a bit about
its potential only, please do not put words in to my mouth like below.
Before denigrating a programming language
If some one has a quick way I would like to learn from them or get a
On 10.05.2013, at 1:20PM, Sudheer Joseph sudheer.jos...@yahoo.com wrote:
If some one has a quick way I would like to learn from them or get a
referecence
where the formatting part is described which was
my intention while posting here. As I have been using fortran I just tried
to use it
10.05.2013 08:47, Eli Bressert kirjoitti:
[clip: renaming ptp]
valuerange() appears to the best most favored one.
range(), arange(), valuerange()
I'm not really a big fan of changing the name of this function at this
stage, as it seems to me that whether it's a gain or not is somewhat a
matter
Sudheer,
This is not really numpy specific. There are many options for output
formatting in python. For the specific question you have, you could do:
print '{0}{1:8.3f}{2:8.3f}{3:8.3f}{4:8.3f}{5:8.3f}'.format(s,x1,x2,x3,x4,x5)
format is a built-in python string method (see python docs). The
On 10/05/2013 13:20, Sudheer Joseph wrote:
Hi,
I am trying to learn Python after feeling its utility in coding and
also reading a bit aboutits potential only, please do not put words
in to my mouth like below.
I didn't put words in your mouth, I simply quoted emails you sent to the
list and
That's a good point regarding the range function names. But, I think
the issue still stands on the readability of the ptp function.
Regarding PEP20 it's stated that readability counts.
If you regard what ptp is supposed to replace, array.max() -
array.min(), the aforementioned follows the PEP20
10.05.2013 16:04, Eli Bressert kirjoitti:
That's a good point regarding the range function names. But, I think
the issue still stands on the readability of the ptp function.
Regarding PEP20 it's stated that readability counts.
I think here it has to be kept in mind that this function has been
On Fri, May 10, 2013 at 2:04 PM, Eli Bressert ebress...@gmail.com wrote:
That's a good point regarding the range function names. But, I think
the issue still stands on the readability of the ptp function.
Regarding PEP20 it's stated that readability counts.
If you regard what ptp is supposed
On Fri, May 10, 2013 at 3:17 PM, Robert Kern robert.k...@gmail.com wrote:
On Fri, May 10, 2013 at 2:04 PM, Eli Bressert ebress...@gmail.com wrote:
That's a good point regarding the range function names. But, I think
the issue still stands on the readability of the ptp function.
Regarding
On 10.05.2013, at 2:51PM, Daniele Nicolodi dani...@grinta.net wrote:
If you wish to format numpy arrays preceding them with a variable name,
the following is a possible solution that gives the same formatting as
in your example:
import numpy as np
import sys
def format(out, v, name):
On Fri, May 10, 2013 at 2:27 PM, Ralf Gommers ralf.gomm...@gmail.com wrote:
On Fri, May 10, 2013 at 3:17 PM, Robert Kern robert.k...@gmail.com wrote:
On Fri, May 10, 2013 at 2:04 PM, Eli Bressert ebress...@gmail.com wrote:
That's a good point regarding the range function names. But, I
On May 10, 2013 3:18 PM, Robert Kern robert.k...@gmail.com wrote:
Sure, it's probably more readable
I am not sure of it. I would have to check the docs to see what it means.
The mathematical term is range, but it already has a meaning in Python, so
it is not a good way to go, being perhaps
Hi,
it popped again on the Theano mailing list that this don't work:
np.arange(10) = a_theano_vector.
The reason is that __array_priority__ isn't respected for that class of
operation.
This page explain the problem and give a work around:
I'm trying to do it, but each time I want to test something, it takes a
long time to rebuild numpy to test it. Is there a way to don't recompile
everything for each test?
thanks
Fred
On Fri, May 10, 2013 at 1:34 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Fri, May 10, 2013 at
Here is my set up:
Mac OS 10.7.5
Xcode 4.5.1
Intel Fortran 12.1
Python 2.7.3 built from source
Numpy 1.6.2 built from source, using MKL 11.0
nose 0.11.4 installed
I run the numpy tests as documented (python -c 'import numpy; numpy.test()'),
but get this output:
tkacvins@macomsim python -c
On Fri, 2013-05-10 at 15:35 -0400, Frédéric Bastien wrote:
I'm trying to do it, but each time I want to test something, it takes
a long time to rebuild numpy to test it. Is there a way to don't
recompile everything for each test?
Are you using current master? It defaults to use
thanks, I'll look at it.
I made a PR: https://github.com/numpy/numpy/pull/3324
Where should I put the tests about this?
thanks
Fred
On Fri, May 10, 2013 at 4:03 PM, Sebastian Berg
sebast...@sipsolutions.netwrote:
On Fri, 2013-05-10 at 15:35 -0400, Frédéric Bastien wrote:
I'm trying to do
On Fri, May 10, 2013 at 9:41 PM, KACVINSKY Tom tom.kacvin...@3ds.comwrote:
Here is my set up:
Mac OS 10.7.5
Xcode 4.5.1
Intel Fortran 12.1
Python 2.7.3 built from source
Numpy 1.6.2 built from source, using MKL 11.0
nose 0.11.4 installed
I run the numpy tests as documented (python -c
np.array ((0,0))
Out[10]: array([0, 0]) ok, it's 2 dimensional
In [11]: np.array ((0,0)).shape
Out[11]: (2,) except, it isn't
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Hi Neal,
On Fri, May 10, 2013 at 7:36 PM, Neal Becker ndbeck...@gmail.com wrote:
np.array ((0,0))
Out[10]: array([0, 0]) ok, it's 2 dimensional
Think you may have confused yourself :-). It's 1 dimensional with 2 elements...
In [11]: np.array ((0,0)).shape
Out[11]: (2,) except, it isn't
Neal Becker wrote:
np.array ((0,0))
Out[10]: array([0, 0]) ok, it's 2 dimensional
In [11]: np.array ((0,0)).shape
Out[11]: (2,) except, it isn't
Sorry for the stupid question - please ignore
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It would be convenient if in arithmetic 0-d arrays were just ignored - it would
seem to me to be convenient in generic code where a degenerate array is treated
as nothing
np.zeros ((0,0)) + np.ones ((2,2))
---
ValueError
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