Thank you Camilo,
It is indeed an easy way and also the link is a ready to
use save method. Thanks a lot for sharing it
with best regards,
Sudheer
***
Sudheer Joseph
Indian National Centre for Ocean Information
On Sun, Jun 16, 2013 at 6:40 PM, Eric Firing wrote:
> Github issues 611, 629, and 2490 are duplicates. 611 included patches
> with a test and a fix, both of which were committed long ago, so all
> three issues should be closed.
>
> Please see my comment on 2264 as to why that should be closed.
>
Github issues 611, 629, and 2490 are duplicates. 611 included patches
with a test and a fix, both of which were committed long ago, so all
three issues should be closed.
Please see my comment on 2264 as to why that should be closed.
On 1417, please remove the "component:numpy.ma" label and add
On Sun, Jun 16, 2013 at 10:57 PM, Eric Firing wrote:
> What is the preferred strategy for handling bug fix PRs? Initial fix on
> master, and then a separate PR to backport to v1.7.x? Or the reverse?
> It doesn't look like v1.7.x is being merged into master regularly, so
> the matplotlib pattern
What is the preferred strategy for handling bug fix PRs? Initial fix on
master, and then a separate PR to backport to v1.7.x? Or the reverse?
It doesn't look like v1.7.x is being merged into master regularly, so
the matplotlib pattern (fix on maintenance, merge maintenance into
master) seems
Hi!
I know it is pretty much the same as you did before, but has been useful
for me in the past. Instead of saving each array separately, just create a
dictionary and save the it, something like
d= {put_all_your_arrays_here}
savez_compressed('file.npz', **d)
-- Camilo Jiménez
On Sat, Jun 15,
On Sun, Jun 16, 2013 at 12:56 PM, Warren Weckesser <
warren.weckes...@gmail.com> wrote:
> With Python 3.3.2 (64 bit), and numpy master:
>
> >>> import numpy as np
> >>> np.__version__
> '1.8.0.dev-2a5c2c8'
>
> >>> f = np.float64(1.0)
> >>> i = 2**65
> >>> f*i
> Traceback (most recent call last):
>
With Python 3.3.2 (64 bit), and numpy master:
>>> import numpy as np
>>> np.__version__
'1.8.0.dev-2a5c2c8'
>>> f = np.float64(1.0)
>>> i = 2**65
>>> f*i
Traceback (most recent call last):
File "", line 1, in
TypeError: unsupported operand type(s) for *: 'numpy.float64' and 'int'
Is this the
Here is a solution I got.
I think I need to do the rest with loop. Though I am seeing an interpnd I could
not find a documentation on that or example.
http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.RectBivariateSpline.html#scipy.interpolate.RectBivariateSpline
Thank you
wi
Thank you,
No if the location ( space time or depth) of choice is not
available then the function I was looking for should give an interpolated value
at the choice.
with best regards,
Sudheer
- Original Message -
> From: Henry Gomersall
> To: Discussion of Numerical Py
On Sun, 2013-06-16 at 14:48 +0800, Sudheer Joseph wrote:
> Is it possible to sample a 4D array of numpy at given dimensions with
> out writing loops? ie a smart python way?
It's not clear how what you want to do is different from simply indexing
the array...?
Henry
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