2017-02-22 16:23 GMT+01:00 Alex Rogozhnikov :
> Hi Francesc,
> thanks a lot for you reply and for your impressive job on bcolz!
>
> Bcolz seems to make stress on compression, which is not of much interest
> for me, but the *ctable*, and chunked operations look very
2016-12-22 17:44 GMT+01:00 Nicolas P. Rougier :
>
> Dear all,
>
> I've just put online a (kind of) book on Numpy and more specifically about
> vectorization methods. It's not yet finished, has not been reviewed and
> it's a bit rough around the edges. But I think there
2016-02-20 20:13 GMT+01:00 David Cournapeau <courn...@gmail.com>:
>
>
> On Sat, Feb 20, 2016 at 5:26 PM, Kiko <kikocorre...@gmail.com> wrote:
>
>>
>>
>> 2016-02-20 17:58 GMT+01:00 Ralf Gommers <ralf.gomm...@gmail.com>:
>>
>>>
>&
2016-02-20 17:58 GMT+01:00 Ralf Gommers :
>
>
> On Wed, Feb 17, 2016 at 9:46 PM, Sebastian Berg <
> sebast...@sipsolutions.net> wrote:
>
>> On Mi, 2016-02-17 at 20:59 +0100, Jaime Fernández del Río wrote:
>> > Hi all,
>> >
>> > I just found out there is a PyData Madrid
is it python3.5 compatible? your message and github don't say the same.
2016-01-23 19:12 GMT+01:00, Charles R Harris :
>
>
> Congratulations.
>
> Chuck
>
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BTW, congratulations and thanks for the hard work
2016-01-23 20:12 GMT+01:00, Kiko <kikocorre...@gmail.com>:
> is it python3.5 compatible? your message and github don't say the same.
>
> 2016-01-23 19:12 GMT+01:00, Charles R Harris <charlesr.har...@gmail.com>:
>
2015-10-02 9:38 GMT+02:00 Alex Rogozhnikov :
> I would suggest
>>
>> %matplotlib notebook
>>
>> It will still have to a nice png, but you get an interactive figure when
>> it is live.
>>
>
> Amazing, thanks. I was using mpld3 for this.
> (for some strange reason I need
2015-10-02 9:48 GMT+02:00 Kiko <kikocorre...@gmail.com>:
>
>
> 2015-10-02 9:38 GMT+02:00 Alex Rogozhnikov <alex.rogozhni...@yandex.ru>:
>
>> I would suggest
>>>
>>> %matplotlib notebook
>>>
>>> It will still have to
2015-03-24 11:02 GMT+01:00 questions anon questions.a...@gmail.com:
I would like to find the nearest coord in a netcdf from a given latitude
and longitude.
I found some fantastic code that does this -
, Kiko kikocorre...@gmail.com wrote:
Hi all,
I am trying to calculate a Hessian. I am using numdifftools for this (
https://pypi.python.org/pypi/Numdifftools).
My question is, is it possible to make it using pure numpy?.
The actual code is like this:
*import numdifftools as nd*
*import
2014-08-08 16:37 GMT+02:00 Eelco Hoogendoorn hoogendoorn.ee...@gmail.com:
Do it in pure numpy? How about copying the source of numdifftools?
Of course it is a solution. I was just wondering if it exist something
similar in the numpy/scipy packages so I do not have to use a new third
party
Hi all,
I am trying to calculate a Hessian. I am using numdifftools for this (
https://pypi.python.org/pypi/Numdifftools).
My question is, is it possible to make it using pure numpy?.
The actual code is like this:
*import numdifftools as nd*
*import numpy as np*
*def log_likelihood(params):*
2012/2/23 Vincent Schut sc...@sarvision.nl
On 02/22/2012 10:45 PM, Chao YUE wrote:
Hi all,
Is anyone using some python geospatial package that can do jobs like
intersection, etc. the job is like you automatically extract a region
on a global map etc.
thanks and cheers,
Chao
2012/2/13 Andrea Gavana andrea.gav...@gmail.com
-- Forwarded message --
From: Andrea Gavana andrea.gav...@gmail.com
Date: Feb 13, 2012 11:31 PM
Subject: Re: [Numpy-discussion] Creating parallel curves
To: Jonathan Hilmer jkhil...@gmail.com
Thank you Jonathan for this, it's
Quoting Chao YUE chaoyue...@gmail.com:
Dear all,
I want to change some variable values in a series of NetCDF file. Did
anybody else did this before using python?
Now I use pupynere for reading data from NetCDF files and making plots.
but
the document of pupynere for writing data to
Hi.
I'm trying to read a big netcdf file (445 Mb) using netcdf4-python.
The data are described as:
*The GEBCO gridded data set is stored in NetCDF as a one dimensional array
of 2-byte signed integers that represent integer elevations in metres.
The complete data set gives global coverage. It
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