Re: [Numpy-discussion] Help with bit arrays

[Paul Hobson]

Does using pyplot.imgread work?

On Fri, Apr 29, 2016 at 8:27 AM, Henrique Almeida 
wrote:

>  Any help with this problem ?
>
> 2016-04-27 11:35 GMT-03:00 Henrique Almeida :
> >  Hello, what's the current status on numpy for loading bit-arrays ?
> >
> > I'm currently unable to correctly load black and white (1-bit) TIFF
> > images. Code example follows:
> >
> > from PIL import Image
> > import numpy
> > from matplotlib import pyplot
> >
> > img = Image.open('oi-00.tiff')
> > a = numpy.array(img)
> >
> > ^ does not work for 1-bit TIFF images
> >
> > PIL source shows that it incorrectly uses typestr == '|b1'. I tried to
> > change this to '|t1', but I get :
> >
> > TypeError: data type "|t1" not understood
> >
> > My goal is to make the above code to work for black and white TIFF
> > images the same way it works for grayscale images. Any help ?
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] (no subject)

[Paul Hobson]

Saumyajit,

Numpy's source code is hosted on Github. You can find the contributing
guides there:
https://github.com/numpy/numpy/blob/master/CONTRIBUTING.md

-paul

On Tue, Apr 26, 2016 at 2:35 AM, Saumyajit Dey <
dsaumya...@student.nitw.ac.in> wrote:

> Hi,
>
> This is Saumyajit Dey and I am looking forward to start contributing to
> NumPy.
>
> I have never contributed to any open source projects before so I would
> want to know some tips and guidelines to start contributing.
>
> Regards,
> Saumyajit
>
> Saumyajit Dey
> Junior Undergraduate Student:
> Department of Computer Science and Engineering
> National Institute of Technology
> Warangal (NITW), India
> Cell: +91-8885847028
>
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] linux wheels coming soon

[Paul Hobson]

On Thu, Apr 14, 2016 at 12:07 PM, Nathaniel Smith  wrote:

> On Apr 14, 2016 11:11 AM, "Benjamin Root"  wrote:
> >
> > Are we going to have to have documentation somewhere making it clear
> that the numpy wheel shouldn't be used in a conda environment? Not that I
> would expect this issue to come up all that often, but I could imagine a
> scenario where a non-scientist is simply using a base conda distribution
> because that is what IT put on their system. Then they do "pip install
> ipython" that indirectly brings in numpy (through the matplotlib
> dependency), and end up with an incompatible numpy because they would have
> been linked against different pythons?
> >
> > Or is this not an issue?
>
> There are always issues when you have two different package managers
> maintaining separate and out-of-sync metadata about what they think is
> installed, but that's true for any mixed use of conda and pip.
>
> But:
> - pip won't install a numpy that is incompatible with your python, unless
> Anaconda is actively breaking cpython's standard abi (they aren't) or
> there's a bug in pip (possible, but no reports yet).
> - conda packages for python packages like numpy do generally include the
> .egg-info / .dist-info directories that pip uses to store its installation
> metadata, so pip can "see" packages installed by conda (but not
> vice-versa). So "pip install matplotlib" won't drag in a pypi numpy if
> there's already a conda numpy installed.
>
Minor clarification:. I believe conda can see pip-installed packages.

If I execute "conda list" in an environment, I can see packaged installed
by both pip, conda, and locally (i.e., "pip install . -e").

-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] [Suggestion] Labelled Array

[Paul Hobson]

Just for posterity -- any future readers to this thread who need to do
pandas-like on record arrays should look at matplotlib's mlab submodule.

I've been in situations (::cough:: Esri production ::cough::) where I've
had one hand tied behind my back and unable to install pandas. mlab was a
big help there.

https://goo.gl/M7Mi8B

-paul



On Mon, Feb 15, 2016 at 1:28 PM, Lluís Vilanova  wrote:

> Benjamin Root writes:
>
> > Seems like you are talking about xarray:
> https://github.com/pydata/xarray
>
> Oh, I wasn't aware of xarray, but there's also this:
>
>
> https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/user_guide/data.html#basic-indexing
>
> https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/user_guide/data.html#dimension-oblivious-indexing
>
>
> Cheers,
>   Lluis
>
>
>
> > Cheers!
> > Ben Root
>
> > On Fri, Feb 12, 2016 at 9:40 AM, Sérgio  wrote:
>
> > Hello,
>
>
> > This is my first e-mail, I will try to make the idea simple.
>
>
> > Similar to masked array it would be interesting to use a label array
> to
> > guide operations.
>
>
> > Ex.:
>  x
> > labelled_array(data =
>
> > [[0 1 2]
> > [3 4 5]
> > [6 7 8]],
> > label =
> > [[0 1 2]
> > [0 1 2]
> > [0 1 2]])
>
>
>  sum(x)
> > array([9, 12, 15])
>
>
> > The operations would create a new axis for label indexing.
>
>
> > You could think of it as a collection of masks, one for each label.
>
>
> > I don't know a way to make something like this efficiently without a
> loop.
> > Just wondering...
>
>
> > Sérgio.
>
> > ___
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
>
>
> > ___
> > NumPy-Discussion mailing list
> > NumPy-Discussion@scipy.org
> > https://mail.scipy.org/mailman/listinfo/numpy-discussion
> ___
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Proposal: stop providing official win32 downloads (for now)

[Paul Hobson]

On Tue, Dec 22, 2015 at 1:22 PM, Paul Hobson <pmhob...@gmail.com> wrote:

>
>
> On Tue, Dec 22, 2015 at 11:11 AM, Chris Barker <chris.bar...@noaa.gov>
> wrote:
>>
>>
>> Any way to know how many people are running 32 bit Python on Windows
>> these days??
>>
>> -CHB
>>
>>
> FWIW, most ArcGIS user are probably using 32-bit Windows unless they view
> python as more than "just that thing with the chevron the nerd keeps
> telling me to learn."
>

Sorry, I meant 32-bit python *on Windows*, since that's what Esri ships by
default.

I know how to make ArcWhatever use 64-bit python, but I'd say most don't.
(And I do update the numpy that comes with Esri's python).
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Proposal: stop providing official win32 downloads (for now)

[Paul Hobson]

On Tue, Dec 22, 2015 at 11:11 AM, Chris Barker 
wrote:
>
>
> Any way to know how many people are running 32 bit Python on Windows these
> days??
>
> -CHB
>
>
FWIW, most ArcGIS user are probably using 32-bit Windows unless they view
python as more than "just that thing with the chevron the nerd keeps
telling me to learn."
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] When to stop supporting Python 2.6?

[Paul Hobson]

On Thu, Dec 10, 2015 at 4:20 AM, Julian Taylor <
jtaylor.deb...@googlemail.com> wrote:

> On 12/09/2015 12:10 AM, Ralf Gommers wrote:
>
>>
>>
>> On Wed, Dec 9, 2015 at 12:01 AM, Chris Barker > > wrote:
>>
>> drop 2.6
>>
>> I still don't understand why folks insist that they need to run a
>> (very)) old python on an old OS, but need the latest and greatest
>> numpy.
>>
>> Chuck's list was pretty long and compelling.
>>
>> -CHB
>>
>>
>>
>> On Mon, Dec 7, 2015 at 1:38 AM, Sturla Molden
>> > wrote:
>>
>> Charles R Harris > > wrote:
>>
>> > As a strawman proposal, how about dropping moving to 2.7 and
>> 3.4 minimum
>> > supported version next fall, say around numpy 1.12 or 1.13
>> depending on how
>> > the releases go.
>> >
>> > I would like to here from the scipy folks first.
>>
>>
>> +1 for dropping Python 2.6, 3.2 and 3.3 after branching 1.11.x. We're
>> already behind other projects like ipython, pandas and matplotlib as
>> usual, so there really isn't much point in being the only project
>> (together with scipy) of the core stack to keep on supporting more or
>> less obsolete Python versions.
>>
>> Ralf
>>
>
>
> I don't see how that is a relevant point. NumPy is the lowest component of
> the stack, we have to be the last to drop support for Python 2.6. And we
> aren't yet the last even when only looking at the high profile components.
> Astropy still supports 2.6 for another release.
> Though by the time 1.11 comes out we might be so I'm ok with dropping it
> after that even when I'm not convinced we gain anything significant from
> doing so.


Purely from a user-perspective, I don't understand why the numpy team would
want to continue support Python <= 2.6 and <= 3.3. The old versions of
numpy aren't going anywhere, so they can still be used if, for example,
you're stuck on a 6-yr old license of ArcGIS, and therefore stuck on Python
2.6

I started using Python with version 2.4 or 2.5 and there was zero
discussion about supporting old Python 1.X versions then. I know those
situations are aren't directly comparable, but when can we let the past go?
-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Automatic number of bins for numpy histograms

[Paul Hobson]

On Tue, Apr 14, 2015 at 4:24 PM, Jaime Fernández del Río 
jaime.f...@gmail.com wrote:

 On Tue, Apr 14, 2015 at 4:12 PM, Nathaniel Smith n...@pobox.com wrote:

 On Mon, Apr 13, 2015 at 8:02 AM, Neil Girdhar mistersh...@gmail.com
 wrote:
  Can I suggest that we instead add the P-square algorithm for the dynamic
  calculation of histograms?
  (
 http://pierrechainais.ec-lille.fr/Centrale/Option_DAD/IMPACT_files/Dynamic%20quantiles%20calcultation%20-%20P2%20Algorythm.pdf
 )
 
  This is already implemented in C++'s boost library
  (
 http://www.boost.org/doc/libs/1_44_0/boost/accumulators/statistics/extended_p_square.hpp
 )
 
  I implemented it in Boost Python as a module, which I'm happy to share.
  This is much better than fixed-width histograms in practice.  Rather
 than
  adjusting the number of bins, it adjusts what you really want, which is
 the
  resolution of the bins throughout the domain.

 This definitely sounds like a useful thing to have in numpy or scipy
 (though if it's possible to do without using Boost/C++ that would be
 nice). But yeah, we should leave the existing histogram alone (in this
 regard) and add a new name for this like adaptive_histogram or
 something. Then you can set about convincing matplotlib and friends to
 use it by default :-)


 Would having a negative number of bins mean this many, but with optimized
 boundaries be too clever an interface?


As a user, I think so. Wouldn't np.histogram(..., adaptive=True) do well
enough?
-p
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] [pydata] Pandas v0.16.0 release candidate 1

[Paul Hobson]

Thanks for the all the hard work! Really looking forward to using the
`assign` method in long chained statements.

-Paul

On Fri, Mar 13, 2015 at 8:33 AM, Jeff Reback jeffreb...@gmail.com wrote:

 Hi,

 I'm pleased to announce the availability of the first release candidate of
 Pandas 0.16.0.
 Please try this RC and report any issues here: Pandas Issues
 https://github.com/pydata/pandas/issues
 We will be releasing officially in 1 week or so.

 This is a major release from 0.15.2 and includes a small number of API
 changes, several new features, enhancements, and performance improvements
 along with a large number of bug fixes. We recommend that all users upgrade
 to this version.

- Highlights include:
   - DataFrame.assign method, see *here*
   
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0160-enhancements-assign
   - Series.to_coo/from_coo methods to interact with scipy.sparse, see
   *here*
   
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0160-enhancements-sparse
   - Backwards incompatible change to Timedelta to conform the .seconds 
 attribute
   with datetime.timedelta, see *here*
   
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0160-api-breaking-timedelta
   - Changes to the .loc slicing API to conform with the behavior of
   .ix see *here
   
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#indexing-changes*
   - Changes to the default for ordering in the Categorical constructor,
   see *here
   
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0160-api-breaking-categorical*


 Here are the full whatsnew and documentation links:
 v0.16.0 Whatsnew
 http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html

 Source tarballs, windows builds, and mac wheels are available here:

 Pandas v0.16.0rc1 Release https://github.com/pydata/pandas/releases

 A big thank you to everyone who contributed to this release!

 Jeff

 --
 You received this message because you are subscribed to the Google Groups
 PyData group.
 To unsubscribe from this group and stop receiving emails from it, send an
 email to pydata+unsubscr...@googlegroups.com.
 For more options, visit https://groups.google.com/d/optout.

___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Would like to patch docstring for numpy.random.normal

[Paul Hobson]

On Mon, Mar 9, 2015 at 4:33 PM, Charles R Harris charlesr.har...@gmail.com
wrote:



 On Mon, Mar 9, 2015 at 2:34 PM, Paul Hobson pmhob...@gmail.com wrote:

 I feel your pain. Making it worse, numpy.random.lognormal takes mean
 and sigma as input. If there's ever a backwards incompatible release, I
 hope these things will be cleared up.


 There is a numpy 2.0 milestone ;)


Is it worth submitting PRs against the existing 2.X branch or is that so
far away that the can should be kicked down the road?
-Paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Would like to patch docstring for numpy.random.normal

[Paul Hobson]

On Tue, Mar 10, 2015 at 11:22 AM, Nathaniel Smith n...@pobox.com wrote:

 On Mar 10, 2015 11:15 AM, Paul Hobson pmhob...@gmail.com wrote:
 
 
  On Mon, Mar 9, 2015 at 4:33 PM, Charles R Harris 
 charlesr.har...@gmail.com wrote:
 
 
 
  On Mon, Mar 9, 2015 at 2:34 PM, Paul Hobson pmhob...@gmail.com wrote:
 
  I feel your pain. Making it worse, numpy.random.lognormal takes mean
 and sigma as input. If there's ever a backwards incompatible release, I
 hope these things will be cleared up.
 
 
  There is a numpy 2.0 milestone ;)
 
 
  Is it worth submitting PRs against the existing 2.X branch or is that so
 far away that the can should be kicked down the road?

 Not sure what you mean by the existing 2.X branch (does such a thing
 exist somewhere?), but yeah, don't submit PRs like that. Best case they'd
 bit rot before we ever get around to 2.0, worst case 2.0 may never happen.
 (What, you liked python 2 - 3 so much you want to go through that again?)


It's been a while, but last time I build numpy from the master on github,
numpy.__version__ came back as 2.dev or something like that. So I always
assumed there was a 2.X branch.

It's been a while since I've built numpy, but clearly I'm mistaken.
-p
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Would like to patch docstring for numpy.random.normal

[Paul Hobson]

I feel your pain. Making it worse, numpy.random.lognormal takes mean and
sigma as input. If there's ever a backwards incompatible release, I hope
these things will be cleared up.



On Tue, Mar 3, 2015 at 9:59 PM, Daniel Sank sank.dan...@gmail.com wrote:

 Sturia,

  Change the name of keyword arguments?
  This would be evil.

 Yes, I see this now. It is really a shame these were defined as keyword
 arguments. I've shown the docstring to a few people and so far they all
 agree that it is unnecessarily convoluted.

 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion


___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Subdividing NumPy array into Regular Grid

[Paul Hobson]

I think you want np.meshgrid
-paul

On Sun, Oct 26, 2014 at 2:09 AM, Artur Bercik vbubbl...@gmail.com wrote:

 I have a rectangle with the following coordinates:

 import numpy as np

 ulx,uly = (110, 60) ##uppper left lon, upper left lat
 urx,ury = (120, 60) ##uppper right lon, upper right lat
 lrx, lry = (120, 50) ##lower right lon, lower right lat
 llx, lly = (110, 50) ##lower left lon, lower left lat

 I want to divide that single rectangle into 100 regular grids inside that,
 and want to calculate the (ulx, uly), (urx,ury), (lrx, lry), and (llx, lly)
 for each grid separately:

 lats = np.linspace(60, 50, 10)
 lons = np.linspace(110, 120, 10)

 lats = np.repeat(lats,10).reshape(10,10)
 lons = np.tile(lons,10).reshape(10,10)

 I could not imagine what to do then?

 Is somebody familiar with such kind of problem?

 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion


___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] [SciPy-Dev] Hamming etc. windows are wrong

[Paul Hobson]

On Fri, Sep 26, 2014 at 3:39 AM, Jerry lancebo...@qwest.net wrote:


 On Sep 26, 2014, at 2:56 AM, Daπid davidmen...@gmail.com wrote:


 On 26 September 2014 10:41, Jerry lancebo...@qwest.net wrote:

  I am just learning Python-NumPy-SciPy but it appears as though the
 SciPy situation is that the documentation page above *** mentions the flag
 but the flag has not been implemented into SciPy itself. I would be glad to
 stand corrected.


 Hmm Earlier I was getting a Python error when I tried to supply the
 second argument, sym. That was the reason for my comment but now it accepts
 the second argument without complaint.


Just curious: had you imported pylab at any point? You sample code looks
like you did several from xxx import * and it might have clobbered the
scipy version (or vise versa).
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Generalize hstack/vstack -- stack; Block matrices like in matlab

[Paul Hobson]

Hey Ben,

Side note: I've had to do the same thing for stitching curvilinear model
grid coordinates together. Usings pandas DataFrames indexed by `i` and `j`
is really good for this. You can offset the indices directly, unstack the
DF, and the pandas will align for you.

Happy to send an example along if you're curious.
-p

On Mon, Sep 8, 2014 at 9:55 AM, Benjamin Root ben.r...@ou.edu wrote:

 A use case would be image stitching or even data tiling. I have had to
 implement something like this at work (so, I can't share it, unfortunately)
 and it even goes so far as to allow the caller to specify how much the
 tiles can overlap and such. The specification is ungodly hideous and I
 doubt I would be willing to share it even if I could lest I release
 code-thulu upon the world...

 I think just having this generalize stack feature would be nice start.
 Tetris could be built on top of that later. (Although, I do vote for at
 least 3 or 4 dimensional stacking, if possible).

 Cheers!
 Ben Root


 On Mon, Sep 8, 2014 at 12:41 PM, Eelco Hoogendoorn 
 hoogendoorn.ee...@gmail.com wrote:

 Sturla: im not sure if the intention is always unambiguous, for such more
 flexible arrangements.

 Also, I doubt such situations arise often in practice; if the arrays arnt
 a grid, they are probably a nested grid, and the code would most naturally
 concatenate them with nested calls to a stacking function.

 However, some form of nd-stack function would be neat in my opinion.

 On Mon, Sep 8, 2014 at 6:10 PM, Jaime Fernández del Río 
 jaime.f...@gmail.com wrote:

 On Mon, Sep 8, 2014 at 7:41 AM, Sturla Molden sturla.mol...@gmail.com
 wrote:

 Stefan Otte stefan.o...@gmail.com wrote:

  stack([[a, b], [c, d]])
 
  In my case `stack` replaced `hstack` and `vstack` almost completely.
 
  If you're interested in including it in numpy I created a pull request
  [1]. I'm looking forward to getting some feedback!

 As far as I can see, it uses hstack and vstack. But that means a and b
 have
 to have the same number of rows, c and d must have the same rumber of
 rows,
 and hstack((a,b)) and hstack((c,d)) must have the same number of
 columns.

 Thus it requires a regularity like this:

 BB
 BB
 CCCDDD
 CCCDDD
 CCCDDD
 CCCDDD

 What if we just ignore this constraint, and only require the output to
 be
 rectangular? Now we have a 'tetris game':

 BB
 BB
 BB
 BB
 DD
 DD

 or

 BB
 BB
 BB
 BB
 BB
 BB

 This should be 'stackable', yes? Or perhaps we need another stacking
 function for this, say numpy.tetris?

 And while we're at it, what about higher dimensions? should there be an
 ndstack function too?


 This is starting to look like the second time in a row Stefan tries to
 extend numpy with a simple convenience function, and he gets tricked into
 implementing some sophisticated algorithm...

 For his next PR I expect nothing less than an NP-complete problem. ;-)


 Jaime

 --
 (\__/)
 ( O.o)
 (  ) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus
 planes de dominación mundial.

 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion



 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion



 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion


___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] UNSUBSCRIBE Re: MKL + CPU, GPU + cuBLAS comparison

[Paul Hobson]

We can't manage your account for you. Click here:
http://mail.scipy.org/mailman/listinfo/numpy-discussion
to unsunscribe yourself.
-paul


On Tue, Nov 26, 2013 at 5:42 AM, regikeyz . regi.pub...@gmail.com wrote:

 UNSUBSCRIBE


 On 26 November 2013 13:37, Frédéric Bastien no...@nouiz.org wrote:

 We have such benchmark in Theano:


 https://github.com/Theano/Theano/blob/master/theano/misc/check_blas.py#L177

 HTH

 Fred

 On Tue, Nov 26, 2013 at 7:10 AM, Dinesh Vadhia
 dineshbvad...@hotmail.com wrote:
  Jerome, Thanks for the swift response and tests.  Crikey, that is a
  significant difference at first glance.  Would it be possible to
 compare a
  BLAS computation eg. matrix-vector or matrix-matrix calculation? Thx!
 
  ___
  NumPy-Discussion mailing list
  NumPy-Discussion@scipy.org
  http://mail.scipy.org/mailman/listinfo/numpy-discussion
 
 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion



 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion


___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] How to compile numpy on ubuntu

[Paul Hobson]

On Tue, Aug 13, 2013 at 10:03 AM, Peng Yu pengyu...@gmail.com wrote:

 Hi,

 The instructions for compiling numpy on ubuntu seems to be very
 outdated. Could anybody provide an updated instruction on installing
 numpy? Thanks.

 http://docs.scipy.org/doc/numpy/user/install.html


The latest instructions should still work fine. I didn't have any problems
on Ubuntu 12.04
-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Please stop bottom posting!!

[Paul Hobson]

On Wed, Apr 3, 2013 at 4:28 PM, Doug Coleman doug.cole...@gmail.com wrote:


 Also, gmail bottom-posts by default. It's transparent to gmail users.
 I'd imagine they are some of the biggest offenders.


Interesting. Mine go to the top by default and I always have to expand the
quoted text, trim down as necessary, and then reply below the relevant
bits. A quick gander at gmail's setting doesn't offer anything obvious.
I'll dig deeper later.
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] error of install numpy on linux redhat.

[Paul Hobson]

On Wed, Oct 24, 2012 at 9:01 PM, Jack Bryan dtustud...@hotmail.com wrote:
 Hi, All,

 I am trying to install numpy  from http://www.scipy.org/Download .

 by

 git clone git://github.com/numpy/numpy.git numpy


 But, when I ran

 python setup.py install

 I got:

 SystemError: Cannot compile 'Python.h'. Perhaps you need to install
 python-dev|python-devel

 Where to get python-dev ?

 I tried:

 $ easy_install python-devel
 Searching for python-devel
 Reading http://pypi.python.org/simple/python-devel/
 Couldn't find index page for 'python-devel' (maybe misspelled?)
 Scanning index of all packages (this may take a while)
 Reading http://pypi.python.org/simple/
 No local packages or download links found for python-devel
 error: Could not find suitable distribution for
 Requirement.parse('python-devel')

 and

 $ easy_install python-dev
 Searching for python-dev
 Reading http://pypi.python.org/simple/python-dev/
 Couldn't find index page for 'python-dev' (maybe misspelled?)
 Scanning index of all packages (this may take a while)
 Reading http://pypi.python.org/simple/
 No local packages or download links found for python-dev
 error: Could not find suitable distribution for
 Requirement.parse('python-dev')

Jack,

Since you don't have admin privileges, I'm going to emphatically
recommend that you use AnacondaCE.

Sine you're on RedHat, it's as simple as:
$ curl 
http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/AnacondaCE-1.1-linux.sh
 anaconda.sh
$ bash anaconda.sh

Follow the prompts and you're done with a lot more than just Numpy.
-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Announcing Anaconda version 1.1

[Paul Hobson]

On Fri, Oct 19, 2012 at 9:10 AM, Travis Oliphant tra...@continuum.io wrote:
 I just wanted to let everyone know about our new release of Anaconda which
 now has Spyder and Matplotlib working for Mac OS X and Windows.

 [snip]
 For more information, to download a trial version of Anaconda Pro, or
 download the completely free Anaconda CE, click here.

Travis,

Just downloaded the linux version of this to try out on a server. When
I try to execute the bash script, I get the following message:

-bash-3.2$ ./AnacondaCE-1.1-linux.sh
ERROR: size of AnacondaCE-1.1-linux.sh should be 248021729 bytes

And the file actually is...
-bash-3.2$ ls -l
total 242452
-rwx--x--x 1 pnrfield pnrfield 248017374 Oct 19 16:39 AnacondaCE-1.1-linux.sh

Thanks,
-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Announcing Anaconda version 1.1

[Paul Hobson]

Sorry for the noise. Using curl to directly grab the file (instead of
copying from another machine) does the trick.
-paul

On Mon, Oct 22, 2012 at 9:50 AM, Paul Hobson pmhob...@gmail.com wrote:
 On Fri, Oct 19, 2012 at 9:10 AM, Travis Oliphant tra...@continuum.io wrote:
 I just wanted to let everyone know about our new release of Anaconda which
 now has Spyder and Matplotlib working for Mac OS X and Windows.

 [snip]
 For more information, to download a trial version of Anaconda Pro, or
 download the completely free Anaconda CE, click here.

 Travis,

 Just downloaded the linux version of this to try out on a server. When
 I try to execute the bash script, I get the following message:

 -bash-3.2$ ./AnacondaCE-1.1-linux.sh
 ERROR: size of AnacondaCE-1.1-linux.sh should be 248021729 bytes

 And the file actually is...
 -bash-3.2$ ls -l
 total 242452
 -rwx--x--x 1 pnrfield pnrfield 248017374 Oct 19 16:39 AnacondaCE-1.1-linux.sh

 Thanks,
 -paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Correlation code from NumPy 1.5 Beginner's Guide

[Paul Hobson]

On Sun, May 13, 2012 at 9:48 AM, Dinesh Prasad dprasad...@yahoo.com wrote:
 Hello. I am new to the list thanks for accepting my question.

 I am trying to run the attached code, directly from the book in the title.

 It simply calculates correlation of returns of the stock listed in the
 spreadsheets. could it be that the numPY library is not being recognized on
 my system for some reason?

 I installed the 32 bit version of python/numpy. This is to preserve
 compatibility with another application that will trigger the code. I am on
 Windows 7 64 bit Home Edition, however.

 Thanks for any suggestions.

 -Dinesh

Dinesh,

What errors are you getting? I didn't run your code, but from the
looks of things, you might want to set unpack=False as you read the
data.

In other words, instead of this:
 bhp = numpy.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True)

use this:
 bhp = numpy.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=False)

The unpack keyword argument makes the np.loadtxt function try to
dump each column into its own variable. It may support dumping the
data to a single tuple, but I don't remember that being the case.
-paul
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] What is consensus anyway

[Paul Hobson]

We're kind of drifting again here, but...

Remember when all this discussion happened on usenet? Perhaps we're in
yet another awkward transition period and soon all email list-type
discussions will be on Github, Bitbucket, StackOverflow (e.g. pandas),
etc.

There's advantages and disadvantages to any sort of discussion
paradigm, but I can imagine a future version of Github where each
project has a tab for a StackOverflow-esque forum. As a user, that all
sounds pretty appealing to me. But this is all just speculation and
conjecture...
-paul

On Wed, Apr 25, 2012 at 9:48 PM, Fernando Perez fperez@gmail.com wrote:
 On Wed, Apr 25, 2012 at 6:28 PM, Benjamin Root ben.r...@ou.edu wrote:
 It would be nice if every pull request created a message to this list.
  Is that even possible?

 -Travis


 This ha been a concern of mine for matplotlib as well.  The closest I can
 come is to set up an RSS feed, but all the titles are PR # and a action, so
 I lose track of which ones I want to view.

 Same here for IPython.  If anybody figures out a clean solution,
 please advertise it!  I think a bunch of us want the same thing...

 Cheers,

 f
 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Masked Arrays in NumPy 1.x

[Paul Hobson]

Travis et al,

This isn't a reply to anything specific in your email and I apologize
if there is a better thread or place to share this information. I've
been meaning to participate in the discussion for a long time and
never got around to it. The main thing I'd like to is convey my
typical use of the numpy.ma module as an environmental engineer
analyzing censored datasets --contaminant concentrations that are
either at well understood values (not masked) or some unknown value
below an upper bound (masked).

My basic understanding is that this discussion revolved around how to
treat masked data (ignored vs missing) and how to implement one, both,
or some middle ground between those two concepts. If I'm off-base,
just ignore all of the following.

For my purposes, numpy.ma is implemented in a way very well suited to
my needs. Here's a gist of a something that was *really* hard for me
before I discovered numpy.ma and numpy in general. (this is a bit
much, see below for the highlights)
https://gist.github.com/2361814

The main message here is that I include the upper bounds of the
unknown values (detection limits) in my array and use that to
statistically estimate their values. I must be able to retrieve the
masked detection limits throughout this process. Additionally the
masks as currently implemented allow me sort first the undetected
values, then the detected values (see __rosRanks in the gist).

As boots-on-the-ground user of numpy, I'm ecstatic that this tool
exists. I'm also pretty flexible and don't anticipated any major snags
in my work if things change dramatically as the masked/missing/ignored
functionality evolves.

Thanks to everyone for the hard work and great tools,
-Paul Hobson

On Mon, Apr 9, 2012 at 9:52 PM, Travis Oliphant tra...@continuum.io wrote:
 Hey all,

 I've been waiting for Mark Wiebe to arrive in Austin where he will spend 
 several weeks, but I also know that masked arrays will be only one of the 
 things he and I are hoping to make head-way on while he is in Austin.    
 Nevertheless, we need to make progress on the masked array discussion and if 
 we want to finalize the masked array implementation we will need to finish 
 the design.

 I've caught up on most of the discussion including Mark's NEP, Nathaniel's 
 NEP and other writings and the very-nice mailing list discussion that 
 included a somewhat detailed discussion on the algebra of IGNORED.   I think 
 there are some things still to be decided.  However, I think some things are 
 pretty clear:

        1) Masked arrays are going to be fundamental in NumPy and these should 
 replace most people's use of numpy.ma.   The numpy.ma code will remain as a 
 compatibility layer

        2) The reality of #1 and NumPy's general philosophy to date means that 
 masked arrays in NumPy should support the common use-cases of masked arrays 
 (including getting and setting of the mask from the Python and C-layers).  
 However, the semantic of what the mask implies may change from what numpy.ma 
 uses to having  a True value meaning selected.

        3) There will be missing-data dtypes in NumPy.   Likely only a limited 
 sub-set (string, bytes, int64, int32, float32, float64, complex64, complex32, 
 and object) with an API that allows more to be defined if desired.   These 
 will most likely use Mark's nice machinery for managing the calculation 
 structure without requiring new C-level loops to be defined.

        4) I'm still not sure about whether the IGNORED concept is necessary 
 or not.    I really like the separation that was emphasized between 
 implementation (masks versus bit-patterns) and operations (propagating versus 
 non-propagating).   Pauli even created another dimension which I don't 
 totally grok and therefore can't remember.   Pauli?  Do you still feel that 
 is a necessary construction?  But, do we need the IGNORED concept to indicate 
 what amounts to different default key-word arguments to functions that 
 operate on NumPy arrays containing missing data (however that is 
 represented)?    My current weak view is that it is not really necessary.   
 But, I could be convinced otherwise.

 I think the good news is that given Mark's hard-work and Nathaniel's 
 follow-up we are really quite far along.   I would love to get Nathaniel's 
 opinion about what remains un-done in the current NumPy code-base.   I would 
 also appreciate knowing (from anyone with an interest) opinions of items 1-4 
 above and anything else I've left out.

 Thanks,

 -Travis




 ___
 NumPy-Discussion mailing list
 NumPy-Discussion@scipy.org
 http://mail.scipy.org/mailman/listinfo/numpy-discussion
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion