From hturesson at gmail.com  Fri Aug  1 14:49:06 2014
From: hturesson at gmail.com (Hjalmar Turesson)
Date: Fri, 1 Aug 2014 20:49:06 +0200
Subject: [SciPy-User] Spatio-temporal interpolation
In-Reply-To: <CANtpxC5iwGbGAEB+q1jZShpg1Yvt+ZxSOHP6dLkYr4YCpD+AGQ@mail.gmail.com>
References: <CANtpxC5iwGbGAEB+q1jZShpg1Yvt+ZxSOHP6dLkYr4YCpD+AGQ@mail.gmail.com>
Message-ID: <CAA7EFhTUGN4bCB_PD6GeaX2HQzuvjQcBRA-87EqZwNk3wbSn9g@mail.gmail.com>

Hi Abrham,

You will need to get your excel data over to a numpy array. To do this, you
can save the excel sheets as .csv (comma separated values) and then read in
these files to Python using genfromtxt() (
http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html).
Make sure that you are using consistent delimiters (e.g. commas). Since
your data are in 50 different sheets you will probably need to concatenate
them into one 4-d numpy array containing all data or three arrays with
date, location and values separated.

I found a simple and clear example of 2-d Kriging interpolation using
Python here:
http://connor-johnson.com/2014/03/20/simple-kriging-in-python/

It seems to be possible to do this using sklearn too. Possibly 3-d.
http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcess.html

Yet, another example:
http://girs.googlecode.com/svn/trunk/maths/kriging/kriging.py

Good luck,
Hjalmar




On Wed, Jul 30, 2014 at 12:31 AM, Abrham Melesse <abrham09 at gmail.com> wrote:

>  Dear users,
> I am new to python and trying to work hard in to it.
> I am trying to interpolate daily station climate data to the defined grid
> points. I have more than 20 years of daily climate data for 50 stations
> that I want to interpolate to the daily grid point values. I could handle
> how to interpolate one spatial data in to grid points. My data is very
> bulky.
>
>  I organize my station data in one excel file with 50 sheets, each sheet
> is daily climate data for the station. The data in each sheet has 4 columns
> ( date, latitude, longitude, value/data)
>
> My grid points are given in another excel file that has 3 columns (Id,
> latitude, longitude)
>
>
>  I want the interpolation output be one excel/text file in the form as
> below. I prefer kriging interpolation if someone of you already have the
> code.
>
>
>  date value1 value2 ?.... valueN
>
>
>  Here Value 1 ...valueN are grid point interpolated values for each grid
> point Id from 1 to N.
>
> Thank you,
>
> Abraham
>
> --
> Abrham Melesse
> Arba Minch University
> Water Resource and Irrigation Engineering Department
> PO Box 2552
> Cell Phone +251 912491406
> Arba Minch
> Ethiopia
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at scipy.org
> http://mail.scipy.org/mailman/listinfo/scipy-user
>
>
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From zseder.hlt at gmail.com  Tue Aug  5 04:56:45 2014
From: zseder.hlt at gmail.com (Attila Zseder)
Date: Tue, 5 Aug 2014 10:56:45 +0200
Subject: [SciPy-User] parallelization of cdist with multiprocessing
Message-ID: <CAG=1KRRU4fMDOVdXJL4LvrNWwMWCvaVgbiRWEeQKfUyJzjTw0w@mail.gmail.com>

Hi,

I would like to run scipy.spatial.distance.cdist in parallel, and I created
a stackoverflow question about this.
http://stackoverflow.com/questions/25119316/scipy-parallel-cdist-with-multiprocessing
Do you have any suggestions?
Thank you!

Attila
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From david_baddeley at yahoo.com.au  Wed Aug  6 09:51:23 2014
From: david_baddeley at yahoo.com.au (David Baddeley)
Date: Wed, 6 Aug 2014 06:51:23 -0700
Subject: [SciPy-User] matlab "uigetfile" analog for ipython
In-Reply-To: <CANn2QUzRrd=8iOoGXvzi1tUJdJCcZLGAZ=nFUCO5un=woocA8Q@mail.gmail.com>
References: <CFE5A6CD.A182%maria-rosaria.antonelli@curie.fr>	<CAA7EFhTCHnjePc0VBS2uHX3zFBcznOBNWTfs2v49ZRvH5V8G7Q@mail.gmail.com>
	<CANn2QUzRrd=8iOoGXvzi1tUJdJCcZLGAZ=nFUCO5un=woocA8Q@mail.gmail.com>
Message-ID: <1407333083.70662.YahooMailNeo@web163903.mail.gq1.yahoo.com>

It will depend on which GUI framework you are using.?Hjalmar and Antonio have given answers for tk and Qt respectively. Under wx you'd use wx.FileDialog

e.g. (copy and pasted from a file I had open hence the slightly irrelevant message and wildcard):

import wx
fdialog = wx.FileDialog(None, 'Open 3D scene ...', wildcard='*.stl', style=wx.OPEN)
succ = fdialog.ShowModal()
? ? ? ??
if (succ == wx.ID_OK):
? ?fname = fdialog.GetPath().encode()

If you're using an iPython notebook in a browser (which it sounds like you might be), you're also going to need to tell iPython to start your gui backend of choice using a gui 'magic' - eg: %gui wx

cheers,
David


On Tuesday, 15 July 2014 6:22 AM, Antonino Ingargiola <tritemio at gmail.com> wrote:



Hi,

I use to define a function that uses Qt (either PySide or PyQt) to open a dialog:

try:
? ? from PySide import QtCore, QtGui
except ImportError:
? ? from PyQt4 import QtCore, QtGui

def gui_fname(dir=None):
? ? """Select a file via a dialog and returns the file name.
? ? """
? ? if dir is None: dir ='./'
? ? fname = QtGui.QFileDialog.getOpenFileName(None, "Select data file...",?
? ? ? ? ? ? dir, filter="All files (*)")
? ? return fname[0]


Antonino



On Sun, Jul 13, 2014 at 4:27 AM, Hjalmar Turesson <hturesson at gmail.com> wrote:

Hi,
>
>I used tkFileDialog for that type of thing.
>
>Hjalmar
>
>
>
>
>On Fri, Jul 11, 2014 at 9:39 AM, Antonelli Maria Rosaria <maria-rosaria.antonelli at curie.fr> wrote:
>
>Hi,?
>>
>>
>>Is there any way to select interactively the folder/directory where are stored the data that I want to analyse in my notebook.
>>Matlab command should be 'uigetfile'. This open a window and I select the file I want from this window.
>>
>>
>>Thank you very much.
>>Best,
>>Rosa
>>_______________________________________________
>>SciPy-User mailing list
>>SciPy-User at scipy.org
>>http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>>
>
>_______________________________________________
>SciPy-User mailing list
>SciPy-User at scipy.org
>http://mail.scipy.org/mailman/listinfo/scipy-user
>
>


_______________________________________________
SciPy-User mailing list
SciPy-User at scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user


From camillechambon at yahoo.fr  Thu Aug  7 04:32:56 2014
From: camillechambon at yahoo.fr (Camille)
Date: Thu, 7 Aug 2014 08:32:56 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?integrate=2Eode_sets_t0_values_outside_of_?=
	=?utf-8?q?my_data=09range?=
References: <1406642365.28550.YahooMailNeo@web28904.mail.ir2.yahoo.com>
	<loom.20140730T095843-555@post.gmane.org>
	<CAN3p1sWRoQRuJi717K=wL2wKPAjYd3faKz30KMepqSFCPkqDRg@mail.gmail.com>
Message-ID: <loom.20140807T102209-726@post.gmane.org>

Oleksandr Huziy <guziy.sasha <at> gmail.com> writes:

> 
> Salut Camille:
> 
> Just don't ask for the solution in the last point, since in order to
> calculate a value of the solution at that point it has to go a bit
> beyond (y should be defined from both sides of ti for the derivative
> to exist in ti (this is a necessary condition)), but interpolation
> does not really define values beyond the right limit.
> 
> Here I've modified your example:
>
http://nbviewer.ipython.org/github/guziy/PyNotebooks/blob/master/ode_demo.ipynb
> 
> Cheers
> 

Hello Oleksandr,

Thanks for your answer. 

But I do need the solution in the last point. 

Furthermore, in your modifications you redefine data(t) as: [1, 2, 3,
4]=data([0.,1.33333333,2.66666667,4.], which is not the same as: [1, 2, 3,
4]=data([0.,1.,2.,3.]. I need data(t) to remain as: [1, 2, 3,
4]=data([0.,1.,2.,3.].

Cheers,

Camille



From camillechambon at yahoo.fr  Thu Aug  7 05:05:19 2014
From: camillechambon at yahoo.fr (Camille)
Date: Thu, 7 Aug 2014 09:05:19 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?integrate=2Eode_sets_t0_values_outside_of_?=
	=?utf-8?q?my_data=09range?=
References: <1406642365.28550.YahooMailNeo@web28904.mail.ir2.yahoo.com>
	<loom.20140730T095843-555@post.gmane.org>
	<CAGzF1ueQxHpwM9dt+dwqk7wBC8uEb9UYD2ORo_=gnjHhUBhKYg@mail.gmail.com>
Message-ID: <loom.20140807T103328-717@post.gmane.org>

Warren Weckesser <warren.weckesser <at> gmail.com> writes:

> 
> Camille,
> 
> I added an answer to your question on StackOverflow:
http://stackoverflow.com/questions/25031966/integrate-ode-sets-t0-values-outside-of-my-data-range/
> 
> Summary:
> 
> * It is normal for the ODE solver to evaluate your function at points
beyond the last requested time value.
> 
> * One way to avoid the interpolation problem is to extend the
interpolation data linearly, using the last two data point.
> 
> Warren?
> 
> 

Hello Warren,

Thanks for your answer. Actually, data(t) is a sinusoidal function. Thus I
can not extend the interpolation data linearly. I edited my question on
StackOverflow accordingly.

Cheers,

Camille 




From davidmenhur at gmail.com  Thu Aug  7 07:13:25 2014
From: davidmenhur at gmail.com (=?UTF-8?B?RGHPgGlk?=)
Date: Thu, 7 Aug 2014 13:13:25 +0200
Subject: [SciPy-User] integrate.ode sets t0 values outside of my data
	range
In-Reply-To: <loom.20140807T103328-717@post.gmane.org>
References: <1406642365.28550.YahooMailNeo@web28904.mail.ir2.yahoo.com>
	<loom.20140730T095843-555@post.gmane.org>
	<CAGzF1ueQxHpwM9dt+dwqk7wBC8uEb9UYD2ORo_=gnjHhUBhKYg@mail.gmail.com>
	<loom.20140807T103328-717@post.gmane.org>
Message-ID: <CAJhcF=3=f=vZ76+gaxnFsLVSuKfVLKuwY51HYa2KnC7Yo_0JeQ@mail.gmail.com>

On 7 August 2014 11:05, Camille <camillechambon at yahoo.fr> wrote:

> Thanks for your answer. Actually, data(t) is a sinusoidal function. Thus I
> can not extend the interpolation data linearly. I edited my question on
> StackOverflow accordingly.


Note that you are evaluating it very close to the boundary, so the
interpolation effects will not be so bad. You can check the sensibility
comparing the results with a purposefully "wrong" interpolation, like the
same linear interpolation but with the opposite slope; but I bet the
differences are going to be slim.

If you know your data is sinusoidal, you can use that to make an even
better estimation of the next value. Essentially, you need to provide the
ODE solver with a way to estimate the derivatives of your function at any
point.


/David
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From warren.weckesser at gmail.com  Thu Aug  7 09:23:43 2014
From: warren.weckesser at gmail.com (Warren Weckesser)
Date: Thu, 7 Aug 2014 09:23:43 -0400
Subject: [SciPy-User] integrate.ode sets t0 values outside of my data
	range
In-Reply-To: <CAJhcF=3=f=vZ76+gaxnFsLVSuKfVLKuwY51HYa2KnC7Yo_0JeQ@mail.gmail.com>
References: <1406642365.28550.YahooMailNeo@web28904.mail.ir2.yahoo.com>
	<loom.20140730T095843-555@post.gmane.org>
	<CAGzF1ueQxHpwM9dt+dwqk7wBC8uEb9UYD2ORo_=gnjHhUBhKYg@mail.gmail.com>
	<loom.20140807T103328-717@post.gmane.org>
	<CAJhcF=3=f=vZ76+gaxnFsLVSuKfVLKuwY51HYa2KnC7Yo_0JeQ@mail.gmail.com>
Message-ID: <CAGzF1ueLBSkNLCGTzd+TGsBu7NLJh25pXbbL0bosuDW-iGmp3g@mail.gmail.com>

On 8/7/14, Da?id <davidmenhur at gmail.com> wrote:
> On 7 August 2014 11:05, Camille <camillechambon at yahoo.fr> wrote:
>
>> Thanks for your answer. Actually, data(t) is a sinusoidal function. Thus
>> I
>> can not extend the interpolation data linearly. I edited my question on
>> StackOverflow accordingly.
>
>
> Note that you are evaluating it very close to the boundary, so the
> interpolation effects will not be so bad. You can check the sensibility
> comparing the results with a purposefully "wrong" interpolation, like the
> same linear interpolation but with the opposite slope; but I bet the
> differences are going to be slim.
>
> If you know your data is sinusoidal, you can use that to make an even
> better estimation of the next value. Essentially, you need to provide the
> ODE solver with a way to estimate the derivatives of your function at any
> point.
>
>
> /David
>

I agree with David.  Use whatever extrapolation method is appropriate
for your function.  The main point is to expect your function to be
evaluated a little bit beyond the final time requested in odeint.

I updated my answer on StackOverflow with a suggestion to use either
the "dopri5" or "dop853" solver of the scipy.integrate.ode class.  It
appears that these solvers do not evaluate your function at times
beyond the requested time.  Check out the SO answer for the sample
code: http://stackoverflow.com/questions/25031966/integrate-ode-sets-t0-values-outside-of-my-data-range

Warren


From camillechambon at yahoo.fr  Fri Aug  8 04:32:07 2014
From: camillechambon at yahoo.fr (CamilleC)
Date: Fri, 8 Aug 2014 01:32:07 -0700 (PDT)
Subject: [SciPy-User] integrate.ode sets t0 values outside of my data
	range
In-Reply-To: <CAGzF1ueLBSkNLCGTzd+TGsBu7NLJh25pXbbL0bosuDW-iGmp3g@mail.gmail.com>
References: <1406642365.28550.YahooMailNeo@web28904.mail.ir2.yahoo.com>
	<loom.20140730T095843-555@post.gmane.org>
	<CAGzF1ueQxHpwM9dt+dwqk7wBC8uEb9UYD2ORo_=gnjHhUBhKYg@mail.gmail.com>
	<loom.20140807T103328-717@post.gmane.org>
	<CAJhcF=3=f=vZ76+gaxnFsLVSuKfVLKuwY51HYa2KnC7Yo_0JeQ@mail.gmail.com>
	<CAGzF1ueLBSkNLCGTzd+TGsBu7NLJh25pXbbL0bosuDW-iGmp3g@mail.gmail.com>
Message-ID: <1407486727716-19667.post@n7.nabble.com>

Thanks Warren and David.
The function to be integrated might not be sinusoidal all the time and it's
not possible to find an extrapolation method which is valid all the time.
Thus I will use the "dopri5" or "dop853" solver of the scipy.integrate.ode
class. 
Thanks again.
Camille



--
View this message in context: http://scipy-user.10969.n7.nabble.com/SciPy-User-integrate-ode-sets-t0-values-outside-of-my-data-range-tp19651p19667.html
Sent from the Scipy-User mailing list archive at Nabble.com.


From matthew.brett at gmail.com  Sat Aug  9 20:23:54 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Sat, 9 Aug 2014 17:23:54 -0700
Subject: [SciPy-User] [Numpy-discussion] ANN: NumPy 1.8.2 bugfix release
In-Reply-To: <53E6162A.8050809@googlemail.com>
References: <53E6162A.8050809@googlemail.com>
Message-ID: <CAH6Pt5rzrZtmnuee3zPHSYbA99gBP17rUDaDqqfkZ0s5ssm7jA@mail.gmail.com>

On Sat, Aug 9, 2014 at 5:38 AM, Julian Taylor
<jtaylor.debian at googlemail.com> wrote:
> Hello,
>
> I am pleased to announce the release of NumPy 1.8.2, a
> pure bugfix release for the 1.8.x series.
> https://sourceforge.net/projects/numpy/files/NumPy/1.8.2/
> The upgrade is recommended for all users of the 1.8.x series.
>
> Following issues have been fixed:
> * gh-4836: partition produces wrong results for multiple selections in
> equal ranges
> * gh-4656: Make fftpack._raw_fft threadsafe
> * gh-4628: incorrect argument order to _copyto in in np.nanmax, np.nanmin
> * gh-4642: Hold GIL for converting dtypes types with fields
> * gh-4733: fix np.linalg.svd(b, compute_uv=False)
> * gh-4853: avoid unaligned simd load on reductions on i386
> * gh-4722: Fix seg fault converting empty string to object
> * gh-4613: Fix lack of NULL check in array_richcompare
> * gh-4774: avoid unaligned access for strided byteswap
> * gh-650: Prevent division by zero when creating arrays from some buffers
> * gh-4602: ifort has issues with optimization flag O2, use O1
>
>
> The source distributions have been uploaded to PyPI. The Windows
> installers, documentation and release notes can be found at:
> https://sourceforge.net/projects/numpy/files/NumPy/1.8.2/

OSX wheels now also up on pypi, please let us know of any problems,

Cheers,

Matthew


From rgrant at enthought.com  Mon Aug 11 18:20:16 2014
From: rgrant at enthought.com (Robert Grant)
Date: Mon, 11 Aug 2014 17:20:16 -0500
Subject: [SciPy-User] ANN: DistArray 0.5 Release
Message-ID: <CALK958hdtKQqcbufVhPLq8XRyj9yG4w7aZKukiLzkka6M1nNYA@mail.gmail.com>

=============================================================================
DistArray 0.5 release
=============================================================================

**Mailing List:** distarray at googlegroups.com

**Documentation:** http://distarray.readthedocs.org

**License:** Three-clause BSD

**Python versions:** 2.7, 3.3, and 3.4

**OS support:** \*nix and Mac OS X

What is DistArray?
------------------

DistArray aims to bring the ease-of-use of NumPy to data-parallel
high-performance computing.  It provides distributed multi-dimensional NumPy
arrays, distributed ufuncs, and distributed IO capabilities.  It can
efficiently interoperate with external distributed libraries like Trilinos.
DistArray works with NumPy and builds on top of it in a flexible and natural
way.

0.5 Release
-----------

Noteworthy improvements in this release include:

* closer alignment with NumPy's API,
* support for Python 3.4 (existing support for Python 2.7 and 3.3),
* a performance-oriented MPI-only mode for deployment on clusters and
  supercomputers,
* a way to register user-defined functions to be callable locally on worker
  processes,
* more consistent naming of sub-packages,
* testing with MPICH2 (already tested against OpenMPI),
* improved and expanded examples,
* installed version testable via ``distarray.test()``, and
* performance and scaling improvements.

With this release, DistArray ready for real-world testing and deployment.  The
project is still evolving rapidly and we appreciate the continued input from
the larger scientific-Python community.

Existing features
-----------------

DistArray:

* supports NumPy-like slicing, reductions, and ufuncs on distributed
  multidimensional arrays;
* has a client-engine process design -- data resides on the worker processes,
  commands are initiated from master;
* allows full control over what is executed on the worker processes and
  integrates transparently with the master process;
* allows direct communication between workers, bypassing the master process
  for scalability;
* integrates with IPython.parallel for interactive creation and exploration of
  distributed data;
* supports distributed ufuncs (currently without broadcasting);
* builds on and leverages MPI via MPI4Py in a transparent and user-friendly
  way;
* has basic support for unstructured arrays;
* supports user-controllable array distributions across workers (block,
  cyclic, block-cyclic, and unstructured) on a per-axis basis;
* has a straightforward API to control how an array is distributed;
* has basic plotting support for visualization of array distributions;
* separates the array?s distribution from the array?s data -- useful for
  slicing, reductions, redistribution, broadcasting, and other operations;
* implements distributed random arrays;
* supports ``.npy``-like flat-file IO and hdf5 parallel IO (via ``h5py``);
  leverages MPI-based IO parallelism in an easy-to-use and transparent way;
  and
* supports the distributed array protocol [protocol]_, which allows
  independently developed parallel libraries to share distributed arrays
  without copying, analogous to the PEP-3118 new buffer protocol.

Planned features and roadmap
----------------------------

Near-term features and improvements include:

* array re-distribution capabilities;
* lazy evaluation and deferred computation for latency hiding;
* interoperation with Trilinos [Trilinos]_; and
* distributed broadcasting support.

The longer-term roadmap includes:

* Integration with other packages [petsc]_ that subscribe to the distributed
  array protocol [protocol]_;
* Distributed fancy indexing;
* Out-of-core computations;
* Support for distributed sorting and other non-trivial distributed
  algorithms; and
* End-user control over communication and temporary array creation, and other
  performance aspects of distributed computations.

History and funding
-------------------

Brian Granger started DistArray as a NASA-funded SBIR project in 2008.
Enthought picked it up as part of a DOE Phase II SBIR [SBIR]_ to provide a
generally useful distributed array package.  It builds on NumPy, MPI, MPI4Py,
IPython, IPython.parallel, and interfaces with the Trilinos suite of
distributed HPC solvers (via PyTrilinos [Trilinos]_).

This material is based upon work supported by the Department of Energy under
Award Number DE-SC0007699.

This report was prepared as an account of work sponsored by an agency of the
United States Government.  Neither the United States Government nor any agency
thereof, nor any of their employees, makes any warranty, express or implied,
or assumes any legal liability or responsibility for the accuracy,
completeness, or usefulness of any information, apparatus, product, or process
disclosed, or represents that its use would not infringe privately owned
rights.  Reference herein to any specific commercial product, process, or
service by trade name, trademark, manufacturer, or otherwise does not
necessarily constitute or imply its endorsement, recommendation, or favoring
by the United States Government or any agency thereof.  The views and opinions
of authors expressed herein do not necessarily state or reflect those of the
United States Government or any agency thereof.


.. [protocol] http://distributed-array-protocol.readthedocs.org/en/rel-0.10.0/
.. [Trilinos] http://trilinos.org/
.. [petsc] http://www.mcs.anl.gov/petsc/
.. [SBIR] http://www.sbir.gov/sbirsearch/detail/410257


From ralf.gommers at gmail.com  Mon Aug 18 18:20:22 2014
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Tue, 19 Aug 2014 00:20:22 +0200
Subject: [SciPy-User] sprint @ EuroSciPy, Aug 31
Message-ID: <CABL7CQjM-OKezmheAE-WR-Ft2W5t2RgCTEUT1YsDJP4oqpBuDg@mail.gmail.com>

Hi all,

Here is a reminder that on Sunday 31 August, there will be a Scipy sprint
at EuroSciPy (in Cambridge, UK). Details can be found at
https://www.euroscipy.org/2014/program/sprints/

Newcomers to Scipy development are very welcome; actually one of the main
goals of the sprint is to help new people to get started. Last year's
sprint was excellent - 20 people joined and we still have all-time highs in
the commits per month and contributors per month graph to show for it:
https://www.openhub.net/p/scipy

If you have time and will be at EuroSciPy: please join!

Cheers,
Ralf
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From pav at iki.fi  Sun Aug 17 14:23:49 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Sun, 17 Aug 2014 18:23:49 +0000 (UTC)
Subject: [SciPy-User]
	=?utf-8?q?how_to_share_weave=2Einline_generated_file?=
	=?utf-8?q?s_accross=09machines=3F?=
References: <5CC618D74A5E2747BD49AEB4F19B85AE1A6AECC0@HKW20030007.gbl.ad.hedani.net>
Message-ID: <loom.20140817T201415-432@post.gmane.org>

Jadhav, Alok  <alok.jadhav <at> credit-suisse.com> writes:
> according to the scipy.weave.inline documentation, weave
> stores the catalogue of already compiled code strings in
> an on-disk cache. What happens if the location of weave generated
> pyd files is commong between 2 machines (on a network drive),
> will the catalogue of this data shared between 2 machines? To
> be more precise, if 1 machine generates a pyd file and if we
> copy this pyd file to a different machine and run the same weave.inline
> function will the other machine try to recompile the code? 

Weave caches compiled code on disk, using cache keys derived from
the code string to be compiled. If compiled code is found from the
cache, no recompilation is done.

With network file systems,  I'm not sure how carefully locking is
implemented in Weave, so race conditions may exist if two processes
try to write the same code simultaneously.

-- 
Pauli Virtanen



From jaakko.luttinen at aalto.fi  Thu Aug 21 07:58:30 2014
From: jaakko.luttinen at aalto.fi (Jaakko Luttinen)
Date: Thu, 21 Aug 2014 14:58:30 +0300
Subject: [SciPy-User] ANN: BayesPy 0.2
Message-ID: <53F5DEE6.5050204@aalto.fi>

Dear all,

I am pleased to announce the release of BayesPy version 0.2.

BayesPy provides tools for Bayesian inference in Python. In particular, 
it implements variational message passing framework, which enables 
modular and efficient way to construct models and perform approximate 
posterior inference.

Download: https://pypi.python.org/pypi/bayespy/

Documentation: http://www.bayespy.org

Repository: https://github.com/bayespy/bayespy

Comments, feedback and contributions are welcome.

Best regards,
Jaakko


From parrenin at ujf-grenoble.fr  Fri Aug 22 05:50:26 2014
From: parrenin at ujf-grenoble.fr (=?UTF-8?Q?Fr=C3=A9d=C3=A9ric_Parrenin?=)
Date: Fri, 22 Aug 2014 11:50:26 +0200
Subject: [SciPy-User] time-frequency diagram with wavelet transform
Message-ID: <CABrqr5pHXSMn9wNqNnChC8KOVWnbK4rm_He9ztZpPxXXCDRTAA@mail.gmail.com>

Dear all,


I have a irregularly sampled time series and I would like to plot its
time-frequency diagram using wavelet transform.
(I am relatively new in wavelets, especially in python)

Is there any example on how to do this?
(I could not find any clear example on the internet)

Thanks in advance,

best regards,

Fr?d?ric
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From mailinglists at xgm.de  Wed Aug 27 10:23:30 2014
From: mailinglists at xgm.de (Florian Lindner)
Date: Wed, 27 Aug 2014 16:23:30 +0200
Subject: [SciPy-User] loadmat: Unknown mat file type, version 49
Message-ID: <ltkpl4$l2b$1@ger.gmane.org>

Hello,

when trying to read a matlab file, writen bei petSC, I get this error:

Traceback (most recent call last):
  File "petsc_plot.py", line 9, in <module>
    rbf_results = scipy.io.loadmat("rbf_results")
  File "/usr/lib/python3.4/site-packages/scipy/io/matlab/mio.py", line 131, 
in loadmat
    MR = mat_reader_factory(file_name, appendmat, **kwargs)
  File "/usr/lib/python3.4/site-packages/scipy/io/matlab/mio.py", line 55, 
in mat_reader_factory
    mjv, mnv = get_matfile_version(byte_stream)
  File "/usr/lib/python3.4/site-packages/scipy/io/matlab/miobase.py", line 
237, in get_matfile_version
    % ret)
ValueError: Unknown mat file type, version 49, 49


The file contains a matrix and a vector and looks like that:

%Mat Object:Nodes 1 MPI processes
%  type: seqdense
% Size = 10 2 
Nodes = zeros(10,2);
Nodes = [
7.2003197397953400e-01 4.0114727059134836e-01 
6.1793966542126100e-02 5.2764865382548720e-01 
1.0022337819588500e-02 1.4405427480322786e-01 
1.4463931936456476e-01 9.9650445216117589e-01 
3.9777780919128602e-01 1.0677308875937896e-01 
7.3036588248200474e-02 9.8905332488367748e-01 
1.0386628927366459e-01 7.3131275919223881e-01 
2.5078039364333193e-01 3.0846024921541471e-01 
9.8405227390177075e-01 1.8055644102282642e-01 
7.3851607000756303e-01 9.8604536855510361e-01 
];
%Vec Object:Coefficients 1 MPI processes
%  type: seq
Coefficients = [
5.0245251254608746e+04
2.3659864438721524e+04
-1.4592196188522011e+05
1.4189477757354762e+04
7.8494387129370181e+04
2.5176418131682707e+05
-4.3384330330706594e+04
-1.7567379893104360e+05
6.5349663489607065e+02
-5.4052285933927575e+04
];


The petsc C code:

  PetscViewer viewer;
  PetscViewerASCIIOpen(PETSC_COMM_WORLD, "rbf_results", &viewer);
  PetscViewerSetFormat(viewer, PETSC_VIEWER_ASCII_MATLAB);
  MatView(Nodes, viewer);
  VecView(c, viewer);
  PetscViewerDestroy(&viewer);

Any idea what could be the problem here?

System is ArchLinux, SciPy 0.14, NumPy 1.8.2, Python 3.4.1

Thanks,
Florian



From dphinnstuart at gmail.com  Wed Aug 27 11:08:43 2014
From: dphinnstuart at gmail.com (phinn stuart)
Date: Thu, 28 Aug 2014 00:08:43 +0900
Subject: [SciPy-User] Convert 3d NumPy array into 2d
Message-ID: <CAEqOoUqDVpvP3KqvdC3Vu5HK=uB-uHpdycT4o9zfAbtzsGUM+A@mail.gmail.com>

Hi everyone, how can I convert (1L, 480L, 1440L) shaped numpy array into
(480L, 1440L)?

Thanks in the advance.

phinn
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From maximilian.albert at gmail.com  Wed Aug 27 12:18:17 2014
From: maximilian.albert at gmail.com (Maximilian Albert)
Date: Wed, 27 Aug 2014 17:18:17 +0100
Subject: [SciPy-User] Convert 3d NumPy array into 2d
In-Reply-To: <CAEqOoUqDVpvP3KqvdC3Vu5HK=uB-uHpdycT4o9zfAbtzsGUM+A@mail.gmail.com>
References: <CAEqOoUqDVpvP3KqvdC3Vu5HK=uB-uHpdycT4o9zfAbtzsGUM+A@mail.gmail.com>
Message-ID: <CAGA_dmjP0PVZ8gTC1k1Vr99nOZ_tySytqR7_wedqxwDsibFYGg@mail.gmail.com>

[source]
<http://github.com/numpy/numpy/blob/v1.8.1/numpy/core/fromnumeric.py#L1072>
<http://docs.scipy.org/doc/numpy/reference/generated/numpy.squeeze.html#numpy.squeeze>
2014-08-27 16:08 GMT+01:00 phinn stuart <dphinnstuart at gmail.com>:

> Hi everyone, how can I convert (1L, 480L, 1440L) shaped numpy array into
> (480L, 1440L)?
>

If 'a' is your array then 'a.squeeze()' or 'a.squeeze(axis=0)' will do the
job (see the docs at [1]). You can also say a.reshape(480, 1440) but
obviously this is only useful if you know the exact dimensions of your
array.

Cheers,
Max

[1] http://docs.scipy.org/doc/numpy/reference/generated/numpy.squeeze.html
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From matthew.brett at gmail.com  Wed Aug 27 15:38:11 2014
From: matthew.brett at gmail.com (Matthew Brett)
Date: Wed, 27 Aug 2014 15:38:11 -0400
Subject: [SciPy-User] loadmat: Unknown mat file type, version 49
In-Reply-To: <ltkpl4$l2b$1@ger.gmane.org>
References: <ltkpl4$l2b$1@ger.gmane.org>
Message-ID: <CAH6Pt5oh2_gwGFB0SWZDqwJgPatZf3ZE__jVXP8W9vTYyG3N2Q@mail.gmail.com>

Hi,

On Wed, Aug 27, 2014 at 10:23 AM, Florian Lindner <mailinglists at xgm.de> wrote:
> Hello,
>
> when trying to read a matlab file, writen bei petSC, I get this error:
>
> Traceback (most recent call last):
>   File "petsc_plot.py", line 9, in <module>
>     rbf_results = scipy.io.loadmat("rbf_results")
>   File "/usr/lib/python3.4/site-packages/scipy/io/matlab/mio.py", line 131,
> in loadmat
>     MR = mat_reader_factory(file_name, appendmat, **kwargs)
>   File "/usr/lib/python3.4/site-packages/scipy/io/matlab/mio.py", line 55,
> in mat_reader_factory
>     mjv, mnv = get_matfile_version(byte_stream)
>   File "/usr/lib/python3.4/site-packages/scipy/io/matlab/miobase.py", line
> 237, in get_matfile_version
>     % ret)
> ValueError: Unknown mat file type, version 49, 49
>
>
> The file contains a matrix and a vector and looks like that:
>
> %Mat Object:Nodes 1 MPI processes
> %  type: seqdense
> % Size = 10 2
> Nodes = zeros(10,2);
> Nodes = [
> 7.2003197397953400e-01 4.0114727059134836e-01
> 6.1793966542126100e-02 5.2764865382548720e-01
> 1.0022337819588500e-02 1.4405427480322786e-01
> 1.4463931936456476e-01 9.9650445216117589e-01
> 3.9777780919128602e-01 1.0677308875937896e-01
> 7.3036588248200474e-02 9.8905332488367748e-01
> 1.0386628927366459e-01 7.3131275919223881e-01
> 2.5078039364333193e-01 3.0846024921541471e-01
> 9.8405227390177075e-01 1.8055644102282642e-01
> 7.3851607000756303e-01 9.8604536855510361e-01
> ];
> %Vec Object:Coefficients 1 MPI processes
> %  type: seq
> Coefficients = [
> 5.0245251254608746e+04
> 2.3659864438721524e+04
> -1.4592196188522011e+05
> 1.4189477757354762e+04
> 7.8494387129370181e+04
> 2.5176418131682707e+05
> -4.3384330330706594e+04
> -1.7567379893104360e+05
> 6.5349663489607065e+02
> -5.4052285933927575e+04
> ];
>
>
> The petsc C code:
>
>   PetscViewer viewer;
>   PetscViewerASCIIOpen(PETSC_COMM_WORLD, "rbf_results", &viewer);
>   PetscViewerSetFormat(viewer, PETSC_VIEWER_ASCII_MATLAB);
>   MatView(Nodes, viewer);
>   VecView(c, viewer);
>   PetscViewerDestroy(&viewer);
>
> Any idea what could be the problem here?
>
> System is ArchLinux, SciPy 0.14, NumPy 1.8.2, Python 3.4.1

Sorry - I wasn't sure from what you said whether this is actually a
matlab .mat file in matlab 4 / 5 / 6 binary format?

Cheers,

matthew


From mailinglists at xgm.de  Wed Aug 27 16:33:24 2014
From: mailinglists at xgm.de (Florian Lindner)
Date: Wed, 27 Aug 2014 22:33:24 +0200
Subject: [SciPy-User] loadmat: Unknown mat file type, version 49
References: <ltkpl4$l2b$1@ger.gmane.org>
	<CAH6Pt5oh2_gwGFB0SWZDqwJgPatZf3ZE__jVXP8W9vTYyG3N2Q@mail.gmail.com>
Message-ID: <ltlfak$r9r$1@ger.gmane.org>

Matthew Brett wrote:

> Hi,
> 
> On Wed, Aug 27, 2014 at 10:23 AM, Florian Lindner <mailinglists at xgm.de>
> wrote:
>> Hello,

>> The petsc C code:
>>
>>   PetscViewer viewer;
>>   PetscViewerASCIIOpen(PETSC_COMM_WORLD, "rbf_results", &viewer);
>>   PetscViewerSetFormat(viewer, PETSC_VIEWER_ASCII_MATLAB);
>>   MatView(Nodes, viewer);
>>   VecView(c, viewer);
>>   PetscViewerDestroy(&viewer);
>>
>> Any idea what could be the problem here?
>>
>> System is ArchLinux, SciPy 0.14, NumPy 1.8.2, Python 3.4.1
> 
> Sorry - I wasn't sure from what you said whether this is actually a
> matlab .mat file in matlab 4 / 5 / 6 binary format?

Actually I got no idea. petSC gives no information about the specific 
version. I've pasted the relevant file how it's cat'ted into my console.

Thx,
Florian



From pav at iki.fi  Wed Aug 27 17:16:46 2014
From: pav at iki.fi (Pauli Virtanen)
Date: Thu, 28 Aug 2014 00:16:46 +0300
Subject: [SciPy-User] loadmat: Unknown mat file type, version 49
In-Reply-To: <ltlfak$r9r$1@ger.gmane.org>
References: <ltkpl4$l2b$1@ger.gmane.org>	<CAH6Pt5oh2_gwGFB0SWZDqwJgPatZf3ZE__jVXP8W9vTYyG3N2Q@mail.gmail.com>
	<ltlfak$r9r$1@ger.gmane.org>
Message-ID: <ltlhru$t26$1@ger.gmane.org>

27.08.2014, 23:33, Florian Lindner kirjoitti:
> Matthew Brett wrote:
[clip]
>>>   PetscViewerSetFormat(viewer, PETSC_VIEWER_ASCII_MATLAB);
[clip]
>> Sorry - I wasn't sure from what you said whether this is actually a
>> matlab .mat file in matlab 4 / 5 / 6 binary format?

Given that it says PETSC_VIEWER_ASCII_MATLAB, it's probably saved just
as a text file. scipy.io.loadmat loads files in the matlab binary format.




From ndbecker2 at gmail.com  Fri Aug 29 08:50:12 2014
From: ndbecker2 at gmail.com (Neal Becker)
Date: Fri, 29 Aug 2014 08:50:12 -0400
Subject: [SciPy-User] ANN: NumPy 1.9.0 release candidate 1 available
References: <53FE104C.2020006@googlemail.com> <ltpsm5$rmo$2@ger.gmane.org>
Message-ID: <ltpsu4$rmo$4@ger.gmane.org>

OK, it's fixed by doing:

rm -rf ~/.local/lib/python2.7/site-packages/numpy*
python setup.py install --user

I guess something was not cleaned out from previous packages



From ben.root at ou.edu  Fri Aug 29 09:26:47 2014
From: ben.root at ou.edu (Benjamin Root)
Date: Fri, 29 Aug 2014 09:26:47 -0400
Subject: [SciPy-User] [Numpy-discussion] ANN: NumPy 1.9.0 release
	candidate 1 available
In-Reply-To: <ltpsu4$rmo$4@ger.gmane.org>
References: <53FE104C.2020006@googlemail.com> <ltpsm5$rmo$2@ger.gmane.org>
	<ltpsu4$rmo$4@ger.gmane.org>
Message-ID: <CANNq6Fk-CqT-SdBHZN21caLqdcDSqxwNHavYa5X=+f-6hjqv1A@mail.gmail.com>

It is generally a good idea when switching between releases to execute "git
clean -fxd" prior to rebuilding. Admittedly, I don't know how cleaning out
that directory in .local could have impacted things. Go figure.

Cheers!
Ben Root


On Fri, Aug 29, 2014 at 8:50 AM, Neal Becker <ndbecker2 at gmail.com> wrote:

> OK, it's fixed by doing:
>
> rm -rf ~/.local/lib/python2.7/site-packages/numpy*
> python setup.py install --user
>
> I guess something was not cleaned out from previous packages
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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From cyrille.rossant at gmail.com  Sat Aug 30 09:51:23 2014
From: cyrille.rossant at gmail.com (Cyrille Rossant)
Date: Sat, 30 Aug 2014 14:51:23 +0100
Subject: [SciPy-User] Fwd: ANN: Vispy 0.3,
	high-performance visualization in Python
In-Reply-To: <CA+-1RQR+zo2K_1Y_dsyhC+ZVyoWHj7cKBFdh9k7_nm-YTkRkPA@mail.gmail.com>
References: <CA+-1RQR+zo2K_1Y_dsyhC+ZVyoWHj7cKBFdh9k7_nm-YTkRkPA@mail.gmail.com>
Message-ID: <CA+-1RQR0_ajDGg6sMPtGLY2DPKMggp0KZfpDZqpJncqdUi7Q9w@mail.gmail.com>

Hi,

We are pleased to announce the release of Vispy version 0.3!

This release provides new graphics API (including a scene graph), a
basic OpenGL backend for matplotlib, and experimental integration in
the IPython notebook. Further, we have improved the existing modules
and added several application backends (e.g. wx and sdl2).

We are now focusing our efforts on:

* Consolidating the higher-level graphics APIs: visuals, transforms,
scene graph.
* Improving the performance of the scene graph, notably through batch
rendering (Collections system).
* Adding more features to the matplotlib backend.
* Improving the integration in the IPython notebook: better
performance, improved event loop integration, client-side backend with
WebGL.

Eventually, scientists with no knowledge of OpenGL will be able to
easily create fast and scalable interactive visualizations.

We will greatly appreciate any feedback, bug reports, and feature
requests. See http://vispy.org for downloads and more information.

The Vispy team