gmane.comp.python.scientific.user

quasi random, Halton sequence

2013-06-18T03:25:15

I didn't find any quasi-random sequences in python that is BSD compatible.
The question shows up every few years. Is there anything now?


a quick translation from c to python, (to be translated to cython and
to c (going in a circle))

maybe there is something slightly off (e.g. gap in circle)

Josef

-----------------
# -*- coding: utf-8 -*-
"""

Created on Mon Jun 17 22:12:21 2013

Author: Sebastien Paris
Josef Perktold translation from c

http://www.mathworks.com/matlabcentral/fileexchange/17457-quasi-montecarlo-halton-sequence-generator
"""

#void halton(int dim , int nbpts, double *h  , double *p )
#{
#
#double lognbpts , d , sum;
#
#int i , j , n , t , b;
#
#static int P[11] = {2 ,3 ,5 , 7 , 11 , 13 , 17 , 19 ,  23 , 29 , 31};
#
#
#lognbpts = log(nbpts + 1);
#
#
#for(i = 0 ; i < dim ; i++)
#
#{
#
#b      = P[i];
#
#n      = (int) ceil(lognbpts/log(b));
#
#
#for(t = 0 ; t < n ; t++)
#
#{
#p[t] = pow(b , -(t + 1) );
#}
#
#
#for (j = 0 ; j < nbpts ; j++)
#
#{
#
#d        = j + 1;
#
#sum      = fmod(d , b)*p[0];
#
#
#for (t = 1 ; t < n ; t++)
#
#{
#
#d        = floor(d/b);
#
#sum     += fmod(d , b)*p[t];
#
#}
#
#
#h[j*dim + i] = sum;
#
#}
#
#}
#
#}

from math import log, floor, ceil, fmod
import numpy as np

def halton(dim, nbpts):
    h = np.empty(nbpts * dim)
    h.fill(np.nan)
    p = np.empty(nbpts)
    p.fill(np.nan)
    P = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
    lognbpts = log(nbpts + 1)
    for i in range(dim):
        b = P[i]
        n = int(ceil(lognbpts / log(b)))
        for t in range(n):
            p[t] = pow(b, -(t + 1) )

        for j in range(nbpts):
            d = j + 1
            sum_ = fmod(d, b) * p[0]
            for t in range(1, n):
                d = floor(d / b)
                sum_ += fmod(d, b) * p[t]

            h[j*dim + i] = sum_

    return h.reshape(nbpts, dim)

x = halton(2, 5000);
#plot(x(1 , :) , x(2 , :) , '+')
print x[:5]
import matplotlib.pyplot as plt
plt.figure()
plt.plot(x[:500, 0], x[:500, 1], '+')
plt.title('uniform-distribution (500)')

plt.figure()
plt.plot(x[:, 0], x[:, 1], '+')
plt.title('uniform-distribution')

from scipy import stats
plt.figure()
xn = stats.norm._ppf(x)
plt.plot(xn[:, 0], xn[:, 1], '+')
plt.title('normal-distribution')

plt.figure()
plt.plot(stats.t._ppf(x[:, 0], 3), stats.t._ppf(x[:, 1], 3), '+')
plt.title('t-distribution')

plt.figure()
x0 = xn[:100]
x0 /= np.sqrt((x0*x0 + 1e-100).sum(1))[:,None]
plt.plot(x0[:, 0] , x0[:, 1], '+')
plt.xlim(-1.1, 1.1)
plt.ylim(-1.1, 1.1)
plt.title('uniform on circle')

plt.show()
-----------------

Build a new continuous probability density function

MRE Simulator — 2013-06-16T20:51:04

Hi experts!
Im a newby user of Python, sage and Scipy.

Using scipy.stats module, i'm trying to build a new probability density function, f(x) (not include in scipy module). I wanna call this function in similar way that other probability density function in scipy, i.e.:
from scipy.stats import new_function.
And do some math with it:
new_function.mean(loc=....., scale= -----), etc.
¿What must i do (step by step, including definition of scale and loc)?
Waiting for your answers.
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Covariance matrix from curve_fit

Thomas Robitaille — 2013-06-16T07:24:28

Hi everyone,

I have a question regarding the output from the
scipy.optimize.curve_fit function - in the following example:

"""
    In [1]: import numpy as np

    In [2]: from scipy.optimize import curve_fit

    In [3]: f = lambda x, a, b: a * x + b

    In [4]: x = np.array([0., 1., 2.])

    In [5]: y = np.array([1.2, 4.6, 7.8])

    In [6]: e = np.array([1., 1., 1.])

    In [7]: curve_fit(f, x, y, sigma=e)
    Out[7]:
    (array([ 3.3       ,  1.23333333]),
     array([[ 0.00333333, -0.00333333],
           [-0.00333333,  0.00555556]]))

    In [8]: curve_fit(f, x, y, sigma=e * 100)
    Out[8]:
    (array([ 3.3       ,  1.23333333]),
     array([[ 0.00333333, -0.00333333],
           [-0.00333333,  0.00555556]]))
"""

it's clear that the covariance matrix does not take into account the
uncertainties on the data points. If I do:

"""
popt, pcov = curve_fit(...)
"""

Then pcov[0,0]**0.5 is therefore not the uncertainty on the parameter,
so I was wondering how this should be scaled to give the actual
uncertainty on the parameter?

Thanks!
Tom

Numpy 1.7.1 Crashing with MKL and AVX instructions

Paul Blelloch — 2013-05-24T15:24:37

 I've tried posting this on the numpy list, but it keeps on getting 
bounced, so I'll try here:
 
I found that when I went from numpy 1.7.0 to 1.7.1 I get a crash whenever I 
try an eigenvalue calculation (or any other linalg calculation) on matrices 
bigger than about 200x200.  This happens with both the latest Anaconda and 
WinPython 64-bit Windows distributions (both of which use numpy 1.7.1) and 
occurs on all the HP workstations at my company.  The folks at Continuum 
helped me debug this and determined that the failure was most likely in use 
of AVX instructions in MKL, but we haven't found a workaround yet short of 
sticking to numpy 1.7.0.  The problem is apparently specific to some 
combination of processor and OS.  Here's what I have:

 

OS Name Microsoft Windows 7 Professional
Version 6.1.7601 Service Pack 1 Build 7601
OS Manufacturer Microsoft Corporation
System Name Z420-6
System Manufacturer Hewlett-Packard
System Model HP Z420 Workstation
System Type x64-based PC
Processor Intel(R) Xeon(R) CPU E5-1650 0 < at > 3.20GHz, 3201 Mhz, 6 Core(s), 12 
Logical Processor(s)
BIOS Version/Date Hewlett-Packard J61 v01.14, 7/17/2012
SMBIOS Version 2.7
Windows Directory C:\Windows
System Directory C:\Windows\system32
Boot Device \Device\HarddiskVolume1
Locale United States
Hardware Abstraction Layer Version = "6.1.7601.17514"
Installed Physical Memory (RAM) 32.0 GB
Total Physical Memory 31.9 GB
Available Physical Memory 28.4 GB
Total Virtual Memory 95.8 GB
Available Virtual Memory 92.1 GB
Page File Space 63.9 GB
Page File C:\pagefile.sys

 

I'm surprised that I'm the only person out there running into this.  Is 
there anyone else who's running into this problem with numpy 1.7.1 with MKL 
on 64-bit Windows?

 

-Paul Blelloch
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Storing return values of optimize.fmin()

Jeroen Meidam — 2013-04-22T10:12:45

Hi,

I am using optimize.fmin to minimize a function over 2 parameters.
In the documentation it says that the output is:
    (xopt, {fopt, iter, funcalls, warnflag})

I have no problem putting xopt into a variable, because this is simply done
by writing:
    xopt = fmin(function,x0)
After which I can use xopt for anything I need it for.

What I want however, is to store "fopt" into a variable, like I did with
xopt. In the standard case, fopt is only returned as text in the output
stream:
"
Optimization terminated successfully.
         Current function value: -0.995801  <--- This is what I'm
interested in
         Iterations: 35
         Function evaluations: 71
"

How can I store it into a variable? Is it possible?


Thanks,
Jeroen
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PyFEAST, a feature selection module for python

Calvin Morrison — 2013-04-07T16:23:53

Hello,

I'm happy to announce the release of PyFeast, a feature selection
module for python.

PyFeast is a set of bindings for the FEAST feature selection toolbox
[0], which was originally written in C with a Mex interface to Matlab.

Because Python is also commonly used in computational science, writing
bindings to enable researchers to utilize these feature selection
algorithms in Python was only natural.

At Drexel University's EESI Lab[1], we are using PyFeast to create a
feature selection tool for the Department of Energy's upcoming KBase
platform.[2]

PyFeast contains eleven different feature selection algorithms which
are thoroughly documented, and utilizes numpy arrays, so integration
into current projects is very easy.

PyFeast is available here:
http://github.com/mutantturkey/PyFeast

Please let me know if you have any questions or comments!

Thank you,
Calvin Morrison

[0] http://www.cs.man.ac.uk/~gbrown/fstoolbox/
[1] http://www.ece.drexel.edu/gailr/EESI/
[2] http://kbase.science.energy.gov/developer-zone/api-documentation/fizzy-feature-selection-service/

how do I get the subtrees of dendrogram made byscipy.cluster.hierarchy?

Waleed Hamra — 2013-06-03T19:44:25

I had a confusion regarding this module (scipy.cluster.hierarchy) ... and 
still have some !

For example we have this dendrogram: 
http://img62.imageshack.us/img62/8130/3ieb4.png

My question is how can I extract the coloured subtrees (each one represent a 
cluster) in a nice format, say SIF format ? Now the code to get the plot above 
is:

In [1]: import scipy

In [2]: import scipy.cluster.hierarchy as sch

In [3]: import matplotlib.pylab as plt

In [4]: X = scipy.randn(100,2)

In [5]: d = sch.distance.pdist(X)

In [6]: Z= sch.linkage(d,method='complete')

In [7]: P =sch.dendrogram(Z)

In [8]: plt.savefig('plot_dendrogram.png')

In [9]: T = sch.fcluster(Z, 0.5*d.max(), 'distance')

In [10]: T
Out[10]: 
      array([4, 5, 3, 2, 2, 3, 5, 2, 2, 5, 2, 2, 2, 3, 2, 3, 2, 5, 4, 5, 2, 5, 
2,
      3, 3, 3, 1, 3, 4, 2, 2, 4, 2, 4, 3, 3, 2, 5, 5, 5, 3, 2, 2, 2, 5, 4,
      2, 4, 2, 2, 5, 5, 1, 2, 3, 2, 2, 5, 4, 2, 5, 4, 3, 5, 4, 4, 2, 2, 2,
      4, 2, 5, 2, 2, 3, 3, 2, 4, 5, 3, 4, 4, 2, 1, 5, 4, 2, 2, 5, 5, 2, 2,
      5, 5, 5, 4, 3, 3, 2, 4], dtype=int32)

In [11]: sch.leaders(Z,T)
Out[11]: 
      (array([190, 191, 182, 193, 194], dtype=int32), array([2, 3, 1, 
4,5],dtype=int32))

So now, the output of fcluster() gives the clustering of the nodes (by their 
id's), and leaders() described here is supposed to return 2 arrays:

    first one contains the leader nodes of the clusters generated by Z, here we 
can see we have 5 clusters, as well as in the plot

    and the second one the id's of these clusters

So if this leaders() returns resp. L and M : L[2]=182 and M[2]=1, then cluster 
1 is leaded by node id 182, which doesn't exist in the observations set X, the 
documentation says "... then it corresponds to a non-singleton cluster". But I 
can't get it ...

Also, I converted the Z to a tree by sch.to_tree(Z), that will return an easy-
to-use tree object, which I want to visualize, but which tool should I use as 
a graphical platform that manipulate these kind of tree objects as inputs?

thanks in advance :)

how do I get the subtrees of dendrogram made byscipy.cluster.hierarchy?

Waleed Hamra — 2013-06-03T18:55:53

I had a confusion regarding this module (scipy.cluster.hierarchy) ... and 
still have some !

For example we have this dendrogram: 
http://img62.imageshack.us/img62/8130/3ieb4.png

My question is how can I extract the coloured subtrees (each one represent a 
cluster) in a nice format, say SIF format ? Now the code to get the plot above 
is:

In [1]: import scipy

In [2]: import scipy.cluster.hierarchy as sch

In [3]: import matplotlib.pylab as plt

In [4]: X = scipy.randn(100,2)

In [5]: d = sch.distance.pdist(X)

In [6]: Z= sch.linkage(d,method='complete')

In [7]: P =sch.dendrogram(Z)

In [8]: plt.savefig('plot_dendrogram.png')

In [9]: T = sch.fcluster(Z, 0.5*d.max(), 'distance')

In [10]: T
Out[10]: 
      array([4, 5, 3, 2, 2, 3, 5, 2, 2, 5, 2, 2, 2, 3, 2, 3, 2, 5, 4, 5, 2, 5, 
2,
      3, 3, 3, 1, 3, 4, 2, 2, 4, 2, 4, 3, 3, 2, 5, 5, 5, 3, 2, 2, 2, 5, 4,
      2, 4, 2, 2, 5, 5, 1, 2, 3, 2, 2, 5, 4, 2, 5, 4, 3, 5, 4, 4, 2, 2, 2,
      4, 2, 5, 2, 2, 3, 3, 2, 4, 5, 3, 4, 4, 2, 1, 5, 4, 2, 2, 5, 5, 2, 2,
      5, 5, 5, 4, 3, 3, 2, 4], dtype=int32)

In [11]: sch.leaders(Z,T)
Out[11]: 
      (array([190, 191, 182, 193, 194], dtype=int32), array([2, 3, 1, 
4,5],dtype=int32))

So now, the output of fcluster() gives the clustering of the nodes (by their 
id's), and leaders() described here is supposed to return 2 arrays:

    first one contains the leader nodes of the clusters generated by Z, here we 
can see we have 5 clusters, as well as in the plot

    and the second one the id's of these clusters

So if this leaders() returns resp. L and M : L[2]=182 and M[2]=1, then cluster 
1 is leaded by node id 182, which doesn't exist in the observations set X, the 
documentation says "... then it corresponds to a non-singleton cluster". But I 
can't get it ...

Also, I converted the Z to a tree by sch.to_tree(Z), that will return an easy-
to-use tree object, which I want to visualize, but which tool should I use as 
a graphical platform that manipulate these kind of tree objects as inputs?

thanks in advance :)

Can SciPy deal with a set of non-linear equations?

LU, JIANMIN — 2013-05-22T16:03:46

Hi guys,

I know that non-linear equations can be solved by fsolve() in SciPy. Another question from me would be: Can SciPy handle the multiple precision number problem? Namely, can SciPy define a variable with arbitray precision and can fsolve() take that correctly? Thanks.

Regards,
Jianmin, from U. of South Carolina.
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Can SciPy deal with a set of non-linear equations?

LU, JIANMIN — 2013-05-22T15:50:33

Hi SciPy guys,

I get a set of non-linear equations to be solved with Python and the goal is to find the roots of this set of equations. Can SciPy handle this?

Regards,

Jianmin, from U. of South Carolina.
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noob question: numpy copy vs standard lib copy

psoriasis — 2013-05-13T20:23:59

I'm new to python.  As I understand it, assignment copies by reference
and to do otherwise requires a function like the standard library's
copy or deepcopy functions.  However, from what I see numpy has it's
own copy function and using it on a random object (instance of a test
class I made up not an array etc) doesn't seem to return the expected
copy object.    I did try importing the copy module and that worked
but then the numpy copy module was "shadowed" but I don't know if
that's a problem.

Still, I'm sure numpy users need to copy regular objects so what's the
standard solution to this?


Note: I'm using the latest pythonxy (==> spyder, numpy, etc)

Thanks

problem with scipy.io.wavfile (urgent)

rohan wadnerkar — 2013-05-13T11:00:47

Hello all,

I am trying to read .wav file using scipy.io.wavfile.read(). It reads some
file properly. For some files its giving following error...


Warning (from warnings module):
  File "D:\project\cardiocare-1.0\src\scipy\io\wavfile.py", line 121
    warnings.warn("chunk not understood",
WavFileWarning)WavFileWarning: chunk not understoodTraceback (most
recent call last):
  File "D:\project\cardiocare-1.0\src\ccare\plot.py", line 37, in plot
    input_data = read(p.bitfile)
  File "D:\project\cardiocare-1.0\src\scipy\io\wavfile.py", line 119, in read
    data = _read_data_chunk(fid, noc, bits)
  File "D:\project\cardiocare-1.0\src\scipy\io\wavfile.py", line 56,
in _read_data_chunk
    data = data.reshape(-1,noc)ValueError: total size of new array
must be unchanged


Can any one suggest me any solution?

MLE for discrete distributions: no fit() method onrv_discrete?

Keith Braithwaite — 2013-05-09T16:27:45

Hi,

I would like to find MLEs (and subsequently goodness of fit and so on) 
for empirical data against discrete distributions. I notice that 
scipy.stats.rv_discrete does not have the fit() method that 
scipy.stats.rv_continuous does. Is there a technical (either Pythonic or 
statistical) reason why not? If I roll my own am I going to get nonsense 
results?

Best Regards,
Keith
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ConvexHull: difficult to get vertices

2013-04-28T19:14:33

I am trying to use scipy.spatial.ConvexHull (introduced in scipy 0.12.0) to get the convex polygon surrounding a given polygon (2D).
But there doesn't seem to be any easy way to get the vertices of the convex hull from the result returned from the scipy.spatial.ConvexHull function.
Perhaps (probably?) this is a documentation problem.
The documentation says that it returns an object with attributes: points, simplices, …
The 'points' attribute is supposed to be "Points in the convex hull". But empirically, it has all of the points that were sent as input to the function - not just the points that are in the convex hull.
The 'simplices' attribute has "Indices of points forming the simplical facets of the convex hull". In the case of my 2D polygon, these seem to be the indices of pairs of points forming the line segments of the polygon I want. But they aren't in any particular order (as far as I can see).

I have found that I can get the indices of the points that form the convex polygon that I want by doing the following:

h = scipy.spatial.ConvexHull(polygonVerts)
indices = np.unique(h.simplices.flatten())

And hence the vertices of the convex polygon can be obtained as:

convexPolygonVerts = [polygonVerts[i] for i in indices]

But this seems rather more difficult than it should be.
And this wouldn't work if I had a collection of points in 2D (instead of a polygon) and wanted the bounding polygon.

--
Cameron Hayne
macdev< at >hayne.net

ANN:: Veusz 1.17.1

Jeremy Sanders — 2013-04-14T19:43:50

I'm please to announce the release of the Veusz 1.17.1 python plotting 
module and plotting package. Please find the release notes below.

Veusz 1.17.1
------------
http://home.gna.org/veusz/

Veusz is a scientific plotting package.  It is designed to produce
publication-ready Postscript/PDF/SVG output. Graphs are built-up by
combining plotting widgets. The user interface aims to be simple,
consistent and powerful.

Veusz provides GUI, Python module, command line, scripting, DBUS and
SAMP interfaces to its plotting facilities. It also allows for
manipulation and editing of datasets. Data can be captured from
external sources such as Internet sockets or other programs.

Changes in 1.17.1:
  * Allow coloured points for non-orthogonal plots (polar, ternary)
  * Remove unnecessary exception data

Bug fixes:
  * Fix Print dialog
  * Fix command-line "Print" command
  * Fix duplicate axes drawn in grid
  * Fix crash adding empty polar plot
  * Exit properly on Mac OS X with --export option
  * Fix highlighted button icons missing (Mac OS X binary)

Changes in 1.17:
  * Add new broken axis widget with gaps in the numerical sequence
  * Grid lines are plotted always under (or over) the data
  * Shift+Scroll wheel scrolls left/right (thanks to Dave Hughes)
  * Polar plots can have a "minimum" radius and log axes
  * Many more LaTeX symbols added
  * Add SAMP/VoTable support (thanks to Graham Bell)
  * New shifted-points xy line mode, which plots a stepped line with
    the points shifted to lie between the coordinates given
  * Points can be picked to console and/or clipboard
    (thanks to Valerio Mussi)
  * Allow reversed ternary plot

Bug fixes:
  * Fix unicode characters for \circ and \odot
  * Fix for data type of pickable points
  * Fix sort by group crash bug
  * Many crashes fixed
  * Fix width of key when using long titles/and or multiple columns
  * Fix bold and italic output in SVG output

Features of package:
  Plotting features:
   * X-Y plots (with errorbars)
   * Line and function plots
   * Contour plots
   * Images (with colour mappings and colorbars)
   * Stepped plots (for histograms)
   * Bar graphs
   * Vector field plots
   * Box plots
   * Polar plots
   * Ternary plots
   * Plotting dates
   * Fitting functions to data
   * Stacked plots and arrays of plots
   * Plot keys
   * Plot labels
   * Shapes and arrows on plots
   * LaTeX-like formatting for text
  Input and output:
   * EPS/PDF/PNG/SVG/EMF export
   * Dataset creation/manipulation
   * Embed Veusz within other programs
   * Text, CSV, FITS, NPY/NPZ, QDP, binary and user-plugin importing
   * Data can be captured from external sources
  Extending:
   * Use as a Python module
   * User defined functions, constants and can import external Python 
functions
   * Plugin interface to allow user to write or load code to
      - import data using new formats
      - make new datasets, optionally linked to existing datasets
      - arbitrarily manipulate the document
   * Scripting interface
   * Control with DBUS and SAMP
  Other features:
   * Data picker
   * Interactive tutorial
   * Multithreaded rendering

Requirements for source install:
  Python (2.6 or greater required)
    http://www.python.org/
  Qt >= 4.4 (free edition)
    http://www.trolltech.com/products/qt/
  PyQt >= 4.3 (SIP is required to be installed first)
    http://www.riverbankcomputing.co.uk/software/pyqt/
    http://www.riverbankcomputing.co.uk/software/sip/
  numpy >= 1.0
    http://numpy.scipy.org/

Optional:
  PyFITS >= 1.1 (optional for FITS import)
    http://www.stsci.edu/resources/software_hardware/pyfits
  pyemf >= 2.0.0 (optional for EMF export)
    http://pyemf.sourceforge.net/
  PyMinuit >= 1.1.2 (optional improved fitting)
    http://code.google.com/p/pyminuit/
  For EMF and better SVG export, PyQt >= 4.6 or better is
    required, to fix a bug in the C++ wrapping
  dbus-python, for dbus interface
    http://dbus.freedesktop.org/doc/dbus-python/
  astropy (optional for VO table import)
    http://www.astropy.org/
  SAMPy (optional for SAMP support)
    http://pypi.python.org/pypi/sampy/

Veusz is Copyright (C) 2003-2013 Jeremy Sanders
<jeremy< at >jeremysanders.net> and contributors. It is licenced under the
GPL (version 2 or greater).

For documentation on using Veusz, see the "Documents" directory. The
manual is in PDF, HTML and text format (generated from docbook). The
examples are also useful documentation. Please also see and contribute
to the Veusz wiki: http://barmag.net/veusz-wiki/

Issues with the current version:

   * Due to a bug in the Qt XML processing, some MathML elements
     containing purely white space (e.g. thin space) will give an error.

If you enjoy using Veusz, we would love to hear from you. Please join
the mailing lists at

https://gna.org/mail/?group=veusz

to discuss new features or if you'd like to contribute code. The
latest code can always be found in the Git repository
at https://github.com/jeremysanders/veusz.git.

sqrtm is too slow for matrices of size 1000

Vivek Kulkarni — 2013-04-13T15:47:25

Hi,
I am implementing spectral clustering for my course work and am using the 
sqrtm function to find the square root of a matrix . But its far too slow, 
my matrix is of size (1258,1258). Any suggestions on how I can speed things 
up or some other function that scipy supports which can be used. 

I am implementing the algorithm as described here:
http://books.nips.cc/papers/files/nips14/AA35.pdf

Finding the square root of D^(-1) is way too slow for D^-1 of size 
(1258,1258).


Thanks.
Vivek.
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Laplace Operator with spacing

Carolina Verdugo — 2013-04-10T08:04:54

Good morning,
I would like to calcute de Laplacian Operator of a matrix with spacing
between points and if it were possible the same boundary conditions as the
function del2 does in Matlab. I wish you could help me. Thanks in advance.
Carolina
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Laplacian Operator as del2

Carolina Verdugo Molano — 2013-04-09T21:50:45

Hello, I would like to calculate the Laplacian Operator of a matrix with spacing between points. And if it was possible the same boundary conditions as del2 does in Matlab. I wish you could help me. Thanks in advance.

Ignore characters while reading text

Florian Lindner — 2013-06-14T10:50:17

Hello,

I have a text file with data like

1 (2 3 4) (5 6 7) (8 9 10)
2 (4 5 1) (3 6 8) (1 6 45)

How can I read that file into an array?

[
[1, 2, 3, 5, 6, 7, 8, 9, 10]
[2, 4, 1, 3, ... ]
]

I tried genfromtxt with deletechars="()" but that seems to affect only 'names'. 
I also tried delimiter="() " but that didn't work either.

Thanks!

Florian

hey

Gopalakrishnan Ravimohan — 2013-06-13T15:40:48





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Gopalakrishnan Ravimohan
.......................
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ANN: neo 0.3.0 release

Samuel Garcia — 2013-06-13T09:40:09

We are pleased to announce the 0.3.0 release of the neo.


Neo is a package for representing electrophysiology data in Python, 
together with support for reading a wide range of neurophysiology file 
formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, 
Plexon, Tdt, and support for writing to a subset of these formats plus 
non-proprietary formats including HDF5.

The goal of Neo is to improve interoperability between Python tools for 
analyzing, visualizing and generating electrophysiology data (such as 
OpenElectrophy, NeuroTools, G-node, Helmholtz, PyNN) by providing a 
common, shared object model. In order to be as lightweight a dependency 
as possible, Neo is deliberately limited to represention of data, with 
no functions for data analysis or visualization.

Neo implements a hierarchical data model well adapted to intracellular 
and extracellular electrophysiology and EEG data with support for 
multi-electrodes (for example tetrodes). Neo's data objects build on the 
quantities_ package, which in turn builds on NumPy by adding support for 
physical dimensions. Thus neo objects behave just like normal NumPy 
arrays, but with additional metadata, checks for dimensional consistency 
and automatic unit conversion.



Release 0.3.0 notes:
   * various bug fixes in neo.io
   * added ElphyIO
   * SpikeTrain performence improved
   * An IO class now can return a list of Block (see read_all_blocks in 
IOs)
   * python3 compatibility improved




Home page: http://neuralensemble.org/neo
Mailing list: 
https://groups.google.com/forum/?fromgroups#!forum/neuralensemble
Documentation: http://packages.python.org/neo/



The neo team