numpy-discussion — Discussion list for all users of Numerical Python

Re: [Numpy-discussion] Regarding Matrices

[Sven S. <sve...@gm...>]

Hi,

Satya Upadhya schrieb:

>>>> from Numeric import *

Well this list is about the numpy package, but anyway...

> the power function is giving a resultant matrix in which each element of
> matrix B is raised to the power of 0 so as to make it 1. But, taken as a
> whole i.e. matrix B to the power of 0 should have given the identity
> matrix.

afaik, in numpy terms if you are dealing with a numpy array, such
functions are elementwise by design.
In contrast, if you have a numpy matrix (a special subclass of the array
class) --constructed e.g. as mat(eye(3))-- then power is redefined to be
the matrix power; at least that's the rule for the ** operator, not 100%
sure if for the explicit power() function as well, but I suppose so.

>  
> Also, what is the procedure for taking the log of an entire matrix
> (log(A) where A is a matrix takes the log of every individual element in
> A, but thats not the same as taking the log of the entire matrix)

I don't understand what you want, how do you take the log of a matrix
mathematically?

-Sven

[Numpy-discussion] Regarding Matrices

[Satya U. <sat...@ya...>]

Dear All,
  Just a few queries regarding matrices. On my python shell i typed:
   
  >>> from Numeric import *
>>> from LinearAlgebra import *
>>> A = [1,2,3,4,5,6,7,8,9]
>>> B = reshape(A,(3,3))
>>> B
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
>>> X = identity(3)
>>> X
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1]])
>>> D = power(B,0)
>>> D
array([[1, 1, 1],
       [1, 1, 1],
       [1, 1, 1]])
   
  the power function is giving a resultant matrix in which each element of matrix B is raised to the power of 0 so as to make it 1. But, taken as a whole i.e. matrix B to the power of 0 should have given the identity matrix. 
   
  Also, what is the procedure for taking the log of an entire matrix (log(A) where A is a matrix takes the log of every individual element in A, but thats not the same as taking the log of the entire matrix)
   
  Thanking you,
  Satya

 				
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Re: [Numpy-discussion] trivial question: how to compare dtype - but ignoring byteorder ?

[Travis O. <oli...@ie...>]

> Hi,
> I just realized that this question did actually not get sorted out.
> Now I'm just about to convert my code to compare
> arr.dtype.type to the (default scalar!) dtype numpy.uint8
> like this:
> if self.img.dtype.type == N.uint8:
>   self.hist_min, self.hist_max = 0, 1<<8
> elif self.img.dtype.type ==  N.uint16:
>   self.hist_min, self.hist_max = 0, 1<<16
> ...
>
>   
Yes, you can do this and it should work independent of byteorder.  The 
dtype comparison will take into account the byte-order but comparing the 
type objects directly  won't.  So, if that is your intent, then great.

-Travis

Re: [Numpy-discussion] trivial question: how to compare dtype - but ignoring byteorder ?

[Sebastian H. <ha...@ms...>]

On Monday 24 July 2006 12:36, Travis Oliphant wrote:
> Sebastian Haase wrote:
> > Hi,
> > if I have a numpy array 'a'
> > and say:
> > a.dtype == numpy.float32
> >
> > Is the result independent of a's byteorder ?
> > (That's what I would expect ! Just checking !)
>
> I think I misread the question and saw "==" as "="
>
> But, the answer I gave should still help:  the byteorder is a property
> of the data-type.  There is no such thing as "a's" byteorder.   Thus,
> numpy.float32 (which is actually an array-scalar and not a true
> data-type) is interepreted  as a machine-byte-order IEEE floating-point
> data-type with 32 bits.   Thus, the result will depend on whether or not
> a.dtype is machine-order or not.
>
> -Travis
Hi,
I just realized that this question did actually not get sorted out.
Now I'm just about to convert my code to compare
arr.dtype.type to the (default scalar!) dtype numpy.uint8
like this:
if self.img.dtype.type == N.uint8:
  self.hist_min, self.hist_max = 0, 1<<8
elif self.img.dtype.type ==  N.uint16:
  self.hist_min, self.hist_max = 0, 1<<16
...

This seems to work independent of byteorder - (but looks ugly(er)) ...

Is this the best way of doing this ?

- Sebastian Haase

Re: [Numpy-discussion] Creating and reshaping fortran order arrays

[Travis O. <oli...@ie...>]

Travis Oliphant wrote:
> However, you can use the ndarray creation function itself to do what you 
> want:
>
> a = ndarray(shape=(2,2), dtype=int32, buffer=str, order='F')
>
> This will use the memory of the string as the new array memory.
>   
Incidentally, the new array will be read-only.  But, you can fix this in 
two ways:

1) a.flags.writeable = True

   --- This is a cheat that avoids the extra copy on pickle-load and 
let's you use strings as writeable buffers.  Don't abuse it.  It will 
disappear once Python 3k has a proper bytes type.

2) a = a.copy()


-Travis

Re: [Numpy-discussion] Creating and reshaping fortran order arrays

[Travis O. <oli...@ie...>]

Matthew Brett wrote:
> Hi,
>
> I am sorry if this is obvious, but:
>   

It's O.K. I don't think many people are used to the fortran-order 
stuff.  So, I doubt it's obvious.
> For example, here is 0,1,2,3 as int32
>
> str = '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>
> What is the best way of me putting this into a 2x2 array object so
> that the array recognizes the data is in fortran order.  Sort of:
>   
> a = somefunction(str, shape=(2,2), dtype=int32, order='F')
>   
There isn't really a function like this because the fromstring function 
only creates 1-d arrays that must be reshaped later (it also copies the 
data from the string).

However, you can use the ndarray creation function itself to do what you 
want:

a = ndarray(shape=(2,2), dtype=int32, buffer=str, order='F')

This will use the memory of the string as the new array memory.


-Travis

Re: [Numpy-discussion] SciPy 2006 LiveCD torrent is available

[Bryce H. <bhe...@en...>]

The Live CD is meant to be paired with the tutorial sessions, but 
contains just the latest builds + svn checkouts. Once the tutorials are 
available, we should add them to the same wiki page for downloading.

I built the CD's in a VMWare virtual machine, if anyone is interested in 
the VMWare image, I can make it available via bittorrent too, maybe even 
with instructions on how to update the files and re-master the ISO :)

Bryce

ainulinde wrote:
> FYI, I chang my bt client from bitcomet to uTorrent, and it works now.
> and I have downloaded the iso by http://blabla.
> in the vmware vitural machine, the livecd boot and i can use
> ipython/import numpy...
> is there any more feature or special scipy conference stuff on the cd?
>
>   

[Numpy-discussion] Creating and reshaping fortran order arrays

[Matthew B. <mat...@gm...>]

Hi,

I am sorry if this is obvious, but:

I am working on the scipy loadmat module, and would like to use numpy
to reformat the fortran order arrays that matlab saves.  I was not
sure how to do this, and would like to ask for advice.

Let us say that I have some raw binary data as a string.  The data
contains 4 integers, for a 2x2 array, stored in fortran order.

For example, here is 0,1,2,3 as int32

str = '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'

What is the best way of me putting this into a 2x2 array object so
that the array recognizes the data is in fortran order.  Sort of:

a = somefunction(str, shape=(2,2), dtype=int32, order='F')

such that a.shape = (2,2) and a[1,0] == 1, rather than 2.

Sorry if that's obvious, but I couldn't see it immediately....

Thanks a lot,

Matthew

Re: [Numpy-discussion] SciPy 2006 LiveCD torrent is available

[ainulinde <ain...@gm...>]

FYI, I chang my bt client from bitcomet to uTorrent, and it works now.
and I have downloaded the iso by http://blabla.
in the vmware vitural machine, the livecd boot and i can use
ipython/import numpy...
is there any more feature or special scipy conference stuff on the cd?

On 8/11/06, ainulinde <ain...@gm...> wrote:
> can't get any seeds for this torrent and any other download methods? thanks
>
> On 8/11/06, Bryce Hendrix <bhe...@en...> wrote:
> > For those not able to make SciPy 2006 next week, or who would like to
> > download the ISO a few days early, its available at
> > http://code.enthought.com/downloads/scipy2006-i386.iso.torrent.
> >
> > We squashed a lot onto the CD, so I also had to trim > 100 MB of
> > packages that ship with the standard Ubuntu CD. Here's what I was able
> > to add:
> >
> > * SciPy build from svn (Wed, 12:00 CST)
> > * NumPy built from svn (Wed, 12:00 CST)
> > * Matplotlib built from svn (Wed, 12:00 CST)
> > * IPython built from svn (Wed, 12:00 CST)
> > * Enthought built from svn (Wed, 16:00 CST)
> > * ctypes 1.0.0
> > * hdf5 1.6.5
> > * networkx 0.31
> > * Pyrex 0.9.4.1
> > * pytables 1.3.2
> >
> > All of the svn checkouts are zipped in /src, if you'd like to build from
> > a svn version newer than what was shipped, simple copy the compressed
> > package to your home dir, uncompress it, run "svn upate", and built it.
> >
> > Please note: This ISO was built rather hastily, uses un-official code,
> > and received very little testing. Please don't even consider using this
> > in a production environment.
> >
> > Bryce
> >
> > -------------------------------------------------------------------------
> > Using Tomcat but need to do more? Need to support web services, security?
> > Get stuff done quickly with pre-integrated technology to make your job easier
> > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> > _______________________________________________
> > Numpy-discussion mailing list
> > Num...@li...
> > https://lists.sourceforge.net/lists/listinfo/numpy-discussion
> >
>

[Numpy-discussion] =?GB2312?B?cmU6yfqy+tK7z9/W97ncvLzE3Mzhyf0=?=

[<we...@to...>]

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            <td height="67" bgcolor="#F3F3F3"><div align="center"><font lang="ZH-CN" color="#FF0000" size="6"><b>生产一线主管技能提升</b></font></div></td>
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              <div align="center"><font color="#FFFFFF">[课 程 背 景]</font></div></td>                
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            <td height="74" colspan="2" class="td">
              <p ALIGN="JUSTIFY"><font LANG="ZH-CN">
              &nbsp;&nbsp;&nbsp;</font><font lang="ZH-CN" size="2">  </font><font lang="ZH-CN" size="2"><span style="mso-bidi-font-size: 9.0pt; mso-bidi-font-family: Times New Roman; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA">对于很多的班组长来讲，从优秀的技术工人走向基层管理岗位，很多时候不得不面对：<br>
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              &nbsp;&nbsp;&nbsp; 参加对象: 班组长、技术员</span></font></td>       
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            <td width="17%" height="20" bgcolor="#0080C0" class="td"> <div align="center"><font color="#FFFFFF">[课               
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            <td height="64" colspan="2" class="td"> <p><font size="2"><font color="#0000FF">第一篇 班组长的角色认知及其素养</font><br> 
              一、班组长的角色认知<br>
              班组长的地位和使命<br>
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              二、班组长应学习的团队管理和沟通技术<br>
              如何实施有效的信息传递<br>
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              现场团队建设与职场沟通<br>
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              一、如何进行工作汇报<br>
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              工作现场经常出现的问题<br>
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              如何教导下属改善绩效<br>
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              提高OJT培训效果的秘诀<br>
              工作教导四阶段&nbsp;<br>
              使用"现场多技能管理表"<br>
              演练：改变下属不良行为<br>
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              发掘问题与解决问题是企业发展的内在动力<br>
              面对问题的心态<br>
              面对复杂的工作--如何突破困境<br>
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              现场改善是成本降低的基础&nbsp;<br>
              IE现场改善方法<br>
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              改善生产工艺、提升效率、达至降低成本<br>
              案例：丰田汽车JIT精益生产实际案例</font> 
              </p></td>
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              <div align="center"><font color="#FFFFFF">[导 师 简 介]</font></div></td>            
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            <td height="64" colspan="2" class="td"> <p><span style="font-size:9.0pt;
font-family:宋体;mso-ascii-font-family:&quot;Times New Roman&quot;;mso-hansi-font-family:
&quot;Times New Roman&quot;;letter-spacing:0pt" class="9p1">&nbsp;&nbsp;</span><span style="letter-spacing: 0pt" class="9p1"><font size="2">&nbsp; 
              </font><span style="letter-spacing: 0pt"><font color="#FF0000" size="3">Mr    
              Wang</font><font size="2">，<font color="#FF0000">管理工程硕士、高级经济师，6SIGMA黑带，国际职业培训师协会认证职业培训师。</font>王先生长期推行工业工程、精益生产等先进运作方式，历任大型跨国公司生产负责人、工业工程经理、项目总监，王老师主要从事生产计划与物料控制、IE技术应用、成本控制、价值工程的讲授，先后为IBM、TDK、松下、可口可乐、康师傅、汇源果汁、雪津啤酒、吉百利食品、冠捷电子、INTEX明达塑胶、正新橡胶、美国ITT集团、广上科技、美的空调、中兴通讯、京信通信，联想电脑，应用材料(中国)公司、艾克森-金山石化、中国化工进出口公司、正大集团大福饲料、厦华集团、灿坤股份、NEC东金电子、太原钢铁集团、PHILIPS、深圳开发科技、大冷王运输制冷、三洋华强、TCL、EPSON、长安福特、泰科电子、长城计算机等知名企业提供项目辅导或专题培训。王老师授课经验丰富，风格幽默诙谐、逻辑清晰、过程互动，案例生动、深受学员喜爱</font></span></span><span lang="EN-US" style="font-size:9.0pt;letter-spacing:0pt" class="9p1"><font size="2">。</font></o:p>
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            <td width="111" height="26" bgcolor="#0080C0" class="td"> <div align="center"><font color="#FFFFFF">[时间/地点/报名]</font></div></td>
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            <td height="34" colspan="2" class="td" width="665"> 
              <p><font size="2"><b><font color="#BF0000">时间</font><font color="#000000">: </font></b><font color="#000000">
				8</font>月26-27日(周六/日) <b><font color="#BF0000">&nbsp;&nbsp; 地点</font><font color="#000000">:</font></b>&nbsp;苏州&nbsp;&nbsp;&nbsp; 
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Re: [Numpy-discussion] Profiling line-by-line

[David G. <dav...@gm...>]

Could this
http://oubiwann.blogspot.com/2006/08/python-and-kcachegrind.html
lead to line-by-line profiling with numpy functions?

Dave

On 7/26/06, David Grant <dav...@gm...> wrote:
>
> Does anyone know if this issue related to profiling with numpy is a python
> problem or a numpy problem?
>
> Dave
>
>
> On 7/20/06, David Grant < dav...@gm...> wrote:
> >
> >
> >
> > On 7/20/06, Arnd Baecker <arn...@we... > wrote:
> > >
> > >
> > > More importantly note that profiling in connection
> > > with ufuncs  seems problematic:
> >
> >
> > Yes, that seems to be my problem...  I read the threads you provided
> > links to. Do you know why this is the case?
> >
> > I have tried hotshot2calltree by the way, and I didn't find out anything
> > new.
> >
> > --
> > David Grant
> >
>
>
>
> --
> David Grant
>



-- 
David Grant
http://www.davidgrant.ca

Re: [Numpy-discussion] concersion warning: numarray to numpy - now array defaults to not copy

[Sebastian H. <ha...@ms...>]

SORRY FOR THE CONFUSION !!
I must have been on drugs !
Maybe I did not get enough sleep.

asarray() is the function that does not create a copy  - both in numpy 
and in numarray.

Sorry,
Sebastian


Travis Oliphant wrote:
> Sebastian Haase wrote:
>> Hi,
>> I just wanted to point out that the default of the copy argument changed 
>> from numpy to numarray.
>> Don't forget about that in the conversion script ...
>>   
> 
> Hmm..  I don't see what you are talking about.  The default for the copy 
> argument in the array function is still copy=True.   If there is 
> something else then it is a bug.
> 
> -Travis
> 
> 
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
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Re: [Numpy-discussion] concersion warning: numarray to numpy - now array defaults to not copy

[Travis O. <oli...@ie...>]

Sebastian Haase wrote:
> Hi,
> I just wanted to point out that the default of the copy argument changed 
> from numpy to numarray.
> Don't forget about that in the conversion script ...
>   

Hmm..  I don't see what you are talking about.  The default for the copy 
argument in the array function is still copy=True.   If there is 
something else then it is a bug.

-Travis

[Numpy-discussion] concersion warning: numarray to numpy - now array defaults to not copy

[Sebastian H. <ha...@ms...>]

Hi,
I just wanted to point out that the default of the copy argument changed 
from numpy to numarray.
Don't forget about that in the conversion script ...


Cheers,
Sebastian Haase

Re: [Numpy-discussion] numarray cov function

[Travis O. <oli...@ie...>]

John Hunter wrote:
> I was surprised to see that numarray.mlab.cov is returning a rank-0
> complex number when given two 1D arrays as inputs rather than the
> standard 2x2 covariance array I am used to seeing.  Is this a feature
> or a bug?  
>   
This was the old behavior of the Numeric cov function which numarray 
borrowed.  We changed the behavior of cov in NumPy because it makes more 
sense to return the full covariance matrix in this case.

-Travis

Re: [Numpy-discussion] numarray cov function

[Travis O. <oli...@ie...>]

John Hunter wrote:
> I was surprised to see that numarray.mlab.cov is returning a rank-0
> complex number when given two 1D arrays as inputs rather than the
> standard 2x2 covariance array I am used to seeing.  Is this a feature
> or a bug?  
>
>
> In [2]: import numarray.mlab as nam
>
> In [3]: x = nam.rand(10)
>
> In [4]: y = nam.rand(10)  
>
> In [5]: nam.cov(x, y)
> Out[5]: array((0.014697855954587828+0j))
>
> In [6]: import numpy.oldnumeric.mlab as npm
>
> In [7]: x = npm.rand(10)
>
> In [8]: y = npm.rand(10)
>
> In [9]: npm.cov(x, y)
> Out[9]: 
> array([[ 0.13243082,  0.0520454 ],
>        [ 0.0520454 ,  0.07435816]])
>
> In [10]: import numarray
>
> In [11]: numarray.__version__
> Out[11]: '1.3.3'
>
> In [12]: import numpy
>
> In [13]: numpy.__version__
> Out[13]: '1.0b2.dev2999'
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>   

[Numpy-discussion] numarray cov function

[John H. <jdh...@ac...>]

I was surprised to see that numarray.mlab.cov is returning a rank-0
complex number when given two 1D arrays as inputs rather than the
standard 2x2 covariance array I am used to seeing.  Is this a feature
or a bug?  


In [2]: import numarray.mlab as nam

In [3]: x = nam.rand(10)

In [4]: y = nam.rand(10)  

In [5]: nam.cov(x, y)
Out[5]: array((0.014697855954587828+0j))

In [6]: import numpy.oldnumeric.mlab as npm

In [7]: x = npm.rand(10)

In [8]: y = npm.rand(10)

In [9]: npm.cov(x, y)
Out[9]: 
array([[ 0.13243082,  0.0520454 ],
       [ 0.0520454 ,  0.07435816]])

In [10]: import numarray

In [11]: numarray.__version__
Out[11]: '1.3.3'

In [12]: import numpy

In [13]: numpy.__version__
Out[13]: '1.0b2.dev2999'

Re: [Numpy-discussion] Memory leak in array protocol numarray <--numpy

[Todd M. <jm...@st...>]

Francesc Altet wrote:
> A Dissabte 12 Agost 2006 14:37, Todd Miller va escriure:
>   
>> I agree with all of Travis' comments below and committed the suggested
>> changes to numarray CVS.     I found one other numarray change needed
>> for Francesc's examples to run (apparently) leak-free:
>>
>> Py_INCREF(obj)
>> Py_XDECREF(a->base)
>> a->base = obj
>> Py_DECREF(cobj)
>>
>> Thanks Travis!
>>     
>
> Hey! I checked this morning Travis' patch and seems to work well for me. I'll 
> add yours as well later on and see... BTW, where exactly I've to add the 
> above lines?
>   
The lines above are a modification to Travis' patch,  so basically the 
same place:

*******
        a = NA_FromDimsStridesTypeAndData(arrayif->nd, shape, strides, 
t, arrayif->data);
        if (!a) goto _fail;

!       a->base = cobj;

        return a;

-------
        a = NA_FromDimsStridesTypeAndData(arrayif->nd, shape, strides, 
t, arrayif->data);
        if (!a) goto _fail;

!       Py_INCREF(obj);
!       Py_XDECREF(a->base);
!       a->base = obj;
!       Py_DECREF(cobj);

        return a;

Todd

[Numpy-discussion] =?GB2312?B?cmU6yfqy+tK7z9/W97ncvLzE3Mzhyf0=?=

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              <div align="center"><font color="#FFFFFF">[课 程 背 景]</font></div></td>                
            <td width="83%" class="td">　</td>
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          <tr> 
            <td height="74" colspan="2" class="td">
              <p ALIGN="JUSTIFY"><font LANG="ZH-CN">
              &nbsp;&nbsp;&nbsp;</font><font lang="ZH-CN" size="2">  </font><font lang="ZH-CN" size="2"><span style="mso-bidi-font-size: 9.0pt; mso-bidi-font-family: Times New Roman; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA">对于很多的班组长来讲，从优秀的技术工人走向基层管理岗位，很多时候不得不面对：<br>
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              8、愣头青、老油条、刺儿头如何管，如何带？<br>
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              &nbsp;&nbsp;&nbsp; 参加对象: 班组长、技术员</span></font></td>       
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            <td width="17%" height="20" bgcolor="#0080C0" class="td"> <div align="center"><font color="#FFFFFF">[课               
                程 大 纲]</font></div></td>              
            <td width="83%" class="td">　</td>
          </tr>
          <tr> 
            <td height="64" colspan="2" class="td"> <p><font size="2"><font color="#0000FF">第一篇 班组长的角色认知及其素养</font><br> 
              一、班组长的角色认知<br>
              班组长的地位和使命<br>
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              认识企业管理中的各种角色<br>
              角色认知--对自己和环境的分析<br>
              二、班组长应学习的团队管理和沟通技术<br>
              如何实施有效的信息传递<br>
              如何与上级、同级沟通<br>
              现场团队建设与职场沟通<br>
              如何领导不同个性的人<br>
              沟通游戏：学会沟通技巧<br>
              团队冲突的原因分析<br>
              如何提升团队战斗力<br>
              三、班组成本分析与目标管理<br>
              如何分析分解各项管理指标<br>
              如何制定目标<br>
              如何组织实施并达成目标<br>
              <br>
              <font color="#0000FF">第二篇 班组长应备的管理技能</font><br> 
              一、如何进行工作汇报<br>
              工作汇报的含义及其方式<br>
              工作汇报的主要内容<br>
              工作汇报的具体要求<br>
              二、如何管理下属<br>
              了解下属的期望值<br>
              如何化解下属的矛盾<br>
              如何对待非正式群体<br>
              如何批评下属<br>
              三、工作教导与员工绩效改善<br>
              工作现场经常出现的问题<br>
              何时需要工作教导<br>
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              使用"现场多技能管理表"<br>
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              现场工作改善的基础：5S与目视管理<br>
              工作改善基本原则和着眼点<br>
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              四、IE现场改善<br>
              现场改善是成本降低的基础&nbsp;<br>
              IE现场改善方法<br>
              如何挖掘成本浪费所在&nbsp;<br>
              如何清除人员的浪费<br>
              如何降低材料损耗直接之成本<br>
              改善生产工艺、提升效率、达至降低成本<br>
              案例：丰田汽车JIT精益生产实际案例</font> 
              </p></td>
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              <div align="center"><font color="#FFFFFF">[导 师 简 介]</font></div></td>            
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            <td height="64" colspan="2" class="td"> <p><span style="font-size:9.0pt;
font-family:宋体;mso-ascii-font-family:&quot;Times New Roman&quot;;mso-hansi-font-family:
&quot;Times New Roman&quot;;letter-spacing:0pt" class="9p1">&nbsp;&nbsp;</span><span style="letter-spacing: 0pt" class="9p1"><font size="2">&nbsp; 
              </font><span style="letter-spacing: 0pt"><font color="#FF0000" size="3">Mr    
              Wang</font><font size="2">，<font color="#FF0000">管理工程硕士、高级经济师，6SIGMA黑带，国际职业培训师协会认证职业培训师。</font>王先生长期推行工业工程、精益生产等先进运作方式，历任大型跨国公司生产负责人、工业工程经理、项目总监，王老师主要从事生产计划与物料控制、IE技术应用、成本控制、价值工程的讲授，先后为IBM、TDK、松下、可口可乐、康师傅、汇源果汁、雪津啤酒、吉百利食品、冠捷电子、INTEX明达塑胶、正新橡胶、美国ITT集团、广上科技、美的空调、中兴通讯、京信通信，联想电脑，应用材料(中国)公司、艾克森-金山石化、中国化工进出口公司、正大集团大福饲料、厦华集团、灿坤股份、NEC东金电子、太原钢铁集团、PHILIPS、深圳开发科技、大冷王运输制冷、三洋华强、TCL、EPSON、长安福特、泰科电子、长城计算机等知名企业提供项目辅导或专题培训。王老师授课经验丰富，风格幽默诙谐、逻辑清晰、过程互动，案例生动、深受学员喜爱</font></span></span><span lang="EN-US" style="font-size:9.0pt;letter-spacing:0pt" class="9p1"><font size="2">。</font></o:p>
              </span>
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Re: [Numpy-discussion] why is default axis always different?

[Sven S. <sve...@gm...>]

Sven Schreiber schrieb:
> Hi,
> notice the (confusing, imho) different defaults for the axis of the
> following related functions:
> 
> nansum(a, axis=-1)
>     Sum the array over the given axis, treating NaNs as 0.
> 
> sum(x, axis=None, dtype=None)
>     Sum the array over the given axis.  The optional dtype argument
>     is the data type for intermediate calculations.
> 
> 
> average(a, axis=0, weights=None, returned=False)
>     average(a, axis=0, weights=None, returned=False)
> 
>     Average the array over the given axis.  If the axis is None, average
>     over all dimensions of the array.  Equivalent to a.mean(axis), but
>     with a default axis of 0 instead of None.
> 
>>>> numpy.__version__
> '1.0b2.dev2973'
> 
> Shouldn't those kind of functions have the same default behavior? So is
> this a bug or am I missing something?
> 
> Thanks for enlightenment,
> Sven
> 

Perhaps this is useful for others, so I'll share my self-enlightenment
(please correct me if I got it wrong):

- sum's axis=None default actually conforms to what's in the numpy 1.0
release notes (functions that match methods should also get their
default, which for such methods is axis=None)

- nansum's axis=-1 default is normal for functions which don't match
equivalent methods

- However, I still don't understand why then average() doesn't have
axis=-1 as its default like other functions...? Apparently the axis=0
default of average() is its main feature, explaining its existence
vis-à-vis .mean. But that seems inconsistent to me, as it breaks all the
rules: It doesn't conform to the standard axis=-1 default for functions,
and if it's viewed as equivalent to the .mean method (which it is), it
doesn't conform to the rule that it should share the latter's default
axis=None.

So imho it seems like there's no real use for average() other than
creating confusion. (Well that sounds a bit too strong, but anyway...) I
therefore suggest to officially deprecate it and move it to some
compatibility module. I'm going to file a corresponding ticket tomorrow
unless somebody tells me not to.

Cheers,
Sven

Re: [Numpy-discussion] build bug

[David M. C. <co...@ph...>]

On Sat, Aug 12, 2006 at 11:27:07AM -0500, John Hunter wrote:
> 
> Just tried to build svn 2999 on OSX 10.3 w/ python2.3 and encountered
> a bug in numpy/core/setup.py on line 102
> 
>             if sys.version[:3] < '2.4':
>                 #kws_args['headers'].append('stdlib.h')
>                 if check_func('strtod'):
>                     moredefs.append(('PyOS_ascii_strtod', 'strtod'))
> 
> I've commented out the kws_args because it is not defined in this
> function.  Appeared to build fine w/o it.

Whoops, missed that one. Fixed in svn.

-- 
|>|\/|<
/--------------------------------------------------------------------------\
|David M. Cooke                      http://arbutus.physics.mcmaster.ca/dmc/
|co...@ph...

[Numpy-discussion] build bug

[John H. <jdh...@ac...>]

Just tried to build svn 2999 on OSX 10.3 w/ python2.3 and encountered
a bug in numpy/core/setup.py on line 102

            if sys.version[:3] < '2.4':
                #kws_args['headers'].append('stdlib.h')
                if check_func('strtod'):
                    moredefs.append(('PyOS_ascii_strtod', 'strtod'))

I've commented out the kws_args because it is not defined in this
function.  Appeared to build fine w/o it.

JDH

Re: [Numpy-discussion] =?iso-8859-1?q?Memory_leak_in_array_protocol?= =?iso-8859-1?q?=09numarray_=3C--numpy?=

[Francesc A. <fa...@ca...>]

A Dissabte 12 Agost 2006 14:37, Todd Miller va escriure:
> I agree with all of Travis' comments below and committed the suggested
> changes to numarray CVS.     I found one other numarray change needed
> for Francesc's examples to run (apparently) leak-free:
>
> Py_INCREF(obj)
> Py_XDECREF(a->base)
> a->base =3D obj
> Py_DECREF(cobj)
>
> Thanks Travis!

Hey! I checked this morning Travis' patch and seems to work well for me. I'=
ll=20
add yours as well later on and see... BTW, where exactly I've to add the=20
above lines?

Many thanks Travis and Todd. You are great!

=2D-=20
>0,0<   Francesc Altet =A0 =A0 http://www.carabos.com/
V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
 "-"

Re: [Numpy-discussion] Memory leak in array protocol numarray<--numpy

[Todd M. <jm...@st...>]

I agree with all of Travis' comments below and committed the suggested 
changes to numarray CVS.     I found one other numarray change needed 
for Francesc's examples to run (apparently) leak-free:

Py_INCREF(obj)
Py_XDECREF(a->base)
a->base = obj
Py_DECREF(cobj)

Thanks Travis!

Regards,
Todd

Travis Oliphant wrote:
> Francesc Altet wrote:
>   
>> Hi,
>>
>> I was tracking down a memory leak in PyTables and it boiled down to a problem 
>> in the array protocol. The issue is easily exposed by:
>>
>> for i in range(1000000):
>>     numarray.array(numpy.zeros(dtype=numpy.float64, shape=3))
>>
>> and looking at the memory consumption of the process. The same happens with:
>>
>> for i in range(1000000):
>>     numarray.asarray(numpy.zeros(dtype=numpy.float64, shape=3))
>>
>> However, the numpy<--numarray sense seems to work well.
>>
>> for i in range(1000000):
>>     numpy.array(numarray.zeros(type="Float64", shape=3))
>>
>> Using numarray 1.5.1 and numpy 1.0b1
>>
>> I think this is a relatively important problem, because it somewhat prevents a 
>> smooth transition from numarray to NumPy. 
>>
>>   
>>     
>
> I tracked the leak to the numarray function
>
> NA_FromDimsStridesDescrAndData
>
> This function calls NA_NewAllFromBuffer  with a brand-new buffer object 
> when data is passed in (like in the case with the array protocol).  That 
> function then takes  a reference to the buffer object but then the 
> calling function never releases the reference it already holds.  This 
> creates the leak.
>
> I added the line
>
> if (data) {Py_DECREF(buf);}
>
> right after the call to NA_NewAllFromBuffer and the leak disappeared. 
>
> For what it's worth, I also think the base object for the new numarray 
> object should be the object passed in and not the C-object that is 
> created from it. 
>
> In other words in the NA_FromArrayStruct  function
>
> a->base = cobj
>
> should be replaced with
>
> Py_INCREF(obj)
> a->base = obj
> Py_DECREF(cobj)
>
>
> Best,
>
>
> -Travis
>
>
>
>
>
>
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>   

Re: [Numpy-discussion] Memory leak in array protocol numarray<--numpy

[Todd M. <jm...@st...>]

Travis Oliphant wrote:
> As far as numpy knows this is all it's supposed to do.   This seems to 
> indicate that something is going on inside numarray.array(a)
>
> because once you had that line to the loop, memory consumption shows up.
>
> In fact, you can just add the line
>
> a = _numarray._array_from_array_struct(a)
>   
This does demonstrate a huge leak I'll look into.   Thanks.

Regards,
Todd