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

[Numpy-discussion] Fwd: bdist_mpkg

[Christopher F. <fon...@ma...>]

Never mind, I see that it is in setupegg.py.

C.

Begin forwarded message:

> From: lis...@ma...
> Date: July 8, 2006 11:11:02 AM EDT
> To: num...@li...
> Subject: bdist_mpkg
>
> As of recent svn updates, I can no longer build numpy binaries for  
> OS X using bdist_mpkg. How do I restore this capability, or is  
> there a better way of building Mac binaries?
>
> Thanks,
> C.
>
> --
> Christopher Fonnesbeck
> + Atlanta, GA
> + fonnesbeck at mac.com
> + Contact me on AOL IM using email address
>
>

--
Christopher Fonnesbeck
+ Atlanta, GA
+ fonnesbeck at mac.com
+ Contact me on AOL IM using email address

[Numpy-discussion] bdist_mpkg

[<lis...@ma...>]

As of recent svn updates, I can no longer build numpy binaries for OS  
X using bdist_mpkg. How do I restore this capability, or is there a  
better way of building Mac binaries?

Thanks,
C.

--
Christopher Fonnesbeck
+ Atlanta, GA
+ fonnesbeck at mac.com
+ Contact me on AOL IM using email address

Re: [Numpy-discussion] NumPy beta release plan

[Darren D. <dd...@co...>]

On Saturday 08 July 2006 4:04 am, Stephan Tolksdorf wrote:
> Hi Travis,
>
> thanks for putting all the effort into NumPy.
>
>  > Unless there is a serious issue pointed out, the only thing that should
>  > be changed at this point are bug-fixes, documentation strings, and added
>  > tests.  Once 1.0b1 is released nothing but those things can be added.
>
> The beginning of the beta phase might also be the best time to address
> the most severe NumPy.distutils issues, so that all changes can be
> extensively tested during the beta:

The number of people in my lab using numpy/scipy/matplotlib is starting to 
increase. Last week I was trying to get a couple of graduate students set up. 
They were using MacOS X 10.4 with gcc3.3, and we were never successful in 
getting numpy to build. I think the problem was the same as reported at 
http://mail.python.org/pipermail/pythonmac-sig/2006-March/016703.html. We 
ended up getting things installed by installing scipy with fink.

>   - warning messages are only shown if compilation fails (NumPy should
> build warning free)

One of the guys I was working with tried installing every compiler out there 
to get rid of all the compiler not found messages that were reported during 
the attempted numpy builds. I guess he thought these messages were critical 
errors, since they are rendered in red lettering.

Darren

Re: [Numpy-discussion] Args for rand and randn: call for a vote

[Scott R. <sr...@nr...>]

> * Should numpy.rand and numpy.randn accept sequences of dimensions as  
> arguments, like rand((3,3)), as an alternative to rand(3,3)?

+1

-- 
Scott M. Ransom            Address:  NRAO
Phone:  (434) 296-0320               520 Edgemont Rd.
email:  sr...@nr...             Charlottesville, VA 22903 USA
GPG Fingerprint: 06A9 9553 78BE 16DB 407B  FFCA 9BFA B6FF FFD3 2989

Re: [Numpy-discussion] Args for rand and randn: call for a vote

[Darren D. <dd...@co...>]

On Saturday 08 July 2006 7:07 am, Ed Schofield wrote:
> * Should numpy.rand and numpy.randn accept sequences of dimensions as
> arguments, like rand((3,3)), as an alternative to rand(3,3)?

+1

-- 
Darren S. Dale, Ph.D.
dd...@co...

Re: [Numpy-discussion] What's wrong with matrices?

[Bill B. <wb...@gm...>]

On 7/8/06, Travis Oliphant <oli...@ie...> wrote:
>
> > Okay, here are a few that come to mind.
> > 1) Functions that take a matrix but return an array.  Maybe these are
> > all fixed now.  But they better be fixed not just in numpy but in
> > scipy too.  To me this implies there needs to be some standard idiom
> > for how to write a generic array-protocol-using function so that you
> > don't have to think about it.
>
> A lot of these are fixed.  The mechanism for handling this is in-place:
> either using asanyarray in the function or (more generally) using a
> decorator that wraps the arguments with asarray and returns the output
> with __array_wrap__.


That sounds good.  Is there a description of what that does and how to use
it anywhere you could point me to?

> 5) I've got squeezes like crazy all over my matrix-using code.  Maybe
> > this was a bug in 0.9.5 or so that's been fixed?  I do seem to recall
> > some problem with indexing or c_ or something that was causing
> > matrices to grow extra levels of length 1 axes.  Again, like the
> > scalar*matrix bug, things like that shouldn't happen.
> Sure, but it's going to happen in a beta release...  That's why we need
> testers.   As I recall, most bugs with matrices have been fixed fairly
> quickly as soon as they are reported.


What do you mean by "beta release"?  Are the odds considered betas and evens
releases?  Or do you mean just everything prior to 1.0 is actually beta?  I
haven't seen anything actually labeled as "beta" yet.

And yes, you've done a fantastic job fixing bugs quickly, and getting
releases out in a pretty timely manner too.  Many thanks for that.

>
> > 6) No good way to do elementwise operations?  Sometimes you just want
> > to do an elementwise mult or divide or exponentiation.  I guess you're
> > supposed to do  Z = asmatrix(X.A * Y.A).  Yah, right.
> This is a problem with a dearth of infix operators.   In fact, if we had
> a good way to write matrix multiplication as an infix operator, perhaps
> there wouldn't be any need for matrices.


Actually I was more after what you mentioned later -- the multiply(a,b)
function.  I see they're all there -- multiply, divide, power.  That's all I
wanted, because I know the Python operator overload situation doesn't really
allow more than that.

> ...So I'd like matrices to be able to have ndim>2.
> I suppose this restriction could be lifted.


I think that would be an improvement for people who want to use matrix as
their only ndarray data type.

>     b) On the other end, I think ndim<2 is useful sometimes too.  Take
> > a function like mean(), for example.  With no arguments  the return
> > value is a 1x1 matrix (as opposed to a scalar).
> Have you checked lately.  It's a scalar now...  This has been fixed.


Nope, haven't checked more recently than the latest release. I'm in the
process of trying to build numpy, though.

> Or take indexing.  It seems odd to me that where() reurns a tuple of
> > shape==(1,N) objects instead of just (N,) .




The way to fix some of these is to return arrays for indexing instead of
> allowing matrices.    But, matrices that are less than 2-d just don't
> make sense.


I guess not.  Looking at what matlab does more closely I see that it does
report size (1,1) for all scalars, and either 1,N or N,1 for all vectors.
It just hides it better.  Namely when printing you don't get as many
superfluous brackets (in fact matlab doesn't print any brackets).

> Maybe I can get over that though, as long as it works for indexing
> > (which it seems it does).   But I think the scalar return case is a
> > real issue.  Here's another: sum().  For an array you can do
> > sum(sum(a)) and get a scalar if a is 2-d, but for matrix sum(sum(m))
> > is the same as sum(m).   And along these lines, m[newaxis] just
> > silently doesn't do anything.  That doesn't seem right.
>
> These are just semantic questions.   It's no surprise that sum(sum(m))
> returns the same as sum(m) for a matrix because summing over the same
> axis won't change the result.   You have to sum over both axes in a
> matrix.


Right, it is perfectly logical given how sum is implemented.    I guess I'm
just used to Matlab's way which acts more like sum on arrays.   But maybe
numpy's way is ok too.

--bill

Re: [Numpy-discussion] Args for rand and randn: call for a vote

[Bill B. <wb...@gm...>]

> > * Should numpy.rand and numpy.randn accept sequences of dimensions as
> > arguments, like rand((3,3)), as an alternative to rand(3,3)?
> >


+1

Re: [Numpy-discussion] Args for rand and randn: call for a vote

[Paul B. <peb...@gm...>]

On 7/8/06, Ed Schofield <sch...@ft...> wrote:
>
> Last week's discussion on rand() and randn() seemed to indicate a
> sentiment that they ought to take tuples for consistency with ones,
> zeros, eye, identity, and empty -- that, although they are supposed
> to be convenience functions, they are inconvenient precisely because
> of their inconsistency with these other functions.  This issue has
> been raised many times over the past several months.
>
> Travis made a change in r2572 to allow tuples as arguments, then took
> it out again a few hours later, apparently unsure about whether this
> was a good idea.
>
> I'd like to call for a vote on what people would prefer, and then ask
> Travis to make a final pronouncement before the feature freeze.
>
>
>
>
> * Should numpy.rand and numpy.randn accept sequences of dimensions as
> arguments, like rand((3,3)), as an alternative to rand(3,3)?

+1     --- I'm all for consistency!

> OR
>
>
> * Should rand((3,3)) and randn((3,3)) continue to raise a TypeError?

Re: [Numpy-discussion] Args for rand and randn: call for a vote

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

Ed Schofield schrieb:

> 
> I'd like to call for a vote on what people would prefer, and then ask  
> Travis to make a final pronouncement before the feature freeze.
> 
> 
> 
> 
> * Should numpy.rand and numpy.randn accept sequences of dimensions as  
> arguments, like rand((3,3)), as an alternative to rand(3,3)?
> 
+1

(Thanks Ed for trying to reach a conclusion on this.)
-sven

Re: [Numpy-discussion] building ATLAS on Win32 - endless compile loop

[Peter <pe...@so...>]

> My laptop is a Pentium M, which isn't one of the options on the
> architecture list.  So I picked "unknown".
> 

In that case ATLAS propably does a full search instead of the much
smaller architectural default one. This takes much longer, but not
infinitely.

Cheers,

Peter

Re: [Numpy-discussion] What's wrong with matrices?

[Bill B. <wb...@gm...>]

My preferred way to import numpy currently is:
import numpy  as num

It would be nice if I could do:
import numpy.matrix as num

And basically have access to all the same stuff that's in the base numpy but
have everything set up in a matrix-like way, so num.ones returns a matrix
instead of an array, etc.  (I don't care so much about the particular
name... matlib, matrixlib, matrixdefault, or whatever -- I just care about
the functionality)

The way I understand this matlib module is that I would do something along
the lines of one of these:
1)  import numpy as num
     from numpy.matlib import ones, zeros,...
or
2)  import numpy as num
     import numpy.matlib as M

Either way, the matrix functions aren't in the same namespace as general
numpy functions, which makes it feel like something of a bolt-on rather than
a second equally valid way to use numpy.  So what I'd like is

3) import numpy.matlib as num

I think the way to get 3) may be to just do a "from numpy import *" at the
beginning of the numpy.matlib module, before redefining particular functions
with matrix versions.  Maybe that's a bad idea, though?

Or maybe this is the way Travis and others planned for it to work from the
beginning?  At any rate, versions 1) and 2) should also be supported for the
times you don't want to use matlib as the default, but still want access to
it.


--bill

Re: [Numpy-discussion] Args for rand and randn: call for a vote

[Ed S. <sch...@ft...>]

On 08/07/2006, at 1:07 PM, Ed Schofield wrote:

> Last week's discussion on rand() and randn() seemed to indicate a
> sentiment that they ought to take tuples for consistency with ones,
> zeros, eye, identity, and empty -- that, although they are supposed
> to be convenience functions, they are inconvenient precisely because
> of their inconsistency with these other functions.  This issue has
> been raised many times over the past several months.
>
> Travis made a change in r2572 to allow tuples as arguments, then took
> it out again a few hours later, apparently unsure about whether this
> was a good idea.
>
> I'd like to call for a vote on what people would prefer, and then ask
> Travis to make a final pronouncement before the feature freeze.
>
>
> * Should numpy.rand and numpy.randn accept sequences of dimensions as
> arguments, like rand((3,3)), as an alternative to rand(3,3)?

+1

[Numpy-discussion] Args for rand and randn: call for a vote

[Ed S. <sch...@ft...>]

Last week's discussion on rand() and randn() seemed to indicate a  
sentiment that they ought to take tuples for consistency with ones,  
zeros, eye, identity, and empty -- that, although they are supposed  
to be convenience functions, they are inconvenient precisely because  
of their inconsistency with these other functions.  This issue has  
been raised many times over the past several months.

Travis made a change in r2572 to allow tuples as arguments, then took  
it out again a few hours later, apparently unsure about whether this  
was a good idea.

I'd like to call for a vote on what people would prefer, and then ask  
Travis to make a final pronouncement before the feature freeze.




* Should numpy.rand and numpy.randn accept sequences of dimensions as  
arguments, like rand((3,3)), as an alternative to rand(3,3)?


OR


* Should rand((3,3)) and randn((3,3)) continue to raise a TypeError?

[Numpy-discussion] building ATLAS on Win32 - endless compile loop

[Bill B. <wb...@gm...>]

Trying to compile Atlas from source on Win32 with Cygwin.  Anyone seen 'make
install' for ATLAS go into and endless loop before?

More info:
Following the instructions here:
   Installing SciPy/Windows -
SciPy.org<http://www.scipy.org/Installing_SciPy/Windows?highlight=%28%28----%28-%2A%29%28%5Cr%29%3F%5Cn%29%28.%2A%29CategoryInstallation%5Cb%29>
At this step:
    "5) As prompted by the config script, execute
 make install arch=YOUR_ARCHITECTURE  and wait for approx > 15min."

It was taking much longer than 15 minutes, but I figured my laptop is just
slow or something.
So I left it on when I went to bed.  It was still cranking away 6 hours
later when I got up.

Just thought I'd check if anyone had seen that before.

My laptop is a Pentium M, which isn't one of the options on the architecture
list.  So I picked "unknown".

--bb

Re: [Numpy-discussion] I think something broke badly today in numpy/scipy...

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

Fernando Perez wrote:
> Hi all,
>
> I updated earlier today (about 4 hours ago) numpy/scipy SVN, and all
> of a sudden my codes broke left and right.  Backing off to
>
>   
Hey Fernando.   I think I found the problem.   It was the same problem 
causing the BFGS test to fail in SciPy.

It can be shown by looking at

sk = [0,-1.0,1.0]
print 250.0*sk[:,newaxis]

This should give something useful (not zeros). 

The problem was a seemingly harmless change in _IsContiguous that 
allowed for 0-strided arrays to be called contiguous.  This caused havoc 
with the multiplication function later which relied on an accurate 
_IsContiguous function. 

This change was in r2765 (I suspect you checked out a change with that 
in it).  The change could cause problems where-ever you use newaxis with 
scalar multiplication.

The problem should be fixed in SVN.

Very sorry...

-Travis

Re: [Numpy-discussion] NumPy beta release plan

[Stephan T. <st...@si...>]

Hi Travis,

thanks for putting all the effort into NumPy.

 > Unless there is a serious issue pointed out, the only thing that should
 > be changed at this point are bug-fixes, documentation strings, and added
 > tests.  Once 1.0b1 is released nothing but those things can be added.

The beginning of the beta phase might also be the best time to address 
the most severe NumPy.distutils issues, so that all changes can be 
extensively tested during the beta:
  - warning messages are only shown if compilation fails (NumPy should 
build warning free)
  - there are some problems on Windows (ticket #114)
  - 64bit/Python 2.5 issues?

Ciao,
   Stephan

[Numpy-discussion] NumPy beta release plan

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

Some of you may have noticed that things have been changing rapidly in 
the NumPy world (if you were out of the office in June then all the 
activity may seem overwhelming). 

All of this activity is based on the fact that the upcoming beta release 
will mean a feature freeze for NumPy. 

As a review:

The biggest changes over the past 3 months have been

1) Capitalized-type-names (Float32, Complex64, Int8, etc) are now
   only available in the numpy.oldnumeric namespace (this is the
   namespace that convertcode.py replaces for Numeric).   We are trying
   to wean you off character codes as much as possible.  They are still
   there, of course but should only be used in special cases not as
   a general rule.

2) Un-specified data-types now default to floating point. 

   To help with code you have that relies on integer data-types you
   can either use functions from numpy.oldnumeric where the functions
   are still defined with the integer-default data-types or use
   functions in numpy/lib/convdtype to replace type-less versions of
   ones, zeros, empty with empty(..., dtype=int).

3) C-API names have prefix PyArray_ (like always), NPY_ or npy_. 
 
   The NPY_ and npy_ prefixes are new and were done to remove the
   likelihood of name collisions when NumPy is used with another
   library.  The old (and un-prefixed) names are accessible by
   importing numpy/noprefix.h  instead of numpy/arrayobject.h

   It is fine to use noprefix.h in-place of arrayobject.h if you
   expect to have no naming conflicts. This is what NumPy itself does.

4) The flag combinations with _FLAGS in the name have the _FLAGS
   removed (but are prefixed with NPY_).  Again the old names are
   still available in numpy/noprefix.h

5) The Numarray C-API is now available as numpy/libnumarray.h as
   long as you use the directory returned from numpy.get_numarray_include()
   as an argument to -I in the compile command.


More minor changes:

1) ctypes attribute added for ease of working with ctypes data
2) T attribute added as a convenience for .transpose()


Personally, I don't want to make any more changes so that we can make a 
1.0 beta release that will freeze the API.  Because of my guarantee to 
keep SVN versions of NumPy/SciPy/matplotlib working, changes probably 
cause more headache for me than anyone else. 

Unless there is a serious issue pointed out, the only thing that should 
be changed at this point are bug-fixes, documentation strings, and added 
tests.  Once 1.0b1 is released nothing but those things can be added.

I'd like July to be a much more calm month.  We should get the 1.0b1 out 
in the next couple of weeks.  That way perhaps 1.0b2 can be out by the 
SciPy conference.    I can see the beta release period taking several 
months with only bug-fixes/docstring/testing improvements happening over 
that time. 

So, take this message as a warning for the up-coming feature freeze on 
NumPy and an invitation to contribute docstrings and unit-tests.

I hope the rapid pace of June development did not scare too many 
people.  Please voice your concerns if you have them.

Best regards,

-Travis O.

[Numpy-discussion] =?GB2312?B?1MvTw0VYQ0VMuMS9+LLGzvG53MDtus3K0LOh06rP+jxhZD4=?=

[<jk...@to...>]

<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=gb2312">
<title>无标题文档</title>
<style type="text/css">
<!--
.td {
	font-size: 12px;
	color: #313131;
	line-height: 20px;
	font-family: "Arial", "Helvetica", "sans-serif";
}
-->
</style>
</head>

<body leftmargin="0" background="http://bo.sohu.com//images/img20040502/dj_bg.gif">
<table width="100%" border="0" cellspacing="0" cellpadding="0">
  <tr>
    <td height="31" background="http://igame.sina.com.cn/club/images/topmenu/topMenu_8.gif" class="td"><div align="center"><font color="#FFFFFF">主办单位：
		易腾企业管理咨询有限公司</font></div></td>
  </tr>
</table>
<br>
<table width="684" border="0" align="center" cellpadding="0" cellspacing="0" height="1287">
  <tr>
    <td height="71" bgcolor="#8C8C8C"> 
      <div align="center"> 
        <table width="100%" border="0" cellspacing="1" cellpadding="0" height="76">
          <tr>
            <td height="74" bgcolor="#F3F3F3"><div align="center">
                <span lang="zh-cn"><font size="6" color="#000080">
				运用EXCEL改进财务管理和市场营销</font></span></div></td>
          </tr>
        </table>
      </div></td>
  </tr>
  <tr>
    <td height="1211" bgcolor="#FFFFFF"> <div align="center"> 
        <table width="99%" border="0" cellspacing="0" cellpadding="0" height="48">
          <tr> 
            <td width="17%" height="22" bgcolor="#BF0000" class="td"> 
              <div align="center"><font color="#FFFFFF">[课 程 背 景]</font></div></td>                      
            <td width="83%" class="td" height="22">　</td>
          </tr>
          <tr> 
            <td height="26" colspan="2" class="td">
              <p ALIGN="JUSTIFY"><font LANG="ZH-CN">
              &nbsp;&nbsp;&nbsp;&nbsp; </font><font color="#000000" size="2">   
				不管您在什么岗位上工作，利用Excel电子表格进行数据分析几乎已经成为每个经理人的必备工具，尤其是在财务和营销管理上，电子表格能够帮助你筛选数据、分析数据并制作管理图表。如果你打算利用Excel提高工作质量和效率，那么这个课程就是为你定制的。<br>
				</font><b><font color="#ff0000" size="2">培 训 收 益：</font></b><font color="#000000" size="2"><br>
				提高EXCEL实际操作能力，提高工作效率；<br>
				掌握如何利用各种函数建立数学模型进行高效财务分析；<br>
				掌握快速实现产品、客户分类的方法，使公司效益倍增；<br>
				掌握建立多因素量本利敏感分析模型，使你直观地发现最佳盈利模式；<br>
				掌握利用各种预测工具，寻找营销方案；<br>
				掌握如何制作令老板满意的各类图表</font><font LANG="ZH-CN"><font 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">。</span></font> </font></td> 
          </tr>
        </table>
      </div>
      <div align="center" style="width: 671; height: 1"> </div>
      <div align="center"> 
        <table width="99%" height="84" border="0" cellpadding="0" cellspacing="0">
          <tr> 
            <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 color="#FF0000">
			<span style="font-size: 10.0pt; font-family: 宋体">一、<span style="font-family:宋体; font-size:10.0pt">EXC</span>EL<span style="font-family: 宋体; font-size:10.0pt">操作技巧</span></span></font><span lang="EN-US"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">数据处理：</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			数据输入、数据格式、建立公式、数据编辑、调用函数（基本函数、逻辑、统计、数据库、财务类等）、图表制作</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">数据管理：</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			排序、筛选、记录单、分类汇总</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">数据分析：</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">数据透视表</span><span lang="EN-US" style="font-size:10.0pt">(</span><span style="font-size: 10.0pt; font-family: 宋体">图</span><span lang="EN-US" style="font-size:10.0pt">)</span><span style="font-size: 10.0pt; font-family: 宋体">、数据敏感分析、单变量求解、模拟运算表、方案管理器、规划求解</span><span lang="EN-US"><br>
			<br>
			</span><span style="font-family: 宋体; font-size:10.0pt">
			<font color="#FF0000">二、如何运用图表进行行政事务处理和工作报告</font></span><span lang="EN-US"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			怎样快速创建出你需要的图表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">如何创建动态图</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">如何因地制宜地使用图表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">行政管理表格设计</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">人力资源管理表格设计</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			如何自动生成员工考核工资表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">企业销售业绩的图表表达</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">产品市场占有率的图表表达</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">如何运用</span><span lang="EN-US" style="font-size:10.0pt">EXCEL</span><span style="font-size: 10.0pt; font-family: 宋体">分析市场调查问卷</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">如何运用</span><span lang="EN-US" style="font-size:10.0pt">EXCEL</span><span style="font-size: 10.0pt; font-family: 宋体">制作和分析销售报表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">如何运用</span><span lang="EN-US" style="font-size:10.0pt">EXCEL</span><span style="font-size: 10.0pt; font-family: 宋体">制作和分析财务报表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			人事、物料、财务数据库的链接和自动处理</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span lang="EN-US"><br>
			</span><font color="#FF0000">
			<span style="font-family: 宋体; font-size: 10.0pt">三、如何运用EXCEL进行销售和财务管理</span></font><span lang="EN-US"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">成本费用分析与管理</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">销售业务管理与决策</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">动态本量利模型分析</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">固定资产折旧计算</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">工资及个人所得税计算</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			现金日报及现金流量表的编制</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			由资产负债表自动生成现金流量表</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			工资、固定资产投资、折旧方案筛选等实际运用模板建立和应用分析</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			量本利分析、回归分析、方案预测、销售客户产品分析等实战演练</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			定性指标的定量化分析技术应用的模拟演练</span><span lang="EN-US" style="font-size:10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">
			运用数据透视表（图）进行经营分析的分析思路和模拟演练</span><span lang="EN-US" style="font-size: 10.0pt"><br>
			</span><span style="font-size: 10.0pt; font-family: 宋体">投资项目评价与决策<br>
			<br>
			<font color="#FF0000">四、公司实际案例分析与演练</font></span></p></td>
          </tr>
        </table>
        <table width="99%" height="84" border="0" cellpadding="0" cellspacing="0">
          <tr> 
            <td width="17%" height="20" bgcolor="#0080C0" class="td"> 
              <div align="center"><font color="#FFFFFF">[导 师 简 介]</font></div></td>                   
            <td width="83%" class="td">　</td>
          </tr>
          <tr> 
            <td height="64" colspan="2" class="td"> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;   
			<font color="#FF0000">&nbsp; </font><font color="#000000">
			<font color="#ff0000">Mr Wang </font>
			，管理工程硕士、高级经济师，国际职业培训师协会认证职业培训师，历任跨国公司生产负责人、工业工程经理、管理会计分析师、营运总监等高级管理职务多年，同时还担任    
			&lt; 价值工程 &gt; 杂志审稿人、辽宁省营口市商业银行独立董事等职务，对企业管理有较深入的研究。    
			王老师主要从事成本控制、财务管理、管理会计决策等课程的讲授，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    
			、美的汽车、上海贝尔阿尔卡特、天津扎努西、上海卡博特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>。<font color="#FF0000"> </font>            
              </p></td>
          </tr>
        </table>
      </div>
      <div align="center"> 
        <table width="669" border="0" cellpadding="0" cellspacing="0" height="57">
          <tr> 
            <td width="132" height="26" bgcolor="#0080C0" class="td"> <div align="center"><font color="#FFFFFF">[时间/地点/联系方式]</font></div></td>
            <td width="536" class="td" height="26">　</td>
          </tr>
          <tr> 
            <td height="31" colspan="2" class="td" width="669"> 
              <p><b>&nbsp;</b><font size="2"><b>时间: </b>7月15-16日（周六/日）<b>&nbsp; 地点:&nbsp; </b></font>
				上海 
				<font size="2"><b> &nbsp; </b>1980元/人 (此课程提供电脑/学习)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 四人以上参加，赠送一名名额</font>  
          </p>
              </td>
          </tr>
        </table>
      </div>
      <table width="99%" height="51" border="0" align="center" cellpadding="0" cellspacing="0">
        <tr> 
          <td height="51" class="td"> <font size="2"><b>&nbsp;电话：&lt;</b>0 2 1 - 5 1 1 8 7 1 2 6 
			&gt;&nbsp;  
            &nbsp; 谢小姐&nbsp;&nbsp; 注:如您不需要此邮件,请回信  
            ts...@to...并在标题注明订退</font></td>      
        </tr>
      </table> </td>
  </tr>
</table>

</body>
</html>

[Numpy-discussion] SciPy should now compile and install

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

All changes needed for scipy to compile and install with the new NumPy 
are now done.

As a side benefit, the numarray C-API compatibility module also received 
a test as it is now used to compile scipy/ndimage

So, SVN versions of NumPy / SciPy / and Matplotlib should all work 
together now.

-Travis

[Numpy-discussion] Easy way to get NumPy code to compile

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

If you have been a user of NumPy.  The easy way to update your code so 
that it compiles even with the latest changes to the naming-scheme is to 
replace

#include "numpy/arrayobject.h"

with

#include "numpy/noprefix.h"

This will generate the prefix-less names (and the other _FLAGS and 
OWN_DATA names that your code may depend on).   It also includes 
arrayobject.h and so there is no need to do that twice.

This should help people who already have code that works with NumPy. 

-Travis

Re: [Numpy-discussion] .T Transpose shortcut for arrays again

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

Sasha wrote:
> On 7/6/06, Bill Baxter <wb...@gm...> wrote:
>> ...
>> Yep, like Tim said.  The usage is say a N sets of basis vectors.  Each set
>> of basis vectors is a matrix.
> 
> This brings up a feature that I really miss from numpy: an ability to do
> 
> array([f(x) for x in a])
> 
> without python overhead.  APL-like languages have a notion of "adverb"
> - a higher level operator that maps a function to a function. Numpy
> has some adverbs implemented as ....
<snip>

Hi,
I was just reading through this thread and noticed that the above
might be possibly done best with(a little extended version of) the
numexpr module.

Am I right !?  Just wanted to post this comment about a package I'm
really looking forward to using once I convert from numarray.

Thanks for numpy !!
Sebastian Haase
UCSF

Re: [Numpy-discussion] Call for a vote on .M .A .T .H attributes

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

Travis Oliphant wrote:
> Travis Oliphant wrote:
>> This is a call for a vote on each of the math attributes.  Please post 
>> your vote as
>>
>> +1 :  support
>> +0 :  don't care so go ahead
>> -0 :  don't care so why do it
>> -1 :  against
>>
>> Vote on the following issues separately: 
>>
>>
>>
>> 1) .T  Have some kind of .T attribute
>>
>>   
  +1
>>    If >0 on this then:
>>
>>    a) .T == .swapaxes(-2,-1)
>>   
  +1
>>    b) .T == .transpose()
>>   
  -0
>>    c) .T raises error for ndim > 2
>>   
  -1
>>    d) .T returns (N,1) array for length (N,) array
>>
>>   
  +1
>>    e) .T returns self for ndim < 2
>>
>>   
  -1
>> 2) .H  returns .T.conj()
>>
>>
>>   
  +0
> 
>> 3) .M  returns matrix version of array
>>
>>   
  +0
>> 4) .A  returns basearray (useful for sub-classes).
>> 

  +0


Sebastian Haase

Re: [Numpy-discussion] NumPy C-API now has prefixes

[Stefan v. d. W. <st...@su...>]

On Fri, Jul 07, 2006 at 07:06:58PM -0600, Fernando Perez wrote:
> On 7/7/06, Travis Oliphant <oli...@ee...> wrote:
> > I just committed a big change to the NumPy SVN (r2773-r2777) which ad=
ds
> > the prefix npy_ or NPY_ to all names not otherwise pre-fixed.
> >
> > There is also a noprefix.h  header that allows you to use the names
> > without the prefixes defined, as before
> >
> > Plus:
> >
> > 1) The special FLAG names with _FLAGS  now have the _FLAGS removed
> > 2) The PY_ARRAY_TYPES_PREFIX is ignored.
> > 3) The tMIN/tMAX macros are removed
> > 4) MAX_DIMS --> NPY_MAXDIMS
> > 5) OWN_DATA --> NPY_OWNDATA
>=20
> Make sure scipy builds after these, I think I just saw it not build
> with 'OWN_DATA' errors.  Maybe I just caught you in-between
commits...

I see the following before the compilation breaks:

Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98PyObject* ARGOUT_argout=
(char*, char*, int*, int)=E2=80=99:
Lib/cluster/src/vq_wrap.cpp:734: error: =E2=80=98OWN_DATA=E2=80=99 was no=
t declared in this scope
Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98int char_to_size(char)=E2=
=80=99:
Lib/cluster/src/vq_wrap.cpp:582: warning: control reaches end of non-void=
 function
Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98int char_to_numtype(cha=
r)=E2=80=99:
Lib/cluster/src/vq_wrap.cpp:590: warning: control reaches end of non-void=
 function
Lib/cluster/src/vq_wrap.cpp: At global scope:
Lib/cluster/src/vq_wrap.cpp:147: warning: =E2=80=98void* SWIG_TypeQuery(c=
onst char*)=E2=80=99 defined but not used
Lib/cluster/src/vq_wrap.cpp:301: warning: =E2=80=98void SWIG_addvarlink(P=
yObject*, char*, PyObject* (*)(), int (*)(PyObject*))=E2=80=99 defined bu=
t not used
Lib/cluster/src/vq_wrap.cpp:315: warning: =E2=80=98int SWIG_ConvertPtr(Py=
Object*, void**, swig_type_info*, int)=E2=80=99 defined but not used
Lib/cluster/src/vq_wrap.cpp:516: warning: =E2=80=98PyObject* l_output_hel=
per(PyObject*, PyObject*)=E2=80=99 defined but not used
In file included from /usr/include/python2.4/Python.h:8,
                 from Lib/cluster/src/vq_wrap.cpp:176:
/usr/include/python2.4/pyconfig.h:835:1: warning: "_POSIX_C_SOURCE" redef=
ined
In file included from /usr/include/string.h:26,
                 from Lib/cluster/src/vq_wrap.cpp:27:
/usr/include/features.h:150:1: warning: this is the location of the previ=
ous definition
In file included from Lib/cluster/src/vq_wrap.cpp:499:
Lib/cluster/src/vq.h:57:7: warning: no newline at end of file
Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98PyObject* ARGOUT_argout=
(char*, char*, int*, int)=E2=80=99:
Lib/cluster/src/vq_wrap.cpp:734: error: =E2=80=98OWN_DATA=E2=80=99 was no=
t declared in this scope
Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98int char_to_size(char)=E2=
=80=99:
Lib/cluster/src/vq_wrap.cpp:582: warning: control reaches end of non-void=
 function
Lib/cluster/src/vq_wrap.cpp: In function =E2=80=98int char_to_numtype(cha=
r)=E2=80=99:
Lib/cluster/src/vq_wrap.cpp:590: warning: control reaches end of non-void=
 function
Lib/cluster/src/vq_wrap.cpp: At global scope:
Lib/cluster/src/vq_wrap.cpp:147: warning: =E2=80=98void* SWIG_TypeQuery(c=
onst char*)=E2=80=99 defined but not used
Lib/cluster/src/vq_wrap.cpp:301: warning: =E2=80=98void SWIG_addvarlink(P=
yObject*, char*, PyObject* (*)(), int (*)(PyObject*))=E2=80=99 defined bu=
t not used
Lib/cluster/src/vq_wrap.cpp:315: warning: =E2=80=98int SWIG_ConvertPtr(Py=
Object*, void**, swig_type_info*, int)=E2=80=99 defined but not used
Lib/cluster/src/vq_wrap.cpp:516: warning: =E2=80=98PyObject* l_output_hel=
per(PyObject*, PyObject*)=E2=80=99 defined but not used
error: Command "g++ -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -=
fPIC -I/home/stefan/lib/python2.4/site-packages/numpy/core/include -I/usr=
/include/python2.4 -c Lib/cluster/src/vq_wrap.cpp -o build/temp.linux-i68=
6-2.4/Lib/cluster/src/vq_wrap.o" failed with exit status 1
Error building package.


St=C3=A9fan

Re: [Numpy-discussion] NumPy C-API now has prefixes

[Albert S. <fu...@gm...>]

Hello all

> Please,
> 
> try out the new C-API and let's get the bugs wrinkled out.
> 
> Hopefully this will give us a more solid foundation for the future...
> I've already committed changes to matplotlib SVN that allow it to work
> with old and new NumPy.

What implications, if any, do these changes have for C extension skeleton on
the wiki?

http://www.scipy.org/Cookbook/C_Extensions

Regards,

Albert

[Numpy-discussion] I think something broke badly today in numpy/scipy...

[Fernando P. <fpe...@gm...>]

Hi all,

I updated earlier today (about 4 hours ago) numpy/scipy SVN, and all
of a sudden my codes broke left and right.  Backing off to

In [3]: numpy.__version__
Out[3]: '0.9.9.2737'

In [4]: scipy.__version__
Out[4]: '0.5.0.2044'

things are OK again.  I am really sorry not to be able to provide a
narrow test case, but I am utterly swamped right now, and need to
finish a number of things.  Given how bracketing this particular
problem takes about 1/2 hour in recompilations alone for each
revision, I really can't do it right now.

There is no exception, no traceback, it's just that various numerical
algorithms fail to converge.  This code is complex and it uses numpy,
scipy and weave, so the problem could be anywhere.

Again, my apologies for the super-vague report.  But perhaps knowing
that the change is fairly recent, someone may have an eureka moment :)

If nobody finds anything obvious, I might be able to spend some
serious debugging time next week on this, but for now I'll just
retreat to a working version and try to finish my things.

Cheers,

f