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From: Travis O. <oli...@ie...> - 2006-08-04 03:49:19
|
I'd like to release NumPy beta 2.0 on Saturday to get ready for the SciPy 2006 conference. Please post any bugs and commit any fixes by then. I suspect there will be 4 or 5 beta releases and then a couple of release candidates before the final release comes out at the first of October. -Travis |
|
From: <mis...@ya...> - 2006-08-04 00:28:54
|
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|
From: Mathew Y. <my...@jp...> - 2006-08-03 23:28:38
|
Here is a similar problem I wish could be fixed. In scipy.io.mio is
savemat with the line
if type(var) != ArrayType
which, I believe should be changed to
if not isinstance(var,ArrayType):
so I can use savemat with memory mapped arrays.
Mathew
Travis Oliphant wrote:
> Sebastian Haase wrote:
>
>> On Wednesday 02 August 2006 22:43, Travis Oliphant wrote:
>>
>>> Sebastian Haase wrote:
>>>
>>>> Thanks,
>>>> I just found
>>>> numpy.isscalar() and numpy.issctype() ?
>>>> These sound like they would do what I need - what is the difference
>>>> between the two ?
>>>>
>>> Oh, yeah.
>>>
>>> numpy.issctype works with type objects
>>> numpy.isscalar works with instances
>>>
>>> Neither of them distinguish between scalars and "numbers."
>>>
>>> If you get errors with isscalar it would be nice to know what they are.
>>>
>> I'm still trying to reproduce the exception, but here is a first comparison
>> that - honestly - does not make much sense to me:
>> (type vs. instance seems to get mostly the same results and why is there a
>> difference with a string ('12') )
>>
>
> These routines are a little buggy. I've cleaned them up in SVN to
> reflect what they should do. When the dtype object came into
> existence a lot of what the scalar types where being used for was no
> longer needed. Some of these functions weren't updated to deal with
> the dtype objects correctly either.
>
> This is what you get now:
> >>> import numpy as N
> >>> N.isscalar(12)
>
> True
>
> >>> N.issctype(12)
>
> False
>
> >>> N.isscalar('12')
>
> True
>
> >>> N.issctype('12')
>
> False
>
> >>> N.isscalar(N.array([1]))
>
> False
>
> >>> N.issctype(N.array([1]))
>
> False
>
> >>> N.isscalar(N.array([1]).dtype)
>
> False
>
> >>> N.issctype(N.array([1]).dtype)
>
> True
>
> >>> N.isscalar(N.array([1])[0].dtype)
>
> False
>
> >>> N.issctype(N.array([1])[0].dtype)
>
> True
>
> >>> N.isscalar(N.array([1])[0])
>
> True
>
> >>> N.issctype(N.array([1])[0])
>
> False
>
>
> -Travis
>
>
>>>>> N.isscalar(12)
>>>>>
>> True
>>
>>
>>>>> N.issctype(12)
>>>>>
>> True
>>
>>
>>>>> N.isscalar('12')
>>>>>
>> True
>>
>>
>>>>> N.issctype('12')
>>>>>
>> False
>>
>>
>>>>> N.isscalar(N.array([1]))
>>>>>
>> False
>>
>>
>>>>> N.issctype(N.array([1]))
>>>>>
>> True
>>
>>
>>>>> N.isscalar(N.array([1]).dtype)
>>>>>
>> False
>>
>>
>>>>> N.issctype(N.array([1]).dtype)
>>>>>
>> False
>>
>> # apparently new 'scalars' have a dtype attribute !
>>
>>
>>>>> N.isscalar(N.array([1])[0].dtype)
>>>>>
>> False
>>
>>
>>>>> N.issctype(N.array([1])[0].dtype)
>>>>>
>> False
>>
>>
>>>>> N.isscalar(N.array([1])[0])
>>>>>
>> True
>>
>>
>>>>> N.issctype(N.array([1])[0])
>>>>>
>> True
>>
>> -Sebastian
>>
>
> -------------------------------------------------------------------------
> Take Surveys. Earn Cash. Influence the Future of IT
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> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
|
|
From: Sebastian H. <ha...@ms...> - 2006-08-03 20:42:31
|
On Thursday 03 August 2006 13:19, Travis Oliphant wrote:
> Sebastian Haase wrote:
> >On Wednesday 02 August 2006 22:43, Travis Oliphant wrote:
> >>Sebastian Haase wrote:
> >>>Thanks,
> >>>I just found
> >>>numpy.isscalar() and numpy.issctype() ?
> >>>These sound like they would do what I need - what is the difference
> >>>between the two ?
> >>
> >>Oh, yeah.
> >>
> >>numpy.issctype works with type objects
> >>numpy.isscalar works with instances
> >>
> >>Neither of them distinguish between scalars and "numbers."
> >>
> >>If you get errors with isscalar it would be nice to know what they are.
> >
> >I'm still trying to reproduce the exception, but here is a first
> > comparison that - honestly - does not make much sense to me:
> >(type vs. instance seems to get mostly the same results and why is there
> > a difference with a string ('12') )
>
> These routines are a little buggy. I've cleaned them up in SVN to
> reflect what they should do. When the dtype object came into
> existence a lot of what the scalar types where being used for was no
> longer needed. Some of these functions weren't updated to deal with
> the dtype objects correctly either.
>
> This is what you get now:
> >>> import numpy as N
> >>> N.isscalar(12)
>
> True
>
> >>> N.issctype(12)
>
> False
>
> >>> N.isscalar('12')
>
> True
>
> >>> N.issctype('12')
>
> False
>
> >>> N.isscalar(N.array([1]))
>
> False
>
> >>> N.issctype(N.array([1]))
>
> False
>
> >>> N.isscalar(N.array([1]).dtype)
>
> False
>
> >>> N.issctype(N.array([1]).dtype)
>
> True
>
> >>> N.isscalar(N.array([1])[0].dtype)
>
> False
>
> >>> N.issctype(N.array([1])[0].dtype)
>
> True
>
> >>> N.isscalar(N.array([1])[0])
>
> True
>
> >>> N.issctype(N.array([1])[0])
>
> False
>
>
> -Travis
Great!
Just wanted to point out that '12' is a scalar -
I suppose that's what it is.
(To determine if something is a number it seems
best to implement a try: ... except: ... something like float(x) - as Chris
has suggested )
-S.
|
|
From: Travis O. <oli...@ee...> - 2006-08-03 20:37:44
|
Sebastian Haase wrote:
>On Wednesday 02 August 2006 22:43, Travis Oliphant wrote:
>>Sebastian Haase wrote:
>>>Thanks,
>>>I just found
>>>numpy.isscalar() and numpy.issctype() ?
>>>These sound like they would do what I need - what is the difference
>>>between the two ?
>>
>>Oh, yeah.
>>
>>numpy.issctype works with type objects
>>numpy.isscalar works with instances
>>
>>Neither of them distinguish between scalars and "numbers."
>>
>>If you get errors with isscalar it would be nice to know what they are.
>
>I'm still trying to reproduce the exception, but here is a first comparison
>that - honestly - does not make much sense to me:
>(type vs. instance seems to get mostly the same results and why is there a
>difference with a string ('12') )
These routines are a little buggy. I've cleaned them up in SVN to
reflect what they should do. When the dtype object came into
existence a lot of what the scalar types where being used for was no
longer needed. Some of these functions weren't updated to deal with
the dtype objects correctly either.
This is what you get now:
>>> import numpy as N
>>> N.isscalar(12)
True
>>> N.issctype(12)
False
>>> N.isscalar('12')
True
>>> N.issctype('12')
False
>>> N.isscalar(N.array([1]))
False
>>> N.issctype(N.array([1]))
False
>>> N.isscalar(N.array([1]).dtype)
False
>>> N.issctype(N.array([1]).dtype)
True
>>> N.isscalar(N.array([1])[0].dtype)
False
>>> N.issctype(N.array([1])[0].dtype)
True
>>> N.isscalar(N.array([1])[0])
True
>>> N.issctype(N.array([1])[0])
False
-Travis
>>>>N.isscalar(12)
>
>True
>
>>>>N.issctype(12)
>
>True
>
>>>>N.isscalar('12')
>
>True
>
>>>>N.issctype('12')
>
>False
>
>>>>N.isscalar(N.array([1]))
>
>False
>
>>>>N.issctype(N.array([1]))
>
>True
>
>>>>N.isscalar(N.array([1]).dtype)
>
>False
>
>>>>N.issctype(N.array([1]).dtype)
>
>False
>
> # apparently new 'scalars' have a dtype attribute !
>
>>>>N.isscalar(N.array([1])[0].dtype)
>
>False
>
>>>>N.issctype(N.array([1])[0].dtype)
>
>False
>
>>>>N.isscalar(N.array([1])[0])
>
>True
>
>>>>N.issctype(N.array([1])[0])
>
>True
>
>-Sebastian
|
|
From: Stefan v. d. W. <st...@su...> - 2006-08-03 18:35:50
|
Hi Mark On Thu, Aug 03, 2006 at 11:25:26AM -0400, Mark Heslep wrote: > Stefan van der Walt wrote: > > Binary thresholding can be added to ndimage easily, if further speed > > improvement is needed. > > > > Regards > > St=E9fan > Yes Id like to become involved in that effort. Whats the status of=20 > ndimage now? Has it all been brought over from numarray and placed,=20 > where? Is there a template of some kind for adding new code? You can find 'ndimage' in scipy. Travis also recently added the STSCI image processing tools to the sandbox. St=E9fan |
|
From: Christopher B. <Chr...@no...> - 2006-08-03 17:34:06
|
Sebastian Haase wrote:
> Finally I traced the problem down to a utility function:
> "is_number" - it is simply implemented as
> def is_number(val):
> return (type(val) in [type(0.0),type(0)])
> OK - how should this have been done right ?
Well, as others have said, python is uses "duck typing", so you really
shouldn't be checking for specific types anyway -- if whatever is passed
in acts like it should, that's all you need not know. However, sometimes
it does make sense to catch the error sooner, rather than later, so that
it can be obvious, or handled properly, or give a better error message,
or whatever.
In this case, I still use a "duck typing" approach: I don't need to know
exactly what type it is, I just need to know that I can use it in the
way I want, and an easy way to do that is to turn it into a known type:
def is_number(val):
try:
float(val)
return True
except ValueError:
return False
Though more often, I'd just call float on it, and pass that along,
rather than explicitly checking
This works at least with numpy float64scalar and float32scalar, and it
should work with all numpy scalar types, except perhaps the long types
that don't fit into a Python float.
it'll also turn string into floats if it can, which may or may not be
what you want.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
NOAA/OR&R/HAZMAT (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
Chr...@no...
|
|
From: Sebastian H. <ha...@ms...> - 2006-08-03 16:32:34
|
On Wednesday 02 August 2006 22:43, Travis Oliphant wrote:
> Sebastian Haase wrote:
> > Thanks,
> > I just found
> > numpy.isscalar() and numpy.issctype() ?
> > These sound like they would do what I need - what is the difference
> > between the two ?
>
> Oh, yeah.
>
> numpy.issctype works with type objects
> numpy.isscalar works with instances
>
> Neither of them distinguish between scalars and "numbers."
>
> If you get errors with isscalar it would be nice to know what they are.
I'm still trying to reproduce the exception, but here is a first comparison
that - honestly - does not make much sense to me:
(type vs. instance seems to get mostly the same results and why is there a
difference with a string ('12') )
>>> N.isscalar(12)
True
>>> N.issctype(12)
True
>>> N.isscalar('12')
True
>>> N.issctype('12')
False
>>> N.isscalar(N.array([1]))
False
>>> N.issctype(N.array([1]))
True
>>> N.isscalar(N.array([1]).dtype)
False
>>> N.issctype(N.array([1]).dtype)
False
# apparently new 'scalars' have a dtype attribute !
>>> N.isscalar(N.array([1])[0].dtype)
False
>>> N.issctype(N.array([1])[0].dtype)
False
>>> N.isscalar(N.array([1])[0])
True
>>> N.issctype(N.array([1])[0])
True
-Sebastian
|
|
From: Charles R H. <cha...@gm...> - 2006-08-03 15:38:27
|
Heh, This is fun. Two more variations with 1000 reps instead of 100 for better timing: def numpy_nmean_conv_nl_tweak1(list,n): b = numpy.ones(n,dtype=float) a = numpy.convolve(list,b,mode="full") a[:n] /= numpy.arange(1, n + 1) a[n:] /= n return a[:len(list)] def numpy_nmean_conv_nl_tweak2(list,n): b = numpy.ones(n,dtype=float) a = numpy.convolve(list,b,mode="full") a[:n] /= numpy.arange(1, n + 1) a[n:] *= 1.0/n return a[:len(list)] Which gives numpy convolve took: 2.630000 sec. numpy convolve noloop took: 0.320000 sec. numpy convolve noloop tweak1 took: 0.250000 sec. numpy convolve noloop tweak2 took: 0.240000 sec. Chuck On 8/2/06, Phil Ruggera <pru...@gm...> wrote: > > A variation of the proposed convolve routine is very fast: > > regular python took: 1.150214 sec. > numpy mean slice took: 2.427513 sec. > numpy convolve took: 0.546854 sec. > numpy convolve noloop took: 0.058611 sec. > > Code: > > # mean of n values within an array > import numpy, time > def nmean(list,n): > a = [] > for i in range(1,len(list)+1): > start = i-n > divisor = n > if start < 0: > start = 0 > divisor = i > a.append(sum(list[start:i])/divisor) > return a > > t = [1.0*i for i in range(1400)] > start = time.clock() > for x in range(100): > reg = nmean(t,50) > print "regular python took: %f sec."%(time.clock() - start) > > def numpy_nmean(list,n): > a = numpy.empty(len(list),dtype=float) > for i in range(1,len(list)+1): > start = i-n > if start < 0: > start = 0 > a[i-1] = list[start:i].mean(0) > return a > > t = numpy.arange(0,1400,dtype=float) > start = time.clock() > for x in range(100): > npm = numpy_nmean(t,50) > print "numpy mean slice took: %f sec."%(time.clock() - start) > > def numpy_nmean_conv(list,n): > b = numpy.ones(n,dtype=float) > a = numpy.convolve(list,b,mode="full") > for i in range(0,len(list)): > if i < n : > a[i] /= i + 1 > else : > a[i] /= n > return a[:len(list)] > > t = numpy.arange(0,1400,dtype=float) > start = time.clock() > for x in range(100): > npc = numpy_nmean_conv(t,50) > print "numpy convolve took: %f sec."%(time.clock() - start) > > def numpy_nmean_conv_nl(list,n): > b = numpy.ones(n,dtype=float) > a = numpy.convolve(list,b,mode="full") > for i in range(n): > a[i] /= i + 1 > a[n:] /= n > return a[:len(list)] > > t = numpy.arange(0,1400,dtype=float) > start = time.clock() > for x in range(100): > npn = numpy_nmean_conv_nl(t,50) > print "numpy convolve noloop took: %f sec."%(time.clock() - start) > > numpy.testing.assert_equal(reg,npm) > numpy.testing.assert_equal(reg,npc) > numpy.testing.assert_equal(reg,npn) > > On 7/29/06, David Grant <dav...@gm...> wrote: > > > > > > > > On 7/29/06, Charles R Harris <cha...@gm...> wrote: > > > > > > Hmmm, > > > > > > I rewrote the subroutine a bit. > > > > > > > > > def numpy_nmean(list,n): > > > a = numpy.empty(len(list),dtype=float) > > > > > > b = numpy.cumsum(list) > > > for i in range(0,len(list)): > > > if i < n : > > > a[i] = b[i]/(i+1) > > > else : > > > a[i] = (b[i] - b[i-n])/(i+1) > > > return a > > > > > > and got > > > > > > regular python took: 0.750000 sec. > > > numpy took: 0.380000 sec. > > > > > > I got rid of the for loop entirely. Usually this is the thing to do, at > > least this will always give speedups in Matlab and also in my limited > > experience with Numpy/Numeric: > > > > def numpy_nmean2(list,n): > > > > a = numpy.empty(len(list),dtype=float) > > b = numpy.cumsum(list) > > c = concatenate((b[n:],b[:n])) > > a[:n] = b[:n]/(i+1) > > a[n:] = (b[n:] - c[n:])/(i+1) > > return a > > > > I got no noticeable speedup from doing this which I thought was pretty > > amazing. I even profiled all the functions, the original, the one > written by > > Charles, and mine, using hotspot just to make sure nothing funny was > going > > on. I guess plain old Python can be better than you'd expect in certain > > situtations. > > > > -- > > David Grant > > ------------------------------------------------------------------------- > Take Surveys. Earn Cash. Influence the Future of IT > Join SourceForge.net's Techsay panel and you'll get the chance to share > your > opinions on IT & business topics through brief surveys -- and earn cash > http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > |
|
From: Mark H. <ma...@mi...> - 2006-08-03 15:25:32
|
Stefan van der Walt wrote: > Binary thresholding can be added to ndimage easily, if further speed > improvement is needed. > > Regards > St=E9fan Yes Id like to become involved in that effort. Whats the status of=20 ndimage now? Has it all been brought over from numarray and placed,=20 where? Is there a template of some kind for adding new code? Regards, Mark |
|
From: Mikolai F. <mf...@gm...> - 2006-08-03 14:49:46
|
Hello,
I have noticed some that the 1d histogram and 2d histogram. The
histogram function bins everything between the elements of edges, and
then includes everything greater than the last edge element in the
last bin. The histrogram2d function only bins in the range specified
by edges. Is there a reason these two functions do not operate in the
same way?
--
-Mikolai Fajer-
|
|
From: David M. C. <co...@ph...> - 2006-08-03 09:27:25
|
On Thu, Aug 03, 2006 at 11:02:11AM +0200, Rudolph van der Merwe wrote: > Is the current 1.0b1 version of Numpy a maintenace release of the > stable 1.0 release, or is it a BETA release for the UPCOMMING 1.0 > release of Numpy? Beta. Maintenance releases will have version numbers like 1.0.1. -- |>|\/|< /--------------------------------------------------------------------------\ |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ |co...@ph... |
|
From: <xa...@16...> - 2006-08-03 09:11:26
|
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<td height="67" bgcolor="#F3F3F3"><div align="center"><font lang="ZH-CN" color="#FF0000" size="6"><b>车间管理人员八项修炼</b></font></div></td>
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<p ALIGN="JUSTIFY"><font LANG="ZH-CN">
</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">车间是生产型企业的中心，车间和制造部门管理的好坏，直接影响着产品“质量、成本、交货期”各项指标的完成，伴随着微利时代的到来和组织结构趋向扁平化的今天，车间管理在企业中将扮演愈加重要的角色！<br>
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3、生产过程不稳定，机器故障和产品质量问题常常发生，如何有效的控制提高质量和提高设备利用率<br>
4、现场很多事情需要依靠下属和同级部门共同努力，如何有效沟通和协调激发下属的主动性和责任心？ </span></font></td>
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<td height="64" colspan="2" class="td"> <p><font size="2"><b><font color="#0000FF">◇ 车间管理人员的角色定位</font></b><br>
车间管理人员的职责与角色认知<br>
如何建立好的管理的基础<br>
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车间管理工作的重心与要点<br>
<b><font color="#0000FF">◇ 如何有效的推进车间管理工作</font></b><br>
车间整体工作的推进体系<br>
车间管理项目的指标化<br>
如何将目标与指标展开为具体的实施方案<br>
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如何建立适宜的标准，作为暴露问题的指针<br>
<b><font color="#0000FF">◇ 车间管理的基础--如何运用5S和目视管理</font></b><br>
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5S的核心与实质<br>
精益目视管理<br>
5S信息板,KANBAN卡片<br>
创建和应用不同类型的视觉控制工具<br>
案例研讨<br>
<b><font color="#0000FF">◇ 车间进度与过程控制</font></b><br>
生产作业计划的追踪实施<br>
如何控制最佳的生产节拍，保持有效产出<br>
如何提高生产管理系统的柔性<br>
如何减少运输时间，缩短交期<br>
运用U型生产线布置方式提高生产线的效率<br>
如何降低换线时间适应生产线的转换<br>
如何利用多能工随时调整生产安排<br>
如何化解瓶颈环节的制约<br>
如何通过快速换型技术实现多品种生产转换<br>
换型物料车与换型工具车的使用<br>
<b><font color="#0000FF">◇ 现场质量改进</font></b><br>
如何识别质量问题<br>
如何运用品管圈活动改进质量管理<br>
推移管理与预防性问题发现<br>
质量问题的对应流程与要点<br>
质量改善活动的四阶段十步骤<br>
<b><font color="#0000FF">◇ 现场成本控制</font></b><br>
盈亏平衡点――学习老板的经营观<br>
现场成本管理的主要指标<br>
降低制造成本的主要途径<br>
减少现场浪费的活动方法<br>
放大镜――从宏观到微观的工具<br>
标准成本与标准工时的测定<br>
标准成本/标准工时的差异分析<br>
<b><font color="#0000FF">◇ 现场设备管理TPM</font></b><br>
设备管理的八大支柱<br>
数字化的综合效率管理<br>
设备的六大损失<br>
改善慢性损失，向零故障挑战 <br>
设备初期清扫与困难源对策<br>
自主保养的七步骤<br>
<b><font color="#0000FF">◇ 车间人员管理</font></b><br>
新型的上下级关系<br>
自我培养与培养下属的意识<br>
如何有效的指导与辅导下属<br>
如何塑造持续学习与改善的现场氛围<br>
如何有效的向上级沟通与汇报<br>
同级部门之间沟通与反馈的技巧<br>
人际技巧与关系处理<br>
激励下属的技巧与方法<br>
<b><font color="#0000FF">◇ 案例讨论</font></b></font>
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<td height="64" colspan="2" class="td"> <p><font size="2"><font color="#FF0000"> Mr Wang，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，对企业管理有较深入的研究。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、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、艾克森-金山石化、正大集团、厦华集团、灿坤股份、NEC东金电子、太原钢铁集团、PHILIPS、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>
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<td height="12" class="td"> <font size="2"><font color="#000000"><b>报名/咨询电话:</b></font> 谢小姐<font color="#000000">
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From: Rudolph v. d. M. <rva...@sk...> - 2006-08-03 09:02:15
|
Is the current 1.0b1 version of Numpy a maintenace release of the stable 1.0 release, or is it a BETA release for the UPCOMMING 1.0 release of Numpy? -- Rudolph van der Merwe |
|
From: Travis O. <oli...@ie...> - 2006-08-03 05:43:52
|
Sebastian Haase wrote: > Thanks, > I just found > numpy.isscalar() and numpy.issctype() ? > These sound like they would do what I need - what is the difference > between the two ? > Oh, yeah. numpy.issctype works with type objects numpy.isscalar works with instances Neither of them distinguish between scalars and "numbers." If you get errors with isscalar it would be nice to know what they are. -Travis |
|
From: Phil R. <pru...@gm...> - 2006-08-03 05:41:30
|
A variation of the proposed convolve routine is very fast:
regular python took: 1.150214 sec.
numpy mean slice took: 2.427513 sec.
numpy convolve took: 0.546854 sec.
numpy convolve noloop took: 0.058611 sec.
Code:
# mean of n values within an array
import numpy, time
def nmean(list,n):
a = []
for i in range(1,len(list)+1):
start = i-n
divisor = n
if start < 0:
start = 0
divisor = i
a.append(sum(list[start:i])/divisor)
return a
t = [1.0*i for i in range(1400)]
start = time.clock()
for x in range(100):
reg = nmean(t,50)
print "regular python took: %f sec."%(time.clock() - start)
def numpy_nmean(list,n):
a = numpy.empty(len(list),dtype=float)
for i in range(1,len(list)+1):
start = i-n
if start < 0:
start = 0
a[i-1] = list[start:i].mean(0)
return a
t = numpy.arange(0,1400,dtype=float)
start = time.clock()
for x in range(100):
npm = numpy_nmean(t,50)
print "numpy mean slice took: %f sec."%(time.clock() - start)
def numpy_nmean_conv(list,n):
b = numpy.ones(n,dtype=float)
a = numpy.convolve(list,b,mode="full")
for i in range(0,len(list)):
if i < n :
a[i] /= i + 1
else :
a[i] /= n
return a[:len(list)]
t = numpy.arange(0,1400,dtype=float)
start = time.clock()
for x in range(100):
npc = numpy_nmean_conv(t,50)
print "numpy convolve took: %f sec."%(time.clock() - start)
def numpy_nmean_conv_nl(list,n):
b = numpy.ones(n,dtype=float)
a = numpy.convolve(list,b,mode="full")
for i in range(n):
a[i] /= i + 1
a[n:] /= n
return a[:len(list)]
t = numpy.arange(0,1400,dtype=float)
start = time.clock()
for x in range(100):
npn = numpy_nmean_conv_nl(t,50)
print "numpy convolve noloop took: %f sec."%(time.clock() - start)
numpy.testing.assert_equal(reg,npm)
numpy.testing.assert_equal(reg,npc)
numpy.testing.assert_equal(reg,npn)
On 7/29/06, David Grant <dav...@gm...> wrote:
>
>
>
> On 7/29/06, Charles R Harris <cha...@gm...> wrote:
> >
> > Hmmm,
> >
> > I rewrote the subroutine a bit.
> >
> >
> > def numpy_nmean(list,n):
> > a = numpy.empty(len(list),dtype=float)
> >
> > b = numpy.cumsum(list)
> > for i in range(0,len(list)):
> > if i < n :
> > a[i] = b[i]/(i+1)
> > else :
> > a[i] = (b[i] - b[i-n])/(i+1)
> > return a
> >
> > and got
> >
> > regular python took: 0.750000 sec.
> > numpy took: 0.380000 sec.
>
>
> I got rid of the for loop entirely. Usually this is the thing to do, at
> least this will always give speedups in Matlab and also in my limited
> experience with Numpy/Numeric:
>
> def numpy_nmean2(list,n):
>
> a = numpy.empty(len(list),dtype=float)
> b = numpy.cumsum(list)
> c = concatenate((b[n:],b[:n]))
> a[:n] = b[:n]/(i+1)
> a[n:] = (b[n:] - c[n:])/(i+1)
> return a
>
> I got no noticeable speedup from doing this which I thought was pretty
> amazing. I even profiled all the functions, the original, the one written by
> Charles, and mine, using hotspot just to make sure nothing funny was going
> on. I guess plain old Python can be better than you'd expect in certain
> situtations.
>
> --
> David Grant
|
|
From: Alan G I. <ai...@am...> - 2006-08-03 05:33:46
|
On Wed, 02 Aug 2006, Sebastian Haase apparently wrote: > Recently someone (Torgil Svensson) here suggested to unify > the default argument between a method and a function > - I think the discussion was about numpy.var and it's > "axis" argument. I would be a clear +1 on unifying these > and have a clean design of numpy. Consequently the old way > of different defaults should be absorbed by the oldnumeric > sub module. +1 I think this consistency is *really* important for the easy acceptance of numpy by new users. (For a user's perspective, I also think is is just good design.) I expect many new users to be "burned" by this inconsistency. However, as an intermediate run (say 1 year) transition measure to the consistent use, I would be comfortable with the numpy functions requiring an axis argument. One user's view, Alan Isaac |
|
From: Sebastian H. <ha...@ms...> - 2006-08-03 05:17:11
|
Travis Oliphant wrote: > Sebastian Haase wrote: >> Hi! >> I just finished maybe a total of 5 hours tracking down a nasty bug. >> >> Finally I traced the problem down to a utility function: >> "is_number" - it is simply implemented as >> def is_number(val): >> return (type(val) in [type(0.0),type(0)]) >> >> As I said - now I finally saw that I always got >> False since the type of my number (0.025) is >> <type 'float64scalar'> >> and that's neither <type 'float'> nor <type 'int'> >> >> OK - how should this have been done right ? >> >> > > Code that depends on specific types like this is going to be hard to > maintain in Python because many types could reasonably act like a > number. I do see code like this pop up from time to time and it will > bite you more with NumPy (which has a whole slew of scalar types). > > The scalar-types are in a hierarchy and so you could replace the code with > > def is_number(val): > return isinstance(val, (int, float, numpy.number)) > > But, this will break with other "scalar-types" that it really should > work with. It's best to look at what is calling is_number and think > about what it wants to do with the object and just try it and catch the > exception. > > -Travis > Thanks, I just found numpy.isscalar() and numpy.issctype() ? These sound like they would do what I need - what is the difference between the two ? (I found that issctype worked OK while isscalar gave some exception in some cases !? ) - Sebastian |
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From: Travis O. <oli...@ie...> - 2006-08-03 05:02:53
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Sebastian Haase wrote:
> Hi!
> I just finished maybe a total of 5 hours tracking down a nasty bug.
>
> Finally I traced the problem down to a utility function:
> "is_number" - it is simply implemented as
> def is_number(val):
> return (type(val) in [type(0.0),type(0)])
>
> As I said - now I finally saw that I always got
> False since the type of my number (0.025) is
> <type 'float64scalar'>
> and that's neither <type 'float'> nor <type 'int'>
>
> OK - how should this have been done right ?
>
>
Code that depends on specific types like this is going to be hard to
maintain in Python because many types could reasonably act like a
number. I do see code like this pop up from time to time and it will
bite you more with NumPy (which has a whole slew of scalar types).
The scalar-types are in a hierarchy and so you could replace the code with
def is_number(val):
return isinstance(val, (int, float, numpy.number))
But, this will break with other "scalar-types" that it really should
work with. It's best to look at what is calling is_number and think
about what it wants to do with the object and just try it and catch the
exception.
-Travis
|
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From: Sebastian H. <ha...@ms...> - 2006-08-03 04:55:36
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Travis Oliphant wrote: > Torgil Svensson wrote: > >>> They are supposed to have different defaults because the functional >>> forms are largely for backward compatibility where axis=0 was the default. >>> >>> -Travis >>> >>> >> Isn't backwards compatibility what "oldnumeric" is for? >> >> >> > > As this discussion indicates there has been a switch from numpy 0.9.8 to > numpy 1.0b of how to handle backward compatibility. Instead of > importing old names a new sub-package numpy.oldnumeric was created. > This mechanism is incomplete in the sense that there are still some > backward-compatible items in numpy such as defaults on the axis keyword > for functions versus methods and you still have to make the changes that > convertcode.py makes to the code to get it to work. > > I'm wondering about whether or not some additional effort should be > placed in numpy.oldnumeric so that replacing Numeric with > numpy.oldnumeric actually gives no compatibility issues (i.e. the only > thing you have to change is replace imports with new names). In > other words a simple array sub-class could be created that mimics the > old Numeric array and the old functions could be created as well with > the same arguments. > > The very same thing could be done with numarray. This would make > conversion almost trivial. > > Then, the convertcode script could be improved to make all the changes > that would take a oldnumeric-based module to a more modern numpy-based > module. A similar numarray script could be developed as well. > > What do people think? Is it worth it? This could be a coding-sprint > effort at SciPy. > > > -Travis Hi, Just as thought of cautiousness: If people actually get "too much" encouraged to just always say " from numpy.oldnumeric import * " or as suggested maybe soon also something like " from numpy.oldnumarray import * " - could this not soon lead to a great state of confusion when later people on this mailing list ask questions and nobody really knows which of the submodules they are referring to !? Recently someone (Torgil Svensson) here suggested to unify the default argument between a method and a function - I think the discussion was about numpy.var and it's "axis" argument. I would be a clear +1 on unifying these and have a clean design of numpy. Consequently the old way of different defaults should be absorbed by the oldnumeric sub module. All I'm saying then is that this could cause confusion later on - and therefore the whole idea of "easy backwards compatibility" should be qualified by encouraging people to adopt the most problematic changes (like new default values) rather sooner than later. I'm hoping that numpy will find soon an increasingly broader acceptance in the whole Python community (and the entire scientific community for that matter ;-) ). Thanks for all your work, Sebastian Haase |
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From: Robert K. <rob...@gm...> - 2006-08-03 04:43:43
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Sebastian Haase wrote: > Hi! > I just finished maybe a total of 5 hours tracking down a nasty bug. > So I thought I would share this: > I'm keeping a version of (old) SciPy's 'plt' module around. > (I know about matplotlib - anyway - ...) > I changed the code some time ago from Numeric to numarray - no problem. > Now I switched to numpy ... and suddenly the zooming does not work > anymore: it always zooms to "full view". > > Finally I traced the problem down to a utility function: > "is_number" - it is simply implemented as > def is_number(val): > return (type(val) in [type(0.0),type(0)]) > > As I said - now I finally saw that I always got > False since the type of my number (0.025) is > <type 'float64scalar'> > and that's neither <type 'float'> nor <type 'int'> > > OK - how should this have been done right ? It depends on how is_number() is actually used. Probably the best thing to do would be to take a step back and reorganize whatever is calling it to not require specific types. Quick-and-dirty: use isinstance() instead since float64scalar inherits from float. However, float32scalar does not, so this is not a real solution, just a hack to get you on your merry way. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco |
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From: Sebastian H. <ha...@ms...> - 2006-08-03 04:31:44
|
Hi!
I just finished maybe a total of 5 hours tracking down a nasty bug.
So I thought I would share this:
I'm keeping a version of (old) SciPy's 'plt' module around.
(I know about matplotlib - anyway - ...)
I changed the code some time ago from Numeric to numarray - no problem.
Now I switched to numpy ... and suddenly the zooming does not work
anymore: it always zooms to "full view".
Finally I traced the problem down to a utility function:
"is_number" - it is simply implemented as
def is_number(val):
return (type(val) in [type(0.0),type(0)])
As I said - now I finally saw that I always got
False since the type of my number (0.025) is
<type 'float64scalar'>
and that's neither <type 'float'> nor <type 'int'>
OK - how should this have been done right ?
Anyway, I'm excited about the new numpy
and am looking forward to it's progress
Thanks,
Sebastian Haase
|
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From: Stefan v. d. W. <st...@su...> - 2006-08-03 00:45:45
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On Wed, Aug 02, 2006 at 04:51:07PM -0400, Mark Heslep wrote: > I need a binary threshold and numpy.where() seems very slow on numpy=20 > 0.9.9.2800: >=20 > python -m timeit -n 10 -s "import numpy as n;a=3Dn.ones((512,512),=20 > n.uint8)*129" > "a_bin=3Dn.where( a>128, 128,0)" > 10 loops, best of 3: 37.9 msec per loop Using numpy indexing brings the time down by a factor of 10 or so: In [46]: timeit b =3D N.where(a>128,128,0) 10 loops, best of 3: 27.1 ms per loop In [47]: timeit b =3D (a > 128).astype(N.uint8) * 128 100 loops, best of 3: 3.45 ms per loop Binary thresholding can be added to ndimage easily, if further speed improvement is needed. Regards St=E9fan |
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From: Robert K. <rob...@gm...> - 2006-08-02 23:25:44
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Stephan Tolksdorf wrote: > Hi David, > >> I updated that patch to work (it's in ticket #114, btw, for those following >> along), and integrated it last week. Please give the current svn a try to see >> how it works. > > I'm really sorry I overlooked your changes. Thanks a lot for your > efforts. I will try the various windows builds in the next days and > address the remaining issues. > > > I had it done mid-July, but I guess you didn't get the Trac email? > > I haven't received any email notfication from Trac. Is there something I > can do about the missing notifications? You can sign up for the numpy-tickets mailing list. http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco |
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From: David M. C. <co...@ph...> - 2006-08-02 23:22:27
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On Thu, 03 Aug 2006 01:00:06 +0200 Stephan Tolksdorf <st...@si...> wrote: > I haven't received any email notfication from Trac. Is there something I > can do about the missing notifications? When logged in, check "Settings" (upper-right corner, besides Logout). Make sure your email address is in there. -- |>|\/|< /--------------------------------------------------------------------------\ |David M. Cooke http://arbutus.physics.mcmaster.ca/dmc/ |co...@ph... |
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