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From: Nadav H. <na...@vi...> - 2004-05-19 16:43:15
|
The simplest solution is to use "solve_linear_equations" functin from = numarray.linear_algebra. For more sophisticate solution you may try = scipy (www.scipy.org) Nadav -----Original Message----- From: Craig Rasmussen [mailto:cra...@la...] Sent: Wed 19-May-04 17:59 To: numarray Cc:=09 Subject: [Numpy-discussion] Linear solvers I need to solve a matrix equation of the form A u =3D q for u, where u and q are N-vectors and A is a=20 symmetric-positive-definite matrix. For now, at least, A is a 2-D matrix. I would like to use Python and numarray. Are there Python modules that I could use to obtain a solution for u? Thanks, Craig ------------------------------------------------------- This SF.Net email is sponsored by: SourceForge.net Broadband Sign-up now for SourceForge Broadband and get the fastest 6.0/768 connection for only $19.95/mo for the first 3 months! http://ads.osdn.com/?ad_id=3D2562&alloc_id=3D6184&op=3Dclick _______________________________________________ Numpy-discussion mailing list Num...@li... https://lists.sourceforge.net/lists/listinfo/numpy-discussion |
|
From: Craig R. <cra...@la...> - 2004-05-19 14:59:48
|
I need to solve a matrix equation of the form
A u = q
for u, where u and q are N-vectors and A is a
symmetric-positive-definite matrix.
For now, at least, A is a 2-D matrix.
I would like to use Python and numarray. Are there Python modules that
I could use to obtain a solution for u?
Thanks,
Craig
|
|
From: Lumber W. <ne...@lu...> - 2004-05-17 23:42:46
|
<body bgcolor=3D"#FFFFFF" text=3D"#000000"> <html>
<head>
<title>Composite Decking, Ipe, Western Red Cedar, Wood Lattice & Trellis, i=
ronwood, Hardwood, Redwood, Cedar Decking, Treated Pine, Trex, Wood Polymer=
s, Fencing, Preservatives, Siding </title>
<style>
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h1
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page-break-after:avoid;
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gn=3D"center" border=3D"0">
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<table height=3D"1" cellSpacing=3D"0" cellPadding=3D"3" width=3D"100%" =
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</td>
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<td class=3D"lbr" bgColor=3D"#eef1e7" height=3D"1">
=20
<p class=3D"MsoNormal"><b><span style=3D"font-family:Ar=
ial">IPE=20
Ironwood</span></b><span style=3D"font-family:Arial"> 5/4 x 6 =
$1.89/ LF (Tabebula=20
spp)</span></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">IPE</sp=
an></b><span style=3D"font-family:Arial">=20
heartwood is light to dark olive brown, often with attractive light=
er or=20
darker striping and striking contrasts with the lighter color sapwo=
od.=20
It has a fine texture, medium luster; IPE is very hard (two to thre=
e=20
times as hard as oak), very strong and very heavy</span></p>
<p class=3D"MsoNormal"><span style=3D"font-family:Arial"> </sp=
an></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">Cambara=
</span>
</b><span style=3D"font-family:Arial">5/4 x 6 $1.39/ LF (Astr=
onium=20
Graveolens)</span></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">Cambara=
</span>
</b><span style=3D"font-family:Arial">trees produce wood with a med=
ium to=20
coarser texture for a more casual look. Ranging from pale to pinkis=
h=20
brow to browns tinged with maroon and lit red. It blends will with =
cedar=20
and is an excellent substitute for Redwood.</span></p>
<p class=3D"MsoNormal"><span style=3D"font-family:Arial"> </sp=
an></p>
<h1>Massaranduba <span style=3D"font-weight:normal">5/4 x 6 $=
1.49/ LF (Manilkara=20
Bidentata)</span></h1>
<p class=3D"MsoNormal"><b>Massaranduba </b>is also known as<b> Braz=
ilian=20
Redwood</b>. This wood is fine and as hard as IPE with a beautiful=
=20
consistent color that ranges from light to dark reddish brown with=
=20
occasional dark stripes. It has a high resistance to moisture=20
absorption, decay fungi and termites, and is widely used for water =
works=20
in the canals of Holland.</p>
<p class=3D"MsoNormal"> </p>
<p class=3D"MsoNormal"><b>Western Red Cedar </b>2 x 6 $1.49/ =
LF <b>(</b><i>Thuja=20
Plicata</i><b>)</b></p>
<p class=3D"MsoNormal"><b>Western Red Cedar</b> is renowned for its=
=20
natural beauty and outstanding physical properties that make it one=
of=20
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<p class=3D"MsoNormal"> </p>
<p class=3D"MsoNormal"><b>TigerWood </b>5/4 x 6 $1.69/ LF <b>=
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<p class=3D"MsoNormal"><b>TigerWood</b> stands out for its resistan=
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" height=3D"168" cellSpacing=3D"0" cellPadding=3D"0" width=3D"57%" border=
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<font color=3D"#663300">IPE / </font></a> </font></b></font><=
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<font face=3D"Verdana, Arial, Helvetica, sans-serif" color=3D=
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<font color=3D"#663300">MASSARANDUBA</font></a></font></b><br=
>
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SOLUTIONS</font></b><font face=3D"Arial, Helvetica, sans-serif" siz=
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TX 78758 <br>
<a href=3D"mailto:Sa...@lu...">Sales@LumberSolutions.=
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|
From: Lumber W. <ne...@lu...> - 2004-05-17 23:42:45
|
<body bgcolor=3D"#FFFFFF" text=3D"#000000"> <html>
<head>
<title>Composite Decking, Ipe, Western Red Cedar, Wood Lattice & Trellis, i=
ronwood, Hardwood, Redwood, Cedar Decking, Treated Pine, Trex, Wood Polymer=
s, Fencing, Preservatives, Siding </title>
<style>
<!--
h1
{margin-bottom:.0001pt;
page-break-after:avoid;
font-size:12.0pt;
font-family:Arial;
margin-left:0in; margin-right:0in; margin-top:0in}
-->
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<table height=3D"1" cellSpacing=3D"0" cellPadding=3D"3" width=3D"100%" =
border=3D"0">
<tr>
<td width=3D"100%" background=3D"http://www.lumbersolutions.com/gra=
d.jpg" bgColor=3D"#aaba8b" height=3D"19">
</td>
</tr>
<tr>
<td class=3D"lbr" bgColor=3D"#eef1e7" height=3D"1">
=20
<p class=3D"MsoNormal"><b><span style=3D"font-family:Ar=
ial">IPE=20
Ironwood</span></b><span style=3D"font-family:Arial"> 5/4 x 6 =
$1.89/ LF (Tabebula=20
spp)</span></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">IPE</sp=
an></b><span style=3D"font-family:Arial">=20
heartwood is light to dark olive brown, often with attractive light=
er or=20
darker striping and striking contrasts with the lighter color sapwo=
od.=20
It has a fine texture, medium luster; IPE is very hard (two to thre=
e=20
times as hard as oak), very strong and very heavy</span></p>
<p class=3D"MsoNormal"><span style=3D"font-family:Arial"> </sp=
an></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">Cambara=
</span>
</b><span style=3D"font-family:Arial">5/4 x 6 $1.39/ LF (Astr=
onium=20
Graveolens)</span></p>
<p class=3D"MsoNormal"><b><span style=3D"font-family:Arial">Cambara=
</span>
</b><span style=3D"font-family:Arial">trees produce wood with a med=
ium to=20
coarser texture for a more casual look. Ranging from pale to pinkis=
h=20
brow to browns tinged with maroon and lit red. It blends will with =
cedar=20
and is an excellent substitute for Redwood.</span></p>
<p class=3D"MsoNormal"><span style=3D"font-family:Arial"> </sp=
an></p>
<h1>Massaranduba <span style=3D"font-weight:normal">5/4 x 6 $=
1.49/ LF (Manilkara=20
Bidentata)</span></h1>
<p class=3D"MsoNormal"><b>Massaranduba </b>is also known as<b> Braz=
ilian=20
Redwood</b>. This wood is fine and as hard as IPE with a beautiful=
=20
consistent color that ranges from light to dark reddish brown with=
=20
occasional dark stripes. It has a high resistance to moisture=20
absorption, decay fungi and termites, and is widely used for water =
works=20
in the canals of Holland.</p>
<p class=3D"MsoNormal"> </p>
<p class=3D"MsoNormal"><b>Western Red Cedar </b>2 x 6 $1.49/ =
LF <b>(</b><i>Thuja=20
Plicata</i><b>)</b></p>
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|
From: Todd M. <jm...@st...> - 2004-05-17 20:47:34
|
On Mon, 2004-05-17 at 12:31, Álvaro Tejero Cantero wrote:
> Hello,
>
> I'm trying to rewrite my simulation using masked arrays. For that I
> define masks for triangular_inferior NxN and
> diagonal_triangular_superior NxN arrays.
>
> I'm unsuccesfully trying to know what happens with the program when I
> operate over a MaskedArray: it always says
>
> Traceback (most recent call last):
> File "<stdin>", line 31, in ?
> File "<stdin>", line 26, in run
> File "/usr/lib/python2.3/site-packages/numarray/ma/MA.py", line 990,
> in __pow__
> return power(self, other, third)
> File "/usr/lib/python2.3/site-packages/numarray/ma/MA.py", line 1585,
> in power
> return masked_array(Numeric.power(fa, fb), m)
> File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 877,
> in _cache_miss2
> mode, win1, win2, wout, cfunc, ufargs = \
> File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 921,
> in _setup
> intypes = (in1._type.name, in2._type.name)
> AttributeError: 'ObjectArray' object has no attribute '_type'
>
>
> For reference, I copy a simplified version of the program. The statement
> that causes the AttributeError is the last from run(N,D,S):
>
> ---------------------------
>
> import numarray.ma as mka
> import numarray.random_array as rnd
> import numarray as nmr
>
> def relative(x, msk):
> (N,D) = x.shape
> return mka.array([nmr.subtract.outer(x[:,i],x[:,i]) for i in nmr.arange(D)],
> mask = nmr.array([msk for i in nmr.arange(D)],typecode=mka.MaskType))
>
> def scalar(x,y):
> return mka.add.reduce(x*y,axis=0)
>
> def run(D,N,S):
> trig_inf = nmr.array(([[(0,1)[i<j] for i in nmr.arange(N)] for j in nmr.arange(N)]), typecode=mka.MaskType)
> diag_trig_sup = 1 - trig_inf #print mka.is_mask(diag_trig_sup) gives "1"
>
> # positions and velocities
> r = rnd.random([N,D]) #should be overlap-checked
> v = rnd.random([N,D])
>
> # relative positions and velocities
> r_rel = relative(r, diag_trig_sup)
> v_rel = relative(v, diag_trig_sup)
>
> RV, VV, RR = scalar(r_rel,v_rel), scalar(v_rel,v_rel), scalar(r_rel,r_rel)
> discr = RV**2 - VV*(RR-S**2) #<<<<----------PROBLEM HERE----------------
>
>
>
> if __name__ == '__main__':
> run(D=3,N=4,S=1e-8)
>
>
> -------------------------
>
> Any other advice that you may give me is very wellcome. My deadline is
> approaching and I'm really reluctant to rewrite all this in C++ but I
> don't see the light.
>
> Thank you in advance,
>
> á.
Here is a work around I put in numarray's MA.py which is now in CVS:
cvs diff: Diffing Packages/MA/Lib
Index: Packages/MA/Lib/MA.py
===================================================================
RCS file: /cvsroot/numpy/numarray/Packages/MA/Lib/MA.py,v
retrieving revision 1.7
retrieving revision 1.9
diff -c -r1.7 -r1.9
*** Packages/MA/Lib/MA.py 1 Oct 2003 15:44:35 -0000 1.7
--- Packages/MA/Lib/MA.py 17 May 2004 20:21:58 -0000 1.9
***************
*** 1263,1270 ****
--- 1263,1274 ----
result = value
result.shape = d.shape
else:
+ if not m.is_c_array():
+ m = m.copy()
try:
result = Numeric.array(d, typecode=d.typecode(),
copy=1)
+ if not result.is_c_array():
+ result = result.copy()
Numeric.putmask(result, m, value)
except:
result = obj.choose(m, (d, value))
You can either get the new MA from Source Forge CVS (project numpy, down
at the moment) or use this diff by editing your own MA.py and adding the
lines shown with +'s. The root problem is limitations in numarray's
putmask() function which are avoided by making contiguous copies of some
of the arrays in MA's filled() function.
Regards,
Todd
--
Todd Miller <jm...@st...>
|
|
From: Todd M. <jm...@st...> - 2004-05-17 19:03:10
|
On Thu, 2004-05-13 at 12:57, Todd Miller wrote: > On Thu, 2004-05-13 at 07:13, Álvaro Tejero Cantero wrote: > > I have a matrix of particle collision times: times[i,j] gives the time > > for particle "i" to collide with particle "j". > > > > If I do, in order to get the first expected collision time for each > > particle, the following (random array for testing purposes) : > > > > >>> N=30 > > >>> times = rnd.random([N,N]) > > >>> choose(argmin(times,transpose(times)) > > Segmentation fault (of the python interactive shell!!!) > > > > With N=100 I get a more informative traceback, and rest within the > > shell: > > > > Traceback (most recent call last): > > File "<stdin>", line 1, in ? > > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1670, in choose > > return _choose(selector, population, outarr, clipmode) > > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1579, in __call__ > > result = self._doit(computation_mode, woutarr, cfunc, ufargs, 0) > > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1564, in _doit > > blockingparameters) > > ValueError: _operator_compute: too many inputs + outputs > > > > > > For N=10,N=15 I get the expected output, but for N=20 I get again the > > brutal segfault... > > > > > > regards, > > á. I tracked this down today and understand the problem: there's a bug in the ufunc error checking and a ridiculous limit in choose. I'm still working out the "real" solution but a quick workaround is to edit numarray.h and set MAXARGS to a larger number, say N+10. Regards, Todd -- Todd Miller <jm...@st...> |
|
From: T. C. <al...@an...> - 2004-05-17 16:24:23
|
Hello,
I'm trying to rewrite my simulation using masked arrays. For that I
define masks for triangular_inferior NxN and
diagonal_triangular_superior NxN arrays.
I'm unsuccesfully trying to know what happens with the program when I
operate over a MaskedArray: it always says
Traceback (most recent call last):
File "<stdin>", line 31, in ?
File "<stdin>", line 26, in run
File "/usr/lib/python2.3/site-packages/numarray/ma/MA.py", line 990,
in __pow__
return power(self, other, third)
File "/usr/lib/python2.3/site-packages/numarray/ma/MA.py", line 1585,
in power
return masked_array(Numeric.power(fa, fb), m)
File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 877,
in _cache_miss2
mode, win1, win2, wout, cfunc, ufargs =3D \
File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 921,
in _setup
intypes =3D (in1._type.name, in2._type.name)
AttributeError: 'ObjectArray' object has no attribute '_type'
For reference, I copy a simplified version of the program. The statement
that causes the AttributeError is the last from run(N,D,S):
---------------------------
import numarray.ma as mka
import numarray.random_array as rnd
import numarray as nmr
def relative(x, msk):
(N,D) =3D x.shape
return mka.array([nmr.subtract.outer(x[:,i],x[:,i]) for i in nmr.arange=
(D)],
mask =3D nmr.array([msk for i in nmr.arange(D)],typeco=
de=3Dmka.MaskType))
def scalar(x,y):
return mka.add.reduce(x*y,axis=3D0)
def run(D,N,S):
trig_inf =3D nmr.array(([[(0,1)[i<j] for i in nmr.arange(N)] for j in n=
mr.arange(N)]), typecode=3Dmka.MaskType)
diag_trig_sup =3D 1 - trig_inf #print mka.is_mask(diag_trig_sup) gives =
"1"
# positions and velocities
r =3D rnd.random([N,D]) #should be overlap-checked
v =3D rnd.random([N,D])
# relative positions and velocities
r_rel =3D relative(r, diag_trig_sup)
v_rel =3D relative(v, diag_trig_sup)
RV, VV, RR =3D scalar(r_rel,v_rel), scalar(v_rel,v_rel), scalar(r_rel,r=
_rel)
discr =3D RV**2 - VV*(RR-S**2) #<<<<----------PROBLEM HERE--------=
--------
if __name__ =3D=3D '__main__':
run(D=3D3,N=3D4,S=3D1e-8)
-------------------------
Any other advice that you may give me is very wellcome. My deadline is
approaching and I'm really reluctant to rewrite all this in C++ but I
don't see the light.
Thank you in advance,
=E1.
--=20
=C1lvaro Tejero Cantero
http://alqua.org -- documentos libres
free documents
|
|
From: eric j. <er...@en...> - 2004-05-17 04:35:06
|
Hello, This is a bit of a status report I guess for SciPy (www.scipy.org) and Enthought (www.enthought.com), but, really, it is a long winded job posting. Among other things, Enthought develops scientific software for clients. Python is central to to our strategy in this arena. We have long supported SciPy and believe strongly in Open Source Software. The purpose is to give people a feeling about what we do, the technical challenges we face, and the skills needed to meet them. SciPy ----- There has been a lot of work on SciPy lately by the Travis Oliphant, Travis Vaught, Pearu Peterson, Joe Cooper, and on the website by Jon-Eric Steinbomer and Janet Swisher. The site is now upgraded to Plone and we have a SciPy 0.3 package out. If you're interested in seeing the activity level at scipy.org, please visit. http://www.scipy.org/map?rmurl=http%3A//scipy.net/scipyusage/ It looks like we're averaging about a 100 downloads a day if you add up all source and binary distributions. When SciPy 0.1 was released a couple a years ago, I think we averaged about 10. Its extremely difficult to extrapolate a user base from such information, the obvious growth is good news. Other Stuff ----------- In addition to SciPy we have multiple projects going on right now that either already plug into or will be plugins to a Python-based scientific application framework called Envisage (sorta like an IDE for science apps) that we're working on. The idea is: (1) To lower the bar as far as work required to get a GUI put on the front end of a scientific algorithm. (2) Develop scientific plugins that can play well together so that, for instance, an electromagnetics simulator built by one person can be used in conjunction with an optimization plugin built by another person and results can be visualized with a 3D plugin. This is a hard, long path that requires much work and though. We have started the process and actually have built an app on a very early version of the Envisage framework. The app works great, but it's usage of the framework is a little mixed at this point. There were a lot of compromises we ended up making on the framework side in order to ship on time. Also, I am not sure whether "easy-to-program" and "flexible architecture for compatible plugins" are not mutually exclusive. I always have in my mind that I want a smart scientist to be able to build the GUI in a short amount of time after the algorithm (which is the "hard part") is finished. I still harbor this wish, but the lessons I've had over the last couple of years suggest that the GUI is actually the most expensive part of development for large apps. This is partially due to the fact that commercial apps are rarely built around "research" algorithms -- meaning that the algorithm code usually already exists in some form. Still, UIs take tons of time. Testing them is hard. Flexibility requires more complexity (factories, adapters, etc.) than seems necessary at first. Further, building *good* UI's is really hard. Scientist rarely have the patience or perspective for it -- and yet it is very often difficult to build a good UI for science apps without a scientist's understanding of the underlying problem. We have a number of tools along with SciPy that we're building that are pieces of this puzzle. 1. Traits -- They at the heart of everything we do. They provide some explicit typing, default GUI's, an observer event model for anything that uses them. They can also require some getting used to. 2. Kiva/Enable -- This is the generic drawing system for Python. 3. Chaco -- Interactive 2D Plotting 4. PyFace -- MVC layer on top of wxPython. Supports trees, menus, and a few other things right now. This will grow. 5. Envisage -- Plugin based GUI framework for scientific applications. Beyond the basic GUI that comes from Envisage, we already have a couple plugins for profiling code using hotshot and also searching for memory leaks. David Morrill wrote these and they are called gotcha and enroll. Martin Chilvers wrote a simple scintilla based plugin for editing scripts, and a PyCrust shell is available by default in the app (not a plugin at the moment). We would love to see a number of other plugins: a. Debugger. b. IPython-like PyCrust going. c. A more full featured script editing plugin. (there is a huge list of features here). d. Mayavi e. etc. These are all used actively in commercial products that we deliver. However, they are also in various stages of development and will continue to evolve and grow. Some are still pretty green. Portions of these are openly available now. All five of the listed tool sets will be open at some point this summer when they are cleaned up a bit. Jobs ---- All of this leads to the fact that we are hiring and looking for smart, pleasant, communicative people with integrity that are excited about building this sort of stuff. There is a lot of software architecture to be done that needs talented designers. There is UI design/development that needs scientists that care about UIs or UI developers/designers that are really quick at grasping scientific problems. We also need really strong scientists/scientific developers to do some of the backend coding for commercial products and also SciPy development. If you have a background in geophysics, electromagnetics (especially multi-pole methods) that is a big plus. For this, a PhD is helpful, but not required. Parallel computing skills wouldn't hurt. 3D visualization a la VTK/Python is a major need. So is knowledge about 2D rendering to help finish out Kiva and the Enable toolset. Strong Python skills, Python extension writing knowledge and strong C/C++ skills are a big benefit. Design/development with or of Java NetBeans or Eclipse-like architecture is great -- or any other solid GUI architecture. Dedication to writing clean/clear code meant for other humans to read is a requirement. We have 3 or 4 scientific/python positions to fill over the coming months, and we're looking for candidates that have the best mix of the above skills. You'll join our existing team of scientist/engineers, computer scientists, technical writers, and an HCI specialist. We like this mix and feel it is the best way to build this sort of software. If you are interested in working at Enthought, please send us your resume at jo...@en.... If not, please send this posting to the smartest people you know that fit some part of the above description (Python experience not explicitly required). Salaries are competitive. Candidates must be willing to relocate to Austin, Tx. thanks, eric jones PS: There are additional positions listed at http://www.enthought.com/careers.htm for those interested in business application development (not necessarily with Python). |
|
From: Jon S. <js...@wm...> - 2004-05-14 10:03:54
|
What about an object (TimeSeries) which can be "explored" (optionally) during its init method and, if so, it creates the dictionary with those values? If it is not explored, the dictionary would be assigned to None and, if requested, the "exploratory" statistics would be computed then. This could be the basis for other computations on time series. Just my two cents. Jon Saenz. | Tfno: +34 946012445 Depto. Fisica Aplicada II | Fax: +34 946013500 Facultad de Ciencias. \\ Universidad del Pais Vasco \\ Apdo. 644 \\ 48080 - Bilbao \\ SPAIN On Thu, 13 May 2004, Russell E Owen wrote: > At 9:27 AM -0400 2004-05-13, Perry Greenfield wrote: > >... One has to trade off the number of such functions > >against the speed savings. Another example is getting max and min values > >for an array. I've long thought that this is so often done they could > >be done in one pass. There isn't a function that does this yet though. > > Statistics is another area where multiple return values could be of > interest -- one may want the mean and std dev, and making two passes > is wasteful (since some of the same info needs to be computed both > times). > > A do-all function that computes min, min location, max, max location, > mean and std dev all at once would be nice (especially if the > returned values were accessed by name, rather than just being a tuple > of values, so they could be referenced safely and readably). > > -- Russell > > > ------------------------------------------------------- > This SF.Net email is sponsored by: SourceForge.net Broadband > Sign-up now for SourceForge Broadband and get the fastest > 6.0/768 connection for only $19.95/mo for the first 3 months! > http://ads.osdn.com/?ad_id=2562&alloc_id=6184&op=click > _______________________________________________ > Numpy-discussion mailing list > Num...@li... > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > |
|
From: Perry G. <pe...@st...> - 2004-05-13 21:40:11
|
> > A do-all function that computes min, min location, max, max location, > > mean and std dev all at once would be nice (especially if the > > returned values were accessed by name, rather than just being a tuple > > of values, so they could be referenced safely and readably). > > > > -- Russell > > > We will definitely add something like this for 1.0 or 1.1. > (but probably for min and max location, it will just be > for the first encountered). > > Perry > To elaborate on this a bit, I'm thinking that the minmax capability probably should be separated from statistics (it's very common to want to do min max without wanting to do mean or stdev, plus as others have noted, the statistics can be a bit more involved). There is one other aspect that needs user input. How to handle ieee special values for things like minmax. Right now the ufunc reductions have some odd behavior that is a result of underlying C library behavior. A NaN isn't consistently handled in such comparisons (it appears to depend on the order when comparing a NaN to a regular float, whichever appears second appears to be accepted in pairwise comparisons). I was thinking that one could add an exclude keyword to the functions to indicate that ieee special values should not be included (well, I suppose Inf should be for min max) otherwise they would be. Any thoughts on this? Perry |
|
From: Perry G. <pe...@st...> - 2004-05-13 21:30:23
|
Álvaro Tejero Cantero wrote: > > but this gives > >>> subtract.outer(r,r).shape > (10, 3, 10, 3) > > that is, subtracts y coordinates to x coordinates which is not intended. > AFAIK the outer solution is MUCH faster than the nested for loops, so > what I do now is > > >>> r_rel = transpose(array([subtract.outer(r[:,0],r[:,0]), > subtract.outer(r[:,1],r[:,1]), > subtract.outer(r[:,2],r[:,2])])) > >>> r_rel.shape #as with the double loop > (10,10,3) > > > My question is then if there is any more elegant way to do this, > especially giving as a result independence of the number of dimensions). > Not that I can think of at the moment. > Maybe an "axis" (=0 in this case?) keyword for the outer function would > be useful in this context? > Perhaps. Right now these ufunc methods are pretty complicated so it may not be easy to do, but I understand that there is certainly utility in being able to do that. We'll look into it (but not right away so don't hold your breath). Perry |
|
From: Bruce S. <so...@ui...> - 2004-05-13 21:21:12
|
Hi,
Raymond D. Hettinger is writing a general statistics module 'statistics.py A
collection of functions for summarizing data' that is somewhere in a Python
CVS (I can not find the exact reference but it appeared in a fairly recent
Python thread). He uses a one-pass algorithm from Knuth for the variance that
has good numerical stability.
Below is a rather rough version modified from my situation (masked arrays) which
uses Knuth's algorithm for the variance. It lacks features like checking
dimensions (assumes variance can be computed) and documentation.
Regards
Bruce Southey
import numarray
def SummaryStats(Matrix):
mshape=Matrix.getshape()
nrows=mshape[0]
ncols=mshape[1]
#print nrows, ncols
# Create matrices to hold statistics
N_obs =numarray.zeros(ncols, type='Float64')
Sum =numarray.zeros(ncols, type='Float64')
Var =numarray.zeros(ncols, type='Float64')
Min =numarray.zeros(ncols, type='Float64')
Max =numarray.zeros(ncols, type='Float64')
Mean =numarray.zeros(ncols, type='Float64')
AdjM =numarray.zeros(ncols, type='Float64')
NewM =numarray.zeros(ncols, type='Float64')
DifM =numarray.zeros(ncols, type='Float64')
for row in range(nrows):
for col in range(ncols):
t_value=Matrix[row,col]
N_obs[col] = N_obs[col] + 1
Sum[col] = Sum[col] + t_value
if t_value > Max[col]:
Max[col]=t_value
if t_value < Min[col]:
Min[col]=t_value
if N_obs[col]==1:
Mean[col]=t_value
AdjM[col]=(t_value-Mean[col])/(N_obs[col])-DifM[col]
NewM[col]=Mean[col]+AdjM[col]
DifM[col]=(NewM[col]-Mean[col])-AdjM[col]
Var[col] = Var[col] +
(t_value-Mean[col])*(t_value-NewM[col])
Mean[col] = NewM[col]
print 'N_obs\n', N_obs
print 'Sum\n', Sum
print 'Mean\n', Mean
print 'Var\n', Var/(nrows-1)
if __name__ == '__main__':
MValues=numarray.array([[1,2,1],[3,2,2],[5,1,1],[4,3,2]])
SummaryStats(MValues)
---- Original message ----
>Date: Thu, 13 May 2004 15:42:30 -0400
>From: "Perry Greenfield" <pe...@st...>
>Subject: RE: [Numpy-discussion] Getting the indexes of the myarray.min()
>To: "Russell E Owen" <rowen@u.washington.edu>, "numarray"
<num...@li...>
>
>> Russell E Owen wrote:
>>
>> At 9:27 AM -0400 2004-05-13, Perry Greenfield wrote:
>> >... One has to trade off the number of such functions
>> >against the speed savings. Another example is getting max and min values
>> >for an array. I've long thought that this is so often done they could
>> >be done in one pass. There isn't a function that does this yet though.
>>
>> Statistics is another area where multiple return values could be of
>> interest -- one may want the mean and std dev, and making two passes
>> is wasteful (since some of the same info needs to be computed both
>> times).
>>
>> A do-all function that computes min, min location, max, max location,
>> mean and std dev all at once would be nice (especially if the
>> returned values were accessed by name, rather than just being a tuple
>> of values, so they could be referenced safely and readably).
>>
>> -- Russell
>>
>We will definitely add something like this for 1.0 or 1.1.
>(but probably for min and max location, it will just be
>for the first encountered).
>
>Perry
>
>
>-------------------------------------------------------
>This SF.Net email is sponsored by: SourceForge.net Broadband
>Sign-up now for SourceForge Broadband and get the fastest
>6.0/768 connection for only $19.95/mo for the first 3 months!
>http://ads.osdn.com/?ad_id=2562&alloc_id=6184&op=click
>_______________________________________________
>Numpy-discussion mailing list
>Num...@li...
>https://lists.sourceforge.net/lists/listinfo/numpy-discussion
|
|
From: Warren F. <fo...@sl...> - 2004-05-13 21:09:01
|
On Thu, 13 May 2004, Russell E Owen wrote: > Statistics is another area where multiple return values could be of > interest -- one may want the mean and std dev, and making two passes > is wasteful (since some of the same info needs to be computed both > times). Single pass std deviations don't work very well if you've got a lot of data points and the std deviation is small compared to the average. I'm not arguing aginst including them, but maybe the documentation for such a function should include a caveat. Warren Focke |
|
From: Perry G. <pe...@st...> - 2004-05-13 19:42:44
|
> Russell E Owen wrote: > > At 9:27 AM -0400 2004-05-13, Perry Greenfield wrote: > >... One has to trade off the number of such functions > >against the speed savings. Another example is getting max and min values > >for an array. I've long thought that this is so often done they could > >be done in one pass. There isn't a function that does this yet though. > > Statistics is another area where multiple return values could be of > interest -- one may want the mean and std dev, and making two passes > is wasteful (since some of the same info needs to be computed both > times). > > A do-all function that computes min, min location, max, max location, > mean and std dev all at once would be nice (especially if the > returned values were accessed by name, rather than just being a tuple > of values, so they could be referenced safely and readably). > > -- Russell > We will definitely add something like this for 1.0 or 1.1. (but probably for min and max location, it will just be for the first encountered). Perry |
|
From: Russell E O. <rowen@u.washington.edu> - 2004-05-13 19:23:00
|
At 9:27 AM -0400 2004-05-13, Perry Greenfield wrote: >... One has to trade off the number of such functions >against the speed savings. Another example is getting max and min values >for an array. I've long thought that this is so often done they could >be done in one pass. There isn't a function that does this yet though. Statistics is another area where multiple return values could be of interest -- one may want the mean and std dev, and making two passes is wasteful (since some of the same info needs to be computed both times). A do-all function that computes min, min location, max, max location, mean and std dev all at once would be nice (especially if the returned values were accessed by name, rather than just being a tuple of values, so they could be referenced safely and readably). -- Russell |
|
From: T. C. <al...@an...> - 2004-05-13 18:50:02
|
On Thu, 2004-05-13 at 20:40, Paul Dubois wrote: > Use MA, it is designed to solve this problem. Is it as fast as using numarray arrays? Are you aware of example code floating around, apart from the docs? Thank you! --=20 =C1lvaro Tejero Cantero http://alqua.org -- documentos libres free documents |
|
From: Clare B. <lil...@ho...> - 2004-05-13 18:10:06
|
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|
From: Todd M. <jm...@st...> - 2004-05-13 17:01:39
|
On Thu, 2004-05-13 at 12:34, Álvaro Tejero Cantero wrote: > > I think what you're seeing is this: > > > > where(condition, expression1, expression2) > > > > Even though condition is selecting parts of expression1 which are valid, > > expression1 is still fully evaluated. > > Is this behaviour what is intended, or do you consider it a shortcoming > of the implementation?. In theory avoiding unneeded evaluations of > expression1 is very desirable... This just falls out of Python function call semantics: parameters are evaluated before a function is called. It is a property of Python so I think we're stuck with it. Two things come to mind to get rid of the warnings: 1. Stuff the input arrays with values which avoid the warnings. condition = logical_and(RV<0,discr>0) discr[condition] = 1 VV[condition] = 1 newtimes = where(condition, (-RV-sqrt(discr))/VV, far_future) 2. Use the Error system to suppress the warnings: Error.pushMode(dividebyzero="ignore", invalid="ignore") newtimes = where(logical_and(RV<0,discr>0), (-RV-sqrt(discr))/VV, far_future) Error.popMode() Regards, Todd |
|
From: Perry G. <pe...@st...> - 2004-05-13 16:59:17
|
Álvaro Tejero Cantero wrote: > > > I think what you're seeing is this: > > > > where(condition, expression1, expression2) > > > > Even though condition is selecting parts of expression1 which are valid, > > expression1 is still fully evaluated. > > Is this behaviour what is intended, or do you consider it a shortcoming > of the implementation?. In theory avoiding unneeded evaluations of > expression1 is very desirable... > It's about the only way it could work given how an array-based library works (unless there were a mechanism to postpone evaluation of expressions until their context can be determined). The expression is evaluated before it is passed to the where function. Perry Greenfield |
|
From: Todd M. <jm...@st...> - 2004-05-13 16:57:21
|
On Thu, 2004-05-13 at 07:13, Álvaro Tejero Cantero wrote: > I have a matrix of particle collision times: times[i,j] gives the time > for particle "i" to collide with particle "j". > > If I do, in order to get the first expected collision time for each > particle, the following (random array for testing purposes) : > > >>> N=30 > >>> times = rnd.random([N,N]) > >>> choose(argmin(times,transpose(times)) > Segmentation fault (of the python interactive shell!!!) > > With N=100 I get a more informative traceback, and rest within the > shell: > > Traceback (most recent call last): > File "<stdin>", line 1, in ? > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1670, in choose > return _choose(selector, population, outarr, clipmode) > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1579, in __call__ > result = self._doit(computation_mode, woutarr, cfunc, ufargs, 0) > File "/usr/lib/python2.3/site-packages/numarray/ufunc.py", line 1564, in _doit > blockingparameters) > ValueError: _operator_compute: too many inputs + outputs > > > For N=10,N=15 I get the expected output, but for N=20 I get again the > brutal segfault... > > > regards, > á. I was able to reproduce this bug, logged it on Source Forge, and will get to it as soon as possible... probably tomorrow or Monday. Regards, Todd |
|
From: Ray S. <ra...@bl...> - 2004-05-13 16:49:40
|
At 06:37 PM 5/13/2004 +0200, =C1lvaro Tejero Cantero wrote: >Hello, > >Do you know of publicly available example code with numarray?. Short of >the tutorial and the page by Magnus Hetland, and the intro by David >Mertz, I haven't found much code written in numarray through google. Try: http://rjs.org/astro/1004x/Python/CAP7X.PY I might have a few others around I can share... Ray Schumacher Blue Cove Interactive 7220 Trade Street, Suite 101 San Diego, CA 92121 858.695.8801 http://Blue-Cove.com |
|
From: T. C. <al...@an...> - 2004-05-13 16:30:28
|
Hello, Do you know of publicly available example code with numarray?. Short of the tutorial and the page by Magnus Hetland, and the intro by David Mertz, I haven't found much code written in numarray through google. It would be helpful for me to have more code to look at. I am explicitly discarding numpy code in order to keep away from subtle differences, but perhaps this is overcautious. Thanks! =E1. --=20 =C1lvaro Tejero Cantero http://alqua.org -- documentos libres free documents |
|
From: T. C. <al...@an...> - 2004-05-13 16:28:01
|
> I think what you're seeing is this: >=20 > where(condition, expression1, expression2) >=20 > Even though condition is selecting parts of expression1 which are valid, > expression1 is still fully evaluated. Is this behaviour what is intended, or do you consider it a shortcoming of the implementation?. In theory avoiding unneeded evaluations of expression1 is very desirable... --=20 =C1lvaro Tejero Cantero http://alqua.org -- documentos libres free documents |
|
From: Perry G. <pe...@st...> - 2004-05-13 13:28:13
|
Robert Kern wrote: > > Well, yes. But when you ask to find all the things that equal > > the minimum, you pretty much must look twice (if you want to know > > where they all are if more than one). Once to determine the > > minimum, the next time to locate all of them. > > Nah, you can accumulate indices corresponding to the current minimum > value as you go. Discard the list of indices and start again if you get > a new minimum value. > True enough (though I suspect some special cases may not work any faster, e.g., an array of all zeros with the last element equal to -1; you spend all the time copying indices for nearly the whole damn thing for no purpose). There are a bunch of things that can be done to eliminate multiple passes but they tend to lead to many different specialized functions. One has to trade off the number of such functions against the speed savings. Another example is getting max and min values for an array. I've long thought that this is so often done they could be done in one pass. There isn't a function that does this yet though. > >>Yes... although for the problem at hand that motivated my query, my > >>times matrix is symmetric... I don't really need all the minima, but > >>does numarray have any special datatype for symmetric matrixes, that > >>prevents storage of unneded (e.g. supradiagonal) elements?. > >> > > > > Not for special cases like this. One could probably write a special > > subclass to do this, but for a savings of a factor of 2 in memory, > > it usually would not be worth the trouble (unlike sparse matrices) > > OTOH, having subclasses that follow LAPACK's symmetric packed storage > scheme would be very useful not because of the space factor but the time > saved by being able to use the symmetric algorithms in LAPACK. I think. > I'd agree that that would be a much stronger motivating factor. Perry |
|
From: Todd M. <jm...@st...> - 2004-05-13 13:04:13
|
On Thu, 2004-05-13 at 07:54, Álvaro Tejero Cantero wrote: > Hi, > > I cannot understand why I do receive these warnings: > > Warning: Encountered invalid numeric result(s) in sqrt > Warning: Encountered invalid numeric result(s) in divide > > after > > >>> newtimes = where(logical_and(RV<0,discr>0), (-RV-sqrt(discr))/VV, far_future) > > > RV, discr, RR, VV are NxN arrays of reals (Float64), while > far_future=1e20 (scalar). > > 1. sqrt(<0) should be impossible, since it's explicitly ruled out that > discr<0 in the where condition. > > 2. /0 should also be impossible, since VV==0 is True only for the > diagonal... > > I've done some testing and where(logical_and(RV<0, discr>0), discr, 666) > doesn't show any negative numbers, neither does > where(logical_and(RV<0,discr>0),VV,666) show any zeroes... > > > > Please.. what am I doing wrong here? I think what you're seeing is this: where(condition, expression1, expression2) Even though condition is selecting parts of expression1 which are valid, expression1 is still fully evaluated. Regards, Todd -- Todd Miller <jm...@st...> |
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