Mailman 3 May 2004 - NumPy-Discussion

IPE Cumaru Massaranduba Cedar
by Lumber Wholesale May 17, 2004

May 17, 2004

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IPE Cumaru Massaranduba Cedar
by Lumber Wholesale May 17, 2004

May 17, 2004

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MaskedArray problem?
by Álvaro Tejero Cantero May 17, 2004

May 17, 2004

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. -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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ANN: Job Openings at Enthought
by eric jones May 16, 2004

May 16, 2004

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 jobs(a)enthought.com. 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).

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Getting the indexes of the myarray.min()
by Álvaro Tejero Cantero May 14, 2004

May 14, 2004

Hello, I am new to numarray. I have been searching the documentation, and see no evident vectorial and concise way to get the indexes for the minimum of an array times.min(). Currently my two alternatives are: (times is NxN array) husband, wife = nonzero(equal(times.min(),times)) #gives tuple of 1x1 arrays, each containing one index and (even uglier) husband = compress(times.min()==times,indices([N,N])[0]) wife = compress(times.min()==times,indices([N,N])[1]) These are weird ways to get something as simple. I am surely missing something, but I have tried several slicing strategies before without success. For getting the minimum times in each row I use: choose(argmin(times),transpose(times)) What are the idioms in numpy for these tasks? Thank you very much in advance, á. I would -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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RE: [Numpy-discussion] Getting the indexes of the myarray.min()
by Bruce Southey May 13, 2004

May 13, 2004

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" <perry(a)stsci.edu> >Subject: RE: [Numpy-discussion] Getting the indexes of the myarray.min() >To: "Russell E Owen" <rowen(a)u.washington.edu>, "numarray" <numpy-discussion(a)lists.sourceforge.net> > >> 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 >Numpy-discussion(a)lists.sourceforge.net >https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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Re: [Numpy-discussion] Unexpected warnings in where
by Álvaro Tejero Cantero May 13, 2004

May 13, 2004

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! -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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May 13, 2004

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Unexpected warnings in where
by Álvaro Tejero Cantero May 13, 2004

May 13, 2004

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? -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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Example code
by Álvaro Tejero Cantero May 13, 2004

May 13, 2004

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! á. -- Álvaro Tejero Cantero http://alqua.org -- documentos libres free documents

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