Mailman 3 August 2012 - SciPy-User

Notification when array is modified
by John Jumper Aug. 21, 2012

Aug. 21, 2012

I am looking for a method so that I can be notified when the contents of a numpy array change. A simplified class that illustrates the desired functionality is below. class ComputeDerived(object): def __init__(self, n): self.A = np.zeros(n) self.B = np.zeros(n) self.change_mask = np.ones(n, dtype=np.bool) self._Derived = expensive(self.A,self.B) def set_A(self, i, x): self.A[i] = x self.change_mask[i] = True def set_B(self, i, x): self.B[i] = x self.change_mask[i] = True def Derived(self): if np.any(self.change_mask): for i in np.nonzero(self.change_mask)[0]: self._Derived[i] = expensive(A[i],B[i]) self.change_mask[:] = False return self._Derived This class works, but I lack the power of numpy's indexing syntax to change A or B. I would like to be able to write compute_derived.A[(3,5,6)] = 1 and then be notified that A has changed. Otherwise, if I want to use a numpy interface for A and B, I would have to store old copies of A and B and traverse these copies every time Derived is called. Is there any interface in numpy so that I could expose A and B directly as writeable numpy arrays but be notified when they are modified? Alternatively, if I subclass ndarray, is there some exhaustive list of methods that can change the underlying data (I would be worried about view objects here)? In case it makes a difference, my actual use case is a C++ class wrapped using Boost::Python. The numpy arrays A and B would point at aligned float* members of my C++ class. Thank you, John Jumper

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Re: [SciPy-User] minimization of constrained mltivariate problem
by The Helmbolds Aug. 20, 2012

Aug. 20, 2012

Take alook at SciPy's "optimize" stuff, especially fmin_bfgs and fmin_cobyla. The writeups on these may be hard to follow, but we are working an better documentation of these and other "optimize" routines.

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How to minimize a simple multi variable function
by Roderick Bartolo Aug. 20, 2012

Aug. 20, 2012

Hi all, I would like to minimize the following simple function f(x,y,z) = x*y*z with some constraints x<=20 + y and y < z+2 using Pulp. Does any body know how this can be done please?

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UnivariateSpline returning NaN
by Kevin Gullikson Aug. 19, 2012

Aug. 19, 2012

Hi all, I am having a weird issue with UnivariateSpline return nan when trying to interpolate some data. I have had this happen before a couple times, but it was always because a nan was in the input arrays. Here, they are all real numbers. Here is some minimal code that recreates my problem: import numpy x,y = numpy.loadtxt("test.out", usecols=(0,1), unpack=True) from scipy.interpolate import UnivariateSpline fcn = UnivariateSpline(x,y,s=0) print fcn(numpy.median(x)) (the result of the print is nan) Here is the data that I used: 960.38174 2218580.3 2205540 960.3904 2209433.1 2205465.9 960.39906 2202205.2 2205391.9 960.40773 2209385.9 2205317.8 960.41639 2218550.3 2205243.7 960.42505 2220656.8 2205169.7 960.43372 2220779.4 2205095.6 960.44238 2220771.8 2205021.5 960.45104 2220764.3 2204947.5 960.67545 2220409.5 2203030.8 960.68725 2220387.5 2202930.1 960.69905 2220365.5 2202829.5 960.71085 2220343.4 2202728.8 960.72265 2220321.4 2202628.2 960.73445 2220299.3 2202527.6 960.74625 2220277.3 2202427 960.75805 2220222.8 2202326.4 960.76985 2220200.7 2202225.7 960.78165 2220178.7 2202125.2 960.79345 2220156.6 2202024.6 960.80525 2220134.6 2201924 960.81705 2220080.1 2201823.4 961.16055 2219013.1 2198897.2 961.16699 2218983.4 2198842.3 961.17344 2218953.6 2198787.4 961.17988 2218923.8 2198732.5 961.18788 2218886.9 2198664.3 961.19587 2218882.5 2198596.2 961.20386 2218910.5 2198528.1 961.21186 2218906.1 2198459.9 961.21963 2218837.8 2198393.7 961.2274 2218769.4 2198327.4 961.23517 2218701.1 2198261.2 961.24294 2218697.7 2198194.9 961.25071 2218661.8 2198128.7 961.25695 2218633 2198075.6 961.26317 2218604.3 2198022.5 961.27485 2218550.4 2197922.9 961.28653 2218496.5 2197823.4 961.2982 2218475 2197723.8 961.30988 2218421.1 2197624.3 961.32156 2218367.3 2197524.8 961.75259 2216379.3 2193857.4 961.75259 2225525.5 2193857.4 962.04986 2222983.5 2191338.5 962.05175 2222942.3 2191322.5 962.69908 2217396.7 2185841.9 962.71837 2217210.7 2185678.5 962.73766 2217024.8 2185515.2 962.75695 2214605.6 2185352 962.77624 2180663.5 2185188.7 962.79552 2080767.6 2185025.6 962.81481 2027222.1 2184862.4 962.83411 2080471.4 2184699.3 962.8534 2180022.7 2184536.2 962.87269 2213620.2 2184373.1 Thanks, Kevin Gullikson

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[SciPy-user] Avoiding inner for loops
by mdekauwe Aug. 19, 2012

Aug. 19, 2012

Hi, I need to avoid (at least) two inner for loops in what I am trying to do otherwise my processing takes forever. What is the best way to transfer what I am doing into a more "numpy way"? Essentially I am trying to call a model again for various different parameter combinations. The example is fictional, by the grid_size would ideally grow > 500 and by doing so the processing speed becomes very slow the way I have set things up.. thanks. example. import numpy as np def fake_model(data1, data2, p1, p2, p3): """ complete nonsense """ return data1 + data2 * p1 * p2 * p3 data1 = np.random.rand(10) # the size of this arrays varies might be 10 might be 15 etc data2 = np.random.rand(10) # the size of this arrays varies might be 10 might be 15 etc obs = np.random.rand(10) # the size of this arrays varies might be 10 might be 15 etc grid_size = 10 # Ideally this would be a large number param1 = np.linspace(5.0, 350, grid_size) param2 = np.linspace(5.0, 550, grid_size) param3 = np.linspace(1E-8, 10.5, grid_size) ss = np.zeros(0) for p1 in param1: for p2 in param2: for p3 in param3: ans = fake_model(data1, data2, p1, p2, p3) ss = np.append(ss, np.sum(obs - ans)**2) print np.sum(obs - ans)**2 -- View this message in context: http://old.nabble.com/Avoiding-inner-for-loops-tp34319609p34319609.html Sent from the Scipy-User mailing list archive at Nabble.com.

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Strange import behaviour
by Carlos Baptista Aug. 19, 2012

Aug. 19, 2012

Dear all, I have come across a very strange behaviour of Scipy. It has to do with importing and using Scipy functions. For example, this works: >>> import scipy.sparse as ss >>> ss <module 'scipy.sparse' from '/usr/lib/python2.7/dist-packages/scipy/sparse/__init__.pyc'> However this does not work: >>> import scipy as sp >>> sp.sparse Traceback (most recent call last): File "<ipython-input-2-0c35c470f8a4>", line 1, in <module> sp.sparse AttributeError: 'module' object has no attribute 'sparse' The above example is merely a condensation of my real problem (using spsolve from scipy.sparse.linalg). What might cause this problem? Kind regards, Carlos

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brentq and brenth
by The Helmbolds Aug. 16, 2012

Aug. 16, 2012

It would seem more logical to call brentq "brentp" ("p" for parabola) to follow the same pattern as "brenth" ("h" for hyperbola). Unless there is an inside joke to use "q" because it is "p" reflected about a vertical axis, and hence represents Brent's "inverse parabolic" estimate of the root?

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ANN: FiPy 3.0
by Jonathan Guyer Aug. 16, 2012

Aug. 16, 2012

We are pleased to announce the release of FiPy 3.0. http://www.ctcms.nist.gov/fipy The bump in major version number reflects more on the substantial increase in capabilities and ease of use than it does on a break in compatibility with FiPy 2.x. Few, if any, changes to your existing scripts should be necessary. The significant changes since version 2.1 are: • Coupled and vector equations are now supported. • A more robust mechanism for specifying boundary conditions is now used. • Most Meshes can be partitioned by meshing with Gmsh. • PyAMG and SciPy have been added to the solvers. • FiPy is capable of running under Python 3. • “getter” and “setter” methods have been pervasively changed to Python properties. • The test suite now runs much faster. • Tests can now be run on a full install using fipy.test(). This release addresses 66 tickets. ======================================================================== FiPy is an object oriented, partial differential equation (PDE) solver, written in Python, based on a standard finite volume (FV) approach. The framework has been developed in the Metallurgy Division and Center for Theoretical and Computational Materials Science (CTCMS), in the Material Measurement Laboratory (MML) at the National Institute of Standards and Technology (NIST). The solution of coupled sets of PDEs is ubiquitous to the numerical simulation of science problems. Numerous PDE solvers exist, using a variety of languages and numerical approaches. Many are proprietary, expensive and difficult to customize. As a result, scientists spend considerable resources repeatedly developing limited tools for specific problems. Our approach, combining the FV method and Python, provides a tool that is extensible, powerful and freely available. A significant advantage to Python is the existing suite of tools for array calculations, sparse matrices and data rendering. The FiPy framework includes terms for transient diffusion, convection and standard sources, enabling the solution of arbitrary combinations of coupled elliptic, hyperbolic and parabolic PDEs. Currently implemented models include phase field treatments of polycrystalline, dendritic, and electrochemical phase transformations as well as a level set treatment of the electrodeposition process.

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scipy-0.10.1 compilation on 64 bit windows in debug mode
by Santosh Pratap Singh Aug. 16, 2012

Aug. 16, 2012

Hi All, I am trying to compile a debug version of the scipy-0.10.1 extension on 64 bit windows. I have also compiled the python-2.7.3 and numpy-1.6.2 in debug mode. When I am building scipy, I ran into the problem C:\cpython\Debug\scipy\0_10_1\scipy-0.10.1>python_d setup.py config --compiler=msvc --fcompiler=intelvem build --debug compile options: '-g -IC:\cpython\Debug\CPython\2_7_3\winx64\Debug\python\lib\site-packages\numpy\core\include -c' ifort.exe:f77: scipy\fftpack\src\dfftpack\dcosqb.f ifort: command line warning #10131: extension 'M' not supported ignored in optio n '/Qax' *ifort: command line error: option '/g' is ambiguous* error: Command "C:\Program Files (x86)\Intel\Compiler\11.1\060\Bin\intel64\ifort.exe -FI -w90 -w95 /nologo /MD /nbs /Qlowercase /us /O1 /QaxM -g -IC:\cpython\Debug\CPython\2_7_3\winx64\Debug\python\lib\site-packages\numpy\core\include -c /c scipy\fftpack\src\dfftpack\dcosqb.f /Fobuild\temp.win-amd64-2.7\scipy\fftpack\src\dfftpack\dcosqb.o" failed with exit status 1 Can some one help me in this? Am I missing something? Thanks. Santosh

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integrals and quad
by nicky van foreest Aug. 16, 2012

Aug. 16, 2012

Hi, Given some function f it is easy with scipy.integrate.quad to compute the integral of f for some given endpoint. However, I need the integral at many endpoints, that is, I want to plot \int_0^t f(x) dx. How can this be done in an efficient and elegant way? To illustrate I used the following code. from numpy import cumsum, linspace, vectorize from scipy.integrate import quad from pylab import plot, show def f(x): return x F = vectorize(lambda t: quad(f, 0, t)[0]) # must be wasteful t = linspace(0,3, 50) FF = cumsum(f(t))*(t[1]-t[0]) # simple, but inaccurate, note that t[1]-t[0] is the grid size, a bit like dx in the integral plot(t,f(t)) plot(t, F(t)) plot(t, F) show() I suspect that calling F at many values is wasteful, since the integral is evaluated at the same points many times. The trick with using cumsum must save some work (an O(n) algo), but is less accurate as is shown by the graphs. So, I don't like to use cumsum, and I also don't like to use a vectorized quad. Is there something better? thanks Nicky

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