Mailman 3 January 2015 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn July 28, 2022

July 28, 2022

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Normalization for optimization in python
by Yuxiang Wang April 27, 2015

April 27, 2015

Dear all, During optimization, it is often helpful to normalize the input parameters to make them on the same order of magnitude, so the convergence can be much better. For example, if we want to minimize f(x), while a reasonable approximation is x0=[1e3, 1e-4], it might be helpful to normalize x0[0] and x0[1] to about the same order of magnitude (often O(1)). My question is, I have been using scipy.optimize and specifically, the L-BFGS-B algorithm. I was wondering that, do I need to normalize that manually by writing a function, or the algorithm already did it for me? Thank you! -Shawn -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw5aj(a)virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/

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fmin_l_bfgs_b stdout gets mixed into following Python stdout
by Daan Wynen Feb. 3, 2015

Feb. 3, 2015

Hi, I couldn't find anything about this issue, and it is not critical but annoying. Whenever I run the solver, the standard output order gets mixed up. What I want to see is the following (blank lines stripped out for saving space): ... ME SAYS: Unoptimized objective: 0.947251976788 RUNNING THE L-BFGS-B CODE * * * Machine precision = 2.220D-16 N = 50 M = 20 At X0 0 variables are exactly at the bounds At iterate 0 f= 9.47252D-01 |proj g|= 1.04635D-01 At iterate 1 f= 7.95655D-01 |proj g|= 9.16550D-02 ... At iterate 3769 f= 7.12543D-05 |proj g|= 2.07939D-05 At iterate 3770 f= 7.12527D-05 |proj g|= 1.51021D-04 * * * Tit = total number of iterations ... Total User time 0.000E+00 seconds. ME SAYS: After optimization: 7.1252665476e-05 ... Instead, what I get is this: ... ME SAYS: Unoptimized objective: 0.947251976788 RUNNING THE L-BFGS-B CODE * * * Machine precision = 2.220D-16 N = 50 M = 20 At X0 0 variables are exactly at the bounds At iterate 0 f= 9.47252D-01 |proj g|= 1.04635D-01 At iterate 1 f= 7.95655D-01 |proj g|= 9.16550D-02 ... At iterate 3147 f= 8.09457D-05 |proj g|= 5.97659D-05 ME SAYS: After optimization: 7.1252665476e-05 At iterate 3148 f= 8.09298D-05 |proj g|= 5.19516D-05 ... At iterate 3769 f= 7.12543D-05 |proj g|= 2.07939D-05 At iterate 3770 f= 7.12527D-05 |proj g|= 1.51021D-04 * * * Tit = total number of iterations ... Total User time 0.000E+00 seconds. ... Obviously, when I run an experiment, I would like to see the result at the end, and not buried somewhere in the middle of the iteration outputs. I tried passing callback=lambda _: sys.stdout.flush() and putting another flush after the whole optimization, but it didn't help. The example is on a very fast toy calculation, but this also happens when each iteration takes multiple seconds. I am using: python 2.7.3 (virtualenv from Fedora 18 distribution) scipy 0.14.1 numpy 1.9.1 But this also happens on Canopy. Is this maybe just to be expected when interfacing with fortran, or am I using this the wrong way? Best Regards Daan Wynen

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Need clarification on paper "The NumPy array: a structure for efficient numerical computation": what does "vectorize" really mean?
by Brian Merchant Feb. 1, 2015

Feb. 1, 2015

In "The NumPy array: a structure for efficient numerical computation" (2011, http://arxiv.org/abs/1102.1523), the authors use the verb/adjective "vectorize" in such a way that I need clarification. On page 3, in subsection "Numerical operations on arrays: vectorization", I get the (perhaps incorrect?) impression that "vectorization" refers to "an operation that can be run using C for loops over C arrays". Already, the vocabulary seems a little weird to me, since if "vectorization" really just means "for loops in a low level language rather than a high level language"...why create a word for the concept based on the root word "vector"? Perhaps there is some history there that I am missing, but I can accept that definition. The answer to the following StackOverflow seems to suggest that "vectorization" means "implemented in a lower level language": http://stackoverflow.com/questions/17483042/explain-the-speed-difference-be… However, on page 5, I see the following sentence: "In a non-vectorized language, no temporary arrays need to be allocated when the output values are calculated in a nested for-loop, e.g. (in C)". Hold on -- how does "vectorized" make sense here? Should I interpret it as "in a low level language, no temporary arrays need to be allocated when the output values are calculated..."? If yes, well...why would temporary arrays need to be allocated in a higher level language? Can you help me make sense of my confusion? Kind regards, Brian

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Problem with sherpa fitting
by emmaonaw Jan. 30, 2015

Jan. 30, 2015

Hi, I don't know if this is the right forum to ask but I cannot find any forum where to get help with sherpa fitting, so here I go :). I am trying to fit an image using a model of background, PSF and gaussian source, using sherpa in python. I set up my model using the standard tools: show_all() Data Set: 1 Filter: Circle(112.75,164.45,111.12) name = filename.fits x0 = Float64[97344] x1 = Float64[97344] y = Float32[97344] shape = (312, 312) staterror = None syserror = None sky = None eqpos = world crval = [ 278.5712, -7.067 ] crpix = [ 157., 157.] cdelt = [ 0.009, 0.009] crota = 0 epoch = 2000 equinox = 2000 coord = logical Model: 1 (tablemodel.bkgmap + usermodel.Src1) Param Type Value Min Max Units ----- ---- ----- --- --- ----- bkgmap.ampl thawed 1 -3.40282e+38 3.40282e+38 Src1.sigma1 frozen 7.0006 1 30 Src1.ampl thawed 10 -100 100000 Src1.size thawed 10.0009 0 55.5603 Src1.xpos thawed 146.943 124.719 169.167 Src1.ypos thawed 158.894 136.608 181.18 and everything looks okay until I try to fit(): ERROR: Internal Python error in the inspect module. Below is the traceback from this internal error. Traceback (most recent call last): File "/Users/emma/anaconda/lib/python2.7/site-packages/IPython/core/ultratb.py", line 776, in structured_traceback records = _fixed_getinnerframes(etb, context, tb_offset) File "/Users/emma/anaconda/lib/python2.7/site-packages/IPython/core/ultratb.py", line 230, in wrapped return f(*args, **kwargs) AssertionError Unfortunately, your original traceback can not be constructed. I am stuck here, tried several things but not really sure how to continue debugging this, any ideas? is it possible crval should be positive? Thanks a lot Emma -- View this message in context: http://scipy-user.10969.n7.nabble.com/Problem-with-sherpa-fitting-tp19935.h… Sent from the Scipy-User mailing list archive at Nabble.com.

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Help of understanding C code in weave
by Zheng Ruan Jan. 29, 2015

Jan. 29, 2015

Hi Scipy users, I am trying to read code that uses scipy.weave and I don't understand some of the code in C. To make it easy and clear, I'll just post the part that confused me. I have a numpy array (a) with a shape of (2, 623, 3, 333). And another array numpy array (d) with a shape of (1, 623, 623). In the C code part I have something like this: code = """ ... c = *(a + b); *(d+b) += 1; ... """ In the above code, b and c are float type in C. I just don't understand how the c value are calculated and what does "*(d+b) += 1;" do. The code is very old and I saw some deprecated warnings when I compile it. Thank you so much and any hints are welcome!!! Zheng

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Solving equations in SciPy
by Gabriele Brambilla Jan. 29, 2015

Jan. 29, 2015

Hi all, is it possible to finding roots of equations in Scipy? Is there any function that does this task? For example an equation solver able to find the complex roots of an equation like X^4 + aX^3 + ... = 0 ? thanks Gabriele

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General scientific python mailing list
by Todd Jan. 26, 2015

Jan. 26, 2015

Hi, I see a lot of mailing lists for specific scientific python projects, but no mailing list for scientific python in general. Is there any mailing list for general scientific python users?

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