Mailman 3 December 2013 - SciPy-User

How to display a RGB image (MFISH)?
by jp 02 Apr '23

02 Apr '23

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 28 Jul '22

28 Jul '22

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

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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leastsq and multiprocessing
by Frédéric Parrenin 30 May '14

30 May '14

Dear all, Following these posts: http://stackoverflow.com/questions/10489134/multithreaded-calls-to-the-obje… It seems it is possible to make leastsq take part of multiple processors. I was wondering: given that the tendency of processors is to have more and more cores nowadays, why this is not done by default in leastsq? Best regards, Frédéric Parrenin

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Allan Variance
by Daniele Nicolodi 04 Feb '14

04 Feb '14

Hello, does anyone know or have written and is willing to share some code to compute the Allan variance, modified Allan variance and friends, ideally both from frequency than from phase data? I'm writing my own code, it is not that difficult, but I would prefer the time required for testing and validation doing something more fun. If there is no available code implementing Allan variance, would it be interesting to include in in SciPy? Thank you so much. Best, Daniele

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Re: [SciPy-User] SciPy-User Digest, Vol 120, Issue 12
by Michal Romaniuk 24 Jan '14

24 Jan '14

Hi, > Hi, > > On Mon, Aug 19, 2013 at 7:44 AM, Michal Romaniuk > <michal.romaniuk06(a)imperial.ac.uk> wrote: >> Hi, >> >> I'm saving a large batch of data using savemat and although I get no >> errors, the files produced are not readable for either matlab or scipy. >> Is there a limit on file size? > > Ah - yes there is - the individual matrices in the mat file cannot be > larger than 4GB. Is it possible you hit this limit? > > Sorry, I only realized this when Richard Llewellyn pointed this out a > couple of weeks ago on the list: > > http://scipy-user.10969.n7.nabble.com/SciPy-User-scipy-io-loadmat-throws-Ty… > > The current scipy code has an error message for matrices that are too large. > > Cheers, > > Matthew Well, I managed to work around the problem to some extent by setting do_compression=True. Now Matlab can read those files (so they must be valid to some extent) but SciPy can't (even though they were written with SciPy). I get this error: PATH/lib/python2.6/site-packages/scipy/io/matlab/mio.pyc in loadmat(file_name, mdict, appendmat, **kwargs) 173 variable_names = kwargs.pop('variable_names', None) 174 MR = mat_reader_factory(file_name, appendmat, **kwargs) --> 175 matfile_dict = MR.get_variables(variable_names) 176 if mdict is not None: 177 mdict.update(matfile_dict) PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in get_variables(self, variable_names) 290 continue 291 try: --> 292 res = self.read_var_array(hdr, process) 293 except MatReadError, err: 294 warnings.warn( PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in read_var_array(self, header, process) 253 `process`. 254 ''' --> 255 return self._matrix_reader.array_from_header(header, process) 256 257 def get_variables(self, variable_names=None): PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:5401)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:4849)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.read_real_complex (scipy/io/matlab/mio5_utils.c:5602)() ValueError: total size of new array must be unchanged The size of the main array is about 9 GB before compression, but the compressed files are less than 500 MB and closer to 400 MB. There are some other arrays in the file too but they are much smaller. Any ideas on how I could get SciPy to read this data back? Right now I can only think of storing the data in single precision format... Thanks, Michal

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Problems with weave under windows
by laughingrice 30 Dec '13

30 Dec '13

I've been fighting with getting weave working under windows 8.1 using Canopy (scipy 0.13.2-1) Turned out that compilation errors were Microsoft complaining that the command like is too long. Changing line 95 in scipy/weave/catalog.py from return base + sha256(expr).hexdigest() to return base + sha256(expr).hexdigest()[:-30] or doing the same in line 126 of scipy/weave/platform_info.py chk_sum = check_sum(exe_path) to chk_sum = check_sum(exe_path)[:-30] Solved the problem for me (a combination of them also worked removing less characters in each, although this would depend on user name length as well) This is with both visual studio 2010 and 2012 (had to set VS90COMNTOOLS to point to either VS100COMNTOOLS or VS110COMNTOOLS for weave to find vcvarsall.bat as well). Anyone else see this problem and has a better solution? Thanks -- View this message in context: http://scipy-user.10969.n7.nabble.com/Problems-with-weave-under-windows-tp1… Sent from the Scipy-User mailing list archive at Nabble.com.

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setuptools messing with sdists using numpy.distutils and Fortran libraries
by Juan Luis Cano 26 Dec '13

26 Dec '13

I'm trying to build a Python package using some Fortran libraries, and started using also setuptools at a certain point because I wanted to take advantage of "setup.py develop". However, despite being stated that one should just import setuptools before numpy.distutils imports: http://mail.scipy.org/pipermail/numpy-discussion/2013-September/067784.html I found that "setup.py sdist" works differently whether setuptools has been imported or not - i.e. not the same files get excluded or included. In particular, I had problems with the .pyf files of a Fortran library I created, included using config.add_extension. Without setuptools it works, and the .pyf files get included, but with setuptools they are missing. This results in failed installations later on. In case you want the specific example, here is the diff between the working and non-working setup.py: https://github.com/Pybonacci/poliastro/compare/0.1.x...master#diff-29 The use of setuptools is not crucial for this project, but I'm interested in knowing what's going on here. Thanks in advance!

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scipy and python 3.3
by Mark Dixon 23 Dec '13

23 Dec '13

Hi, I'm dipping my toe in python3-land and trying to build a few packages on a 64-bit Intel CentOS 6.5 box, but I have problems getting scipy 0.13.2 to pass some of its tests. Should I be opening tickets about these, or am I being really dumb? * python 3.3.2 * nose 1.3.0 * numpy 1.8.0 (with PTATLAS=None) * atlas 3.10.1 (made into a full LAPACK with netlib LAPACK 3.4.2) * gcc 4.4.7 (as shipped with CentOS 6.5) (I've built each of these from source, apart from gcc) When I run scipy's tests, I get 3 failures in (full output with verbose=2 appended below): test_basic.test_xlogy test_lambertw.test_values test_lambertw.test_ufunc I don't get any failures with the same stack built against python 2.7.6. numpy passes its tests with either stack. Any pointers would be greatly appreciated, please! Cheers, Mark ====================================================================== FAIL: test_basic.test_xlogy ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/scipy/special/tests/test_basic.py", line 2736, in test_xlogy assert_func_equal(special.xlogy, w2, z2, rtol=1e-13, atol=1e-13) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/scipy/special/_testutils.py", line 87, in assert_func_equal fdata.check() File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/scipy/special/_testutils.py", line 292, in check assert_(False, "\n".join(msg)) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 44, in assert_ raise AssertionError(msg) AssertionError: Max |adiff|: 712.557 Max |rdiff|: 1028 Bad results (3 out of 6) for the following points (in output 0): 0j (nan+0j) => (-0+0j) != (nan+nanj) (rdiff 0.0) (1+0j) (2+0j) => (-711.8625285635226+1.5707963267948752j) != (0.6931471805599453+0j) (rdiff 1028.0030375952847) (1+0j) 1j => (-711.8625285635226+1.5707963267948752j) != 1.5707963267948966j (rdiff 453.18576089112065) ====================================================================== FAIL: test_lambertw.test_values ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py", line 21, in test_values assert_equal(lambertw(inf,1).real, inf) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 304, in assert_equal raise AssertionError(msg) AssertionError: Items are not equal: ACTUAL: nan DESIRED: inf ====================================================================== FAIL: test_lambertw.test_ufunc ---------------------------------------------------------------------- Traceback (most recent call last): File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 581, in chk_same_position assert_array_equal(x_id, y_id) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 718, in assert_array_equal verbose=verbose, header='Arrays are not equal') File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not equal (mismatch 66.66666666666666%) x: array([False, True, True], dtype=bool) y: array([False, False, False], dtype=bool) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/nose-1.3.0-py3.3.egg/nose/case.py", line 198, in runTest self.test(*self.arg) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/scipy/special/tests/test_lambertw.py", line 93, in test_ufunc lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873]) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 811, in assert_array_almost_equal header=('Arrays are not almost equal to %d decimals' % decimal)) File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 607, in assert_array_compare chk_same_position(x_isnan, y_isnan, hasval='nan') File "/scratch/bob/p3/pylibs/lib/python3.3/site-packages/numpy/testing/utils.py", line 587, in chk_same_position raise AssertionError(msg) AssertionError: Arrays are not almost equal to 6 decimals x and y nan location mismatch: x: array([ 0.+0.j, nan+0.j, nan+0.j]) y: array([ 0. , 1. , 0.567]) ----------------------------------------------------------------------

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Plotly Beta: web-based, publication-quality graphing for Python and IPython
by Matt Sundquist 21 Dec '13

21 Dec '13

Hi SciPy users, My name is Matt, and I'm part of Plotly <https://plot.ly>. We're working on a scientific graphing library <http://plot.ly/api/python> for Python (fork here <https://github.com/plotly/python-api>) that allows you to create interactive, web-based graphs in IPython and your browser. Our gallery of Notebooks is here<http://nbviewer.ipython.org/github/plotly/IPython-plotly/tree/master/> . We would love to be a useful resource for the SciPy community. As we're quite new (just a few months into our beta), we benefit from and very much appreciate help and expert feedback. We would love your opinions, advice, and thoughts. Plotly lets you style interactive, publication-quality graphs. You can make Plotly graphs with NumPy, pandas, Datetime, and LaTeX. Plotly has bubble charts, box plots, line charts, scatter plots, histograms, 2D histograms, and heatmaps. Plotly supports log axes, error bars, date axes, multiple axes, and subplots. The gallery, here<http://nbviewer.ipython.org/github/plotly/IPython-plotly/tree/master/>, has examples. You can edit with code or the GUI in Plotly, share a graph online, via a download, or as part of a NB. You can also embed with an iframe (Washington Post example<http://www.washingtonpost.com/blogs/wonkblog/wp/2013/06/14/do-low-taxes-on-…> ). Data and graphs live together, and can be shared with collaborators (like a Google Doc). For an example, here is a graph<https://plot.ly/~IPython.Demo/402/> made with Python, styled with this NB<http://nbviewer.ipython.org/github/plotly/IPython-plotly/blob/master/Bubble…>. Plotly is set up like GitHub. You control privacy and sharing. It's free for public use, you can fork the APIs (and we welcome pull requests), and has a premium subscription for heavy private use. Thanks a bunch. It would mean a lot to hear your thoughts, advice, and feedback. All my best, Matt

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