Mailman 3 September 2013 - 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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Allan Variance
by Daniele Nicolodi Feb. 4, 2014

Feb. 4, 2014

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 Jan. 24, 2014

Jan. 24, 2014

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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1.8.0rc1
by Charles R Harris Oct. 7, 2013

Oct. 7, 2013

Hi All, NumPy 1.8.0rc1 is up now on sourceforge<http://sourceforge.net/projects/numpy/files/NumPy/1.8.0rc1/>.The binary builds are included except for Python 3.3 on windows, which will arrive later. Many thanks to Ralf for the binaries, and to those who found and fixed the bugs in the last beta. Any remaining bugs are all my fault ;) I hope this will be the last release before final, so please test it thoroughly. Chuck

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Strange results by scipy.spatial Delaunay
by Nils Wagner Oct. 6, 2013

Oct. 6, 2013

Hi all, I tried to create a convex hull of a set of points distributed on a cylindrical surface by the following script. The needed input file coor.dat is attached. How can I fix the problem with the distorted mesh (convex.png) ? Nils from scipy.spatial import Delaunay import numpy as np points = np.loadtxt('coor.dat',usecols =(1,2,3)) nid = np.loadtxt('coor.dat',usecols=(0,)) m,n = points.shape print 'Number of points m =', m tri = Delaunay(points) faces = [] for ia, ib, ic in tri.convex_hull: x1 = points[ia] x2 = points[ib] x3 = points[ic] area = 0.5*np.linalg.norm(np.cross(x2-x1,x1-x3)) print 'Area of face', area faces.append(points[[ia, ib, ic]]) import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d.art3d import Poly3DCollection fig = plt.figure() ax = fig.gca(projection='3d') items = Poly3DCollection(faces, facecolors=[(0, 0, 0, 0.1)]) ax.add_collection(items) ax.scatter(points[:,0], points[:,1], points[:,2], 'o') ax.legend(loc=0,shadow=True) plt.show()

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Fitting data with optimize.curve_fit
by TFSM Oct. 3, 2013

Oct. 3, 2013

lab1.py <http://scipy-user.10969.n7.nabble.com/file/n18692/lab1.py> I have a couple questions. The data show as counts is the total number of counts in 60 seconds. When using the count rate instead of the total counts as the y data, curve_fit does not want to give a meaningful answer. It gives the co-variance as infinity and the cosine that is fit does not match the data. Using total counts y*60, the co-variance is reasonable and the cosine fits the data. Why does increasing the counts by 60 allow curve_fit to give a reasonable answer? A similar problem happens when trying to fit the first harmonic to this data, A11*cos(3x/pi) + A31*cos(3x/pi) but I must increase the counts artificially by at least 10 times for curve_fit to give me a curve that resembles the data being fit. Is there a better way to fit this data? Is what I am doing here legitimate artificially increase y to get a fit then just dividing by that amount to get the data back to count rate? Sorry for the noob questions and thanks. -- View this message in context: http://scipy-user.10969.n7.nabble.com/Fitting-data-with-optimize-curve-fit-… Sent from the Scipy-User mailing list archive at Nabble.com.

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Re: [SciPy-User] scipy cblas return value
by Radim Řehůřek Oct. 1, 2013

Oct. 1, 2013

Thanks guys, these comments are very helpful. But I don't know what BLAS/compiler the users will be using. That was the whole point of trying to go through SciPy -- I thought there was a translation/interface that would allow me to call the standard routines in a stable way, no matter what idiosyncratic BLAS hid underneath... I figured SciPy itself must be doing that already. But `scipy.linalg.blas.sdot` gives me the wrong result in Python, too. The same as when called from C through _cpointer. So now I'm confused -- surely this issue has something to do with SciPy? Scipy itself uses a combination of options 1 and 2, but the wrapper > functions are not exposed via _cpointer (they don't have the standard > BLAS API), which points to the "raw" BLAS function. > Aha. Ok. I'm sifting through the SciPy sources now, to better understand what wraps what, where... it may take a while. In the meanwhile, does my original idea make any sense? To let SciPy do all the necessary wrapping/compiling/linking, but otherwise offer BLAS using the smallest call overhead possible, with a stable signature. I know the strides and all, I don't need any extra ifs or input checks or Python. Cheers, Radim

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[ANN] MATLAB fmincon now available in Python2
by Dmitrey Sept. 30, 2013

Sept. 30, 2013

Hi all, current state of Python <-> MATLAB connection soft doesn't allow passing of function handlers, however, a walkaround has been implemented via some tricks, so now MATLAB function fmincon is available in Python-written OpenOpt and FuncDesigner frameworks (with possibility of automatic differentiation, example ). Future plans include MATLAB fsolve, ode23, ode45 (unlike scipy fsolve and ode they can handle sparse matrices), fgoalattain, maybe global optimization toolbox solvers. I intend to post the message to several forums, so to keep discussion in a single place use OpenOpt forum thread http://forum.openopt.org/viewtopic.php?id=769 Regards, D.

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