Mailman 3 April 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

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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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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Playing numpy array over speakers
by Todd Oct. 17, 2015

Oct. 17, 2015

Is anyone aware of a well-maintained, simple, cross-platform python package that can play a numpy array as sound over speakers? I am aware of https://wiki.python.org/moin/Audio/. However, in all the cases there, as far as I can find they either do not support numpy arrays, are not cross-platform, cannot playback sound at all, or are unmaintained. There is also PySoundCard, which would do what I need but also appears to be unmaintained (no release in over a year, and no commits in 5 months, no release with serious bugfixes mentioned in commits).

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Is there existing code to log-with-bells-on for runtime algorithm diagnostics?
by Rob Clewley May 6, 2015

May 6, 2015

Hi, I'm in need of a system for logging the step-wise results and diagnostic metadata about a python function implementation of an algorithm that I'm developing. The specific algorithm is not of great consequence except that it's for scientific computing and may produce large (e.g., '00s or maybe '000s, but not "big data" scale) amounts of intermediate numerical data that can be complex to understand when debugging its progress. In fact, I'm trying to build a general purpose tool for exploring the inner workings of numerical algorithms for teaching and learning purposes, e.g. for graduate student training or for figuring out parameter choices in difficult applications. I want to be able to insert commands inside of the loops that log certain variable states, completed stages of the algorithm (assume it's hierarchical), text warnings, and possibly even 'pointers' to graphical output objects of some of the intermediate data (e.g. matplotlib object handles for lines, points). Then I can trace the work done afterwards or step through the process in an IDE debugger and make interactive calls to access recent steps, plot certain relationships in the current state, etc. The basic logger's "levels" of output don't really apply here. I at least want categories, if not hierarchical sub-categories. I don't think the built-in logger is sophisticated enough for this, being a flat record of freeform text AFAIU, but the API looks appealing. I'm considering an in-memory sqlite DB to store structured records at any logged step, and an accompanying dictionary to store references to any python object metadata, keyed by a unique ID in the DB log. Then I'd write an API for it that resembles the logger's. It's not super hard for me to write my own thing here, but I'm wondering if anyone has come across any existing solutions in this vein, or has any advice before I go further in designing a solution? I can't really believe that no-one has attempted this before, but it's been really hard to find any existing work through online search. Thanks, Rob

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Norm for Sparse Matrix
by Wenlei Xie May 1, 2015

May 1, 2015

Hi, The function numpy.linalg.norm doesn't seem to work with sparse matrices. I am wondering if there is any function to get the norm for sparse matrices? Thank you! Best, Wenlei

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signal.firls
by Gregory Allen April 30, 2015

April 30, 2015

I was surprised to find that there was no signal.firls function in scipy for designing FIR filters using least-squares error minimization. I wrote this based on a post in this list [http://mail.scipy.org/pipermail/scipy-user/2009-November/023101.html], added band weights, and fleshed it out in the style of signal.remez. It’s not as full-featured as the matlab version, but it solved a need for me. :) It could be pasted at the bottom of signal/fir_filter_design.py. I include it here in case it is of any use to someone else, or to be included in scipy. Thanks, -Greg --- import numpy as np from scipy.special import sinc def firls(numtaps, bands, desired, weight=None, Hz=1): """ FIR filter design using least-squares error minimization. Calculate the filter coefficients for the finite impulse response (FIR) filter which has the best approximation to the desired frequency response described by `bands` and `desired` in the least squares sense. Parameters ---------- numtaps : int The number of taps in the FIR filter. `numtaps` must be odd. bands : array_like A monotonic sequence containing the band edges in Hz. All elements must be non-negative and less than half the sampling frequency as given by `Hz`. desired : array_like A sequence half the size of bands containing the desired gain in each of the specified bands. weight : array_like, optional A relative weighting to give to each band region. The length of `weight` has to be half the length of `bands`. Hz : scalar, optional The sampling frequency in Hz. Default is 1. Returns ------- out : ndarray A rank-1 array containing the coefficients of the optimal (in a least squares sense) filter. Example ------- We want to construct a filter with a passband at 0.2-0.3 Hz, and stop bands at 0-0.1 Hz and 0.4-0.5 Hz. Note that this means that the behavior in the frequency ranges between those bands is unspecified and may overshoot. >>> from scipy import signal >>> bpass = signal.firls(71, [0, 0.1, 0.2, 0.3, 0.4, 0.5], [0, 1, 0]) >>> freq, response = signal.freqz(bpass) >>> ampl = np.abs(response) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax1 = fig.add_subplot(111) >>> ax1.semilogy(freq/(2*np.pi), ampl, 'b-') # freq in Hz >>> plt.show() """ if numtaps%2 == 0: raise ValueError("numtaps must be odd.") L = (numtaps-1)//2 # normalize bands and make it 2 columns bands = np.asarray(bands).flatten()/Hz if len(bands)%2 == 1: raise ValueError("bands must contain frequency pairs.") bands = bands.reshape(-1,2) # check remaining params if len(bands) != len(desired): raise ValueError("desired must have one entry per band.") if weight is None: weight = np.ones_like(desired) # set up the linear matrix equation to be solved, Ax = b k = np.arange(L+1)[np.newaxis] m = k.T A,b = 0,0 for i, (f0,f1) in enumerate(bands): Ai = f1 * (sinc(2*(m+k)*f1) + sinc(2*(m-k)*f1)) \ - f0 * (sinc(2*(m+k)*f0) + sinc(2*(m-k)*f0)) bi = desired[i] * (2*f1*sinc(2*m*f1) - 2*f0*sinc(2*m*f0)) A += Ai * abs(weight[i]**2) b += bi * abs(weight[i]**2) # solve and return x = np.linalg.solve(A,b).squeeze() h = np.hstack((x[:0:-1]/2, x[0], x[1:]/2)) return h

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ANN: SciPy 2015 Tutorial Schedule Posted - Register Today - Already 30% Sold Out
by Courtenay Godshall (Enthought) April 30, 2015

April 30, 2015

**The <https://plus.google.com/s/%23SciPy2015> #SciPy2015 Conference (Scientific Computing with <https://plus.google.com/s/%23Python> #Python) Tutorial Schedule is up! It is 1st come, 1st served and already 30% sold out. Register today!** http://www.scipy2015.scipy.org/ehome/115969/289057/? <http://www.scipy2015.scipy.org/ehome/115969/289057/?&> &.This year you can choose from 16 different SciPy tutorials OR select the 2 day Software Carpentry course on scientific Python that assumes some programming experience but no Python knowledge. Please share! Tutorials include: *Introduction to NumPy (Beginner) *Machine Learning with Scikit-Learn (Intermediate) *Cython: Blend of the Best of Python and C/++ (Intermediate) *Image Analysis in Python with SciPy and Scikit-Image (Intermediate) *Analyzing and Manipulating Data with Pandas (Beginner) *Machine Learning with Scikit-Learn (Advanced) *Building Python Data Applications with Blaze and Bokeh (Intermediate) *Multibody Dynamics and Control with Python (Intermediate) *Anatomy of Matplotlib (Beginner) *Computational Statistics I (Intermediate) *Efficient Python for High-Performance Parallel Computing (Intermediate) *Geospatial Data with Open Source Tools in Python (Intermediate) *Decorating Drones: Using Drones to Delve Deeper into Intermediate Python (Intermediate) *Computational Statistics II (Intermediate) *Modern Optimization Methods in Python (Advanced) *Jupyter Advanced Topics Tutorial (Advanced)

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Re: [SciPy-User] Crashing
by Andres Gomez-Lievano April 29, 2015

April 29, 2015

Daπid <davidmenhur <at> gmail.com> writes: > > > > On 23 April 2015 at 23:08, Andres Gomez-Lievano <agomez26 <at> asu.edu> wrote: > > # Initializing the parameters and data of the problem > Xc = np.array([80.0, 230.0, 132.0]) > Xp = np.array([74.0, 200.0, 91.0, 77.0]) > size_c = len(Xc) > size_p = len(Xp) > > > Your problem is impossible. You have a 4 by 3 matrix of elements between 0 and 1, and are trying to get sums of more than 200. Try first with a problem with existing solution:_mat = np.random.random((4,3))Xc = _mat.sum(axis=0)Xp = _mat.sum(axis=1) > > Also, Numpy offers functions like reshape, that can replace your vec2mat. > > Hard constraints are something that minimisation algorithms don't like. It is usually better to add them as a penalty to your target function. See here:import numpy as npfrom scipy.optimize import minimize# Defining functionsdef entropy(p_vec): # Log missbehaves if there are negative values, so just take them away. p_vec = np.abs(p_vec) return -1.0 * np.dot(p_vec, np.log(p_vec))def const_rows(p_vec, rows, shape): mat = p_vec.reshape(*shape) diff = mat.sum(axis=0) - rows return diff.dot(diff)def const_cols(p_vec, cols, shape): mat = p_vec.reshape(*shape) diff = mat.sum(axis=1) - cols return diff.dot(diff)def function(x): # Adjustable parameters lambda_1 = 100. lambda_2 = 100. shape = (4, 3) e = entropy(x) rows_violation = const_rows(x, Xc, shape) cols_violation = const_cols(x, Xp, shape) return -e + lambda_1 * rows_violation + lambda_2 * cols_violation# Initializing the parameters and data of the problem_mat = np.random.random((4,3))Xc = _mat.sum(axis=0)Xp = _mat.sum(axis=1)size_p = len(Xp)size_c = len(Xc)# Generating the bounds for each probability. NB: we are not using them right now!zeroonebounds = tuple((0,1) for _ in xrange(size_p * size_c))# Generating the initial vector to start the 'minimize' algorithminit_vec = np.random.random(len(Xc) * len(Xp))# Finally, running the algorithmresult = minimize(function, init_vec)# Display resultsres_mat = result.x.reshape(size_p, size_c)print np.abs(res_mat - _mat).sum()# Violations of the boundsprint res_mat.sum(axis=1) - Xpprint res_mat.sum(axis=0) - Xc > > /David. > > > > > > > > > _______________________________________________ > SciPy-User mailing list > SciPy-User <at> scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user > David, thank you for your help. My problem was well-posed (i.e., is not impossible!). The rows or columns of the probability matrix do not have to add up to the vectors Xc and Xp that are externally provided. But in any case, your suggestions helped me solve the problem. As you did, I included the constraints into the objective function, and the program is now working. THANK YOU! ... Now, other problems have emerged... but those will be the topic of another post. Best, Andres

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fun with debugging: np.linalg.inv with singular matrix
by josef.pktd＠gmail.com April 29, 2015

April 29, 2015

reordering an array changes results by 1021173647741.1094 It's obvious if I calculate 1 / floating_point_noise and floating_point_noise depends on mathematically irrelevant details. But it took me a while to remember that noise doesn't follow the simple math. inv with nonsingular matrix - great >>> xm = np.corrcoef(x[:,::-2], rowvar=0) >>> np.linalg.inv(xm)[:, -3:] array([[ 0.38794217, 0.40358464, 0.12232308], [ 1.21001936, 0.39702137, -0.10164338], [ 0.39702137, 1.20088551, 0.03021732], [-0.10164338, 0.03021732, 1.03114181]]) >>> np.linalg.inv(xm[::-1,::-1])[::-1,::-1][:, -3:] array([[ 0.38794217, 0.40358464, 0.12232308], [ 1.21001936, 0.39702137, -0.10164338], [ 0.39702137, 1.20088551, 0.03021732], [-0.10164338, 0.03021732, 1.03114181]]) >>> np.max(np.abs(np.linalg.inv(xm[::-1,::-1])[::-1,::-1] - np.linalg.inv(xm))) 1.1102230246251565e-16 >>> inv with singular matrix - blowing up path dependent numerical noise >>> xm = np.corrcoef(x, rowvar=0) >>> np.linalg.inv(xm)[:, -3:] array([[ 4.68288729e-02, -1.86395743e-01, 1.47198710e-02], [ -1.35971574e-01, 2.90029902e-01, 3.41148182e-01], [ 9.77422379e-03, -1.17494713e-01, -2.43669915e-01], [ -1.00183312e+14, -1.00183312e+14, -1.00183312e+14], [ -1.00183312e+14, -1.00183312e+14, -1.00183312e+14], [ -1.00183312e+14, -1.00183312e+14, -1.00183312e+14], [ -1.00183312e+14, -1.00183312e+14, -1.00183312e+14], [ -1.00183312e+14, -1.00183312e+14, -1.00183312e+14]]) >>> np.linalg.inv(xm[::-1,::-1])[::-1,::-1][:, -3:] array([[ 5.01007835e-02, -1.84819296e-01, 1.57372647e-02], [ -1.46454280e-01, 2.88649950e-01, 3.38451911e-01], [ 1.13948216e-02, -1.18632618e-01, -2.43817631e-01], [ -1.01204486e+14, -1.01204486e+14, -1.01204486e+14], [ -1.01204486e+14, -1.01204486e+14, -1.01204486e+14], [ -1.01204486e+14, -1.01204486e+14, -1.01204486e+14], [ -1.01204486e+14, -1.01204486e+14, -1.01204486e+14], [ -1.01204486e+14, -1.01204486e+14, -1.01204486e+14]]) >>> np.max(np.abs(np.linalg.inv(xm[::-1,::-1])[::-1,::-1] - np.linalg.inv(xm))) 1021173647741.1094 >>> Josef

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