Mailman 3 August 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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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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cross-compile support
by 庞小锋 Aug. 26, 2015

Aug. 26, 2015

Hi: I am a scipy user who needs scipy ,pandas and numpy to resolve a problem related to calssification.The algorithm used is SVM. My question is as follows: scipy and other related project can support cross-compiling with mips64-octeon or not ? If yes,what should i do ? Many thanks! Xiaofeng 夏日畅销榜大牌美妆只要1元夏日畅销榜大牌美妆只要1元夏日畅销榜大牌美妆只要1元

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numpy.lexsort
by Gabriele Brambilla Aug. 21, 2015

Aug. 21, 2015

Hi I want to order a numpy.array using lexsort. >>> import numpy as np >>> surnames = ['Hertz', 'Galilei', 'Hertz'] >>> names = ['Heinrich', 'Galilao', 'Gustav'] >>> ind = np.lexsort((names,surnames)) >>> ind array([1, 2, 0]) But how can I have an array ordered according to this indexes? I would do surnames2 = surnames.copy() for i, a in enumerate(ind): surnames2[i] = surnames[a] But I think there should be another way without writing the cycle by myself. Thanks GB

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ANN: mpldatacursor v0.6 - Interactive annotation boxes for Matplotlib
by Joe Kington Aug. 17, 2015

Aug. 17, 2015

Hi folks, mpldatacursor version 0.6 has been released (it's long overdue). mpldatacursor provides interactive “data cursors” (clickable annotation boxes) for matplotlib. Major Changes in v0.6 Version 0.6 adds: - Better handling of date-formatted axes. - “Popup” text boxes can be interactively hidden by right-clicking (controllable through the hide_button and display_button kwargs). - Proper support for twinned axes. - Better unicode support for the formatter function. Note that this makes mpldatacursor incompatibile with early 3.x versions (3.0, 3.1, and 3.2). However, it remains compatible with Python >= 3.3 (e.g. 3.3, 3.4, and 3.5) as well as 2.6 and 2.7. - Annotation boxes will now try to stay visible inside the figure by default. Specify keep_inside=False to disable this. - Added basic support for extracting the z-value of 3D artists. - Made the precision of the default x & y formatting depend on the range of the axes. - Full support for interactive IPython notebooks through the nbagg backend. Note that the performance on the nbagg may be very poor. - Workarounds for annotation issues in Matplotlib v1.4.3 - Numerous bugfixes (Thanks to everyone for the reports!). Basic Usage mpldatacursor offers a few different styles of interaction through the datacursor function. As an example, this displays the x, y coordinates of the selected artist in an annotation box: import matplotlib.pyplot as plt import numpy as np from mpldatacursor import datacursor data = np.outer(range(10), range(1, 5)) fig, ax = plt.subplots() lines = ax.plot(data) ax.set_title('Click somewhere on a line') datacursor(lines) plt.show() <https://github.com/joferkington/mpldatacursor/blob/master/examples/basic.py> Installation mpldatacursor can be installed from PyPi using easy_install/pip/etc. (e.g. pip install mpldatacursor) or you may download the source and install it directly with python setup.py install. Thanks, -Joe

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Examples how to call NumPy from C++
by Alexander Kalinin Aug. 13, 2015

Aug. 13, 2015

Dear SciPy users, I have some software in C++. A want to call NumPy python procedures from C++, for example some matrix multiplication procedures. Does anybody know some examples how to pass arrays from C++ to NumPy and call procedure like matrix multiplication? Sincerely, Alexander

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ANN: Numpy 1.10.0b1 release
by Charles R Harris Aug. 11, 2015

Aug. 11, 2015

Hi All, give this release a whirl and report any problems either on the numpy-discussion list or by opening an issue on github. I'm pleased to announce the first beta release of Numpy 1.10.0. There is over a year's worth of enhancements and bug fixes in the 1.10.0 release, so please give this release a whirl and report any problems either on the numpy-discussion list or by opening an issue on github. Tarballs, installers, and release notes may be found in the usual place at Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.10.0b1/>. Chuck

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[ANN] HCO Challenge Workshop "Parameter and State Estimation: Methods - Software - Applications", Nov. 10-11, 2015, Heidelberg, Germany
by Sebastian Walter Aug. 10, 2015

Aug. 10, 2015

Dear SciPy community, There seems to be a significant interest in nonlinear optimization and parameter estimation methods in the SciPy community (numerous discussions on the mailing list, the ongoing GSoC project by Evgeni Burovski and Nikolay Mayorov, etc.). I am myself a regular user of the NumPy/SciPy stack, and maintainer of some humble Python packages (AlgoPy, PyAdolc). The Heidelberg Collaboratory for Industrial Optimization (HCO) is hosting a Challenge Workshop on "Parameter and State Estimation: Methods - Software - Applications", Nov. 10-11, 2015, Heidelberg. I believe this workshop could be of interest to the SciPy developers and community. Here is the official announcement: --------------- start announcement ---------------------- We cordially invite you to participate in the *HCO Challenge Workshop**“Parameter and State Estimation: Methods - Software - Applications”* November 10-11, 2015 International Academic Forum Heidelberg *Hauptstr. 42, 69117 Heidelberg, Germany* *Homepage*: *http://hco.iwr.uni-heidelberg.de <http://hco.iwr.uni-heidelberg.de/>* *Scope of the workshop:* Model-based simulation and optimization methods become increasingly important for the development of new products and more efficient processes. Fundamental requirement to leverage the full potential of model-based methods is the development of *predictive mathematical models* and their quantitative validation. This complex task requires robust and efficient numerical methods for parameter estimation. Furthermore, advanced control of processes in real time needs fast and reliable numerical methods for state estimation. Industrial practice shows that in order to realize the full potential of modelling complex processes we have to deal with a number of new challenges. Because of the high relevance of *parameter and state estimation* methods for current and future research and development, the Heidelberg Collaboratory for Industrial Optimization (HCO) invites decision makers, researchers and practitioners from academia and industry to participate in this workshop. Primary goals: - present an overview of state-of-the-art methods for parameter and state estimation - discuss current challenges from industry and academia - identify requirements for the next generation of parameter and state estimation methods and software - foster synergies and collaborations We kindly invite your contribution and participation. Note that the number of participants and remaining presentation slots is limited. *Please register at* *http://hco.iwr.uni-heidelberg.de* <http://hco.iwr.uni-heidelberg.de>. *Workshop Fee:* Early Bird: 100 EUR Normal: 150 EUR *Important Deadlines*: Abstract Submission: Sept. 30, 2015 Registration (early bird): Sept. 30, 2015 *Confirmed speakers include representatives of:* Austrian Institute of Technology, BASF SE, Deutsche ACCUmotive GmbH & Co. KG, Daimler AG, HSKA Karlsruhe, TU Berlin, TU Dortmund, SAS Institute GmbH *The event is kindly supported by: *Committee for Mathematical Modeling, Simulation and Optimization (KoMSO) Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University Mathematics Center Heidelberg (MATCH) --------------- end announcement ----------------------

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