[Numpy-discussion] 16ᵗʰ Advanced Scientific Programming in Python in Heraklion, Crete, Greece, 25 August – 1 September, 2024
ASPP2024: 16ᵗʰ Advanced Scientific Programming in Python Summer School == https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are interactive and allow students to acquire direct hands-on experience with the topics. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Python is the standard tool for the programming scientist due to clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization. This school is targeted at PhD students, postdocs and more senior researchers from all areas of science. Competence in Python or in another language such as Java, JavaScript, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and git or another version control system is assumed. Participants without any prior experience with Python or git should work through the proposed introductory material before the course. We care for diversity and inclusion, and strive for a welcoming atmosphere to programming scientists of all levels. In particular, we have focused on recruiting an international and gender-balanced pool of students. Date & Location === 25 August – 1 September, 2024. Heraklion, Crete, Greece Application === You can apply online: https://aspp.school Application deadline: 23:59 UTC, Wednesday 1 May, 2024. There will be no deadline extension, so be sure to apply on time. Invitations and notification of rejection will be sent by Sunday 26 May, 2024. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program === • Large-scale collaborative scientific code development with git and GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Scientific programming patterns in Python • Writing parallel applications in Python • Profiling and speeding up scientific code • Programming in teams Faculty === • Aitor Morales-Gregorio, Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich Germany • Jenni Rinker, Department of Wind and Energy Systems, Technical University of Denmark, Lyngby Denmark • Lisa Schwetlick, Laboratory of Psychophysics, EPFL, Lausanne Switzerland • Pamela Hathway, YPOG, Berlin/Nürnberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, innoCampus, Technische Universität Berlin Germany • Verjinia Metodieva, NeuroCure, Charité – Universitätsmedizin Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers == Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, innoCampus, Technische Universität Berlin Germany Organization team in Heraklion: • Sara Moberg, Department of Biology, Humboldt-Universität zu Berlin Germany • Athanasia Papoutsi, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Maria Diamantaki, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Zampeta Kalogeropoulou, Digital Science & Research Solutions Ltd., Heraklion Greece Sponsors We are able to hold this year's ASPP school thanks to the financial support of the Tübingen AI Center. The Institute of Molecular Biology & Biotechnology of the Foundation for Research and Technology – Hellas is hosting us in Heraklion and is taking care of the local organization. Website: https://aspp.school Contact: info@aspp.school ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] [ANN] 10ᵀᴴ Advanced Scientific Programming in Python in Nikiti, Greece, August 28—September 2, 2017
10ᵀᴴ Advanced Scientific Programming in Python == a Summer School by the G-Node and the Municipality of Sithonia Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. You can apply online: https://python.g-node.org Application deadline: 23:59 UTC, May 31, 2017. There will be no deadline extension, so be sure to apply on time ;-) Be sure to read the FAQ before applying. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Date & Location === August 28—September 2, 2017. Nikiti, Sithonia, Halkidiki, Greece Program === → Best Programming Practices • Best practices for scientific programming • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization → Software Carpentry • Test-driven development • Debugging with a debuggger • Profiling code → Scientific Tools for Python • Advanced NumPy → Advanced Python • Decorators • Context managers • Generators → The Quest for Speed • Writing parallel applications • Interfacing to C with Cython • Memory-bound problems and memory profiling • Data containers: storage and fast access to large data → Practical Software Development • Group project Preliminary Faculty === • Francesc Alted, freelance consultant, author of Blosc, Castelló de la Plana, Spain • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, VA USA • Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne Switzerland • Juan Nunez-Iglesias, Victorian Life Sciences Computation Initiative, University of Melbourne, Australia • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Nicolas P. Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Disease, University of Bordeaux, France • Bartosz Teleńczuk, European Institute for Theoretical Neuroscience, CNRS, Paris, France • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA USA • Nelle Varoquaux, Berkeley Institute for Data Science, UC Berkeley, CA USA • Tiziano Zito, freelance consultant, Berlin, Germany Organizers == For the German Neuroinformatics Node of the INCF (G-Node) Germany: • Tiziano Zito, freelance consultant, Berlin, Germany • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, Fairfax, USA • Jakob Jordan, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich GmbH, Germany • Etienne Roesch, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, UK Website: https://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] NumPy 1.15.0rc1 released
On Sun 08 Jul, 22:35 -0400, Sandro Tosi wrote: The Python versions supported by this release are 2.7, 3.4-3.6. The wheels are linked with OpenBLAS 3.0, which should fix some of the linalg problems reported for NumPy 1.14, and the source archives were created using Cython 0.28.2 and should work with the upcoming Python 3.7. just checking: in Debian we're currently linking against libblas/liblapack (as available from http://www.netlib.org/lapack/) - should we start investigating switching to OpenBLAS? Well, as far as I can tell numpy in Debian is built using the /etc/alternatives method, i.e. you can choose which BLAS implementation to use at run time if more then one implementation is installed. In my case, it links to openblas already: """ $ ldd /usr/lib/python3/dist-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so linux-vdso.so.1 (0x7ffe3bf7b000) libblas.so.3 => /usr/lib/x86_64-linux-gnu/libblas.so.3 (0x7f23df471000) libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x7f23debaa000) libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x7f23de98c000) libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x7f23de5d2000) /lib64/ld-linux-x86-64.so.2 (0x7f23df2e9000) libopenblas.so.0 => /usr/lib/x86_64-linux-gnu/libopenblas.so.0 (0x7f23dc35f000) libgfortran.so.4 => /usr/lib/x86_64-linux-gnu/libgfortran.so.4 (0x7f23dbf8b000) libquadmath.so.0 => /usr/lib/x86_64-linux-gnu/libquadmath.so.0 (0x7f23dbd4b000) libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x7f23dbb2d000) libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x7f23db915000) $ ls -l /usr/lib/x86_64-linux-gnu/libblas.so.3 lrwxrwxrwx 1 root root 47 Sep 11 2017 /usr/lib/x86_64-linux-gnu/libblas.so.3 -> /etc/alternatives/libblas.so.3-x86_64-linux-gnu $ ls -l /etc/alternatives/libblas.so.3-x86_64-linux-gnu lrwxrwxrwx 1 root root 47 Sep 11 2017 /etc/alternatives/libblas.so.3-x86_64-linux-gnu -> /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 $ update-alternatives --display libblas.so.3-x86_64-linux-gnu libblas.so.3-x86_64-linux-gnu - auto mode link best version is /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 link currently points to /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 link libblas.so.3-x86_64-linux-gnu is /usr/lib/x86_64-linux-gnu/libblas.so.3 /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3 - priority 35 /usr/lib/x86_64-linux-gnu/blas/libblas.so.3 - priority 10 /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 - priority 40 """ So, it seems to me there's no problem to solve in Debian? Ciao! Tiziano ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] 12ᵗʰ Advanced Scientific Programming in Python in Camerino, Italy, 2—7 September, 2019
12ᵗʰ Advanced Scientific Programming in Python == a Summer School by the G-Node and the University of Camerino https://python.g-node.org Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location === 2–7 September, 2019. Camerino, Italy. Application === You can apply online: https://python.g-node.org/wiki/applications Application deadline: 23:59 UTC, 26 May, 2019. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://python.g-node.org/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program === • Version control with git and how to contribute to open source projects with GitHub • Tidy data analysis and visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty === • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark • Juan Nunez-Iglesias, Bioimage Analysis Research Fellow, Monash University, Australia • Nelle Varoquaux, Department of Statistics, UC Berkeley, CA, USA • Pamela Hathway, Neural Reckoning, Imperial College London, UK • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA, USA • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany Organizers == For the German Neuroinformatics Node of the INCF (G-Node), Germany: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw, Poland • Jakob Jordan, Department of Physiology, University of Bern, Switzerland For the University of Camerino, Italy: • Barbara Re, Computer Science Division, School of Science and Technology, University of Camerino Italy Website: https://python.g-node.org Contact: python-i...@g-node.org ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] 13ᵗʰ Advanced Scientific Programming in Python in Ghent, Belgium, 31 August—5 September, 2020
13ᵗʰ Advanced Scientific Programming in Python == a Summer School by the ASPP faculty and the Ghent University https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location === 31 August–5 September, 2020. Ghent, Belgium. Application === You can apply online: https://aspp.school/wiki/applications Application deadline: 23:59 UTC, Sunday 24 May, 2020 There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://aspp.school/wiki/faq Participation is for free, i.e. no fee is charged! Accommodation in the student residence comes at no costs for participants. We are trying to arrange financial coverage for food expenses too, but this may not work. Participants however should take care of travel expenses by themselves. Program === • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty === • Caterina Buizza, Personal Robotics Lab, Imperial College London UK • Lisa Schwetlick, Experimental and Biological Psychology, Universität Potsdam Germany • Nelle Varoquaux, CNRS, TIMC-IMAG, University Grenoble Alpes France • Nicolas P. Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Disease, University of Bordeaux France • Pamela Hathway, Neural Reckoning, Imperial College London UK • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers == Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany Local team: • Nina Turk, Photonics Research Group, INTEC, Ghent University – imec Belgium • Freya Acar, Office for Data and Information, City of Ghent Belgium • Joan Juvert Institutional organizers: • Wim Bogaerts, Photonics Research Group, INTEC, Ghent University –imec Belgium • Sven Degroeve, VIB-UGent Center for Medical Biotechnology, Ghent Belgium • Jeroen Famaey, Department of Mathematics and Computer Science, University of Antwerp – iMinds Belgium • Bernard Manderick, Artificial Intelligence Lab, Vrije Universiteit Brussel Belgium Website: https://aspp.school Contact: info@aspp.school ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] log of negative real numbers -> RuntimeWarning: invalid value encountered in log
Would a "complex default" mode ever make it into numpy, to behave more like Matlab and other packages with respect to complex number handling? Sure it would make it marginally slower if enabled, but it might open the door to better compatibility when porting code to Python. numpy already has the "complex default" log function: numpy.lib.scimath.log . There are other useful gems in that module, for example a "complex default" sqrt function Ciao, Tiziano ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] 14ᵗʰ Advanced Scientific Programming in Python in Bilbao Spain, 5–11 September, 2022
ASPP2022: 14ᵗʰ Advanced Scientific Programming in Python a Summer School by the ASPP faculty and the Faculty of Engineering of the Mondragon University, Bilbao https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location === 5–11 September, 2022. Bilbao, Spain. Application === You can apply online: https://aspp.school Application deadline: 23:59 UTC, Sunday 1 May, 2022. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://aspp.school/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. We are in the process of securing some funds for supporting students with accommodation and living costs. Program === • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty === • Jakob Jordan, Department of Physiology, University of Bern Switzerland • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde Denmark • Lisa Schwetlick, Experimental and Biological Psychology, Universität Potsdam Germany • Nicolas Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Diseases, University of Bordeaux France • Pamela Hathway, GfK, Nuremberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers == Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin Germany Organization team in Bilbao: • Aitor Morales-Gregorio, Theoretical Neuroanatomy, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich, Germany • Carlos Cernuda, Data Analysis & Cybersecurity, Faculty of Engineering, Mondragon Unibertsitatea, Bilbao Spain Website: https://aspp.school Contact: info@aspp.school ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: Add a new inf whose data type is int
Hi, there's no integer inf, but you can use np.iinfo(np.int64).max or np.iinfo(np.int64).min if you need an upper/lower bound for integers in np.max/np.min Ciao! Tiziano On Tue 22 Nov, 06:27 -, 2601536...@qq.com wrote: Hi, I am a student and I have very little knowledge about python and numpy, so my suggestion may seem really stupid. One day I was use numpy to implement Floyd algorithm and then I found the data type of np.inf is float, so i can not use min or max function because other data type are int. So I wonder whether you can add a new inf whose data type is int. Thanks ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: opossumn...@gmail.com ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] 15ᵗʰ Advanced Scientific Programming in Python in Heraklion, Crete, Greece, 27 August – 3 September,2023
ASPP2023: 15ᵗʰ Advanced Scientific Programming in Python a Summer School https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are interactive and allow students to acquire direct hands-on experience with the topics. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Python is the standard tool for the programming scientist due to clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization. This school is targeted at PhD students, postdocs and more senior researchers from all areas of science. Competence in Python or in another language such as Java, JavaScript, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and git or another version control system is assumed. Participants without any prior experience with Python or git should work through the proposed introductory material before the course. We care for diversity and inclusion, and strive for a welcoming atmosphere to programming scientists of all levels. In particular, we have focused on recruiting an international and gender-balanced pool of students. Date & Location === 27 August – 3 September, 2023. Heraklion, Crete, Greece Application === You can apply online: https://aspp.school Application deadline: 23:59 UTC, Monday 1 May, 2023. There will be no deadline extension, so be sure to apply on time. Invitations and notification of rejection will be sent by Sunday 28 May, 2023. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program === • Large-scale collaborative scientific code development with git and GitHub • Best practices in data visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Scientific programming patterns in Python • Writing parallel applications in Python • Profiling and speeding up scientific code • Programming in teams Faculty === • Aitor Morales-Gregorio, Theoretical Neuroanatomy, Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich Germany • Guillermo Aguilar, Department of Computational Psychology, Technische Universität Berlin Germany • Jakob Jordan, Department of Physiology, University of Bern Switzerland • Lisa Schwetlick, Experimental and Biological Psychology, Universität Potsdam Germany • Pamela Hathway, Orange Business, Berlin/Nürnberg Germany • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, innoCampus, Technische Universität Berlin Germany • Verjinia Metodieva, NeuroCure, Charité – Universitätsmedizin Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers == Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, innoCampus, Technische Universität Berlin Organization team from IMBB/FORTH: • Athanasia Papoutsi, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Greece • Emmanouil Froudarakis, Institute of Molecular Biology and Biotechnology of the Foundation for Research and Technology – Hellas, Heraklion Sponsors We are able to hold this year's ASPP school thanks to the financial support of the Tübingen AI Center. The Institute of Molecular Biology & Biotechnology of the Foundation for Research and Technology – Hellas is hosting us in Heraklion and is taking care of the local organization. We also explicitly thank Prof. Felix Wichmann at the Neural Information Processing Group, Eberhard Karls Universität Tübingen, Germany, for his invaluable help. Website: https://aspp.school Contact: info@aspp.school ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.pytho
[Numpy-discussion] Re: array.T.max() vs array.max(axis=0) performance difference
Hi George,
what you see is due to the memory layout of numpy arrays. If you switch your
array to F-order you'll see that the two functions have the same timings, i.e.
both are fast (on my machine 25 times faster for the 1_000_000 points case).
Try:
vertices = np.array(np.random.random((n, 2)), order='F')
When your array doesn't fit in L1-cache anymore, either order 'C' or order 'F'
becomes (much) more efficient depending on which dimension you are internally
looping through.
You can read more about it here:
https://numpy.org/doc/stable/dev/internals.html#internal-organization-of-numpy-arrays
and
https://numpy.org/doc/stable/reference/arrays.ndarray.html#internal-memory-layout-of-an-ndarray
Hope that helps,
Tiziano
On Fri 21 Mar, 12:10 +0200, George Tsiamasiotis via NumPy-Discussion
wrote:
Hello NumPy community!
I was writing a function that calculates the bounding box of a polygon (the
smallest rectangle that fully contains the polygon, and who's sides are
parallel to the x and y axes). The input is a (N,2) array containing the
vertices of the polygon, and the output is a 4-tuple containing the vertices of
the 2 corners of the bounding box.
I found two ways to do that using the np.min() and np.max() methods, and I was
surprised to see a significant speed difference, even though they seemingly do
the same thing.
While for small N the speed is essentially the same, the difference becomes
noticeable for larger N. >From my testing, the difference seems to plateau,
with the one way being around 4-5 times faster than the other.
Is there an explanation for this?
Here is a small benchmark I wrote (must be executed with IPython):
import numpy as np
from IPython import get_ipython
vertices = np.random.random((1000, 2))
def calculate_bbox_normal(vertices: np.ndarray) -> tuple[np.float64]:
xmin, ymin = vertices.min(axis=0)
xmax, ymax = vertices.max(axis=0)
return xmin, ymin, xmax, ymax
def calculate_bbox_transpose(vertices: np.ndarray) -> tuple[np.float64]:
xmin = vertices.T[0].min()
xmax = vertices.T[0].max()
ymin = vertices.T[1].min()
ymax = vertices.T[1].max()
return xmin, ymin, xmax, ymax
bbox_normal = calculate_bbox_normal(vertices)
bbox_transpose = calculate_bbox_transpose(vertices)
print(f"Equality: {bbox_normal == bbox_transpose}")
for n in [10, 100, 1000, 10_000, 100_000, 1_000_000]:
print(f"Number of points: {n}")
vertices = np.random.random((n, 2))
print("Normal: ", end="")
get_ipython().run_line_magic("timeit", "calculate_bbox_normal(vertices)")
print("Transpose: ", end="")
get_ipython().run_line_magic("timeit", "calculate_bbox_transpose(vertices)")
print()
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[Numpy-discussion] 17ᵗʰ Advanced Scientific Programming in Python in Plovdiv, Bulgaria, 21–28 September, 2025
ASPP2025: 17ᵗʰ Advanced Scientific Programming in Python Summer School == https://aspp.school Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are interactive and allow students to acquire direct hands-on experience with the topics. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Python is the standard tool for the programming scientist due to clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization. This school is targeted at PhD students, postdocs and more senior researchers from all areas of science. Competence in Python or in another language such as Java, JavaScript, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and git or another version control system is assumed. Participants without any prior experience with Python or git should work through the proposed introductory material before the course. We care for diversity and inclusion, and strive for a welcoming atmosphere to programming scientists of all levels. In particular, we have focused on recruiting an international and gender-balanced pool of students. Date & Location === 21–28 September, 2025. Plovdiv, Bulgaria. Application === You can apply online: https://aspp.school Application deadline: 23:59 UTC, Sunday 1 June, 2025. There will be no deadline extension, so be sure to apply on time. Invitations and notifications of rejection will be sent by Sunday 15 June, 2025. Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program === • Large-scale collaborative scientific code development with git and code forges • Testing and debugging scientific code • Organizing, documenting, and distributing scientific code • Data in scientific programming • Scientific programming patterns in Python • What every scientist should know about computer architecture • Writing parallel applications in Python • Programming in teams Faculty === • Aitor Morales-Gregorio, Faculty of Mathematics and Physics, Charles University, Prague, Czechia • Guillermo Aguilar, Department of Computational Psychology, Technische Universität Berlin, Germany • Jenni Rinker, Department of Wind and Energy Systems, Technical University of Denmark, Lyngby, Denmark • Lisa Schwetlick, Laboratory of Psychophysics, EPFL, Lausanne, Switzerland • Pamela Hathway, YPOG, Berlin/Nürnberg, Germany • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Tiziano Zito, innoCampus, Technische Universität Berlin, Germany • Victoria Shevchenko, Inria Saclay Palaiseau and Université Paris Cité, France • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw, Poland Organizers == Head of the organization for ASPP and responsible for the scientific program: • Tiziano Zito, innoCampus, Technische Universität Berlin, Germany Organization team in Plovdiv: • Maya Ivanova Nikolova, MNKnowledge, Sofia, Bulgaria • Verjinia Metodieva, Charité – Universitätsmedizin Berlin, ECN, Germany Sponsors ASPP2025 is hosted by the Technical University of Sofia, Plovdiv branch. The organization is done in collaboration with MNKnowledge and with the financial and institutional support of the Tübingen AI Center. Website: https://aspp.school Contact: info@aspp.school ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
