From mansingtakale at gmail.com Sat Feb 1 05:57:49 2020 From: mansingtakale at gmail.com (mansing takale) Date: Sat, 1 Feb 2020 16:27:49 +0530 Subject: [SciPy-User] How to create a request to change a function? In-Reply-To: References:

<886c08fe7170d397bed4f067bb2e014db1fc771d.camel@lma.cnrs-mrs.fr> Message-ID: Great, Nice contribution On Sat, Feb 1, 2020 at 4:11 AM Augusto Dufloth wrote: > Thanks for your email! > > My point is: for the sake of the formal definition of ode, the function is > lacking a convenient feature. > > Since you have to input as an argument a time array, it would be super > useful to have the option to also input time-varying arguments of the same > length as t. > > Checking stackoverflow you find that many people would benefit from this > addition. > > Because of the lack of this feature, I had to create my own ODE as a C > function. In a practical example the time to solve my system was reduced by > 20 fold. As compared to the solution you provided (and I also tried in the > past). I believe this could be even faster, but I lack the skills of code > optimization. > > Kind regards > > > > On Sat, Feb 1, 2020 at 0:18 Fabrice Silva wrote: > >> see https://pastebin.com/TKt12AEE for a readable version of the code >> sample >> >> Le vendredi 31 janvier 2020, Fabrice Silva a ?crit : >> >> Le vendredi 31 janvier 2020, Augusto Dufloth a ?crit : >> >> Hello, >> >> I have a request to change one of scipy?s function. >> >> I am trying to use odeint to integrate a set of equations of motion. My >> state equations rely on time dependent inputs. And these inputs are numpy >> arrays. >> >> When I do a flight path reconstruction, the inputs comes from the >> inertial system of the aircraft and I plug it into the state equations, so >> I get the deterministic position of the aircraft and than I can compare it >> to the measured values. >> >> The issue is that I can?t input an array. Searching stackoverflow I saw >> solutions using for-loops and assigning an arg for each time step. This is >> counter productive and create a huge time calculation for long arrays. >> >> I created my own C-dll to deal with this for loop issue, but it?s super >> counter productive. >> >> Can we get an option that the input args are arrays if same size of t, >> and as the state is integrated, the index of the arg follows the states? >> >> >> Hi, >> As far as I understand, you want to use time-varying parameters (which >> includes right-hand side terms of ODEs) specified as numpy array (i.e., at >> discrete time values) while ODEs are defined in the continuous-time domain. >> Specification of an extrapolation method is required for the well-posedness >> of the problem: the most trivial is the "Sample-and-hold" process which >> maintain values until next sample step. >> >> One simple solution (maybe one that you arleady tested) is to use the ode >> class and its integrate method. >> Adapting the example given in the docs >> >> https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.ode.html#scipy.integrate.ode >> >> from scipy.integrate import ode >> >> >> def f(t, y, arg1): >> >> return [1j*arg1*y[0] + y[1], -arg1*y[1]**2] >> >> def jac(t, y, arg1): >> >> return [[1j*arg1, 1], [0, -arg1*2*y[1]]] >> >> >> r = ode(f, jac).set_integrator('zvode', method='bdf') >> >> >> t = np.arange(0, 10, 1) >> >> results = np.empty((len(t), 1 + len(y0)), dtype=float) + np.nan >> >> >> r.set_initial_value([1.0j, 2.0], t[0]) >> >> for ind in range(len(t) - 1): >> >> r.set_f_params(param[ind]).set_jac_params(param[ind]) >> >> tmp_result = r.integrate(t[ind+1]) >> >> if not r.successful(): >> >> break >> >> results[ind] = tmp_result >> >> >> This introduced a reasonnable overhead in performance while begin quite >> readable. >> >> Best regards >> >> Fabrice >> >> >> _______________________________________________ >> SciPy-User mailing list >> SciPy-User at python.org >> https://mail.python.org/mailman/listinfo/scipy-user >> > -- > "The greatest challenge to any thinker is stating the problem in a way > that will allow a solution." > - Bertrand Russell > _______________________________________________ > SciPy-User mailing list > SciPy-User at python.org > https://mail.python.org/mailman/listinfo/scipy-user > -- Sincerely Yours*,* *Dr. Mansing V. Takale*, Assistant Professor, Department of Physics, Shivaji University, Kolhapur-416004 India (M.S.) Contact: +91-9673041222 -------------- next part -------------- An HTML attachment was scrubbed... URL: From opossumnano at gmail.com Thu Feb 13 10:41:29 2020 From: opossumnano at gmail.com (ASPP) Date: Thu, 13 Feb 2020 07:41:29 -0800 (PST) Subject: [SciPy-User] =?utf-8?b?W0FOTl0gMTPhtZfKsCBBZHZhbmNlZCBTY2llbnRp?= =?utf-8?q?fic_Programming_in_Python_in_Ghent=2C_Belgium=2C_31_August?= =?utf-8?q?=E2=80=945_September=2C_2020?= Message-ID: <5e456e29.1c69fb81.9303c.f5be@mx.google.com> 13?? 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, Universit?t 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-Universit?t zu Berlin Germany ? Tiziano Zito, Department of Psychology, Humboldt-Universit?t zu Berlin Germany ? Zbigniew J?drzejewski-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-Universit?t 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 at aspp.school From opossumnano at gmail.com Thu Feb 13 10:44:12 2020 From: opossumnano at gmail.com (Tiziano Zito) Date: Thu, 13 Feb 2020 07:44:12 -0800 (PST) Subject: [SciPy-User] =?utf-8?b?W0FOTl0gMTPhtZfKsCBBZHZhbmNlZCBTY2llbnRp?= =?utf-8?q?fic_Programming_in_Python_in_Ghent=2C_Belgium=2C_31_August?= =?utf-8?q?=E2=80=945_September=2C_2020?= Message-ID: <5e456ecc.1c69fb81.dd39f.e268@mx.google.com> 13?? 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, Universit?t 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-Universit?t zu Berlin Germany ? Tiziano Zito, Department of Psychology, Humboldt-Universit?t zu Berlin Germany ? Zbigniew J?drzejewski-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-Universit?t 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 at aspp.school From mikofski at berkeley.edu Thu Feb 27 03:21:03 2020 From: mikofski at berkeley.edu (Dr. Mark Alexander Mikofski PhD) Date: Thu, 27 Feb 2020 00:21:03 -0800 Subject: [SciPy-User] [ANN] pvlib participating in GSoC Message-ID: Excited to announce that pvlib python is participating in its first ever Google Summer of Code GSoC under the NumFOCUS umbrella. If you are a student interested in modeling renewable solar energy please apply: https://summerofcode.withgoogle.com/organizations/4727917315096576/ For project ideas, take a look at the pvlib github wiki: https://summerofcode.withgoogle.com/organizations/4727917315096576/ and reach out on our Google Groups forum: https://groups.google.com/forum/#!forum/pvlib-python We look forward to hearing from you! -- Mark Mikofski, PhD (2005) *Fiat Lux* -------------- next part -------------- An HTML attachment was scrubbed... URL: From mikofski at berkeley.edu Thu Feb 27 16:27:16 2020 From: mikofski at berkeley.edu (Dr. Mark Alexander Mikofski PhD) Date: Thu, 27 Feb 2020 13:27:16 -0800 Subject: [SciPy-User] [ANN] pvlib participating in GSoC In-Reply-To: References: Message-ID: Sorry, the correct link to the wiki list of project ideas is here: https://github.com/pvlib/pvlib-python/wiki/GSoC-2020-Project On Thu, Feb 27, 2020 at 12:21 AM Dr. Mark Alexander Mikofski PhD < mikofski at berkeley.edu> wrote: > Excited to announce that pvlib python > is participating in its > first ever Google Summer of Code GSoC under the NumFOCUS umbrella. If you > are a student interested in modeling renewable solar energy please apply: > > https://summerofcode.withgoogle.com/organizations/4727917315096576/ > > > For project ideas, take a look at the pvlib github wiki: > > https://summerofcode.withgoogle.com/organizations/4727917315096576/ > > > and reach out on our Google Groups forum: > > https://groups.google.com/forum/#!forum/pvlib-python > > > We look forward to hearing from you! > > -- > Mark Mikofski, PhD (2005) > *Fiat Lux* > -- Mark Mikofski, PhD (2005) *Fiat Lux* -------------- next part -------------- An HTML attachment was scrubbed... URL: