This is going to be a fun live-coding session using NumPy and SoundDevice. The goal of this talk is to make the computer produce realistic-sounding instrument sounds, using nothing but math.<\/p>\n
All the code will be written live and we'll hear the audio that it produces.<\/p>\n
We\u2019ll start with creating a simple sound using a sine wave. We\u2019ll gradually make it sound more like a real instrument, learning a little\nbit about music theory on the way. We\u2019ll add features one-by-one until by the end of the talk, we\u2019ll hear our synthesizer play a piece\nof classical music.<\/p>\n","pubDate":"Thu, 23 Jul 2020 00:00:00 +0000","guid":"tag:pyvideo.org,2020-07-23:\/europython-2020\/live-coding-a-music-synthesizer.html","category":["EuroPython 2020","europython","europython-2020","europython-online","Agile","Algorithms","Fun and Humor","Physics","PyPy"]},{"title":"Radio Astronomy with Python","link":"https:\/\/pyvideo.org\/europython-2020\/radio-astronomy-with-python.html","description":"
Gaussian Processes and Neural Networks applied to photometric redshift reconstruction<\/p>\n
Looking at higher redshifts is equivalent to looking back in time: they improve the studies of cosmology, expanding our knowledge of the universe. It allows us to study various physical phenomena like the power spectrum of galaxies which describes the distribution of galaxies on a range of scales, galaxy clustering, and large scales, the detection of the Baryon Acoustic Oscillation feature.\nAs a result, a significant amount of work has been done to increase the efficiency and accuracy of the process via new algorithms and optimization of existing ones.\nAstronomical datasets are undergoing a rapid growth in size and complexity as past, ongoing and future surveys produce massive multi-temporal and multi-wavelength datasets, with huge information to be extracted and analyzed.\nThe alternative to a full spectroscopic survey is to obtain multi-color images of the sky and perform photometric redshift estimates for the galaxies we have available.\nWhen dealing with this problem, there are two main approaches: model-driven data analysis (template fitting methods) and data-driven analysis, which can use machine learning methods.\nTo solve this problem, we use data-driven analysis, more specifically GPz (which uses Gaussian processes) and ANNz2 (which mainly uses neural networks), both python software.<\/p>\n
Prerequisites: machine learning and basic math knowledge<\/p>\n","pubDate":"Thu, 23 Jul 2020 00:00:00 +0000","guid":"tag:pyvideo.org,2020-07-23:\/europython-2020\/radio-astronomy-with-python.html","category":["EuroPython 2020","europython","europython-2020","europython-online","Big Data","Data","Machine-Learning","Physics"]},{"title":"The Joy of Creating Art with Code.","link":"https:\/\/pyvideo.org\/europython-2020\/the-joy-of-creating-art-with-code.html","description":"
Art is everywhere and it\u2019s beautiful. Unleash the creative artist inside you with the beauty of Generative Art. Learn how algorithms are used to create these aesthetic art forms, how motion and structures emit sounds and what toolkits are required to do so. This talk looks at Python as an artistic and visual programming language with the simplicity and beauty of generative art using Processing, PyCairo and webGL. The audience will see an evolution of generative art over the last 50 years, how autonomously these art forms are created using algorithms and how we can stimulate paints and other media. The talk will be showing how to create artworks inspired by geometric and mathematical patterns which also includes randomness with hands-on examples (Two such examples are added here: https:\/\/imgur.com\/a\/lycAYnj<\/a> ).<\/p>\n","pubDate":"Thu, 23 Jul 2020 00:00:00 +0000","guid":"tag:pyvideo.org,2020-07-23:\/europython-2020\/the-joy-of-creating-art-with-code.html","category":["EuroPython 2020","europython","europython-2020","europython-online","Algorithms","Analytics","JavaScript","Physics","Python 3"]},{"title":"Theoretical physics with sympy","link":"https:\/\/pyvideo.org\/pycon-de-2017\/theoretical-physics-with-sympy.html","description":" Florian Th\u00f6le<\/strong> (@florian_thl)<\/p>\n PhD student in Computational Materials Science. Enthusiastic about teaching. Instructor for Software Carpentry.<\/p>\n Abstract<\/strong><\/p>\n In this talk, I will introduce the basics of sympy. Using a simple model system in magnetism, we'll play around with simplifications, then do a bit of numerical optimization and in the end make psychedelic-looking figures.<\/p>\n Description<\/strong><\/p>\n I will introduce the basic functionalities of the sympy package to do symbolic computing, with a special focus on vector and matrix operations. Then, I'll briefly explain a real-world model from the description of 2D layered magnetic materials and use sympy to deal with the resulting expressions. We'll evaluate those expressions to visualize the results of the model and obtain a numerical estimate of a transition point.<\/p>\n The aim of this talk is to give a light-hearted introduction into the world of symbolic computing to someone who has more fun working with computers than pen and paper.<\/p>\n Recorded at<\/strong> PyCon.DE 2017 Karlsruhe: https:\/\/de.pycon.org\/<\/a><\/p>\n Video editing<\/strong>: Sebastian Neubauer & Andrei Dan<\/p>\n Tools<\/strong>: Blender, Avidemux & Sonic Pi<\/p>\n","pubDate":"Wed, 25 Oct 2017 00:00:00 +0000","guid":"tag:pyvideo.org,2017-10-25:\/pycon-de-2017\/theoretical-physics-with-sympy.html","category":["PyCon DE 2017","physics","science","sympy"]},{"title":"Validazione e decodifica di file XML con Python","link":"https:\/\/pyvideo.org\/pycon-italia-2017\/validazione-e-decodifica-di-file-xml-con-python.html","description":" Per la schematizzazione di documenti XML sono disponibili diversi\nstandard, tra questi XML Schema. La prima problematica \u00e8 quella di poter\nvalidare file XML secondo il loro rispettivo schema. Un\u2019altra esigenza \u00e8\nla conversione dell\u2019XML in strutture dati in cui siano valorizzati i\ntipi base definiti nello schema e la struttura nidificata in cui sono\ninseriti, similmente a quello che \u00e8 gi\u00e0 reso disponibile per JSON.<\/p>\n In ambito scientifico, nell\u2019ambito delle attivit\u00e0 del progetto europeo\nMaX (Materials design at the Exascale), per semplificare l\u2019interscambio\ndi dati dati XML tra diversi software di simulazione, si \u00e8 deciso di\nadottare un approccio unitario e sistematico alla validazione e alla\ndecodifica dei dati XML prodotti.<\/p>\n Pertanto \u00e8 in fase di sviluppo una nuova libreria (xmlschema), basata\nsulla libreria standard ElementTree di Python, che permette di validare\ne decodificare dati XML con schemi XSD. La decodifica di un documento\nXML produce un dizionario nidificato con i valori terminali appartenenti\nai corrispondenti tipi elementari.<\/p>\n Nella presentazione parlerei delle problematiche degli schemi XSD,\ndell\u2019ambito scientifico in cui \u00e8 stata sviluppata la libreria e della\nstruttura della libreria stessa, illustrando e magari raccogliendo\nqualche idea per migliorarla.<\/p>\n","pubDate":"Sat, 08 Apr 2017 00:00:00 +0000","guid":"tag:pyvideo.org,2017-04-08:\/pycon-italia-2017\/validazione-e-decodifica-di-file-xml-con-python.html","category":["PyCon Italia 2017","xml","mathematical-modelling","encoding","Python","simulation","validation","physics","metadata"]},{"title":"Catching Neutrinos with Python and KM3NeT","link":"https:\/\/pyvideo.org\/pycon-italia-2017\/catching-neutrinos-with-python-and-km3net.html","description":" KM3NeT is the next generation underwater neutrino telescope located in\nthe deepest seas of the Mediterranean. Once completed, it will have an\ninstrumented volume of several cubic kilometres. One of the technical\nchallenges in such a huge project is providing software tools, which can\nbe rapidly developed and maintained while keeping a focus on\nportability, compatibility and usability. The area of application ranges\nfrom live monitoring over offline processing and analysis to data\nvisualisation and 3D event displays. Python with it\u2019s wonderful standard\nlibrary combined with extraordinary open source frameworks proves to be\nable to handle all these different tasks with ease. This talk covers\nsome of the projects used in our collaboration to make our lives easier,\nfeaturing technologies such as Cython, Numpy, Scipy, Pandas, Matplotlib,\nJupyter, Tornado, PyOpenGL, urwid and more, and focusses on one of our\nkey ingredients: a framework called KM3Pipe, which provides a\npipeline-based workflow to allow us building complex analysis chains by\nstitching together modules.<\/p>\n","pubDate":"Fri, 07 Apr 2017 00:00:00 +0000","guid":"tag:pyvideo.org,2017-04-07:\/pycon-italia-2017\/catching-neutrinos-with-python-and-km3net.html","category":["PyCon Italia 2017","physics","pipeline","neutrinos","hephysiscs"]},{"title":"GPU-accelerated data analysis in Python: a study case in Material Sciences","link":"https:\/\/pyvideo.org\/pycon-italia-2018\/gpu-accelerated-data-analysis-in-python-a-study-case-in-material-sciences.html","description":" The Max Planck Computing and Data Facility is engaged in the development\nand optimization of algorithms and applications for high performance\ncomputing as well as for data-intensive projects. As programming\nlanguage in data science, Python is now used at MPCDF in the scientific\narea of \u201catom probe crystallography\u201d (APT): a Fourier analysis in 3D\nspace can be simulated in order to reveal composition and\ncrystallographic structure at the atomic scale of billions APT\nexperimental data sets.<\/p>\n The Python data ecosystem has proved to be well suited to this, as it\nhas grown beyond the confines of single machines to embrace scalability.\nThe talk aims to describe our approach to scaling across multiple GPUs,\nand the role of visualization methods too.<\/p>\n Our data workflow analysis relies on the GPU-accelerated Python software\npackage PyNX, an open source library which provides fast parallel\ncomputation scattering. The code takes advantage of the high throughput\nof GPUs, using the pyCUDA library.<\/p>\n Exploratory data analysis, high productivity and rapid prototyping with\nhigh performance are enabled through Jupyter Notebooks and Python\npackages e.g., pandas, matplotlib\/plotly. In production stage,\ninteractive visualization is realized by using standard scientific tool,\ne.g. Paraview, an open-source 3D visualization program which requires\nPython modules to generate visualization components within VTK files.<\/p>\n in __on sabato 21 aprile<\/strong> at 14:45 **See\nschedule**<\/a><\/p>\n","pubDate":"Sat, 21 Apr 2018 00:00:00 +0000","guid":"tag:pyvideo.org,2018-04-21:\/pycon-italia-2018\/gpu-accelerated-data-analysis-in-python-a-study-case-in-material-sciences.html","category":["PyCon Italia 2018","GPUComputing","visualization","mathematical-modelling","image-processing","bigdata","matplotlib","analytics","data-visualization","data-analysis","Data Mining","scientific-computing","physics","python3"]},{"title":"Project based introduction to scientific computing for physics majors","link":"https:\/\/pyvideo.org\/scipy-2014\/project-based-introduction-to-scientific-computin.html","description":" This talk will present an overview of a project-based introductory\ncourse in scientific computing using python for physics majors at Cal\nPoly San Luis Obispo.<\/p>\n Computational tools and skills are as critical to the training of\nphysics majors as calculus and math, yet they receive much less emphasis\nin the undergraduate curriculum. One-off courses that introduce\nprogramming and basic numerical problem-solving techniques with\ncommercial software packages for topics that appear in the traditional\nphysics curriculum are insufficient to prepare students for the\ncomputing demands of modern technical careers. Yet tight budgets and\nrigid degree requirements constrain the ability to expand computational\ncourse offerings for physics majors.<\/p>\n This talk will present an overview of a recently revamped course at Cal\nPoly San Luis Obispo that uses Python and associated scientific\ncomputing libraries to introduce the fundamentals of open-source tools,\nversion control systems, programming, numerical problem solving and\nalgorithmic thinking to undergraduate physics majors. The spirit of the\ncourse is similar to the bootcamps organized by Software\nCarpentry<\/a> for researchers in science\nbut is offered as a ten-week for-credit course. In addition to having a\ntraditional in-class component, students learn the basics of Python by\ncompleting tutorials on Codecademy<\/a>'s\nPython track and practice their algorithmic thinking by tackling\nProject Euler<\/a> problems. This approach of\nincorporating online training may provide a different way of thinking\nabout the role of MOOCs in higher education. The early part of the\ncourse focuses on skill-building, while the second half is devoted to\napplication of these skills to an independent research-level\ncomputational physics project. Examples of recent projects and their\nresults will be presented.<\/p>\n","pubDate":"Wed, 09 Jul 2014 00:00:00 +0000","guid":"tag:pyvideo.org,2014-07-09:\/scipy-2014\/project-based-introduction-to-scientific-computin.html","category":["SciPy 2014","education","physics"]}]}}Description<\/h3>
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