https://community.wolfram.com RSS Feed for Wolfram Community showing any discussions tagged with Graphics and Visualization sorted by most likes. https://community.wolfram.com/groups/-/m/t/2593151 **Introducing our brand new YouTube channel, [Wolfram R&D][1]! Our channel features livestreams, behind-the-scenes creator presentations, insider videos, and more.** ---------- Join us for the unique Wolfram R&D livestreams on [Twitch][2] and [YouTube][3] led by our developers! You will see **LIVE** stream indicators on these channels on the dates listed below. The live streams provide tutorials and behind the scenes look at Mathematica and the Wolfram Language directly from developers. Join our livestreams every Wednesday at 11 AM CST and interact with developers who work on data science, machine learning, image processing, visualization, geometry, and other areas. ---------- ⭕ **UPCOMING** EVENTS - Jan 29 -- Reinforcement Learning Applied to Feedback Control with [Suba Thomas][61] ---------- ✅ **PAST** EVENTS - April 24 -- [FeynCalc][60] - April 3 -- [Explore the Total Solar Eclipse of April 2024][59] - Mar 22 -- [20 Years of xAct Tensor Computer Algebra][58] - Feb 28 -- [Zero Knowledge Authentication][57] - Jan 17 -- [Nutrients by the Numbers][56] - Dec 13 -- [Understanding Graphics][55] - Oct 18 -- [Overview of Number Theory][54] - Sep 27 -- [QMRITools: Processing Quantitative MRI Data][52] - Sep 13 -- [Make High Quality Graph Visualization][51] - Sep 6 -- [Insider's View of Graphs & Networks][53] - Aug 30 -- [Labeling Everywhere][49] - Aug 22 -- [Equation Generator for Equation-of-Motion Coupled Cluster Assisted by CAS][48] - Aug 16 -- [Foreign Function Interface][4] - July 26 -- [Modeling Fluid Circuits][6] - July 19 -- [Geocomputation][5] - July 5 -- [Protein Visualization][7] - Jun 14 -- [Chat Notebooks bring the power of Notebooks to LLMs][8] - May 31-- [Probability and Statistics: Random Sampling][9] - May 24 -- [Problem Solving][10] - May 17 -- [The state of Optimization][11] - May 10 -- [Building a video game with Wolfram notebooks][12] - April 26 -- [Control Systems: An Overview][13] - April 19 -- [MaXrd: A crystallography package developed for research support][14] - April 5th -- [Relational database in the Wolfram Language][15] - Mar 29th -- [Build your first game in the Wolfram Language with Unity game engine][16] - Mar 22nd -- [Everything to know about Mellin-Barnes Integrals - Part II][17] - Mar 15th -- [Building your own Shakespearean GPT - a ChatGPT like GPT model][18] - Mar 8th -- [Understand Time, Date and Calendars][19] - Mar 1st -- [Introducing Astro Computation][20] - Feb 22nd -- [Latest features in System Modeler][21] - Feb 15th -- [Everything to know about Mellin-Barnes Integrals][22] - Feb 8th -- [Dive into Video Processing][23] - Feb 1st -- [PDE Modeling][24] - Jan. 25th -- [Ask Integration Questions to Oleg Marichev][25] - Jan. 18th -- [My Developer Tools][26] - Jan. 11th -- [Principles of Dynamic Interfaces][27] - Dec. 14th -- [Wolfram Resource System: Repositories & Archives][28] - Dec. 7th -- [Inner Workings of ImageStitch: Image Registration, Projection and Blending][29] - Nov. 30th -- [Q&A for Calculus and Algebra][30] - Nov. 23rd -- [xAct: Efficient Tensor Computer Algebra][31] - Nov. 16th -- [Latest in Machine Learning][32] - Nov. 9th -- [Computational Geology][33] - Nov. 2nd -- [Behind the Scenes at the Wolfram Technology Conference 2022][34] - Oct 26th -- [Group Theory Package (GTPack) and Symmetry Principles in Condensed Matter][35] - Oct 12th -- [Tree Representation for XML, JSON and Symbolic Expressions][36] - Oct. 5th -- [A Computational Exploration of Alcoholic Beverages][37] - Sept. 28th -- [Q&A with Visualization & Graphics Developers][38] - Sept. 14th -- [Paclet Development][39] - Sept. 7th -- [Overview of Chemistry][40] - Aug. 24th -- [Dive into Visualization][41] - Aug. 17th -- [Latest in Graphics & Shaders][42] - Aug. 10th -- [What's new in Calculus & Algebra][43] > **What are your interests? 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It was a blast to see all the amazing art you created. After deliberation by our judges, these are the winners: - **Honorable Mention**: ARTIST: [Daniel Hoffmann][1], EXHIBIT: "[The Memory of Persistence][2]" - **Staff Winner**: ARTIST: [Anton Antonov][3], EXHIBIT: "[Rorschach mask animations projected over 3D surfaces][4]" - **3rd Place**: ARTIST: [Jacqueline Doan][5], EXHIBIT: "[Kuramoto oscillators with phase lag][6]" - **2nd Place**: ARTIST: [Tom Verhoeff][7], EXHIBIT: "[Sculpture from 18 congruent pieces][8]" - **1st Place**: ARTIST: [Frederick Wu][9], EXHIBIT: "[Love heart jewelry IV: the giving tree][10]" ![enter image description here][11] ---------- # CONTEST Flex your creative and computational skill with Wolfram's Computational Art Contest that kicks off today, Monday, March 28th! Share your work with the community, and potentially win free Wolfram merchandise. Programmers and artists of all skill levels are encouraged to participate! This contest is inspired by Genuary, an annual project releasing generative art prompts during the month of January. We're elated to see the creative works of our users and engage with the community while exploring the scope of computational art within the Wolfram Language. ## Rules & Guidelines ## - Submission deadline is April 25th at 9am Central Time. Posts posted after will not be included in judging - Participants must fill out a detailed Community profile ( example: https://community.wolfram.com/web/claytonshonkwiler ) and create a Community post about their submission. The post must include the code used to create graphics and the final piece of art placed at the top of the post. An explanation of how their code works is required, moreover participants are encouraged to write more about their creative process. - Participants submit their entry by commenting on this post with an image of their art, along with a link to their Community post - Multiple submissions per participant are allowed, but please keep the number of submissions under three - Each participant can only win once. Participants' best-performing piece, as determined by the judges, will be used when determining winners - Both static images and animations can be submitted. Animations are preferred in a GIF format; if the animation is too large for a GIF, the post can point to a public YouTube video. - Submissions will be judged by a handful of Wolfram experts, with the following parameters: - Visual aesthetics - Wolfram Language code - Creativity - Explanation of process - Submissions from all areas of computational art are welcome - Submissions from former or current Wolfram employees are allowed, but will be judged as their own category with only one winner - Submitting previous work/posts is allowed, but must meet the requirements stated above ## Encouragements ## - Not sure where to start? We encourage you to look at other user's submission for inspiration, or look at some of the work in the visual arts group of Community: - Artists' group: https://wolfr.am/ART-examples - Artist (see Staff Picks section): https://community.wolfram.com/web/claytonshonkwiler - We encourage you to vote and comment on other people's submissions. - Please spread the word about this competition, among your friends and other social media! ## Prizes ## First, second, and third place winners will be featured on all of Wolfram's social media accounts, as well as receiving their choice of free Wolfram merchandise. We are able to ship merchandise to countries listed on the Wolfram Store: https://store.wolfram.com. If your country is not listed on the Wolfram Store, we strongly encourage you to still submit an entry, as we will feature winners submissions regardless of location. ### Important ### All contest rules have been explained above under Rules & Guidelines. It's encouraged for all participants to read the rules carefully to prevent disqualification. If you have any additional questions, ask directly in the thread comments or contact us by email at t-artcontest@wolfram.com . We recommend reading other people comments as they clarify the nature of the contest as well. Comments deemed by moderators as superfluous to the thread may be removed or transferred by moderators to keep competition professional. [1]: https://community.wolfram.com/web/danielsanderhoffmann [2]: https://community.wolfram.com/groups/-/m/t/2518220 [3]: https://community.wolfram.com/web/antononcube [4]: https://community.wolfram.com/groups/-/m/t/2518279 [5]: https://community.wolfram.com/web/jacquelinengocdoan [6]: https://community.wolfram.com/groups/-/m/t/2509110 [7]: https://community.wolfram.com/web/tverhoeff [8]: https://community.wolfram.com/groups/-/m/t/2513265 [9]: https://community.wolfram.com/web/wufei1978 [10]: https://community.wolfram.com/groups/-/m/t/2430827 [11]: https://community.wolfram.com//c/portal/getImageAttachment?filename=news-congrads-kkluyshnik-02-04-19.jpg&userId=11733 Eryn Gillam 2022-03-28T17:35:43Z https://community.wolfram.com/groups/-/m/t/1872608 *Short URL to share this post*: https://wolfr.am/coronavirus ***JOIN*** *our* ***Medical Sciences*** *group for the latest updates & best networking*: https://wolfr.am/MedicalSciences ---------- This post is intended to be the hub for Wolfram resources related to novel coronavirus disease COVID-19 that originated in Wuhan, China. The larger aim is to provide a forum for disseminating ways in which Wolfram technologies and coding can be utilized to shed light on the virus and pandemic. Possibilities include using the Wolfram Language for data-mining, modeling, analysis, visualizations, and so forth. Among other things, we encourage comments and feedback on these resources. Please note that this is intended for technical analysis and discussion supported by computation. Aspects outside this scope and better suited for different forums should be avoided. Thank you for your contribution! ## ________________________________________ ## FEATURED CONTENT - [COVID-19 Livestream Notebook March 24][8] by Stephen Wolfram - [Agent-Based Networks Models for COVID-19][9] by Christopher Wolfram - [Live-Stream: Exploring Pandemic Data][10] by Stephen & Christopher Wolfram + guests - [Live-Stream: Exploring and Explaining Epidemic Modeling][11] by Stephen & Christopher Wolfram + guests ## ________________________________________ ## [CALL for Making COVID-19 Data Computable (*link*)][12] More pandemic-related information and data sets emerging every day. We invite people in the community to contribute to making more data surrounding this topic computable. Here is a call to action with some recommendations for people who want to do more, whether it's just pointing out relevant data sources, or taking the time to make some of that data computable and more instantly ready for other people to explore: https://wolfr.am/COVID-19-DATA . ## ________________________________________ ## [Curated Computable Data (*link*)][13] [FOLLOW THIS LINK][14] to see all available COVID-19 data repositories ready for computation in the Wolfram Language . [Changes in Updates to SARS-CoV-2 Sequences in the Wolfram Data Repository][16] We have published and are continuously updating the Wolfram Data Repository entries. Below are a few key ones. Follow the link above to browse all repositories. We encourage you to make [*your own contributions*][15] of curated data relevant to COVID-19. > **Pandemic Data for Novel Coronavirus COVID-19** > https://www.wolframcloud.com/obj/resourcesystem/published/DataRepository/resources/Epidemic-Data-for-Novel-Coronavirus-COVID-19 > **Genetic Sequences for the SARS-CoV-2 Coronavirus** > https://datarepository.wolframcloud.com/resources/Genetic-Sequences-for-the-SARS-CoV-2-Coronavirus > **Patient Medical Data for Novel Coronavirus COVID-19** > https://datarepository.wolframcloud.com/resources/Patient-Medical-Data-for-Novel-Coronavirus-COVID-19 > **COVID-19 Hospital Resource Use Projections** > https://datarepository.wolframcloud.com/resources/COVID-19-Hospital-Resource-Use-Projections > **OECD Data: Hospital Beds Per Country** > https://datarepository.wolframcloud.com/resources/OECD-Data-Hospital-Beds-Per-Country > **Hospital Beds Per US State** > https://datarepository.wolframcloud.com/resources/Hospital-Beds-Per-US-State ## ________________________________________ ## [Computational Publications (*link*)][17] We encourage you to share your computational explorations relevant to coronavirus on Wolfram Community as stand-alone articles and then comment with their URL links on this discussion thread. We will summarize these articles in the following list: ### ________________________________ ###FEATURED > **COVID-19 Livestream Notebook March 24** by Stephen Wolfram > https://www.wolframcloud.com/obj/s.wolfram/Published/COVID-19-Livestream-March-24.nb > **Agent-Based Networks Models for COVID-19** by Christopher Wolfram > https://community.wolfram.com/groups/-/m/t/1907703 > **Epidemiological Models for Influenza and COVID-19** by Robert Nachbar > https://community.wolfram.com/groups/-/m/t/1896178 > **Epidemic simulation with a polygon container** by Francisco Rodríguez > https://community.wolfram.com/groups/-/m/t/1901002 > **Distance to nearest confirmed US COVID-19 case** by Chip Hurst > https://community.wolfram.com/groups/-/m/t/1911583 ### ________________________________ ### EPIDEMIC MODELING: SIMULATION > **Epidemic simulation with a polygon container** by Francisco Rodríguez > https://community.wolfram.com/groups/-/m/t/1901002 > **Agent based epidemic simulation** by Jon McLoone > https://community.wolfram.com/groups/-/m/t/1900481 > **Modeling the spatial spread of infection diseases in the US** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/1889072 > **Geo-spatial-temporal COVID-19 simulations and visualizations over USA** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/1900514 > **Life, Liberty, and Lockdowns: cellular automaton approach** by Philip Maymin > https://community.wolfram.com/groups/-/m/t/2181433 ### ________________________________ ### EPIDEMIC MODELING: COMPARTMENTAL > **Teaching notebook on disease models** by Gareth Russell > https://community.wolfram.com/groups/-/m/t/2694698 > **Stochastic Epidemiology Models with Applications to the COVID-19** by Robert Nachbar > https://community.wolfram.com/groups/-/m/t/1980051 > **COVID19: Italian SIRD estimates and prediction** by Christos Papahristodoulou > https://community.wolfram.com/groups/-/m/t/1984320 > **Solver for COVID-19 epidemic model with the Caputo fractional derivatives** by Alexander Trounev > https://community.wolfram.com/groups/-/m/t/1976589 > **EpiPlay: using Mathematica to gamify education in epidemiology** by Rui Alves > https://community.wolfram.com/groups/-/m/t/2535927 > **Epidemiological Model for repetitive rapid testing for COVID-19** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/2075883 > **Phase transition of a SIR agent-based models** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/1977230 > **A simple estimate of covid-19 fatalities based on past data** by Kay Herbert > https://community.wolfram.com/groups/-/m/t/1959438 > **SIR Model with Log-normal infected periods** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/1946292 > **SEI2HR-Econ model with quarantine and supplies scenarios** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1937880 > **COVID-19 - Policy Simulator - Can you find the perfect policy?** by Jan Brugard > https://community.wolfram.com/groups/-/m/t/1931352 > **Epidemiological Models for Influenza and COVID-19** by Robert Nachbar > https://community.wolfram.com/groups/-/m/t/1896178 > **Exploring Epidemiological Modeling** by Jordan Hasler > https://community.wolfram.com/groups/-/m/t/1920119 > **SEI2HR model with quarantine scenarios** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1926505 > **The SIR Model for Spread of Disease** by Arnoud Buzing > https://community.wolfram.com/groups/-/m/t/1903289 > **COVID-19 - R0 and Herd Immunity - are we getting closer?** by Jan Brugard > https://community.wolfram.com/groups/-/m/t/1911422 > **Basic experiments workflow for simple epidemiological models** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1895675 > **Scaling of epidemiology models with multi-site compartments** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1897377 > **WirVsVirus 2020 hackathon participation** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1907256 > **An SEIR like model that fits the coronavirus infection data** by Enrique Garcia Moreno > https://community.wolfram.com/groups/-/m/t/1888335 > **A SEIRD Model For COVID-19 Using DDEs** by Luis Borgonovo > https://community.wolfram.com/groups/-/m/t/1996374 > **A Neat Package for Compartmental Model Diagrams** by Hamza Alsamraee > https://community.wolfram.com/groups/-/m/t/2078640 > **Redesign of didactics of S(E)IR(D) -> SI(EY)A(CD) models of epidemics** by Thomas Colignatus > https://community.wolfram.com/groups/-/m/t/2004784 > **COVID-19 SIR models: transmission, vaccination, herd immunity dynamics revealed** by Athanasios Paraskevopoulos > https://community.wolfram.com/groups/-/m/t/3008488 ### ________________________________ ### EPIDEMIC MODELING: LOGISTIC > **COVID-19 pandemic data in Italy** by Riccardo Fantoni > https://community.wolfram.com/groups/-/m/t/1909687 > **Predicting Coronavirus Epidemic in United States** by Robert Rimmer >https://community.wolfram.com/groups/-/m/t/1906954 > **Tracking Coronavirus Testing in the United States** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1902302 > **Logistic Model for Quarantine Controlled Epidemics** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1900530 > **Updated: coronavirus logistic growth model: China** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1890271 > **Coronavirus logistic growth model: China** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1887435 > **Coronavirus logistic growth model: Italy and South Korea** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1887823 > **Coronavirus logistic growth model: South Korea** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1894561 > **Logistic growth model for epidemic Covid-19 in Colombia** by Diego Ramos > https://community.wolfram.com/groups/-/m/t/2092786 ### ________________________________ ### GENOMICS > **Analyzing the spread of SARS-CoV-2 variants in California** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/2205357 > **Analyzing the spread of SARS-CoV-2 variants in Florida** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/2206874 > **Analyzing Nextstrain Data with WFR Newick Functions (COVID-19/SARS-CoV-2)** by John Cassel > https://community.wolfram.com/groups/-/m/t/1958952 > **Finding and analyzing a COVID subvariant in Australia** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/2342489 > **Analyzing SARS-CoV-2 Genetic Sequences** by John Cassel & Daniel Lichtblau > https://blog.wolfram.com/2021/08/19/newick-trees-proximity-resources-and-accessions-analyzing-sars-cov-2-genetic-sequences/ > **Estimating the number of times the SARS CoV-2 virus has replicated** by Carlos Munoz > https://community.wolfram.com/groups/-/m/t/1943243 >**From sequenced SARS-CoV-2 genomes to a phylogenetic tree** by Daniel Lichtblau >https://community.wolfram.com/groups/-/m/t/1961461 > **Genome analysis and the SARS-nCoV-2** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/1874816 > **Visualizing Sequence Alignments from the COVID-19** by Jessica Shi > https://community.wolfram.com/groups/-/m/t/1875352 > **A walk-through of the SARS-CoV-2 nucleotide Wolfram resource** by John Cassel > https://community.wolfram.com/groups/-/m/t/1887456 > **Geometrical analysis of genome for COVID-19 vs SARS-like viruses** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1878824 > **Chaos Game For Clustering of Novel Coronavirus COVID-19** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1875994 ### ________________________________ ### DATA ANALYSIS > **Optimal Annual COVID-19 Vaccine Boosting Dates Following Previous Booster Vaccination or Breakthrough Infection** by Jeffrey Townsend > https://community.wolfram.com/groups/-/m/t/3341399 > **Probability of early infection extinction depends linearly on the virus clearance rate** by Nóra Juhász > https://community.wolfram.com/groups/-/m/t/3307502 > **Detecting Global Community Structure in a COVID-19 Activity Correlation Network** by Hiroki Sayama > https://community.wolfram.com/groups/-/m/t/3056172 > **Analyzing trends of COVID-19 through public news feeds** by Silvia Hao > https://community.wolfram.com/groups/-/m/t/2569395 > **Deep neural network detection & clinical staging of COVID-19 chest X-rays** by Peter Riley > https://community.wolfram.com/groups/-/m/t/2389110 > **COVID-19 - The Swedish Experiment - Is it working?** by Jan Brugard > https://community.wolfram.com/groups/-/m/t/1974412 > **A simple COVID-19 spread model** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/1945196 > **COVID19: The performance of the Swedish strategy** by Christos Papahristodoulou > https://community.wolfram.com/groups/-/m/t/1990972 > **Exploring social trends on Covid-19 pandemic using WikipediaData** by Jofre Espigule-Pons > https://community.wolfram.com/groups/-/m/t/1931508 > **Google Mobility Data** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1946686 > **Understanding Aggregate COVID Curves** by Christopher Wolfram > https://community.wolfram.com/groups/-/m/t/2068457 > **Apple mobility trends data visualization** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1942813 > **Computing COVID-19 Spread Rates in US Cities** by Daniel Lichtblau > https://community.wolfram.com/groups/-/m/t/1930261 > **COVID-19 data and the Newcomb Benford Distribution** by Gustavo Delfino > https://community.wolfram.com/groups/-/m/t/1913908 > **Short-time trends for COVID-19**, by Fabian Wenger > https://community.wolfram.com/groups/-/m/t/1912710 > **What countries are hit hard by COVID19 outbreak?** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1904507 > **COVID19 in Iran: under-diagnosis issue** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1891140 > **Coronavirus analysis: descriptive statistics with SQL functions** by Damian Calin > https://community.wolfram.com/groups/-/m/t/2206078 > **Covid-19 vaccine campaigns efficacy analysis** by Damian Calin > https://community.wolfram.com/groups/-/m/t/2383314 > **Argentina: COVID-19 Data Analysis** by Tobias Canavesi > https://community.wolfram.com/groups/-/m/t/1932910 > **Analysis of the Change in Phillips Curve After COVID-19 with Regression** by Seojin Yoon > https://community.wolfram.com/groups/-/m/t/2055704 > **COVID wave alert: statistical analysis and visualization** by Antonio Neves > https://community.wolfram.com/groups/-/m/t/2115658 > **Predicting COVID-19 using cough sounds classification** by Siria Sadeddin > https://community.wolfram.com/groups/-/m/t/2166833 > **Covid-19 vaccination data analysis using SQL functions** by Damian Calin > https://community.wolfram.com/groups/-/m/t/2324474 > **Analyzing COVID-19 vaccine sentiment over time** by Arshaan Sayed > https://community.wolfram.com/groups/-/m/t/2317293 > **VAERS data analysis using SQL functions** by Damian Calin > https://community.wolfram.com/groups/-/m/t/2351726 > **Correlating COVID-19 government measures to biweekly/daily outbreaks** by Arshaan Sayed > https://community.wolfram.com/groups/-/m/t/2362327 > **Plotting Covid19 sentiment in different regions of Chennai** by Aditya Sairam Prakash > https://community.wolfram.com/groups/-/m/t/2388236 ### ________________________________ ### DATA VISUALIZATIONS > **CDC COVID19 vaccination data across US counties** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/2282418 > **Top 20 COVID countries HeatMap by absolute death and death in ppm** by Rodrigo Murta > https://community.wolfram.com/groups/-/m/t/2004800 > **COVIDWORLD app: current data and visualizations for SARS-CoV2 pandemic** by Rui Alves > https://community.wolfram.com/groups/-/m/t/2473065 > **US Counties COVID-19 confirmed cases by population density timelines** by Bob Sandheinrich > https://community.wolfram.com/groups/-/m/t/1992898 > **3D Modeling of the SARS-CoV-2 Virus in the Wolfram Language** by Jeff Bryant > https://community.wolfram.com/groups/-/m/t/1989540 > **California COVID19 Data** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/2132204 > **COVID-19 progress in Peru macro regions: coast vs mountain vs jungle** by Francisco Rodríguez > https://community.wolfram.com/groups/-/m/t/1965079 > **COVID-19 reopening criterion: a simple visualization** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1962615 > **100 Days of COVID19 Over US Counties** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1956368 > **Population Density Map** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1955760 > **Google Mobility Data** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1946686 > **COVID19 Case-Fatality Ratio, Income, and Age: Simple Visualization** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1939045 > **Data Analysis of Coronavirus in Mexico** by Ivan Martinez > https://community.wolfram.com/groups/-/m/t/1927657 > **Confirmed COVID-19 Cases in Catalonia** by Bernat Espigulé Pons > https://community.wolfram.com/groups/-/m/t/1919468 > **Distance to nearest confirmed US COVID-19 case** by Chip Hurst > https://community.wolfram.com/groups/-/m/t/1911583 > **COVID19 Confirmed Cases: US Counties** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/1950980 > **COVID19 data visualization across US counties** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/2119049 > **Maps for Visualizing Covid-19's Effect** by Eric Mockensturm > https://community.wolfram.com/groups/-/m/t/1934457 > **US Counties COVID-19 deaths plot** by Bob Sandheinrich > https://community.wolfram.com/groups/-/m/t/1918332 > **Comparing the spread of COVID-19 between countries**, Jan Brugard > https://community.wolfram.com/groups/-/m/t/1905992 > **NY Times COVID-19 data visualization** by Anton Antonov > https://community.wolfram.com/groups/-/m/t/1911668 > **COVID-19 cases for each administrative division in Spain** by Bernat Espigulé Pons > https://community.wolfram.com/groups/-/m/t/1910116 > **Propagation risk of COVID-19 by local contact in Spain (10 - 14 March)** by Bernat Espigulé Pons > https://community.wolfram.com/groups/-/m/t/1898126 > **Visualizing the Pandemic Data COVID-19** by Martijn Froeling > https://community.wolfram.com/groups/-/m/t/1899870 > **COVID-19 visualization of turning point** by Isao Maruyama > https://community.wolfram.com/groups/-/m/t/1899911 > **Mapping "Live" COVID Data on a Globe** by Gabriel Lemieux > https://community.wolfram.com/groups/-/m/t/1902102 > **Novel Coronavirus COVID-19 in Brazil** by Estevao Teixeira > https://community.wolfram.com/groups/-/m/t/1905950 > **Mapping Novel Coronavirus COVID-19 Outbreak** by Jofre Espigule-Pons > https://community.wolfram.com/groups/-/m/t/1868945 > **Ways to visualize COVID-19 simulation results?** by Kyle Keane > https://community.wolfram.com/groups/-/m/t/1962739 > **General and COVID-19 deaths in Sweden** by Oscar Rodriguez > https://community.wolfram.com/groups/-/m/t/2006377 > **COVID19 Tokyo per days of the week** Isao Maruyama > https://community.wolfram.com/groups/-/m/t/2133807 ### ________________________________ ### DATA PROCESSING > **Cov-Tell: Daily COVID-19 Updates with Alexa (made with Wolfram APIFunction)** by Jessica Shi > https://community.wolfram.com/groups/-/m/t/1958307 > **Build a COVID-19 Chest X-Ray Image Uploader with Cloud & Data Drop** by Jofre Espigule-Pons > https://community.wolfram.com/groups/-/m/t/1919770 > **Scraping OpenTable's "State of the Industry" page** by Aaron Enright > https://community.wolfram.com/groups/-/m/t/1911043 > **City-level Search Tool for Coronavirus (COVID-19) Confirmed Cases** by David Lomiashvili > https://community.wolfram.com/groups/-/m/t/1913247 > **Web Scraper: New York Times Coronavirus Data** by Robert Rimmer > https://community.wolfram.com/groups/-/m/t/1894426 > **TraCOV: Personalized COVID-19 Risk Analysis Tool** by Jessica Shi > https://community.wolfram.com/groups/-/m/t/1977700 > **Mobility changes data: transforming to Wolfram Language dataset** by Mads Bahrami > https://community.wolfram.com/groups/-/m/t/2160386 ### ________________________________ ### MASKS > **Effect of mandatory mask usage in COVID cases** by Diego Zviovich > https://community.wolfram.com/groups/-/m/t/1919060 > **Face mask detection: classifying image data** by Siria Sadeddin > https://community.wolfram.com/groups/-/m/t/2139499 ## ________________________________________ ## [Livestream Archives (*link*)][18] - Stephen & Christopher Wolfram + guests [Exploring Pandemic Data][19] - Stephen & Christopher Wolfram + guests [Exploring and Explaining Epidemic Modeling][20] - Robert Nachbar - [Epidemiological Models for Influenza and COVID-19][21] - Brian Wood - [COVID-19 Dashboard Visualizations][22] - John Cassel - [Behind the Genetic Sequences for Novel Coronavirus SARS-CoV-2][23] - Keiko Hirayama - [Patient Data Exploration for the Novel Coronavirus COVID-19][24] - Keiko Hirayama - [Pandemic Data Exploration for the Novel Coronavirus COVID-19][25] - Diego Zviovich - [Geo-spatial-temporal COVID-19 Simulations and Visualizations Over USA][26] - Anton Antonov - [COVID19 Epidemic Modeling: Compartmental Models][27] - Anton Antonov - [Scaling of Epidemiology Models with Multi-site Compartments][28] - Anton Antonov - [Simple Economic Extension of Compartmental Epidemiological Models][29] - Juan Klopper - [Coronavirus medical data analysis][30] - Juan Klopper - [Coronavrirus epidemiological data analysis][31] - Rory Foulger - [Coronavirus Data Exploration - Wolfram Livecoding with Students][32] ## ________________________________________ ## Other useful resources - Arnoud Buzing [GitHub][33] repository and [Notebook Gallery][34] for coronavirus - [Modeling a Pandemic like Ebola with the Wolfram Language](https://blog.wolfram.com/2014/11/04/modeling-a-pandemic-like-ebola-with-the-wolfram-language) - [Epidemics at Wolfram Demonstrations](https://demonstrations.wolfram.com/search.html?query=epidemic) - [IGSIRProcess - IGraph Epidemic models][35] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=1China_c.png&userId=1624544 [2]: https://community.wolfram.com//c/portal/getImageAttachment?filename=1.5US_c.png&userId=1624544 [3]: https://community.wolfram.com//c/portal/getImageAttachment?filename=2World_c.png&userId=1624544 [4]: https://community.wolfram.com//c/portal/getImageAttachment?filename=3Genetic_c.png&userId=1624544 [5]: https://community.wolfram.com//c/portal/getImageAttachment?filename=4Patient_c.png&userId=1624544 [6]: https://community.wolfram.com//c/portal/getImageAttachment?filename=5Resources_c.png&userId=1624544 [7]: https://www.wolframcloud.com/obj/examples/COVID19Preview.png [8]: https://www.wolframcloud.com/obj/s.wolfram/Published/COVID-19-Livestream-March-24.nb [9]: https://community.wolfram.com/groups/-/m/t/1907703 [10]: https://youtu.be/Vs5APySGYnk [11]: https://youtu.be/kC6LHAv_lx0 [12]: https://community.wolfram.com/groups/-/m/t/1908923 [13]: https://datarepository.wolframcloud.com/search/?i=COVID-19 [14]: https://datarepository.wolframcloud.com/search/?i=COVID-19 [15]: https://reference.wolfram.com/language/workflow/SubmitToTheWolframDataRepository.html [16]: https://community.wolfram.com/groups/-/m/t/2238214 [17]: http://wolfr.am/StaffPicks [18]: https://www.youtube.com/playlist?list=PLxn-kpJHbPx3_hUbroRYC_7NxcOwZ1SWa [19]: https://youtu.be/Vs5APySGYnk [20]: https://youtu.be/kC6LHAv_lx0 [21]: https://youtu.be/pcFB6_yrxGE [22]: https://youtu.be/vUq8qx7kTYA [23]: https://youtu.be/HCJgv3N_kDo [24]: https://youtu.be/MlI_8o4A3BA [25]: https://youtu.be/P86ZY-znE64 [26]: https://youtu.be/Kjk-sYlg-U0 [27]: https://youtu.be/LRs9rYCXIzs [28]: https://youtu.be/b8oCNjRI0gY [29]: https://youtu.be/C-sjXQiPE7s [30]: https://youtu.be/gA0TPQZgNY0 [31]: https://youtu.be/I-n3zN4aU6c [32]: https://youtu.be/4xCfPIiredM [33]: https://github.com/arnoudbuzing/wolfram-coronavirus [34]: https://wolfr.am/JZNRriEE [35]: http://szhorvat.net/mathematica/IGDocumentation/#epidemic-models Vitaliy Kaurov 2020-02-04T15:18:14Z https://community.wolfram.com/groups/-/m/t/3241186 A Wolfram U Daily Study Group on ["Introduction to Calculus"][1] begins on Monday, August 12, 2024. Join a cohort of fellow mathematics enthusiasts to learn about the fundamentals of calculus from the recent [Introduction to Calculus][2] ebook by John Clark and myself. Our topics will include functions and limits, differential and integral calculus, and practical applications of calculus. The study group will be led by expert Wolfram U instructor [Luke Titus][4], and I will stop by occasionally to check in with the group. It should be a lot of fun! No prior Wolfram Language experience is required. Please feel free to use this thread to collaborate and share ideas, materials and links to other resources with fellow learners. **Dates** August 12- September 6, 2024, 11am-12pm CT (4-5pm GMT) > **[REGISTER HERE][5]** ![enter image description here][6] [1]: https://www.bigmarker.com/series/introduction-to-calculus-wsg56/series_details?utm_bmcr_source=community [2]: https://www.wolfram-media.com/products/introduction-to-calculus/ [3]: https://www.wolfram.com/wolfram-u/courses/mathematics/introduction-to-calculus/ [4]: https://community.wolfram.com/web/luket [5]: https://www.bigmarker.com/series/introduction-to-calculus-wsg56/series_details?utm_bmcr_source=community [6]: https://community.wolfram.com//c/portal/getImageAttachment?filename=wolframu-banner.png&userId=26786 Devendra Kapadia 2024-08-05T20:01:27Z https://community.wolfram.com/groups/-/m/t/122095 The latest way I have found to use my expensive math software for frivolous entertainment is this. Here's is a way to describe it. [list] [*]1000 dancers assume random positions on the dance-floor. [*]Each randomly chooses one "friend" and one "enemy". [*]At each step every dancer [list] [*]moves 0.5% closer to the centre of the floor [*]then takes a large step towards their friend [*]and a small step away from their enemy. [/list] [*]At random intervals one dancer re-chooses their friend and enemy [/list] Randomness is deliberately injected. Here is the dance... [mcode]n = 1000; r := RandomInteger[{1, n}]; f := (#/(.01 + Sqrt[#.#])) & /@ (x[[#]] - x) &; s := With[{r1 = r}, p[[r1]] = r; q[[r1]] = r]; x = RandomReal[{-1, 1}, {n, 2}]; {p, q} = RandomInteger[{1, n}, {2, n}]; Graphics[{PointSize[0.007], Dynamic[If[r < 100, s]; Point[x = 0.995 x + 0.02 f[p] - 0.01 f[q]]]}, PlotRange -> 2][/mcode] [img]/c/portal/getImageAttachment?filename=OPTfnlfrnds.gif&userId=11733[/img] Thanks to Vitaliy for posting this on my behalf, complete with animations :-) Background: I had read somewhere that macro-scale behaviour of animal swarms (think of flocks of starlings or shoals of herring) is explained by each individual following very simple rules local to their vicinity, essentially 1) try to keep up and 2) try not to collide. I started trying to play with this idea in Mathematica, but it was rather slow to identify the nearest neighbours of each particle. So I wondered what would happen if each particle acted according to the locations of two other particles, regardless of their proximity. The rule was simply to move away from one and towards the other. The contraction (x = 0.995 x) was added to prevent the particle cloud from dispersing towards infinity or drifting away from the origin. I tweaked the "towards" and "away" step sizes to strike a balance between the tendency to clump together and to spread apart (if you make the step sizes equal you get something more like a swarm of flies). With each particle's attractor and repeller fixed, the system finds a sort of dynamic equilibrium, so to keep things changing I added a rule to periodically change the attractor and repeller for one of the particles. The final adjustment was to make the "force" drop towards zero for particles at very close range. This helps to stop the formation of very tight clumps, and also prevents a division-by-zero error when a particle chooses itself as its attractor or repeller. The description of the system as a dance was an attempt to explain the swirling pattern on the screen without using mathematical language. I'd love to see what other "dances" can be created with other simple rules. Simon Woods 2013-09-11T18:31:12Z https://community.wolfram.com/groups/-/m/t/1432072 *NOTE: This is a long page with many images. Scroll through to find some gems.* ---------- ![enter image description here][1] [WordFrequencyData][4] is a nifty instrument for mining oceans of texts and discovering wonderful historical semantic curiosities. **This post is a call for you to share your discoveries of interesting word histories**. Rules are very simple. - Post you discovery as a comment on this thread - Your discovery should be curious histories of some words that can be seen in their WordFrequencyData - Start your comment with a title clearly indicating the meaning of your discovery (use # as the first character to make a title) - Your comment must contain a plot WordFrequencyData of your terms. You can use the function I provide below. Alternatively you can use your own what to visualize WordFrequencyData. - Your comment must contain Wolfram Language code you use to make the plot - Your comment must contain some text explaining why you think the words you found are curious and interesting in your opinion - *If you want to comment on someone's work please click REPLY to his/her specific post so it is clear to what you refer and nested structure of comments is preserved.* Please see comment below for good examples. ---------- ### FUNCTION for PLOTs ---------- Feel free to use this function for your visualizations and change or improve it if you wish. Note what kind of options you can provide to this plot. I tried to limit those options to only very important once, fixing other options to make a nice plot. ClearAll@WordFrequencyPlot; Options[WordFrequencyPlot]= {"YearStart"->1800,"YearEnd"->Now,"Case"->True, "Smooth"->3,"Scaling"->None,"Style"->Automatic}; WordFrequencyPlot[words_,OptionsPattern[]]:= With[{ $data=WordFrequencyData[words,"TimeSeries", {OptionValue["YearStart"],OptionValue["YearEnd"]}, IgnoreCase->OptionValue["Case"]]}, DateListPlot[ MapThread[Callout, {MeanFilter[#,Quantity[OptionValue["Smooth"],"Years"]]&/@ Values[$data],words}], ScalingFunctions->OptionValue["Scaling"], PlotRange->All, PlotTheme->"Detailed", PlotStyle->OptionValue["Style"], FrameTicks->{Automatic,None}, ImageSize->Large, FrameLabel->{"YEAR","FREQUENCY in TEXT"}] ] [1]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2018-09-04at5.02.04PM.png&userId=11733 [2]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2018-08-30at8.19.02PM.png&userId=20103 [3]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2018-09-04at12.48.22PM.png&userId=11733 [4]: http://reference.wolfram.com/language/ref/WordFrequencyData.html Vitaliy Kaurov 2018-08-30T21:41:25Z https://community.wolfram.com/groups/-/m/t/1659553 Dear all, inspired by another [great post][1] of [@Anton Antonov][at0] and in particular there by a remark of [@Vitaliy Kaurov][at1] pointing to [the art of knitting images][2] I could not resist trying with Mathematica. Clearly - this problem is crying out loudly for **Radon transform**! ![enter image description here][3] I start by choosing some example image, convert it to inverse grayscale and perform the Radon transform. ClearAll["Global`*"] img0 = RemoveBackground[ ImageTrim[ ExampleData[{"TestImage", "Girl3"}], {{80, 30}, {250, 240}}], {"Background", {"Uniform", .29}}]; img1 = ImageAdjust[ColorNegate@ColorConvert[RemoveAlphaChannel[img0], "Grayscale"]]; {xDim, yDim} = {180, 400}; (* i.e. angles between 1\[Degree] and 180\[Degree] *) rd0 = Radon[img1, {xDim, yDim}]; ImageCollage[{img0, ImageAdjust@rd0}, Method -> "Rows", Background -> None, ImagePadding -> 10] ![enter image description here][4] Every column of the Radon image represents a different angle of projection. So next I separate these columns into (here 180) single Radon images and do an inverse Radon transform on each: maskLine[a_] := Table[If[a == n, 1, 0], {n, 1, xDim}]; maskImg = Table[Image[ConstantArray[maskLine[c], yDim]], {c, 1, xDim}]; rdImgs = rd0 maskImg; ProgressIndicator[Dynamic[n], {1, xDim}] invRadImgs = Table[{ImageApply[If[# > 0, #, 0] &, InverseRadon[rdImgs[[n]]]], -(n - 91) \[Degree]}, {n, 1, xDim}]; These data already represent the angle dependent intensities for backpropagation. Now one just has *somehow* to translate these intensities into discretely spaced lines (because this is the actual task in analogy to the above mentioned knitting ). Here is my simple attempt, which e.g. for 69° gives the following result (I am not really happy with this - there is definitely room for improvement!): ![enter image description here][5] valsAngle[invRads_] := Module[{img, angle, data, l2}, angle = Last@invRads; data = Max /@ (Transpose@*ImageData@*ImageRotate @@ invRads); l2 = Round[Length[data]/2]; data = MapIndexed[{First[#2] - l2, #1} &, data]; {Select[ Times @@@ ({#1, If[#2 > .0003, 1, 0]} & @@@ ((Mean /@ # &)@*Transpose /@ Partition[data, 5])), # != 0 &], angle} (* limiting value of 0.0003 is just empirical! *) ]; va = valsAngle /@ invRadImgs; graphicsData[va_] := Module[{u, angle}, {u, angle} = va; InfiniteLine[# {Cos[angle], -Sin[angle]}, {Sin[angle], Cos[angle]}] & /@ u]; gd = graphicsData /@ va; Graphics[{Thickness[.0003], gd}, ImageSize -> 600, PlotRange -> {{-170, 170}, {-220, 220}}] ... and the result is a bunch of lines: ![enter image description here][6] [at0]: https://community.wolfram.com/web/antononcube [at1]: https://community.wolfram.com/web/vitaliyk [1]: https://community.wolfram.com/groups/-/m/t/1555648?p_p_auth=T7A50bYl [2]: http://artof01.com/vrellis/works/knit.html [3]: https://community.wolfram.com//c/portal/getImageAttachment?filename=ImageOfLines.gif&userId=32203 [4]: https://community.wolfram.com//c/portal/getImageAttachment?filename=img0rd0.jpg&userId=32203 [5]: https://community.wolfram.com//c/portal/getImageAttachment?filename=linesample.png&userId=32203 [6]: https://community.wolfram.com//c/portal/getImageAttachment?filename=ImageOfLines.png&userId=32203 Henrik Schachner 2019-04-13T20:01:08Z https://community.wolfram.com/groups/-/m/t/1868945 *MODERATOR NOTE: coronavirus resources & updates:* https://wolfr.am/coronavirus ---------- ![enter image description here][1] &[Wolfram Notebook][2] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=4686Wuhan_Coronavirus_Outbreak_Jan29.gif&userId=95400 [2]: https://www.wolframcloud.com/obj/c2eafc69-4016-4e05-a8ad-b1d22a37379f Jofre Espigule-Pons 2020-01-29T02:18:52Z https://community.wolfram.com/groups/-/m/t/842698 NOTE: the actual APP that does some analysis of Shakespeare's "Romeo and Juliet" is located [**HERE**][1]. Please wait through a potential little load or evaluation times, it is computing ! ;-) Read below and through comments for many ideas on Shakespeare's data mining. [![enter image description here][2]][3] I also highly recommend reading recent related [blog by Jofre Espigule][4] and checking out his [Wolfram Cloud app][5] that does some social and linguistic visualizations of the Shakespeare's texts: [![enter image description here][6]][5] April 23, 2016 marks 400th anniversary of Shakespeare’s death. Just a few decades of life's work produced texts that fascinate humanity for already 400 years. This centuries-old fascination tells us Shakespeare's works highlight the perpetual social and cultural phenomena. And also that Shakespeare is a seldom genius, a true master of the written and spoken word. But have you ever thought that Shakespeare's texts can be deemed as data? Perhaps Emerging filed of digital humanities can tell us what to read between the lines. Modern technologies can provide a new insight into social networks of characters, semantic, statistical and other properties of corpus that is usually considered of only high artistic value. Is there a pattern in the art? **Could you think of data mining analysis or visualizations to apply to Shakespeare's works? Please share your thoughts! Dive with Wolfram technologies into infinite depths of Shakespeare's data.** EXAMPLE: Storyline ------- Imagine I would like to see in a few quick pictures how the dramatic development of events propagates through a play. I consider "Romeo and Juliet" and download full text as a string (lower-casing all words): romeojuliet = ToLowerCase[Import["http://shakespeare.mit.edu/romeo_juliet/full.html"]]; Now I will write a function `drama` that displays the **density** of a specific word in a play. It is done by indexing positions of words in the text and then running [SmoothKernelDistribution][7] algorithm hidden inside SmoothHistogram function that also plots the density: drama[keywords_List] := With[ {pos = StringPosition[romeojuliet, #][[All, 1]] & /@ keywords}, SmoothHistogram[pos, Frame -> None, BaseStyle -> White, PlotLegends -> Placed[keywords, {{.93, .8}}], AspectRatio -> 1/3, ImageSize -> 700, PlotTheme -> "Marketing", PlotStyle -> {Automatic, Automatic, Dashed, Dashed, Dashed}, Filling -> {1 -> {2}}, FillingStyle -> Directive[White, Opacity[.8]]]] And now with a few computations `drama` reads the play and announces the verdict with just 3 images. Visually we see clearly what was important as the time went by. The 3rd image of interplay between "love", "hate", "life", and "death" speaks the most. drama[{"romeo", "juliet", "life", "death"}] drama[{"romeo", "juliet", "love", "hate"}] drama[{"love", "hate", "life", "death"}] ![enter image description here][8] To make a cloud app, we need to modify function a bit and use [CloudDeploy][9]. dramaFORM[keywords_String] := Rasterize@Module[ {pos, leg, keys = TextWords[ToLowerCase[keywords]]}, leg = {"romeo", "juliet"}~Join~keys; pos = StringPosition[romeojuliet, #][[All, 1]] & /@ leg; SmoothHistogram[DeleteCases[pos, {} | {_Integer}], Frame -> None, PlotLegends -> Placed[leg, Bottom], AspectRatio -> 1/3, ImageSize -> 700, PlotTheme -> "Marketing", PlotStyle -> {Automatic, Automatic}~Join~Table[Dashed, {Length[leg] - 2}], Filling -> {1 -> {2}}, FillingStyle -> Directive[White, Opacity[.8]]]] CloudDeploy[FormFunction[{ "x" -> <|"Label" -> "", "Interpreter"->"String", "Hint"->"hint: love, death", "Help"->Style["type Shakespeare's words separated by spaces or comma, be patient, wait, behold ;-)",Italic]|>}, dramaFORM[#x]&, AppearanceRules-><| "Title" -> Grid[{{"Evolution of topics through Romeo & Juliet"},{Spacer[{10,5}]},{img}},Alignment->Center], "Description" -> "DETAILS: http://wolfr.am/RomeoJuliet "|>, FormTheme -> "Black"], "RomeoAndJuliet", Permissions->"Public"] EXAMPLE: Wordcloud ------------------ It is also interesting to know how modern society sees Shakespeare. The code below for the word cloud runs over Encyclopedia Britannica article about Shakespeare. text=Import["http://www.britannica.com/print/article/537853"]; base[w_]:=With[{tmp=WordData[w,"BaseForm","List"]}, If[(Head[tmp]===Missing)||tmp==={},w,tmp[[1]]]]; SetAttributes[base,Listable]; tst=Quiet[base[TextWords[StringDelete[DeleteStopwords[ToLowerCase[text]],DigitCharacter..]]]]; blackLIST={"shakespeare","william","th","iii","iv","vi"}; WordCloud[DeleteCases[DeleteCases[tst,_First],Alternatives@@blackLIST], WordOrientation->{{-\[Pi]/4,\[Pi]/4}},AspectRatio->1/3, ScalingFunctions->(#^.01&),ImageSize->800] ![enter image description here][10] DATA & CODE SOURCES: -------------------- - [William Shakespeare Plays][11] - [Wolfram Demonstrations][12] - [The Complete Works of William Shakespeare, MIT][13] - [Open Source Shakespeare][14] [at0]: http://community.wolfram.com/web/jofreep [1]: https://www.wolframcloud.com/objects/vitaliyk/RomeoAndJuliet [2]: http://community.wolfram.com//c/portal/getImageAttachment?filename=2016-04-22_05-56-43.png&userId=11733 [3]: https://www.wolframcloud.com/objects/vitaliyk/RomeoAndJuliet [4]: http://blog.wolfram.com/2016/04/21/analyzing-shakespeares-texts-on-the-400th-anniversary-of-his-death/ [5]: https://www.wolframcloud.com/objects/jofree/Othello [6]: http://community.wolfram.com//c/portal/getImageAttachment?filename=2016-04-24_16-50-23.png&userId=11733 [7]: http://reference.wolfram.com/language/ref/SmoothKernelDistribution.html [8]: http://community.wolfram.com//c/portal/getImageAttachment?filename=sdfr45wtrhgfdasaf.png&userId=11733 [9]: http://reference.wolfram.com/language/ref/CloudDeploy.html [10]: http://community.wolfram.com//c/portal/getImageAttachment?filename=sadfsdgshdt5342.png&userId=11733 [11]: https://datahub.io/dataset/william-shakespeare-plays [12]: http://demonstrations.wolfram.com/search.html?query=shakespeare [13]: http://shakespeare.mit.edu/ [14]: http://www.opensourceshakespeare.org/ Vitaliy Kaurov 2016-04-20T17:19:46Z https://community.wolfram.com/groups/-/m/t/1257547 # Intro One of the most popular Reddit's channels **Data Is Beautiful** (with multi-million membership of subscribers) has just started **Battle Competitions** for data visualizations that will run monthly. This is a call to Wolfram Community members to collaborate on **JAN 2018 Battle**. ***Direct reference to the JAN 2018 Battle***: https://redd.it/7nm6ed ## Solutions - **Heatmap of inter- and intra- species comparison** *by Vitaliy Kaurov*: - http://community.wolfram.com/groups/-/m/t/1257577 - **Bubble chart for 4D data** *by Sander Huisman*: - http://community.wolfram.com/groups/-/m/t/1257885 - **Population - pyramid like visualization**: *by George Varnavides* - http://community.wolfram.com/groups/-/m/t/1258056 - **Growth Rate in "Intensity Space"** *by Henrik Schachner* - http://community.wolfram.com/groups/-/m/t/1258281 - **Intraspecies comparison using RadarChart** *by Diego Zviovich* - http://community.wolfram.com/groups/-/m/t/1260507 - **Interspecies comparison using HeatmapPlot** by *Anton Antonov* - http://community.wolfram.com/groups/-/m/t/1261444 - **Scatter plot slices of temperature dynamics** *by Vitaliy Kaurov* - http://community.wolfram.com/groups/-/m/t/1261948 - **RadarChart for each pecies** by *Anton Antonov* - http://community.wolfram.com/groups/-/m/t/1261438 ## Rules of this thread Reddi requires direct links to the images. Hence a separate post is necessary. Here are the steps: - Make a separate post solving the challenge with detailed title describing your specific method of visualization and starting with tag [Reddit-DiBB0118] (Data is Beautiful Battle 01/2018) - Make a comment in this thread simply stating the title and copying your post URL. See my example in the comments. I will collect the solutions in the "solutions" section above. This method enables you to post your own posts on Reddit if you want to keeping your authorship. ## Goal I simply suggest that Wolfram Community members brainstorm in the comments below about how the best to visualize the dataset. Feel free to submit **your own** solutions to Reddit if you want to as they require the original authors. The main goal though is simply to have fun here on Community. **Don't forget to vote up the posts you like**. ## Important Battles have simple rules explained clearly in the Reddit battle thread linked above. To not get disqualified it is advised to read rules carefully. You can ask Reddit admins any additional questions directly in the thread comments. I recommend reading other people comments as they clarify the nature of the dataset. ## Getting the data w/ Wolfram Language (WL) The dataset is located at a web page: http://aquatext.com/tables/algaegrwth.htm The nature of the data is clear from the website description. It is easy to get the raw data with the following WL command: raw = Import["http://aquatext.com/tables/algaegrwth.htm", "Data"] /."0..06" -> .06; You need `/."0..06" -> .06` because the data has a clerical error resulting in the import of a string instead of a number. One way of obtaining a simple rectangular array / table of data is: data=Cases[raw,{_String,__?NumberQ},Infinity]/. x_List/;First[x]=="Temperature":>{"Temperature",5,5,10,10,25,25,30,30}; which can be viewed as TableForm[data] ![enter image description here][3] [1]: https://www.reddit.com/r/dataisbeautiful/ [2]: http://community.wolfram.com/groups/-/m/t/1257577 [3]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2018-01-02at6.20.30PM.png&userId=11733 Vitaliy Kaurov 2018-01-03T00:33:35Z https://community.wolfram.com/groups/-/m/t/526743 [Wolfram Science Summer School][1] and [Wolfram Innovation Summer School][2] are happening now in Boston at Bentley University. The goal of this Code Jam is to post interesting code snippets that fit the Wolfram Language functionality described in detail below. By "interesting" I mean simple programs that generate complex patterns. We will be jamming with our students, but everyone is welcome to join. Let's have some fun! In this Code Jam we will take a look at the CellularAutomaton (CA) function and its syntax **for rules defined as functions**. This may sound a bit confusing, so let's take a look at some examples. First, if you haven't yet, you have to make yourself familiar with the [CellularAutomaton][3] function. The most well known cases are integer Wolfram indexes for [CA rules][4], for instance for the celebrated rule 30: ArrayPlot[CellularAutomaton[30, RandomInteger[1, 100], 50]] ![enter image description here][5] But CellularAutomaton can take a function as a rule. Below are some examples. Use an arbitrary symbolic function f as the rule to apply to range-1 neighbors: CellularAutomaton[{f[#] &, {}, 1}, {a, b, c}, 1] `{{a, b, c}, {f[{c, a, b}], f[{a, b, c}], f[{b, c, a}]}}` Set up a "Pascal's triangle cellular automaton": CellularAutomaton[{f[#] &, {}, 1/2}, {a, b, c}, 1] `{{a, b, c}, {f[{c, a}], f[{a, b}], f[{b, c}]}}` CellularAutomaton[{Total[#] &, {}, 1/2}, {{1}, 0}, 3] `{{1, 0, 0, 0}, {1, 1, 0, 0}, {1, 2, 1, 0}, {1, 3, 3, 1}}` Additive cellular automaton modulo 4: ArrayPlot[ CellularAutomaton[{Mod[Total[#], 4] &, {}, 1}, {{1}, 0}, 50], ColorFunction -> "Rainbow"] ![enter image description here][7] The second argument to the function is the step number: CellularAutomaton[{f[#1, #2] &, {}, 1}, {a, b, c}, 1] `{{a, b, c}, {f[{c, a, b}, 1], f[{a, b, c}, 1], f[{b, c, a}, 1]}}` CellularAutomaton[{f, {}, 1}, {a, b, c}, 1] `{{a, b, c}, {f[{c, a, b}, 1], f[{a, b, c}, 1], f[{b, c, a}, 1]}}` Change the rule at successive steps; #2 gives the step number: ArrayPlot[ CellularAutomaton[{Mod[Total[#] + #2, 4] &, {}, 1}, {{1}, 0}, 30], ColorFunction -> "Rainbow"] ![enter image description here][8] Use continuous values for cells: ArrayPlot[CellularAutomaton[{Mod[Total[#]/2, 1] &, {}, 1}, {{1}, 0}, 50]] ![enter image description here][9] Manipulate[ ArrayPlot[ CellularAutomaton[{Mod[s Total[#], 1] &, {}, 2}, {{1}, 0}, 50], ColorFunction -> "Rainbow", PixelConstrained -> 3] , {s, .01, .99}] ![enter image description here][10] Try different functions and different initial conditions. They can be strings, numbers, graphics or expressions. Try ArrayPlot but also try other visualization tools like Grid. Try them out. Post code, images, and text comments. You can also comment on other people's code. [1]: https://www.wolframscience.com/summerschool/ [2]: http://education.wolfram.com/summer/innovation/ [3]: http://reference.wolfram.com/language/ref/CellularAutomaton.html [4]: http://www.wolframscience.com/nksonline/page-53 [5]: /c/portal/getImageAttachment?filename=SSCJSS2015_1.png&userId=11733 [6]: /c/portal/getImageAttachment?filename=SSCJSS2015_2.png&userId=11733 [7]: /c/portal/getImageAttachment?filename=SSCJSS2015_3.png&userId=11733 [8]: /c/portal/getImageAttachment?filename=SSCJSS2015_4.png&userId=11733 [9]: /c/portal/getImageAttachment?filename=SSCJSS2015_5.png&userId=11733 [10]: /c/portal/getImageAttachment?filename=4926wsdaf345678iruyjtdhfgsdasrt4.gif&userId=11733 Vitaliy Kaurov 2015-07-09T08:46:23Z https://community.wolfram.com/groups/-/m/t/2451238 ![enter image description here][1] ![enter image description here][2] An [n-gonal antiprism or n-antiprism][3] is a polyhedron composed of two parallel copies of an n-sided polygon, connected by a band of 2n triangles. Grid@Transpose@Table[PolyhedronData[{"Antiprism",k},#]&/@{"Image","Net"},{k,3,7}] ![enter image description here][4] To form an almost perfect ring of 4-antiprisms firstly we need a set of vertices and coordinates: offset =#+{0,0,Sqrt[1-1/4 Sec[π/8]^2]+4.05} &/@(2PolyhedronData[{"Antiprism",4}, "VertexCoordinates"]); face={{5,1,2,6},{8,4,7,3},{6,4,8},{2,7,4},{1,3,7},{5,8,3},{6,2,4},{2,1,7},{1,5,3},{5,6,8}}; Then we can make 13 copies: Graphics3D[ Table[GraphicsComplex[RotationMatrix[k 2 Pi/13,{0,1,0}].#&/@offset,Polygon/@face],{k,0,12}], Boxed-> False, SphericalRegion->True] ![13 antiprisms][5] This is **not** exact, but it's very close. ![13 antiprisms side][6] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=antiptism4.gif&userId=11733 [2]: https://community.wolfram.com//c/portal/getImageAttachment?filename=asdf43qasdf.jpg&userId=11733 [3]: https://mathworld.wolfram.com/Antiprism.html [4]: https://community.wolfram.com//c/portal/getImageAttachment?filename=sdfq3dfag43.jpg&userId=11733 [5]: https://community.wolfram.com//c/portal/getImageAttachment?filename=13anti.jpg&userId=21530 [6]: https://community.wolfram.com//c/portal/getImageAttachment?filename=13antiside.jpg&userId=21530 Ed Pegg 2022-01-20T20:12:49Z https://community.wolfram.com/groups/-/m/t/1085633 The neural network and machine learning framework has become one of the key features of the latest releases of the Wolfram Language. Training neural networks can be very time consuming on a standard CPU. Luckily the Wolfram Language offers an incredible easy way to use a GPU to train networks - and do lots of other cool stuff. The problem with this was/is that most current Macs do not have an NVIDIA graphics card, which is necessary to access this framework within the Wolfram Language. Therefore, Wolfram Inc. had decided to drop support for GPUs on Macs. There is however a way to use GPUs on Macs. For example you can use an [external GPU like the one offered by Bizon][1]. ![enter image description here][2] Apart from the BizonBox there a couple of cables and a power supply. You can buy/configure different versions of the BizonBox: there is a range of different graphics cards available and you can buy a the BizonBox 2s which basically connects via Thunderbolt and the BizonBox 3 which connects to USB-C. Luckily, Wolfram have decided to reintroduce support for GPUs in Mathematica 11.1.1 - see [the discussion here][3]. I have a variety of these BizonBoxes (both 2s and 3) and a range of Macs. I thought it would be a good idea to post a how-to. The essence of what I will be describing in this post should work for most Macs. I ran Sierra on all of them. Here is the recipe to get the thing to work: Installation of the BizonBox, the required drivers, and compilers ----------------------------------------------------------------- 0. I will assume that you have Sierra installed and that Xcode is running. One of the really important steps if you want to use compilers is to ***downgrade*** the command line tools to version 7.3 You will have to log into your Apple Developer account and download the Command Line Tools version 7.3. Install the tools and run the terminal command (not in Mathematica!): sudo xcode-select --switch /Library/Developer/CommandLineTools 1. Reboot your Mac into safe mode, i.e. hold CMD+R while rebooting. 2. Open a terminal (under item Utilities at the top of the screen). 3. Enter csrutil disable 4. Shut the computer down. 5. Connect your BizonBox to the mains and to either the thunderbolt or USB-C port of your Mac. 6. Restart your Mac. 7. Click on the Apple symbol in the top left. Then "About this Mac" and "System Report". In the Thunderbolt section you should see something like this: ![enter image description here][4] 8. In the documentation of the BizonBox you will find a link to a program called bizonboxmac.zip. Download that file and unzip it. 9. Open the folder and click on "bizonbox.prefPane" to install. (If prompted to, do update!) 10. You should see this window: ![enter image description here][6] 11. Click on Activate. Type in password if required to do so. It should give something like this: ![enter image description here][7] Then restart. 12. Install the CUDA Toolkit: [https://developer.nvidia.com/cuda-downloads][8]. You'll have to click through some questions for the download. ![enter image description here][9] what you download should be something like cuda_8.0.61_mac.dmg and it should be more or less 1.44 GB worth. 13. Install the toolkit with all its elements. ![enter image description here][10] 14. Restart your computer. First tests ----------- Now you should be good to go. Open Mathematica 11.1.1. Execute Needs["CUDALink`"] Needs["CCompilerDriver`"] CUDAResourcesInstall[] Then try: CUDAResourcesInformation[] which should look somewhat like this: ![enter image description here][11] Then you should check SystemInformation[] Head to Links and then CUDA.This should look similar to this: ![enter image description here][12] So far so good. Next is the really crucial thing: CUDAQ[] should give TRUE. If that's what you see you are good to go. Be more daring and try CUDAImageConvolve[ExampleData[{"TestImage","Lena"}], N[BoxMatrix[1]/9]] // AbsoluteTiming ![enter image description here][13] You might notice that the non-GPU version of this command runs faster: ImageConvolve[ExampleData[{"TestImage","Lena"}], N[BoxMatrix[1]/9]] // AbsoluteTiming runs in something like 0.0824 seconds, but that's ok. Benchmarking (training neural networks) --------------------------------------- Let's do some Benchmarking. Download some example data: obj = ResourceObject["CIFAR-10"]; trainingData = ResourceData[obj, "TrainingData"]; RandomSample[trainingData, 5] You can check whether it worked: RandomSample[trainingData, 5] should give something like this: ![enter image description here][14] These are the classes of the 50000 images: classes = Union@Values[trainingData] ![enter image description here][15] Let's build a network module = NetChain[{ConvolutionLayer[100, {3, 3}], BatchNormalizationLayer[], ElementwiseLayer[Ramp], PoolingLayer[{3, 3}, "PaddingSize" -> 1]}] net = NetChain[{module, module, module, module, FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, "Input" -> NetEncoder[{"Image", {32, 32}}], "Output" -> NetDecoder[{"Class", classes}]] When you train the network: {time, trained} = AbsoluteTiming@NetTrain[net, trainingData, Automatic, "TargetDevice" -> "GPU"]; you should see something like this: ![enter image description here][16] So the thing started 45 secs ago and it supposed to finish in 2m54s. In fact, it finished after 3m30s. If we run the same on the CPU we get: ![enter image description here][17] The estimate kept changing a bit, but it settled down at about 18h20m.That is slower by a factor of about 315, which is quite substantial. Use of compiler --------------- Up to now we have not needed the actual compiler. Let's try this, too. Let's grow a Mandelbulb: width = 4*640; height = 4*480; iconfig = {width, height, 1, 0, 1, 6}; config = {0.001, 0.0, 0.0, 0.0, 8.0, 15.0, 10.0, 5.0}; camera = {{2.0, 2.0, 2.0}, {0.0, 0.0, 0.0}}; AppendTo[camera, Normalize[camera[[2]] - camera[[1]]]]; AppendTo[camera, 0.75*Normalize[Cross[camera[[3]], {0.0, 1.0, 0.0}]]]; AppendTo[camera, 0.75*Normalize[Cross[camera[[4]], camera[[3]]]]]; config = Join[{config, Flatten[camera]}]; pixelsMem = CUDAMemoryAllocate["Float", {height, width, 3}] srcf = FileNameJoin[{$CUDALinkPath, "SupportFiles", "mandelbulb.cu"}] Now this should work: mandelbulb = CUDAFunctionLoad[File[srcf], "MandelbulbGPU", {{"Float", _, "Output"}, {"Float", _, "Input"}, {"Integer32", _, "Input"}, "Integer32", "Float", "Float"}, {16}, "UnmangleCode" -> False, "CompileOptions" -> "--Wno-deprecated-gpu-targets ", "ShellOutputFunction" -> Print] Under certain circumstances you might want to specify the location of the compiler like so: mandelbulb = CUDAFunctionLoad[File[srcf], "MandelbulbGPU", {{"Float", _, "Output"}, {"Float", _, "Input"}, {"Integer32", _, "Input"}, "Integer32", "Float", "Float"}, {16}, "UnmangleCode" -> False, "CompileOptions" -> "--Wno-deprecated-gpu-targets ", "ShellOutputFunction" -> Print, "CompilerInstallation" -> "/Developer/NVIDIA/CUDA-8.0/bin/"] This should give: ![enter image description here][18] Now mandelbulb[pixelsMem, Flatten[config], iconfig, 0, 0.0, 0.0, {width*height*3}]; pixels = CUDAMemoryGet[pixelsMem]; Image[pixels] gives ![enter image description here][19] So it appears that all is working fine. Problems -------- I did come up with some problems though. There is quite a number of CUDA functions: Names["CUDALink`*"] ![enter image description here][20] Many work just fine. res = RandomReal[1, 5000]; ListLinePlot[res] ![enter image description here][21] ListLinePlot[First@CUDAImageConvolve[{res}, {GaussianMatrix[{{10}, 10}]}]] ![enter image description here][22] The thing is that some don't and I am not sure why (I have a hypothesis though). Here are some functions that do **not** appear to work: CUDAColorNegate CUDAClamp CUDAFold CUDAVolumetricRender CUDAFluidDynamics and some more. I would be very grateful if someone could check these on OSX (and perhaps Windows?). I am not sure if the this is due to some particularity of my systems or something that could be flagged up to Wolfram Inc for checking. When I wanted to try that systematically I wanted to use the function WolframLanguageData to look for the first example in the documentation of the CUDA functions, but it appears that no CUDA function is in the WolframLanguageData. I think tit would be great to have them there, too, and am not sure why they wouldn't be there. In spite of these problems I hope that this post will help some Mac users to get CUDA going. It is a great framework and simple to use in the Wolfram Language. With the BizonBox and Mathematica 11.1.1 Mac users are no longer excluded from accessing this feature. Cheers, Marco PS: Note, that there is anecdotal evidence that one can even use the BizonBox under Windows running in a virtual box under OSX. I don't have Windows, but I'd like to hear if anyone get this running. [1]: https://bizon-tech.com [2]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at22.09.10.png&userId=48754 [3]: http://community.wolfram.com/groups/-/m/t/902394 [4]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot1.png&userId=48754 [5]: http://bizon-tech.com/bizonboxmac.zip [6]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2.png&userId=48754 [7]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot3.png&userId=48754 [8]: https://developer.nvidia.com/cuda-downloads [9]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot6.png&userId=48754 [10]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot7.png&userId=48754 [11]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at22.38.22.png&userId=48754 [12]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-06at18.46.46.png&userId=48754 [13]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at22.49.15.png&userId=48754 [14]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at22.52.43.png&userId=48754 [15]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at22.53.30.png&userId=48754 [16]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at20.37.20.png&userId=48754 [17]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at20.39.02.png&userId=48754 [18]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at23.04.38.png&userId=48754 [19]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at21.50.42.png&userId=48754 [20]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at23.10.05.png&userId=48754 [21]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at23.15.59.png&userId=48754 [22]: http://community.wolfram.com//c/portal/getImageAttachment?filename=ScreenShot2017-05-07at23.16.36.png&userId=48754 Marco Thiel 2017-05-07T22:21:42Z https://community.wolfram.com/groups/-/m/t/560469 *WOLFRAM MATERIALS for the ARTICLE:* > Szabolcs Horvát, Jakub Podkalicki, Gábor Csárdi, Tamás Nepusz, Vincent Traag, Fabio Zanini, Daniel Noom, (2022). > *IGraph/M: graph theory and network analysis for Mathematica*. > arXiv:2209.09145 **[physics.soc-ph]**. > https://doi.org/10.48550/arXiv.2209.09145 [](https://igraph.discourse.group) [](https://github.com/szhorvat/IGraphM/releases) [](https://github.com/szhorvat/IGraphM#contributions) [](https://doi.org/10.5281/zenodo.1134932) ---- ##Article abstract IGraph/M is an efficient general purpose graph theory and network analysis package for Mathematica. IGraph/M serves as the Wolfram Language interfaces to the igraph C library, and also provides several unique pieces of functionality not yet present in igraph, but made possible by combining its capabilities with Mathematica's. The package is designed to support both graph theoretical research as well as the analysis of large-scale empirical networks. ---- ##Introduction The post below was written for the original release of IGraph/M. The package has come a long way since then and now contains ~300 functions. See http://szhorvat.net/mathematica/IGraphM for more details on the current release. Compatibility: 64-it Windows/macOS/Linux or Raspberry Pi; Mathematica <del>10.0</del> 11.0 or later. <a href="http://szhorvat.net/mathematica/IGraphM"><img src="https://community.wolfram.com//c/portal/getImageAttachment?filename=IGraphM-ad-3.png&userId=38370" width="300"></a> ---- I would like to announce IGraph/M, a new igraph interface for Mathematica: http://szhorvat.net/mathematica/IGraphM [igraph](http://igraph.org/) is a graph manipulation and analysis package. IGraph/M makes its functionality available from Mathematica. This initial release, version 0.1, covers only some igraph functions, as I focused on the things that I need personally. However the main framework is complete, and new functions can be added quickly. If anyone would like to contribute, please contact me. Binary packages for OS X (10.9 or later) and Linux can be downloaded [from GitHub](https://github.com/szhorvat/IGraphM/releases). Unfortunately, I was unable to compile the development version of igraph for Windows, so I cannot provide a Windows version. If you can help with compiling igraph itself (not IGraph/M) on Windows, please let me know! Functionality in this release that is not built into Mathematica: * Vertex betweenness centrality for weighted graphs * Estimates of vertex betweenness, edge betweenness and closeness centrality; for large graphs * Minimum feedback arc set for weighted and unweighted graphs * Find all cliques (not just maximal ones) * Count 3- and 4-motifs * Rewire edges, keeping either the density or the degree sequence * Alternative algorithms for isomorphism testing: Bliss, VF2 * Subgraph isomorphism * Test if a degree sequence is graphical * Alternative algorithms for generating random graphs with given degree sequence * Layout algorithms that take weights into account Note that IGraph/M is *not a replacement* for Mathematica's graphs and networks functionality. It is meant to complement what is already available in Mathematica, thus it primarily focuses on adding functionality that is not already present. Why did I release the package before covering most of the igraph functionality? I do not have time to work on things I do not personally need or use, so I am unlikely to extend it further unless the need comes up. I do think that the functions that are included in v0.1 can already be useful to others too. I would also like to give the opportunity for people to contribute to the project if they wish to. The groundwork has been laid, so further extensions should be quick and relatively easy. Also check out a related project, [IGraphR](https://github.com/szhorvat/IGraphR), which makes igraph available for Mathematica users through RLink. I wrote IGraph/M because I needed higher performance and greater reliability (especially for parallel computing) than what RLink could provide. ---- **A request:** If any of you have used IGraphR in the past to access igraph from Mathematica, please post a response to this thread and let me know which specific functions you were using. &[Wolfram Notebook][1] [1]: https://www.wolframcloud.com/obj/94639221-60b4-47e3-8862-d996caa40388 Szabolcs Horvát 2015-09-06T12:55:14Z https://community.wolfram.com/groups/-/m/t/813449 ![enter image description here][1] The other day I was thinking, could I make a certain plot in Lego? Well, Let's start at the start and make a simple n*m lego-brick. The basic measurements are: brickstyle=Sequence[Red,EdgeForm[AbsoluteThickness[1]]]; gropts=Sequence[Boxed->False,ViewVector->(10{2.4, -1.3, 2.}),ViewAngle->8*Degree]; dims={dimx,dimy,dimz}={8.0,8.0,9.6}/8; (* size of a unit cell in lego-world *) knobd=4.8/8; (* knob diameter *) knobh=1.8/8; (* knob height *) botdi=4.8/8; (* bottom pillar inner diameter*) botdo=6.51/8; (* bottom pillar outer diameter*) wall=1.2/8; (* wall thickness*) thickness=1.0/8; (* top thickness *) And here a function that will make simple brick: ClearAll[DrawLego] DrawLego[{nx_Integer,ny_Integer,nz_Integer:1},detailed:(True|False|None):True]:=Module[{ptsout,ptsin,sides,bottom,rimi,rimo,knobs,knobs2}, ptsout=Tuples[{{0,0,0},{nx,ny,nz}dims}\[Transpose]]; ptsin=Tuples[{{wall,wall,0},{nx,ny,nz}dims-{wall,wall,thickness}}\[Transpose]]; sides=If[BooleanQ[detailed],If[TrueQ[detailed],{ptsin,ptsout},{ptsout}],{ptsout}]; sides=GraphicsComplex[#,{Polygon[{1,2,6,5}],Polygon[{3,4,8,7}],Polygon[{1,2,4,3}],Polygon[{5,6,8,7}],Polygon[{2,4,8,6}]}]&/@sides; If[TrueQ[detailed], ptsout=Tuples[{{0,0,0},{nx,ny,0}dims}\[Transpose]]; ptsin=Tuples[{{wall,wall,0},{nx,ny,nz}dims-{wall,wall,thickness}}\[Transpose]]; rimo=ptsout[[1;;;;2]][[{1,3,4,2}]]; rimi=ptsin[[1;;;;2]][[{1,3,4,2}]]; rimo=Partition[rimo,2,1,1]; rimi=Partition[rimi,2,1,1]; bottom=MapThread[Polygon[#1~Join~Reverse[#2]]&,{rimo,rimi}]; ]; If[BooleanQ[detailed], knobs=Tuples[Range[1,#]&/@({nx,ny})]-1/2; knobs=Cylinder[{Append[#{dimx,dimy},nz dimz],Append[#{dimx,dimy},nz dimz+knobh]}&/@knobs,knobd/2]; ]; If[TrueQ[detailed], knobs2=Tuples[Range[1,#]&/@({nx,ny}-1)]; knobs2=Tube[{Append[#{dimx,dimy},0],Append[#{dimx,dimy},nz dimz-thickness]}&/@knobs2,botdo/2]; ]; If[BooleanQ[detailed], If[TrueQ[detailed], {sides,bottom,knobs,{CapForm[None],knobs2}} , {sides,knobs} ] , {sides} ] ] DrawLego[{nx_Integer,ny_Integer,nz_Integer:1},p:{px_,py_,pz_},detailed_:True]:=Translate[DrawLego[{nx,ny,nz},detailed],p{1,1,dimz}-{0.5,0.5,0}] So we can draw any brick now, at any place, and we have the option to have it detailed or not... Graphics3D[{brickstyle, DrawLego[{4, 2}]}, Lighting -> "Neutral", Boxed -> False, Axes -> True] Graphics3D[{brickstyle, DrawLego[{4, 2, 1}]}, Lighting -> "Neutral", Boxed -> False, Axes -> True] Graphics3D[{brickstyle, DrawLego[{4, 2, 1}, {1, 1, 1}]}, Lighting -> "Neutral", Boxed -> False, Axes -> True] Graphics3D[{brickstyle, DrawLego[{4, 2, 1}, {1, 1, 1}, False]}, Lighting -> "Neutral", Boxed -> False, Axes -> True] Graphics3D[{brickstyle, DrawLego[{4, 2, 1}, {1, 1, 1}, None]}, Lighting -> "Neutral", Boxed -> False, Axes -> True] giving: ![enter image description here][2] So now that we can 'plot' any brick we can make a function that will cover a layer in brick-world with bricks of decreasingly smaller sizes iteratively, and will alternately go in the horizontal x and y directions: ClearAll[TileWithLego,CreateLegos,TransformLego] TileWithLego[slice_List/;MatrixQ[slice],sizes_List,greedy:(True|False),bricks_List:{}]:=Module[{size,bounds,sizex,sizey,shift,dims,dimx,dimy,greedy\[Lambda],stepi,stepj,newarr,part,newbricks}, size={sizex,sizey}=First[sizes]; dims={dimy,dimx}=Dimensions[newarr=slice]; shift=Floor[First[size]/2]; {stepi,stepj}=If[greedy,{1,1},{sizex,sizey}]; greedy\[Lambda]=Boole[!greedy]; newbricks=Reap[Do[ bounds={{j,j+sizey-1},{i,i+sizex-1}}; part=Take[newarr,##]&@@bounds; If[Total[part,2]===sizex sizey, newarr[[Span@@bounds[[1]],Span@@bounds[[2]]]]=0; Sow[bounds]; ] , {j,1,dimy-sizey+1,stepj} , {i,1+greedy\[Lambda] Mod[(j-1)/sizey,2]shift,dimx-sizex+1,stepi} ]][[2]]; If[newbricks==={},newbricks={{}}]; newbricks=bricks~Join~newbricks[[1]]; If[Length[sizes]>1, TileWithLego[newarr,Rest[sizes],greedy,newbricks] , Reverse/@newbricks ] ] CreateLegos[slice_List/;MatrixQ[slice],sizes_List,rotate:(True|False),greedy:(True|False)]:=If[rotate,Reverse/@TileWithLego[slice\[Transpose],sizes,greedy],TileWithLego[slice,sizes,greedy]] TransformLego[slices_List,bricks_List,greedy:(True|False|Automatic)]:=Module[{len,greedies,heights,rotates,brickies,brickspec}, len=Length[slices]; heights=Range[len]; rotates=(#=!=0)&/@Mod[heights,2]; greedies=Switch[greedy,True,ConstantArray[True,len],False,ConstantArray[False,len],_,Switch[len,1,{False},2,{False,False},_,{False,False}~Join~ConstantArray[True,len-2]]]; brickies=MapThread[CreateLegos[#1,bricks,#2,#3]&,{slices,rotates,greedies}]; brickspec=MapThread[{#1[[All,All,2]]-#1[[All,All,1]]+1,{#1[[All,All,1]],ConstantArray[#2,Length[#1]]}\[Transpose]}\[Transpose]&,{brickies,heights}]; brickspec=Catenate[brickspec]; brickspec[[All,2]]=Flatten/@brickspec[[All,2]]; brickies=DrawLego[#1,#2,False (* detailed *)]&@@@brickspec; {Graphics3D[{brickstyle,brickies},Boxed->False,ImageSize->700],brickspec} ] Let's turn a simple plot in to its Lego-presentation: heightmap=Table[8+Round[3.5Sin[0.1(0.1x^2+y)]/1.2],{x,-15,24,2},{y,-30,28,2}]; ListPlot3D[%,Mesh->None,InterpolationOrder->0] minmax=MinMax[heightmap]+0.5{-1,1}; slices=UnitStep[heightmap-#+1]&/@Range@@minmax; {gr,bricks}=TransformLego[slices,{{4,2},{3,2},{2,2},{4,1},{3,1},{2,1},{1,1}},Automatic]; gr giving: ![enter image description here][3] ![enter image description here][4] We can try different shapes, namely a sphere: slices=DiskMatrix[{9/dimz,9,9},20]; {gr,bricks}=TransformLego[slices,{{4,2},{3,2},{2,2},{4,1},{3,1},{2,1},{1,1}},Automatic]; gr ![enter image description here][5] Or a pyramid: slices=DiamondMatrix[{8,8,8},18][[10;;]]; {gr,bricks}=TransformLego[slices,{{4,2},{3,2},{2,2},{4,1},{3,1},{2,1},{1,1}},Automatic]; gr ![enter image description here][6] The price (according to the online lego shop), would be: prices = {{2, 4} -> 0.23, {1, 2} -> 0.11, {1, 1} -> 0.08, {1, 3} -> 0.15, {1, 4} -> 0.15, {2, 2} -> 0.15, {2, 3} -> 0.19}; Total[(Sort /@ bricks[[All, 1]]) /. prices] 18.76 We can go now and make some instructions for making this pyramid! Because I can't build something without instructions. Let's create some layer-by-layer instructions: ClearAll[CreatePage,CreatePages] CreatePage[slices_List,pagenumber_Integer]:=Module[{add,old,image,gr,gr3,opts,width=500}, {add,old}=TakeDrop[slices,-1]; image=(DrawLego[#1,#2,False]&@@@#)&/@slices; add=Flatten[add,1]; add=SortBy[Minus@*First][Reverse/@Tally[Sort/@add[[All,1]]]]; add[[All,1]]=Style[Row[{#,"\[Cross]"}],16,Black]&/@add[[All,1]]; add[[All,2]]=Graphics3D[{brickstyle,DrawLego[#]},gropts,ImageSize->50,Background->None]&/@add[[All,2]]; add=Grid[add]; gr3=Graphics3D[{brickstyle,image},Boxed->False,ViewPoint->(10{2.4, -1.3, 2.}),ImageSize->2width/3]; gr=Graphics[ {LightBlue,Rectangle[{0,0},{1,1.5}], Inset[gr3,Scaled@{0.5,0.5}], Inset[Style[ToString[pagenumber],30,Black],Scaled@{0.5,0.05},Scaled@{0.5,0}], Inset[add,Scaled@{0.05,1},Scaled@{0,1}] }, Axes->False, Frame->False, ImageSize->(width{1,1.5}), PlotRange->{{0,1},{0,1.5}}, AspectRatio->Full ] ] CreatePages[bricks_List]:=Module[{brickslices,out}, brickslices=SortBy[Part[#,1,-1,-1]&][GatherBy[bricks,Part[#,-1,-1]&]]; out = Map[CreatePage[brickslices[[;;#]],#]&,Range[Length[brickslices]]]; Rasterize[#,"Image"]& /@ out ] So let's call the function: CreatePages[bricks] ![enter image description here][7] gives me back 8 pages of instructions, including the bricks I need for that 'layer' ! Lastly, let's make one from a 3D model: brickstyle=Sequence[RGBColor[0.55,0.38,0.19],EdgeForm[AbsoluteThickness[1]]]; bg=ExampleData[{"Geometry3D","Triceratops"},"BoundaryMeshRegion"] bounds={xbounds,ybounds,zbounds}=CoordinateBounds[ExampleData[{"Geometry3D","Triceratops"},"VertexData"]]; rmf=RegionMember[bg]; \[Delta]=2^-3; alldata=Boole[Table[rmf[{x,y,z}],{x,xbounds[[1]],xbounds[[2]],\[Delta]},{y,ybounds[[1]],ybounds[[2]],\[Delta]},{z,zbounds[[1]],zbounds[[2]],\[Delta]}]]; alldata=Transpose[alldata,{3,2,1}]; {gr,bricks}=TransformLego[alldata,{{4,2},{3,2},{2,2},{4,1},{3,1},{2,1},{1,1}},False]; gr ![enter image description here][8] Now feel free to turn your own plots, 3d-scans, and models to Legos! ![enter image description here][9] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=ezgif-7-d6220bc85b47.gif&userId=11733 [2]: http://community.wolfram.com//c/portal/getImageAttachment?filename=106061.png&userId=73716 [3]: http://community.wolfram.com//c/portal/getImageAttachment?filename=39362.png&userId=73716 [4]: http://community.wolfram.com//c/portal/getImageAttachment?filename=65983.png&userId=73716 [5]: http://community.wolfram.com//c/portal/getImageAttachment?filename=15884.png&userId=73716 [6]: http://community.wolfram.com//c/portal/getImageAttachment?filename=73285.png&userId=73716 [7]: http://community.wolfram.com//c/portal/getImageAttachment?filename=16926.png&userId=73716 [8]: http://community.wolfram.com//c/portal/getImageAttachment?filename=18467.png&userId=73716 [9]: http://community.wolfram.com//c/portal/getImageAttachment?filename=3141out.gif&userId=73716 Sander Huisman 2016-02-29T21:57:12Z https://community.wolfram.com/groups/-/m/t/2135869 Introduction ------------ The goal of this post is to start from images like this example one : ![Girl3][1] and generate pictures like: ![Girl3Gray][2] or ![Girl3Color][3] Mathematica at least 12.1 will be required since Mixed Integer programming is used. Explanation ----------- Let's take the first image (black and white) as an example. Let's assume we have a collection of 16 tiles: ![GrayTiles][4] The problem to solve is how to place the tiles on the picture so that the gray content of a tile is close to the gray content of the picture below it and at the same time the topological constraints are satisfied. By topological constraints, I mean that the tiles must be compatible. This is allowed: ![Allowed][5] This is forbidden because the dark horizontal band is continuing as a white horizontal band. ![Forbidden][6] We are going to translate this problem into a set of equations on integer variables and with a linear cost function to optimize. The final problem will be solved with the LinearOptimization function from Mathematica 12.1. Each pixel of the image is encoded by a vector ![eq1][7] because there are 16 different tiles in this example. The components of the vector can be either 0 or 1. This is expressed as: VectorLessEqual[{0, v}], VectorLessEqual[{v, 1}], v \[Element] Vectors[nbTiles, Integers] For each pixel, only one tile can be used. We cannot put several tiles on a pixel but only choose one and only one. If we use the constraint: ![eq2][8] then we express that only one tile can be used. Indeed, since the component are integers and equal to 0 or 1, then the only way to satisfy this equation is that one of the components, and only one, is equal to one. Expressing the topological constraints is similar. We have equations like: ![eq3][9] This equation is describing a relationship between pixel (x,y) and pixel (x+1,y). The values on left and right side can either be 0 or 1 (at same time). When zero it means : none of the tiles is used. When one, it means one of the tile is used. So, the translation of the equation is: If the tile 0,3 or 5 are used at pixel (x,y) then the tiles 3,6,9 or 11 must be used at pixel (x+1,y). (I have not checked if it makes sense with the set of tiles I am using as example. The constraints for those tiles are probably different.). To describe the topological constraint of the tiles, we have functions like: rightSide[tileA[{a_,b_,c_}]] :={b,c}; This is giving a key describing the right side of the tile. Those keys are then used in associations to build the topological constraints. The key can be anything so if you want to add new tiles, you can just use the key you want to describe the sides of your tiles. The generic function rightSide must be extended with new cases when new tiles are added. Then, we need to express how good the tiles are approximating the original picture. For this, an error function is created. It is a sum of terms: ![eq4][10] It means that if the tile i is selected for pixel (x,y) then the approximation error is `Subscript[f, k]` The function averageColor must be extended with new tiles. It returns the color content of a tile : a RGBColor. The code is using a color distance to compute the errors. That's why the input picture is always converted to RGB and the alpha channel removed. So, finally we have translated our problem into a set of integer constraints and with a linear cost function to optimize. It is a mixed integer programming problem which can be solved with LinearOptimization. The tiles must be displayed. It is done by the function tileDraw and the tile is drawn in a square from (0,0) to (1,1) corners. The code is rasterizing those graphics into 50x50 pixel images. I have had lots of problems with those pictures due to rounding errors ... probably due to the very old GPU on my very old computer. So I tuned the vector code assuming the final tile image is 50x50 pixels. Now the pictures are well aligned, there is no more one row or one column of wrong pixels on the boundary of the tiles. But this may cause a problem on your configuration. So, if the tile pictures are not rendering correctly on your side, you'll need to tune my vector graphic code again. If the picture is too big, solving the full mixed integer programming problem may take too long. But we can solve a sub-optimal problem. We divide the picture into sub-pictures and solve the problem on each sub-picture then we recombine the solutions. For it to work : we must add new constraints to express compatibility between the pictures. For instance, the left side of a picture at (row,col) must be compatible with the right side of the picture at (row,col-1). So, the problem must be solved in a given order so that the constraints can be propagated from one sub-picture to the other. For some tiles, the sub-optimal solution can be very good from an artistic point of view (the dark tiles below are working well). For other tiles (the smith tiles in the notebook) either the sub-optimal problem cannot always be solved (because the constraints coming from the previous pictures can't be satisfied) or the sub-optimal problem will look bad from time to time. So this idea of using sub-picture is really dependent on the kind of tiles used. You need to experiment. But it is art after all. Example of use -------------- First, we get an example picture: srcImage = ImageCrop[ExampleData[{"TestImage", "Girl3"}], {190, 270}] The picture is resized, converted to RGB and any alpha channel removed. imgToAnalyze = ImageResize[ ImageAdjust[ RemoveAlphaChannel[ColorConvert[srcImage, "RGB"], Black]], {50, Automatic}] For the dark tiles (knots), we decide to only use gray levels. First color is the background of the tiles. Other colors are for the circles and vertical and horizontal bands. tileData = mkDarkTiles[RGBColor[ 0.5, 0.5, 0.5], {RGBColor[0., 0., 0.], RGBColor[1., 1., 1.]}]; It gives a total of 16 tiles. The more tiles, the more difficult it is to solve the problem. 16 is ok on my old computer. tileData["allTilesImg"] // Length The problem is solved on 15x15 sub pictures. solution = partitionSolve[tileData, imgToAnalyze, 15]; The final picture is generated from the tiles and the solution. img = createPict[tileData, solution]; And you'll get: ![Girl3Gray][2] Have fun ! I hope the vectorial code will not have to be tuned to generate the tile pictures (rounding errors). The notebook is attached to the post. [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Girl3.png&userId=89693 [2]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Girl3Gray.png&userId=89693 [3]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Girl3Color.png&userId=89693 [4]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Tiles.png&userId=89693 [5]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Allowed.png&userId=89693 [6]: https://community.wolfram.com//c/portal/getImageAttachment?filename=Forbidden.png&userId=89693 [7]: https://community.wolfram.com//c/portal/getImageAttachment?filename=eq1.png&userId=89693 [8]: https://community.wolfram.com//c/portal/getImageAttachment?filename=eq2.png&userId=89693 [9]: https://community.wolfram.com//c/portal/getImageAttachment?filename=eq3.png&userId=89693 [10]: https://community.wolfram.com//c/portal/getImageAttachment?filename=eq4.png&userId=89693 [11]: https://community.wolfram.com//c/portal/getImageAttachment?filename=LenaGray.png&userId=89693 Christophe Favergeon 2020-12-11T16:08:18Z https://community.wolfram.com/groups/-/m/t/2435403 ![Designing Townscaper town on computable base-grid][1] &[Wolfram Notebook][2] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=frames2.gif&userId=20103 [2]: https://www.wolframcloud.com/obj/0d953f64-4678-4d55-9d6b-17997fa2f42a Silvia Hao 2022-01-02T11:23:53Z https://community.wolfram.com/groups/-/m/t/229505 Hey all, I'm a student and am just starting to get the hang of Mathematica. I tried to make a Mathematica version of the [b][url=http://gabrielecirulli.github.io/2048]game 2048[/url][/b]. My program works now, so I thought I'd share. I'd love to hear if there are any better ways to do it, or any suggestions you have to make it better.  I originally wanted to see the distribution of how many moves a random game would last. So, now you can use all the normal Mathematica tools to discover cool patterns in 2048. Thanks, hope you enjoy! [mcode]Shift[list_]:=PadRight[Cases[list,Except[0]],4]; Merge[list_]:=Flatten[list//.{x___,c_,c_,y___}->{x,{2*c},y}]; SlideRow[list_]:=Shift[Merge[Shift[list]]]; Slide[list_,l]:=Table[SlideRow[list[[j]]],{j,1,4}]; Slide[list_,r]:=Table[Reverse[SlideRow[Reverse[list[[j]]]]],{j,1,4}]; Slide[list_,u]:=Transpose[Table[SlideRow[Transpose[list][[j]]],{j,1,4}]]; Slide[list_,d]:=Transpose[Table[Reverse[SlideRow[Reverse[Transpose[list][[j]]]]],{j,1,4}]]; RandInsert[list_]:=ReplacePart[list,RandomChoice[Position[list,0]]->RandomChoice[{2,4}]] (*these functions slide each row in the given direction,combine like terms,and add one random number to an empty tile*) Col[n_]:=Graphics[{Blend[{Yellow,Cyan,Purple,Red},((Log[n+1]/Log[2]))/11],Rectangle[]}]; Visual[list_]:=ImageCompose[GraphicsGrid[Table[Table[Col[list[[j,i]]],{i,1,4}],{j,1,4}]],GraphicsGrid[list,Frame->All]] (*the tiles,with colors and numbers*) Nex[list_,move_]:=If[FreeQ[list,0],ConstantArray[Infinity,{4,4}],RandInsert[Slide[list,move]]] (*the update rule*) (*the game itself.input from {u,d,l,r} to move*) game=ConstantArray[0,{4,4}];Print[Dynamic[Visual[game]]];While[game!=ConstantArray[Infinity,{4,4}],game=Nex[game,Input[]]][/mcode] [img]/c/portal/getImageAttachment?filename=ssdfsdret5654344254cfbrev.gif&userId=20103[/img] Edit: When I compared my results with the excellent [url=http://artent.net/2014/03/17/an-ai-for-2048-part-1/]artent[/url] article below, I noticed two important bugs. A random tile was added even after moves that didn't change anything. And the game ended after the board was full, even if further moves were possible. To fix this, I changed the Nex update rule. It's a little clunky, but should work. Uh, right now for the sake of analysis I have the final state be the total score instead of the grid, but you can change it easily enough. Thanks everyone for the comments![mcode]Nex[list_, move_] := Piecewise[{{RandInsert[list], Total[Flatten[list]] == 0}, {Total[Flatten[list]], Slide[list, u] == Slide[list, d] == Slide[list, l] == Slide[list, r] == list}, {list, Slide[list, move] == list}}, RandInsert[Slide[list, move]]][/mcode] Robert Stoughton 2014-04-01T18:47:05Z https://community.wolfram.com/groups/-/m/t/2342501 *MODERATOR NOTE: related resource function can be found here* https://resources.wolframcloud.com/FunctionRepository/resources/MandelbrotSetRemap ---- ![enter image description here][1] &[Wolfram Notebook][2] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=frac_hero.jpg&userId=20103 [2]: https://www.wolframcloud.com/obj/17f4f067-3491-4d7a-bf84-5601782ad11e Mark Greenberg 2021-08-14T14:58:17Z https://community.wolfram.com/groups/-/m/t/1124967 > Website: https://visx.io/ > Announcement video: https://youtu.be/Ulz96RZXHAY > Beta version call: https://community.wolfram.com/groups/-/m/t/2713297 Hi everyone. I'm working on a visual interface to the Wolfram Language called [visX][1], and I'd like to ask what you all think of it. Wolfram Language code can often be thought of as a set of blocks, each of which takes some inputs, does something, and produces an output. VisX lets you write WL code exactly this way - you draw a digram, connecting blocks with links. For example, say you want to count how many times each digit (0 to 9) occurs in the first 30 digits of Pi. With text-based WL code, you'd write digits = RealDigits[N[Pi, 30]][[1]] Count[digits, #] & /@ Range[0, 9] In visX, you'd draw this: ![digits of Pi][2] I guess it's pretty self-explanatory. In addition to using built-in WL blocks, you can write your own, like the CountInList block. Normally, blocks just transform inputs to output, but the CountInList block is mapped over its input which is indicated by the little brackets on the outside of its connection ports. (That's basically visual syntactic sugar for "/@" or Map.) The 4 in the upper-right corner indicates that results inside this block are showing results from the 4-th time through the map. The block with "digits" in it sets a variable, which is then referenced in the CountInList block. You define blocks (which are basically functions) by just making an empty rectangle and dragging contents in then wiring them together, then you can use copies of the block wherever you want. A change in any copy of the block will be reflected in all other copies. There's no real difference between a defining a block and using it. Recursion can be specified by just including a copy of the block inside itself. Blocks can call other blocks in the same manner. Just like regular WL code, visX blocks can be nested deeply, but with the visual interface, it's easy to zoom in and out. At any point, the UI will show you the right amount of detail for each block - sometimes no detail at all, sometimes its name and labels on its inputs, sometimes its actual contents (which can then be edited or further zoomed...). visX is stand-alone software that runs locally on your machine, evaluates the diagram using your local Mathematica kernel, and receives the results and puts them back in the diagram. You can load data files using Import as usual. One of the problems that I've seen with visual languages in the past is that while simple things are easy to do, the code quickly gets too complex to manage and the visual interface starts to get in the way. With the Wolfram Language in theory everything is an expression, and this can lead you to write functional-style programs which are easily thought of as a diagram, but that's not always the most natural way to express a computation. Sometimes you just need a little for loop. Consider calculating Fibonacci numbers. Start the sequence with 1, 1, ... then each element of the sequence is the sum of the previous two. Yes, you can write a recursive algorithm to do this, but most people just want to write a little for loop. In visX, you can do this (calculates the 6th Fibonacci number): ![embedded code][4] I've tried to let you use blocks-and-links when that's the most natural thing (which is usually), and text-based code when that's better. Of course, you can mix them together however you want. A second problem I've found with visual programming languages is that it can actually be much slower to use then writing out text, because you have to laboriously drag and drop every single block. Even simple algebraic expressions like Sin[x]^2 + Cos[x]^2 2x^2 + 4x*y + 8y^2 would involve a lot of blocks because of all the Plus, Times, and Power blocks, as well as all the constants and symbols. With visX, you can enter Wolfram Language code snippets like those, and it will parse them and transform them into blocks which you can then insert into your diagram all at once and edit at will. This makes it much faster to get your idea onto the screen so that you can start evaluating it and developing it. I'm also working on the ability to take a visX block and give you back the Wolfram Language code that it represents. The examples given here are simple, but of course you can use this interface for putting together a complex piece of code as well. I find it especially handy when building up a calculation with lots of intermediate results along the way, or to rapidly prototype an algorithm where I want to be able to easily switch the data flows around. Does this project seem useful to anyone? I'd like to get some feedback - what do you think of it? Would you use it? For what? If there's interest, I could do a small-scale alpha test in about a month from now. More info at [visx.io][5]. -Nicholas Hoff *edited to clarify block definitions and recursion* [1]: http://visx.io [2]: http://community.wolfram.com//c/portal/getImageAttachment?filename=pi_digits_without_chrome.png&userId=1124239 [3]: http://community.wolfram.com//c/portal/getImageAttachment?filename=pi_digits.png&userId=1124239 [4]: http://community.wolfram.com//c/portal/getImageAttachment?filename=embeded_wl.png&userId=1124239 [5]: http://visx.io Nicholas Hoff 2017-06-20T10:02:52Z