395 skills found · Page 8 of 14
pepprseed / Svgdatashapesa compact set of python functions for creating many types of plots and data displays in SVG for use in web pages.
datarob / TermstrcThe R package offers a wide range of functions for term structure estimation based on static and dynamic coupon bond and yield data sets. The implementation focuses on the cubic splines approach of McCulloch (1971, 1975) and the Nelson and Siegel (1987) method with extensions by Svensson (1994), Diebold and Li (2006) and De Pooter (2007). We propose a weighted constrained optimization procedure with analytical gradients and a globally optimal start parameter search algorithm. Extensive summary statistics and plots are provided to compare the results of the different estimation methods. Several demos are available using data from European government bonds and yields.
arcesoftware / FractalsThe code uses the HTML5 canvas element to render the Fractals, and it utilizes the requestAnimationFrame() function to continuously draw new points on the canvas. It also includes a feature to plot the dots in the Fractals depending on the distance from the origin, with a color gradient that ranges from green to red.
fbob / MplFOAMSome functions to plot OpenFOAM data with Matplotlib
region-spotteR / PrepPlotA loose collections of functions to prepare plots in R
minoue-xx / BarChartRaceAnimationThis repository provides a function that generates a bar chart race plot.
VManuelSM / Membership FunctionsMembership functions for fuzzy logic, encoded and plotted in python.
XsarfrazX / CDF PDF MatlabPlotting and analysing Cumulative Distribution Function(CDF) and Probability Density Function(PDF) of Uniform and Gaussian Distribution
kisonecat / Phase PlotPlot complex functions
Marinov-Ocean-Group / Climate Science AnalysisVarious climate science analysis and plots: Sea Surface Temperature, Winds, Sea Ice, NAO, Empirical Orthogonal Functions, etc
David-OConnor / FplotFunction plots with less code
GistNoesis / VisualizeGradientPlot gradient map for 1d and 2d functions
JohannHM / Easy Plot EEG Brain Network MatlabHow to plot a brain network if you have the data? This is for you. This function in Matlab® allows to plot a brain functional network based on 4 EEG layouts of (31, 56, 61 and 64) channels and different options according the user needs.
EvangelosKatsavrias / Matlab OOP CAGDThis is an OOD framework in Matlab, designed for development of CAGD (Computer Aided Geometric Design) applications. Currently it includes several processing and plotting functions using B-Splines and NURBS for 1D, 2D and 3D topologies.
eshamay / InterfacemdTools for working with data from molecular dynamics simulations. Trajectory analysis, computational spectroscopy, density plots, distribution functions, atoms/molecules/systems - specializing in aqueous (water) systems made using Amber, Gromacs, and CP2K simulation packages.
sentisci / FheatmapR function to plot high quality, elegant heatmap using 'ggplot2' graphics . Some of the important features of this package are, coloring of row/column side tree with respect to the number of user defined cuts in the cluster, add annotations to both columns and rows, option to input annotation palette for tree and column annotations and multiple parameters to modify aesthetics (style, color, font) of texts in the plot.
shibo769 / UHDmediUHDmedi is an R package for estimating total, indirect, and direct effects in ultra-high dimensional mediation. It offers functions for simulating data, loading datasets, performing EDA, effect estimation via debiased lasso, bootstrap CI, and diagnostic plotting. Methods from https://arxiv.org/pdf/2412.08827.
GroupAYECS765P / BDP 05 Large Scale ClusteringBDP 05: CLUSTERING OF LARGE UNLABELED DATASETS OVERVIEW Real world data is frequently unlabeled and can seem completely random. In these sort of situations, unsupervised learning techniques are a great way to find underlying patterns. This project looks at one such algorithm, KMeans clustering, which searches for boundaries separating groups of points based on their differences in some features. The goal of the project is to implement an unsupervised clustering algorithm using a distributed computing platform. You will implement this algorithm on the stack overflow user base to find different ways the community can be divided, and investigate what causes these groupings. The clustering algorithm must be designed in a way that is appropriate for data intensive parallel computing frameworks. Spark would be the primary choice for this project, but it could also be implemented in Hadoop MapReduce. Algorithm implementations from external libraries such as Spark MLib may not be utilised; the code must be original from the students. However, once the algorithm is completed, a comparison between your own results and that generated by MLlib could be interesting and aid your investigation. Stack Overflow is the main dataset for this project, but alternative datasets can be adopted after consultation with the module organiser. Additionally, different clustering algorithms may be utilised, but this must be discussed and approved y the module organiser. DATASET The project will use the Stack Overflow dataset. This dataset is located in HDFS at /data/stackoverflow The dataset for StackOverflow is a set of files containing Posts, Users, Votes, Comments, PostHistory and PostLinks. Each file contains one XML record per line. For complete schema information: Click here In order to define the clustering use case, you must define what should be the features of each post that will be used to cluster the data. Have a look at the different fields to define your use case. ALGORITHM The project will implement the k-means algorithm for clustering. This algorithm iteratively recomputes the location of k centroids (k is the number of clusters, defined beforehand), that aim to classify the data. Points are labelled to the closest centroid, with each iteration updating the centroids location based on all the points labelled with that value. Spark and Map/Reduce can be utilised for implementing this problem. Spark is recommended for this task, due to its performance benefits in . However, note that the MLib extension of Spark is not allowed to be used as the primary implementation. The group must code its own original implementation of the algorithm. However, it is possible to also use the mllib implementation, in order to evaluate the results from each clustering implementation. Report Contents Brief literature survey on clustering algorithms, including the challenges on implementing them at scale for parallel frameworks. The report should then justify the chosen algorithm (if changed) and the implementation. Definition of the project use case, where the implemented project will be part of the solution. Implementation in MapReduce or Spark of a clustering algorithm(KMeans). Must take into account the potential enormous size of the dataset, and develop sensible code that will scale and efficiently use additional computing nodes. The code will also need to potentially convert the dataset from its storage format to an in-memory representation. Source code should not be included in the report. However, the algorithms should be explained in the report. Results section. Adequate figures and tables should be used to present the results. The effectiveness of the algorithm should also be shown, including performance indications. Not really sure if this can be done for clustering. Critical evaluation of the results should be provided. Experiments demonstrating the technique can successfully group users in the dataset. Representation of the results, and discussion of the findings in a critical manner. ASSESSMENT The project according to the specification has a base difficulty of 85/100. This means that a perfect implementation and report would get a 85. Additional technical features and experimentation would raise the difficulty in order to opt for a full 100/100 mark. Report presentation: 20% Appropriate motivation for the work. Lack of typos/grammar errors, adequate format. Clear flow and style. Related work section including adequate referencing. Technical merit: 50% Completeness of the implementation. [25%] Provided source code. Code is documented. [10%] Design rationale of the code is provided. [10%] Efficient, and appropriate implementation for the chosen platform. [5%] Results/Analysis: 30% Experiments have been carried out on the full dataset. [10%] Adequate plots/tables are provided, with captions. [10%] Results are not only presented but discussed appropriately. [10%] Additional project goals: Implementation of additional functions beyond the base specification can raise the base mark up to 100. A non-exhaustive list of expansion ideas include: Exploration and discussion of hyperparameter tuning (e.g. the number of k groups to cluster the data into) [up to 10 marks] Comparative evaluation of clustering technique with existing implementations (e.g. mllib) [up to 10 marks] Bringing in additional datasets from stackoverflow, such as user badges, to aid in clustering [up to 5 marks] Cluster additional datasets (such as posts) [up to 10 marks] LEAD DEMONSTRATOR For specific queries related to this coursework topic, please liaise with Mr/Ms TBD, who will be the lead demonstrator for this project, as well as with the module organiser. SUBMISSION GUIDELINES The report will have a maximum length of 8 pages, not counting cover page and table of contents. The report must include motivation of the problem, brief literature survey, explanation of the selected technique, implementation details and discussion of the obtained results, and references used in the work. Additionally, the source code must be included as a separate compressed file in the submission.
mrc1234 / Liri Bot2019# LIRI Bot ### Overview In this assignment, you will make LIRI. LIRI is like iPhone's SIRI. However, while SIRI is a Speech Interpretation and Recognition Interface, LIRI is a _Language_ Interpretation and Recognition Interface. LIRI will be a command line node app that takes in parameters and gives you back data. ### Before You Begin 1. LIRI will search Spotify for songs, Bands in Town for concerts, and OMDB for movies. 2. Make a new GitHub repository called liri-node-app and clone it to your computer. 3. To retrieve the data that will power this app, you'll need to send requests to the Bands in Town, Spotify and OMDB APIs. You'll find these Node packages crucial for your assignment. * [Node-Spotify-API](https://www.npmjs.com/package/node-spotify-api) * [Request](https://www.npmjs.com/package/request) * You'll use Request to grab data from the [OMDB API](http://www.omdbapi.com) and the [Bands In Town API](http://www.artists.bandsintown.com/bandsintown-api) * [Moment](https://www.npmjs.com/package/moment) * [DotEnv](https://www.npmjs.com/package/dotenv) ## Submission Guide Make sure you use the normal GitHub. Because this is a CLI App, there will be no need to deploy it to Heroku. This time, though, you need to include screenshots, a gif, and/or a video showing us that you got the app working with no bugs. You can include these screenshots or a link to a video in a `README.md` file. * Include screenshots (or a video) of typical user flows through your application (for the customer and if relevant the manager/supervisor). This includes views of the prompts and the responses after their selection (for the different selection options). * Include any other screenshots you deem necessary to help someone who has never been introduced to your application understand the purpose and function of it. This is how you will communicate to potential employers/other developers in the future what you built and why, and to show how it works. * Because screenshots (and well-written READMEs) are extremely important in the context of GitHub, this will be part of the grading. If you haven't written a markdown file yet, [click here for a rundown](https://guides.github.com/features/mastering-markdown/), or just take a look at the raw file of these instructions. ### Submission on BCS * Please submit the link to the Github Repository! ### Instructions 1. Navigate to the root of your project and run `npm init -y` — this will initialize a `package.json` file for your project. The `package.json` file is required for installing third party npm packages and saving their version numbers. If you fail to initialize a `package.json` file, it will be troublesome, and at times almost impossible for anyone else to run your code after cloning your project. 2. Make a `.gitignore` file and add the following lines to it. This will tell git not to track these files, and thus they won't be committed to Github. ``` node_modules .DS_Store .env ``` 3. Make a JavaScript file named `keys.js`. * Inside keys.js your file will look like this: ```js console.log('this is loaded'); exports.spotify = { id: process.env.SPOTIFY_ID, secret: process.env.SPOTIFY_SECRET }; ``` 4. Next, create a file named `.env`, add the following to it, replacing the values with your API keys (no quotes) once you have them: ```js # Spotify API keys SPOTIFY_ID=your-spotify-id SPOTIFY_SECRET=your-spotify-secret ``` * This file will be used by the `dotenv` package to set what are known as environment variables to the global `process.env` object in node. These are values that are meant to be specific to the computer that node is running on, and since we are gitignoring this file, they won't be pushed to github — keeping our API key information private. * If someone wanted to clone your app from github and run it themselves, they would need to supply their own `.env` file for it to work. 5. Make a file called `random.txt`. * Inside of `random.txt` put the following in with no extra characters or white space: * spotify-this-song,"I Want it That Way" 6. Make a JavaScript file named `liri.js`. 7. At the top of the `liri.js` file, add code to read and set any environment variables with the dotenv package: ```js require("dotenv").config(); ``` 8. Add the code required to import the `keys.js` file and store it in a variable. * You should then be able to access your keys information like so ```js var spotify = new Spotify(keys.spotify); ``` 9. Make it so liri.js can take in one of the following commands: * `concert-this` * `spotify-this-song` * `movie-this` * `do-what-it-says` ### What Each Command Should Do 1. `node liri.js concert-this <artist/band name here>` * This will search the Bands in Town Artist Events API (`"https://rest.bandsintown.com/artists/" + artist + "/events?app_id=codingbootcamp"`) for an artist and render the following information about each event to the terminal: * Name of the venue * Venue location * Date of the Event (use moment to format this as "MM/DD/YYYY") 2. `node liri.js spotify-this-song '<song name here>'` * This will show the following information about the song in your terminal/bash window * Artist(s) * The song's name * A preview link of the song from Spotify * The album that the song is from * If no song is provided then your program will default to "The Sign" by Ace of Base. * You will utilize the [node-spotify-api](https://www.npmjs.com/package/node-spotify-api) package in order to retrieve song information from the Spotify API. * The Spotify API requires you sign up as a developer to generate the necessary credentials. You can follow these steps in order to generate a **client id** and **client secret**: * Step One: Visit <https://developer.spotify.com/my-applications/#!/> * Step Two: Either login to your existing Spotify account or create a new one (a free account is fine) and log in. * Step Three: Once logged in, navigate to <https://developer.spotify.com/my-applications/#!/applications/create> to register a new application to be used with the Spotify API. You can fill in whatever you'd like for these fields. When finished, click the "complete" button. * Step Four: On the next screen, scroll down to where you see your client id and client secret. Copy these values down somewhere, you'll need them to use the Spotify API and the [node-spotify-api package](https://www.npmjs.com/package/node-spotify-api). 3. `node liri.js movie-this '<movie name here>'` * This will output the following information to your terminal/bash window: ``` * Title of the movie. * Year the movie came out. * IMDB Rating of the movie. * Rotten Tomatoes Rating of the movie. * Country where the movie was produced. * Language of the movie. * Plot of the movie. * Actors in the movie. ``` * If the user doesn't type a movie in, the program will output data for the movie 'Mr. Nobody.' * If you haven't watched "Mr. Nobody," then you should: <http://www.imdb.com/title/tt0485947/> * It's on Netflix! * You'll use the request package to retrieve data from the OMDB API. Like all of the in-class activities, the OMDB API requires an API key. You may use `trilogy`. 4. `node liri.js do-what-it-says` * Using the `fs` Node package, LIRI will take the text inside of random.txt and then use it to call one of LIRI's commands. * It should run `spotify-this-song` for "I Want it That Way," as follows the text in `random.txt`. * Edit the text in random.txt to test out the feature for movie-this and concert-this. ### BONUS * In addition to logging the data to your terminal/bash window, output the data to a .txt file called `log.txt`. * Make sure you append each command you run to the `log.txt` file. * Do not overwrite your file each time you run a command. ### Reminder: Submission on BCS * Please submit the link to the Github Repository! - - - ### Minimum Requirements Attempt to complete homework assignment as described in instructions. If unable to complete certain portions, please pseudocode these portions to describe what remains to be completed. Adding a README.md as well as adding this homework to your portfolio are required as well and more information can be found below. - - - ### Create a README.md Add a `README.md` to your repository describing the project. Here are some resources for creating your `README.md`. Here are some resources to help you along the way: * [About READMEs](https://help.github.com/articles/about-readmes/) * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) - - - ### Add To Your Portfolio After completing the homework please add the piece to your portfolio. Make sure to add a link to your updated portfolio in the comments section of your homework so the TAs can easily ensure you completed this step when they are grading the assignment. To receive an 'A' on any assignment, you must link to it from your portfolio. - - - ### One More Thing If you have any questions about this project or the material we have covered, please post them in the community channels in slack so that your fellow developers can help you! If you're still having trouble, you can come to office hours for assistance from your instructor and TAs. **Good Luck!**
thomasfuhringer / LybnizFunction graph plotter
