50 skills found · Page 1 of 2
pa7 / Heatmap.js🔥 JavaScript Library for HTML5 canvas based heatmaps
LKremer / Ggpointdensity:chart_with_upwards_trend: :bar_chart: Introduces geom_pointdensity(): A Cross Between a Scatter Plot and a 2D Density Plot.
jamesotto852 / GgdensityAn R package for interpretable visualizations of bivariate density estimates
kevinschaul / BinifyA command-line tool to better visualize crowded dot density maps.
hhcho / DensvisDensity-preserving data visualization tools den-SNE and densMAP
powellgenomicslab / NebulosaR package to visualize gene expression data based on weighted kernel density estimation
Przemol / Seqplots:chart_with_upwards_trend:SeqPlots - An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps.
datawan-labs / SchoolsVisualizing School Distribution and Population Density in Indonesia, By mapping where people live alongside school locations, this study shows how spatial data can reveal patterns of educational access across the country.
reddyprasade / Machine Learning With Scikit Learn Python 3.xIn general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
tyrasd / Osm Node Density:city_sunrise: a visualization of OpenStreetMaps node density
peterwang1996 / QianFangGaoNengA small Chrome extension for visualizing the density of damuku from videos in Bilibili
chgloor / PedsimPEDSIM is a microscopic pedestrian crowd simulation system. It is suitable for use in crowd simulations (e.g. indoor evacuation simulation, large scale outdoor simulations), where quantitative measurements like pedestrian density or evacuation time matter. The quality of the individual agent's trajectory is high enough for creating massive pedestrian crowd animations (e.g. for motion pictures or architectural visualization). Since libpedsim is easy to use and extend, it is a good starting point for science projects.
Superzchen / IFeatureOmega GUIiFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.
Superzchen / IFeatureOmega CLIiFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, iFeatureOmega supplies the largest number of feature extraction and analysis approaches for most molecule types compared to other pipelines. Three versions (i.e. iFeatureOmega-Web, iFeatureOmega-GUI and iFeatureOmega-CLI) of iFeatureOmega have been made available to cater to both experienced bioinformaticians and biologists with limited programming expertise. iFeatureOmega also expands its functionality by integrating 15 feature analysis algorithms (including ten cluster algorithms, three dimensionality reduction algorithms and two feature normalization algorithms) and providing nine types of interactive plots for statistical features visualization (including histogram, kernel density plot, heatmap, boxplot, line chart, scatter plot, circular plot, protein three dimensional structure plot and ligand structure plot). iFeatureOmega is an open-source platform for academic purposes. The web server can be accessed through http://ifeature2.erc.monash.edu and the GUI and CLI versions can be download at: https://github.com/Superzchen/iFeatureOmega-GUI and https://github.com/Superzchen/iFeatureOmega-CLI, respectively.
HwangTaehyun / Oh My Agentic ScoreOh My Agentic Score (OMAS) — Measure and visualize AI agent thread-based engineering capabilities. 4-dimension scoring: More (Parallelism), Longer (Autonomy), Thicker (Density), Fewer (Trust).
CDFER / Ceramic Capacitor DeratingJupyter Notebook to analyze and visualize DC bias derating of ceramic capacitors. Uses Murata SimSurfing CSV data to plot capacitance density (µF/mm²) vs. DC bias, highlighting effective capacitance at a user-defined target voltage. Helps in selecting appropriate MLCCs by showing their real-world performance under bias conditions.
aromanro / DFTQuantumDotDensity Functional Theory with plane waves basis, applied on a 'quantum dot'. Volumetric visualization of orbitals with VTK
alexing / DatadataPulling data using both the Genius API and the Spotify API I've been able to analyze Jorge Drexler's music and get some insights and visualizations on his creative process and his songs in general; both from the lyrics side and the musical theory side. Wordcount, lexical and lyrical density, sentiment analysis and analysis of musical components like tempo, time signature and key are all taken into account. Also, in the end, gloom_index is used combining both lyrics and music.
spyke / SpykeA Python application for visualizing, navigating, and spike sorting high-density multichannel neuronal extracellular waveform data
Query-farm / TextplotA DuckDB community extension that enables text-based data visualization directly in SQL queries, including ASCII/Unicode bar charts, density plots, and sparklines for lightweight analytics and dashboards.
