Search Results for "dataset"
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Showing 23 open source projects for "dataset"
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Parquet.jl
Julia implementation of Parquet columnar file format reader
A parquet file or dataset can be loaded using the read_parquet function. A parquet dataset is a directory with multiple parquet files, each of which is a partition belonging to the dataset. -
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HDF5.jl
Save and load data in the HDF5 file format from Julia
HDF5 stands for Hierarchical Data Format v5 and is closely modeled on file systems. In HDF5, a "group" is analogous to a directory, a "dataset" is like a file. HDF5 also uses "attributes" to associate metadata with a particular group or dataset. HDF5 uses ASCII names for these different objects, and objects can be accessed by Unix-like pathnames, e.g., "/sample1/tempsensor/firsttrial" for a top-level group "sample1", a subgroup "tempsensor", and a dataset "firsttrial". ... -
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Charts.css
Open source CSS framework for data visualization
Charts.css is a modern CSS framework. It uses CSS utility classes to style HTML elements as charts. No dependencies. 72kb file size. Less than 6kb gzipped file size! Visualization helps end-users understand data. Charts.css help frontend developers turn data into beautiful charts and graphs using simple CSS classes. The data is structured using semantic HTML tags and styled using CSS classes which change the visual representation displayed to the end-user. The framework offers developers... -
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HyperTools
A Python toolbox for gaining geometric insights
...HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. -
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React Chart.js
React components for Chart.js, the most popular charting library
...In order to improve performance, offer new features, and improve maintainability, it was necessary to break backwards compatibility, but we aimed to do so only when worth the benefit. You will find that any event which causes the chart to re-render, such as hover tooltips, etc., will cause the first dataset to be copied over to other datasets, causing your lines and bars to merge together. This is because to track changes in the dataset series, the library needs a key to be specified. If none is found, it can't tell the difference between the datasets while updating. Specify a different property to be used as a key by passing a datasetIdKey prop to your chart component. -
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ggplot2
An implementation of the Grammar of Graphics in R
...With ggplot2 you simply provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it will take care of the rest. ggplot2 is over 10 years old and is used by hundreds of thousands of people all over the world for plotting. In most cases using ggplot2 starts with supplying a dataset and aesthetic mapping (with aes()); adding on layers (like geom_point() or geom_histogram()), scales (like scale_colour_brewer()), and faceting specifications (like facet_wrap()); and finally, coordinating systems. ggplot2 has a rich ecosystem of community-maintained extensions for those looking for more innovation. ggplot2 is a part of the tidyverse, an ecosystem of R packages designed for data science. -
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UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation
A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments. -
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JLD2
HDF5-compatible file format in pure Julia
JLD2 saves and loads Julia data structures in a format comprising a subset of HDF5, without any dependency on the HDF5 C library. JLD2 is able to read most HDF5 files created by other HDF5 implementations supporting HDF5 File Format Specification Version 3.0 (i.e. libhdf5 1.10 or later) and similarly, those should be able to read the files that JLD2 produces. JLD2 provides read-only support for files created with the JLD package. The save and load functions, provided by FileIO, provide a... -
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Sweetviz
Visualize and compare datasets, target values and associations
...Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Shows how a target value (e.g. "Survived" in the Titanic dataset) relates to other features. Sweetviz integrates associations for numerical (Pearson's correlation), categorical (uncertainty coefficient) and categorical-numerical (correlation ratio) datatypes seamlessly, to provide maximum information for all data types. Automatically detects numerical, categorical and text features, with optional manual overrides. min/max/range, quartiles, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness. -
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Digital Earth Australia notebooks
Repository for Digital Earth Australia Jupyter Notebooks
The knowledge hub brings together information about Digital Earth Australia’s products and services, allowing you to utilize our free and open-source satellite imagery archive. Browse our catalog of data products to find supporting information and ways to access the data. The Digital Earth Australia notebooks and tools repository (dea-notebooks) hosts Jupyter Notebooks, Python scripts and workflows for analyzing Digital Earth Australia (DEA) satellite data and derived products. This... -
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Kst is the fastest real-time large-dataset viewing and plotting tool available and has basic data analysis functionality. Kst contains many powerful built-in features and is expandable with plugins and extensions.
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seaborn
Statistical data visualization in Python
...Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. -
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DeepH-pack
Deep neural networks for density functional theory Hamiltonian
DeepH-pack is the official implementation of the DeepH (Deep Hamiltonian) method described in the paper Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation and in the Research Briefing. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS. -
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missingno
Missing data visualization module for Python
...missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. At a glance, date, time, the distribution of injuries, and the contribution factor of the first vehicle appear to be completely populated, while geographic information seems mostly complete, but spottier. ... -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates. -
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ManifoldLearning
Package for manifold learning and nonlinear dimensionality reduction
A Julia package for manifold learning and nonlinear dimensionality reduction. Most of the methods use k-nearest neighbors method for constructing local subspace representation. By default, neighbors are computed from a distance matrix of a dataset. This is not an efficient method, especially, for large datasets. -
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Facets
Visualizations for machine learning datasets
...Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income dataset, used for predicting whether an individual’s income exceeds $50K/yr based on their census data. The census data contains features such as age, education level, and occupation for each individual. ... -
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xmgrace python interface
xmgrace and qtgrace plotting interface in python
This is a python module providing a nearly complete interface to the graphing program xmgrace and the new qtgrace (great project... grace works under windows, too, now!). It allows creation of grace plots from within python, which can be customized using python, or through grace after creation. -
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Evita is a prototype system for the exploration of terascale dataset by using a a wavelet-based representational scheme allowing ranked access to macroscopic data features.
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Yggdrasill
3D Evolutionary Tree Visualization
...Currently available features include procedural tree generation of evolutionary data provided by the Tree of Life (ToL) Web Project (http://tolweb.org/tree/), full three dimensional camera controls as well as a web search, an extinct species overlay and more. Later updates will include the full ToL dataset, improve tree generation and overall accessability, as well as adding access to other datasets. Special thanks go to the Tree of Life Web Project and all its contributors for providing the data and to Unity Technologies for free access to Unity 5. -
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CIG-P
CIG-P is a simple yet flexible data visualization tool
...CIG-P can be used to compare a) different AP-MS datasets of various baits or b) a particular bait under various perturbations (lenticular section CIG-P). The output of CIG-P is a simple and intuitively easy to grasp visualization of a complex dataset. Publication: CIG-P: Cicular Interaction Graph for Proteomics http://www.biomedcentral.com/1471-2105/15/344/ Previously known as PIVOT (Protein Interaction Visualization and Observation Tool) -
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SciChart
Interactive Swing based Charting library to display science data
This free charting library supporting in the initial version line plot and bar plots. Provides: Axis sharing independent rescaling and panning of axis and datasets. Basic tooltips Legend displayer component with ability to select the active dataset. It is designed upon the Model View Controller paradigm. This mean that the dataset related API is abstracted in a model. This model is used by the swing components. Display related functionality is limited to the swing components with no interaction with the model. -
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JData Explorer
Allows SQL query of CSV to produce graphical visualization of data.
Data Explorer allows the user to load a CSV dataset, then query the dataset via standard SQL, then feed the resultant dataset into over 15 different visualizations.
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