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Showing 236 open source projects for "format"
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MAT.jl
Julia module for reading MATLAB files
Julia module for reading MATLAB files. Read and write MATLAB files in Julia. This library can read MATLAB .mat files, both in the older v4/v5/v6/v7 format, as well as the newer v7.3 format. -
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Koop
Transform, query, and download geospatial data on the web
Koop is a JavaScript toolkit for making requests to spatial APIs. It exposes a Node.js web server that facilitates on-the-fly transformations of geospatial data from one format to another and delivers it to clients by HTTP. Koop allows you to keep your data in its native format while making it accessible in any format required. Out-of-the-box, Koop can translate your data into the GeoServices specification supported by ArcGIS products. Its plugin architecture supports output in other formats including vector-tile, WMS, and plain old GeoJSON. ... -
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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. -
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UnROOT.jl
Native Julia I/O package to work with CERN ROOT files objects
UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python. -
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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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JLD.jl
Saving and loading julia variables while preserving native types
JLD, for which files conventionally have the extension .jld, is a widely used format for data storage with the Julia programming language. JLD is a specific "dialect" of HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data. By comparison with "plain" HDF5, JLD files automatically add attributes and naming conventions to preserve type information for each object. For lossless storage of arbitrary Julia objects, the only other complete solution appears to be Julia's serializer, which can be accessed via the serialize and deserialize commands. ... -
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PlantUML
Generate diagrams from textual description
...The output is an image, which either appears in the other software, or is written to an image file on disk. Diagrams are defined using a simple and intuitive language. (see PlantUML Language Reference Guide). Images can be generated in PNG, in SVG or in LaTeX format. It is also possible to generate ASCII art diagrams (only for sequence diagrams). -
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XMLTV 1.4.0
Utilities to obtain, generate, and post-process TV listings data
XMLTV is a suite of utilities to obtain, generate, and post-process TV listings data in XMLTV format. It provides tools to gather television listings, process listings data, and help organize TV viewing. -
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JDF.jl
Julia DataFrames serialization format
JDF is a DataFrames serialization format with the following goals, fast save and load times, compressed storage on disk, enabled disk-based data manipulation (not yet achieved), and support for machine learning workloads, e.g. mini-batch, sampling (not yet achieved). JDF stores a DataFrame in a folder with each column stored as a separate file. There is also a metadata.jls file that stores metadata about the original DataFrame. -
DAT Freight and Analytics - DATDAT Freight & Analytics operates DAT One, North America’s largest truckload freight marketplace; DAT iQ, the industry’s leading freight data analytics service; and Trucker Tools, the leader in load visibility. Shippers, transportation brokers, carriers, news organizations, and industry analysts rely on DAT for market trends and data insights, informed by nearly 700,000 daily load posts and a database exceeding $1 trillion in freight market transactions. Founded in 1978, DAT is a business unit of Roper Technologies (Nasdaq: ROP), a constituent of the Nasdaq 100, S&P 500, and Fortune 1000. Headquartered in Beaverton, Ore., DAT continues to set the standard for innovation in the trucking and logistics industry.
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CSV Lint
CSV Lint plug-in for Notepad++ for syntax highlighting
CSV Lint plug-in for Notepad++ for syntax highlighting, csv validation, automatic column and datatype detecting fixed width datasets, change datetime format, decimal separator, sort data, count unique values, convert to xml, json, sql etc. A plugin for data cleaning and working with messy data files. Use CSV Lint for metadata discovery, technical data validation, and reformatting on tabular data files. It is not meant to be a replacement for spreadsheet programs like Excel or SPSS, but rather it's a quality control tool to examine, verify or polish up a dataset before further processing. -
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Gource
Software version control visualization
Software projects are displayed by Gource as an animated tree with the root directory of the project at its centre. Directories appear as branches with files as leaves. Developers can be seen working on the tree at the times they contributed to the project. Gource includes built-in log generation support for Git, Mercurial, Bazaar and SVN. Gource can also parse logs produced by several third party tools for CVS repositories. Gource is a visualization tool for source control repositories. The... -
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janitor
Simple tools for data cleaning in R
janitor provides simple, convenient tools for data cleaning, formatting, and exploration in R. It is especially useful for cleaning messy data frames, removing duplicates, formatting column names, and producing frequency tables in a tidy workflow. -
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Logstash
Centralize, transform and stash your data
Logstash is a server-side data processing pipeline that dynamically ingests data from numerous sources, transforms it, and ships it to your favorite “stash” regardless of format or complexity. It supports and ingests data of all shapes, sizes and sources, dynamically transforms and prepares this data, and transports it to the output of your choice. Logstash is extensible, with over 200 plugins available to let you create and configure your pipeline how you choose. -
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Recap
Recap tracks and transform schemas across your whole application
Recap is a schema language and multi-language toolkit to track and transform schemas across your whole application. Your data passes through web services, databases, message brokers, and object stores. Recap describes these schemas in a single language, regardless of which system your data passes through. Recap schemas can be defined in YAML, TOML, JSON, XML, or any other compatible language. -
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Gradle Task Tree
Gradle plugin that adds a 'taskTree' task that prints task dependency
A Gradle plugin that adds a taskTree task to your build. Running it prints out a hierarchical, easy‑to‑read task dependency tree, helping you visualize the build execution order. -
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cuDF
GPU DataFrame Library
Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming. For additional examples, browse our complete API documentation, or check out our more detailed notebooks. cuDF can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel. cuDF is supported only on Linux, and with Python versions 3.7 and later. ... -
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JuliaConnectoR
A functionally oriented interface for calling Julia from R
...It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
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TimeZones.jl
IANA time zone database access for the Julia programming language
IANA time zone database access for the Julia programming language. TimeZones.jl extends the Date/DateTime support for Julia to include a new time zone-aware TimeType: ZonedDateTime. -
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targets
Function-oriented Make-like declarative workflows for R
...It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping computation for up-to-date targets so that unchanged parts of the workflow are not recomputed. Targets can represent files or R objects, and tracking file changes etc is incorporated. -
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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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pprof
pprof is a tool for visualization and analysis of profiling data
pprof is a profiling visualization and analysis tool that ingests profiles in the profile.proto format and generates human-readable and graph-based reports. It supports multiple profile types (CPU, heap, allocations, contention, etc.) and can present data as text tables, call graphs (via Graphviz/dot), flame graphs, and interactive web UIs. The tool helps developers find hot paths, quantify resource usage, and compare profiles across runs to validate performance changes. -
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DataQualityDashboard
A tool to help improve data quality standards in data science
The goal of the Data Quality Dashboard (DQD) project is to design and develop an open-source tool to expose and evaluate observational data quality. This package will run a series of data quality checks against an OMOP CDM instance (currently supports v5.4, v5.3 and v5.2). It systematically runs the checks, evaluates the checks against some pre-specified threshold, and then communicates what was done in a transparent and easily understandable way. The quality checks were organized according... -
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ImageInTerminal.jl
Julia package for displaying images in the terminal using ANSI colors
ImageInTerminal is a drop-in package that once imported changes how a single Colorant and whole Colorant arrays (regular images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session. By default, this package will detect if your running terminal supports 24-bit colors (true colors). If it does, the image will be displayed in 24-bit colors, otherwise, it falls back to 8-bit (256 colors). -
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gganimate
A Grammar of Animated Graphics
gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customize how it should change with time. Here we take a simple boxplot of fuel consumption as a function of cylinders and let it transition between the number of gears available in the cars. As this is a discrete split (gear being best described as an ordered factor) we use... -
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GDAL.jl
Thin Julia wrapper for GDAL - Geospatial Data Abstraction Library
Julia wrapper for GDAL - Geospatial Data Abstraction Library. This package is a binding to the C API of GDAL/OGR. It provides only a C style usage, where resources must be closed manually, and datasets are pointers. Other packages can build on top of this to provide a more Julian user experience. See for example ArchGDAL.jl. Most users will want to use ArchGDAL.jl instead of using GDAL.jl directly.
