Search Results for "create-react-app" - Page 3
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Showing 467 open source projects for "create-react-app"
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NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data... -
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Querybook
Big Data Querying UI, combining collocated table metadata
...The Query Editor is aware of your tables and their columns, as such it provides autocompletion, syntax highlighting, and the ability to hover or click on a table to view its information. No need to leave Querybook to create charts to quickly visualize your results. With a familiar interface easily create line, bar, stacked area, pie, horizontal bar, donut, scatter, and table charts. Add them then to your DataDoc to complete your data narrative. -
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Promviz
Visualize traffic of your clusters in realtime from Prometheus data
Promviz is a visualization tool for Prometheus, enabling users to create custom dashboards and analyze Prometheus metrics interactively. -
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SQLPad
Web-based SQL editor run in your own private cloud
A web app for writing and running SQL queries and visualizing the results. Supports Postgres, MySQL, SQL Server, ClickHouse, Crate, Vertica, Trino, Presto, SAP HANA, Cassandra, Snowflake, Google BigQuery, SQLite, TiDB, and many more via ODBC. The docker image runs on port 3000 and uses /var/lib/sqlpad for the embedded database directory. latest tag is continuously built from latest commit in repo. -
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ipychart
The power of Chart.js with Python
Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive, and modular and are displayed directly in the output of the cells of your jupyter notebook environment. Charts are fully interactive, you can hover it to display tooltips and select the information you want to see directly from the output cell of your notebook. -
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CommonMark.jl
A CommonMark-compliant parser for Julia
A CommonMark-compliant parser for Julia. -
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D-Tale
Visualizer for pandas data structures
D-Tale is the combination of a Flask backend and a React front-end to bring you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex. D-Tale was the product of a SAS to Python conversion. What was originally a perl script wrapper on top of SAS's insight function is now a lightweight web client on top of Pandas data structures. ... -
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Comonicon
Your best CLI generator in JuliaLang
Roger's magic book for command line interfaces. -
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Books.jl
Create books with Julia
In a nutshell, this package is meant to generate books (or reports or dashboards) with embedded Julia output. Via Pandoc, the package can live serve a website and build various outputs including a website and PDF. For many standard output types, such as DataFrames and plots, the package can run your code and will automatically handle proper embedding in the output documents, and also try to guess suitable captions and labels. Also, it is possible to work via the live server, which shows... -
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ggpubr
'ggplot2' Based Publication Ready Plots
ggpubr is an R package that provides easy-to-use wrapper functions around ggplot2 to create publication-ready visualizations with minimal code. It streamlines plot creation for researchers and analysts, allowing features such as statistical annotation, theme customization, and plot arrangement with fewer lines of code. -
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Vizzu
Library for animated data visualizations and data stories
Vizzu is a free, open-source Javascript/C++ library utilizing a generic dataviz engine that generates many types of charts and seamlessly animates between them. It can be used to create static charts but more importantly, it is designed for building animated data stories and interactive explorers as Vizzu enables showing different perspectives of the data that the viewers can easily follow due to the animation. Create a placeholder element that will contain the rendered chart. The Vizzu constructor has to load Vizzu's WebAssembly module asynchronously to prevent blocking the whole page. ... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
...A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. ... -
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billboard.js
Re-usable, easy interface JavaScript chart library based on D3.js
billboard.js is a re-usable, easy interface JavaScript chart library, based on D3.js. The name "billboard" comes from the famous "billboard chart" which everybody knows. billboard.js provides the easiest way to create a 'chart' instantly. Chart generation is super easy. With extensive options, you can create a chart instantly! Yes, billboard.js works on D3 v4+; what everybody was waiting for! All of the code was written as an ESM(ES Module) with ES6+ syntax. Combine hundreds of options to satisfy your needs. Checkout the examples to see in action! ... -
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TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
...It adds a lot of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more. Create special chart by collecting charts tags in ‘scalars’. Note that this function can only be called once for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called before or after the training loop. -
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Countly iOS SDK
Countly Product Analytics iOS SDK with macOS, watchOS and tvOS support
Countly SDK for iOS is an analytics SDK for integrating real-time event tracking, user profiles, crash reporting, and push notifications into iOS apps. It provides detailed insights into user behavior and app performance. -
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Helix Toolkit
Helix Toolkit is a collection of 3D components for .NET
...Adds variety of functionalities/models on the top of internal WPF 3D models (Media3D namespace). dds variety of functionalities/models on the top of internal .NET Core WPF 3D models (Media3D namespace). Custom 3D Engine and XAML/MVVM compatible Scene Graphs based on SharpDX(DirectX 11) for Universal Windows App. Custom 3D Engine and Scene Graphs based on SharpDX(DirectX 11) for netstandard and .NET Core. Custom 3D Engine and XAML/MVVM compatible Scene Graphs based on SharpDX(DirectX 11) for WinUI 3 (Project Reunion - experimental). -
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prettymaps
A small set of Python functions to draw pretty maps from OpenStreetMap
A Python package to draw maps with customizable styles from OpenStreetMap data. Created using the osmnx, matplotlib, shapely and vsketch packages. -
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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. ... -
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JuiceFS
JuiceFS is a distributed POSIX file system built on top of Redis
...JuiceFS is designed to bring back the gold-old memories and experience of file systems in local disks to the cloud. JuiceFS is POSIX compliant and is fully compatible with HDFS and S3. Cloud app building or migrating, file sharing cross-geo and cross-cloud has become easier than ever before. Whether it's a public cloud, private cloud, or hybrid cloud, JuiceFS is available on any cloud of your choice and delivers flexibility, availability, scalability and strong consistency for your data-intensive applications. Purposely built to serve big data scenarios such as self-driving model training, recommendation engine, and Next-generation Gene Sequencing, JuiceFS specializes in high performance and easier management of tens of billion of files management. ... -
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Chokidar
Minimal and efficient cross-platform file watching library
...Node.js fs.watch often reports events twice, emits most changes as rename, and it does not provide an easy way to recursively watch file trees nor supports recursive watching on Linux. Same as with Node.js fs.watchFile. Therefore, Chokidar resolves these problems. Initially made for Brunch (an ultra-swift web app build tool), it is now used in Microsoft's Visual Studio Code, gulp, karma, PM2, browserify, webpack, BrowserSync, and many others. It has proven itself in production environments. Chokidar does still rely on the Node.js core fs module, but when using fs.watch and fs.watchFile for watching, it normalizes the events it receives, often checking for truth by getting file stats and/or dir contents. -
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ResumableFunctions.jl
C# style generators a.k.a. semi-coroutines for Julia
C# has a convenient way to create iterators using the yield return statement. The package ResumableFunctions provides the same functionality for the Julia language by introducing the @resumable and the @yield macros. These macros can be used to replace the Task switching functions produce and consume which were deprecated in Julia v0.6. Channels are the preferred way for inter-task communication in Julia v0.6+, but their performance is subpar for iterator applications. -
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Vespa
The open big data serving engine
...At any scale, with unbeatable performance. Vespa is a full-featured text search engine and supports both regular text search and fast approximate vector search (ANN). This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do. Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. ... -
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leafmap
A Python package for interactive mapping and geospatial analysis
A Python package for geospatial analysis and interactive mapping in a Jupyter environment. Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is a spin-off project of the geemap Python package, which was designed specifically to work with Google Earth Engine (GEE). However, not everyone in the geospatial community has access to the GEE cloud computing platform. Leafmap is designed to fill this gap for non-GEE users. It... -
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Bumper.jl
Bring Your Own Stack
Bumper.jl is a package that aims to make working with bump allocators (also known as arena allocators) easier and safer. You can dynamically allocate memory to these bump allocators, and reset them at the end of a code block, just like Julia's stack. Allocating to a bump allocator with Bumper.jl can be just as efficient as stack allocation. Bumper.jl is still a young package, and may have bugs. Let me know if you find any. -
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NetCDF.jl
NetCDF support for the julia programming language
NetCDF support for the Julia programming language, there is a high-level and a medium-level interface for writing and reading netcdf files. The dimensions "x1" and "t" of the variable are called "x1" and "t" in this example. If the dimensions do not exist yet in the file, they will be created. The dimension "x1" will be of length 10 and have the values 11..20, and the dimension "t" will have length 20 and the attribute "units" with the value "s".
