Search Results for "bcftools-windows" - Page 2
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Showing 456 open source projects for "bcftools-windows"
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electricityMap
A real-time visualisation of the CO2 emissions of electricity
Real-time visualization of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity consumption built with d3.js and mapbox GL. Real-time data is defined as a data source with an hourly (or better) frequency, delayed by less than 2hrs. It should provide a breakdown by generation type. Often fossil fuel generation (coal/gas/oil) is combined under a single heading like 'thermal' or 'conventional', this is not a problem. Citizens should not be responsible for the emissions... -
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Astropy
Repository for the Astropy core package
The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages. Astropy is a Python library for use in astronomy. Learn Astropy provides a portal to all of the Astropy educational material through a single dynamically searchable web page. It allows you to filter tutorials by keywords, search for filters, and make search queries in tutorials and documentation simultaneously. The Anaconda Python... -
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Pathway
Python ETL framework for stream processing, real-time analytics, LLM
Pathway is an open-source framework designed for building real-time data applications using reactive and declarative paradigms. It enables seamless integration of live data streams and structured data into analytical pipelines with minimal latency. Pathway is especially well-suited for scenarios like financial analytics, IoT, fraud detection, and logistics, where high-velocity and continuously changing data is the norm. Unlike traditional batch processing frameworks, Pathway continuously... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. 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... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to... -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking... -
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Positron
Positron, a next-generation data science IDE
Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding... -
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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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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,... -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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Python for Data Analysis
Materials and IPython notebooks for "Python for Data Analysis"
Python for Data Analysis is the official companion repository for Python for Data Analysis, 3rd Edition by Wes McKinney. It contains the datasets, examples, and IPython notebooks used throughout the book. The repository helps readers practice Python data analysis concepts directly in Jupyter Notebook. Its chapters cover Python basics, NumPy, pandas, data loading, cleaning, wrangling, visualization, time series, modeling libraries, and full analysis examples. The project includes setup... -
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two... -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes,... -
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JILL.py
A cross-platform installer for the Julia programming language
The enhanced Python fork of JILL, Julia Installer for Linux (and every other platform), Light. -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
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CleanVision
Automatically find issues in image datasets
CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset! The quality of machine learning models hinges on the quality of the data used... -
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Neuroglancer
WebGL-based viewer for volumetric data
Neuroglancer is a WebGL-based visualization tool designed for exploring large-scale volumetric and neuroimaging datasets directly in the browser. It allows users to interactively view arbitrary 2D and 3D cross-sections of volumetric data alongside 3D meshes and skeleton models, enabling precise examination of neural structures and biological imaging results. Its multi-pane interface synchronizes multiple orthogonal views with a central 3D viewport, making it ideal for analyzing complex brain... -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you... -
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PySyft
Data science on data without acquiring a copy
Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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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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Run Page
Make your own running home page
GitHub Actions manages automatic synchronization of runs and generation of new pages. Gatsby-generated static pages, fast. Support for Vercel (recommended) and GitHub Pages automated deployment. React Hooks. Mapbox for map display. Supports most sports apps such as nike strava. Automatically backup gpx data for easy backup and uploading to other software. -
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classic.tplx
A more accurate representation of jupyter notebooks
A more accurate representation of Jupyter notebooks when converting to pdfs. This template was designed to make converted Jupyter notebooks look (almost) identical to the actual notebook. If something doesn't exist in the original notebook then it doesn't belong in the conversion. As of nbconvert 5.5.0, the majority of these improvements have been merged into nbconvert's default template. Version 3.x of this package will continue to support nbconvert 5.5.0 and lower, whereas in the future...
