Showing 5 open source projects for "effects"

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  • 1
    VisPy

    VisPy

    Main repository for Vispy

    Vispy is an open-source, high-performance interactive visualization library in Python, designed for creating scientific visualizations and interactive plots. It leverages the power of modern Graphics Processing Units (GPUs) through OpenGL to render large datasets efficiently. Vispy supports a wide range of visualization types, including 2D plots, 3D visualizations, volume rendering, and more, making it suitable for scientific research, data analysis, and educational purposes.
    Downloads: 0 This Week
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  • 2
    F1 Race Replay

    F1 Race Replay

    An interactive Formula 1 race visualisation and data analysis tool

    ...It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. ...
    Downloads: 0 This Week
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  • 3
    nb-clean

    nb-clean

    Clean Jupyter notebooks of outputs, metadata, and empty cells

    ...You may prefer this to the Git filter if your project already uses the pre-commit framework. Note that the Git filter and pre-commit hook work differently, with different effects on your working directory. The pre-commit hook operates on the notebook on disk, cleaning the copy in your working directory. The Git filter cleans notebooks as they are added to the index, leaving the copy in your working directory dirty.
    Downloads: 0 This Week
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  • 4
    MCPower

    MCPower

    MCPower — simple Monte Carlo power analysis for complex models

    ...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation.
    Downloads: 4 This Week
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  • 5
    Software package for stochastic and hybrid simulation of microtubule dynamics and the effects of anti-mitotic drugs and microtubule regulatory proteins.
    Downloads: 0 This Week
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