Showing 4 open source projects for "graphics"

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    seaborn

    seaborn

    Statistical data visualization in Python

    Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. 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. ...
    Downloads: 5 This Week
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  • 2
    MORPHE

    MORPHE

    MORphological PHenotype Extraction

    MORphological PHenotype Extraction (MORPHE) is a suite of automated image processing, visualization, and classification algorithms to facilitate the analysis of heritable and clonal red-to-green transitions that occurred during the growth of a colony.
    Downloads: 0 This Week
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  • 3

    GENIE (GEne-geNe IntEraction)

    GPU based Parallel Gene-Gene Interaction Analysis

    Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. ...
    Downloads: 0 This Week
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  • 4
    Myrtle

    Myrtle

    A simple programmable spreadsheet for learning statistics.

    ...Myrtle offers a unique bookmarking facility which allows students to create and reuse named references to their favorite cell ranges. This can help students focus attention on the important relationships among particular rows or columns of data. Myrtle's graphics and reporting features allow students to report back to their instructors their mastery of course content.
    Downloads: 3 This Week
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