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strawberry-graphql-with-litestar
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public |
A library for creating GraphQL APIs (with litestar)
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2025-10-10 |
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strawberry-graphql-with-opentelemetry
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public |
A library for creating GraphQL APIs (with opentelemetry)
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2025-10-10 |
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strawberry-graphql-with-channels
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public |
A library for creating GraphQL APIs (with channels)
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2025-10-10 |
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strawberry-graphql-with-aiohttp
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public |
A library for creating GraphQL APIs (with aiohttp)
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2025-10-10 |
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strawberry-graphql-with-chalice
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public |
A library for creating GraphQL APIs (with chalice)
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2025-10-10 |
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strawberry-graphql-with-pyinstrument
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public |
A library for creating GraphQL APIs (with pyinstrument)
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2025-10-10 |
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strawberry-graphql-with-django
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public |
A library for creating GraphQL APIs (with django)
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2025-10-10 |
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strawberry-graphql-with-debug-server
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public |
A library for creating GraphQL APIs (with debug-server)
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2025-10-10 |
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strawberry-graphql-with-fastapi
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public |
A library for creating GraphQL APIs (with fastapi)
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2025-10-10 |
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strawberry-graphql-with-debug
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public |
A library for creating GraphQL APIs (with debug)
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2025-10-10 |
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strawberry-graphql-with-asgi
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public |
A library for creating GraphQL APIs (with asgi)
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2025-10-10 |
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strawberry-graphql
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public |
A library for creating GraphQL APIs
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2025-10-10 |
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strawberry-graphql-with-sanic
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public |
A library for creating GraphQL APIs (with sanic)
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2025-10-10 |
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furones
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public |
Compute the Approximate Independent Set for undirected graph encoded in DIMACS format.
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2025-10-10 |
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glab
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public |
A GitLab CLI tool bringing GitLab to your command line
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2025-10-10 |
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pydantic-xml
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public |
pydantic xml extension
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2025-10-10 |
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msgraph-sdk
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public |
The Microsoft Graph Python SDK
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2025-10-10 |
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pbs-installer
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public |
Installer for Python Build Standalone
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2025-10-10 |
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hklpy2
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public |
2nd generation controls for diffractometers as PseudoPositioners in the Bluesky Framework.
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2025-10-10 |
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sccache
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public |
sccache is ccache with cloud storage
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2025-10-10 |
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pymomentum-gpu
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public |
A library for human kinematic motion and numerical optimization solvers to apply human motion
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2025-10-10 |
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erddapcfg
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public |
Python program to work with ERDDAP configurations.
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2025-10-10 |
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carapace
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public |
A multi-shell completion binary
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2025-10-10 |
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outlines
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public |
Probabilistic Generative Model Programming
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2025-10-10 |
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r-datavisualizations
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public |
Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
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2025-10-10 |
