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Source channel @githubtrending · Post #15608 · Apr 7

#other Use Karpathy-inspired guidelines in a single CLAUDE.md file to fix Claude's coding flaws like wrong assumptions, overcomplicated code, unnecessary edits, and poor goal-setting. Follow four rules: think explicitly before coding, prioritize simplicity, make only required changes, and use tests for verifiable success. Install via Claude plugin or curl command. You benefit with cleaner, minimal code, fewer errors, proactive questions, and self-correcting AI that delivers precise results faster. https://github.com/forrestchang/andrej-karpathy-skills

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djangoproject

@djangoproject · Post #423 · 08/26/2017, 08:39 AM

http://scitools.org.uk/iris/docs/latest/userguide/index.html Iris seeks to provide a powerful, easy to use, and community-driven Python library for analysing and visualising #meteorological and #oceanographic data sets. With Iris you can: Use a single #API to work on your data, irrespective of its original format. Read and write (CF-)netCDF, GRIB, and PP files. Easily produce graphs and maps via integration with #matplotlib and #cartopy.

djangoproject

@djangoproject · Post #424 · 08/26/2017, 08:43 AM

http://scitools.org.uk/cartopy/docs/latest/index.html Cartopy is a Python package designed to make drawing maps for data analysis and visualisation as easy as possible. #Cartopy makes use of the powerful #PROJ.4, #numpy and #shapely libraries and has a simple and intuitive drawing interface to #matplotlib for creating publication quality maps. Some of the key features of cartopy are: object oriented projection definitions point, line, vector, polygon and image transformations between projections integration to expose advanced mapping in matplotlib with a simple and intuitive interface powerful vector data handling by integrating shapefile reading with Shapely capabilities