{"library":"spright","title":"spright","type":"library","description":"Bayesian radius-density-mass relation for small planets, using a hierarchical Bayesian model to infer interior composition from observed mass and radius. Current version 25.6.3, released monthly.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install spright"],"cli":null},"imports":["from spright import SPRight"],"auth":{"required":false,"env_vars":[]},"links":{"homepage":"https://spright.readthedocs.io","github":"https://github.com/hpparvi/spright","docs":null,"changelog":null,"pypi":"https://pypi.org/project/spright/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null},"quickstart":{"code":"from spright import SPRight\n\n# Initialize the model\nmodel = SPRight()\n\n# Evaluate the posterior for a given radius and mass (Earth units)\nradius = 1.5  # R_earth\nmass = 3.0    # M_earth\n\n# Get posterior samples of core mass fraction and water mass fraction\nsamples = model.predict(mass, radius)\nprint(samples)\n","lang":"python","description":"Quickstart: initialize SPRight, then call predict with mass and radius in Earth units to obtain posterior samples.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}