{"library":"spotpy","title":"spotpy - A Statistical Parameter Optimization Tool","type":"library","description":"spotpy is a Python library for statistical parameter optimization, calibration, and sensitivity analysis of environmental models. It provides a range of algorithms including Monte Carlo, Markov chain Monte Carlo (MCMC), Shuffled Complex Evolution (SCE-UA), DREAM, and various sensitivity analysis methods like FAST and Morris. Current version: 1.6.7. Release cadence is irregular, with several minor releases in recent years.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install spotpy"],"cli":null},"imports":["import spotpy","from spotpy.examples import spotpy_setup as setup","from spotpy import demcz"],"auth":{"required":false,"env_vars":[]},"links":{"homepage":"https://spotpy.readthedocs.io","github":"https://github.com/thouska/spotpy","docs":"https://spotpy.readthedocs.io","changelog":"https://github.com/thouska/spotpy/blob/master/CHANGELOG.md","pypi":"https://pypi.org/project/spotpy/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null},"quickstart":{"code":"import spotpy\nfrom spotpy.examples.spotpy_setup_hymod import spotpy_setup as setup\n\n# Create a test setup\nspotpy_setup = setup()\n\n# Select algorithm (here: Monte Carlo)\nsampler = spotpy.algorithms.mc(spotpy_setup, dbname='MC_test', dbformat='csv')\nsampler.sample(100)\n\n# Get results\nresults = sampler.getdata()\nprint(results.keys())","lang":"python","description":"Basic Monte Carlo sampling using spotpy with a built-in example setup (HYMOD model).","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}