Package: hydrating
hydratingOverviewhydrating is a python package for fitting hydrological rating curves.This initial preview only provides basic powerlaw fitting.Development Status: Pre-Alpha. Due to personal obligations, development is mostly on pause until second half of 2023.
Consider the API unstable, it may change at short/no notice.Requirements and installationRequirements:numpy
pandas
lmfitInstall from pypi using pippip install hydratingGeneral description and example usageFunctionality is currently limited to fitting basic powerlaw rating curve# imports for creating demo datafromnumpy.randomimportSeedSequence,default_rngimportpandasaspdfromlmfitimportParameters# imports for hydratingfromhydratingimportRatingCurvefromhydrating.modelsimportPowerLaw# Test data for power law ratinga=0.1b=2.5h0=0.65rng=default_rng(SeedSequence(123))stage=rng.uniform(1,10,size=20)#+ rng.normal(0, 0.01, size=20) # uncomment for stage with some noisedischarge_c=PowerLaw().func(stage,a,h0,b)discharge=discharge_c#+  discharge_c * rng.normal(0, 0.1, size=20) # uncomment for discharge with some noisedata_df=pd.DataFrame({'stage_key':stage,'discharge_key':discharge})# fit by passing numpy arrays of the datarc_c=RatingCurve(PowerLaw)rc_c.fit(x=stage,y=discharge)rc_c.fit_summary()# for a fit summaryrc_c.fit_best_parametersOut:{'a':0.09999999999999991,'h0':0.6499999999999992,'b':2.5000000000000004}# fit by adding a dataframe. In future versions this will allow for more options adding# other metadata to the rating curverc_c=RatingCurve(PowerLaw)rc_c.add_data(data_df,stage='stage_key',discharge='discharge_key')rc_c.fit()# will automatically use keys provided in add_data, but can also pass other keys here# specify parameter bounds and fixed values, using lmfit.Parameters objectsinput_parameters=Parameters()input_parameters.add('a',value=1)input_parameters.add('b',value=2.6,vary=False)input_parameters.add('h0',value=0,max=0.4)rc_c=RatingCurve(PowerLaw,initial_parameters=input_parameters)rc_c.fit(x=stage,y=discharge)Out:{'a':0.07495641841621854,'b':2.6,'h0':0.3999999978452029}# one can also let hydrating create the parameters simpy by ommiting# initial_parameters keyword, and then modify them# refer to lmfit documentationrc_c=RatingCurve(PowerLaw)rc_c.initial_parameters['b'].value=2.55rc_c.initial_parameters['b'].vary=Falserc_c.fit(x=stage,y=discharge)rc_c.fit_best_parametersOut:{'a':0.08681743671415193,'h0':0.5320384707736181,'b':2.55}Feedback and issuesPlease report issues here:https://github.com/rhkarls/hydrating/issuesGeneral feedback is most welcome, please post that as well under issues.MIT LicenseCopyright (c) 2022, Reinert Huseby KarlsenPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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