smps.fit.LogNormalFitResults#
- class smps.fit.LogNormalFitResults(params, error_matrix, fittedvalues, modes, **kwargs)#
Fit parameters for the LogNormal distribution.
This class is returned by the LogNormal model and is not typically created from scratch.
- Parameters
- paramsarray-like
The parameters of the fitted model as an array.
- error_matrixarray-like
The error matrix for the params.
- fittedvaluesarray-like
The fitted values from the LogNormal model.
- modesint
The number of modes fit.
See also
- predict(X, weight='number')#
Predict new values using the fit model.
- Parameters
- Xarray-like
An array of particle diameters at which to predict the number concentration based on the fit model.
- weight: str, default=’number’
The moment of the model to fit at. Should be one of (‘number’, ‘surface’, ‘volume’).
- Returns
- An array of number concentrations.
Examples
Predict the number concentration at 1 and 2.5 µm:
>>> from smps.fit import LogNormal >>> >>> model = LogNormal() >>> >>> results = model.fit(obj.midpoints, obj.dndlogdp.mean(), modes=1) >>> >>> results.predict([1., 2.5])
- summary()#
Summary statistics for the fit LogNormal model.
- Returns
- statsmodels.iolib.table.SimpleTable
Examples
>>> from smps.fit import LogNormal >>> >>> model = LogNormal() >>> >>> results = model.fit(obj.midpoints, obj.dndlogdp.mean(), modes=1) >>> results.summary()