LogParabola¶
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class
sherpa.models.basic.LogParabola(name='logparabola')[source] [edit on github]¶ Bases:
sherpa.models.model.RegriddableModel1DOne-dimensional log-parabolic function.
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ref¶ The reference point for the normalization.
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c1¶ The power-law index (gamma).
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c2¶ The curvature of the parabola (beta).
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ampl¶ The amplitude of the model.
Notes
The functional form of the model for points is:
f(x) = ampl * (x / ref) ^ (-c1 - c2 * log_10 (x / ref))
The grid version is evaluated by numerically intgerating the function over each bin using a non-adaptive Gauss-Kronrod scheme suited for smooth functions [1], falling over to a simple trapezoid scheme if this fails.
References
[1] https://www.gnu.org/software/gsl/manual/html_node/QNG-non_002dadaptive-Gauss_002dKronrod-integration.html Attributes Summary
thawedparhardmaxesthawedparhardminsthawedparmaxesthawedparminsthawedparsMethods Summary
apply(outer, *otherargs, **otherkwargs)calc(pars, xlo, *args, **kwargs)get_center()guess(dep, *args, **kwargs)Set an initial guess for the parameter values. regrid(*arrays)reset()set_center(*args, **kwargs)startup()Called before a model may be evaluated multiple times. teardown()Called after a model may be evaluated multiple times. Attributes Documentation
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thawedparhardmaxes¶
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thawedparhardmins¶
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thawedparmaxes¶
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thawedparmins¶
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thawedpars¶
Methods Documentation
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apply(outer, *otherargs, **otherkwargs) [edit on github]¶
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calc(pars, xlo, *args, **kwargs) [edit on github]¶
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get_center() [edit on github]¶
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guess(dep, *args, **kwargs) [edit on github]¶ Set an initial guess for the parameter values.
Attempt to set the parameter values, and ranges, for the model to match the data values. This is intended as a rough guess, so it is expected that the model is only evaluated a small number of times, if at all.
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regrid(*arrays) [edit on github]¶
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reset() [edit on github]¶
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set_center(*args, **kwargs) [edit on github]¶
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startup() [edit on github]¶ Called before a model may be evaluated multiple times.
See also
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teardown() [edit on github]¶ Called after a model may be evaluated multiple times.
See also
setup()
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