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__init__(self,
verbosity=1)
Class for to set settings for minimisation and dispersion target
functions for minimisation. |
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set_settings_minfx(self,
scaling_matrix=None,
min_algor=' simplex ' ,
c_code=True,
constraints=False,
chi2_jacobian=False,
func_tol=1e-25,
grad_tol=None,
max_iterations=10000000)
Setup options to minfx. |
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list
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estimate_x0_exp(self,
times=None,
values=None)
Estimate starting parameter x0 = [r2eff_est, i0_est], by converting
the exponential curve to a linear problem. |
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numpy array
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func_exp(self,
params=None,
times=None)
Calculate the function values of exponential function. |
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numpy array
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func_exp_residual(self,
params=None,
times=None,
values=None)
Calculate the residual vector betwen measured values and the function
values. |
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numpy array
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numpy array
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func_exp_grad(self,
params=None,
times=None)
The gradient (Jacobian matrix) of func_exp for Co-variance
calculation. |
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float
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func_exp_chi2(self,
params=None,
times=None,
values=None,
errors=None)
Target function for minimising chi2 in minfx, for exponential fit. |
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numpy array
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func_exp_chi2_grad(self,
params=None,
times=None,
values=None,
errors=None)
Target function for the gradient (Jacobian matrix) to minfx, for
exponential fit . |
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numpy array
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func_exp_chi2_grad_array(self,
params=None,
times=None,
values=None,
errors=None)
Return the gradient (Jacobian matrix) of func_exp_chi2() for
parameter co-variance error estimation. |
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