def residuals(fitparams, params, func, data, uncert, indparams, stepsize,
pmin, pmax, prior, priorlow, priorup):
"""
Calculate the weighted residuals between data and a model, accounting
also for parameter priors.
Parameters
----------
fitparams: 1D ndarray
The model free parameters.
params: 1D ndarray
Model parameters (including fixed and shared parameters).
func: Callable
Function that models data, being called as:
model = func(params, *indparams)
data: 1D ndarray
The data set being modeled.
uncert: 1D ndarray
Data uncertainties.
indparams: Tuple
Additional arguments for the func function.
stepsize: 1D ndarray
Array indicating which params are free params (stepsize > 0), the
fixed params (stepsize=0) and shared parameters (stepsize < 0).
pmin: 1D ndarray
Lower boundaries of params.
pmax: 1D ndarray
Upper boundaries of params.
prior: 1D ndarray
Priors array.
priorlow: 1D ndarray
Priors lower uncertainties.
priorup: 1D ndarray
Priors upper uncertainties.
Returns
-------
Array of weighted data-model and prior-params residuals.
"""
# Get free and shared indices:
ifree = np.where(stepsize > 0)[0]
ishare = np.where(stepsize < 0)[0]
# Combine fitparams into func params:
params[ifree] = fitparams
# Keep parameters within boundaries:
params = np.clip(params, pmin, pmax)
# Update shared parameters:
for s in ishare:
params[s] = params[-int(stepsize[s])-1]
# Compute model:
fargs = [params] + indparams
model = func(*fargs)
# Find the parameters that have prior:
iprior = np.where(priorlow != 0)[0]
prioroff = params - prior
# Calculate residuals:
residuals = cs.residuals(model, data, uncert,
prioroff[iprior], priorlow[iprior], priorup[iprior])
#print("Params: %s"%str(params))
#print("Prior: %s"%str(prior))
#print(prioroff[iprior], priorlow[iprior], priorup[iprior])
#print(residuals[-4:])
return residuals
def residuals(fitparams, params, func, data, uncert, indparams, stepsize, pmin,
pmax, prior, priorlow, priorup):
"""
Calculate the weighted residuals between data and a model, accounting
also for parameter priors.
Parameters
----------
fitparams: 1D ndarray
The model free parameters.
params: 1D ndarray
Model parameters (including fixed and shared parameters).
func: Callable
Function that models data, being called as:
model = func(params, *indparams)
data: 1D ndarray
The data set being modeled.
uncert: 1D ndarray
Data uncertainties.
indparams: Tuple
Additional arguments for the func function.
stepsize: 1D ndarray
Array indicating which params are free params (stepsize > 0), the
fixed params (stepsize=0) and shared parameters (stepsize < 0).
pmin: 1D ndarray
Lower boundaries of params.
pmax: 1D ndarray
Upper boundaries of params.
prior: 1D ndarray
Priors array.
priorlow: 1D ndarray
Priors lower uncertainties.
priorup: 1D ndarray
Priors upper uncertainties.
Returns
-------
Array of weighted data-model and prior-params residuals.
"""
# Get free and shared indices:
ifree = np.where(stepsize > 0)[0]
ishare = np.where(stepsize < 0)[0]
# Combine fitparams into func params:
params[ifree] = fitparams
# Keep parameters within boundaries:
params = np.clip(params, pmin, pmax)
# Update shared parameters:
for s in ishare:
params[s] = params[-int(stepsize[s]) - 1]
# Compute model:
fargs = [params] + indparams
model = func(*fargs)
# Find the parameters that have prior:
iprior = np.where(priorlow != 0)[0]
prioroff = params - prior
# Calculate residuals:
residuals = cs.residuals(model, data, uncert, prioroff[iprior],
priorlow[iprior], priorup[iprior])
#print("Params: %s"%str(params))
#print("Prior: %s"%str(prior))
#print(prioroff[iprior], priorlow[iprior], priorup[iprior])
#print(residuals[-4:])
return residuals
def residuals(fitparams, params, func, data, uncert, indparams, stepsize,
prior, priorlow, priorup, ifree, ishare, iprior):
"""
Calculate the weighted residuals between data and a model, accounting
also for parameter priors.
Parameters
----------
fitparams: 1D ndarray
The model free parameters.
params: 1D ndarray
The model parameters (including fixed and shared parameters).
func: Callable
The fitting function to model the data, called as:
model = func(params, *indparams)
data: 1D ndarray
Dependent data fitted by func.
uncert: 1D ndarray
1-sigma uncertainty of data.
indparams: tuple
Additional arguments required by func (if required).
stepsize: 1D ndarray
Parameters' jump scale (same size as params).
If the stepsize is positive, the parameter is free for fitting.
If the stepsize is 0, keep the parameter value fixed.
If the stepsize is a negative integer, copy (share) the parameter value
from params[np.abs(stepsize)+1], which can be free or fixed.
prior: 1D ndarray
Model parameters' (Gaussian) prior values (same size as params).
Considered only when priolow != 0. priorlow and priorup are the
lower and upper 1-sigma width of the Gaussian prior, respectively.
priorlow: 1D ndarray
Parameters' lower 1-sigma Gaussian prior (same size as params).
priorup: 1D ndarray
Paraneters' upper 1-sigma Gaussian prior (same size as params).
ifree: 1D bool ndarray
Indices of the free parameters in params.
ishare: 1D bool ndarray
Indices of the shared parameters in params.
iprior: 1D bool ndarray
Indices of the prior parameters in params.
Returns
-------
Array of weighted data-model and prior-params residuals.
"""
# Update params with fitparams:
params[ifree] = fitparams
# Update shared parameters:
for s in ishare:
params[s] = params[-int(stepsize[s]) - 1]
# Compute model:
model = func(params, *indparams)
# Find the parameters that have prior:
prioroff = params - prior
# Calculate residuals:
residuals = cs.residuals(model, data, uncert, prioroff[iprior],
priorlow[iprior], priorup[iprior])
return residuals
def residuals(fitparams, params, func, data, uncert, indparams, stepsize,
prior, priorlow, priorup, ifree, ishare, iprior):
"""
Calculate the weighted residuals between data and a model, accounting
also for parameter priors.
Parameters
----------
fitparams: 1D ndarray
The model free parameters.
params: 1D ndarray
The model parameters (including fixed and shared parameters).
func: Callable
The fitting function to model the data, called as:
model = func(params, *indparams)
data: 1D ndarray
Dependent data fitted by func.
uncert: 1D ndarray
1-sigma uncertainty of data.
indparams: tuple
Additional arguments required by func (if required).
stepsize: 1D ndarray
Parameters' jump scale (same size as params).
If the stepsize is positive, the parameter is free for fitting.
If the stepsize is 0, keep the parameter value fixed.
If the stepsize is a negative integer, copy (share) the parameter value
from params[np.abs(stepsize)+1], which can be free or fixed.
prior: 1D ndarray
Model parameters' (Gaussian) prior values (same size as params).
Considered only when priolow != 0. priorlow and priorup are the
lower and upper 1-sigma width of the Gaussian prior, respectively.
priorlow: 1D ndarray
Parameters' lower 1-sigma Gaussian prior (same size as params).
priorup: 1D ndarray
Paraneters' upper 1-sigma Gaussian prior (same size as params).
ifree: 1D bool ndarray
Indices of the free parameters in params.
ishare: 1D bool ndarray
Indices of the shared parameters in params.
iprior: 1D bool ndarray
Indices of the prior parameters in params.
Returns
-------
Array of weighted data-model and prior-params residuals.
"""
# Update params with fitparams:
params[ifree] = fitparams
# Update shared parameters:
for s in ishare:
params[s] = params[-int(stepsize[s])-1]
# Compute model:
model = func(params, *indparams)
# Find the parameters that have prior:
prioroff = params - prior
# Calculate residuals:
residuals = cs.residuals(model, data, uncert,
prioroff[iprior], priorlow[iprior], priorup[iprior])
return residuals
