splat/model.py
def _modelFitEMCEE_bestparameters(values,lnp,**kwargs):
    '''
    Return three sets of parameters: by quantiles, the weighted mean, and the best values
    '''
    parameter_weights = kwargs.get('parameter_weights',numpy.ones(values.shape[-1]))
    quantiles = kwargs.get('quantiles',[16,50,84])
    verbose = kwargs.get('verbose',False)

    quant_parameters = []
    best_parameters = []
    mean_parameters = []
    for i in range(values.shape[-1]):
        q = numpy.percentile(values[:,i],quantiles)
        quant_parameters.append([q[1],q[2]-q[1],q[1]-q[0]])
        mean_parameters.append(numpy.sum(parameter_weights*values[:,i])/numpy.sum(parameter_weights))
        best_parameters.append(values[numpy.where(lnp == numpy.max(lnp)),i].reshape(-1)[0])
    return best_parameters,mean_parameters,quant_parameters

    Return three sets of parameters: by quantiles, the weighted mean, and the best values
    
Return three sets of parameters: by quantiles, the weighted mean, and the best values
Return three sets of parameters : by quantiles the weighted mean and the best values
def _modelFitEMCEE_bestparameters(values,lnp,**kwargs):
    parameter_weights = kwargs.get('parameter_weights',numpy.ones(values.shape[-1]))
    quantiles = kwargs.get('quantiles',[16,50,84])
    verbose = kwargs.get('verbose',False)
    quant_parameters = []
    best_parameters = []
    mean_parameters = []
    for i in range(values.shape[-1]):
        q = numpy.percentile(values[:,i],quantiles)
        quant_parameters.append([q[1],q[2]-q[1],q[1]-q[0]])
        mean_parameters.append(numpy.sum(parameter_weights*values[:,i])/numpy.sum(parameter_weights))
        best_parameters.append(values[numpy.where(lnp == numpy.max(lnp)),i].reshape(-1)[0])
    return best_parameters,mean_parameters,quant_parameters
def _modelFitEMCEE_bestparameters ( values , lnp , ** kwargs ) : parameter_weights = kwargs . get ( 'parameter_weights' , numpy . ones ( values . shape [ - 1 ] ) ) quantiles = kwargs . get ( 'quantiles' , [ 16 , 50 , 84 ] ) verbose = kwargs . get ( 'verbose' , False ) quant_parameters = [ ] best_parameters = [ ] mean_parameters = [ ] for i in range ( values . shape [ - 1 ] ) : q = numpy . percentile ( values [ : , i ] , quantiles ) quant_parameters . append ( [ q [ 1 ] , q [ 2 ] - q [ 1 ] , q [ 1 ] - q [ 0 ] ] ) mean_parameters . append ( numpy . sum ( parameter_weights * values [ : , i ] ) / numpy . sum ( parameter_weights ) ) best_parameters . append ( values [ numpy . where ( lnp == numpy . max ( lnp ) ) , i ] . reshape ( - 1 ) [ 0 ] ) return best_parameters , mean_parameters , quant_parameters
Return three sets of parameters: by quantiles, the weighted mean, and the best values
Return three sets of parameters : by quantiles the weighted mean and the best values
'''
    Return three sets of parameters: by quantiles, the weighted mean, and the best values
    '''
values
lnp