isochrones/starmodel_old.py
def prop_triangle(self, **kwargs):
        """
        Makes corner plot of only observable properties.

        The idea here is to compare the predictions of the samples
        with the actual observed data---this can be a quick way to check
        if there are outlier properties that aren't predicted well
        by the model.

        :param **kwargs:
            Keyword arguments passed to :func:`StarModel.triangle`.

        :return:
            Figure object containing corner plot.
         
        """
        truths = []
        params = []
        for p in self.properties:
            try:
                val, err = self.properties[p]
            except:
                continue

            if p in self.ic.bands:
                params.append('{}_mag'.format(p))
                truths.append(val)
            elif p=='parallax':
                params.append('distance')
                truths.append(1/(val/1000.))
            else:
                params.append(p)
                truths.append(val)
        return self.triangle(params, truths=truths, **kwargs)

        Makes corner plot of only observable properties.

        The idea here is to compare the predictions of the samples
        with the actual observed data---this can be a quick way to check
        if there are outlier properties that aren't predicted well
        by the model.

        :param **kwargs:
            Keyword arguments passed to :func:`StarModel.triangle`.

        :return:
            Figure object containing corner plot.
         
        
Makes corner plot of only observable properties.
The idea here is to compare the predictions of the samples
with the actual observed data---this can be a quick way to check
if there are outlier properties that aren't predicted well
by the model.
Makes corner plot of only observable properties . The idea here is to compare the predictions of the samples with the actual observed data --- this can be a quick way to check if there are outlier properties that aren ' t predicted well by the model .
def prop_triangle(self, **kwargs):
        truths = []
        params = []
        for p in self.properties:
            try:
                val, err = self.properties[p]
            except:
                continue
            if p in self.ic.bands:
                params.append('{}_mag'.format(p))
                truths.append(val)
            elif p=='parallax':
                params.append('distance')
                truths.append(1/(val/1000.))
            else:
                params.append(p)
                truths.append(val)
        return self.triangle(params, truths=truths, **kwargs)
def prop_triangle ( self , ** kwargs ) : truths = [ ] params = [ ] for p in self . properties : try : val , err = self . properties [ p ] except : continue if p in self . ic . bands : params . append ( '{}_mag' . format ( p ) ) truths . append ( val ) elif p == 'parallax' : params . append ( 'distance' ) truths . append ( 1 / ( val / 1000. ) ) else : params . append ( p ) truths . append ( val ) return self . triangle ( params , truths = truths , ** kwargs )
Makes corner plot of only observable properties.
Makes corner plot of only observable properties .
"""
        Makes corner plot of only observable properties.

        The idea here is to compare the predictions of the samples
        with the actual observed data---this can be a quick way to check
        if there are outlier properties that aren't predicted well
        by the model.

        :param **kwargs:
            Keyword arguments passed to :func:`StarModel.triangle`.

        :return:
            Figure object containing corner plot.
         
        """
Figure object containing corner plot.
Figure object containing corner plot .
self
**kwargs
Keyword arguments passed to :func:`StarModel.triangle`.
Keyword arguments passed to : func : ` StarModel . triangle ` .