__init__.py 文件源码

def summary(self):
        s = OrderedDict()
        s['source kind'] = self.sourcekind
        s['source'] = self.source
        if self.params is not None:
            for param, value in self.params._asdict().items():
                s['parameter: %s' % param] = value
            s['log-variance theoretical half-life'] = self.params.logvarhalflife()
            s['log-variance theoretical unconditional s.d.'] = np.sqrt(self.params.logvaruncondvar())
        s['log-return sample mean'] = np.mean(self.svdf['logreturn'])
        s['log-return sample s.d.'] = np.sqrt(np.var(self.svdf['logreturn']))
        if 'logvar' in self.svdf.columns:
            s['log-variance sample mean'] = np.mean(self.svdf['logvar'])
            s['log-variance sample s.d.'] = np.sqrt(np.var(self.svdf['logvar']))
        s['correlation timing'] = self.cortiming        
        s['log-return forward?'] = self.logreturnforward
        s['log-return scale'] = self.logreturnscale
        return s