StellarModel.py 文件源码

def __call__(self, T, metal, vsini=0.0, return_xypoint=True, **kwargs):
        """
        Given parameters, return an interpolated spectrum

        If return_xypoint is False, then it will only return
          a numpy.ndarray with the spectrum

        Before interpolating, we will do some error checking to make
        sure the requested values fall within the grid
        """

        # Scale the requested values
        T = (T - self.T_scale[0]) / self.T_scale[1]
        metal = (metal - self.metal_scale[0]) / self.metal_scale[1]

        # Get the minimum and maximum values in the grid
        T_min = min(self.grid[:, 0])
        T_max = max(self.grid[:, 0])
        metal_min = min(self.grid[:, 1])
        metal_max = max(self.grid[:, 1])
        input_list = (T, metal)

        # Check to make sure the requested values fall within the grid
        if (T_min <= T <= T_max and
                        metal_min <= metal <= metal_max):

            y = self.interpolator(input_list)
        else:
            if self.debug:
                warnings.warn("The requested parameters fall outside the model grid. Results may be unreliable!")
            print(T, T_min, T_max)
            print(metal, metal_min, metal_max)
            y = self.NN_interpolator(input_list)

        # Test to make sure the result is valid. If the requested point is
        # outside the Delaunay triangulation, it will return NaN's
        if np.any(np.isnan(y)):
            if self.debug:
                warnings.warn("Found NaNs in the interpolated spectrum! Falling back to Nearest Neighbor")
            y = self.NN_interpolator(input_list)

        model = DataStructures.xypoint(x=self.xaxis, y=y)
        vsini *= units.km.to(units.cm)
        model = Broaden.RotBroad(model, vsini, linear=self.rebin)


        # Return the appropriate object
        if return_xypoint:
            return model
        else:
            return model.y