polyml.py 文件源码

def calcmlmags(self, lightcurve):
        """
        Returns a "lc.mags"-like array made using the ml-parameters.
        It has the same size as lc.mags, and contains the microlensing to be added to them.
        The lightcurve object is not changed !

        For normal use, call getmags() from the lightcurve.

        Idea : think about only returning the seasons mags to speed it up ? Not sure if reasonable, as no seasons defined outside ?
        """
        jds = lightcurve.jds[self.season.indices] # Is this already a copy ? It seems so. So no need for an explicit copy().
        # We do not need to apply shifts (i.e. getjds()), as anyway we "center" the jds.


        # Old method :
        if self.mltype == "poly":

            refjd = np.mean(jds)
            jds -= refjd # This is apparently safe, it does not shifts the lightcurves jds.

            allmags = np.zeros(len(lightcurve.jds))
            allmags[self.season.indices] = np.polyval(self.params, jds) # probably faster then +=
            return allmags


        # Legendre polynomials :
        if self.mltype == "leg":

            rjd = (np.max(jds) - np.min(jds))/2.0
            cjd = (np.max(jds) + np.min(jds))/2.0
            jds = (jds - cjd)/rjd

            allmags = np.zeros(len(lightcurve.jds))

            for (n, p) in enumerate(self.params):
                allmags[self.season.indices] += p * ss.legendre(n)(jds)

            return allmags