def regression(self): # Prior on mean function : mean_mag = np.mean(self.mags) def meanprior(query): return (0.0 * query + mean_mag) # Ok we try this, but with a spline #def meanprior(jds): # inshape = jds.shape # return spline.eval(jds=jds.reshape(inshape[0])).reshape(inshape) self.regfct = pymcgp.regression(self.jds, self.mags, self.magerrs, meanprior) #npts = (gpr.jds[-1] - gpr.jds[0])*2.0 # xs = np.linspace(gpr.jds[0], gpr.jds[-1], npts) # (ys, yerrs) = gpr.regfct(xs)
def regression(self):
# Prior on mean function :
mean_mag = np.mean(self.mags)
def meanprior(query):
return (0.0 * query + mean_mag)
# Ok we try this, but with a spline
#def meanprior(jds):
# inshape = jds.shape
# return spline.eval(jds=jds.reshape(inshape[0])).reshape(inshape)
self.regfct = pymcgp.regression(self.jds, self.mags, self.magerrs,
meanprior)
#npts = (gpr.jds[-1] - gpr.jds[0])*2.0
# xs = np.linspace(gpr.jds[0], gpr.jds[-1], npts)
# (ys, yerrs) = gpr.regfct(xs)
def factory(l, pad=300, pd=2, plotcolour=None, covkernel="matern", pow=1.5, amp=2.0, scale=200.0, errscale=5.0): """ Give me a lightcurve, I return a regularly sampled light curve, by performing some regression. :param pad: the padding, in days :param pd: the point density, in points per days. The points live on a regular grid in julian days, 0.0, 0.1, 0.2, 0.3 ... The parameters pow, amp, scale, errscale are passed to the GPR, see its doc. """ if plotcolour == None: plotcolour = l.plotcolour name = l.object jds = l.jds.copy() timeshift = l.timeshift mags = l.getmags(noml=True) magerrs = l.getmagerrs() minjd = np.round(jds[0] - pad) maxjd = np.round(jds[-1] + pad) npts = int(maxjd - minjd)*pd rsjds = np.linspace(minjd, maxjd, npts) # rs for regularly sampled # The regression itself mean_mag = np.mean(mags) def meanprior(query): return (0.0 * query + mean_mag) regfct = pymcgp.regression(jds, mags, magerrs, meanprior, covkernel=covkernel, pow=pow, amp=amp, scale=scale, errscale=errscale) (rsmags, rsmagerrs) = regfct(rsjds) # that f****r does not want to be executed in a multiprocessing loop. Why ? return rslc(rsjds, rsmags, rsmagerrs, pad, pd, timeshift=timeshift, name=name, plotcolour=plotcolour)
def factory(l, pad=300, pd=2, plotcolour=None, pow=1.5, amp=2.0, scale=200.0, errscale=5.0): """ Give me a lightcurve, I return a regularly sampled light curve, by performing some regression. :param pad: the padding, in days :param pd: the point density, in points per days. The points live on a regular grid in julian days, 0.0, 0.1, 0.2, 0.3 ... The parameters pow, amp, scale, errscale are passed to the GPR, see its doc. """ if plotcolour == None: plotcolour = l.plotcolour name = l.object jds = l.jds.copy() timeshift = l.timeshift mags = l.getmags(noml=True) magerrs = l.getmagerrs() minjd = np.round(jds[0] - pad) maxjd = np.round(jds[-1] + pad) npts = int(maxjd - minjd)*pd rsjds = np.linspace(minjd, maxjd, npts) # rs for regularly sampled # The regression itself mean_mag = np.mean(mags) def meanprior(query): return (0.0 * query + mean_mag) regfct = pymcgp.regression(jds, mags, magerrs, meanprior, pow=pow, amp=amp, scale=scale, errscale=errscale) (rsmags, rsmagerrs) = regfct(rsjds) return rslc(rsjds, rsmags, rsmagerrs, pad, pd, timeshift=timeshift, name=name, plotcolour=plotcolour)
def factory(l, pad=300, pd=2, plotcolour=None): """ Give me a lightcurve, I return a regularly sampled light curve, by performing some regression. :param pad: the padding, in days :param pd: the point density, in points per days. The points live on a regular grid in julian days, 0.0, 0.1, 0.2, 0.3 ... """ if plotcolour == None: plotcolour = l.plotcolour name = l.object jds = l.jds.copy() timeshift = l.timeshift mags = l.getmags(noml=True) magerrs = l.getmagerrs() minjd = np.round(jds[0] - pad) maxjd = np.round(jds[-1] + pad) npts = int(maxjd - minjd)*pd rsjds = np.linspace(minjd, maxjd, npts) # rs for regularly sampled # The regression itself mean_mag = np.mean(mags) def meanprior(query): return (0.0 * query + mean_mag) regfct = pymcgp.regression(jds, mags, magerrs, meanprior) (rsmags, rsmagerrs) = regfct(rsjds) return rslc(rsjds, rsmags, rsmagerrs, pad, pd, timeshift=timeshift, name=name, plotcolour=plotcolour)
