def tweakml_PS_NUMBER(lcs, spline):
return twk.tweakml_PS(lcs,
spline,
B_PARAM,
f_min=1 / 300.0,
psplot=False,
verbose=False,
interpolation='linear')
def tweakml_PS_4(lcs, spline):
return twk.tweakml_PS(lcs,
spline,
0.12500000000000003,
f_min=1 / 300.0,
psplot=False,
verbose=False,
interpolation='linear',
A_correction=0.9883240534172314)
def tweakml_PS_3(lcs, spline):
return twk.tweakml_PS(lcs,
spline,
1.5499999999999998,
f_min=1 / 300.0,
psplot=False,
verbose=False,
interpolation='linear',
A_correction=0.9497062988061292)
def tweakml_PS_2(lcs, spline):
return twk.tweakml_PS(lcs,
spline,
0.12500000000000003,
f_min=1 / 300.0,
psplot=False,
verbose=False,
interpolation='linear',
A_correction=1.0439695004938903)
def tweakml_PS_1(lcs, spline):
return twk.tweakml_PS(lcs,
spline,
0.12500000000000003,
f_min=1 / 300.0,
psplot=False,
verbose=False,
interpolation='linear',
A_correction=1.0385175125538744)
def tweakml_PS(lcs, spline, B, A_correction):
return twk.tweakml_PS(lcs,
spline,
B,
f_min=1 / 300.0,
psplot=False,
save_figure_folder=None,
verbose=self.verbose,
interpolation='linear',
A_correction=A_correction)
for l in lcs:
ml.append(l.ml.spline.copy())
B_vec = np.linspace(0.1, 1.0, 10)
for B in B_vec:
#set back the original microelnsing :
for i, l in enumerate(lcs):
l.ml.spline = ml[i]
# lcs[i].ml.spline.display()
twk.tweakml_PS([lcs[0]],
spline,
B,
f_min=1 / 300.0,
save_figure_folder='./',
psplot=False,
verbose=True,
interpolation='linear')
twk.tweakml_PS([lcs[1]],
spline,
B,
f_min=1 / 300.0,
save_figure_folder='./',
psplot=False,
verbose=True,
interpolation='linear')
twk.tweakml_PS([lcs[2]],
spline,
B,
f_min=1 / 300.0,
picklepath = "../Simulation/" + object + '_' + dataname + '/' + 'spl1_ks%i_splml_ksml_%i/' % (
kntstp, ml_kntstep)
picklename = "initopt_%s_ks%i_ksml%i.pkl" % (dataname, kntstp, ml_kntstep)
curve = 0
(lcs, spline) = pycs.gen.util.readpickle(picklepath + picklename)
A = 1.0 #this is the maximum frequency you want to add (in unit of the Nymquist frequency of your signal)
B = [0.1] #this is the scaling of the power spectrum
# #To check what does the microlensing curve looks like
rls = pycs.gen.stat.subtract(lcs, spline)
# lcs[0].ml.spline.display()
# lcs[1].ml.spline.display()
# lcs[2].ml.spline.display()
print "Nb coefficient before tweak :", len(lcs[curve].ml.spline.c)
print "before tweak :", pycs.gen.stat.resistats(rls[0])
twk.tweakml_PS(lcs[curve],
spline,
B,
f_min=1 / 300.0,
save_figure_folder='./',
psplot=True,
verbose=True,
interpolation='linear')
# lcs[0].ml.spline.display()
# lcs[1].ml.spline.display()
# lcs[2].ml.spline.display()
print "Nb coefficient after tweak :", len(lcs[curve].ml.spline.c)