plt.savefig('test_r_hesfit_' + np.str(ilc) + '.pdf')
return (parmout, covout, freq, tplot, xplot)
#!!!!!!!!!!!!!!!!!!! test the code using this program
timeall = []
sigall = []
yall = []
nlc = 1
for i in range(nlc):
#generate test light curve and add noise
datpre = mylcgen(tlo=0, thi=100, dt=0.03, iseed=132423)
npre = np.shape(datpre[:, 0])[0]
datmean = np.std(datpre[:, 1])
snow = np.ones(npre) / 10 * datmean
dat = mrs.myresample(dir='',
fname=[''],
dtave=2.0,
sampmin=0.8,
sampcode=3,
datin=np.array((datpre[:, 0], datpre[:, 1], snow)).T)
ndat = np.shape(dat[:, 0])[0]
sig = dat[:, 2]
for i in range(ndat):
dat[i, 1] = normdis(1, dat[i, 1], sig[i])[0]
sigall.append(sig)
yall.append(dat[:, 1] + 50)
import numpy as np
from mylcgen import *
from myrandom import *
import matplotlib.pylab as plt
import mysinecostrans as msc
#generate test light curve and add noise
dat = mylcgen(tlo=0, thi=100, dt=2.0, iseed=42342332)
ndat = np.shape(dat[:, 0])[0]
datmean = np.std(dat[:, 1])
sig = np.ones(ndat) / 10 * datmean
for i in range(ndat):
dat[i, 1] = normdis(1, dat[i, 1], sig[i])[0]
y = dat[:, 1]
time = dat[:, 0]
tlo = np.min(time)
thi = np.max(time)
dt = np.mean(time[1:] - time[:-1])
flo = 0.5 / (thi - tlo)
fhi = 5. / dt
msc.sct(time, y, sig, freq=np.arange(flo, fhi + flo, flo), costrans=0)
#test the routine above and do it manually to compare
tlo = np.min(time)
thi = np.max(time)
dt = np.mean(time[1:] - time[:-1])
import numpy as np
from mylcgen import *
from myrandom import *
import matplotlib.pylab as plt
import myresample as mrs
#generate test light curve and add noise
datpre = mylcgen(tlo=0, thi=100, dt=0.03, iseed=34245)
ndat = np.shape(datpre[:, 0])[0]
datmean = np.std(datpre[:, 1])
sig = np.ones(ndat) / 10 * datmean
dat = mrs.myresample(dir='',
fname=[''],
dtave=2.0,
sampmin=0.8,
sampcode=3,
datin=np.array((datpre[:, 0], datpre[:, 1], sig)).T)
ndat = np.shape(dat[:, 0])[0]
sig = dat[:, 2]
for i in range(ndat):
dat[i, 1] = normdis(1, dat[i, 1], sig[i])[0]
y = dat[:, 1] + 50
time = dat[:, 0]
tlo = np.min(time)
thi = np.max(time)
dt = np.mean(time[1:] - time[:-1])
##!!!!!!!!!!!!!!!!!!! test the code using this program # # #generate test light curve and add noise cadence = 3.0 timeall = [] sigall = [] yall = [] datpre = mylcgen(tlo=0,thi=100,dt=cadence,iseed=132423) npre = np.shape(datpre[:,0])[0] sig = np.std(datpre[:,1])/10 #add noise sigpre = np.ones(npre)*sig datpre[:,1] = np.random.randn(npre)*sig + datpre[:,1] #add bad data to test sigma clipping datpre[10,1] = datpre[10,1] + 100*sig datpre[5,1] = datpre[5,1] - 40*sig timeall = datpre[:,0] + np.random.randn(npre)*0.1#npre,datpre[:,0],0.1) yall = datpre[:,1] sigall = sigpre