if smoothtype2 == "s": # smoothing
smoothtype = raw_input('--> Boxcar or Gaussian smoothing? (b/g) ')
while smoothtype != "b" and smoothtype != "g":
smoothtype = raw_input('--> Please enter b or g: ')
while True:
try:
kern = int(raw_input('--> Smoothing kernel: '))
break
except ValueError:
print '--> Please enter an integer'
print ' '
# smoothing method
if smoothtype == "b": # boxcar smoothing
sap_flux2, smth_flux = smoothing.boxsmooth(time, sap_flux, kern)
elif smoothtype == "g": # gaussian smoothing
sap_flux2, smth_flux = smoothing.gausssmooth(time, sap_flux, kern)
# plotting phase curve
plt.figure(2)
plt.plot(time, smth_flux, 'ro', markersize=3)
plt.xlabel('Time mod %f days' % foldper)
plt.ylabel('Fractional Intensity')
exec("plt.title('%d')" % kic)
exec("plt.savefig('kic%d_phase.png')" % kic)
elif smoothtype2 == "b": # binning
while True:
try:
index = (j + 1) * k + (x - k) * j
if img == 0:
pass
else:
flux2 = second_flux[index,:]
# creating a blend array to remove NaNs
blend = np.array([time, flux2])
blend = np.transpose(blend)
blend2 = np.ma.compress_rows(np.ma.fix_invalid(blend))
time2 = blend2[:,0]
flux2 = blend2[:,1]
if smoothtype == "b": # boxcar smoothing
flux3, smth_flux = smoothing.boxsmooth(time2, flux2, kern)
elif smoothtype == "g": # gaussian smoothing
flux3, smth_flux = smoothing.gausssmooth(time2, flux2, kern)
exec("pixel%d_flux = flux3" % index)
exec("pixel%d_time = time2" % index)
exec("tempflux = pixel%d_flux" % index)
exec("temptime = pixel%d_time" % index)
clip = inp * np.std(tempflux)
meanflux = np.mean(tempflux)
upperbound = meanflux + clip
lowerbound = meanflux - clip
sap_flux_0 = table_0['SAP_FLUX']
#sap_flux_err_0 = table_0['SAP_FLUX_ERR']
time_0 = table_0['TIME']
hdulist.close()
# creating a blend array to remove NaNs
blend_0 = np.array([time_0, sap_flux_0])
blend_0 = np.transpose(blend_0)
blend2_0 = np.ma.compress_rows(np.ma.fix_invalid(blend_0))
time_0 = blend2_0[:,0]
sap_flux_0 = blend2_0[:,1]
if smoothtype == "b": # boxcar smoothing
sap_flux2_0, smth_flux_0 = smoothing.boxsmooth(time_0, sap_flux_0, kern)
elif smoothtype == "g": # gaussian smoothing
sap_flux2_0, smth_flux_0 = smoothing.gausssmooth(time_0, sap_flux_0, kern)
### FIRST QUARTER ###
hdulist = pyfits.open('kplr00skeleton-2009166043257_llc.fits')
table_1 = hdulist[1].data
sap_flux_1 = table_1['SAP_FLUX']
#sap_flux_err_1 = table_1['SAP_FLUX_ERR']
time_1 = table_1['TIME']
hdulist.close()