def load_from_smart(cls, radpath, name=None):
# Read-in .rad file
wlhr, wno, solar_spec, TOA_flux, rad_streams = readsmart.rad(
radfile, getdata=True)
# Calculate Hi-res reflectivity spectrum
Ahr = (TOA_flux / solar_spec) #* np.pi / planet.Phi
def smart_observation(radfile,
itime,
telescope,
planet,
star,
ref_lam=0.55,
tag='',
plot=True,
saveplot=False,
savedata=False):
"""Uses coronagraph noise model to create an observation of high resolution SMART output.
Parameters
----------
radfile : string
Location and name of file to be read in
itime : float
Integration time (hours)
telescope : Telescope
Telescope object
planet : Planet
Planet object
star : Star
Star object
tag : string
ID for output files
plot : boolean
Set to True to make plot
saveplot : boolean
Set to True to save the plot as a PDF
savedata : boolean
Set to True to save data file of observation
Returns
----------
lam : array
Wavelength grid of observed spectrum
spec : array
Albedo grid of observed spectrum
sig : array
One sigma errorbars on albedo spectrum
rwl : array
Wavelength grid of SMART output
Ahr : array
Albedo grid of SMART output
Output
---------
If saveplot=True then plot will be saved
If savedata=True then data will be saved
"""
# Read-in .rad file
wlhr, wno, solar_spec, TOA_flux, rad_streams = readsmart.rad(radfile,
getdata=True)
# Calculate Hi-res reflectivity spectrum
Ahr = (TOA_flux / solar_spec) #* np.pi / planet.Phi
# Possibly convolve with gaussian?
# Skip call_noise and just call: noise
lam, dlam, A, q, Cratio, cp, csp, cz, cez, cD, cR, cth, DtSNR = \
make_noise(Ahr, wlhr, solar_spec, telescope, planet, star, wantsnr=10.0, COMPUTE_LAM=True)
# Calculate background photon count rate
cb = (cz + cez + csp + cD + cR + cth)
# Calculate the SNR of observation
time = itime * 3600. # Convert hours to seconds
#SNR = calc_SNR(time, cp, cb)
# Generate noisy spectrum by drawing data points from a normal distribution
#spec, sig = draw_noisy_spec(A, SNR)
# Calculate SNR, sigma, and noised-up spectrum
spec, sig, SNR = process_noise(time, Cratio, cp, cb)
if plot:
# Set string for plot text
if itime > 2.0:
timestr = "{:.0f}".format(itime) + ' hours'
else:
timestr = "{:.0f}".format(itime * 60) + ' mins'
plot_text = r'Distance = '+"{:.1f}".format(planet.distance)+' pc'+\
'\n Integration time = '+timestr
# If a reference wavelength is specified then return the SNR at that wl
# corresponding to the integration time given
if ref_lam:
ireflam = find_nearest(lam, ref_lam)
ref_SNR = SNR[ireflam]
plot_text = plot_text + '\n SNR = '+"{:.1f}".format(ref_SNR)+\
' at '+"{:.2f}".format(lam[ireflam])+r' $\mu$m'
# Plot observed spectrum; save pdf if saveplot=True
lammin, lammax = np.min(lam) - 0.1, np.max(lam) + 0.1
Amin, Amax = np.min(A) - np.max(sig) * 1.1, np.max(
A) + np.max(sig) * 1.1
tmin = np.min(Ahr[(wlhr > lammin) & (wlhr < lammax)])
tmax = np.max(Ahr[(wlhr > lammin) & (wlhr < lammax)])
if tmin < Amin: Amin = tmin
if tmax > Amax: Amax = tmax
#ymin,ymax = np.min(A), np.max(A)
plot_tag = 'observed_smart_' + tag + '.pdf'
fig = plt.figure(figsize=(15, 10))
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
ax0.plot(wlhr, Ahr, alpha=0.5, c='k')
if telescope.mode != 'Imaging':
ax0.plot(lam,
A,
alpha=0.7,
color='orange',
drawstyle='steps-mid',
lw=2.0)
else:
ax0.plot(lam, A, 'o', alpha=0.7, color='orange', ms=10.0)
telescope.filter_wheel.plot(ax=ax0)
ax0.errorbar(lam, spec, yerr=sig, fmt='o', color='k', ms=10.0)
ax0.set_ylabel('Fp/Fs')
ax0.set_xlabel('Wavelength [$\mu$m]')
ax0.set_xlim([lammin, lammax])
ax0.set_ylim([Amin, Amax])
#ax0.set_ylim([-0.01,ymax+0.1])
#ax0.set_ylim([-0.01,1.01])
ax0.text(0.99, 0.99, plot_text,\
verticalalignment='top', horizontalalignment='right',\
transform=ax0.transAxes,\
color='black', fontsize=20)
# Save plot if saveplot==True
if saveplot:
fig.savefig(plot_tag)
print 'Saved: ' + plot_tag
fig.show()
# Save Synthetic data file (wavelength, albedo, error) if savedata=True
if savedata:
data_tag = 'observed_smart_' + tag + '.txt'
y_sav = np.array([lam, spec, sig])
np.savetxt(data_tag, y_sav.T)
print 'Saved: ' + data_tag
# Return Synthetic data and high-res spec
return lam, spec, sig, wlhr, Ahr
def smart_observation(radfile, itime, telescope, planet, star,
ref_lam=0.55, tag='', plot=True, saveplot=False, savedata=False):
"""Uses coronagraph noise model to create an observation of high resolution SMART output.
Parameters
----------
radfile : string
Location and name of file to be read in
itime : float
Integration time (hours)
telescope : Telescope
Telescope object
planet : Planet
Planet object
star : Star
Star object
tag : string
ID for output files
plot : boolean
Set to True to make plot
saveplot : boolean
Set to True to save the plot as a PDF
savedata : boolean
Set to True to save data file of observation
Returns
----------
lam : array
Wavelength grid of observed spectrum
spec : array
Albedo grid of observed spectrum
sig : array
One sigma errorbars on albedo spectrum
rwl : array
Wavelength grid of SMART output
Ahr : array
Albedo grid of SMART output
Output
---------
If saveplot=True then plot will be saved
If savedata=True then data will be saved
"""
# Read-in .rad file
wlhr, wno, solar_spec, TOA_flux, rad_streams = readsmart.rad(radfile,getdata=True)
# Calculate Hi-res reflectivity spectrum
Ahr = (TOA_flux / solar_spec) #* np.pi / planet.Phi
# Possibly convolve with gaussian?
# Skip call_noise and just call: noise
lam, dlam, A, q, Cratio, cp, csp, cz, cez, cD, cR, cth, DtSNR = \
make_noise(Ahr, wlhr, solar_spec, telescope, planet, star, wantsnr=10.0, COMPUTE_LAM=True)
# Calculate background photon count rate
cb = (cz + cez + csp + cD + cR + cth)
# Calculate the SNR of observation
time = itime * 3600. # Convert hours to seconds
#SNR = calc_SNR(time, cp, cb)
# Generate noisy spectrum by drawing data points from a normal distribution
#spec, sig = draw_noisy_spec(A, SNR)
# Calculate SNR, sigma, and noised-up spectrum
spec, sig, SNR = process_noise(time, Cratio, cp, cb)
if plot:
# Set string for plot text
if itime > 2.0:
timestr = "{:.0f}".format(itime)+' hours'
else:
timestr = "{:.0f}".format(itime*60)+' mins'
plot_text = r'Distance = '+"{:.1f}".format(planet.distance)+' pc'+\
'\n Integration time = '+timestr
# If a reference wavelength is specified then return the SNR at that wl
# corresponding to the integration time given
if ref_lam:
ireflam = find_nearest(lam,ref_lam)
ref_SNR = SNR[ireflam]
plot_text = plot_text + '\n SNR = '+"{:.1f}".format(ref_SNR)+\
' at '+"{:.2f}".format(lam[ireflam])+r' $\mu$m'
# Plot observed spectrum; save pdf if saveplot=True
lammin,lammax = np.min(lam)-0.1, np.max(lam)+0.1
Amin, Amax = np.min(A)-np.max(sig)*1.1, np.max(A)+np.max(sig)*1.1
tmin = np.min(Ahr[(wlhr > lammin) & (wlhr < lammax)])
tmax = np.max(Ahr[(wlhr > lammin) & (wlhr < lammax)])
if tmin < Amin: Amin = tmin
if tmax > Amax: Amax = tmax
#ymin,ymax = np.min(A), np.max(A)
plot_tag = 'observed_smart_'+tag+'.pdf'
fig = plt.figure(figsize=(15,10))
gs = gridspec.GridSpec(1, 1)
ax0 = plt.subplot(gs[0])
ax0.plot(wlhr, Ahr, alpha=0.5, c='k')
if telescope.mode != 'Imaging':
ax0.plot(lam, A, alpha=0.7, color='orange', drawstyle='steps-mid', lw=2.0)
else:
ax0.plot(lam, A, 'o', alpha=0.7, color='orange', ms = 10.0)
telescope.filter_wheel.plot(ax=ax0)
ax0.errorbar(lam, spec, yerr=sig, fmt='o', color='k', ms=10.0)
ax0.set_ylabel('Fp/Fs')
ax0.set_xlabel('Wavelength [$\mu$m]')
ax0.set_xlim([lammin,lammax])
ax0.set_ylim([Amin, Amax])
#ax0.set_ylim([-0.01,ymax+0.1])
#ax0.set_ylim([-0.01,1.01])
ax0.text(0.99, 0.99, plot_text,\
verticalalignment='top', horizontalalignment='right',\
transform=ax0.transAxes,\
color='black', fontsize=20)
# Save plot if saveplot==True
if saveplot:
fig.savefig(plot_tag)
print 'Saved: '+plot_tag
fig.show()
# Save Synthetic data file (wavelength, albedo, error) if savedata=True
if savedata:
data_tag = 'observed_smart_'+tag+'.txt'
y_sav = np.array([lam,spec,sig])
np.savetxt(data_tag, y_sav.T)
print 'Saved: '+data_tag
# Return Synthetic data and high-res spec
return lam, spec, sig, wlhr, Ahr
def load_from_smart(cls, radpath, name=None):
# Read-in .rad file
wlhr, wno, solar_spec, TOA_flux, rad_streams = readsmart.rad(radfile,getdata=True)
# Calculate Hi-res reflectivity spectrum
Ahr = (TOA_flux / solar_spec) #* np.pi / planet.Phi
def from_file(cls, path):
# Read path file
if path.endswith(".rad"):
# Read rad
wavelength, wavenumber, star_flux, toa_flux, rad_streams = rs.rad(
path)
geo_albedo = toa_flux / star_flux
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
flux_transmission, absorbing_radius, tdepth = tmp, tmp, tmp
elif path.endswith(".tran"):
# Read tran
wavelength, flux_transmission, absorbing_radius = rs.tran(path)
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
wavenumber, toa_flux, star_flux, geo_albedo, tdepth = tmp, tmp, tmp, tmp, tmp
elif path.endswith(".trnst"):
# Read trnst
wavelength, wavenumber, absorbing_radius, tdepth = rs.trnst(path)
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
toa_flux, star_flux, geo_albedo, flux_transmission = tmp, tmp, tmp, tmp
elif path.endswith("data.txt"):
# Read dat file from Will/Shawn's version
dat = np.genfromtxt(path, skip_header=8)
# Parse
wavelength = dat[:, 0]
wavenumber = dat[:, 1]
star_flux = dat[:, 2]
toa_flux = dat[:, 3]
geo_albedo = dat[:, 4]
# Fill transit arrays with empty strings
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
flux_transmission, absorbing_radius, tdepth = tmp, tmp, tmp
elif path.endswith(".flx"):
# Read dat file from Will/Shawn's version
dat = np.genfromtxt(path, skip_header=8)
# Parse
wavelength = dat[:, 0]
wavenumber = dat[:, 1]
star_flux = dat[:, 2]
toa_flux = dat[:, 3]
geo_albedo = dat[:, 4]
# Fill transit arrays with empty strings
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
flux_transmission, absorbing_radius, tdepth = tmp, tmp, tmp
elif path.endswith(".trn"):
# Read dat file from Will/Shawn's version
dat = np.genfromtxt(path, skip_header=8)
# Parse
wavelength = dat[:, 0]
absorbing_radius = dat[:, 1]
tdepth = dat[:, 2]
# Fill transit arrays with empty strings
tmp = np.chararray(len(wavelength), itemsize=1)
tmp[:] = ""
toa_flux, star_flux, geo_albedo, flux_transmission, wavenumber = tmp, tmp, tmp, tmp, tmp
else:
print "%s is an invalid path." % path
# Return new class instance
return cls(wavelength=wavelength,
wavenumber=wavenumber,
toa_flux=toa_flux,
star_flux=star_flux,
geo_albedo=geo_albedo,
flux_transmission=flux_transmission,
absorbing_radius=absorbing_radius,
tdepth=tdepth)
def smart_observation(radfile, itime, telescope, planet, star,
ref_lam=0.55, tag='', plot=True, saveplot=False, savedata=False,
THERMAL=False, wantsnr=10.):
"""Uses coronagraph noise model to create an observation of high resolution SMART output.
Parameters
----------
radfile : string
Location and name of file to be read in
itime : float
Integration time (hours)
telescope : Telescope
Telescope object
planet : Planet
Planet object
star : Star
Star object
tag : string
ID for output files
plot : boolean
Set to True to make plot
saveplot : boolean
Set to True to save the plot as a PDF
savedata : boolean
Set to True to save data file of observation
Returns
----------
lam : array
Wavelength grid of observed spectrum
spec : array
Albedo grid of observed spectrum
sig : array
One sigma errorbars on albedo spectrum
rwl : array
Wavelength grid of SMART output
Ahr : array
Albedo grid of SMART output
Output
---------
If saveplot=True then plot will be saved
If savedata=True then data will be saved
"""
# try importing readsmart
try:
import readsmart as rs
except ImportError:
print "Module 'readsmart' not found. Please install on your local machine \
to proceed with this function. The source can be found at: \
https://github.com/jlustigy/readsmart"
return None, None, None, None, None
# Read-in .rad file
wlhr, wno, solar_spec, TOA_flux, rad_streams = rs.rad(radfile,getdata=True)
# Calculate Hi-res reflectivity spectrum
Ahr = (TOA_flux / solar_spec) #* np.pi / planet.Phi
# Possibly convolve with gaussian?
# Skip call_noise and just call: noise
lam, dlam, A, q, Cratio, cp, csp, cz, cez, cD, cR, cth, DtSNR = \
make_noise(Ahr, wlhr, solar_spec, telescope, planet, star, wantsnr=wantsnr, COMPUTE_LAM=True, THERMAL=THERMAL)
# Calculate background photon count rate
cb = (cz + cez + csp + cD + cR + cth)
# Calculate the SNR of observation
time = itime * 3600. # Convert hours to seconds
#SNR = calc_SNR(time, cp, cb)
# Generate noisy spectrum by drawing data points from a normal distribution
#spec, sig = draw_noisy_spec(A, SNR)
# Calculate SNR, sigma, and noised-up spectrum
spec, sig, SNR = process_noise(time, Cratio, cp, cb)
if plot:
plot_coronagraph_spectrum(lam, spec, sig, itime, planet.distance, ref_lam, SNR, truth=Cratio)
# Save Synthetic data file (wavelength, albedo, error) if savedata=True
if savedata:
data_tag = 'observed_smart_'+tag+'.txt'
y_sav = np.array([lam,spec,sig])
np.savetxt(data_tag, y_sav.T)
print 'Saved: '+data_tag
# Return Synthetic data and high-res spec
return lam, spec, sig, wlhr, Ahr
