def __init__(self,
spectrum,
atmgrid,
telescope,
disperser,
target,
header,
filename=""):
"""
Args:
filename (:obj:`str`): path to the image
"""
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.spectrum = spectrum
self.header = spectrum.header
self.disperser = disperser
self.target = target
self.telescope = telescope
self.atmgrid = atmgrid
self.lambdas = atmgrid[0, index_atm_data:]
self.lambda_binwidths = np.gradient(self.lambdas)
self.spectragrid = None
self.filename = ""
if filename != "":
self.filename = filename
self.load_spectrum(filename)
if parameters.VERBOSE:
print self.header
def SpectractorSimGrid(filename, outputdir):
""" SpectractorSimGrid
Main function to simulate several spectra
A grid of spectra will be produced for a given target, airmass and pressure
Args:
filename (:obj:`str`): filename of the image (data)
outputdir (:obj:`str`): path to the output directory
"""
my_logger = parameters.set_logger(__name__)
my_logger.info('\n\tStart SPECTRACTORSIMGRID')
# Initialisation
spectrum, telescope, disperser, target = SpectractorInit(
filename, outputdir)
# Set output path
ensure_dir(outputdir)
# extract the basename : simimar as os.path.basename(file)
base_filename = filename.split('/')[-1]
output_filename = os.path.join(
outputdir, base_filename.replace('spectrum', 'spectrasim'))
output_atmfilename = os.path.join(
outputdir, base_filename.replace('spectrum', 'atmsim'))
# SIMULATE ATMOSPHERE GRID
# ------------------------
airmass = spectrum.header['AIRMASS']
pressure = spectrum.header['OUTPRESS']
temperature = spectrum.header['OUTTEMP']
atm = AtmosphereGrid(airmass, pressure, temperature, filename)
# test if file already exists
#if os.path.exists(output_atmfilename) and os.path.getsize(output_atmfilename)>MINFILESIZE:
# filesize= os.path.getsize(output_atmfilename)
# infostring=" atmospheric simulation file %s of size %d already exists, thus load it ..." % (output_atmfilename,filesize)
# my_logger.info(infostring)
# atmgrid,header=atm.loadfile(output_atmfilename)
#else:
atmgrid = atm.compute()
header = atm.savefile(filename=output_atmfilename)
atmsim.CleanSimDir()
if parameters.VERBOSE:
infostring = '\n\t ========= Atmospheric simulation : ==============='
my_logger.info(infostring)
atm.plot_transmission() # plot all atm transp profiles
atm.plot_transm_img() # plot 2D image summary of atm simulations
# SPECTRA-GRID
#-------------
# in any case we re-calculate the spectra in case of change of transmission function
spectra = SpectrumSimGrid(spectrum, atmgrid, telescope, disperser, target,
header)
spectragrid = spectra.compute()
spectra.save_spectra(output_filename)
if parameters.VERBOSE:
infostring = '\n\t ========= Spectra simulation : ==============='
spectra.plot_spectra()
spectra.plot_spectra_img()
def __init__(self, filtername=""):
"""
Args:
filename (:obj:`str`): path to the data filename (for info only)
"""
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.filtername = filtername
self.load_transmission()
def __init__(self, airmass, pressure, temperature, filenamedata):
Atmosphere.__init__(self, airmass, pressure, temperature)
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.filenamedata = filenamedata
# create the numpy array that will contains the atmospheric grid
self.atmgrid = np.zeros(
(NB_ATM_POINTS + 1, NB_atm_HEADER + NB_atm_DATA))
self.atmgrid[0, index_atm_data:] = WL
self.header = fits.Header()
def __init__(self, spectrum, atmosphere, telescope, disperser):
"""
Args:
filename (:obj:`str`): path to the image
"""
Spectrum.__init__(self)
for k, v in spectrum.__dict__.items():
self.__dict__[k] = copy.copy(v)
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.disperser = disperser
self.telescope = telescope
self.atmosphere = atmosphere
self.lambdas = None
self.data = None
self.err = None
def __init__(self, label, verbose=False):
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.label = label
self.ra = None
self.dec = None
self.coord = None
self.type = None
self.redshift = 0
self.wavelengths = []
self.spectra = []
self.verbose = verbose
self.emission_spectrum = False
self.hydrogen_only = False
self.sed = None
self.load()
def __init__(self, logbook="ctiofulllogbook_jun2017_v5.csv"):
"""Load and initialise the logbook
Args:
logbook (str): path to the logbook. Must be a CSV file.
"""
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.logbook = logbook
if not os.path.isfile(logbook):
self.my_logger.error(
'CSV logbook file {} not found.'.format(logbook))
sys.exit()
self.csvfile = open(self.logbook, 'rU')
self.reader = csv.DictReader(self.csvfile,
delimiter=';',
dialect=csv.excel_tab)
def __init__(self, filename, atmgrid_filename="", live_fit=False):
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.filename = filename
self.live_fit = live_fit
self.A1 = 1.0
self.A2 = 0.1
self.ozone = 300.
self.pwv = 3
self.aerosols = 0.03
self.reso = 10.
self.shift = 1e-3
self.p = np.array([self.A1, self.A2, self.ozone, self.pwv, self.aerosols, self.reso, self.shift])
self.lambdas = None
self.model = None
self.model_err = None
self.model_noconv = None
self.labels = ["$A_1$", "$A_2$", "ozone", "PWV", "VAOD", "reso", "$\lambda_{\\mathrm{shift}}$"]
self.bounds = ((0, 0, 0, 0, 0, 1, -20), (np.inf, 1.0, np.inf, 10, 1.0, 100, 20))
self.title = ""
self.spectrum, self.telescope, self.disperser, self.target = SpectractorSimInit(filename)
self.airmass = self.spectrum.header['AIRMASS']
self.pressure = self.spectrum.header['OUTPRESS']
self.temperature = self.spectrum.header['OUTTEMP']
self.use_grid = False
if atmgrid_filename == "":
self.atmosphere = Atmosphere(self.airmass, self.pressure, self.temperature)
else:
self.use_grid = True
self.atmosphere = AtmosphereGrid(filename, atmgrid_filename)
if parameters.VERBOSE:
self.my_logger.info('\n\tUse atmospheric grid models from file %s. ' % atmgrid_filename)
self.p[0] *= np.max(self.spectrum.data) / np.max(self.simulation(self.spectrum.lambdas, *self.p))
self.get_truth()
if 0. in self.spectrum.err:
self.spectrum.err = np.ones_like(self.spectrum.err)
if parameters.DEBUG:
fig = plt.figure()
for i in range(10):
a = self.atmosphere.interpolate(300, i, 0.05)
plt.plot(self.atmosphere.lambdas, a, label='pwv=%dmm' % i)
plt.grid()
plt.xlabel('$\lambda$ [nm]')
plt.ylabel('Atmospheric transmission')
plt.legend(loc='best')
plt.show()
def SpectractorInit(filename, outputdir):
""" SpectractorInit
Main function to simulate several spectra
A grid of spectra will be produced for a given target, airmass and pressure
Args:
filename (:obj:`str`): filename of the image (data)
outputdir (:obj:`str`): path to the output directory
"""
my_logger = parameters.set_logger(__name__)
my_logger.info('\n\tStart SPECTRACTORSIM initialisation')
# Load data spectrum
spectrum = Spectrum(filename)
# TELESCOPE TRANSMISSION
# ------------------------
telescope = TelescopeTransmission(spectrum.filter)
if parameters.VERBOSE:
infostring = '\n\t ========= Telescope transmission : ==============='
my_logger.info(infostring)
telescope.plot_transmission()
# DISPERSER TRANSMISSION
# ------------------------
disperser = Hologram(label=spectrum.disperser)
if parameters.VERBOSE:
infostring = '\n\t ========= Disperser transmission : ==============='
my_logger.info(infostring)
disperser.plot_transmission()
# STAR SPECTRUM
# ------------------------
target = spectrum.target
if parameters.VERBOSE:
infostring = '\n\t ========= SED : %s ===============' % target.label
my_logger.info(infostring)
target.plot_spectra()
return spectrum, telescope, disperser, target
def ImageSim(filename,
outputdir,
guess,
target,
pwv=5,
ozone=300,
aerosols=0,
A1=1,
A2=0,
with_rotation=True,
with_stars=True):
""" The basic use of the pipeline consists first to define:
- the path to the fits image from which to extract the image,
- the path of the output directory to save the extracted spectrum (created automatically if does not exist yet),
- the rough position of the object in the image,
- the name of the target (to search for the extra-atmospheric spectrum if available).
Then different parameters and systematics can be set:
- pwv: the pressure water vapor (in mm)
- ozone: the ozone quantity (in XX)
- aerosols: the vertical aerosol optical depth
- A1: a global grey absorption parameter for the spectrum
- A2: the relative amplitude of second order compared with first order
- with_rotation: rotate the spectrum according to the disperser characteristics (True by default)
- with_stars: include stars in the image field (True by default)
"""
my_logger = parameters.set_logger(__name__)
my_logger.info('\n\tStart IMAGE SIMULATOR')
# Load reduced image
image = ImageModel(filename, target=target)
if parameters.DEBUG:
image.plot_image(scale='log10', target_pixcoords=guess)
# Find the exact target position in the raw cut image: several methods
my_logger.info('\n\tSearch for the target in the image...')
target_pixcoords = image.find_target_2Dprofile(guess)
# Background model
my_logger.info('\n\tBackground model...')
background = BackgroundModel(level=image.target_bkgd2D(0, 0),
frame=(1600, 1650))
if parameters.DEBUG:
background.plot_model()
# Target model
my_logger.info('\n\tStar model...')
star = StarModel(target_pixcoords,
image.target_star2D,
image.target_star2D.amplitude.value,
target=image.target)
reso = star.sigma
if parameters.DEBUG:
star.plot_model()
# Star field model
starfield = None
if with_stars:
my_logger.info('\n\tStar field model...')
starfield = StarFieldModel(image, threshold=0.01 * star.amplitude)
if parameters.VERBOSE:
image.plot_image(scale='log10',
target_pixcoords=starfield.pixcoords)
starfield.plot_model()
# Spectrum model
my_logger.info('\n\tSpectum model...')
lambdas = np.arange(parameters.LAMBDA_MIN, parameters.LAMBDA_MAX)
airmass = image.header['AIRMASS']
pressure = image.header['OUTPRESS']
temperature = image.header['OUTTEMP']
telescope = TelescopeTransmission(image.filter)
spectrumsim = SpectractorSimCore(image,
telescope,
image.disperser,
image.target,
lambdas,
airmass,
pressure,
temperature,
pwv=pwv,
ozone=ozone,
aerosols=aerosols)
spectrum = SpectrumModel(image,
spectrumsim,
sigma=reso,
A1=A1,
A2=A2,
reso=reso,
rotation=with_rotation)
# Image model
my_logger.info('\n\tImage model...')
image.compute(star, background, spectrum, starfield=starfield)
image.add_poisson_noise()
image.convert_to_ADU_units()
if parameters.VERBOSE:
image.plot_image(scale="log",
title="Image simulation",
target_pixcoords=target_pixcoords,
units=image.units)
# Set output path
ensure_dir(outputdir)
output_filename = filename.split('/')[-1]
output_filename = (output_filename.replace('reduc',
'sim')).replace('trim', 'sim')
output_filename = os.path.join(outputdir, output_filename)
# Save images and parameters
image.header['A1'] = A1
image.header['A2'] = A2
image.header['OZONE'] = ozone
image.header['PWV'] = pwv
image.header['VAOD'] = aerosols
image.header['reso'] = reso
image.header['ROTATION'] = int(with_rotation)
image.header['STARS'] = int(with_stars)
image.save_image(output_filename, overwrite=True)
return image
def SpectractorSim(filename,
outputdir,
lambdas,
pwv=5,
ozone=300,
aerosols=0.05,
overwrite=True):
""" SpectractorSim
Main function to simulate several spectra
A grid of spectra will be produced for a given target, airmass and pressure
Args:
filename (:obj:`str`): filename of the image (data)
outputdir (:obj:`str`): path to the output directory
pwv (:obj:`float`): pressure water vapor
ozone (:obj:`float`): ozone quantity
aerosols (:obj:`float`): VAOD Vertical Aerosols Optical Depth
"""
my_logger = parameters.set_logger(__name__)
my_logger.info('\n\tStart SPECTRACTORSIM')
# Initialisation
spectrum, telescope, disperser, target = SpectractorInit(
filename, outputdir)
# SIMULATE ATMOSPHERE
# -------------------
airmass = spectrum.header['AIRMASS']
pressure = spectrum.header['OUTPRESS']
if pressure < 700: # sometimes the weather data are bad so force a reasonable pressure
pressure = 782.5
temperature = spectrum.header['OUTTEMP']
if pressure < 700: # sometimes the weather data are bad so force a reasonable pressure
temperature = 10.0 # Celcius degrees
atmosphere = Atmosphere(airmass, pressure, temperature)
atmosphere.simulate(pwv, ozone, aerosols)
if parameters.VERBOSE:
infostring = '\n\t ========= Atmospheric simulation : ==============='
my_logger.info(infostring)
infostring = "\n\t outputdir = " + outputdir
my_logger.info(infostring)
atmosphere.plot_transmission() # plot all atm transp profiles
# SPECTRUM SIMULATION
#--------------------
spectrum_simulation = SpectrumSimulation(spectrum, atmosphere, telescope,
disperser)
spectrum_simulation.simulate(lambdas)
if parameters.VERBOSE:
infostring = '\n\t ========= Spectra simulation : ==============='
spectrum_simulation.plot_spectrum(nofit=True)
# SAVE SPECTRUM
#------------------
if outputdir != None:
if parameters.VERBOSE:
infostring = "\n\t Simulated spectrum will be saved in outputdir = " + outputdir
my_logger.info(infostring)
# extract the basename : simimar as os.path.basename(file)
base_filename = filename.split('/')[
-1] # get "reduc_20170530_213.fits"
tag_filename = base_filename.split('_')[0] # get "reduc_"
search_str = '^%s_(.*)' % (tag_filename) # get "^reduc_(.*)"
root_filename = re.findall(search_str,
base_filename)[0] # get "20170530_213.fits'
output_filename = 'specsim_' + root_filename # get "spectrasim_20170530_213.fits"
output_filename = os.path.join(outputdir, output_filename)
if parameters.VERBOSE:
infostring = 'output filename =' + output_filename
my_logger.info(infostring)
spectrum_simulation.save_spectrum(output_filename, overwrite=True)
return spectrum_simulation
def __init__(self, airmass, pressure, temperature):
self.my_logger = parameters.set_logger(self.__class__.__name__)
self.airmass = airmass
self.pressure = pressure
self.temperature = temperature
self.transmission = lambda x: np.ones_like(x).astype(float)