def radius_sky_portion(CCD_structure):
'''
This function uses the information on the CCD to estimate the radius of the portion of the sky visualized
Parameters
----------
CCD_structure : array (float elements)
This object contains the information about the measure of the CCD
[0] : the length of the CCD on the x axis in mm
[1] : the length of the CCD on the y axis in mm
[2] : the length of a single pixel in mm
Output
------
radius : astropy.units.quantity.Quantity
it is the radius in an astropy undestandable format
'''
log.debug(hist())
pixel_on_x = CCD_structure[0]/CCD_structure[2]
pixel_on_y = CCD_structure[1]/CCD_structure[2]
diagonal_diameter = np.sqrt((pixel_on_x)**2+(pixel_on_y)**2)
radius_on_pixel = diagonal_diameter/2
radius = radius_on_pixel*CCD_structure[3]/60
radius = (radius/60)*u.deg
return radius
def VEGA_to_AB(magnitudo, photo_filter):
'''
This function takes the magnitudo of a star in the VEGA system and converts it in the AB system.
It download the conversion table calling data_loader
Parameters
----------
magnitudo : array (float elements)
An element of magnitudo is the magnitudo of a star in a specific range (photo_filter)
photo_filter : list (string elements)
It is a list where a element is a string that identifies a filter, e.g. "U","R","I"
Output
------
magnitudo : array
The array contains the elements converted
'''
log.debug(hist(photo_filter))
sub = DATA["Convertion_table"]
convertion_table = [sub[k] for k in photo_filter if k in sub]
for i in range(len(magnitudo)-1):
if magnitudo[i]== 0:
magnitudo[i]= 40
else:
magnitudo[i] = magnitudo[i] - convertion_table[i]
magnitudo[i] = magnitudo[i] - convertion_table[i]
return magnitudo
def photons_to_electrons(photons, photo_filter):
'''
This function takes the number of photons/sec and converts them in number of electron/sec on the CCD.
It calls data_loader to download the quantum efficiency of the CCD
Parameters
----------
photons : array
The elements of the array are the number of photons for a specific range
photo_filter : list (string elements)
It is a list where a element is a string that identifies a filter, e.g. "U","R","I"
Output
electron : array
The elements of the array are the number of electrons for a specific range
'''
log.debug(hist())
sub = DATA["Quantum_efficiency"]
quantum_efficiency = [sub[k] for k in photo_filter if k in sub]
electrons = photons
for i in range(len(photons)-1):
electrons[i] = photons[i]*quantum_efficiency[i]
return electrons
def magnitudo_to_photons(magnitudo, photo_filter):
'''
This function convertes the magnitudo of a star in number of photons/second.
It download informations calling data_loader
Parameters
----------
magnitudo : array (float elements)
An element of magnitudo is the magnitudo of a star in a specific range (photo_filter)
photo_filter : list (string elements)
It is a list where a element is a string that identifies a filter, e.g. "U","R","I"
Output
------
photons : array
The elements of the array are the number of photons for a specific range
'''
log.debug(hist())
sub = DATA["Central_wavelenght"]
central_wavelenght = [sub[k] for k in photo_filter if k in sub]
number = magnitudo
for i in range(len(magnitudo)-1):
if magnitudo[i] == 0:
number[i] = 0
else:
exp = 6.74-0.4*magnitudo[i]
number[i] = (10**exp)#/(central_wavelenght[i])
return number
def atmospheric_attenuation(magnitudo, photo_filter, AirMass):
'''
This function takes the magnitudo of a star and gives back the same magnitudo attenuated by the atmosphere.
It download the extinction coefficients calling data_loader
Parameters
----------
magnitudo : array (float elements)
An element of magnitudo is the magnitudo of a star in a specific range (photo_filter)
photo_filter : list (string elements)
It is a list where a element is a string that identifies a filter, e.g. "U","R","I"
AirMass : float
The AirMass provide the thickness of the atmosphere crossed by the light
Output
------
magnitudo : array
The array contains the elements attenuated
'''
log.debug(hist())
sub = DATA["Extinction_coefficient"]
extinction_coefficient = [sub[k] for k in photo_filter if k in sub]
for i in range(len(magnitudo)-1):
#if magnitudo[i] == 0:
# magnitudo[i] = 0
#else:
# magnitudo[i] = magnitudo[i]-AirMass*extinction_coefficient[i]
magnitudo[i] = magnitudo[i]+AirMass*extinction_coefficient[i]
return magnitudo
def _parse_result(self, response, verbose=False):
"""
Parses the results form the HTTP response to `~astropy.table.Table`.
Parameters
----------
response : `requests.Response`
The HTTP response object
verbose : bool, optional
Defaults to `False`. When true it will display warnings whenever
the VOtable returned from the Service doesn't conform to the
standard.
Returns
-------
table : `~astropy.table.Table`
"""
if not verbose:
commons.suppress_vo_warnings()
content = response.text
log.debug(content)
# Check if results were returned
if 'The catalog is not in the list' in content:
raise Exception("Catalogue not found")
# Check that object name was not malformed
if 'Either wrong or missing coordinate/object name' in content:
raise Exception("Malformed coordinate/object name")
# Check that the results are not of length zero
if len(content) == 0:
raise Exception("The LCOGT server sent back an empty reply")
# Read it in using the astropy VO table reader
try:
first_table = votable.parse(six.BytesIO(response.content),
pedantic=False).get_first_table()
except Exception as ex:
self.response = response
self.table_parse_error = ex
raise TableParseError("Failed to parse LCOGT votable! The raw "
" response can be found in self.response,"
" and the error in self.table_parse_error.")
# Convert to astropy.table.Table instance
table = first_table.to_table()
# Check if table is empty
if len(table) == 0:
warnings.warn(
"Query returned no results, so the table will "
"be empty", NoResultsWarning)
return table
def read_array_field(self, fieldlist):
# Turn an iraf record array field into a numpy array
fieldline = [l.split() for l in fieldlist[1:]]
# take only the first 3 columns
# identify writes also strings at the end of some field lines
xyz = [l[:3] for l in fieldline]
try:
farr = np.array(xyz)
except Exception:
log.debug("Could not read array field {}".format(fieldlist[0].split()[0]))
return farr.astype(np.float64)
def Data_structure():
'''
This function simply creates the data structure used in query
'''
log.debug(hist())
Coord_x = []
Coord_y = []
Flux_tot = []
data = [Coord_x, Coord_y, Flux_tot]
return data
def from_cache(self, cache_location):
request_file = self.request_file(cache_location)
try:
with open(request_file, "rb") as f:
response = pickle.load(f)
if not isinstance(response, requests.Response):
response = None
except IOError: # TODO: change to FileNotFoundError once drop py2 support
response = None
if response:
log.debug("Retrieving data from {0}".format(request_file))
return response
def from_cache(self, cache_location):
request_file = self.request_file(cache_location)
try:
with open(request_file, "rb") as f:
response = pickle.load(f)
if not isinstance(response, requests.Response):
response = None
except IOError: # TODO: change to FileNotFoundError once drop py2 support
response = None
if response:
log.debug("Retrieving data from {0}".format(request_file))
return response
def magnitudo_to_electrons(magnitudo, photo_filters, AirMass, exposure_time, Controll):
'''
This functions calls VEGA_to_AB, atmospheric_attenuation, magnitudo_to_photons, photons_to_electrons
to obtain the total electrons generated in a CCD by the light of a star
Parameters
----------
magnitudo : array (float elements)
An element of magnitudo is the magnitudo of a star in a specific range (photo_filter)
photo_filter : list (string elements)
It is a list where a element is a string that identifies a filter, e.g. "U","R","I"
AirMass : float
The AirMass provide the thickness of the atmosphere crossed by the light
exposure_time : int
It is the exposure time used to obtain the image, it is in seconds
Output
------
tot : float
It is the total number of electrons generated by the star on the CCD
'''
log.debug(hist())
magnitudo = VEGA_to_AB(magnitudo, photo_filters)
magnitudo = atmospheric_attenuation(magnitudo, photo_filters, 1)
photons = magnitudo_to_photons(magnitudo, photo_filters)
electrons = photons_to_electrons(photons, photo_filters)
if Controll:
for n, i in enumerate(photo_filters):
if i == "V":
if electrons[n] <= 3e-4:
electrons[n] = electrons[-1]
tot = (sum(electrons)-electrons[-1])*exposure_time
else:
tot = sum(electrons)*exposure_time
return tot
def find_radius_cumul(self, fraction):
"""
Find for each model the radius containing a fraction of the flux.
Parameters
----------
fraction: float
The fraction to use when determining the radius
"""
log.debug("Calculating radii containing %g%s of the flux" %
(fraction * 100., '%'))
radius = np.zeros(self.n_models, dtype=self.flux.dtype) * u.au
if self.apertures is None:
return radius
else:
required = fraction * self.flux[:, -1]
# Linear interpolation - need to loop over apertures for vectorization
for ia in range(len(self.apertures) - 1):
calc = (required >= self.flux[:, ia]) & (required <
self.flux[:, ia + 1])
radius[calc] = (required[calc] - self.flux[calc, ia]) / \
(self.flux[calc, ia + 1] - self.flux[calc, ia]) * \
(self.apertures[ia + 1] - self.apertures[ia]) + \
self.apertures[ia]
calc = (required < self.flux[:, 0])
radius[calc] = self.apertures[0]
calc = (required >= self.flux[:, -1])
radius[calc] = self.apertures[-1]
return radius
def find_radius_cumul(self, fraction):
"""
Find for each model the radius containing a fraction of the flux.
Parameters
----------
fraction: float
The fraction to use when determining the radius
"""
log.debug("Calculating radii containing %g%s of the flux" % (fraction * 100., '%'))
radius = np.zeros(self.n_models, dtype=self.flux.dtype) * u.au
if self.apertures is None:
return radius
else:
required = fraction * self.flux[:, -1]
# Linear interpolation - need to loop over apertures for vectorization
for ia in range(len(self.apertures) - 1):
calc = (required >= self.flux[:, ia]) & (required < self.flux[:, ia + 1])
radius[calc] = (required[calc] - self.flux[calc, ia]) / \
(self.flux[calc, ia + 1] - self.flux[calc, ia]) * \
(self.apertures[ia + 1] - self.apertures[ia]) + \
self.apertures[ia]
calc = (required < self.flux[:, 0])
radius[calc] = self.apertures[0]
calc = (required >= self.flux[:, -1])
radius[calc] = self.apertures[-1]
return radius
def find_radius_sigma(self, fraction):
"""
Find for each model a fractional surface brightness radius
This is the outermost radius where the surface brightness is larger
than a fraction of the peak surface brightness.
Parameters
----------
fraction: float
The fraction to use when determining the radius
"""
log.debug("Calculating %g%s peak surface brightness radii" %
(fraction * 100., '%'))
sigma = np.zeros(self.flux.shape, dtype=self.flux.dtype)
sigma[:, 0] = self.flux[:, 0] / self.apertures[0]**2
sigma[:, 1:] = (self.flux[:, 1:] - self.flux[:, :-1]) / \
(self.apertures[1:] ** 2 - self.apertures[:-1] ** 2)
maximum = np.max(sigma, axis=1)
radius = np.zeros(self.n_models, dtype=self.flux.dtype) * u.au
# Linear interpolation - need to loop over apertures backwards for vectorization
for ia in range(len(self.apertures) - 2, -1, -1):
calc = (sigma[:, ia] > fraction * maximum) & (radius == 0.)
radius[calc] = (sigma[calc, ia] - fraction * maximum[calc]) / \
(sigma[calc, ia] - sigma[calc, ia + 1]) * \
(self.apertures[ia + 1] - self.apertures[ia]) + \
self.apertures[ia]
calc = sigma[:, -1] > fraction * maximum
radius[calc] = self.apertures[-1]
return radius
def find_radius_sigma(self, fraction):
"""
Find for each model a fractional surface brightness radius
This is the outermost radius where the surface brightness is larger
than a fraction of the peak surface brightness.
Parameters
----------
fraction: float
The fraction to use when determining the radius
"""
log.debug("Calculating %g%s peak surface brightness radii" % (fraction * 100., '%'))
sigma = np.zeros(self.flux.shape, dtype=self.flux.dtype)
sigma[:, 0] = self.flux[:, 0] / self.apertures[0] ** 2
sigma[:, 1:] = (self.flux[:, 1:] - self.flux[:, :-1]) / \
(self.apertures[1:] ** 2 - self.apertures[:-1] ** 2)
maximum = np.max(sigma, axis=1)
radius = np.zeros(self.n_models, dtype=self.flux.dtype) * u.au
# Linear interpolation - need to loop over apertures backwards for vectorization
for ia in range(len(self.apertures) - 2, -1, -1):
calc = (sigma[:, ia] > fraction * maximum) & (radius == 0.)
radius[calc] = (sigma[calc, ia] - fraction * maximum[calc]) / \
(sigma[calc, ia] - sigma[calc, ia + 1]) * \
(self.apertures[ia + 1] - self.apertures[ia]) + \
self.apertures[ia]
calc = sigma[:, -1] > fraction * maximum
radius[calc] = self.apertures[-1]
return radius
def _convolve_model_dir_1(model_dir, filters, overwrite=False):
for f in filters:
if f.name is None:
raise Exception("filter name needs to be set")
if f.central_wavelength is None:
raise Exception("filter central wavelength needs to be set")
# Create 'convolved' sub-directory if needed
if not os.path.exists(model_dir + "/convolved"):
os.mkdir(model_dir + "/convolved")
# Find all SED files to convolve
sed_files = (
glob.glob(model_dir + "/seds/*.fits.gz")
+ glob.glob(model_dir + "/seds/*/*.fits.gz")
+ glob.glob(model_dir + "/seds/*.fits")
+ glob.glob(model_dir + "/seds/*/*.fits")
)
par_table = load_parameter_table(model_dir)
if len(sed_files) == 0:
raise Exception("No SEDs found in %s" % model_dir)
else:
log.info("{0} SEDs found in {1}".format(len(sed_files), model_dir))
# Find out apertures
first_sed = SED.read(sed_files[0])
n_ap = first_sed.n_ap
apertures = first_sed.apertures
# Set up convolved fluxes
fluxes = [
ConvolvedFluxes(
model_names=np.zeros(len(sed_files), dtype="U30" if six.PY3 else "S30"),
apertures=apertures,
initialize_arrays=True,
)
for i in range(len(filters))
]
# Set up list of binned filters
binned_filters = []
binned_nu = None
# Loop over SEDs
b = ProgressBar(len(sed_files))
for im, sed_file in enumerate(sed_files):
log.debug("Convolving {0}".format(os.path.basename(sed_file)))
# Read in SED
s = SED.read(sed_file, unit_freq=u.Hz, unit_flux=u.mJy, order="nu")
# Check if filters need to be re-binned
try:
assert binned_nu is not None
np.testing.assert_array_almost_equal_nulp(s.nu.value, binned_nu.value, 100)
except AssertionError:
log.info("Rebinning filters")
binned_filters = [f.rebin(s.nu) for f in filters]
binned_nu = s.nu
b.update()
# Convolve
for i, f in enumerate(binned_filters):
fluxes[i].central_wavelength = f.central_wavelength
fluxes[i].apertures = apertures
fluxes[i].model_names[im] = s.name
if n_ap == 1:
fluxes[i].flux[im] = np.sum(s.flux * f.response)
fluxes[i].error[im] = np.sqrt(np.sum((s.error * f.response) ** 2))
else:
fluxes[i].flux[im, :] = np.sum(s.flux * f.response, axis=1)
fluxes[i].error[im] = np.sqrt(np.sum((s.error * f.response) ** 2, axis=1))
for i, f in enumerate(binned_filters):
fluxes[i].sort_to_match(par_table["MODEL_NAME"])
fluxes[i].write(model_dir + "/convolved/" + f.name + ".fits", overwrite=overwrite)
def convolve_model_dir_monochromatic(model_dir,
overwrite=False,
max_ram=8,
wav_min=-np.inf * u.micron,
wav_max=np.inf * u.micron):
"""
Convolve all the model SEDs in a model directory
Parameters
----------
model_dir : str
The path to the model directory
overwrite : bool, optional
Whether to overwrite the output files
max_ram : float, optional
The maximum amount of RAM that can be used (in Gb)
wav_min : float, optional
The minimum wavelength to consider. Only wavelengths above this value
will be output.
wav_max : float, optional
The maximum wavelength to consider. Only wavelengths below this value
will be output.
"""
modpar = parfile.read(os.path.join(model_dir, 'models.conf'), 'conf')
if modpar.get('version', 1) > 1:
raise ValueError(
"monochromatic filters are no longer used for new-style model directories"
)
# Create 'convolved' sub-directory if needed
if not os.path.exists(model_dir + '/convolved'):
os.mkdir(model_dir + '/convolved')
# Find all SED files to convolve
sed_files = sorted(
glob.glob(model_dir + '/seds/*.fits.gz') +
glob.glob(model_dir + '/seds/*/*.fits.gz') +
glob.glob(model_dir + '/seds/*.fits') +
glob.glob(model_dir + '/seds/*/*.fits'))
par_table = load_parameter_table(model_dir)
# Find number of models
n_models = len(sed_files)
if n_models == 0:
raise Exception("No SEDs found in %s" % model_dir)
else:
log.info("{0} SEDs found in {1}".format(n_models, model_dir))
# Find out apertures and wavelengths
first_sed = SED.read(sed_files[0])
n_ap = first_sed.n_ap
apertures = first_sed.apertures
n_wav = first_sed.n_wav
wavelengths = first_sed.wav
# For model grids that are very large, it is not possible to compute all
# fluxes in one go, so we need to process in chunks in wavelength space.
chunk_size = min(
n_wav, int(np.floor(max_ram * 1024.**3 / (4. * 2. * n_models * n_ap))))
if chunk_size == n_wav:
log.info("Producing all monochromatic files in one go")
else:
log.info("Producing monochromatic files in chunks of {0}".format(
chunk_size))
filters = Table()
filters['wav'] = wavelengths
filters['filter'] = np.zeros(wavelengths.shape, dtype='S10')
# Figure out range of wavelength indices to use
# (wavelengths array is sorted in reverse order)
jlo = n_wav - 1 - (wavelengths[::-1].searchsorted(wav_max) - 1)
jhi = n_wav - 1 - wavelengths[::-1].searchsorted(wav_min)
chunk_size = min(chunk_size, jhi - jlo + 1)
# Loop over wavelength chunks
for jmin in range(jlo, jhi, chunk_size):
# Find upper wavelength to compute
jmax = min(jmin + chunk_size - 1, jhi)
log.info('Processing wavelengths {0} to {1}'.format(jmin, jmax))
# Set up convolved fluxes
fluxes = [
ConvolvedFluxes(model_names=np.zeros(
n_models, dtype='U30' if six.PY3 else 'S30'),
apertures=apertures,
initialize_arrays=True) for i in range(chunk_size)
]
b = ProgressBar(len(sed_files))
# Loop over SEDs
for im, sed_file in enumerate(sed_files):
b.update()
log.debug('Processing {0}'.format(os.path.basename(sed_file)))
# Read in SED
s = SED.read(sed_file, unit_freq=u.Hz, unit_flux=u.mJy, order='nu')
# Convolve
for j in range(chunk_size):
fluxes[j].central_wavelength = wavelengths[j + jmin]
fluxes[j].apertures = apertures
fluxes[j].model_names[im] = s.name
if n_ap == 1:
fluxes[j].flux[im] = s.flux[0, j + jmin]
fluxes[j].error[im] = s.error[0, j + jmin]
else:
fluxes[j].flux[im, :] = s.flux[:, j + jmin]
fluxes[j].error[im, :] = s.error[:, j + jmin]
for j in range(chunk_size):
fluxes[j].sort_to_match(par_table['MODEL_NAME'])
fluxes[j].write('{0:s}/convolved/MO{1:03d}.fits'.format(
model_dir, j + jmin + 1),
overwrite=overwrite)
filters['filter'][j + jmin] = "MO{0:03d}".format(j + jmin + 1)
return filters
def to_cache(response, cache_file):
log.debug("Caching data to {0}".format(cache_file))
with open(cache_file, "wb") as f:
pickle.dump(response, f)
def to_cache(response, cache_file):
log.debug("Caching data to {0}".format(cache_file))
with open(cache_file, "wb") as f:
pickle.dump(response, f)
def convolve_model_dir(model_dir, filters, overwrite=False):
"""
Convolve all the model SEDs in a model directory
Parameters
----------
model_dir : str
The path to the model directory
filters : list
A list of :class:`~sedfitter.filter.Filter` objects to use for the
convolution
overwrite : bool, optional
Whether to overwrite the output files
"""
for f in filters:
if f.name is None:
raise Exception("filter name needs to be set")
if f.central_wavelength is None:
raise Exception("filter central wavelength needs to be set")
# Create 'convolved' sub-directory if needed
if not os.path.exists(model_dir + '/convolved'):
os.mkdir(model_dir + '/convolved')
# Find all SED files to convolve
sed_files = (glob.glob(model_dir + '/seds/*.fits.gz') +
glob.glob(model_dir + '/seds/*/*.fits.gz') +
glob.glob(model_dir + '/seds/*.fits') +
glob.glob(model_dir + '/seds/*/*.fits'))
par_table = load_parameter_table(model_dir)
if len(sed_files) == 0:
raise Exception("No SEDs found in %s" % model_dir)
else:
log.info("{0} SEDs found in {1}".format(len(sed_files), model_dir))
# Find out apertures
first_sed = SED.read(sed_files[0])
n_ap = first_sed.n_ap
apertures = first_sed.apertures
# Set up convolved fluxes
fluxes = [ConvolvedFluxes(model_names=np.zeros(len(sed_files), dtype='U30' if six.PY3 else 'S30'), apertures=apertures, initialize_arrays=True) for i in range(len(filters))]
# Set up list of binned filters
binned_filters = []
binned_nu = None
# Loop over SEDs
b = ProgressBar(len(sed_files))
for im, sed_file in enumerate(sed_files):
log.debug('Convolving {0}'.format(os.path.basename(sed_file)))
# Read in SED
s = SED.read(sed_file, unit_freq=u.Hz, unit_flux=u.mJy, order='nu')
# Check if filters need to be re-binned
try:
assert binned_nu is not None
np.testing.assert_array_almost_equal_nulp(s.nu.value, binned_nu.value, 100)
except AssertionError:
log.info('Rebinning filters')
binned_filters = [f.rebin(s.nu) for f in filters]
binned_nu = s.nu
b.update()
# Convolve
for i, f in enumerate(binned_filters):
fluxes[i].central_wavelength = f.central_wavelength
fluxes[i].apertures = apertures
fluxes[i].model_names[im] = s.name
if n_ap == 1:
fluxes[i].flux[im] = np.sum(s.flux * f.response)
fluxes[i].error[im] = np.sqrt(np.sum((s.error * f.response) ** 2))
else:
fluxes[i].flux[im, :] = np.sum(s.flux * f.response, axis=1)
fluxes[i].error[im] = np.sqrt(np.sum((s.error * f.response) ** 2, axis=1))
for i, f in enumerate(binned_filters):
fluxes[i].sort_to_match(par_table['MODEL_NAME'])
fluxes[i].write(model_dir + '/convolved/' + f.name + '.fits',
overwrite=overwrite)
def _convolve_model_dir_1(model_dir, filters, overwrite=False):
for f in filters:
if f.name is None:
raise Exception("filter name needs to be set")
if f.central_wavelength is None:
raise Exception("filter central wavelength needs to be set")
# Create 'convolved' sub-directory if needed
if not os.path.exists(model_dir + '/convolved'):
os.mkdir(model_dir + '/convolved')
# Find all SED files to convolve
sed_files = (glob.glob(model_dir + '/seds/*.fits.gz') +
glob.glob(model_dir + '/seds/*/*.fits.gz') +
glob.glob(model_dir + '/seds/*.fits') +
glob.glob(model_dir + '/seds/*/*.fits'))
par_table = load_parameter_table(model_dir)
if len(sed_files) == 0:
raise Exception("No SEDs found in %s" % model_dir)
else:
log.info("{0} SEDs found in {1}".format(len(sed_files), model_dir))
# Find out apertures
first_sed = SED.read(sed_files[0])
n_ap = first_sed.n_ap
apertures = first_sed.apertures
# Set up convolved fluxes
fluxes = [
ConvolvedFluxes(model_names=np.zeros(
len(sed_files), dtype='U30' if six.PY3 else 'S30'),
apertures=apertures,
initialize_arrays=True) for i in range(len(filters))
]
# Set up list of binned filters
binned_filters = []
binned_nu = None
# Loop over SEDs
b = ProgressBar(len(sed_files))
for im, sed_file in enumerate(sed_files):
log.debug('Convolving {0}'.format(os.path.basename(sed_file)))
# Read in SED
s = SED.read(sed_file, unit_freq=u.Hz, unit_flux=u.mJy, order='nu')
# Check if filters need to be re-binned
try:
assert binned_nu is not None
np.testing.assert_array_almost_equal_nulp(s.nu.value,
binned_nu.value, 100)
except AssertionError:
log.info('Rebinning filters')
binned_filters = [f.rebin(s.nu) for f in filters]
binned_nu = s.nu
b.update()
# Convolve
for i, f in enumerate(binned_filters):
fluxes[i].central_wavelength = f.central_wavelength
fluxes[i].apertures = apertures
fluxes[i].model_names[im] = s.name
if n_ap == 1:
fluxes[i].flux[im] = np.sum(s.flux * f.response)
fluxes[i].error[im] = np.sqrt(np.sum(
(s.error * f.response)**2))
else:
fluxes[i].flux[im, :] = np.sum(s.flux * f.response, axis=1)
fluxes[i].error[im] = np.sqrt(
np.sum((s.error * f.response)**2, axis=1))
for i, f in enumerate(binned_filters):
fluxes[i].sort_to_match(par_table['MODEL_NAME'])
fluxes[i].write(model_dir + '/convolved/' + f.name + '.fits',
overwrite=overwrite)
def convolve_model_dir_monochromatic(model_dir, overwrite=False, max_ram=8,
wav_min=-np.inf * u.micron, wav_max=np.inf * u.micron):
"""
Convolve all the model SEDs in a model directory
Parameters
----------
model_dir : str
The path to the model directory
overwrite : bool, optional
Whether to overwrite the output files
max_ram : float, optional
The maximum amount of RAM that can be used (in Gb)
wav_min : float, optional
The minimum wavelength to consider. Only wavelengths above this value
will be output.
wav_max : float, optional
The maximum wavelength to consider. Only wavelengths below this value
will be output.
"""
modpar = parfile.read(os.path.join(model_dir, 'models.conf'), 'conf')
if modpar.get('version', 1) > 1:
raise ValueError("monochromatic filters are no longer used for new-style model directories")
# Create 'convolved' sub-directory if needed
if not os.path.exists(model_dir + '/convolved'):
os.mkdir(model_dir + '/convolved')
# Find all SED files to convolve
sed_files = (glob.glob(model_dir + '/seds/*.fits.gz') +
glob.glob(model_dir + '/seds/*/*.fits.gz') +
glob.glob(model_dir + '/seds/*.fits') +
glob.glob(model_dir + '/seds/*/*.fits'))
par_table = load_parameter_table(model_dir)
# Find number of models
n_models = len(sed_files)
if n_models == 0:
raise Exception("No SEDs found in %s" % model_dir)
else:
log.info("{0} SEDs found in {1}".format(n_models, model_dir))
# Find out apertures and wavelengths
first_sed = SED.read(sed_files[0])
n_ap = first_sed.n_ap
apertures = first_sed.apertures
n_wav = first_sed.n_wav
wavelengths = first_sed.wav
# For model grids that are very large, it is not possible to compute all
# fluxes in one go, so we need to process in chunks in wavelength space.
chunk_size = min(n_wav, int(np.floor(max_ram * 1024. ** 3 / (4. * 2. * n_models * n_ap))))
if chunk_size == n_wav:
log.info("Producing all monochromatic files in one go")
else:
log.info("Producing monochromatic files in chunks of {0}".format(chunk_size))
filters = Table()
filters['wav'] = wavelengths
filters['filter'] = np.zeros(wavelengths.shape, dtype='S10')
# Figure out range of wavelength indices to use
# (wavelengths array is sorted in reverse order)
jlo = n_wav - 1 - (wavelengths[::-1].searchsorted(wav_max) - 1)
jhi = n_wav - 1 - wavelengths[::-1].searchsorted(wav_min)
chunk_size = min(chunk_size, jhi - jlo + 1)
# Loop over wavelength chunks
for jmin in range(jlo, jhi, chunk_size):
# Find upper wavelength to compute
jmax = min(jmin + chunk_size - 1, jhi)
log.info('Processing wavelengths {0} to {1}'.format(jmin, jmax))
# Set up convolved fluxes
fluxes = [ConvolvedFluxes(model_names=np.zeros(n_models, dtype='U30' if six.PY3 else 'S30'), apertures=apertures, initialize_arrays=True) for i in range(chunk_size)]
b = ProgressBar(len(sed_files))
# Loop over SEDs
for im, sed_file in enumerate(sed_files):
b.update()
log.debug('Processing {0}'.format(os.path.basename(sed_file)))
# Read in SED
s = SED.read(sed_file, unit_freq=u.Hz, unit_flux=u.mJy, order='nu')
# Convolve
for j in range(chunk_size):
fluxes[j].central_wavelength = wavelengths[j + jmin]
fluxes[j].apertures = apertures
fluxes[j].model_names[im] = s.name
if n_ap == 1:
fluxes[j].flux[im] = s.flux[0, j + jmin]
fluxes[j].error[im] = s.error[0, j + jmin]
else:
fluxes[j].flux[im, :] = s.flux[:, j + jmin]
fluxes[j].error[im, :] = s.error[:, j + jmin]
for j in range(chunk_size):
fluxes[j].sort_to_match(par_table['MODEL_NAME'])
fluxes[j].write('{0:s}/convolved/MO{1:03d}.fits'.format(model_dir, j + jmin + 1),
overwrite=overwrite)
filters['filter'][j + jmin] = "MO{0:03d}".format(j + jmin + 1)
return filters
