def get_photometry(ID=None,extra_fields=['_r','_RAJ2000','_DEJ2000'],**kwargs):
"""
Download all available photometry from a star to a record array.
For extra kwargs, see L{_get_URI} and L{mast2phot}
"""
to_units = kwargs.pop('to_units','erg/s/cm2/AA')
master_ = kwargs.get('master',None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
if source=='galex':
results,units,comms = galex(ID=ID,**kwargs)
else:
results,units,comms = search(source,ID=ID,**kwargs)
if results is not None:
master = mast2phot(source,results,units,master,extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave','f8'),('cmeas','f8'),('e_cmeas','f8'),('cunit','a50')]
cols = [[],[],[],[]]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
zp = filters.get_info(master['photband'])
for i in range(len(master)):
try:
value,e_value = conversions.convert(master['unit'][i],to_units,master['meas'][i],master['e_meas'][i],photband=master['photband'][i])
except ValueError: # calibrations not available
value,e_value = np.nan,np.nan
except AssertionError: # postive flux and errors!
value,e_value = np.nan,np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units)
master = numpy_ext.recarr_addcols(master,cols,dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_,master.tolist())
elif master_ is not None:
master = master_
#-- and return the results
return master
def dobb(x, T, **kwargs):
wave_units = kwargs.get('wave_units', 'AA')
flux_units = kwargs.get('flux_units', 'erg/s/cm2/AA')
#-- prepare input
#-- what kind of units did we receive?
curr_conv = constants._current_convention
# X: wavelength/frequency
x_unit_type = conversions.get_type(wave_units)
x = conversions.convert(wave_units, curr_conv, x)
# T: temperature
if isinstance(T, tuple):
T = conversions.convert(T[1], 'K', T[0])
# Y: flux
y_unit_type = conversions.change_convention('SI', flux_units)
#-- if you give Jy vs micron, we need to first convert wavelength to frequency
if y_unit_type == 'kg1 rad-1 s-2' and x_unit_type == 'length':
x = conversions.convert(
conversions._conventions[curr_conv]['length'], 'rad/s', x)
x_unit_type = 'frequency'
elif y_unit_type == 'kg1 m-1 s-3' and x_unit_type == 'frequency':
x = conversions.convert(
'rad/s', conversions._conventions[curr_conv]['length'], x)
x_unit_type = 'length'
#-- correct for rad
if x_unit_type == 'frequency':
x /= (2 * np.pi)
print y_unit_type
#-- run function
I = fctn((x, x_unit_type), T)
#-- prepare output
disc_integrated = kwargs.get('disc_integrated', True)
ang_diam = kwargs.get('ang_diam', None)
if disc_integrated:
I *= np.sqrt(2 * np.pi)
if ang_diam is not None:
scale = conversions.convert(ang_diam[1], 'sr',
ang_diam[0] / 2.)
I *= scale
I = conversions.convert(curr_conv, flux_units, I)
return I
def dobb(x,T,**kwargs):
wave_units = kwargs.get('wave_units','AA')
flux_units = kwargs.get('flux_units','erg/s/cm2/AA')
#-- prepare input
#-- what kind of units did we receive?
curr_conv = constants._current_convention
# X: wavelength/frequency
x_unit_type = conversions.get_type(wave_units)
x = conversions.convert(wave_units,curr_conv,x)
# T: temperature
if isinstance(T,tuple):
T = conversions.convert(T[1],'K',T[0])
# Y: flux
y_unit_type = conversions.change_convention('SI',flux_units)
#-- if you give Jy vs micron, we need to first convert wavelength to frequency
if y_unit_type=='kg1 rad-1 s-2' and x_unit_type=='length':
x = conversions.convert(conversions._conventions[curr_conv]['length'],'rad/s',x)
x_unit_type = 'frequency'
elif y_unit_type=='kg1 m-1 s-3' and x_unit_type=='frequency':
x = conversions.convert('rad/s',conversions._conventions[curr_conv]['length'],x)
x_unit_type = 'length'
#-- correct for rad
if x_unit_type=='frequency':
x /= (2*np.pi)
print y_unit_type
#-- run function
I = fctn((x,x_unit_type),T)
#-- prepare output
disc_integrated = kwargs.get('disc_integrated',True)
ang_diam = kwargs.get('ang_diam',None)
if disc_integrated:
I *= np.sqrt(2*np.pi)
if ang_diam is not None:
scale = conversions.convert(ang_diam[1],'sr',ang_diam[0]/2.)
I *= scale
I = conversions.convert(curr_conv,flux_units,I)
return I
def get_photometry(ID=None, extra_fields=['dist', 'ra', 'dec'], **kwargs):
"""
Download all available photometry from a star to a record array.
For extra kwargs, see L{_get_URI} and L{gator2phot}
Example usage:
>>> import pylab
>>> import vizier
>>> name = 'kr cam'
>>> master = vizier.get_photometry(name,to_units='erg/s/cm2/AA',extra_fields=[])
>>> master = get_photometry(name,to_units='erg/s/cm2/AA',extra_fields=[],master=master)
>>> p = pylab.figure()
>>> wise = np.array(['WISE' in photband and True or False for photband in master['photband']])
>>> p = pylab.errorbar(master['cwave'],master['cmeas'],yerr=master['e_cmeas'],fmt='ko')
>>> p = pylab.errorbar(master['cwave'][wise],master['cmeas'][wise],yerr=master['e_cmeas'][wise],fmt='ro',ms=8)
>>> p = pylab.gca().set_xscale('log')
>>> p = pylab.gca().set_yscale('log')
>>> p = pylab.show()
Other examples:
>>> master = get_photometry(ra=71.239527,dec=-70.589427,to_units='erg/s/cm2/AA',extra_fields=[],radius=1.)
>>> master = get_photometry(ID='J044458.39-703522.6',to_units='W/m2',extra_fields=[],radius=1.)
"""
kwargs['ID'] = ID
to_units = kwargs.pop('to_units', 'erg/s/cm2/AA')
master_ = kwargs.get('master', None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
results, units, comms = search(source, **kwargs)
if results is not None:
master = gator2phot(source,
results,
units,
master,
extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave', 'f8'), ('cmeas', 'f8'), ('e_cmeas', 'f8'),
('cunit', 'a50')]
cols = [[], [], [], []]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
zp = filters.get_info(master['photband'])
for i in range(len(master)):
try:
value, e_value = conversions.convert(
master['unit'][i],
to_units,
master['meas'][i],
master['e_meas'][i],
photband=master['photband'][i])
except ValueError: # calibrations not available
value, e_value = np.nan, np.nan
except AssertionError: # the error or flux must be positive number
value, e_value = np.nan, np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units)
master = numpy_ext.recarr_addcols(master, cols, dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_, master.tolist())
elif master_ is not None:
master = master_
#-- and return the results
return master
def combine(list_of_spectra,R=200.,lambda0=(950.,'AA'),lambdan=(3350.,'AA')):
"""
Combine and weight-average spectra on a common wavelength grid.
C{list_of_spectra} should be a list of lists/arrays. Each element in the
main list should be (wavelength,flux,error).
If you have FUSE fits files, use L{cc.ivs.fits.read_fuse}.
If you have IUE FITS files, use L{cc.ivs.fits.read_iue}.
After Peter Woitke.
@param R: resolution
@type R: float
@param lambda0: start wavelength, unit
@type lambda0: tuple (float,str)
@param lambdan: end wavelength, unit
@type lambdan: tuple (float,str)
@return: binned spectrum (wavelengths,flux, error)
@rtype: array, array, array
"""
l0 = conversions.convert(lambda0[1],'AA',lambda0[0])
ln = conversions.convert(lambdan[1],'AA',lambdan[0])
#-- STEP 1: define wavelength bins
Delta = np.log10(1.+1./R)
x = np.arange(np.log10(l0),np.log10(ln)+Delta,Delta)
x = 10**x
lamc_j = 0.5*(np.roll(x,1)+x)
#-- STEP 2: rebinning of data onto newly defined wavelength bins
Ns = len(list_of_spectra)
Nw = len(lamc_j)-1
binned_fluxes = np.zeros((Ns,Nw))
binned_errors = np.inf*np.ones((Ns,Nw))
for snr,(wave,flux,err) in enumerate(list_of_spectra):
wave0 = np.roll(wave,1)
wave1 = np.roll(wave,-1)
lam_i0_dc = 0.5*(wave0+wave)
lam_i1_dc = 0.5*(wave1+wave)
dlam_i = lam_i1_dc-lam_i0_dc
for j in range(Nw):
A = np.min(np.vstack([lamc_j[j+1]*np.ones(len(wave)),lam_i1_dc]),axis=0)
B = np.max(np.vstack([lamc_j[j]*np.ones(len(wave)),lam_i0_dc]),axis=0)
overlaps = scipy.stats.threshold(A-B,threshmin=0)
norm = np.sum(overlaps)
binned_fluxes[snr,j] = np.sum(flux*overlaps)/norm
binned_errors[snr,j] = np.sqrt(np.sum((err*overlaps)**2))/norm
#-- STEP 3: all available spectra sets are co-added, using the inverse
# square of the bin uncertainty as weight
binned_fluxes[np.isnan(binned_fluxes)] = 0
binned_errors[np.isnan(binned_errors)] = 1e300
weights = 1./binned_errors**2
totalflux = np.sum(weights*binned_fluxes,axis=0)/np.sum(weights,axis=0)
totalerr = np.sqrt(np.sum((weights*binned_errors)**2,axis=0))/np.sum(weights,axis=0)
totalspec = np.sum(binned_fluxes>0,axis=0)
#-- that's it!
return x[:-1],totalflux,totalerr,totalspec
def get_photometry(ID=None,
extra_fields=['_r', '_RAJ2000', '_DEJ2000'],
**kwargs):
"""
Download all available photometry from a star to a record array.
For extra kwargs, see L{_get_URI} and L{mast2phot}
"""
to_units = kwargs.pop('to_units', 'erg/s/cm2/AA')
master_ = kwargs.get('master', None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
if source == 'galex':
results, units, comms = galex(ID=ID, **kwargs)
else:
results, units, comms = search(source, ID=ID, **kwargs)
if results is not None:
master = mast2phot(source,
results,
units,
master,
extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave', 'f8'), ('cmeas', 'f8'), ('e_cmeas', 'f8'),
('cunit', 'a50')]
cols = [[], [], [], []]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
zp = filters.get_info(master['photband'])
for i in range(len(master)):
try:
value, e_value = conversions.convert(
master['unit'][i],
to_units,
master['meas'][i],
master['e_meas'][i],
photband=master['photband'][i])
except ValueError: # calibrations not available
value, e_value = np.nan, np.nan
except AssertionError: # postive flux and errors!
value, e_value = np.nan, np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units)
master = numpy_ext.recarr_addcols(master, cols, dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_, master.tolist())
elif master_ is not None:
master = master_
#-- and return the results
return master
def get_photometry(ID=None,extra_fields=[],**kwargs):
"""
Download all available photometry from a star to a record array.
Extra fields will not be useful probably.
For extra kwargs, see L{_get_URI} and L{gcpd2phot}
"""
to_units = kwargs.pop('to_units','erg/s/cm2/AA')
master_ = kwargs.get('master',None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
results,units,comms = search(source,ID=ID,**kwargs)
if results is not None:
master = gcpd2phot(source,results,units,master,extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave','f8'),('cmeas','f8'),('e_cmeas','f8'),('cunit','a50')]
cols = [[],[],[],[]]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
try:
zp = filters.get_info(master['photband'])
except:
print master['photband']
raise
for i in range(len(master)):
to_units_ = to_units+''
try:
value,e_value = conversions.convert(master['unit'][i],to_units,master['meas'][i],master['e_meas'][i],photband=master['photband'][i])
except ValueError: # calibrations not available
# if it is a magnitude color, try converting it to a flux ratio
if 'mag' in master['unit'][i]:
try:
value,e_value = conversions.convert('mag_color','flux_ratio',master['meas'][i],master['e_meas'][i],photband=master['photband'][i])
to_units_ = 'flux_ratio'
except ValueError:
value,e_value = np.nan,np.nan
# else, we are powerless...
else:
value,e_value = np.nan,np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units_)
master = numpy_ext.recarr_addcols(master,cols,dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
#-- if a master is given as a keyword, and data is found in this module,
# append the two
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_,master.tolist())
elif master is None:
master = master_
#-- and return the results
return master
if 'plx' in database and not ('2007' in database['plx']['r']):
data,units,comms = vizier.search('I/311/hip2',ID=ID)
if data is not None and len(data):
if not 'plx' in database:
database['plx'] = {}
database['plx']['v'] = data['Plx'][0]
database['plx']['e'] = data['e_Plx'][0]
database['plx']['r'] = 'I/311/hip2'
#-- fix the spectral type
data,units,comms = vizier.search('B/mk/mktypes',ID=ID)
if data is not None and len(data):
database['spType'] = data['SpType'][0]
if 'jpos' in database:
#-- add galactic coordinates (in degrees)
ra,dec = database['jpos'].split()
gal = conversions.convert('equatorial','galactic',(str(ra),str(dec)),epoch='2000')
gal = float(gal[0])/np.pi*180,float(gal[1])/np.pi*180
database['galpos'] = gal
#-- fix the proper motions
data,units,comms = vizier.search('I/317/sample',ID=ID)
if data is not None and len(data):
if not 'pm' in database:
database['pm'] = {}
database['pm']['pmRA'] = data['pmRA'][0]
database['pm']['pmDE'] = data['pmDE'][0]
database['pm']['epmRA'] = data['e_pmRA'][0]
database['pm']['epmDE'] = data['e_pmDE'][0]
database['pm']['r'] = 'I/317/sample'
return database
if __name__=="__main__":
def get_photometry(ID=None,extra_fields=['dist','ra','dec'],**kwargs):
"""
Download all available photometry from a star to a record array.
For extra kwargs, see L{_get_URI} and L{gator2phot}
Example usage:
>>> import pylab
>>> import vizier
>>> name = 'kr cam'
>>> master = vizier.get_photometry(name,to_units='erg/s/cm2/AA',extra_fields=[])
>>> master = get_photometry(name,to_units='erg/s/cm2/AA',extra_fields=[],master=master)
>>> p = pylab.figure()
>>> wise = np.array(['WISE' in photband and True or False for photband in master['photband']])
>>> p = pylab.errorbar(master['cwave'],master['cmeas'],yerr=master['e_cmeas'],fmt='ko')
>>> p = pylab.errorbar(master['cwave'][wise],master['cmeas'][wise],yerr=master['e_cmeas'][wise],fmt='ro',ms=8)
>>> p = pylab.gca().set_xscale('log')
>>> p = pylab.gca().set_yscale('log')
>>> p = pylab.show()
Other examples:
>>> master = get_photometry(ra=71.239527,dec=-70.589427,to_units='erg/s/cm2/AA',extra_fields=[],radius=1.)
>>> master = get_photometry(ID='J044458.39-703522.6',to_units='W/m2',extra_fields=[],radius=1.)
"""
kwargs['ID'] = ID
to_units = kwargs.pop('to_units','erg/s/cm2/AA')
master_ = kwargs.get('master',None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
results,units,comms = search(source,**kwargs)
if results is not None:
master = gator2phot(source,results,units,master,extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave','f8'),('cmeas','f8'),('e_cmeas','f8'),('cunit','a50')]
cols = [[],[],[],[]]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
zp = filters.get_info(master['photband'])
for i in range(len(master)):
try:
value,e_value = conversions.convert(master['unit'][i],to_units,master['meas'][i],master['e_meas'][i],photband=master['photband'][i])
except ValueError: # calibrations not available
value,e_value = np.nan,np.nan
except AssertionError: # the error or flux must be positive number
value,e_value = np.nan,np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units)
master = numpy_ext.recarr_addcols(master,cols,dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_,master.tolist())
elif master_ is not None:
master = master_
#-- and return the results
return master
def read_corot(fits_file, return_header=False, type_data='hel',
remove_flagged=True):
"""
Read CoRoT data from a CoRoT FITS file.
Both SISMO and EXO data are recognised and extracted accordingly.
type_data is one of:
- type_data='raw'
- type_data='hel': heliocentric unequidistant
- type_data='helreg': heliocentric equidistant
@param fits_file: CoRoT FITS file name
@type fits_file: string
@param return_header: return header information as dictionary
@type return_header: bool
@param type_data: type of data to return
@type type_data: string (one of 'raw','hel' or 'helreg')
@param remove_flagged: remove flagged datapoints
@type remove_flagged: True
@return: CoRoT data (times, flux, error, flags)
@rtype: array, array, array, array(, header)
"""
#-- read in the FITS file
# headers: ['DATE', 'DATEJD', 'DATEHEL', 'STATUS', 'WHITEFLUX', 'WHITEFLUXDEV', 'BG', 'CORREC']
fits_file_ = pyfits.open(fits_file)
if fits_file_[0].header['hlfccdid'][0]=='A':
times,flux,error,flags = fits_file_[type_data].data.field(0),\
fits_file_[type_data].data.field(1),\
fits_file_[type_data].data.field(2),\
fits_file_[type_data].data.field(3)
# extract the header if asked
if return_header:
header = fits_file_[0].header
fits_file_.close()
logger.debug('Read CoRoT SISMO file %s'%(fits_file))
elif fits_file_[0].header['hlfccdid'][0]=='E':
times = fits_file_['bintable'].data.field('datehel')
if 'blueflux' in fits_file_['bintable'].columns.names:
blueflux,e_blueflux = fits_file_['bintable'].data.field('blueflux'),fits_file_['bintable'].data.field('bluefluxdev')
greenflux,e_greenflux = fits_file_['bintable'].data.field('greenflux'),fits_file_['bintable'].data.field('greenfluxdev')
redflux,e_redflux = fits_file_['bintable'].data.field('redflux'),fits_file_['bintable'].data.field('redfluxdev')
#-- chromatic light curves
if type_data=='colors':
flux = np.column_stack([blueflux,greenflux,redflux])
error = np.column_stack([e_blueflux,e_greenflux,e_redflux]).min(axis=1)
#-- white light curves
else:
flux = blueflux + greenflux + redflux
error = np.sqrt(e_blueflux**2 + e_greenflux**2 + e_redflux**2)
else:
flux,error = fits_file_['bintable'].data.field('whiteflux'),fits_file_['bintable'].data.field('whitefluxdev')
flags = fits_file_['bintable'].data.field('status')
# remove flagged datapoints if asked
if remove_flagged:
keep1 = (flags==0)
keep2 = (error!=-1)
logger.info('Remove: flagged (%d) no valid error (%d) datapoints (%d)'%(len(keep1)-keep1.sum(),len(keep2)-keep2.sum(),len(keep1)))
keep = keep1 & keep2
times,flux,error,flags = times[keep], flux[keep], error[keep], flags[keep]
# convert times to heliocentric JD
times = conversions.convert('MJD','JD',times,jtype='corot')
if return_header:
return times, flux, error, flags, header
else:
return times, flux, error, flags
def combine(list_of_spectra,
R=200.,
lambda0=(950., 'AA'),
lambdan=(3350., 'AA')):
"""
Combine and weight-average spectra on a common wavelength grid.
C{list_of_spectra} should be a list of lists/arrays. Each element in the
main list should be (wavelength,flux,error).
If you have FUSE fits files, use L{cc.ivs.fits.read_fuse}.
If you have IUE FITS files, use L{cc.ivs.fits.read_iue}.
After Peter Woitke.
@param R: resolution
@type R: float
@param lambda0: start wavelength, unit
@type lambda0: tuple (float,str)
@param lambdan: end wavelength, unit
@type lambdan: tuple (float,str)
@return: binned spectrum (wavelengths,flux, error)
@rtype: array, array, array
"""
l0 = conversions.convert(lambda0[1], 'AA', lambda0[0])
ln = conversions.convert(lambdan[1], 'AA', lambdan[0])
#-- STEP 1: define wavelength bins
Delta = np.log10(1. + 1. / R)
x = np.arange(np.log10(l0), np.log10(ln) + Delta, Delta)
x = 10**x
lamc_j = 0.5 * (np.roll(x, 1) + x)
#-- STEP 2: rebinning of data onto newly defined wavelength bins
Ns = len(list_of_spectra)
Nw = len(lamc_j) - 1
binned_fluxes = np.zeros((Ns, Nw))
binned_errors = np.inf * np.ones((Ns, Nw))
for snr, (wave, flux, err) in enumerate(list_of_spectra):
wave0 = np.roll(wave, 1)
wave1 = np.roll(wave, -1)
lam_i0_dc = 0.5 * (wave0 + wave)
lam_i1_dc = 0.5 * (wave1 + wave)
dlam_i = lam_i1_dc - lam_i0_dc
for j in range(Nw):
A = np.min(np.vstack(
[lamc_j[j + 1] * np.ones(len(wave)), lam_i1_dc]),
axis=0)
B = np.max(np.vstack([lamc_j[j] * np.ones(len(wave)), lam_i0_dc]),
axis=0)
overlaps = scipy.stats.threshold(A - B, threshmin=0)
norm = np.sum(overlaps)
binned_fluxes[snr, j] = np.sum(flux * overlaps) / norm
binned_errors[snr, j] = np.sqrt(np.sum((err * overlaps)**2)) / norm
#-- STEP 3: all available spectra sets are co-added, using the inverse
# square of the bin uncertainty as weight
binned_fluxes[np.isnan(binned_fluxes)] = 0
binned_errors[np.isnan(binned_errors)] = 1e300
weights = 1. / binned_errors**2
totalflux = np.sum(weights * binned_fluxes, axis=0) / np.sum(weights,
axis=0)
totalerr = np.sqrt(np.sum(
(weights * binned_errors)**2, axis=0)) / np.sum(weights, axis=0)
totalspec = np.sum(binned_fluxes > 0, axis=0)
#-- that's it!
return x[:-1], totalflux, totalerr, totalspec
data, units, comms = vizier.search('I/311/hip2', ID=ID)
if data is not None and len(data):
if not 'plx' in database:
database['plx'] = {}
database['plx']['v'] = data['Plx'][0]
database['plx']['e'] = data['e_Plx'][0]
database['plx']['r'] = 'I/311/hip2'
#-- fix the spectral type
data, units, comms = vizier.search('B/mk/mktypes', ID=ID)
if data is not None and len(data):
database['spType'] = data['SpType'][0]
if 'jpos' in database:
#-- add galactic coordinates (in degrees)
ra, dec = database['jpos'].split()
gal = conversions.convert('equatorial',
'galactic', (str(ra), str(dec)),
epoch='2000')
gal = float(gal[0]) / np.pi * 180, float(gal[1]) / np.pi * 180
database['galpos'] = gal
#-- fix the proper motions
data, units, comms = vizier.search('I/317/sample', ID=ID)
if data is not None and len(data):
if not 'pm' in database:
database['pm'] = {}
database['pm']['pmRA'] = data['pmRA'][0]
database['pm']['pmDE'] = data['pmDE'][0]
database['pm']['epmRA'] = data['e_pmRA'][0]
database['pm']['epmDE'] = data['e_pmDE'][0]
database['pm']['r'] = 'I/317/sample'
return database
def get_law(name,norm='E(B-V)',wave_units='AA',photbands=None,**kwargs):
"""
Retrieve an interstellar reddening law.
Parameter C{name} must be the function name of one of the laws defined in
this module.
By default, the law will be interpolated on a grid from 100 angstrom to
10 micron in steps of 10 angstrom. This can be adjusted with the parameter
C{wave} (array), which B{must} be in angstrom. You can change the units
ouf the returned wavelength array via C{wave_units}.
By default, the curve is normalised with respect to E(B-V) (you get
A(l)/E(B-V)). You can set the C{norm} keyword to Av if you want A(l)/Av.
Remember that
A(V) = Rv * E(B-V)
The parameter C{Rv} is by default 3.1, other reasonable values lie between
2.0 and 5.1
Extra accepted keywords depend on the type of reddening law used.
Example usage:
>>> wave = np.r_[1e3:1e5:10]
>>> wave,mag = get_law('cardelli1989',wave=wave,Rv=3.1)
@param name: name of the interstellar law
@type name: str, one of the functions defined here
@param norm: type of normalisation of the curve
@type norm: str (one of E(B-V), Av)
@param wave_units: wavelength units
@type wave_units: str (interpretable for units.conversions.convert)
@param photbands: list of photometric passbands
@type photbands: list of strings
@keyword wave: wavelength array to interpolate the law on
@type wave: ndarray
@return: wavelength, reddening magnitude
@rtype: (ndarray,ndarray)
"""
#-- get the inputs
wave_ = kwargs.pop('wave',None)
Rv = kwargs.setdefault('Rv',3.1)
#-- get the curve
wave,mag = globals()[name.lower()](**kwargs)
wave_orig,mag_orig = wave.copy(),mag.copy()
#-- interpolate on user defined grid
if wave_ is not None:
if wave_units != 'AA':
wave_ = conversions.convert(wave_units,'AA',wave_)
mag = np.interp(wave_,wave,mag,right=0)
wave = wave_
#-- pick right normalisation: convert to A(lambda)/Av if needed
if norm.lower()=='e(b-v)':
mag *= Rv
else:
#-- we allow ak and av as shortcuts for normalisation in JOHNSON K and
# V bands
if norm.lower()=='ak':
norm = 'JOHNSON.K'
elif norm.lower()=='av':
norm = 'JOHNSON.V'
norm_reddening = model.synthetic_flux(wave_orig,mag_orig,[norm])[0]
logger.info('Normalisation via %s: Av/%s = %.6g'%(norm,norm,1./norm_reddening))
mag /= norm_reddening
#-- maybe we want the curve in photometric filters
if photbands is not None:
mag = model.synthetic_flux(wave,mag,photbands)
wave = filters.get_info(photbands)['eff_wave']
#-- set the units of the wavelengths
if wave_units != 'AA':
wave = conversions.convert('AA',wave_units,wave)
return wave,mag
def get_law(name, norm='E(B-V)', wave_units='AA', photbands=None, **kwargs):
"""
Retrieve an interstellar reddening law.
Parameter C{name} must be the function name of one of the laws defined in
this module.
By default, the law will be interpolated on a grid from 100 angstrom to
10 micron in steps of 10 angstrom. This can be adjusted with the parameter
C{wave} (array), which B{must} be in angstrom. You can change the units
ouf the returned wavelength array via C{wave_units}.
By default, the curve is normalised with respect to E(B-V) (you get
A(l)/E(B-V)). You can set the C{norm} keyword to Av if you want A(l)/Av.
Remember that
A(V) = Rv * E(B-V)
The parameter C{Rv} is by default 3.1, other reasonable values lie between
2.0 and 5.1
Extra accepted keywords depend on the type of reddening law used.
Example usage:
>>> wave = np.r_[1e3:1e5:10]
>>> wave,mag = get_law('cardelli1989',wave=wave,Rv=3.1)
@param name: name of the interstellar law
@type name: str, one of the functions defined here
@param norm: type of normalisation of the curve
@type norm: str (one of E(B-V), Av)
@param wave_units: wavelength units
@type wave_units: str (interpretable for units.conversions.convert)
@param photbands: list of photometric passbands
@type photbands: list of strings
@keyword wave: wavelength array to interpolate the law on
@type wave: ndarray
@return: wavelength, reddening magnitude
@rtype: (ndarray,ndarray)
"""
#-- get the inputs
wave_ = kwargs.pop('wave', None)
Rv = kwargs.setdefault('Rv', 3.1)
#-- get the curve
wave, mag = globals()[name.lower()](**kwargs)
wave_orig, mag_orig = wave.copy(), mag.copy()
#-- interpolate on user defined grid
if wave_ is not None:
if wave_units != 'AA':
wave_ = conversions.convert(wave_units, 'AA', wave_)
mag = np.interp(wave_, wave, mag, right=0)
wave = wave_
#-- pick right normalisation: convert to A(lambda)/Av if needed
if norm.lower() == 'e(b-v)':
mag *= Rv
else:
#-- we allow ak and av as shortcuts for normalisation in JOHNSON K and
# V bands
if norm.lower() == 'ak':
norm = 'JOHNSON.K'
elif norm.lower() == 'av':
norm = 'JOHNSON.V'
norm_reddening = model.synthetic_flux(wave_orig, mag_orig, [norm])[0]
logger.info('Normalisation via %s: Av/%s = %.6g' %
(norm, norm, 1. / norm_reddening))
mag /= norm_reddening
#-- maybe we want the curve in photometric filters
if photbands is not None:
mag = model.synthetic_flux(wave, mag, photbands)
wave = filters.get_info(photbands)['eff_wave']
#-- set the units of the wavelengths
if wave_units != 'AA':
wave = conversions.convert('AA', wave_units, wave)
return wave, mag
def get_photometry(ID=None, extra_fields=[], **kwargs):
"""
Download all available photometry from a star to a record array.
Extra fields will not be useful probably.
For extra kwargs, see L{_get_URI} and L{gcpd2phot}
"""
to_units = kwargs.pop('to_units', 'erg/s/cm2/AA')
master_ = kwargs.get('master', None)
master = None
#-- retrieve all measurements
for source in cat_info.sections():
results, units, comms = search(source, ID=ID, **kwargs)
if results is not None:
master = gcpd2phot(source,
results,
units,
master,
extra_fields=extra_fields)
#-- convert the measurement to a common unit.
if to_units and master is not None:
#-- prepare columns to extend to basic master
dtypes = [('cwave', 'f8'), ('cmeas', 'f8'), ('e_cmeas', 'f8'),
('cunit', 'a50')]
cols = [[], [], [], []]
#-- forget about 'nan' errors for the moment
no_errors = np.isnan(master['e_meas'])
master['e_meas'][no_errors] = 0.
#-- extend basic master
try:
zp = filters.get_info(master['photband'])
except:
print master['photband']
raise
for i in range(len(master)):
to_units_ = to_units + ''
try:
value, e_value = conversions.convert(
master['unit'][i],
to_units,
master['meas'][i],
master['e_meas'][i],
photband=master['photband'][i])
except ValueError: # calibrations not available
# if it is a magnitude color, try converting it to a flux ratio
if 'mag' in master['unit'][i]:
try:
value, e_value = conversions.convert(
'mag_color',
'flux_ratio',
master['meas'][i],
master['e_meas'][i],
photband=master['photband'][i])
to_units_ = 'flux_ratio'
except ValueError:
value, e_value = np.nan, np.nan
# else, we are powerless...
else:
value, e_value = np.nan, np.nan
try:
eff_wave = filters.eff_wave(master['photband'][i])
except IOError:
eff_wave = np.nan
cols[0].append(eff_wave)
cols[1].append(value)
cols[2].append(e_value)
cols[3].append(to_units_)
master = numpy_ext.recarr_addcols(master, cols, dtypes)
#-- reset errors
master['e_meas'][no_errors] = np.nan
master['e_cmeas'][no_errors] = np.nan
#-- if a master is given as a keyword, and data is found in this module,
# append the two
if master_ is not None and master is not None:
master = numpy_ext.recarr_addrows(master_, master.tolist())
elif master is None:
master = master_
#-- and return the results
return master
