def systematics(units='muHz'):
"""
Return a list of known systematic effects from the Kepler satellite.
"""
ffile = config.get_datafile('catalogs/kepler','systematics.dat')
systems = ascii.read2recarray(ffile)
systems['frequency'] = conversions.nconvert(systems['unit'],units,systems['frequency'])
systems['e_frequency'] = conversions.nconvert(systems['unit'],units,systems['e_frequency'])
systems['w_frequency'] = conversions.nconvert(systems['unit'],units,systems['w_frequency'])
return systems
def systematics(units='muHz'):
"""
Return a list of known systematic effects from the Kepler satellite.
"""
ffile = config.get_datafile('catalogs/kepler', 'systematics.dat')
systems = ascii.read2recarray(ffile)
systems['frequency'] = conversions.nconvert(systems['unit'], units,
systems['frequency'])
systems['e_frequency'] = conversions.nconvert(systems['unit'], units,
systems['e_frequency'])
systems['w_frequency'] = conversions.nconvert(systems['unit'], units,
systems['w_frequency'])
return systems
def update_info(zp=None):
"""
Update information in zeropoint file, e.g. after calibration.
Call first L{ivs.sed.model.calibrate} without arguments, and pass the output
to this function.
@param zp: updated contents from C{zeropoints.dat}
@type zp: recarray
"""
zp_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), "zeropoints.dat")
zp_, comms = ascii.read2recarray(zp_file, return_comments=True)
existing = [str(i.strip()) for i in zp_["photband"]]
resp_files = sorted(glob.glob(os.path.join(os.path.dirname(os.path.abspath(__file__)), "filters/*")))
resp_files = [os.path.basename(ff) for ff in resp_files if not os.path.basename(ff) in existing]
resp_files.remove("HUMAN.EYE")
resp_files.remove("HUMAN.CONES")
resp_files.remove("CONES.EYE")
if zp is None:
zp = zp_
logger.info("No new calibrations; previous information on existing response curves is copied")
else:
logger.info("Received new calibrations contents of zeropoints.dat will be updated")
# -- update info on previously non existing response curves
new_zp = np.zeros(len(resp_files), dtype=zp.dtype)
logger.info("Found {} new response curves, adding them with default information".format(len(resp_files)))
for i, respfile in enumerate(resp_files):
new_zp[i]["photband"] = respfile
new_zp[i]["eff_wave"] = float(eff_wave(respfile))
new_zp[i]["type"] = "CCD"
new_zp[i]["vegamag"] = np.nan
new_zp[i]["ABmag"] = np.nan
new_zp[i]["STmag"] = np.nan
new_zp[i]["Flam0_units"] = "erg/s/cm2/AA"
new_zp[i]["Fnu0_units"] = "erg/s/cm2/AA"
new_zp[i]["source"] = "nan"
zp = np.hstack([zp, new_zp])
sa = np.argsort(zp["photband"])
ascii.write_array(
zp[sa],
"zeropoints.dat",
header=True,
auto_width=True,
comments=["#" + line for line in comms[:-2]],
use_float="%g",
)
def get_info(photbands=None):
"""
Return a record array containing all filter information.
The record arrays contains following columns:
- photband
- eff_wave
- type
- vegamag, vegamag_lit
- ABmag, ABmag_lit
- STmag, STmag_lit
- Flam0, Flam0_units, Flam0_lit
- Fnu0, Fnu0_units, Fnu0_lit,
- source
@param photbands: list of photbands to get the information from. The input
order is equal to the output order. If C{None}, all filters are returned.
@type photbands: iterable container (list, tuple, 1Darray)
@return: record array containing all information on the requested photbands.
@rtype: record array
"""
zp_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), "zeropoints.dat")
zp = ascii.read2recarray(zp_file)
for iph in custom_filters:
if iph == "_prefer_file":
continue
if "zp" in custom_filters[iph]:
zp = np.hstack([zp, custom_filters[iph]["zp"]])
zp = zp[np.argsort(zp["photband"])]
# -- list photbands in order given, and remove those that do not have
# zeropoints etc.
if photbands is not None:
order = np.searchsorted(zp["photband"], photbands)
zp = zp[order]
keep = zp["photband"] == photbands
zp = zp[keep]
return zp
def search(ID,radius=1.,filename=None):
"""
Retrieve datafiles from the Coralie catalogue.
We search on coordinates, pulled from SIMBAD. If the star ID is not
recognised, a string search is performed to match the 'targ name' field in the
FITS headers.
Only the s1d_A data are searched.
@param ID: ID of the star, understandable by SIMBAD
@type ID: str
@param radius: search radius around the coordinates
@type radius: 1
@param filename: write summary to outputfile if not None
@type filename: str
@return: record array with summary information on the observations, as well
as their location (column 'filename')
@rtype: numpy rec array
"""
data = ascii.read2recarray(config.get_datafile(os.path.join('catalogs','coralie'),'CoralieFullDataOverview.tsv'),splitchar='\t')
info = sesame.search(ID)
if info:
ra,dec = info['jradeg'],info['jdedeg']
keep = np.sqrt((data['ra']-ra)**2 + (data['dec']-dec)**2) < radius/60.
else:
keep = [((re.compile(ID).search(objectn) is not None) and True or False) for objectn in data['object']]
keep = np.array(keep)
data = data[keep]
logger.info('Found %d spectra'%(len(data)))
if filename is not None:
ascii.write_array(data,filename,auto_width=True,header=True)
else:
return data
def get_lines(teff,logg,z=0,atoms=None,ions=None,wrange=(-inf,inf),\
blend=0.0):
"""
Retrieve line transitions and strengths for a specific stellar type
Selection wavelength range in angstrom.
Ions should be a list of ions to include. This can either be a string or
a number
A lines is considerd a blend if the closest line is closer than C{blend} angstrom.
Returns record array with fields C{wavelength}, C{ion} and C{depth}.
Example usage:
Retrieve all Silicon lines between 4500 and 4600 for a B1V star.
>>> data = get_lines(20000,4.0,atoms=['Si'],wrange=(4500,4600))
>>> p = pl.figure()
>>> p = pl.vlines(data['wavelength'],1,1-data['depth'])
See how the depth of the Halpha line varies wrt temperature:
>>> teffs = range(5000,21000,1000) + range(22000,32000,2000) + range(30000,50000,50000)
>>> depths = np.zeros((len(teffs),7))
>>> for i,teff in enumerate(teffs):
... data = get_lines(teff,5.0,ions=['HI'],wrange=(3800,7000))
... depths[i] = data['depth']
>>> p = pl.figure();p = pl.title('Depth of Balmer lines (Halpha-Heta)')
>>> p = pl.plot(teffs,1-depths,'o-')
>>> p = pl.xlabel('Effective temperature');p = pl.grid()
"""
#-- get filepath
filename = 'mask.%d.%02d.p%02d'%(int(teff),int(logg*10),int(z))
filename = config.get_datafile(stellar,filename)
#-- read in the data and extract relevant columns
data = ascii.read2recarray(filename,skip_lines=1,dtype=[('wavelength','f8'),('ion','f8'),('depth','f8'),('c3','f8'),('c4','f8'),('c5','f8')])
data = pl.mlab.rec_drop_fields(data,['c3','c4','c5'])
data['wavelength'] *= 10.
#-- remove blends
if blend>0:
blends_left = np.hstack([0,np.diff(data['wavelength'])])
blends_right= np.hstack([np.diff(data['wavelength']),1e10])
keep = (blends_left>blend) & (blends_right>blend)
data = data[keep]
#-- only keep those transitions within a certain wavelength range
keep = (wrange[0]<=data['wavelength']) & (data['wavelength']<=wrange[1])
data = data[keep]
#-- only keep those transitions that belong to certain ions or atoms
if atoms is not None or ions is not None:
keep = np.array(np.zeros(len(data)),bool)
else:
keep = np.array(np.ones(len(data)),bool)
if atoms is not None:
#-- convert all atoms to their number and select the appropriate ones
atoms = [(isinstance(atom,str) and atomcode.index(atom.title()) or atom) for atom in atoms]
for atom in atoms:
keep = keep | (np.abs(data['ion']-atom)<0.5)
if ions is not None:
#-- convert all ions to their number and select the appropriate ones
ions = [(isinstance(ion,str) and name2ioncode(ion) or ion) for ion in ions]
for ion in ions:
keep = keep | (np.abs(data['ion']-ion)<0.005)
return data[keep]
#-- store in easy to use recarrays
dtype = [('name', 'a20')] + [(pb.split('.')[-1], 'f8') for pb in photbands]
synthetic = np.array(synthetic, dtype=dtype)
dtype = [('name', 'a20')] + [(pb.split('.')[-1] , 'f8') for pb in photbands] +\
[('e_'+pb.split('.')[-1] , 'f8') for pb in photbands]
observed = np.array(observed, dtype=dtype)
#-- write results to file
ascii.write_array(synthetic, synfile, sep=',')
ascii.write_array(observed, obsfile, sep=',')
else:
#-- load results from file
dtype = [('name', 'a20')] + [(pb.split('.')[-1], 'f8') for pb in photbands]
synthetic = ascii.read2recarray(synfile, splitchar=',', dtype=dtype)
dtype = [('name', 'a20')] + [(pb.split('.')[-1] , 'f8') for pb in photbands] +\
[('e_'+pb.split('.')[-1] , 'f8') for pb in photbands]
observed = ascii.read2recarray(obsfile, splitchar=',', dtype=dtype)
#-- add some minimal error is non is given observationaly
for band in ['e_' + pb.split('.')[-1] for pb in photbands]:
observed[band] = np.where(observed[band] <= minerror, minerror,
observed[band])
#===================================================================================
# Zero point calculation and plotting
#===================================================================================
def fit_zp(ax, band, c1, c2):
def get_lines(teff,logg,z=0,atoms=None,ions=None,wrange=(-inf,inf),\
blend=0.0):
"""
Retrieve line transitions and strengths for a specific stellar type
Selection wavelength range in angstrom.
Ions should be a list of ions to include. This can either be a string or
a number
A lines is considerd a blend if the closest line is closer than C{blend} angstrom.
Returns record array with fields C{wavelength}, C{ion} and C{depth}.
Example usage:
Retrieve all Silicon lines between 4500 and 4600 for a B1V star.
>>> data = get_lines(20000,4.0,atoms=['Si'],wrange=(4500,4600))
>>> p = pl.figure()
>>> p = pl.vlines(data['wavelength'],1,1-data['depth'])
See how the depth of the Halpha line varies wrt temperature:
>>> teffs = range(5000,21000,1000) + range(22000,32000,2000) + range(30000,50000,50000)
>>> depths = np.zeros((len(teffs),7))
>>> for i,teff in enumerate(teffs):
... data = get_lines(teff,5.0,ions=['HI'],wrange=(3800,7000))
... depths[i] = data['depth']
>>> p = pl.figure();p = pl.title('Depth of Balmer lines (Halpha-Heta)')
>>> p = pl.plot(teffs,1-depths,'o-')
>>> p = pl.xlabel('Effective temperature');p = pl.grid()
"""
#-- get filepath
filename = 'mask.%d.%02d.p%02d' % (int(teff), int(logg * 10), int(z))
filename = config.get_datafile(stellar, filename)
#-- read in the data and extract relevant columns
data = ascii.read2recarray(filename,
skip_lines=1,
dtype=[('wavelength', 'f8'), ('ion', 'f8'),
('depth', 'f8'), ('c3', 'f8'),
('c4', 'f8'), ('c5', 'f8')])
data = pl.mlab.rec_drop_fields(data, ['c3', 'c4', 'c5'])
data['wavelength'] *= 10.
#-- remove blends
if blend > 0:
blends_left = np.hstack([0, np.diff(data['wavelength'])])
blends_right = np.hstack([np.diff(data['wavelength']), 1e10])
keep = (blends_left > blend) & (blends_right > blend)
data = data[keep]
#-- only keep those transitions within a certain wavelength range
keep = (wrange[0] <= data['wavelength']) & (data['wavelength'] <=
wrange[1])
data = data[keep]
#-- only keep those transitions that belong to certain ions or atoms
if atoms is not None or ions is not None:
keep = np.array(np.zeros(len(data)), bool)
else:
keep = np.array(np.ones(len(data)), bool)
if atoms is not None:
#-- convert all atoms to their number and select the appropriate ones
atoms = [(isinstance(atom, str) and atomcode.index(atom.title())
or atom) for atom in atoms]
for atom in atoms:
keep = keep | (np.abs(data['ion'] - atom) < 0.5)
if ions is not None:
#-- convert all ions to their number and select the appropriate ones
ions = [(isinstance(ion, str) and name2ioncode(ion) or ion)
for ion in ions]
for ion in ions:
keep = keep | (np.abs(data['ion'] - ion) < 0.005)
return data[keep]
