def get_plotsymbolcolorinfo():
"""
Return the arrays needed to always plot the same photometric system with the same color.
"""
photsystem_file = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'list_photsystems_sorted.dat')
plotcolorvalues_file = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'plotcolorvalues.dat')
try:
sortedphotsystems = ascii.read2array(photsystem_file, dtype='str')
except IOError:
logger.info(
'Loading of {} file failed. No fixed symbol color for each photometric system possible.'
.format(photsystem_file))
try:
plotcolorvalues = ascii.read2array(plotcolorvalues_file)
except IOError:
logger.info(
'Loading of {} file failed. No fixed symbol color for each photometric system possible.'
.format(plotcolorvalues_file))
try:
if len(sortedphotsystems) == len(plotcolorvalues):
return sortedphotsystems.ravel(), plotcolorvalues.ravel()
else:
raise IndexError
print('{} should be of equal length as {}.'.format(
plotcolorvalues_file, photsystem_file))
except NameError:
return None, None
def get_response(photband):
"""
Retrieve the response curve of a photometric system 'SYSTEM.FILTER'
OPEN.BOL represents a bolometric open filter.
Example usage:
>>> p = pl.figure()
>>> for band in ['J','H','KS']:
... p = pl.plot(*get_response('2MASS.%s'%(band)))
If you defined a custom filter with the same name as an existing one and
you want to use that one in the future, set C{prefer_file=False} in the
C{custom_filters} module dictionary.
@param photband: photometric passband
@type photband: str ('SYSTEM.FILTER')
@return: (wavelength [A], response)
@rtype: (array, array)
"""
photband = photband.upper()
prefer_file = custom_filters['_prefer_file']
if photband == 'OPEN.BOL':
return np.array([1, 1e10]), np.array([1 / (1e10 - 1), 1 / (1e10 - 1)])
#-- either get from file or get from dictionary
photfile = os.path.join(basedir, 'filters', photband)
photfile_is_file = os.path.isfile(photfile)
#-- if the file exists and files have preference
if photfile_is_file and prefer_file:
wave, response = ascii.read2array(photfile).T[:2]
#-- if the custom_filter exist
elif photband in custom_filters:
wave, response = custom_filters[photband]['response']
#-- if the file exists but custom filters have preference
elif photfile_is_file:
wave, response = ascii.read2array(photfile).T[:2]
else:
raise IOError, ('{0} does not exist {1}'.format(
photband, custom_filters.keys()))
sa = np.argsort(wave)
return wave[sa], response[sa]
def get_response(photband):
"""
Retrieve the response curve of a photometric system 'SYSTEM.FILTER'
OPEN.BOL represents a bolometric open filter.
Example usage:
>>> p = pl.figure()
>>> for band in ['J','H','KS']:
... p = pl.plot(*get_response('2MASS.%s'%(band)))
If you defined a custom filter with the same name as an existing one and
you want to use that one in the future, set C{prefer_file=False} in the
C{custom_filters} module dictionary.
@param photband: photometric passband
@type photband: str ('SYSTEM.FILTER')
@return: (wavelength [A], response)
@rtype: (array, array)
"""
photband = photband.upper()
prefer_file = custom_filters['_prefer_file']
if photband=='OPEN.BOL':
return np.array([1,1e10]),np.array([1/(1e10-1),1/(1e10-1)])
#-- either get from file or get from dictionary
photfile = os.path.join(basedir,'filters',photband)
photfile_is_file = os.path.isfile(photfile)
#-- if the file exists and files have preference
if photfile_is_file and prefer_file:
wave, response = ascii.read2array(photfile).T[:2]
#-- if the custom_filter exist
elif photband in custom_filters:
wave, response = custom_filters[photband]['response']
#-- if the file exists but custom filters have preference
elif photfile_is_file:
wave, response = ascii.read2array(photfile).T[:2]
else:
raise IOError,('{0} does not exist {1}'.format(photband,custom_filters.keys()))
sa = np.argsort(wave)
return wave[sa],response[sa]
def csv2recarray(filename):
"""
Read a MAST csv (comma-sep) file into a record array.
@param filename: name of the TCSV file
@type filename: str
@return: catalog data columns, units, comments
@rtype: record array, dict, list of str
"""
data, comms = ascii.read2array(filename,
dtype=np.str,
splitchar=',',
return_comments=True)
results = None
units = {}
#-- retrieve the data and put it into a record array
if len(data) > 1:
#-- now convert this thing into a nice dictionary
data = np.array(data)
#-- retrieve the format of the columns. They are given in the
# Fortran format. In rare cases, columns contain multiple values
# themselves (so called vectors). In those cases, we interpret
# the contents as a long string
formats = np.zeros_like(data[0])
for i, fmt in enumerate(data[1]):
if 'string' in fmt or fmt == 'datetime': formats[i] = 'U100'
if fmt == 'integer': formats[i] = 'f8'
if fmt == 'ra': formats[i] = 'f8'
if fmt == 'dec': formats[i] = 'f8'
if fmt == 'float': formats[i] = 'f8'
#-- define dtypes for record array
dtypes = np.dtype([(i, j) for i, j in zip(data[0], formats)])
#-- remove spaces or empty values
cols = []
for i, key in enumerate(data[0]):
col = data[2:, i]
#-- fill empty values with nan
cols.append([(row.isspace() or not row) and np.nan or row
for row in col])
#-- fix unit name
for source in cat_info.sections():
if cat_info.has_option(source, data[0, i] + '_unit'):
units[key] = cat_info.get(source, data[0, i] + '_unit')
break
else:
units[key] = 'nan'
#-- define columns for record array and construct record array
cols = [np.cast[dtypes[i]](cols[i]) for i in range(len(cols))]
results = np.rec.array(cols, dtype=dtypes)
else:
results = None
units = {}
return results, units, comms
def calibrate(wave,flux,header=None):
"""
Rough calibration of Hermes spectrum.
Thanks to Roy Ostensen for computing the response function via detailed
modeling of Feige 66.
"""
#-- read instrument response function
instrument = config.get_datafile('catalogs/hermes','hermes_20110319.resp')
iwave,iflux = ascii.read2array(instrument).T
keep = (iwave.min()<=wave) & (wave<=iwave.max())
#-- interpolate on observed wavelength array
iflux = np.interp(wave[keep],iwave,iflux)
return wave[keep],flux[keep]/iflux,iflux
def calibrate(wave,flux,header=None):
"""
Rough calibration of Hermes spectrum.
Thanks to Roy Ostensen for computing the response function via detailed
modeling of Feige 66.
"""
#-- read instrument response function
instrument = config.get_datafile('catalogs/hermes','hermes_20110319.resp')
iwave,iflux = ascii.read2array(instrument).T
keep = (iwave.min()<=wave) & (wave<=iwave.max())
#-- interpolate on observed wavelength array
iflux = np.interp(wave[keep],iwave,iflux)
return wave[keep],flux[keep]/iflux,iflux
def csv2recarray(filename):
"""
Read a MAST csv (comma-sep) file into a record array.
@param filename: name of the TCSV file
@type filename: str
@return: catalog data columns, units, comments
@rtype: record array, dict, list of str
"""
data,comms = ascii.read2array(filename,dtype=np.str,splitchar=',',return_comments=True)
results = None
units = {}
#-- retrieve the data and put it into a record array
if len(data)>1:
#-- now convert this thing into a nice dictionary
data = np.array(data)
#-- retrieve the format of the columns. They are given in the
# Fortran format. In rare cases, columns contain multiple values
# themselves (so called vectors). In those cases, we interpret
# the contents as a long string
formats = np.zeros_like(data[0])
for i,fmt in enumerate(data[1]):
if 'string' in fmt or fmt=='datetime': formats[i] = 'a100'
if fmt=='integer': formats[i] = 'f8'
if fmt=='ra': formats[i] = 'f8'
if fmt=='dec': formats[i] = 'f8'
if fmt=='float': formats[i] = 'f8'
#-- define dtypes for record array
dtypes = np.dtype([(i,j) for i,j in zip(data[0],formats)])
#-- remove spaces or empty values
cols = []
for i,key in enumerate(data[0]):
col = data[2:,i]
#-- fill empty values with nan
cols.append([(row.isspace() or not row) and np.nan or row for row in col])
#-- fix unit name
for source in cat_info.sections():
if cat_info.has_option(source,data[0,i]+'_unit'):
units[key] = cat_info.get(source,data[0,i]+'_unit')
break
else:
units[key] = 'nan'
#-- define columns for record array and construct record array
cols = [np.cast[dtypes[i]](cols[i]) for i in range(len(cols))]
results = np.rec.array(cols, dtype=dtypes)
else:
results = None
units = {}
return results,units,comms
def fitzpatrick2004(Rv=3.1, **kwargs):
"""
From Fitzpatrick 2004 (downloaded from FTP)
This function returns A(lambda)/A(V).
To get A(lambda)/E(B-V), multiply the return value with Rv (A(V)=Rv*E(B-V))
Extra kwags are to catch unwanted keyword arguments.
@param Rv: Rv (2.1, 3.1 or 5.0)
@type Rv: float
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
filename = 'Fitzpatrick2004_Rv_%.1f.red' % (Rv)
myfile = os.path.join(basename, filename)
wave_inv, elamv_ebv = ascii.read2array(myfile, skip_lines=15).T
logger.info('Fitzpatrick2004 curve with Rv=%.2f' % (Rv))
return 1e4 / wave_inv[::-1], ((elamv_ebv + Rv) / Rv)[::-1]
def fitzpatrick2004(Rv=3.1,**kwargs):
"""
From Fitzpatrick 2004 (downloaded from FTP)
This function returns A(lambda)/A(V).
To get A(lambda)/E(B-V), multiply the return value with Rv (A(V)=Rv*E(B-V))
Extra kwags are to catch unwanted keyword arguments.
@param Rv: Rv (2.1, 3.1 or 5.0)
@type Rv: float
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
filename = 'Fitzpatrick2004_Rv_%.1f.red'%(Rv)
myfile = os.path.join(basename,filename)
wave_inv,elamv_ebv = ascii.read2array(myfile,skip_lines=15).T
logger.info('Fitzpatrick2004 curve with Rv=%.2f'%(Rv))
return 1e4/wave_inv[::-1],((elamv_ebv+Rv)/Rv)[::-1]
def fitzpatrick1999(Rv=3.1, **kwargs):
"""
From Fitzpatrick 1999 (downloaded from ASAGIO database)
This function returns A(lambda)/A(V).
To get A(lambda)/E(B-V), multiply the return value with Rv (A(V)=Rv*E(B-V))
Extra kwags are to catch unwanted keyword arguments.
@param Rv: Rv (2.1, 3.1 or 5.0)
@type Rv: float
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
filename = 'Fitzpatrick1999_Rv_%.1f' % (Rv)
filename = filename.replace('.', '_') + '.red'
myfile = os.path.join(basename, filename)
wave, alam_ebv = ascii.read2array(myfile).T
alam_av = alam_ebv / Rv
logger.info('Fitzpatrick1999 curve with Rv=%.2f' % (Rv))
return wave, alam_av
def chiar2006(Rv=3.1, curve='ism', **kwargs):
"""
Extinction curve at infrared wavelengths from Chiar and Tielens (2006)
We return A(lambda)/E(B-V), by multiplying A(lambda)/Av with Rv.
This is only defined for Rv=3.1. If it is different, this will raise an
AssertionError
Extra kwags are to catch unwanted keyword arguments.
UNCERTAIN NORMALISATION
@param Rv: Rv
@type Rv: float
@param curve: extinction curve
@type curve: string (one of 'gc' or 'ism', galactic centre or local ISM)
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
source = os.path.join(basename, 'Chiar2006.red')
#-- check Rv
assert (Rv == 3.1)
wavelengths, gc, ism = ascii.read2array(source).T
if curve == 'gc':
alam_ak = gc
elif curve == 'ism':
keep = ism > 0
alam_ak = ism[keep]
wavelengths = wavelengths[keep]
else:
raise ValueError, 'no curve %s' % (curve)
alam_aV = alam_ak * 0.09
#plot(1/wavelengths,alam_aV,'o-')
return wavelengths * 1e4, alam_aV
def fitzpatrick1999(Rv=3.1,**kwargs):
"""
From Fitzpatrick 1999 (downloaded from ASAGIO database)
This function returns A(lambda)/A(V).
To get A(lambda)/E(B-V), multiply the return value with Rv (A(V)=Rv*E(B-V))
Extra kwags are to catch unwanted keyword arguments.
@param Rv: Rv (2.1, 3.1 or 5.0)
@type Rv: float
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
filename = 'Fitzpatrick1999_Rv_%.1f'%(Rv)
filename = filename.replace('.','_') + '.red'
myfile = os.path.join(basename,filename)
wave,alam_ebv = ascii.read2array(myfile).T
alam_av = alam_ebv/Rv
logger.info('Fitzpatrick1999 curve with Rv=%.2f'%(Rv))
return wave,alam_av
def chiar2006(Rv=3.1,curve='ism',**kwargs):
"""
Extinction curve at infrared wavelengths from Chiar and Tielens (2006)
We return A(lambda)/E(B-V), by multiplying A(lambda)/Av with Rv.
This is only defined for Rv=3.1. If it is different, this will raise an
AssertionError
Extra kwags are to catch unwanted keyword arguments.
UNCERTAIN NORMALISATION
@param Rv: Rv
@type Rv: float
@param curve: extinction curve
@type curve: string (one of 'gc' or 'ism', galactic centre or local ISM)
@return: wavelengths (A), A(lambda)/Av
@rtype: (ndarray,ndarray)
"""
source = os.path.join(basename,'Chiar2006.red')
#-- check Rv
assert(Rv==3.1)
wavelengths,gc,ism = ascii.read2array(source).T
if curve=='gc':
alam_ak = gc
elif curve=='ism':
keep = ism>0
alam_ak = ism[keep]
wavelengths = wavelengths[keep]
else:
raise ValueError,'no curve %s'%(curve)
alam_aV = alam_ak * 0.09
#plot(1/wavelengths,alam_aV,'o-')
return wavelengths*1e4,alam_aV
pl.show()
sys.exit()
#-- if arguments are given, we assume the user wants to run one of the
# functions with arguments given in the command line
# EXAMPLES:
# $:> python freqanalyse.py find_frequency infile=test.dat full_output=True
# $:> python freqanalyse.py time_frequency infile=test.dat full_output=True
else:
method, args, kwargs = argkwargparser.parse()
print("Running method %s with arguments %s and keyword arguments %s" %
(method, args, kwargs))
if '--help' in args or 'help' in args or 'help' in kwargs:
sys.exit()
full_output = kwargs.get('full_output', False)
times, signal = ascii.read2array(kwargs.pop('infile')).T[:2]
out = globals()[method](times, signal, **kwargs)
#-- when find_frequency is called
if method == 'find_frequency' and full_output:
print(pl.mlab.rec2txt(out[0], precision=8))
pl.figure()
pl.subplot(211)
pl.plot(out[1][0], out[1][1], 'k-')
pl.subplot(212)
pl.plot(times, signal, 'ko', ms=2)
pl.plot(times, out[2], 'r-', lw=2)
pl.show()
elif method == 'find_frequency':
print(pl.mlab.rec2txt(out))
doctest.testmod()
pl.show()
sys.exit()
#-- if arguments are given, we assume the user wants to run one of the
# functions with arguments given in the command line
# EXAMPLES:
# $:> python freqanalyse.py find_frequency infile=test.dat full_output=True
# $:> python freqanalyse.py time_frequency infile=test.dat full_output=True
else:
method,args,kwargs = argkwargparser.parse()
print "Running method %s with arguments %s and keyword arguments %s"%(method,args,kwargs)
if '--help' in args or 'help' in args or 'help' in kwargs:
sys.exit()
full_output = kwargs.get('full_output',False)
times,signal = ascii.read2array(kwargs.pop('infile')).T[:2]
out = globals()[method](times,signal, **kwargs)
#-- when find_frequency is called
if method=='find_frequency' and full_output:
print pl.mlab.rec2txt(out[0],precision=8)
pl.figure()
pl.subplot(211)
pl.plot(out[1][0],out[1][1],'k-')
pl.subplot(212)
pl.plot(times,signal,'ko',ms=2)
pl.plot(times,out[2],'r-',lw=2)
pl.show()
elif method=='find_frequency':
print pl.mlab.rec2txt(out)