ExtData

class measure_extinction.extdata.ExtData(filename=None)

Bases: object

Extinction for a single line-of-sight

Atributes:

typestring

extinction curve type (e.g., elx or alax)

type_rel_bandstring

band name for relative extinction measurement (x in elx)

red_filestring

reddened star filename

comp_filestring

comparison star filename

columnsdict of tuples of column measurements

measurements are A(V), R(V), N(HI), etc. tuples are measurement, uncertainty

wavesdict of key:wavelengths

key is BAND, IUE, IRS, etc.

ext : dict of key:E(lambda-X) or A(lambda)/A(X) measurements

uncs : dict of key:E(lambda-X) or A(lambda)/A(X) measurement uncertainties

stds : dict of key:A(lambda)/A(X) standard deviations (only defined if the curve is an average of a set of curves, in which case the standard deviation is the spread of the sample around the population mean)

npts : dict of key:number of measurements at each wavelength

names : dict of key:names of names of each wavelength (if photometric bands)

fm90list of FM90 parameters tuples

tuples are measurement, uncertainty

modeldict of key:value with model fitting results, including

• waves: np.ndarray with the wavelengths used in the fitting

• exts: np.ndarray with the fitted powerlaw model to the extinction curve

• residuals: np.ndarray with the fractional residuals, i.e. (data-fit)/fit

• params: tuple with the parameters (amplitude, alpha) if data in A(lambda)/A(V) or (amplitude, alpha, A(V)) if data in E(lambda-V)

Parameters:

filenamestring, optional [default=None]

Full filename to a saved extinction curve

Methods Summary

calc_AV([akav])	Calculate A(V) from the observed extinction curve:
calc_AV_JHK()	Calculate A(V) from the observed extinction curve:
calc_EBV()	Calculate E(B-V) from the observed extinction curve
calc_RV()	Calculate R(V) from the observed extinction curve
calc_elx(redstar, compstar[, rel_band])	Calculate the E(lambda-X) basic extinction measurement
calc_elx_bands(red, comp[, rel_band])	Calculate the E(lambda-X) for the photometric band data
calc_elx_spectra(red, comp, src[, rel_band])	Calculate the E(lambda-X) for the spectroscopic data
fit_band_ext()	Fit the observed NIR extinction curve with a powerlaw model, based on the band data between 1 and 40 micron
fit_spex_ext([amp_bounds, index_bounds, ...])	Fit the observed NIR extinction curve with a powerlaw model, based on the SpeX spectra
get_fitdata(req_datasources[, ...])	Get the data to use in fitting the extinction curve
plot(pltax[, color, alpha, alax, wavenum, ...])	Plot an extinction curve
read(ext_filename)	Read in a saved extinction curve from a FITS file
rebin_constres(source, waverange, resolution)	Rebin the source extinction curve to a fixed spectral resolution and min/max wavelength range.
save(ext_filename[, column_info, ...])	Save the extinction curve to a FITS file
trans_elv_alav([av, akav])	Transform E(lambda-V) to A(lambda)/A(V) by normalizing to A(V) and adding 1.
trans_elv_elvebv([ebv])	Transform E(lambda-V) to E(lambda -V)/E(B-V) by normalizing by E(B-V)).

Methods Documentation

calc_AV(akav=0.112)

Calculate A(V) from the observed extinction curve:

• fit a powerlaw to the SpeX extinction curve, if available

• otherwise: extrapolate the K-band extinction

Parameters:

akavfloat [default = 0.112]

Value of A(K)/A(V) default is from Rieke & Lebofsky (1985) van de Hulst No. 15 curve has A(K)/A(V) = 0.0885

Returns:

Updates self.columns[“AV”]

calc_AV_JHK()

Calculate A(V) from the observed extinction curve:

• extrapolate from J, H, & K photometry

• assumes functional form from Rieke, Rieke, & Paul (1989)

Returns:

Updates self.columns[“AV”]

calc_EBV()

Calculate E(B-V) from the observed extinction curve

Returns:

Updates self.columns[“EBV”]

calc_RV()

Calculate R(V) from the observed extinction curve

Returns:

Updates self.columns[“RV”]

calc_elx(redstar, compstar, rel_band='V')

Calculate the E(lambda-X) basic extinction measurement

Parameters:

redstar:class:StarData

Observed data for the reddened star

compstar:class:StarData

Observed data for the comparison star

rel_bandstring

Band to use for relative extinction measurement default = “V”

Returns:

updates self.(waves, exts, uncs, npts, names)

calc_elx_bands(red, comp, rel_band='V')

Calculate the E(lambda-X) for the photometric band data

Separate from the spectral case as the bands in common must be found. In addition, some of the photometric observations are reported as colors (e.g., B-V) with uncertainties on those colors. As colors are what is needed for the extinction curve, we want to work in those colors to preserve the inheritly lower uncertainties.

Parameters:

red:class:StarData

Observed data for the reddened star

comp:class:StarData

Observed data for the comparison star

rel_bandstring

Band to use for relative extinction measurement default = “V”

Returns:

updates self.(waves, exts, uncs, npts, names)[‘BAND’]

calc_elx_spectra(red, comp, src, rel_band='V')

Calculate the E(lambda-X) for the spectroscopic data

Parameters:

red:class:StarData

Observed data for the reddened star

star:class:StarData

Observed data for the comparison star

srcstring

data source (see global _poss_datasources)

rel_bandstring

Band to use for relative extinction measurement default = “V”

Returns:

updates self.(waves, exts, uncs, npts)[src]

fit_band_ext()

Fit the observed NIR extinction curve with a powerlaw model, based on the band data between 1 and 40 micron

Returns:

Updates self.model[“waves”, “exts”, “residuals”, “params”] and self.columns[“AV”] with the fitting results:

• waves: np.ndarray with the wavelengths used in the fitting

• exts: np.ndarray with the fitted powerlaw model to the extinction curve

• residuals: np.ndarray with the fractional residuals, i.e. (data-fit)/fit

• params: tuple with the parameters (amplitude, alpha) if data in A(lambda)/A(V) or (amplitude, alpha, A(V)) if data in E(lambda-V)

fit_spex_ext(amp_bounds=(-1.5, 1.5), index_bounds=(0.0, 5.0), AV_bounds=(0.0, 6.0))

Fit the observed NIR extinction curve with a powerlaw model, based on the SpeX spectra

Parameters:

amp_boundstuple [default=(-1.5,1.5)]

Model amplitude bounds to be used in the fitting

index_boundstuple [default=(0.0,5.0)]

Powerlaw index bounds to be used in the fitting

AV_boundstuple [default=(0.0,6.0)]

A(V) bounds to be used in the fitting

Returns:

Updates self.model[“waves”, “exts”, “residuals”, “params”] and self.columns[“AV”] with the fitting results:

• waves: np.ndarray with the wavelengths used in the fitting

• exts: np.ndarray with the fitted powerlaw model to the extinction curve

• residuals: np.ndarray with the fractional residuals, i.e. (data-fit)/fit

• params: tuple with the parameters (amplitude, alpha) if data in A(lambda)/A(V) or (amplitude, alpha, A(V)) if data in E(lambda-V)

get_fitdata(req_datasources, remove_uvwind_region=False, remove_lya_region=False, remove_irsblue=False)

Get the data to use in fitting the extinction curve

Parameters:

req_datasourceslist of str

list of data sources (e.g., [‘IUE’, ‘BAND’])

remove_uvwind_regionboolean, optional (default=False)

remove the UV wind regions from the returned data

remove_lya_regionboolean, optional (default=False)

remove the Ly-alpha regions from the returned data

remove_irsblueboolean, optional (default=False)

remove the IRS blue photometry from the returned data

Returns:

(wave, y, y_unc)tuple of arrays

wave is wavelength in microns y is extinction (no units) y_unc is uncertainty on y (no units)

plot(pltax, color=None, alpha=None, alax=False, wavenum=False, exclude=[], normval=1.0, yoffset=0.0, rebin_fac=None, annotate_key=None, annotate_wave_range=None, annotate_text=None, annotate_rotation=0.0, annotate_yoffset=0.0, annotate_color='k', legend_key=None, legend_label=None, fontsize=None, model=False)

Plot an extinction curve

Parameters:

pltaxmatplotlib plot object

colormatplotlib color [default=None]

color to use for all the data

alphafloat [default=None]

transparency value (0=transparent, 1=opaque)

alaxboolean [default=False]

convert from E(lambda-X) using A(X), if necessary plot A(lambda)/A(X)

wavenumboolean [default=False]

plot x axis as 1/wavelength as is standard for UV extinction curves

excludelist of strings [default=[]]

List of data type(s) to exclude from the plot (e.g., “IRS”, “IRAC1”,…)

normvalfloat [default=1.0]

Normalization value

yoffsetfloat [default=0.0]

additive offset for the data

rebin_facint [default=None]

factor by which to rebin the extinction curve

annotate_keystring [default=None]

type of data for which to annotate text (e.g., SpeX_LXD)

annotate_wave_rangelist of 2 floats [default=None]

min/max wavelength range for the annotation of the text

annotate_textstring [default=None]

text to annotate

annotate_rotationfloat [default=0.0]

annotation angle

annotate_yoffsetfloat [default=0.0]

y-offset for the annotated text

annotate_colorstring [default=”k”]

color of the annotated text

legend_keystring [default=None]

legend the spectrum using the given data key

legend_labelstring [default=None]

label to use for legend

fontsizeint [default=None]

fontsize for plot

modelboolean

if set and the model exists, plot it

read(ext_filename)

Read in a saved extinction curve from a FITS file

Parameters:

filenamestring

Full filename of the saved extinction curve

rebin_constres(source, waverange, resolution)

Rebin the source extinction curve to a fixed spectral resolution and min/max wavelength range.

Parameters:

sourcestr

source of extinction (i.e. “IUE”, “IRS”)

waverange2 element array of astropy Quantities

Min/max of wavelength range with units

resolutionfloat

Spectral resolution of rebinned extinction curve

Returns:

measure_extinction ExtData

Object with source extinction curve rebinned

save(ext_filename, column_info=None, save_params=None, fm90_best_params=None, fm90_per_params=None, p92_best_params=None, p92_per_params=None)

Save the extinction curve to a FITS file

Parameters:

filenamestring

Full filename to save extinction curve

column_infodict

dictionary with information about the dust column for example: {‘ebv’: 0.1, ‘rv’: 4.2, ‘av’: 0.42}

save_paramsdict

“type” - type of parameters (e.g., FM90, P92) “best” - best fit parameters as tuple (names, values) “per” - percentile parameters as tuple (names, p50s, puncs, muncs)

fm90_best_paramstuple of 2 float vectors

parameter names and best fit values for the FM90 fit (legacy, use save_params instead)

fm90_per_paramstuple of 2 float vectors

parameter names and (p50, +unc, -unc) values for the FM90 fit (legacy, use save_params instead)

p92_best_paramstuple of 2 float vectors

parameter names and best fit values for the P92 fit (legacy, use save_params instead)

p92_per_paramstuple of 2 float vectors

parameter names and (p50, +unc, -unc) values for the P92 fit (legacy, use save_params instead)

trans_elv_alav(av=None, akav=0.112)

Transform E(lambda-V) to A(lambda)/A(V) by normalizing to A(V) and adding 1. If A(V) is in the columns of the extdata object, use that value. If A(V) is passed explicitly, use that value instead. If no A(V) is available, calculate A(V) from the input elx curve.

Parameters:

avfloat [default = None]

value of A(V) to use - otherwise take it from the columns of the object or calculate it

akavfloat [default = 0.112]

Value of A(K)/A(V), only needed if A(V) has to be calculated from the K-band extinction default is from Rieke & Lebofsky (1985) van de Hulst No. 15 curve has A(K)/A(V) = 0.0885

Returns:

Updates self.(exts, uncs)

trans_elv_elvebv(ebv=None)

Transform E(lambda-V) to E(lambda -V)/E(B-V) by normalizing by E(B-V)).

Parameters:

ebvfloat [default = None]

value of E(B-V) to use - otherwise take it from the columns of the object or calculate it from the E(lambda-V) curve

Returns:

Updates self.(exts, uncs)