Class SED

Initialize SED class.

Different ways to initialize:

1. If no previous data are saved:

>>> mysed = SED('HD129929')

2. If previous data exists:

>>> #mysed = SED(photfile='HD129929.phot') # will set ID with 'oname' field from SIMBAD
>>> #mysed = SED(ID='bla', photfile='HD129929.phot') # Sets custom ID

The ID variable is used internally to look up data, so it should be something SIMBAD understands and that designates the target.

Parameters:

• plx (tuple (plx,e_plx)) - parallax (and error) of the object

Overrides: object.__init__

Machine readable string representation of an SED object.

Overrides: object.__repr__

Human readable string representation of an SED object.

Overrides: object.__str__

Search photometry on the net or from the phot file if it exists.

For bright stars, you can set radius a bit higher...

Parameters:

• radius (float.) - search radius (arcseconds)

Retrieve and combine spectra.

WARNING: this function creates FUSE and DIR directories!

Exclude (any) photometry from fitting process.

If called without arguments, all photometry will be excluded.

Set a default scheme of colors/absolute values to fit the SED.

Possible values:

1. scheme = 'abs': means excluding all colors, including all absolute values
2. scheme = 'color': means including all colors, excluding all absolute values
3. scheme = 'combo': means inculding all colors, and one absolute value per system (the one with the smallest relative error)
4. scheme = 'irfm': means mimic infrared flux method: choose absolute values in the infrared (define with infrared), and colours in the optical

Parameters:

• infrared (tuple (value <float>, unit <str>)) - definition of start of infrared for infrared flux method

Add photometry from numpy arrays

By default unflagged, and at the ra and dec of the star. No color and included.

Parameters:

• meas (array) - original measurements (fluxes, magnitudes...)
• e_meas (array) - error on original measurements in same units as meas
• units (array of strings) - units of original measurements
• photbands (array of strings) - photometric passbands of original measurements
• source (array of strings) - source of original measurements

Check if this SED represents the object `name'.

Purpose: solve alias problems. Maybe the ID is 'HD129929', and you are checking for "V* V836 Cen", which is the same target.

Parameters:

• name (str) - object name

Returns: bool

True if this object instance represent the target "name".

Check if this SED has a phot file.

Returns: bool

True if this object instance has a photfile

Get the probability density function for the distance, given a parallax.

If no parallax is given, the catalogue value will be used. If that one is also not available, a ValueError will be raised.

Parallax must be given in mas.

If the parallax is a float without uncertainty, an uncertainty of 0 will be assumed.

If lutz_kelker=True, a probability density function will be given for the distance, otherwise the distance is computed from simply inverting the parallax.

Distance is returned in parsec (pc).

Returns: (float,float)

distance

Compute distance from radius and angular diameter (scale factor).

The outcome is added to the results attribute:

   distance = r/sqrt(scale)

This particularly useful when you added constraints from solar-like oscillations (add_constraint_slo).

Returns: (float,float)

distance,uncertainty (pc)

Clip grid on CI limit, to save memory.

Parameters:

• mtype (str) - type or results to clip
• CI_limit (float (between 0 (clips everything) and 1 (clips nothing))) - confidence limit to clip on

Calculates the Chi2 and reduced Chi2 based on nr of observations and degrees of freedom. If df is not provided, tries to calculate df from the fitting ranges.

Compute confidence interval of all columns in the results grid.

Parameters:

• mtype (str) - type of results to compute confidence intervals of
• chi2_type (str ('raw' or 'red')) - type of chi2 (raw or reduced)
• CI_limit (float (between 0 (clips everything) and 1 (clips nothing))) - confidence limit to clip on

Saves the provided confidence intervals in the result dictionary of self. The provided confidence intervals will be saved in a dictionary: self.results[mtype]['CI'] with as key for the value the name, for cilow: name_l and for cihigh: name_u.

Parameters:

• mtype (str) - the search type
• name (array) - names of the parameters
• value (array) - best fit values
• cilow (array) - lower CI limit
• cihigh (array) - upper CI limit

Fit fundamental parameters using a (pre-integrated) grid search.

If called consecutively, the ranges will be set to the CI_limit of previous estimations, unless set explicitly.

If called for the first time, the ranges will be +/- np.inf by defaults, unless set explicitly.

Manually set the best SED model.

This is particularly useful when working with calibrators for which there is an observed SED.

The label will be used to store the model in the results attribute.

Parameters:

• wave (ndarray) - wavelength array (angstrom)
• flux (ndarray) - flux array (erg/s/cm2/AA)
• label (s str) - key used to store the model and synthetic photometry in results

Retrieve the best SED model.

Warning: the grid search interpolation is also done with interpolation in metallicity, while this is not the case for the best full SED model. Interpolation of the full SED is only done in teff/logg, and the metallicity is chosen to be equal to the closest grid point. On the other hand, while the reddening value is interpolated in the grid search, this is not the case for the best model (the model is simply reddened according the found best value). So don't be surprised if you find differences between the values of self.results[label]['synflux'], which are the value sfor the interpolated photometric points of the best model, and the synthetic photometry obtained by manual integration of the returned full SED model. Those differences should be incredible small and mainly due to the metallicity.

Retrieve an element from the results of a grid search according to the derived probability.

An array of length "NrSamples" containing 'grid-indices' is returned, so the actual parameter values of the corresponding model can be retrieved from the results dictionary.

Parameters:

• NrSamples (int) - the number of samples you wish to draw

Calculate chi2 of best model.

TEMPORARY!!! API WILL CHANGE!!!

Use the distance to compute additional information.

Compute radii, absolute luminosities and masses, and add them to the results.

Extra kwargs go to get_distance_from_plx.

Warning: after calling this function, the labs column in the grid is actually absolutely calibrated and reflects the true absolute luminosity instead of the absolute luminosity assuming 1 solar radius.

Parameters:

• distance (tuple (float,float)) - distance in solar units and error
• mtype (str) - type of results to add the information to

Use diagnostics from solar-like oscillations to put additional constraints on the parameters.

If the results are constrained with the distance before add_constraint_distance, then these results are combined with the SLO constraints.

Parameters:

• numax (3-tuple (value,error,unit)) - frequency of maximum amplitude
• Deltanu0 (3-tuple (value,error,unit)) - large separation (l=0)
• chi2_type (str ('raw' or 'red')) - type of chi2 (raw or reduced)

Use reddening maps to put additional constraints on the parameters.

This constraint assumes that, if upper_limit=False, given the distance to target, the reddening from the reddening maps is similar to the one derived from the SED. All models where this is not the case will be deemed improbable.

If upper_limit=True, all models with an E(B-V) value above ebv will be considered improbable.

When you don't set ebv, the value will, by default, be derived from Drimmel maps if upper_limit=False and from Schlegel if upper_limit=True. You can change this behaviour by setting model manually.

Parameters:

• distance (tuple (float,float)) - distance and uncertainty in parsec
• ebv (float) - E(B-V) reddening in magnitude
• e_ebv (float) - error on the reddening in percentage
• model (str) - model reddening maps
• mtype (str) - type of results to add the information to
• upper_limit (bool) - consider the E(B-V) value as an upper limit

Add constraints on the mass.

mass must be a tuple, if the second element is smaller than the first, it is assumed to be (mu,sigma) from a normal distribution. If the second element is larger than the first, it is assumed to be (lower, upper) from a uniform distribution.

Plot grid as scatter plot

Prameterptype sets the colors of the scattered points (e.g., 'ci_red','z','ebv').

Example usage:

First set the SED:

>>> mysed = SED('HD180642')
>>> mysed.load_fits()

Then make the plots:

>>> p = pl.figure()
>>> p = pl.subplot(221);mysed.plot_grid(limit=None)
>>> p = pl.subplot(222);mysed.plot_grid(x='ebv',y='z',limit=None)
>>> p = pl.subplot(223);mysed.plot_grid(x='teff',y='ebv',limit=None)
>>> p = pl.subplot(224);mysed.plot_grid(x='logg',y='z',limit=None)

]]include figure]]ivs_sed_builder_plot_grid_01.png]

Decorators:

• @standalone_figure

Plot a 2D confidence intervall calculated using the CI2D computation from calculate_iminimize_CI2D. Make sure you first calculate the grid you want using the calculate_iminimize_CI2D function.

Decorators:

• @standalone_figure

Plot only the SED data.

Extra kwargs are passed to plotting functions.

The decorator provides an keyword savefig. When set to True, an image name is generated automatically (very long!), and the figure is closed. When set to a string, the image is saved with that name, and the figure is closed. When savefig is not given, the image will stay open so that the user can still access all plot elements and do further enhancements.

Parameters:

• colors (boolean) - if False, plot absolute values, otherwise plot colors (flux ratios)
• plot_unselected - if True, all photometry is plotted, otherwise only those that are selected

Decorators:

• @standalone_figure

Plot a fitted SED together with the data.

Example usage:

First set the SED:

>>> mysed = SED('HD180642')
>>> mysed.load_fits()

Then make the plots:

>>> p = pl.figure()
>>> p = pl.subplot(121)
>>> mysed.plot_sed(colors=False)
>>> p = pl.subplot(122)
>>> mysed.plot_sed(colors=True)

]]include figure]]ivs_sed_builder_plot_sed_01.png]

Decorators:

• @standalone_figure

Plot chi2 statistic for every datapoint included in the fit.

To plot the statistic from the included absolute values, set colors=False.

To plot the statistic from the included colors, set colors=True.

Example usage:

First set the SED:

>>> mysed = SED('HD180642')
>>> mysed.load_fits()

Then make the plots:

>>> p = pl.figure()
>>> p = pl.subplot(121)
>>> mysed.plot_chi2(colors=False)
>>> p = pl.subplot(122)
>>> mysed.plot_chi2(colors=True)

]]include figure]]ivs_sed_builder_plot_chi2_01.png]

Parameters:

• colors (boolean) - flag to distinguish between colors and absolute values

Decorators:

• @standalone_figure

Decorators:

• @standalone_figure

Grid of models

Decorators:

• @standalone_figure

Decorators:

• @standalone_figure

Decorators:

• @standalone_figure

Size is x and y width in arcminutes

Decorators:

• @standalone_figure

Save master photometry to a file.

Parameters:

• photfile (str) - name of the photfile. Defaults to starname.phot.

Load the contents of the photometry file to the master record.

Parameters:

• photfile (str) - name of the photfile. Defaults to the value of self.photfile.

Save content of SED object to a FITS file.

The .fits file will contain the following extensions if they are present in the object:
   1) data (all previously collected photometric data = content of .phot file)
   2) model_igrid_search (full best model SED table from grid_search)
   3) igrid_search (CI from grid search = actual grid samples)
   4) synflux_igrid_search (integrated synthetic fluxes for best model from grid_search)
   5) model_imc (full best model SED table from monte carlo)
   6) imc (CI from monte carlo = actual monte carlo samples)
   7) synflux_imc (integrated synthetic fluxes for best model from monte carlo)

@param filename: name of SED FITS file
@type filename: string
@param overwrite: overwrite old FITS file if true
@type overwrite: boolean

Example usage:

>>> #mysed.save_fits()
>>> #mysed.save_fits(filename='myname.fits')

Load a previously made SED FITS file. Only works for SEDs saved with
the save_fits function after 14.06.2012.

The .fits file can contain the following extensions:
   1) data (all previously collected photometric data = content of .phot file)
   2) model_igrid_search (full best model SED table from grid_search)
   3) igrid_search (CI from grid search = actual grid samples)
   4) synflux_igrid_search (integrated synthetic fluxes for best model from grid_search)
   5) model_imc (full best model SED table from monte carlo)
   6) imc (CI from monte carlo = actual monte carlo samples)
   7) synflux_imc (integrated synthetic fluxes for best model from monte carlo)

@param filename: name of SED FITS file
@type filename: string
@rtype: bool
@return: true if Fits file could be loaded

Save content of SED object to a HDF5 file. (HDF5 is the successor of FITS files, providing a clearer structure of the saved content.) This way of saving is more thorough that save_fits(), fx. the CI2D confidence intervals are not save to a fits file, but are saved to a hdf5 file. Currently the following data is saved to HDF5 file:

• sed.master (photometry)
• sed.results (results from all fitting methods)
• sed.constraints (extra constraints on the fits)

Parameters:

• filename (string) - name of SED FITS file
• update (bool) - if True, an existing file will be updated with the current information, if False, an existing fill be overwritten

Returns: string

the name of the output HDF5 file.

Load a previously made SED from HDF5 file.

Parameters:

• filename (string) - name of SED FITS file

Returns: bool

True if HDF5 file could be loaded

Convert the bibcodes in a phot file to a bibtex file.

The first line in the bibtex file contains a \citet command citing all photometry.

Prints the stored confidence intervals of the given mtype, the confidence intervals are sorted alfabetically.

Parameters:

• mtype (str) - the search type for which to show the ci

Returns: str

The confidence intervals in a string