Source¶

class mpdaf.sdetect.Source(header, lines=None, mag=None, z=None, spectra=None, images=None, cubes=None, tables=None, mask_invalid=True, filename=None)[source]¶

Bases: object

This class contains a Source object.

Attributes

header	(`astropy.io.fits.Header`) FITS header instance
lines	(`astropy.table.Table`) List of lines
mag	(`astropy.table.Table`) List of magnitudes
z	(`astropy.table.Table`) List of redshifts
spectra	(dict) Spectra dictionary, keys give origin of spectra (`'tot'` for total spectrum, TBC). Values are `Spectrum` objects.
images	(dict) Images dictionary, keys give filter names (`'MUSE_WHITE'` for white image, TBC). Values are `Image` objects.
cubes	(dict) Cubes dictionary, keys give a description of the cube. Values are `Cube` objects.
tables	(dict) Tables dictionary, keys give a description of each table. Values are `astropy.table.Table` objects.
mask_invalid	(bool) Mask invalid values in tables, defaults to True. This can also be done later with the `masked_invalid` method.
filename	(str) File name of the source.

Methods Summary

`add_FSF`(cube)	Compute the mean FSF using the FSF keywords presents if the FITS header of the mosaic cube.
`add_attr`(key, value[, desc, unit, fmt])	Add a new attribute for the current Source object.
`add_comment`(comment, author[, date])	Add a user comment to the FITS header of the Source object.
`add_cube`(cube, name[, size, lbda, ...])	Extract a cube centered on the source center and append it to the cubes dictionary.
`add_history`(text[, author])	Add a history to the FITS header of the Source object.
`add_image`(image, name[, size, minsize, ...])	Extract an image centered on the source center from the input image and append it to the images dictionary.
`add_line`(cols, values[, units, desc, fmt, match])	Add a line to the lines table.
`add_mag`(band, m, errm)	Add a magnitude value to the mag table.
`add_masks`(args, *kwargs)	Use the list of segmentation maps to compute the union mask and the intersection mask and the region where no object is detected in any segmentation map is saved in the sky mask.
`add_narrow_band_image_lbdaobs`(cube, tag, lbda)	Create narrow band image around an observed wavelength value.
`add_narrow_band_images`(cube, z_desc[, eml, ...])	Create narrow band images from a redshift value and a catalog of lines.
`add_seg_images`([tags, DIR, del_sex])	Run SExtractor on all images to create segmentation maps.
`add_table`(tab, name)	Append an astropy table to the tables dictionary.
`add_white_image`(cube[, size, unit_size])	Compute the white images from the MUSE data cube and appends it to the images dictionary.
`add_z`(desc, z[, errz])	Add a redshift value to the z table.
`crack_z`([eml, nlines, cols, z_desc, zguess])	Estimate the best redshift matching the list of emission lines.
`extract_spectra`(cube[, obj_mask, sky_mask, ...])	Extract spectra from the MUSE data cube and from a list of narrow-band images (to define spectrum extraction apertures).
`find_intersection_mask`(seg_tags[, inter_mask])	Use the list of segmentation maps to compute the instersection mask.
`find_sky_mask`(seg_tags[, sky_mask])	Loop over all segmentation images and use the region where no object is detected in any segmentation map as our sky image.
`find_union_mask`(seg_tags[, union_mask])	Use the list of segmentation maps to compute the union mask.
`from_data`(ID, ra, dec, origin[, proba, ...])	Source constructor from a list of data.
`from_file`(filename[, ext, mask_invalid])	Source constructor from a FITS file.
`info`()	Print information.
`masked_invalid`([tables])	Mask where invalid values occur (NaNs or infs or -9999 or ‘’).
`remove_attr`(key)	Remove an Source attribute from the FITS header of the Source object.
`show_ima`(ax, name[, showcenter, cuts, cmap])	Show image.
`show_spec`(ax, name[, cuts, zero, sky, lines])	Display a spectra.
`sort_lines`([nlines_max])	Sort lines by flux in descending order.
`write`(filename)	Write the source object in a FITS file.

Methods Documentation

add_FSF(cube)[source]¶

Compute the mean FSF using the FSF keywords presents if the FITS header of the mosaic cube.

Parameters:

cube : Cube

Input cube MPDAF object.

add_attr(key, value, desc=None, unit=None, fmt=None)[source]¶

Add a new attribute for the current Source object. This attribute will be saved as a keyword in the primary FITS header. This method could also be used to update a simple Source attribute that is saved in the pyfits header.

Equivalent to self.key = (value, comment)

Parameters:

key : str

Attribute name

value : int/float/str

Attribute value

desc : str

Attribute description

unit : astropy.units.Unit

Attribute units

fmt : str

Attribute format (‘.2f’ for example)

add_comment(comment, author, date=None)[source]¶

Add a user comment to the FITS header of the Source object.

Parameters:

comment : str

Comment

author : str

Initial of the author

date : datetime.date

Date By default the current local date is used.

add_cube(cube, name, size=None, lbda=None, unit_size=Unit("arcsec"), unit_wave=Unit("Angstrom"))[source]¶

Extract a cube centered on the source center and append it to the cubes dictionary.

Extracted cube saved in self.cubes[name].

Parameters:

cube : Cube

Input cube MPDAF object.

name : str

Name used to distinguish this cube

size : float

The size to extract. It corresponds to the size along the delta axis and the image is square. If None, the size of the white image extension is taken if it exists.

lbda : (float, float) or None

If not None, tuple giving the wavelength range.

unit_size : astropy.units.Unit

Unit of the size value (arcseconds by default). If None, size is in pixels.

unit_wave : astropy.units.Unit

Wavelengths unit (angstrom by default). If None, inputs are in pixels.

add_history(text, author='')[source]¶

Add a history to the FITS header of the Source object.

Parameters:

text : str

History text

author : str

Initial of the author.

date : datetime.date

Date By default the current local date is used.

add_image(image, name, size=None, minsize=2.0, unit_size=Unit("arcsec"), rotate=False)[source]¶

Extract an image centered on the source center from the input image and append it to the images dictionary.

Extracted image saved in self.images[name].

Parameters:

image : Image

Input image MPDAF object.

name : str

Name used to distinguish this image

size : float

The size to extract. It corresponds to the size along the delta axis and the image is square. If None, the size of the white image extension is taken if it exists.

unit_size : astropy.units.Unit

Unit of size and minsize. Arcseconds by default (use None for size in pixels).

minsize : float

The minimum size of the output image.

rotate : bool

if True, the image is rotated to the same PA as the white-light image.

add_line(cols, values, units=None, desc=None, fmt=None, match=None)[source]¶

Add a line to the lines table.

Parameters:

cols : list of str

Names of the columns

values : list<int/float/str>

List of corresponding values

units : list<astropy.units>

Unity of each column

desc : list of str

Description of each column

fmt : list of str

Fromat of each column.

match : (str, float/int/str, bool)

Tuple (key, value, False/True) that gives the key to match the added line with an existing line. eg (‘LINE’,’LYALPHA1216’, True) If the boolean is True, the line will be added even if it is not matched.

add_mag(band, m, errm)[source]¶

Add a magnitude value to the mag table.

Parameters:

band : str

Filter name.

m : float

Magnitude value.

errm : float

Magnitude error.

add_masks(*args, **kwargs)[source]¶

Use the list of segmentation maps to compute the union mask and the intersection mask and the region where no object is detected in any segmentation map is saved in the sky mask.

Masks are saved as boolean images: - Union is saved in self.images['MASK_UNION']. - Intersection is saved in self.images['MASK_INTER']. - Sky mask is saved in self.images['MASK_SKY'].

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl).

Parameters:

tags : list of str

List of tags of selected segmentation images

add_narrow_band_image_lbdaobs(cube, tag, lbda, size=None, unit_size=Unit("arcsec"), width=8, is_sum=False, subtract_off=True, margin=10.0, fband=3.0)[source]¶

Create narrow band image around an observed wavelength value.

Parameters:

cube : Cube

MUSE data cube.

tag : str

key used to identify the new narrow band image in the images dictionary.

lbda : float

Observed wavelength value in angstrom.

size : float

The total size to extract in arcseconds. It corresponds to the size along the delta axis and the image is square. If None, the size of the white image extension is taken if it exists.

unit_size : astropy.units.Unit

unit of the size value (arcseconds by default) If None, size is in pixels

width : float

Angstrom total width

is_sum : bool

if True the image is computed as the sum over the wavelength axis, otherwise this is the average.

subtract_off : bool

If True, subtracting off nearby data.

margin : float

This off-band is offseted by margin wrt narrow-band limit (in angstrom).

fband : float

The size of the off-band is fband*narrow-band width (in angstrom).

add_narrow_band_images(cube, z_desc, eml=None, size=None, unit_size=Unit("arcsec"), width=8, is_sum=False, subtract_off=True, margin=10.0, fband=3.0)[source]¶

Create narrow band images from a redshift value and a catalog of lines.

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl)

Narrow-band images are saved in self.images['NB_'].

Parameters:

cube : Cube

MUSE data cube.

z_desc : str

Redshift description. The redshift value corresponding to this description will be used.

eml : dict{float: str}

Full catalog of lines Dictionary: key is the wavelength value in Angstrom, value is the name of the line. if None, the following catalog is used:
eml = {1216 : 'LYALPHA', 1908: 'SUMCIII1907',
        3727: 'SUMOII3726', 4863: 'HBETA' ,
        5007: 'OIII5007', 6564: 'HALPHA'}

size : float

The total size to extract. It corresponds to the size along the delta axis and the image is square. If None, the size of the white image extension is taken if it exists.

unit_size : astropy.units.Unit

unit of the size value (arcseconds by default) If None, size is in pixels

width : float

Narrow-band width(in angstrom).

is_sum : bool

if True the image is computed as the sum over the wavelength axis, otherwise this is the average.

subtract_off : bool

If True, subtracting off nearby data. The method computes the subtracted flux by using the algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl):

# if is_sum is False
sub_flux = mean(flux[lbda1-margin-fband*(lbda2-lbda1)/2: lbda1-margin] +
                flux[lbda2+margin: lbda2+margin+fband*(lbda2-lbda1)/2])

# or if is_sum is True:
sub_flux = sum(flux[lbda1-margin-fband*(lbda2-lbda1)/2: lbda1-margin] +
                flux[lbda2+margin: lbda2+margin+fband*(lbda2-lbda1)/2]) /fband

margin : float

This off-band is offseted by margin wrt narrow-band limit (in angstrom).

fband : float

The size of the off-band is fband x narrow-band width (in angstrom).

add_seg_images(tags=None, DIR=None, del_sex=True)[source]¶

Run SExtractor on all images to create segmentation maps.

SExtractor will use the default.nnw, default.param, default.sex and *.conv files present in the current directory. If not present default parameter files are created or copied from the directory given in input (DIR).

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl)

Parameters:

tags : list of str

List of tags of selected images

DIR : str

Directory that contains the configuration files of sextractor

del_sex : bool

If False, configuration files of sextractor are not removed.

add_table(tab, name)[source]¶

Append an astropy table to the tables dictionary.

Parameters:

tab : astropy.table.Table

Input astropy table object.

name : str

Name used to distinguish this table

add_white_image(cube, size=5, unit_size=Unit("arcsec"))[source]¶

Compute the white images from the MUSE data cube and appends it to the images dictionary.

White image saved in self.images[‘MUSE_WHITE’].

Parameters:

cube : Cube

MUSE data cube.

size : float

The total size to extract in arcseconds. It corresponds to the size along the delta axis and the image is square. By default 5x5arcsec

unit_size : astropy.units.Unit

unit of the size value (arcseconds by default) If None, size is in pixels

add_z(desc, z, errz=0)[source]¶

Add a redshift value to the z table.

Parameters:

desc : str

Redshift description.

z : float

Redshidt value.

errz : float or (float,float)

Redshift error (deltaz) or redshift interval (zmin,zmax).

crack_z(eml=None, nlines=inf, cols=('LBDA_OBS', 'FLUX'), z_desc='EMI', zguess=None)[source]¶

Estimate the best redshift matching the list of emission lines.

Algorithm from Johan Richard (johan.richard@univ-lyon1.fr).

This method saves the redshift values in self.z and lists the detected lines in self.lines. self.info() could be used to print the results.

Parameters:

eml : dict{float: str}

Full catalog of lines to test redshift Dictionary: key is the wavelength value in Angtsrom, value is the name of the line. if None, the following catalog is used:

emlines = {
67: 'LYALPHA1216'  , 1550.0: 'CIV1550'       ,
0: 'CIII]1909'     , 2326.0: 'CII2326'       ,
032: '[OII]3726'   , 3728.8149: '[OII]3729'  ,
6001: 'HTHETA3799' , 3834.6599: 'HETA3835'   ,
0: '[NEIII]3869'   , 3888.7: 'HZETA3889'     ,
0: '[NEIII]3967'   , 4102.0: 'HDELTA4102'    ,
0: 'HGAMMA4340'    , 4861.3198: 'HBETA4861'  ,
0: '[OIII]4959'    , 5007.0: '[OIII]5007'    ,
0: '[NII6548]'     , 6562.7998: 'HALPHA6563' ,
0: '[NII]6583'     , 6716.0: '[SII]6716'     ,
0: '[SII]6731'
}

nlines : int

estimated the redshift if the number of emission lines is inferior to this value

cols : (str, str)

tuple (wavelength column name, flux column name) Two columns of self.lines that will be used to define the emission lines.

z_desc : str

Estimated redshift will be saved in self.z table under these name.

zguess : float

Guess redshift. Test if this redshift is a match and fills the detected lines

extract_spectra(cube, obj_mask='MASK_UNION', sky_mask='MASK_SKY', tags_to_try=('MUSE_WHITE', 'NB_LYALPHA', 'NB_HALPHA', 'NB_SUMOII3726'), skysub=True, psf=None, beta=None, lbda=None, unit_wave=Unit("Angstrom"))[source]¶

Extract spectra from the MUSE data cube and from a list of narrow-band images (to define spectrum extraction apertures).

First, this method computes a subcube that has the same size along the spatial axis as the image that contains the mask of the objet.

Then, the no-weighting spectrum is computed as the sum of the subcube weighted by the mask of the object. It is saved in self.spectra['MUSE_TOT'].

The weighted spectra are computed as the sum of the subcube weighted by the corresponding narrow bands image. They are saved in self.spectra[nb_ima] (for nb_ima in tags_to_try).

If psf:

The potential PSF weighted spectrum is computed as the sum of the subcube weighted by mutliplication of the mask of the objetct and the PSF. It is saved in self.spectra[‘MUSE_PSF’]

If skysub:

The local sky spectrum is computed as the average of the subcube weighted by the sky mask image. It is saved in self.spectra['MUSE_SKY']

The other spectra are computed on the sky-subtracted subcube and they are saved in self.spectra['*_SKYSUB'].

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl)

The weighted sum conserves the flux by :

Taking into account bad pixels in the addition.
Normalizing with the median value of weighting sum/no-weighting sum

Parameters:

cube : Cube

MUSE data cube.

obj_mask : str

Name of the image that contains the mask of the object.

sky_mask : str

Name of the sky mask image.

tags_to_try : list of str

List of narrow bands images.

skysub : bool

If True, a local sky subtraction is done.

psf : numpy.ndarray

The PSF to use for PSF-weighted extraction. This can be a vector of length equal to the wavelength axis to give the FWHM of the Gaussian or Moffat PSF at each wavelength (in arcsec) or a cube with the PSF to use. psf=None by default (no PSF-weighted extraction).

beta : float or none

if not none, the PSF is a Moffat function with beta value, else it is a Gaussian

lbda : (float, float) or none

if not none, tuple giving the wavelength range.

unit_wave : astropy.units.Unit

Wavelengths unit (angstrom by default) If None, inputs are in pixels

find_intersection_mask(seg_tags, inter_mask='MASK_INTER')[source]¶

Use the list of segmentation maps to compute the instersection mask.

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl):

1- Select on each segmentation map the object at the centre of the map. (the algo supposes that each objects have different labels) 2- compute the intersection of these selected objects

Parameters:

tags : list of str

List of tags of selected segmentation images

inter_mask : str

Name of the intersection mask image.

find_sky_mask(seg_tags, sky_mask='MASK_SKY')[source]¶

Loop over all segmentation images and use the region where no object is detected in any segmentation map as our sky image.

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl)

Parameters:

seg_tags : list of str

List of tags of selected segmentation images.

sky_mask : str

Name of the sky mask image.

find_union_mask(seg_tags, union_mask='MASK_UNION')[source]¶

Use the list of segmentation maps to compute the union mask.

Algorithm from Jarle Brinchmann (jarle@strw.leidenuniv.nl):

1- Select on each segmentation map the object at the centre of the map. (the algo supposes that each objects have different labels) 2- compute the union of these selected objects

Parameters:

tags : list of str

List of tags of selected segmentation images

union_mask : str

Name of the union mask image.

classmethod from_data(ID, ra, dec, origin, proba=None, confid=None, extras=None, lines=None, mag=None, z=None, spectra=None, images=None, cubes=None, tables=None, mask_invalid=True)[source]¶

Source constructor from a list of data.

Parameters:

ID : int

ID of the source

ra : double

Right ascension in degrees

dec : double

Declination in degrees

origin : tuple (str, str, str, str)

1- Name of the detector software which creates this object 2- Version of the detector software which creates this object 3- Name of the FITS data cube from which this object has been extracted. 4- Version of the FITS data cube

proba : float

Detection probability

confid : int

Expert confidence index

extras : dict{key: value} or dict{key: (value, comment)}

Extra keywords

lines : astropy.table.Table

List of lines

mag : astropy.table.Table

List of magnitudes.

z : astropy.table.Table

List of redshifts

spectra : dict

Dictionary containing spectra. Keys gives the origin of the spectrum (‘tot’ for total spectrum, TBC). Values are Spectrum objects.

images : dict

Dictionary containing images. Keys gives the filter (‘MUSE_WHITE’ for white image, TBC). Values are Image object.

cubes : dict

Dictionary containing small data cubes. Keys gives a description of the cube. Values are Cube objects.

tables : dict

Dictionary containing tables. Keys give a description of each table. Values are astropy.table.Table objects.

mask_invalid: bool

If True (default), iterate on all columns of all tables to mask invalid values (Inf, NaN, and -9999).

classmethod from_file(filename, ext=None, mask_invalid=True)[source]¶

Source constructor from a FITS file.

Parameters:

filename : str

FITS filename

ext : str or list of str

Names of the FITS extensions that will be loaded in the source object. Regular expression accepted.

mask_invalid: bool

If True (default), iterate on all columns of all tables to mask invalid values (Inf, NaN, and -9999).

info()[source]¶: Print information.

masked_invalid(tables=None)[source]¶: Mask where invalid values occur (NaNs or infs or -9999 or ‘’).

remove_attr(key)[source]¶: Remove an Source attribute from the FITS header of the Source object.

show_ima(ax, name, showcenter=None, cuts=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>, **kwargs)[source]¶

Show image.

Parameters:

ax : matplotlib.axes._subplots.AxesSubplot

Matplotlib axis instance (eg ax = fig.add_subplot(2,3,1)).

name : str

Name of image to display.

showcenter : (float, str)

radius in arcsec and color used to plot a circle around the center of the source.

cuts : (float, float)

Minimum and maximum values to use for the scaling.

cmap : matplotlib.cm

Color map.

kwargs : matplotlib.artist.Artist

kwargs can be used to set additional plotting properties.

show_spec(ax, name, cuts=None, zero=False, sky=None, lines=None, **kwargs)[source]¶

Display a spectra.

Parameters:

ax : matplotlib.axes._subplots.AxesSubplot

Matplotlib axis instance (eg ax = fig.add_subplot(2,3,1)).

name : str

Name of spectra to display.

cuts : (float, float)

Minimum and maximum values to use for the scaling.

zero : float

If True, the 0 flux line is plotted in black.

sky : Spectrum

Sky spectra to overplot (default None).

lines : str

Name of a columns of the lines table containing wavelength values. If not None, overplot red vertical lines at the given wavelengths.

kwargs : matplotlib.artist.Artist

kwargs can be used to set additional plotting properties.

sort_lines(nlines_max=25)[source]¶

Sort lines by flux in descending order.

Parameters:

nlines_max : int

Maximum number of stored lines

write(filename)[source]¶

Write the source object in a FITS file.

Parameters:

filename : str

FITS filename