SpecViz utilizes Astropy I/O registry and YAML data serialization language to enable flexible support for a variety of data formats both in FITS and ASCII.

When specviz is called with a filename as argument (see Launching SpecViz), the default formats below are assumed based on file extension:

By examining the YAML definitions in the following sub-sections and their associated example data files, you will be able to create your own YAML file to define most custom data formats (see Creating a Custom Loader). In addition, there are also other custom loaders that come with SpecViz that follow the same rules but are modified to load JWST DADF test data, which you can also use as a reference.

While using YAML is very flexible, it is also very sensitive to slight changes in your file format. For instance, if you have two files from the same instrument but processed differently (say, one was extracted using IRAF and another one using your own IDL program), they might have different formats and will need separate YAML files.

--- !CustomLoader
name: Generic Fits
extension: [fits, mits]
wcs:
hdu: 0
data:
hdu: 1
col: 0
uncertainty:
hdu: 1
col: 1
type: 'std'
meta:
author: Nicholas Earl


This is generic.yaml, which is a built-in YAML definition for a “generic” FITS file.

--- !CustomLoader


The first line states that this YAML file defines our custom loader. This is always the same no matter what kind of format you are defining.

name: Generic Fits
extension: [fits, mits]


These two lines define the format name and accepted extensions, respectively. In SpecViz GUI, this will translate to “Generic Fits (*.fits *.mits)” in the file type drop-down menu.

wcs:
hdu: 0


This instructs the loader to look for WCS information in the PRIMARY (Extension 0) header. SpecViz also uses this header for other look-ups, e.g., flux unit from BUNIT (see below). Therefore, even if there are no WCS information in your file, you always define this block and point the HDU value to the PRIMARY header. If WCS information are available and supported by Astropy WCS, they will be used to establish dispersion values and unit.

In the absence of WCS or the presence of explicit dispersion column, an additional dispersion: block (not shown) can be defined similarly as data: (see below). Unlike flux, TNULL masking is ignored. If its column does not have TUNIT and unit: 'unitname' is defined, SpecViz will fall back to WCS unit. If all these unit look-ups failed, it defaults to null unit. In the presence of unit: definition, it overrides both TUNIT and WCS.

data:
hdu: 1
col: 0


This instructs the loader to look for flux values (data) in Extension 1, the first column (column index starts from 0). If the column complies to FITS standards (see Astropy FITS Table), flux unit (inferred from TUNIT) and data mask (inferred from TNULL) are also extracted from the same column.

If TUNIT is not defined, loader will look for unit: 'unitname' definition within this block, the unit name must be one that is accepted by Astropy Units (case sensitive). If that is undefined as well, flux unit is extracted from BUNIT keyword in the same header that contains WCS information (see wcs: block for details). If all unit look-ups failed, flux unit is assumed to be $$\textnormal{erg} \; \AA^{-1} \; \textnormal{cm}^{-2} \; \textnormal{s}^{-1}$$.

Note that TNULL is not the same as DQ arrays, which can be similarly defined with mask: block (not shown). If both TNULL and DQ are defined, the masks will be combined.

uncertainty:
hdu: 1
col: 1
type: 'std'


This instructs the loader to look for flux uncertainty values in Extension 1, the second column. Uncertainty type 'std' states that the values are standard deviation (as opposed to inverse variance, 'ivar'). Unlike flux data, its TNULL masking is ignored and unit: tag is not supported. If TUNIT is present, loader will attempt to convert the values to flux unit first. Otherwise, its unit is assumed to be the same as flux unit. If inverse variance is given, square-root is applied to the inversed values before being converted to StdDevUncertainty.

meta:
author: Nicholas Earl


The meta: block can contain any metadata tags you wish to include. They do not affect how SpecViz works. In this example, the author: tag identifies Nicholas Earl as the origin author of this YAML file.

--- !CustomLoader
name: ASCII
extension: [txt, dat]
dispersion:
col: 0
unit: 'Angstrom'
data:
col: 1
unit: 'erg / (Angstrom cm2 s)'
uncertainty:
col: 2
type: 'std'
meta:
author: STScI


This is ascii.yaml, which is a built-in YAML definition for a “generic” ASCII file.

--- !CustomLoader


The first line states that this YAML file defines our custom loader. This is always the same no matter what kind of format you are defining.

name: ASCII
extension: [txt, dat]


These two lines define the format name and accepted extensions, respectively. In SpecViz GUI, this will translate to “ASCII (*.txt *.dat)” in the file type drop-down menu. All ASCII files must comply to Astropy ASCII Table standards.

Any header comments with KEY = VALUE format will be extracted as header metadata information (currently not used by SpecViz).

dispersion:
col: 0
unit: 'Angstrom'


Unlike Generic FITS Loader, ASCII table does not contain WCS. Therefore, the dispersion: block is necessary to define the actual dispersion (e.g., wavelength) values. This instructs the loader to look for dispersion values in the first column (column index starts from 0). Its unit, if not defined in the table itself (e.g., via IPAC table format), will be taken from the unit: tag. The given unit name must be one that is accepted by Astropy Units (case sensitive). If unit is defined in both table and tag, the latter is ignore. If unit is not defined anywhere, it defaults to Angstrom.

data:
col: 1
unit: 'erg / (Angstrom cm2 s)'


This instructs the loader to look for flux values (data) in the second column. Flux unit handling is similar to dispersion unit (see above), except that the default unit would be $$\textnormal{erg} \; \AA^{-1} \; \textnormal{cm}^{-2} \; \textnormal{s}^{-1}$$ if undefined.

If there is an associated DQ column, it can be extracted in a similar fashion using a mask: block specifying the column index (unit is not applicable). Like Generic FITS Loader, zero mask values signify good data.

uncertainty:
col: 2
type: 'std'


This instructs the loader to look for flux uncertainty values in the third column. Uncertainty type 'std' states that the values are standard deviation (as opposed to inverse variance, 'ivar'). Its unit must be the same as flux unit. If inverse variance is given, square-root is applied to the inversed values before being converted to StdDevUncertainty.

meta:
author: STScI


The meta: block can contain any metadata tags you wish to include. They do not affect how SpecViz works. In this example, the author: tag identifies STScI as the origin author of this YAML file.

In addition to loaders that come pre-packaged with the software, SpecViz also looks for custom loaders that you created and saved in your ~/.specviz directory, which can be created with the following Unix command:

\$ mkdir ~/.specviz


To create your own loader, you can use either Generic FITS Loader or ASCII Loader as a template. Your YAML file can have any name of your choosing but must end with a .yaml extension.

In this section, we use a MOSFIRE spectrum named spec1d.gds1312_H0.003.emp26177.fits as an example of a custom FITS format for which we must create our own custom YAML definition file from the Generic FITS Loader template.

First, we inspect the file format that we have, as follow.

>>> from astropy.io import fits
>>> pf = fits.open('spec1d.gds1312_H0.003.emp26177.fits')
>>> pf.info()
Filename: spec1d.gds1312_H0.003.emp26177.fits
No.    Name         Type      Cards   Dimensions   Format
0    PRIMARY     PrimaryHDU       4   ()
1                BinTableHDU     23   1R x 3C      [2287E, 2287E, 2287E]


This opens the FITS file and prints out the overall file structure. From this, it is obvious that the table is in Extension 1.

>>> pf[0].header
SIMPLE  =                    T /Dummy Created by MWRFITS v1.4a
BITPIX  =                    8 /Dummy primary header created by MWRFITS
NAXIS   =                    0 /No data is associated with this header
EXTEND  =                    T /Extensions may (will!) be present
XTENSION= 'BINTABLE'           /Binary table written by MWRFITS v1.4a
BITPIX  =                    8 /Required value
...
TFORM3  = '2287E   '           /


This prints all the headers and we find no WCS information in either of the extensions.

>>> from astropy.table import Table
>>> print(tab)
FLUX [2287]      LAMBDA [2287]      IVAR [2287]
---------------- ------------------ ---------------
0.0 .. 0.0989667 14500.0 .. 18223.8 1e-06 .. 422.54


This shows that there are three columns in the table in Extension 1, namely flux, wavelength, and inverse variance. The table has 2287 rows. Knowing the wavelength regime that the instrument is sensitive to and looking at the wavelength values, we can safely assume that the wavelength unit is Angstrom.

>>> from astropy import units as u
>>> u.electron / u.s / u.pix
Unit("electron / (pix s)")


However, the flux unit is not defined anywhere and cannot be easily inferred. So, let’s just say that we already know the unit to be electrons/s/pix. The code above shows us how Astropy can ingest the flux unit that we want.

--- !CustomLoader
name: Keck/MOSFIRE Fits
extension: [fits, mits]
wcs:
hdu: 0
dispersion:
hdu: 1
col: 1
unit: 'Angstrom'
data:
hdu: 1
col: 0
unit: 'electron / (pix s)'
uncertainty:
hdu: 1
col: 2
type: 'ivar'
meta:
author: STScI


Now that we have the format figured out, it is time to write our own YAML file for it. We will name it keck_mosfire.yaml.

--- !CustomLoader


The first line states that this YAML file defines our custom loader.

name: Keck/MOSFIRE Fits
extension: [fits, mits]


These two lines define the format name and accepted extensions, respectively. We will keep the extensions from our FITS template but change the name to identify our new format. In SpecViz GUI, this will translate to “Keck/MOSFIRE Fits (*.fits *.mits)” in the file type drop-down menu.

wcs:
hdu: 0


We do not have WCS nor BUNIT defined, so we will simply leave this the same as our template.

dispersion:
hdu: 1
col: 1
unit: 'Angstrom'


This instructs the loader to look for our wavelength values in Extension 1, the second column. We explicitly set its unit to Angstrom.

data:
hdu: 1
col: 0
unit: 'electron / (pix s)'


This instructs the loader to look for our flux values in Extension 1, the first column, like the template. However, we also explicitly set its unit to electrons/s/pix by providing the appropriate Astropy unit name.

uncertainty:
hdu: 1
col: 2
type: 'ivar'


This instructs the loader to look for flux uncertainty values in Extension 1, the third column. Unlike the template, we define it as inverse variance.

meta:
author: STScI


Since this does not affect how SpecViz works, we do the lazy thing here by leaving it the same as our template.

Once you are done writing your YAML file, be sure to save it in ~/.specviz. Next, start SpecViz as usual. Now, in its open file dialog, you will see your new format listed in the file-type drop-down menu.