Dataclass module

The dataclass module defines a Data class which is very similar to astropy CCDData and uses much of the astropy code, but defines a class that can also include wavelength, response, and sky attributes. We also add a bitmask attribute that is an integer rather than a boolean to allow it to be used as a bitmask (see bitmask module). We also remove the requirement of including units. Finally, the name CCDData is not used, since we may be using digital data that may not come from a CCD!

Unfortunately, without redoing nddata, the methods provided by nddata, such as slicing and arithmetic, drop the new attributes. So beware using those! Internally in pyvista, uncertainties are propagated in the code rather than using the nddata functions.

The Data class includes a write() method to write all of the different attributes to different extentsions in a FITS file. The read() method will read FITS files with these multiple extensions and populate the attributes of a Data object.

dataclass functions

class pyvista.dataclass.Data(*args, **kwd)[source]

Class to include a wavelength array on top of CCDData

add_wave(wave)[source]

Add a wavelength attribute to Data object

Parameters

wave (float, array-like) – Wavelength array to add

add_bitmask(bitmask)[source]

Add a bitmask attribute to Data object

Parameters

bitmask (int, array-like) – Bitmask array to add

add_response(response)[source]

Add a response attribute to Data object

Parameters

response (float, array-like) – Response array to add

add_sky(sky)[source]

Add a sky attribute to Data object

Parameters

sky (flat, array-like) – Sky array to add

add_skyerr(skyerr)[source]

Add a skyerr attribute to Data object

Parameters

skyerr (flat, array-like) – Sky error array to add

to_hdu(hdu_data='FLUX', hdu_bitmask='BITMASK', hdu_uncertainty='UNCERT', hdu_wave='WAVE', hdu_response='RESPONSE', hdu_sky='SKY', hdu_skyerr='SKYERR', wcs_relax=True, key_uncertainty_type='UTYPE', as_image_hdu=True)[source]

Creates an HDUList object from a CCDData object.

Parameters

  • hdu_bitmask (str or None, optional) – If it is a string append this attribute to the HDUList as ~astropy.io.fits.ImageHDU with the string as extension name. Flags are not supported at this time. If None this attribute is not appended. Default is 'BITMASK' for mask, 'UNCERT' for uncertainty

  • hdu_uncertainty (str or None, optional) – If it is a string append this attribute to the HDUList as ~astropy.io.fits.ImageHDU with the string as extension name. Flags are not supported at this time. If None this attribute is not appended. Default is 'BITMASK' for mask, 'UNCERT' for uncertainty

  • hdu_flags (str or None, optional) – If it is a string append this attribute to the HDUList as ~astropy.io.fits.ImageHDU with the string as extension name. Flags are not supported at this time. If None this attribute is not appended. Default is 'BITMASK' for mask, 'UNCERT' for uncertainty

  • wcs_relax (bool) – Value of the relax parameter to use in converting the WCS to a FITS header using ~astropy.wcs.WCS.to_header. The common CTYPE RA---TAN-SIP and DEC--TAN-SIP requires relax=True for the -SIP part of the CTYPE to be preserved.

  • key_uncertainty_type (str, optional) – The header key name for the class name of the uncertainty (if any) that is used to store the uncertainty type in the uncertainty hdu. Default is UTYPE.

  • as_image_hdu (bool) – If this option is True, the first item of the returned ~astropy.io.fits.HDUList is a ~astropy.io.fits.ImageHDU, instead of the default ~astropy.io.fits.PrimaryHDU.

Raises

  • ValueError –

    • If self.mask is set but not a numpy.ndarray. - If self.uncertainty is set but not a astropy uncertainty type. - If self.uncertainty is set but has another unit then self.data.

  • NotImplementedError – Saving flags is not supported.

Returns

hdulist

Return type

~astropy.io.fits.HDUList

write(file, overwrite=True, png=False, imshow=False)[source]

Write Data to file

pyvista.dataclass.fits_data_reader(filename, hdu=0, unit=None, hdu_uncertainty='UNCERT', hdu_bitmask='BITMASK', hdu_wave='WAVE', hdu_response='RESPONSE', hdu_sky='SKY', hdu_skyerr='SKYERR', key_uncertainty_type='UTYPE', **kwd)[source]

Generate a Data object from a FITS file. Modified from astropy fits_ccddata_reader

Parameters

  • filename (str) – Name of fits file.

  • hdu (int, str, tuple of (str, int), optional) – Index or other identifier of the Header Data Unit of the FITS file from which CCDData should be initialized. If zero and no data in the primary HDU, it will search for the first extension HDU with data. The header will be added to the primary HDU. Default is 0.

  • unit (~astropy.units.Unit, optional) – Units of the image data. If this argument is provided and there is a unit for the image in the FITS header (the keyword BUNIT is used as the unit, if present), this argument is used for the unit. Default is None.

  • hdu_uncertainty (str or None, optional) – FITS extension from which the uncertainty should be initialized. If the extension does not exist the uncertainty of the CCDData is None. Default is 'UNCERT'.

  • hdu_bitmask (str or None, optional) – FITS extension from which the bitmask should be initialized. If the extension does not exist the bitmask of the CCDData is None. Default is 'BITMASK'.

  • hdu_flags (str or None, optional) – Currently not implemented. Default is None.

  • key_uncertainty_type (str, optional) – The header key name where the class name of the uncertainty is stored in the hdu of the uncertainty (if any). Default is UTYPE.

  • kwd – Any additional keyword parameters are passed through to the FITS reader in astropy.io.fits; see Notes for additional discussion.

Notes

FITS files that contained scaled data (e.g. unsigned integer images) will be scaled and the keywords used to manage scaled data in astropy.io.fits are disabled.

pyvista.dataclass.transpose(im)[source]

Transpose a Data object

pyvista.dataclass.minmax(data, mask=None, low=3, high=10)[source]

Return min,max scaling factors for input data using median, and MAD

Parameters

  • img – input CCDData

  • low – number of MADs below median to return

  • high – number of MADs above median to retunr

Returns

low and high scaling factors

Return type

min,max