Source code for pypeit.spectrographs.ntt_efosc2

Module for NTT EFOSC2

.. include:: ../include/links.rst
import numpy as np

from pypeit import msgs
from pypeit import telescopes
from pypeit.core import parse
from pypeit.core import framematch
from pypeit.spectrographs import spectrograph
from pypeit.images import detector_container

from IPython import embed

[docs]class NTTEFOSC2Spectrograph(spectrograph.Spectrograph): """ Child of Spectrograph to handle NTT/EFOSC2 specific code """ ndet = 1 # Because each detector is written to a separate FITS file telescope = telescopes.NTTTelescopePar() name = 'ntt_efosc2' header_name = 'EFOSC' camera = 'EFOSC2' url = '' supported = True comment = 'The ESO Faint Object Spectrograph and Camera version 2'
[docs] def init_meta(self): """ Define how metadata are derived from the spectrograph files. That is, this associates the PypeIt-specific metadata keywords with the instrument-specific header cards using :attr:`meta`. """ self.meta = {} # Required (core) self.meta['ra'] = dict(ext=0, card='RA', required_ftypes=['science', 'standard']) self.meta['dec'] = dict(ext=0, card='DEC', required_ftypes=['science', 'standard']) self.meta['target'] = dict(ext=0, card='OBJECT') self.meta['binning'] = dict(card=None, compound=True) #CDELT1 and CDELT2 self.meta['mjd'] = dict(ext=0, card='MJD-OBS') self.meta['datasec'] = dict(card=None, compound=True) self.meta['oscansec'] = dict(card=None, compound=True) self.meta['exptime'] = dict(ext=0, card='EXPTIME') self.meta['airmass'] = dict(ext=0, card='HIERARCH ESO TEL AIRM START', required_ftypes=['science', 'standard']) self.meta['decker'] = dict(card=None, compound=True, required_ftypes=['science', 'standard']) # Extras for config and frametyping self.meta['dispname'] = dict(ext=0, card='HIERARCH ESO INS GRIS1 NAME', required_ftypes=['science', 'standard']) self.meta['idname'] = dict(ext=0, card='HIERARCH ESO DPR CATG') self.meta['instrument'] = dict(ext=0, card='INSTRUME')
[docs] def compound_meta(self, headarr, meta_key): """ Methods to generate metadata requiring interpretation of the header data, instead of simply reading the value of a header card. Args: headarr (:obj:`list`): List of ``_ objects. meta_key (:obj:`str`): Metadata keyword to construct. Returns: object: Metadata value read from the header(s). """ if meta_key == 'binning': binspatial = headarr[0]['CDELT1'] binspec = headarr[0]['CDELT2'] binning = parse.binning2string(int(binspec), int(binspatial)) return binning elif meta_key == 'decker': try: # Science decker = headarr[0]['HIERARCH ESO INS SLIT1 NAME'] except KeyError: # Standard! try: decker = headarr[0]['HIERARCH ESO SEQ SPEC TARG'] except KeyError: return None return decker elif meta_key == 'datasec' or meta_key == 'oscansec': xbin = int(headarr[0]['CDELT2']) data_x = int(headarr[0]['HIERARCH ESO DET OUT1 NX'] * headarr[0]['CDELT1']) #valid pixels along X data_y = int(headarr[0]['HIERARCH ESO DET OUT1 NY'] * headarr[0]['CDELT2']) #valid pixels along Y oscan_y = int(headarr[0]['HIERARCH ESO DET OUT1 OVSCY'] * headarr[0]['CDELT1']) #Overscan region in Y, no overscan in X pscan_x = int(headarr[0]['HIERARCH ESO DET OUT1 PRSCX'] * headarr[0]['CDELT2']) #Prescan region in X, no prescan in Y pscan_y = int(headarr[0]['HIERARCH ESO DET OUT1 PRSCY'] * headarr[0]['CDELT2']) #Prescan region in Y oscan_x = int(headarr[0]['HIERARCH ESO DET OUT1 X']) # X location of output; Not binned max_x = int(headarr[0]['NAXIS1'] * headarr[0]['CDELT2']) # Maximum columns if meta_key == 'datasec': datasec = '[%s:%s,:%s]' % (pscan_y+1, pscan_y+data_y, data_x) return datasec else: oscansec = '[%s:%s,%s:%s]' % (pscan_y+1, pscan_y+data_y, oscan_x+1*xbin, max_x-1*xbin) # Actually two overscan regions, here I only dealing with the region on x-axis return oscansec else: msgs.error("Not ready for this compound meta")
[docs] def config_independent_frames(self): """ Define frame types that are independent of the fully defined instrument configuration. This method returns a dictionary where the keys of the dictionary are the list of configuration-independent frame types. The value of each dictionary element can be set to one or more metadata keys that can be used to assign each frame type to a given configuration group. See :func:`~pypeit.metadata.PypeItMetaData.set_configurations` and how it interprets the dictionary values, which can be None. Returns: :obj:`dict`: Dictionary where the keys are the frame types that are configuration-independent and the values are the metadata keywords that can be used to assign the frames to a configuration group. """ return {'bias': ['binning', 'datasec'], 'dark': ['binning', 'datasec']}
[docs] def configuration_keys(self): """ Return the metadata keys that define a unique instrument configuration. This list is used by :class:`~pypeit.metadata.PypeItMetaData` to identify the unique configurations among the list of frames read for a given reduction. Returns: :obj:`list`: List of keywords of data pulled from file headers and used to constuct the :class:`~pypeit.metadata.PypeItMetaData` object. """ return ['dispname', 'decker', 'binning', 'datasec']
[docs] def raw_header_cards(self): """ Return additional raw header cards to be propagated in downstream output files for configuration identification. The list of raw data FITS keywords should be those used to populate the :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.configuration_keys` or are used in :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.config_specific_par` for a particular spectrograph, if different from the name of the PypeIt metadata keyword. This list is used by :meth:`~pypeit.spectrographs.spectrograph.Spectrograph.subheader_for_spec` to include additional FITS keywords in downstream output files. Returns: :obj:`list`: List of keywords from the raw data files that should be propagated in output files. """ return ['HIERARCH ESO INS GRIS1 NAME', 'HIERARCH ESO INS SLIT1 NAME', 'HIERARCH ESO SEQ SPEC TARG', 'CDELT1', 'CDELT2', 'HIERARCH ESO DET OUT1 NX', 'HIERARCH ESO DET OUT1 NY', 'HIERARCH ESO DET OUT1 OVSCY', 'HIERARCH ESO DET OUT1 PRSCX', 'HIERARCH ESO DET OUT1 PRSCY', 'HIERARCH ESO DET OUT1 X']
[docs] def get_detector_par(self, det, hdu=None): """ Return metadata for the selected detector. .. warning:: Many of the necessary detector parameters are read from the file header, meaning the ``hdu`` argument is effectively **required** for NOT/EFOSC2. The optional use of ``hdu`` is only viable for automatically generated documentation. Args: det (:obj:`int`): 1-indexed detector number. hdu (``_, optional): The open fits file with the raw image of interest. If not provided, frame-dependent parameters are set to a default. Returns: :class:`~pypeit.images.detector_container.DetectorContainer`: Object with the detector metadata. """ if hdu is None: binning = '1,1' datasec = None oscansec = None else: binning = self.get_meta_value(self.get_headarr(hdu), 'binning') datasec = np.atleast_1d(self.get_meta_value(self.get_headarr(hdu), 'datasec')) oscansec = np.atleast_1d(self.get_meta_value(self.get_headarr(hdu), 'oscansec')) # Manual: # Instrument paper: detector_dict = dict( binning = binning, det = 1, # only one detector dataext = 0, specaxis = 0, specflip = False, spatflip = False, platescale = 0.12, # Manual 2.2 darkcurr = 0.0, # e-/pixel/hour saturation = 65535, # Maual Table 8 nonlinear = 0.80, mincounts = -1e10, numamplifiers = 1, gain = np.atleast_1d(0.91), # See fits header ['HIERARCH ESO DET OUT1 GAIN'] ronoise = np.atleast_1d(10.0), # manual page 108 datasec = datasec, oscansec = oscansec, #suffix = '_Thor', ) return detector_container.DetectorContainer(**detector_dict)
[docs] @classmethod def default_pypeit_par(cls): """ Return the default parameters to use for this instrument. Returns: :class:`~pypeit.par.pypeitpar.PypeItPar`: Parameters required by all of PypeIt methods. """ par = super().default_pypeit_par() # Always correct for flexure, starting with default parameters par['flexure']['spec_method'] = 'boxcar' # Adjustments to slit and tilts for NIR par['calibrations']['traceframe']['process']['use_darkimage'] = False par['calibrations']['pixelflatframe']['process']['use_darkimage'] = False par['calibrations']['illumflatframe']['process']['use_darkimage'] = False # Ignore PCA par['calibrations']['slitedges']['sync_predict'] = 'nearest' #par['calibrations']['slitedges']['rm_slits'] = '1:500:120' # remove the fake slit due to bad pixels #edge parameters par['calibrations']['slitedges']['edge_thresh'] = 75. # Tilt parameters par['calibrations']['tilts']['tracethresh'] = 25.0 par['calibrations']['tilts']['spat_order'] = 3 par['calibrations']['tilts']['spec_order'] = 4 # Image processing # The overscan region might cause oversubtraction of the background, set it to False #par['scienceframe']['process']['use_overscan'] = False # 1D wavelength solution par['calibrations']['wavelengths']['method'] = 'full_template' par['calibrations']['wavelengths']['lamps'] = ['HeI', 'ArI'] par['calibrations']['wavelengths']['rms_thresh_frac_fwhm'] = 0.07 par['calibrations']['wavelengths']['sigdetect'] = 10.0 par['calibrations']['wavelengths']['fwhm'] = 4.0 par['calibrations']['wavelengths']['n_final'] = 4 # Flats par['calibrations']['flatfield']['tweak_slits_thresh'] = 0.90 par['calibrations']['flatfield']['tweak_slits_maxfrac'] = 0.10 # Extraction par['reduce']['skysub']['bspline_spacing'] = 0.8 par['reduce']['skysub']['no_poly'] = True par['reduce']['skysub']['bspline_spacing'] = 0.6 par['reduce']['skysub']['joint_fit'] = False par['reduce']['skysub']['global_sky_std'] = False par['reduce']['extraction']['sn_gauss'] = 4.0 par['reduce']['skysub']['sky_sigrej'] = 5.0 return par
[docs] def config_specific_par(self, scifile, inp_par=None): """ Modify the PypeIt parameters to hard-wired values used for specific instrument configurations. Args: scifile (:obj:`str`): File to use when determining the configuration and how to adjust the input parameters. inp_par (:class:`~pypeit.par.parset.ParSet`, optional): Parameter set used for the full run of PypeIt. If None, use :func:`default_pypeit_par`. Returns: :class:`~pypeit.par.parset.ParSet`: The PypeIt parameter set adjusted for configuration specific parameter values. """ # Start with instrument wide par = super().config_specific_par(scifile, inp_par=inp_par) # Wavelength calibrations if self.get_meta_value(scifile, 'dispname') == 'Gr#6': par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr6.fits' elif self.get_meta_value(scifile, 'dispname') == 'Gr#5': par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr5.fits' # Fringes are affecting this Grism significantly, skip flat fielding par['scienceframe']['process']['use_pixelflat'] = False par['scienceframe']['process']['use_illumflat'] = False par['scienceframe']['process']['use_specillum'] = False elif self.get_meta_value(scifile, 'dispname') == 'Gr#4': par['calibrations']['wavelengths']['reid_arxiv'] = 'ntt_efosc2_Gr4.fits' return par
[docs] def check_frame_type(self, ftype, fitstbl, exprng=None): """ Check for frames of the provided type. Args: ftype (:obj:`str`): Type of frame to check. Must be a valid frame type; see frame-type :ref:`frame_type_defs`. fitstbl (`astropy.table.Table`_): The table with the metadata for one or more frames to check. exprng (:obj:`list`, optional): Range in the allowed exposure time for a frame of type ``ftype``. See :func:`pypeit.core.framematch.check_frame_exptime`. Returns: `numpy.ndarray`_: Boolean array with the flags selecting the exposures in ``fitstbl`` that are ``ftype`` type frames. """ good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng) # TODO: Allow for 'sky' frame type, for now include sky in # 'science' category if ftype == 'science': return good_exp & ((fitstbl['idname'] == 'SCIENCE') | (fitstbl['target'] == 'STD,TELLURIC') | (fitstbl['target'] == 'STD,SKY')) if ftype == 'standard': return good_exp & ((fitstbl['target'] == 'STD,FLUX') | (fitstbl['target'] == 'STD')) if ftype == 'bias': return good_exp & ((fitstbl['target'] == 'BIAS') |(fitstbl['target'] == 'DARK')) if ftype in ['pixelflat', 'trace', 'illumflat']: # Flats and trace frames are typed together return good_exp & ((fitstbl['target'] == 'FLAT') | (fitstbl['target'] == 'SKY,FLAT') | (fitstbl['target'] == 'DOME')) if ftype == 'pinhole': # Don't type pinhole return np.zeros(len(fitstbl), dtype=bool) if ftype in ['arc', 'tilt']: return good_exp & ((fitstbl['target'] == 'WAVE')) msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype)) return np.zeros(len(fitstbl), dtype=bool)
[docs] def bpm(self, filename, det, shape=None, msbias=None): """ Generate a default bad-pixel mask. Even though they are both optional, either the precise shape for the image (``shape``) or an example file that can be read to get the shape (``filename`` using :func:`get_image_shape`) *must* be provided. Args: filename (:obj:`str` or None): An example file to use to get the image shape. det (:obj:`int`): 1-indexed detector number to use when getting the image shape from the example file. shape (tuple, optional): Processed image shape Required if filename is None Ignored if filename is not None msbias (`numpy.ndarray`_, optional): Processed bias frame used to identify bad pixels Returns: `numpy.ndarray`_: An integer array with a masked value set to 1 and an unmasked value set to 0. All values are set to 0. """ # Call the base-class method to generate the empty bpm bpm_img = super().bpm(filename, det, shape=shape, msbias=msbias)"Using hard-coded BPM for NTT EFOSC2") binning = self.get_meta_value(filename, 'binning') binspatial = int(binning[0]) binspec = int(binning[2]) bpm_img[int(232/binspec):, int(362/binspatial):int(366/binspatial)] = 1 bpm_img[int(340/binspec):, int(1292/binspatial)] = 1 #bpm_img[int(2050/binspec):, :] = 1 return bpm_img