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# Simulated NIRCam WFSS and reduction for CEERS pointings 5-8
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We present simulated NIRCam WFSS observations and the associated imaging 
for all four CEERS pointings (CEERS 5-8) using MIRAGE version 2.2.1, with 
input sources taken from a mock catalog created using the Santa Cruz 
semi-analytic model for galaxy evolution. NIRCam WFSS exposures in each 
grism are taken with one LWC direct image followed by two LWC out-of-field 
images to identify the sources associated with the spectra. During all CEERS 
WFSS observations and LWC imaging, the SWC observes with F115W to add depth 
to the NIRCam imaging in these fields.


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### Summary:
Instrument: NIRCam
Mode: WFSS
Source of Simulations: MIRAGE v2.2.1, grismconf V3
Calibration Pipeline Used: jwst 1.4.2
Target: CEERS Pointings 5-8 (as primary with MIRI parallels)

Filters: SWC F115W; LWC F356W, GRISMR, GRISMC
Readout: SHALLOW4
Observation specification: 6 groups, 1 integration
Dithers: 4 point dither, defined with MIRI parallels; 
         direct and out-of-field imaging with each set of grism exposures
Comments: Idealized spectral extraction performed.
APT: CEERS ERS 1345

Contact: Nor Pirzkal (npirzkal@stsci.edu)


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### Details
These are simulations of the CEERS WFSS and associated imaging. V3 versions 
of the grismconf configuration files were used to generate these simulations.

Directories:
- uncal: contains all the Mirage raw output, which can/should be ran though 
         the STScI Pipeline
- rates: STAGE 1 + WCS assignement. These files are in DN/s but can be used 
         to extract spectra in the "classic" way. Note that NO flat-field is 
         applied to the data by the pipeline. 
- seeds: the seed images, i.e. noiseless simulations.
- SBE: datacubes containing all of the individual simulated sources. These 
       can be used as a shortcut to extract the data
- catalogs: catalogs and input spectra
- Extracted: Contains extracted spectra using a mock, idealized extraction. 


Spectral Extraction: 
The extracted spectra are Python pickle files containing 1D spectra and 
the following info:
  [ lam,model_ext,data_ext,err_ext,model_ext_flx,data_ext_flx,err_ext_flx], 
where:
  lam: wavelength in Micron
  model_ext: 1D extracted model (e-/s)
  data_ext: 1D extracted data (e-/s)
  err_ext: 1D extracted data error (e-/s)
  model_ext_flx: 1D extracted and flux calibrated model (flam)
  data_ext_flx: 1D extracted and flux calibrated data (flam)
  err_ext_flx: 1D extracted and flux calibrated data error (flam)

Extraction was "idealized" in the sense that while we followed the SBE method 
described in Pirzkal et al. 2017, we use the input source catalog to extract 
(i.e. all sources are there) and their dispersed models (contained in the SBE) 
to determine the contamination and extraction weight for the optimal 
extraction. In that sense, these are "idealized" extractions which reflect 
contamination levels and signal to noise, but errors in telescope pointing 
and undetected sources in the imaging are not reflected.


Notes: 
- These spectra were generated using NIRCam grismconf V3 reference files
  and should therefore be extracted using this version. They will have 
  different shapes and offsets than the real data.

- We expect the continuum to be detected in these spectra with a SN~2 for 
  ~22 mag sources. There are many fainter sources in the input catalog, and 
  therefore many extracted spectra with emission lines only.


To read these simply use the Python pickle module:
import pickle
lam,model_ext,data_ext,err_ext,model_ext_flx,data_ext_flx,err_ext_flx = pickle.load(open(FILENAME.pickle,"rb"))

The files are named after the object ID (from the catalogs) and which GRISM 
(R or C). The name of the module is not reflected as we can combine Module A 
and B data as long as R and C grism spectra are not combined together.