calc.all.ch12: Calculate the 'average' RA and DEC for objects with multiple...
In esplint/IRACpm: Apply Distortion Correction to SPITZER IRAC Data

Description Usage Arguments Details Value Author(s) Examples

Calculate 'average' RA and DEC of all sources from a full dataset of detections from one epoch. calc_all is internal for CD.solver but can be used for actual data, though not recommended.

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calc.all.ch12(data, cda, cores = 4, whichstars = NA, outlier = TRUE)
calc_all(pix1, pars1, pix2, pars2, cda, data, whichstars = NA, 
outlier = TRUE, cores = 4)

`pix1`	Pixel bias correction polynomial: Cryogenic = ca_pix1; Post Cryogenic = NA
`pars1`	Optical distortion correction polynomial: [3.6] = wa_pars1
`pix2`	Pixel bias correction polynomial: Cryogenic = NA; Post Cryogenic = NA
`pars2`	Optical distortion correction polynomial: [4.5] = wa_pars2
`cda`	Matrix containing c(RA,DEC,angle) for each frame in the dataset. Should be the same length as data$image_key$image_id. (The output of CD.solver or CD.solver2). Can also be a list containing the matrix and a vector with an estimated scale factor (i.e., the output of CD.solver3 or CD.solver4).
`data`	Dataset containing all the individual detections (i.e., Output of read.in.data).
`whichstars`	Vector containing the star numbers that should be evaluated. If NA, program will calculate all stars coordinates.
`outlier`	Set to FALSE if no outlier rejection desired. This parameter has no effect calc.all.ch12.
`cores`	Number of cores to run this function for multicore processing. Defaults to 4.

calc_all calculates the straight mean of individual detections, optionally without outlier rejection (outlier=FALSE). This version is used inside CD.solver but should generally not be used for full datasets.

calc.all.ch12 computes the coordinates using a algorithm depending on the number of detections. For one detection, that detections coordinates are returned. For 2 or more detections the median and mad (median absolute deviation) are returned. If an object has more the 4 detections in [3.6] and the mad([3.6] detections)*2 < mad(all detections) then all [4.5] detections are ignored. A similar test is applied to the [4.5] detections.

calc.all.ch12 returns data.frame with following components:

`RA`	Calculated RA
`DEC`	Calculated DEC
`e_RA`	MAD of RA
`e_DEC`	MAD of DEC
`n_detections`	number of detections used to calculate coordinates
`ch1_e_RA`	MAD of RA for just [3.6] detections
`ch1_e_DEC`	MAD of DEC for just [3.6] detections
`ch1_n_detections`	total number of detections from [3.6]
`ch1_FLUX`	median FLUX for [3.6]
`ch1_SNR`	median SNR for [3.6]
`ch2_e_RA`	MAD of RA for just [4.5] detections
`ch2_e_DEC`	MAD of DEC for just [4.5] detections
`ch2_n_detections`	total number of detections from [4.5]
`ch2_FLUX`	median FLUX for [4.5]
`ch2_SNR`	median SNR for [4.5]

calc_all returns matrix with columns of (mean RA, mean DEC, standard deviation RA, standard deviation DEC, total number of detections). It does not distinguish [3.6] and [4.5] detections. If outlier is set to TRUE, a 2-sigma clip is made for sources with more than 2 detections and values are then recomputed.

Taran Esplin

data(data1,CD1,wa_pars1,wa_pars2,ca_pix1)
options(digits=10)
t1 <- calc.all.ch12(data1,CD1,1,c(1542,1840,1844,1847,2074,2089,2133))
t1 <- t1[!is.na(t1[,1]),]
t2 <- calc_all(ca_pix1,wa_pars1,NA,wa_pars2, 
  CD1,data1,c(1542,1840,1844,1847,2074,2089,2133),TRUE,1)
t2 <- t2[!is.na(t2[,1]),]
t1
t2