**Name:**
*opECalib-ddddd.par*,
where *ddddd* is the int(MJD).

**Produced by:**
mop, iop, sop

**Used by:**
mtframes, ssc, ps, frames

**Size:**
approximately 100 Kb

**Archived?**
Yes

mjd 51259 # MJD day number when this file was created. # This should be unique over the collection # of all configuration files for a given # camera. nsteps 7 # The linearity arrays are hardwired to be of # length 13 (we must adopt a size so that two # files with arrays of differing length aren't # read in together, which would lead to a # possibly undetectable bug). This parameter # records the number of array elements which # actually contain data. # i.e., if "nsteps" = 7, then only the first # 7 elements of the linearity arrays contain # real data. The remaining elements should # be ignored. typedef struct { char program[40]; # program name int camRow; # camRow mt=0 spec=0 dsc=9 int camCol; # camCol mt=0 spec=1234 dsc=9 float readNoiseDN0; # in DN = ADU float fullWellDN0; # in DN = ADU float gain0; # in electrons/DN = electrons/ADU float biasLevel0; # in DN = ADU float DN0[13]; # array of DN's where linearity correction # factors apply (after bias subtraction). # If first element < 0, then subsequent # elements are meaningless, and "linearity0" # stores polynomial coefficients rather than # corrects at matching DN levels. float linearity0[13]; # If DN0[0] >= 0, this is an array of # linearity correction factors at the DN levels # if the DN0 array. # True_DN = Instrumental_DN * # linearity(Instrumental_DN). # If DN0[0] < 0, this is an array of polynomial # coefficients used to apply linearity # corrections. # (after bias subtraction) float readNoiseDN1; float fullWellDN1; float gain1; float biasLevel1; float DN1[13]; float linearity1[13]; float readNoiseDN2; float fullWellDN2; float gain2; float biasLevel2; float DN2[13]; float linearity2[13]; float readNoiseDN3; float fullWellDN3; float gain3; float biasLevel3; float DN3[13]; float linearity3[13]; } ECALIB; ECALIB mt_stare 0 0 \ 13 300000 9.36 8300 \ {5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000} \ { 1.0 0.99 0.985 0.98 0.97 0.96 0.955 0.95 0.945 0.94 0.93 0.92 0.91} \ 13 300000 9.36 8300 \ {5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000} \ {1.0 0.99 0.985 0.98 0.97 0.96 0.955 0.95 0.945 0.94 0.93 0.92 0.91} \ 13 300000 9.36 8300 \ {5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000} \ {1.0 0.99 0.985 0.98 0.97 0.96 0.955 0.95 0.945 0.94 0.93 0.92 0.91} \ 13 300000 9.36 8300 \ {5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 55000 60000 65000} \ {1.0 0.99 0.985 0.98 0.97 0.96 0.955 0.95 0.945 0.94 0.93 0.92 0.91}