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}