HOW TO Trend analysis HOW TO perform a trend analysis

0.1 HOW-TO: Trend analysis 0.1 0.1.1 HOW-TO perform a trend analysis and to find outliers Summary This HOW-TO shows how one can do a simple database trend analysis from the awe-prompt. In general to get to a result you need to do the following steps: • Which quantities do you want to do a trendanalysis on? Determine which classes and attributes of objects in AWE are needed to get the desired information for those quantities. • Construct the database query/queries required to get the desired information. • Make a plot of the desired information to graphically detect outliers. • Refine the constraints in the query to encompass only outliers. • Retrieve the outlying objects and inspect them. 0.1.2 Examples Question 1: Make a plot of the bias level of all raw biases of a CCD as a function of modified julian date of observation. Answer 1: awe> awe> awe> awe> awe> q = (RawBiasFrame.chip.name == ’ccd50’) biases = list(q) x = [b.MJD_OBS for b in biases] y = [b.imstat.median for b in biases] pylab.scatter(x,y,s=0.5) This results in the plot in figure 0.1.2 (zoomed, labels added). Question 2: Look for raw biases for ccd50 (WFI) in 2004 for which the level of the trim section differs significantly from the level of the overscan. Answer 2: awe> awe> 419 awe> awe> awe> awe> q = (RawBiasFrame.filename.like(’WFI.2004*_1.fits’)) len(q) biases = list(q) x = [b.MJD_OBS for b in biases] y = [b.imstat.median-b.overscan_x_stat.median for b in biases] pylab.scatter(x,y,s=0.5) This produces a plot as in figure 0.1.2.You can see that there seems to be one case where the difference is 5 ADU. This image will be interesting to look at. We can select it as follows: awe> frames = [b for b in biases if b.imstat.median-b.overscan_x_stat.median > 4] awe> len(frames) 2 awe> for f in frames: print f.filename ... WFI.2004-10-15T15:10:02.248_1.fits WFI.2004-10-15T15:11:52.384_1.fits awe> for f in frames: f.retrieve() ... 1 0.1 HOW-TO: Trend analysis Figure 1: Trend analysis: bias level for all raw biases of CCD against modified julian date of observation It turns out there are in fact two frames of this kind. The images seem to have an uncharacteristic bright region in them; something was obviously wrong during these observations. 2 0.1 HOW-TO: Trend analysis Figure 2: Trend analysis: trend of raw bias level in trim section minus the level in the overscan X region. The difference is plotted as a function of modified julian date of observation. 3