2) QAing the Pedestal/Status Tables


Once the ped/stat tables have been generated, they must be QAed.  I do the QA in two parts:

1) I spot check the pdf files by eye.  I pick about 5 or 6 fills evenly spaced throughout the run and go through each page of the pdf files looking for any strange-looking towers (for example, towers with stuck bits are pretty easy to see, and they don't always get caught by the algorithm).  Yes, this takes a while, but we don't have to look at the pdfs for every fill!
For examples of the bad channels you are looking for, have a look at Suvarna's nice QA of the tables last year:

2) The status and pedestal information is stored in the sqlite database file, and we can spin over this quickly in order to look at the data over many runs/fills.  I have attached a script which is used to analyze the database file l2status2013.sqlite3.  The first step is to download l2status2013.sqlite3 somewhere *locally* where you can look at it, and save statusCheckfill2.py in the same place. In statusCheckfill2.py you should change line 27 to the appropriate run range that you are analyzing, and change 2012-->2013 if necessary.  Then all you need to do to run the code is something like:
python statusCheckfill2.py

This python code allows you to look at the statuses and pedestals from run to run.  At the moment I make lists of the statuses and pedestals for each tower (in the variables u and y -- sorry for my horrible naming scheme!), and then I can print these lists to the screen or graph them.  At the moment I have the code so that it prints the statuses for any tower that has status 1 some, but not all, of the time (line 138).  This script can be used to find channels which change status frequently over the course of the run... for example, sometimes there are channels which are right on the edge of satisfying the criteria for being marked as "cold" and therefore their status alternates 1 18 1 1 18 18 1 1 18 1 18 etc. Then we can look at the pdf files to see if the tower always looks cold, or if its behavior really changes frequently.  If it is truly cold, then at that point we can either adjust the criteria for being marked as cold, or hard-code the channel as status 18 (I typically just hard-code it).
Also I can look at the histograms histoPedRatio and histoPedRatioGood (which are saved out in histogramBEMCfill.root), which are plots of the ratio of the pedestal for a given run to the pedestal of the first run as a function of tower id.  histoPedRatio is filled for every tower for every run (unless the pedestal for that tower in the first run is zero), and histoPedRatioGood is only filled if the tower's status is 1.  I haven't needed to cut out any towers based on these plots, but I think they would be a good way to find any towers whose pedestals are fluctuating wildly over time (or maybe you might want want to plot the difference, not the ratio).
So you can take a look at the code and play around with it so that it allows you to do the QA that you think is best.

If you look at l2status.py, you can see where I've hard-coded a bunch of channels I thought were bad (the lines which hard-code bad channels are commented out right now because I prefer to start from scratch each year).  I've pasted the code here too:

            ## hard code few bad/hot channels
            #if int(tower.softId)==939 : ##hard code hot channel
            #    tower.status |= 2
            #if (int(tower.softId)==3481 or int(tower.softId)==3737) : ##hard code wide ped
            #    tower.status |= 36
            #if (int(tower.softId)==220 or int(tower.softId)==2415 or int(tower.softId)==1612 or int(tower.softId)==4059): ##hard code stuck bit
            #    tower.status |= 72
            if (int(tower.softId)==671 or int(tower.softId)==1612 or int(tower.softId)==2415 or int(tower.softId)==4059) : ##hard code stuck bit (not sure which bit, or stuck on/off)
                tower.status |= 8
            if (int(self.currentFill) > 16664 and (int(tower.softId)==1957 or int(tower.softId)==1958 or int(tower.softId)==1977 or int(tower.softId)==1978 or int(tower.softId)==1979 or int(tower.softId)==1980 or int(tower.softId)==1997 or int(tower.softId)==1998 or int(tower.softId)==1999 or int(tower.softId)==2000 or int(tower.softId)==2017 or int(tower.softId)==2018 or int(tower.softId)==2019 or int(tower.softId)==2020)) : ##hard code stuck bit
                tower.status |= 8
            if (int(tower.softId)==410 or int(tower.softId)==504 or int(tower.softId)==939 or int(tower.softId)==1221 or int(tower.softId)==1409 or int(tower.softId)==1567 or int(tower.softId)==2092) : ##hard code cold channel
                tower.status |= 18
            if (int(tower.softId)==875 or int(tower.softId)==2305 or int(tower.softId)==2822 or int(tower.softId)==3668 or int(tower.softId)==629 or int(tower.softId)==2969 or int(tower.softId)==4006) : ##either cold or otherwise looks weird
                tower.status |= 18

Some of these channels are persistently problematic, so I expect that your list of bad channels will look similar to mine from previous years.

It may take a few iterations of generating the tables, finding bad towers, tweaking the code or hard-coding the bad channels, regenerating the tables, etc before you are satisfied with the quality of the tables.  Once you have run l2status.py one last time and are satisfied with the quality of the tables you have generated, they are ready to be uploaded to the database by the software coordinator.