Rich, Yes, I want to store each row in a db or a flat file, but I fear the FF may get messy, then once I have it somewhere for posterity, I will start to process it. Generally we look at it in the following form, Rejects by mould number Reject type by mould number
Rejects last hour Rejects last X hours Rejects last shift Rejects last 24 hours Other time periods Also we look at a breakdown of reject types on those time periods. At this point I am just trying to get the data stream into a db or a file, once there I can start playing with how to graph it, I used to do RF coverage studies and we graphed all the different parameters on a RF system, signal level, BER, BERT, S/N, etc both historically and real time. It has been a while, but I have a lot of machines here that are trying to supply all manner of status info, and that format is more or less standardized among them all. At this point I am not worried about the graphing, I am trying to figure how to get live data into a file/db so I can graph it from there. On Mon, Jun 23, 2014 at 2:30 PM, Rich Shepard <[email protected]> wrote: > On Mon, 23 Jun 2014, Chuck Hast wrote: > > > Hmmm I guess I did not get the data sample in it. > > Chuck, > > You did include the data sample, but not what questions you had about > it. > > > I know how to get csv files into spead sheets and i have sort of a idea > of > > getting them into a db, but how do I handle a stream of data like that > > below. All of them are CSV formated, so once I can get one into something > > I can graph and twiddle, the rest should be easy. > > So, you want to store each row in a database table. Then you want to > produce some sort of plot (histogram? scatter plot? box-and-whisker plot?) > from selected rows or continuously? What sort of twiddling do you envision? > > I would approach a solution by using a Python script (the psycopg2 > module > is appropriate for the interface between python and postgres, and wxPython > is the GUI I use) to store the rows in a postgres database table as each > row > was sent from a machine, then use pandas to analyze those data and produce > plots using matplotlib. There are probably a gazillion alternatives, > including using R for the analyses, but I suspect that would be overkill > for > your needs. > > Rich > > _______________________________________________ > PLUG mailing list > [email protected] > http://lists.pdxlinux.org/mailman/listinfo/plug > -- Chuck Hast -- KP4DJT -- Glass, five thousand years of history and getting better. The only container material that the USDA gives blanket approval on. _______________________________________________ PLUG mailing list [email protected] http://lists.pdxlinux.org/mailman/listinfo/plug
