utils.dumpList (os.path.join (args.outdir, "SxS.corr"), correlations,
lambda out, item: out.write ("{:%m/%d/%y} {:%m/%d/%y} {:.5f} ({:d}, {:d}, {:d})\n".format (item[0].datetime, item[1].datetime, item [2],
item[3][0],
item[3][1],
item[3][2])))
#remove rows with nonsense pc values
#leave them in and they'll be colored white
#correlations = [t for t in correlations if t[2] <= 1.0]
#make a matrix instead of a list so we can do a matrix plot of everything
corrMatrix = ambient.makeCorrelationMatrix (seeingDays, correlations)
pdfPages = PdfPages (os.path.join (args.outdir, "SxS.pdf"))
utils.plot2dArray (corrMatrix, pdfPages,
"self correlation of \"seeing\" at {}\n{:%m/%d/%Y} - {:%m/%d/%Y}".
format (os.path.basename (args.indir), seeingDays[0].datetime, seeingDays[-1].datetime),
"day", "day", "pearson coefficient")
pdfPages.close ()
#filter just pcs in the range we want
correlations = [t for t in correlations if t [2] >= args.start_pc and t [2] <= args.stop_pc]
#take subset of best (or worst) pcs
if args.best_count > 0 and args.best_count < len (correlations):
correlations = correlations [0:args.best_count + 1]
#add a rank column
correlations = [(index, t[0], t[1], t[2]) for index, t in enumerate (correlations)]
out.write ("{:%m/%d/%y} {:%m/%d/%y} {:.5f} \
({} {}) ({} {}))\n".format (day0.datetime,
day1.datetime,
c[2],
day0.firstGoodMeasurement[0],
day1.lastGoodMeasurement[0],
day1.firstGoodMeasurement[0],
day1.lastGoodMeasurement[0]))
utils.dumpList (os.path.join (args.outdir, "{}-{}.corr").format (args.indirs [0].name, args.indirs [1].name), correlations, writeCorr)
pdfPages = PdfPages (os.path.join (args.outdir, "seeing-{}x{}.pdf").format (args.indirs [0].name, args.indirs [1].name))
matrix = ambient.goodSquareCorrelate (seeing [0], seeing [1], args.min_points)
utils.plot2dArray (matrix, pdfPages,
"correlation of \"seeing\" {} &\n {}".format (args.indirs [0], args.indirs [1]),
"day", "day", "pearson coefficient")
pdfPages.close ()
# p1. plot days across X axis, and 5 sensors ‘X’ on Y axis, like pc-seeing+all.pdf., except color
#missing data points white to distinguish no data from zero correlation data
#make a 2d color plot of days on the x axis and sensors names on the Y axis and each
#grid point (x,y) is pc seeing vs y at day x
def plotSensorSeeingPC (which):
days = seeing [which]
allDays = allSeeing [which]
siteRange = args.indirs [which]
