def makeDayLists (paramIndex):
dayLists = []
for i in range (2):
days = ambient.Days (os.path.join (args.indirs [i].path, ambient.parameterBasenames [paramIndex] + ".orig"), args.start_date, args.stop_date)
dayList = ambient.dayListFromRange (days, args.indirs [i].startDate, args.indirs [i].stopDate)
dayLists.append (dayList)
return dayLists
def plotSensorSeeingPC (which):
days = seeing [which]
allDays = allSeeing [which]
siteRange = args.indirs [which]
yTicks = []
correlationsArray = []
for p in ambient.parameterBasenames [1:]:
otherDays = ambient.Days (os.path.join (siteRange.path, p + ".orig"), args.start_date, args.stop_date)
otherDays.setGoodness (allDays)
yTicks.append (p)
dayList = ambient.dayListFromRange (otherDays, siteRange.startDate, siteRange.stopDate)
correlations = ambient.goodCorrelate (days, dayList, args.min_points)
correlationsArray.append (correlations)
ambient.plotSensorSeeingPC (os.path.join (args.outdir, "pc-seeing+all-{}.pdf").format (siteRange.name),
correlationsArray, yTicks, days,
args.indirs [which].name)
def makeHistogram (paramIndex):
filename = "sites-" + ambient.parameterNames [paramIndex].translate (str.maketrans (" ", "-")) + ".histogram.pdf"
filename = filename.lower ()
title = "Normalized " + ambient.parameterNames [paramIndex]
#get a list of seeing days, 1 Days object for each site
sites = []
for siteRange in args.indirs:
sites.append (ambient.Days (os.path.join (siteRange.path, ambient.parameterBasenames [paramIndex] + ".orig"), args.start_date, args.stop_date))
pages = PdfPages (os.path.join (args.outdir, filename))
fig = plt.figure ()
ax = fig.add_subplot (1, 1, 1)
ax.set_color_cycle (['b','r','g','m','c','y'])
datasets = []
labels = []
#for each site, add a list of values to "datasets"
#also make a corresponding list of labels based on site dir names
for site, siteRange in zip (sites, [siteRange for siteRange in args.indirs]):
labels.append (siteRange)
datasets.append ([])
dayList = ambient.dayListFromRange (site, siteRange.startDate, siteRange.stopDate)
for day in dayList:
if day:
func = day.periodFilter (args.start_minutes * 60, args.stop_minutes * 60, args.past_midnight)
filtered = filter (func, day.measurements)
datasets [-1].extend ([m [1] for m in filtered])
ax.hist (datasets, bins=args.num_bins, label=labels, normed=1)
ax.set_title (title.title ())
ax.set_xlabel (ambient.paramLabel (paramIndex))
ax.legend ()
pages.savefig (fig)
plt.close (fig)
pages.close ()
#parse user's command line arguments args = parser.parse_args () #make sure the output directory exists utils.makeDir (args.outdir) #get list of all seeing days allSeeingDays = ambient.Days (os.path.join (args.indir, ambient.parameterBasenames [0] + ".orig"), args.start_date, args.stop_date) #scripts need start/stop dates now #assert args.start_date #if start-date/stop-date is specified on command line, only look at a subset of days if args.start_date: seeingDays = ambient.dayListFromRange (allSeeingDays, args.start_date, args.stop_date) else: seeingDays = allSeeingDays.days def seeingCorrelations (seeingDays, otherDays): """ :param seeingDays: a list of Day objects for seeing :param otherDays: a list of Day objects for some sensor :return: correlation list of 3-tuples of pc's """ correlations = ambient.crossCorrelate (seeingDays, otherDays, args.start_minutes*60, args.stop_minutes*60, args.past_midnight, args.min_points) #this is a 3-tuple (day, otherDay, pc), day is in seeingDays, otherDay is in otherDays #remove rows with nonsense pc values
return days
#make lists of all seeing days for each site and subranges of days we are interested in
#see if any of the date ranges is for a leap year so we can pad any non leap year ranges
#with a fake leap day
allSeeing = []
seeing = []
leapYear = False
for i in range (2):
allSeeing.append (getSeeingDays (i))
if allSeeing [-1].isLeapYear ():
leapYear = True
for i in range (2):
dayList = ambient.dayListFromRange (allSeeing [i], args.indirs [i].startDate, args.indirs [i].stopDate, leapYear=leapYear)
seeing.append (dayList)
# 1e. calculate PC for each day. Sort from highest to lowest PC
#make a list of (day0, day1, pc)
#day0 is never None but day1 can be None whenever it's lacking a day that site0 has
#pc values > 1.0 mean nonsense, including not enough matching points
correlations = ambient.goodCorrelate (seeing [0], seeing [1], args.min_points)
#sort on pc
correlations = sorted (correlations, key=lambda x: x[2], reverse=not args.worst)
def writeCorr (out, c):
day0 = c [0]
day1 = c [1]
if not day0:
