print "\n * Observations for station %s" % city['name']
# ----------------------------------------------------------------
# - Looping trough tournament/bet dates.
# Note that 'tdate' is the tournament date (e.g., friday)
# and we need the observations for the day before (therefore
# starting the range at -1) for the persistence user,
# plus two days. Actually the day of the tournament would not
# be necessary, processing it just in case we need it somewhere.
# ----------------------------------------------------------------
for day in range(-1,ndays):
date = tdate + dt.timedelta( day )
print "\n * Processing obsevations for day: %s\n" % date
obs = getobs.getobs(config, db, city, date, wmoww )
# - Temperatures
obs.prepare('TTm') #,special='T today 06:00 to today 18:00')
obs.prepare('TTn') #,special='T yesterday 18:00 to today 6:00 ')
obs.prepare('TTd')
# - Wind speed and direction
obs.prepare('dd')
obs.prepare('ff')
obs.prepare('fx')
# - Mean sea level Pressure
obs.prepare('PPP')
# - Cloud cover
obs.prepare('N')
# - Significant weather
obs.prepare('Wv')
for station in db.get_stations_for_city(city["ID"]):
stations.append(station.wmo)
# Store results in an ndarray of length ndays
res = np.ndarray((ndays, len(stations)), dtype="float")
# Base date
base_date = dt.date(2019, 1, 1) # Has to be a leap year!
# - Looping over all cities
j = -1
for city in cities:
# We don't need the obs but use this object as in the computation
# of the points as it includes the get_maximum_Sd method.
obj = getobs.getobs(config, db, city, base_date)
# Looping over stations
for station in db.get_stations_for_city(city.get("ID")):
# Increase column index
j += 1
# Dummy
for i in range(0, ndays):
# Loop date
date = base_date + dt.timedelta(i)
# Getting maximum sunshine duration
sd = obj.get_maximum_Sd([station], date)