def readFileMonthly(fileName,var2Read,yearC,logPath):
out = np.array([])
try:
scale = 84600
dataSet = nc.Dataset(fileName,'r')
data = dataSet.variables[var2Read][:]*scale
lPos = -1
days = int(data.shape[0])
for m in range(len(months)):
fPos = lPos + 1
if(isLeap(int(yearC)) and m == 1 and days==366):
lPos = months[m] + fPos + 1# Leap year
else:
lPos = months[m] + fPos
newMonth = np.mean(data[fPos:lPos,:,:],axis=0)
if out.size != 0:
if out.ndim < 3:
out = np.concatenate((out[...,np.newaxis],newMonth[...,np.newaxis]),axis=2)
else:
out = np.concatenate((out[...],newMonth[...,np.newaxis]),axis=2)
else:
out = newMonth
print 'Data saved: %s' % (yearC)
fid = open(logPath+'log.txt', 'a+')
fid.write('[SAVED] '+fileName+'\n')
fid.close()
dataSet.close()
except:
e = sys.exc_info()[1]
fid = open(logPath+'log.txt', 'a+')
fid.write('[ERROR] '+fileName+' '+str(e)+'\n\n')
fid.close()
return out
def getDate(day, month, year):
if (day < 1) or (month < 1) or (year < date.today().year):
return None
if month > 12:
return None
if year > date.today().year + staticValues["MAX_YEARS_AHEAD_DATES"]:
return None
if month in [1, 3, 5, 7, 8, 10, 12]:
if day > 31:
return None
elif month == 2:
if calendar.isLeap(year):
if day > 29:
return None
else:
if day > 28:
return None
else:
if day > 30:
return None
newDate = date(year, month, day)
return newDate
def readFileSeasonal(fileName, var2Read, yearC, logPath, lastDec):
out = np.array([])
try:
scale = 84600
dataSet = nc.Dataset(fileName, 'r')
data = dataSet.variables[var2Read][:] * scale
seasonMap = [2, 5, 8, 11]
lPos = 0
days = int(data.shape[0])
for s in range(len(seasonsName)):
fPos = lPos
if s > 0:
init = seasonMap[s - 1]
else:
init = 0
for m in range(init, seasonMap[s]):
if (isLeap(int(yearC)) and s == 0 and days == 366 and m == 1):
lPos += months[m] + 1 # Leap year
else:
lPos += months[m]
nSeason = np.mean(data[fPos:lPos, :, :], axis=0)
if s < 1 and lastDec.size != 0:
nSeason = (nSeason + lastDec) / 2
if out.size != 0:
if out.ndim < 3:
out = np.concatenate(
(out[..., np.newaxis], nSeason[..., np.newaxis]),
axis=2)
else:
out = np.concatenate((out[...], nSeason[..., np.newaxis]),
axis=2)
else:
out = nSeason
if s == 3:
lastDec = np.mean(data[lPos:-1, :, :], axis=0)
#print 'f: %d - l: %d ' %(fPos,lPos)
#fPos += 1
print 'Data saved: %s' % (yearC)
fid = open(logPath + 'log.txt', 'a+')
fid.write('[SAVED] ' + fileName + '\n')
fid.close()
dataSet.close()
except:
e = sys.exc_info()[1]
fid = open(logPath + 'log.txt', 'a+')
fid.write('[ERROR] ' + fileName + ' ' + str(e) + '\n\n')
fid.close()
return [out, lastDec]
def readFileSeasonal(fileName,var2Read,yearC,logPath,lastDec):
out = np.array([])
try:
scale = 84600
dataSet = nc.Dataset(fileName,'r')
data = dataSet.variables[var2Read][:]*scale
seasonMap = [2,5,8,11]
lPos = 0
days = int(data.shape[0])
for s in range(len(seasonsName)):
fPos = lPos
if s > 0:
init = seasonMap[s-1]
else:
init = 0
for m in range(init,seasonMap[s]):
if(isLeap(int(yearC)) and s == 0 and days==366 and m==1):
lPos += months[m] + 1# Leap year
else:
lPos += months[m]
nSeason = np.mean(data[fPos:lPos,:,:],axis=0)
if s < 1 and lastDec.size != 0:
nSeason = (nSeason+lastDec)/2
if out.size != 0:
if out.ndim < 3:
out = np.concatenate((out[...,np.newaxis],nSeason[...,np.newaxis]),axis=2)
else:
out = np.concatenate((out[...],nSeason[...,np.newaxis]),axis=2)
else:
out = nSeason
if s == 3:
lastDec = np.mean(data[lPos:-1,:,:],axis=0)
#print 'f: %d - l: %d ' %(fPos,lPos)
#fPos += 1
print 'Data saved: %s' % (yearC)
fid = open(logPath+'log.txt', 'a+')
fid.write('[SAVED] '+fileName+'\n')
fid.close()
dataSet.close()
except:
e = sys.exc_info()[1]
fid = open(logPath+'log.txt', 'a+')
fid.write('[ERROR] '+fileName+' '+str(e)+'\n\n')
fid.close()
return [out,lastDec]
def readFileMonthly(fileName, var2Read, yearC, logPath):
out = np.array([])
try:
scale = 84600
dataSet = nc.Dataset(fileName, 'r')
data = dataSet.variables[var2Read][:] * scale
lPos = -1
days = int(data.shape[0])
for m in range(len(months)):
fPos = lPos + 1
if (isLeap(int(yearC)) and m == 1 and days == 366):
lPos = months[m] + fPos + 1 # Leap year
else:
lPos = months[m] + fPos
newMonth = np.mean(data[fPos:lPos, :, :], axis=0)
if out.size != 0:
if out.ndim < 3:
out = np.concatenate(
(out[..., np.newaxis], newMonth[..., np.newaxis]),
axis=2)
else:
out = np.concatenate((out[...], newMonth[..., np.newaxis]),
axis=2)
else:
out = newMonth
print 'Data saved: %s' % (yearC)
fid = open(logPath + 'log.txt', 'a+')
fid.write('[SAVED] ' + fileName + '\n')
fid.close()
dataSet.close()
except:
e = sys.exc_info()[1]
fid = open(logPath + 'log.txt', 'a+')
fid.write('[ERROR] ' + fileName + ' ' + str(e) + '\n\n')
fid.close()
return out
