def main():
# Set up list of years from command line arguments
choice = ''
if len(sys.argv) > 1:
argstart = 1
if sys.argv[1] in ['b1', 'b2', 'All', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009', 'y2010', 'y2011', 'y2013']:
choice = sys.argv[1]
argstart = 2
years = []
for arg in sys.argv[argstart:]:
try:
years.append(int(arg))
except:
print sys.argv
sys.exit("Argument Error")
print years
else:
years = []
while len(years) <1:
try:
yearsFromUser = raw_input("Give years as space separated list: ")
yearsAsString = yearsFromUser.strip().split(' ')
for yearString in yearsAsString:
years.append(int(yearString))
except:
years = []
#
# Run main function
for year in years:
strYear = str(year)
dataLoc = '../InData/' + strYear
# Temperature data. Following two lines are example of input format:
# Date,Temp
# 2010-01-25,-8.3
weather = 'weather' + strYear + '.csv'
TfileName = os.path.join(dataLoc, weather)
# Read Temperature data from csv file and convert dates to julian days. Date format '%Y-%m-%d' is SMHI's
TinFile = open(TfileName,'rb')
dates = []
times = []
temps = []
for line in csv.DictReader(TinFile, delimiter=','):
dates.append(line['Date'].strip())
date = datetime.strptime(line['Date'].strip(),'%Y-%m-%d')
jdate = datetime.strftime(date,'%j')
times.append(int(jdate))
temps.append(float(line['Temp'].strip()))
TinFile.close()
# Stake data. Following two lines are example of input format:
# Stake,Easting,Northing,Elevation,Bw,Bs,Bn,Surface
# 04C,651103.586397,7536381.86553,1219,0.334,2.53,-2.196,ice
stakeFileName = 'StakeData' + strYear + '.csv'
SfileName =os.path.join(dataLoc, stakeFileName)
# Get settings for model: AWS elevation, date of snow probing, dates for model export, first date in shading file (could start this at 1 by default but
# shading file may be created for limited range of dates to reduce file size)
refElev, jdayBw, jdatelist, startday = getSettings(dataLoc, times[-1])
print "For year %s following settings used: " %(strYear)
print "refElev set to %s" %(refElev)
print "jdayBw set to %s" %(jdayBw)
#
# Directory for output
outputDir = os.path.join('../Output/', strYear)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
outputname = strYear +'_DDM_'
#
# Truthing of data against field survey data. Each survey stored in sperate csv file.
# The trudb is used to store both data and assessment of results
truthDir = os.path.join(dataLoc,"truthing")
try:
truthfiles = filelist(truthDir,'csv')
trudb = {}
print "Truthing files: "
for file in truthfiles:
if int(file.split('.')[0]) not in jdatelist:
print "%s does not match date given in settings file: %s" % (file, jdatelist)
else:
print file
trudb[file.split('.')[0]] = import2vector(os.path.join(truthDir,file), importError = 'no')
trudbKeys = trudb.keys()
trudbKeys.sort()
except:
print "No truthing data found."
#
# Read stake data
SinFile = open(SfileName,'rb')
stakeData = {}
# Read point data file with position and winter balance as of last winter probing (jdayBw) and send to model
for line in csv.DictReader(SinFile, delimiter=','):
stakeName = line['Stake'].strip()
stakeData[stakeName] = {}
# Coordinates
stakeData[stakeName]['Easting'] = float(line['Easting'].strip())
stakeData[stakeName]['Northing'] = float(line['Northing'].strip())
stakeData[stakeName]['Elevation'] = float(line['Elevation'].strip())
# Get shading factor for location
# Create vector for shade values
vals = []
for d in range(366):
vals.append(1)
try:
stakeData[stakeName]['Shadevals'] = GetShadeVals(stakeData[stakeName]['Easting'], stakeData[stakeName]['Northing'], raster, transf, bandcount, vals, startday)
except:
stakeData[stakeName]['Shadevals'] = vals
print "No shade value obtained for ", stakeName
# Get the measured winter balance
try:
stakeData[stakeName]['Org_Bw'] = float(line['Bw'].strip())
except:
print "No winter balance data found (Bw column)"
break
# Get the measured summer balance
try:
stakeData[stakeName]['Org_Bs'] = float(line['Bs'].strip())
except:
pass
# Get the measured net balance
try:
stakeData[stakeName]['Org_Bn'] = float(line['Bn'].strip())
except:
pass
#
# Prepare database for passing into loop and model run
stakeNames = stakeData.keys()
stakeNames.sort()
data = copy.deepcopy(stakeData)
best = 9999
#
# Set parameters for the melt model
if choice in ['b1', 'b2', 'All']:
choice = choice + '_score'
elif choice in ['y2005', 'y2006', 'y2007', 'y2008', 'y2009', 'y2010', 'y2011', 'y2013']:
choice = choice.strip('y')
print "Choice of parameters: {}".format(choice)
derivedParameters = {}
derivedParameters['2005'] = {'ddfSnow':0.0046, 'ddfSi':0.0054, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0044, 'elevLapse':(700), 'sfe':1.4, 'ELA':1500}
derivedParameters['2006'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0040, 'elevLapse':(800), 'sfe':1.1, 'ELA':1500}
derivedParameters['2007'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0040, 'ddfIce':0.0040, 'lapse':0.0052, 'elevLapse':(800), 'sfe':1.3, 'ELA':1500}
derivedParameters['2008'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0040, 'lapse':0.0044, 'elevLapse':(900), 'sfe':1.5, 'ELA':1500}
derivedParameters['2009'] = {'ddfSnow':0.0036, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0052, 'elevLapse':(800), 'sfe':1.6, 'ELA':1500}
derivedParameters['2010'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0048, 'lapse':0.0040, 'elevLapse':(600), 'sfe':1.2, 'ELA':1500}
derivedParameters['2011'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0060, 'lapse':0.0040, 'elevLapse':(900), 'sfe':1.1, 'ELA':1500}
derivedParameters['2013'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0060, 'lapse':0.0068, 'elevLapse':(800), 'sfe':1.3, 'ELA':1500}
derivedParameters['All_score'] = {'ddfSnow':0.0042, 'ddfSi':0.0056, 'ddfFirn':0.0044, 'ddfIce':0.0056, 'lapse':0.0048, 'elevLapse':(800), 'sfe':1.4, 'ELA':1500}
derivedParameters['All_weight'] = {'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(800), 'sfe':1.4, 'ELA':1500}
derivedParameters['b1_score'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0054, 'ddfIce':0.0060, 'lapse':0.0040, 'elevLapse':(800), 'sfe':1.4, 'ELA':1500}
derivedParameters['b1_weight'] ={'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(800), 'sfe':1.4, 'ELA':1500}
derivedParameters['b2_score'] = {'ddfSnow':0.0038, 'ddfSi':0.0052, 'ddfFirn':0.0042, 'ddfIce':0.0056, 'lapse':0.0056, 'elevLapse':(750), 'sfe':1.4, 'ELA':1500}
derivedParameters['b2_weight'] ={'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(750), 'sfe':1.4, 'ELA':1500}
#
try:
paramDict = {}
paramDict['ddfSnow'] = derivedParameters[choice]['ddfSnow']
paramDict['ddfSi'] = derivedParameters[choice]['ddfSi']
paramDict['ddfFirn'] = derivedParameters[choice]['ddfFirn']
paramDict['ddfIce'] = derivedParameters[choice]['ddfIce']
paramDict['lapse'] = derivedParameters[choice]['lapse']
paramDict['elevLapse'] = derivedParameters[choice]['elevLapse']
paramDict['sfe'] = derivedParameters[choice]['sfe']
paramDict['ELA'] = derivedParameters[choice]['ELA']
paramDict['refElev'] = refElev
best, resNorm = snowCalc(data, stakeNames, temps, times, jdatelist, jdayBw, paramDict, trudbKeys, trudb, outputDir, outputname, best)
except:
print "Running {0:d} on all paramater combinations\n".format(year)
ddfSnow = range(36,59,4)
ddfSnow = np.array(ddfSnow)*0.0001
ddfSnow = list(ddfSnow)
ddfSi = range(48,61,4)
ddfSi = np.array(ddfSi)*0.0001
ddfSi = list(ddfSi)
ddfFirn = range(36,46,4)
ddfFirn = np.array(ddfFirn)*0.0001
ddfFirn = list(ddfFirn)
ddfIce = range(40,65,4)
ddfIce = np.array(ddfIce)*0.0001
ddfIce = list(ddfIce)
lapse = range(36,61,4)
lapse = np.array(lapse)*0.0001
lapse = list(lapse)
elevLapse = range(100,1501,100) # Elevation dependant lapse rate
sfe = range(8,15) # Shading factor exponent (adjusts the shading value at each point)
sfe = np.array(sfe)*0.1
sfe = list(sfe)
ELA = [1500] # Equilibrium line, for firn or ice under snow
parUsage = {'BsScore':[], 'ddfSnow':[], 'ddfSi':[], 'ddfFirn':[], 'ddfIce':[], 'lapse':[], 'elevLapse':[], 'sfe':[], 'ELA':[], 'refElev':[]}
iterationcount = len(ddfSnow)*len(ddfSi)*len(ddfFirn)*len(ddfIce)*len(lapse)*len(elevLapse)*len(sfe)*len(ELA)
print "Total number of runs: %s" % (iterationcount)
for it1, it2, it3, it4, it5, it6, it7, it8 in itertools.product(ddfSnow, ddfSi, ddfFirn, ddfIce, lapse, elevLapse, sfe, ELA):
paramDict = {}
paramDict['ddfSnow'] = it1
paramDict['ddfSi'] =it2
paramDict['ddfFirn'] = it3
paramDict['ddfIce'] = it4
paramDict['lapse'] = it5
paramDict['elevLapse'] = it6
paramDict['sfe'] = it7
paramDict['ELA'] = it8
paramDict['refElev'] = refElev
check = best
best, resNorm = snowCalc(data, stakeNames, temps, times, jdatelist, jdayBw, paramDict, trudbKeys, trudb, outputDir, outputname, best, iterationcount)
parUsage = usageUpdate(parUsage, paramDict, resNorm)
if best < check:
print "{0:d} improved score".format(iterationcount)
iterationcount = iterationcount - 1
if iterationcount % 100 == 0:
print iterationcount
parameterCheckWrite(outputDir, year, parUsage)
def main():
"""
Start script with e.g.
$ python LSASO.py 2005
$ python LSASO.py p 2005
$ python LSASO.py 2005 2006 2007
$ python LSASO.py p 2005 2006 2007
where 'p' indicates using parameters values for melt rates specified in settings.txt file for each year
To run this you will need the following:
A shading file where each pixel represents shading on the glacier at each julian day to be modelled
A temperature data file for each julian day to be modelled
A stake data file containing winter balance values and coordinates for each stake
A settings file
The directory structure I have used here is as follows ('yyyy' should be replaced by the year):
/InData/yyyy
/InData/yyyy/weatheryyyy.csv
/InData/yyyy/StakeReadings.csv
/InData/settings.txt
/InData/Shades/SG_shade.tif This file is here as it is the output of another script
/Output/
/Output/yyyy/ These are created by this script as needed
/Scripts/ Run the script from here.
Format examples:
settings.txt (note that if 'ExportDate' omitted then all dates between bW and final temperature reading exported):
Elevation=1150
jdayBw=115
ExportDate=236,256
ShadeStart=100
ELA may be left out and the programme will use the optimisation algorithm to guess at a best value (not an estimate of ELA per se)
ELA=1500
These parameter settings must ALL be present or those present will be ignored
Snow=0.0046
Si=0.0054
Firn=0.0058
Ice=:0.0064
lapse=0.0044
elevLapse=950
sfe=1.5
weatheryyyy.csv:
Date,Temp
2010-01-25,-8.3
StakeDatayyyy.csv:
Stake,Easting,Northing,Elevation,Bw,Bs,Bn,Surface
04C,651103.586397,7536381.86553,1219,0.334,2.53,-2.196,ice
"""
# Set up list of years from command line arguments
paramChoice = ""
if len(sys.argv) > 1:
argstart = 1
if sys.argv[1] == "p":
paramChoice = "p"
argstart = 2
years = []
for arg in sys.argv[argstart:]:
try:
years.append(int(arg))
except:
print sys.argv
sys.exit("Argument Error")
print years
else:
print main.__doc__
years = []
while len(years) < 1:
try:
yearsFromUser = raw_input("Give years as space separated list or (q)uit: ")
yearsAsString = yearsFromUser.strip().split(" ")
for yearString in yearsAsString:
years.append(int(yearString))
except:
if yearsFromUser == "q":
sys.exit()
else:
years = []
#
# Run main function
for year in years:
print year
strYear = str(year)
dataLoc = "../InData/" + strYear
# Temperature data. Following two lines are example of input format:
# Date,Temp
# 2010-01-25,-8.3
weather = "weather" + strYear + ".csv"
TfileName = os.path.join(dataLoc, weather)
# Read Temperature data from csv file and convert dates to julian days. Date format '%Y-%m-%d' is SMHI's
TinFile = open(TfileName, "rb")
dates = []
times = []
temps = []
for line in csv.DictReader(TinFile, delimiter=","):
dates.append(line["Date"].strip())
date = datetime.strptime(line["Date"].strip(), "%Y-%m-%d")
jdate = datetime.strftime(date, "%j")
times.append(int(jdate))
temps.append(float(line["Temp"].strip()))
TinFile.close()
# Stake data. Following two lines are example of input format:
# Stake,Easting,Northing,Elevation,Bw,Bs,Bn,Surface
# 04C,651103.586397,7536381.86553,1219,0.334,2.53,-2.196,ice
stakeFileName = "StakeData" + strYear + ".csv"
SfileName = os.path.join(dataLoc, stakeFileName)
# Get settings for model: AWS elevation, date of snow probing, dates for model export, first date in shading file (could start this at 1 by default but
# shading file may be created for limited range of dates to reduce file size)
refElev, jdayBw, jdatelist, startday, paramDict = getSettings(dataLoc, times[-1])
print "For year %s following settings used: " % (strYear)
print "refElev set to %s" % (refElev)
print "jdayBw set to %s" % (jdayBw)
#
# Directory for output
outputDir = os.path.join("../Output/", strYear)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
outputname = strYear + "_DDM_"
#
# Truthing of data against field survey data. Each survey stored in sperate csv file.
# The trudb is used to store both data and assessment of results
truthDir = os.path.join(dataLoc, "truthing")
try:
truthfiles = filelist(truthDir, "csv")
trudb = {}
print "Truthing files: "
for file in truthfiles:
if int(file.split(".")[0]) not in jdatelist:
print "%s does not match date given in settings file: %s" % (file, jdatelist)
else:
print file
trudb[file.split(".")[0]] = import2vector(os.path.join(truthDir, file), importError="no")
trudbKeys = trudb.keys()
trudbKeys.sort()
except:
print "No truthing data found."
#
# Read stake data
SinFile = open(SfileName, "rb")
stakeData = {}
# Read point data file with position and winter balance as of last winter probing (jdayBw) and send to model
for line in csv.DictReader(SinFile, delimiter=","):
stakeName = line["Stake"].strip()
stakeData[stakeName] = {}
# Coordinates
stakeData[stakeName]["Easting"] = float(line["Easting"].strip())
stakeData[stakeName]["Northing"] = float(line["Northing"].strip())
stakeData[stakeName]["Elevation"] = float(line["Elevation"].strip())
# Get shading factor for location
# Create vector for shade values
vals = []
for d in range(366):
vals.append(1)
try:
stakeData[stakeName]["Shadevals"] = GetShadeVals(
stakeData[stakeName]["Easting"],
stakeData[stakeName]["Northing"],
raster,
transf,
bandcount,
vals,
startday,
)
except:
stakeData[stakeName]["Shadevals"] = vals
print "No shade value obtained for ", stakeName
# Get the measured winter balance
try:
stakeData[stakeName]["Org_Bw"] = float(line["Bw"].strip())
except:
print "No winter balance data found (Bw column)"
break
# Get the measured summer balance
try:
stakeData[stakeName]["Org_Bs"] = float(line["Bs"].strip())
except:
pass
# Get the measured net balance
try:
stakeData[stakeName]["Org_Bn"] = float(line["Bn"].strip())
except:
pass
#
paramDict["refElev"] = refElev
# Prepare database for passing into loop and model run
stakeNames = stakeData.keys()
stakeNames.sort()
data = copy.deepcopy(stakeData)
best = 9999
#
if len(paramDict.keys()) > 2 and paramChoice == "p":
resNorm = snowCalc(
data, stakeNames, temps, times, jdatelist, jdayBw, paramDict, trudbKeys, trudb, outputDir, outputname
)
else:
if len(paramDict.keys()) == 2:
x0 = [0.0050, 0.0050, 0.0050, 0.0050, 0.0050, 1000, 1.0]
else:
x0 = [0.0050, 0.0050, 0.0050, 0.0050, 0.0050, 1000, 1.0, 1500]
def trigger(x):
if len(x0) == 7:
resNorm = modelOptimiser(
data,
stakeNames,
temps,
times,
jdatelist,
jdayBw,
refElev,
trudbKeys,
trudb,
x,
ELA=paramDict["ELA"],
)
else:
resNorm = modelOptimiser(
data, stakeNames, temps, times, jdatelist, jdayBw, refElev, trudbKeys, trudb, x
)
return resNorm
res = minimize(trigger, x0, method="nelder-mead", options={"xtol": 1e-8, "disp": True})
print (res.x)
paramDict["ddfSnow"] = res.x[0]
paramDict["ddfSi"] = res.x[1]
paramDict["ddfFirn"] = res.x[2]
paramDict["ddfIce"] = res.x[3]
paramDict["lapse"] = res.x[4]
paramDict["elevLapse"] = res.x[5]
paramDict["sfe"] = res.x[6]
if "ELA" not in paramDict.keys():
paramDict["ELA"] = res.x[7]
resNorm = snowCalc(
data, stakeNames, temps, times, jdatelist, jdayBw, paramDict, trudbKeys, trudb, outputDir, outputname
)
for key, value in paramDict.iteritems():
print "{} = {}".format(key, value)
print "\n{} final score: {:2.4f}\n".format(year, resNorm)
def main():
# Set up list of years from command line arguments
if len(sys.argv) > 1:
years = []
for arg in sys.argv[1:]:
try:
years.append(int(arg))
except:
print sys.argv
sys.exit("Argument Error")
print years
else:
years = [2005,2006,2007,2008,2009,2010,2011,2012,2013]
#
# Get shading data
# Location of file containing a multiband raster, each band represents the shading on one day. 1 = no shade, 0 = really quite dark
shadefile = '../InData/Shades/SG_shade.tif'
# shadefile = '../InData/Shades/Reduced/SG_shade.tif'
# Read the shade factor raster in to memory
raster, transf, bandcount = getShadeFile(shadefile)
writeScores = 'no' # 'yes' Can be ignored, will be set to yes if test set to run over multiple parameters (5)
#
# Set plotOn to 1 to plot differences between modelled and measured
plotOn = 1
#
for year in years:
# Set parameters for the melt model
choice = 'All_score'
derivedParameters = {}
derivedParameters['2005'] = {'ddfSnow':0.0046, 'ddfSi':0.0054, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0044, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2006'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0040, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2007'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0040, 'ddfIce':0.0040, 'lapse':0.0052, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2008'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0040, 'lapse':0.0044, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2009'] = {'ddfSnow':0.0036, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0064, 'lapse':0.0052, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2010'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0048, 'lapse':0.0040, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2011'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0058, 'ddfIce':0.0060, 'lapse':0.0040, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['2013'] = {'ddfSnow':0.0036, 'ddfSi':0.0044, 'ddfFirn':0.0058, 'ddfIce':0.0060, 'lapse':0.0068, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['All_score'] = {'ddfSnow':0.0042, 'ddfSi':0.0056, 'ddfFirn':0.0044, 'ddfIce':0.0056, 'lapse':0.0048, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['All_weight'] = {'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['b1_score'] = {'ddfSnow':0.0058, 'ddfSi':0.0056, 'ddfFirn':0.0054, 'ddfIce':0.0060, 'lapse':0.0040, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['b1_weight'] = {'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['b2_score'] = {'ddfSnow':0.0038, 'ddfSi':0.0052, 'ddfFirn':0.0042, 'ddfIce':0.0056, 'lapse':0.0056, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
derivedParameters['b2_weight'] = {'ddfSnow':0.0047, 'ddfSi':0.0050, 'ddfFirn':0.0049, 'ddfIce':0.0052, 'lapse':0.0058, 'elevLapse':(2100 - 1150), 'sfe':1.5, 'ELA':1500}
if choice in derivedParameters.keys():
ddfSnow = [derivedParameters[choice]['ddfSnow']]
ddfSi = [derivedParameters[choice]['ddfSi']]
ddfFirn = [derivedParameters[choice]['ddfFirn']]
ddfIce = [derivedParameters[choice]['ddfIce']]
lapse = [derivedParameters[choice]['lapse']]
elevLapse = [derivedParameters[choice]['elevLapse']]
sfe = [derivedParameters[choice]['sfe']]
ELA = [derivedParameters[choice]['ELA']]
else:
writeScores = 'yes'
ddfSnow = range(36,60,2)
ddfSnow = np.array(ddfSnow)*0.0001
ddfSnow = list(ddfSnow)
ddfSi = range(44,58,2)
ddfSi = np.array(ddfSi)*0.0001
ddfSi = list(ddfSi)
ddfFirn = range(40,60,2)
ddfFirn = np.array(ddfFirn)*0.0001
ddfFirn = list(ddfFirn)
ddfIce = range(40,66,2)
ddfIce = np.array(ddfIce)*0.0001
ddfIce = list(ddfIce)
lapse = range(40,80,4)
lapse = np.array(lapse)*0.0001
lapse = list(lapse)
rangeZ = (2100 - 1150)
elevLapse = [rangeZ] # Elevation dependant lapse rate
sfe = [1.5] # Shading factor exponent (adjusts the shading value at each point)
ELA = [1500] # Equilibrium line, for firn or ice under snow
#
strYear = str(year)
dataLoc = '../InData/' + strYear
# Temperature data. Following two lines are example of input format:
# Date,Temp
# 2010-01-25,-8.3
weather = 'weather' + strYear + '.csv'
TfileName = os.path.join(dataLoc, weather)
# Read Temperature data from csv file and convert dates to julian days. Date format '%Y-%m-%d' is SMHI's
TinFile = open(TfileName,'rb')
dates = []
times = []
temps = []
for line in csv.DictReader(TinFile, delimiter=','):
dates.append(line['Date'].strip())
date = datetime.strptime(line['Date'].strip(),'%Y-%m-%d')
jdate = datetime.strftime(date,'%j')
times.append(int(jdate))
temps.append(float(line['Temp'].strip()))
TinFile.close()
# Stake data. Following two lines are example of input format:
# Stake,Easting,Northing,Elevation,Bw,Bs,Bn,Surface
# 04C,651103.586397,7536381.86553,1219,0.334,2.53,-2.196,ice
stakeFileName = 'StakeData' + strYear + '.csv'
SfileName =os.path.join(dataLoc, stakeFileName)
# Get settings for model: AWS elevation, date of snow probing, dates for model export, first date in shading file (could start this at 1 by default but
# shading file may be created for limited range of dates to reduce file size)
refElev, jdayBw, jdatelist, startday = getSettings(dataLoc, times[-1])
print "For year %s following settings used: " %(strYear)
print "refElev set to %s" %(refElev)
print "jdayBw set to %s" %(jdayBw)
#
#
# Settings and counters for testing multiple parameter values
counter = 0
# bestBsR2 = -9999 # name refers to R-squared test but maybe replaced by norm of residuals
# BsR2 = np.nan # name refers to R-squared test but maybe replaced by norm of residuals
bestResNorm = 9999 # Norm of residuals version
ResNorm = np.nan # Norm of residuals version
writeTest = 0
# Directory for output
outputDir = os.path.join('../Output/', strYear)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
outputname = strYear +'_DDM_'
#
# Truthing of data against field survey data. Each survey stored in sperate csv file.
# The trudb is used to store both data and assessment of results
truthDir = os.path.join(dataLoc,"truthing")
try:
truthfiles = filelist(truthDir,'csv')
trudb = {}
print "Truthing files: "
for file in truthfiles:
if int(file.split('.')[0]) not in jdatelist:
print "%s does not match date given in settings file: %s" % (file, jdatelist)
else:
print file
trudb[file.split('.')[0]] = import2vector(os.path.join(truthDir,file))
trudbKeys = trudb.keys()
trudbKeys.sort()
except:
print "No truthing data found."
#
# Read stake data
SinFile = open(SfileName,'rb')
stakeData = {}
# Read point data file with position and winter balance as of last winter probing (jdayBw) and send to model
for line in csv.DictReader(SinFile, delimiter=','):
stakeName = line['Stake'].strip()
stakeData[stakeName] = {}
# Coordinates
stakeData[stakeName]['Easting'] = float(line['Easting'].strip())
stakeData[stakeName]['Northing'] = float(line['Northing'].strip())
stakeData[stakeName]['Elevation'] = float(line['Elevation'].strip())
# Get shading factor for location
# Create vector for shade values
vals = []
for d in range(366):
vals.append(1)
try:
stakeData[stakeName]['Shadevals'] = GetShadeVals(stakeData[stakeName]['Easting'], stakeData[stakeName]['Northing'], raster, transf, bandcount, vals, startday)
except:
stakeData[stakeName]['Shadevals'] = vals
print "No shade value obtained for ", stakeName
# Get the measured winter balance
try:
stakeData[stakeName]['Org_Bw'] = float(line['Bw'].strip())
except:
print "No winter balance data found (Bw column)"
break
# Get the measured summer balance
try:
stakeData[stakeName]['Org_Bs'] = float(line['Bs'].strip())
except:
pass
# Get the measured net balance
try:
stakeData[stakeName]['Org_Bn'] = float(line['Bn'].strip())
except:
pass
# Iterate over all possible parameter value combinations
# Scoring for each parameter
scores = {'ddfSnow':{}, 'ddfSi':{}, 'ddfFirn':{}, 'ddfIce':{}, 'lapse':{}, 'elevLapse':{}, 'sfe':{}, 'ELA':{}, 'refElev':{}}
parUsage = {'BsScore':[], 'ddfSnow':[], 'ddfSi':[], 'ddfFirn':[], 'ddfIce':[], 'lapse':[], 'elevLapse':[], 'sfe':[], 'ELA':[], 'refElev':[]}
iterationcount = len(ddfSnow)*len(ddfSi)*len(ddfFirn)*len(ddfIce)*len(lapse)*len(elevLapse)*len(sfe)*len(ELA)
print "Total number of runs: %s" % (iterationcount)
for it1, it2, it3, it4, it5, it6, it7, it8 in itertools.product(ddfSnow, ddfSi, ddfFirn, ddfIce, lapse, elevLapse, sfe, ELA):
paramDict = {}
paramDict['ddfSnow'] = it1
paramDict['ddfSi'] =it2
paramDict['ddfFirn'] = it3
paramDict['ddfIce'] = it4
paramDict['lapse'] = it5
paramDict['elevLapse'] = it6
paramDict['sfe'] = it7
paramDict['ELA'] = it8
paramDict['refElev'] = refElev
#
# 'data' is a copy of the original 'stakeData'
data = copy.deepcopy(stakeData)
stakeNames = stakeData.keys()
stakeNames.sort()
data['DataSets'] = {}
for stake in stakeNames:
# For ordered headers/keys
data[stake]['Headers'] = ['MeltModel', 'Shadevals', 'Easting', 'Northing', 'Elevation', 'Org_Bw']
if 'Org_Bn' in data[stake].keys():
data[stake]['Headers'].append('Org_Bn')
# Send input data to Degree Day Model object
data[stake]['MeltModel'] = DdfCell(data[stake]['Easting'], data[stake]['Northing'], data[stake]['Elevation'], data[stake]['Org_Bw'], jdayBw, data[stake]['Shadevals'], paramDict)
# For each julian day in the "times" vector call the meltInst method for each point object, passing the temperature and the day number.
# This is what runs the model at each time step in the temperature time series file
for i in range(len(temps)):
data[stake]['MeltModel'].meltInst(temps[i],times[i])
for day in jdatelist:
# Fetch modelled melt and net balance for each julian day specific in settings and create new entry for each
loc = data[stake]['MeltModel'].jTimeSeries.index(day)
data[stake]['Mod_Bs_' + str(day)] = round(data[stake]['MeltModel'].meltSumSeries[loc],3)
data[stake]['Mod_Bn_' + str(day)] = round(data[stake]['MeltModel'].BnSeries[loc],3)
data[stake]['Headers'].append('Mod_Bs_' + str(day))
data[stake]['Headers'].append('Mod_Bn_' + str(day))
# Fetch any truthing data available
if 'trudbKeys' in locals():
if str(day) in trudbKeys:
try:
loc = np.where(trudb[str(day)]['Stake']==stake)[0][0]
data[stake]['Org_Bs_' + str(day)] = round(trudb[str(day)]['Bs'][loc],3)
data[stake]['Org_Bn_' + str(day)] = round(trudb[str(day)]['Bn'][loc],3)
data[stake]['Mod_Bw_' + str(day)] = round((data[stake]['Org_Bn_' + str(day)] +data[stake]['Mod_Bs_' + str(day)]), 3)
except:
data[stake]['Org_Bs_' + str(day)] = np.nan
data[stake]['Org_Bn_' + str(day)] = np.nan
data[stake]['Mod_Bw_' + str(day)] = np.nan
data[stake]['Headers'].insert(-2, 'Org_Bs_' + str(day))
data[stake]['Headers'].insert(-2, 'Org_Bn_' + str(day))
data[stake]['Headers'].insert(-2, 'Mod_Bw_' + str(day))
# Add values to lists for calculating R2 later
if 'Mod_Bs_' + str(day) not in data['DataSets'].keys():
data['DataSets']['Mod_Bs_' + str(day)] = []
data['DataSets']['Mod_Bs_' + str(day)].append(data[stake]['Mod_Bs_' + str(day)])
if 'Org_Bs_' + str(day) not in data['DataSets'].keys():
data['DataSets']['Org_Bs_' + str(day)] = []
data['DataSets']['Org_Bs_' + str(day)].append(data[stake]['Org_Bs_' + str(day)])
dataKeys = data.keys()
dataKeys.sort()
#
if len(data['DataSets']) > 0:
report = copy.deepcopy(paramDict)
setKeys = data['DataSets'].keys()
# Order all Mod first, then all Org
setKeys.sort()
start = 0
end = len(setKeys)
middle = end/2
i = start
while i < end/2:
# Calculate R2
modBs = np.array(data['DataSets'][setKeys[i]])
obsBs = np.array(data['DataSets'][setKeys[middle]])
# Fit regression line through differences. Not good test of model
# popt, pcov = curve_fit(func,obsBs, modBs)
# variance = np.diagonal(pcov)
# SE = np.sqrt(variance)
#
modBsmean = nanmean(modBs)
obsBsmean = nanmean(obsBs)
obsBsMinModBs = obsBs - modBs
obsBsMinMean = obsBs - obsBsmean
SSres = (np.nansum(obsBsMinModBs**2))
SStot = (np.nansum(obsBsMinMean**2))
ResNorm = SSres**0.5
# BsR2 = 1 - (SSres / SStot) # BsR2 version
# report[(setKeys[i]+'_R2')] = BsR2 # BsR2 version
report[(setKeys[i]+'_RN')] = ResNorm # Norm of residuals version
# scores = scoring(scores, paramDict, BsR2) # BsR2 version
scores = scoring(scores, paramDict, ResNorm) # Norm of residuals version
# parUsage = usageUpdate(parUsage, paramDict, BsR2) #BsR2 version
parUsage = usageUpdate(parUsage, paramDict, ResNorm) # Norm of residuals version
if i == 0:
# if BsR2 >= bestBsR2: # BsR2 version
# bestBsR2 = copy.copy(BsR2) # BsR2 version
if ResNorm <= bestResNorm: # Norm of residuals version
bestResNorm = copy.copy(ResNorm) # Norm of residuals version
writeTest = 1
print "\nRun: {0} of {1}".format(counter+1, iterationcount)
reportKeys = report.keys()
reportKeys.sort()
for k in reportKeys:
print k, report[k]
# scoreWrite(scores, outputDir) # Write out scores for each parameter after each iteration. No real need
# print scores
i = i+1
middle = middle+1
if writeTest == 1:
# Output model data to file
flnm = str(counter)
outDir = makeDir(outputDir, flnm)
meltDataWrite(data, outDir)
# Write report to text file
reportWrite(report, outDir)
# Plot model results
x = []
for stake in stakeNames:
x.append(data[stake]['Elevation'])
if len(data['DataSets']) > 0:
setKeys = data['DataSets'].keys()
# Order all Mod first, then all Org
setKeys.sort()
# Plot differences between measured and modelled
if plotOn == 1:
start = 0
end = len(setKeys)
middle = end/2
i = start
while i < end/2:
modBs = np.array(data['DataSets'][setKeys[i]])
obsBs = np.array(data['DataSets'][setKeys[middle]])
bsDiff = obsBs - modBs
pltnmBs = outputname + setKeys[i] + '_measured'
plotDifElev(pltnmBs, outDir, setKeys[i], x, bsDiff, 'r')
# pltnmBsmm = outputname + setKeys[i] + '_modmeas'
# plotDif(pltnmBsmm, outDir, setKeys[i], obsBs, modBs, 'b')
i = i+1
middle = middle+1
if counter %100 == 0:
print "%s of %s" % (counter, iterationcount)
counter = counter+1
writeTest = 0
if writeScores == 'yes':
scoreWrite(scores, outputDir)
parameterCheckWrite(outputDir, year, parUsage)