def processAlgorithm(self, progress):
RES_FOLDER = self.getParameterValue(MDWF_PlotResults.RES_FOLDER)
RES_TYPE = self.getParameterValue(MDWF_PlotResults.RES_TYPE)
RES_VAR = self.getParameterValue(MDWF_PlotResults.RES_VAR)
REACH_ID = self.getParameterValue(MDWF_PlotResults.REACH_ID)
SUB_ID = self.getParameterValue(MDWF_PlotResults.SUB_ID)
HRU_ID = self.getParameterValue(MDWF_PlotResults.HRU_ID)
RES_OBSFILE = self.getParameterValue(MDWF_PlotResults.RES_OBSFILE)
TEMP_RES = self.getParameterValue(MDWF_PlotResults.TEMP_RES)
if RES_TYPE == 0:
RES_UNIT = RES_OUTSPECS.REACH_UNITS[RES_VAR]
RES_VARCOL = RES_OUTSPECS.REACH_RES_COLS[RES_VAR]
RES_VAR = RES_OUTSPECS.RESULT_VARIABLES[RES_VAR]
## data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.REACH_DELIMITER,RES_OUTSPECS.REACH_SKIPROWS,RES_OUTSPECS.REACH_OUTNAME)
data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.REACH_SKIPROWS,RES_OUTSPECS.REACH_OUTNAME)
data_ex = read_SWAT_out.reach_SWAT_ts(data,REACH_ID,RES_VARCOL,RES_VAR)
stime = read_SWAT_out.read_SWAT_time(RES_FOLDER)
if (TEMP_RES == 2) & (stime[-1] != 0):
raise GeoAlgorithmExecutionException('According to master watershed file (file.cio) the reach output file is not printed with a monthly time step.')
else:
read_SWAT_out.reach_tsplot(stime,data_ex,REACH_ID,RES_VAR,RES_UNIT,RES_FOLDER,RES_OUTSPECS.PYEX_DATE_OFFSET,RES_OBSFILE,TEMP_RES)
elif RES_TYPE == 1:
RES_UNIT = RES_OUTSPECS.SUB_UNITS[RES_VAR]
RES_VARCOL = RES_OUTSPECS.SUB_RES_COLS[RES_VAR]
RES_VAR = RES_OUTSPECS.RESULT_VARIABLES[RES_VAR]
## data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.SUB_DELIMITER,RES_OUTSPECS.SUB_SKIPROWS,RES_OUTSPECS.SUB_OUTNAME)
data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.SUB_SKIPROWS,RES_OUTSPECS.SUB_OUTNAME)
data_ex = read_SWAT_out.sub_SWAT_ts(data,SUB_ID,RES_VARCOL,RES_VAR)
stime = read_SWAT_out.read_SWAT_time(RES_FOLDER)
read_SWAT_out.sub_tsplot(stime,data_ex,SUB_ID,RES_VAR,RES_UNIT,RES_FOLDER,RES_OUTSPECS.PYEX_DATE_OFFSET,RES_OBSFILE)
elif RES_TYPE == 2:
RES_UNIT = RES_OUTSPECS.HRU_UNITS[RES_VAR]
RES_VARCOL = RES_OUTSPECS.HRU_RES_COLS[RES_VAR]
RES_VAR = RES_OUTSPECS.RESULT_VARIABLES[RES_VAR]
## data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.HRU_DELIMITER,RES_OUTSPECS.HRU_SKIPROWS,RES_OUTSPECS.HRU_OUTNAME)
data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.HRU_SKIPROWS,RES_OUTSPECS.HRU_OUTNAME)
data_ex = read_SWAT_out.hru_SWAT_ts(data,SUB_ID,HRU_ID,RES_VARCOL,RES_VAR)
stime = read_SWAT_out.read_SWAT_time(RES_FOLDER)
read_SWAT_out.hru_tsplot(stime,data_ex,SUB_ID,HRU_ID,RES_VAR,RES_UNIT,RES_FOLDER,RES_OUTSPECS.PYEX_DATE_OFFSET,RES_OBSFILE)
elif RES_TYPE == 3:
RES_UNIT = RES_OUTSPECS.RSV_UNITS[RES_VAR]
RES_VARCOL = RES_OUTSPECS.RSV_RES_COLS[RES_VAR]
RES_VAR = RES_OUTSPECS.RESULT_VARIABLES[RES_VAR]
data = read_SWAT_out.read_SWAT_out(RES_FOLDER,RES_OUTSPECS.RSV_SKIPROWS,RES_OUTSPECS.RSV_OUTNAME)
(data_ex, RSV_ID) = read_SWAT_out.rsv_SWAT_ts(RES_FOLDER,data,SUB_ID,RES_VARCOL,RES_VAR)
stime = read_SWAT_out.read_SWAT_time(RES_FOLDER)
read_SWAT_out.rsv_tsplot(stime,data_ex,SUB_ID,RSV_ID,RES_VAR,RES_UNIT,RES_FOLDER,RES_OUTSPECS.PYEX_DATE_OFFSET,RES_OBSFILE)
else:
raise GeoAlgorithmExecutionException('Result type not supported at the moment')
def processAlgorithm(self, progress):
SRC_FOLDER = self.getParameterValue(OSFWF_Assimilate_b.SRC_FOLDER)
MOD_DESC = self.getParameterValue(OSFWF_Assimilate_b.MOD_DESC)
ASS_FOLDER = self.getParameterValue(OSFWF_Assimilate_b.ASS_FOLDER)
OBS_FILE = self.getParameterValue(OSFWF_Assimilate_b.OBS_FILE)
# Extract total number of subbasins from model description file
NBRCH = ReadNoSubs(MOD_DESC)
# Get startdate from SWAT file.cio and compare with startdate of assimilation to determine header in SWAT output files
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(date(int(SWAT_time_info[1]),1,1) + timedelta(days=int(SWAT_time_info[2])-1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(date(int(SWAT_time_info[1]+SWAT_time_info[4]),1,1))
SWAT_enddate = date2num(date(int(SWAT_time_info[0]+SWAT_time_info[1]-1),1,1)) + SWAT_time_info[3]-1
ASS_module2_ErrorModel.ErrorModel_discharge(OBS_FILE, ASS_FOLDER, NBRCH, SWAT_enddate, SWAT_startdate)
def processAlgorithm(self, progress):
SRC_FOLDER = self.getParameterValue(OSFWF_Assimilate_a.SRC_FOLDER)
MOD_DESC = self.getParameterValue(OSFWF_Assimilate_a.MOD_DESC)
ASS_FOLDER = self.getParameterValue(OSFWF_Assimilate_a.ASS_FOLDER)
# Extract total number of subbasins from model description file
NBRCH = ReadNoSubs(MOD_DESC)
# Get startdate from SWAT file.cio and compare with startdate of assimilation to determine header in SWAT output files
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(date(int(SWAT_time_info[1]),1,1) + timedelta(days=int(SWAT_time_info[2])-1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(date(int(SWAT_time_info[1]+SWAT_time_info[4]),1,1))
SWAT_enddate = date2num(date(int(SWAT_time_info[0]+SWAT_time_info[1]-1),1,1)) + SWAT_time_info[3]-1
header = OUTSPECS.REACH_SKIPROWS
ASS_module1_PrepData.CreateTextFiles(NBRCH, SRC_FOLDER, ASS_FOLDER, header, SWAT_startdate, SWAT_enddate)
def processAlgorithm(self, progress):
SRC_FOLDER = self.getParameterValue(OSFWF_Assimilate_d.SRC_FOLDER)
MOD_DESC = self.getParameterValue(OSFWF_Assimilate_d.MOD_DESC)
ASS_FOLDER = self.getParameterValue(OSFWF_Assimilate_d.ASS_FOLDER)
STARTDATE = self.getParameterValue(OSFWF_Assimilate_d.STARTDATE)
OBS_FILE = self.getParameterValue(OSFWF_Assimilate_d.OBS_FILE)
# Extract total number of subbasins from model description file
NBRCH = ReadNoSubs(MOD_DESC)
# Get startdate from SWAT file.cio
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(date(int(SWAT_time_info[1]),1,1) + timedelta(days=int(SWAT_time_info[2])-1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(date(int(SWAT_time_info[1]+SWAT_time_info[4]),1,1))
SWAT_enddate = date2num(date(int(SWAT_time_info[0]+SWAT_time_info[1]-1),1,1)) + SWAT_time_info[3]-1
# Assimilation startdate is equal to SWAT start date
ASS_startdate = SWAT_startdate
# Assimilation enddate is equal to SWAT end date
ASS_enddate = SWAT_enddate
ASS_module3_Assimilation.kf_flows(OBS_FILE, ASS_FOLDER, NBRCH, ASS_enddate, ASS_startdate, SWAT_enddate, SWAT_startdate)
def processAlgorithm(self, progress):
SRC_FOLDER = self.getParameterValue(OSFWF_Assimilate_b.SRC_FOLDER)
MOD_DESC = self.getParameterValue(OSFWF_Assimilate_b.MOD_DESC)
ASS_FOLDER = self.getParameterValue(OSFWF_Assimilate_b.ASS_FOLDER)
OBS_FILE = self.getParameterValue(OSFWF_Assimilate_b.OBS_FILE)
# Extract total number of subbasins from model description file
NBRCH = ReadNoSubs(MOD_DESC)
# Get startdate from SWAT file.cio and compare with startdate of assimilation to determine header in SWAT output files
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(
date(int(SWAT_time_info[1]), 1, 1) +
timedelta(days=int(SWAT_time_info[2]) - 1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(
date(int(SWAT_time_info[1] + SWAT_time_info[4]), 1, 1))
SWAT_enddate = date2num(
date(int(SWAT_time_info[0] + SWAT_time_info[1] - 1), 1,
1)) + SWAT_time_info[3] - 1
ASS_module2_ErrorModel.ErrorModel_discharge(OBS_FILE, ASS_FOLDER,
NBRCH, SWAT_enddate,
SWAT_startdate)
def processAlgorithm(self, progress):
SRC_FOLDER = self.getParameterValue(OSFWF_Assimilate_a.SRC_FOLDER)
MOD_DESC = self.getParameterValue(OSFWF_Assimilate_a.MOD_DESC)
ASS_FOLDER = self.getParameterValue(OSFWF_Assimilate_a.ASS_FOLDER)
# Extract total number of subbasins from model description file
NBRCH = ReadNoSubs(MOD_DESC)
# Get startdate from SWAT file.cio and compare with startdate of assimilation to determine header in SWAT output files
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(
date(int(SWAT_time_info[1]), 1, 1) +
timedelta(days=int(SWAT_time_info[2]) - 1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(
date(int(SWAT_time_info[1] + SWAT_time_info[4]), 1, 1))
SWAT_enddate = date2num(
date(int(SWAT_time_info[0] + SWAT_time_info[1] - 1), 1,
1)) + SWAT_time_info[3] - 1
header = OUTSPECS.REACH_SKIPROWS
ASS_module1_PrepData.CreateTextFiles(NBRCH, SRC_FOLDER, ASS_FOLDER,
header, SWAT_startdate,
SWAT_enddate)
def processAlgorithm(self, progress):
progress.setConsoleInfo("Downloading new weather data...")
SRC_FOLDER = 'C:\Users\s113332\Desktop\Kavango_Showcase\SWAT_model\Scenarios\Default\TxtInOut' #SWAT output folder
ASS_FOLDER = 'C:\Users\s113332\Desktop\Kavango_Showcase\SWAT_model\Assimilation_2016' #storage location for assimilation input
OBS_FILE = 'C:\Users\s113332\Desktop\Kavango_Showcase\In-situ_discharge\\Rundu.csv' #file with in-situ observations
pcp_folder = 'C:\Users\s113332\Desktop\Kavango_Showcase\NOAA-GFS_climate_forcing\\APCP'
tmax_folder = 'C:\Users\s113332\Desktop\Kavango_Showcase\NOAA-GFS_climate_forcing\\TMAX'
tmin_folder = 'C:\Users\s113332\Desktop\Kavango_Showcase\NOAA-GFS_climate_forcing\\TMIN'
forecast_dates_file = "C:\Users\s113332\Desktop\Kavango_Showcase\\ForecastDates.txt"
logfilename = "C:\Users\s113332\Desktop\Kavango_Showcase\\log.txt"
climstatfilename = "C:\Users\s113332\Desktop\Kavango_Showcase\NOAA-GFS_climate_forcing\Climate_stations\\KavangoStations.txt"
subcatchmap_res = 0.01
correct_factor = 0.67
model = ModelFile(
"C:\Users\s113332\Desktop\Kavango_Showcase\\ModelDescription.txt")
NBRCH = 12 #number of reaches in the model
REACH_ID = 10 #reach for which in-situ data is available
OUTSPECS = SWAT_output_format_specs()
SWAT_EXE = SRC_FOLDER + os.sep + "swat2009DtuEnv.exe"
Issue_Date = date.today()
Ass_Out_Folder = 'C:\Users\s113332\Desktop\Kavango_Showcase\SWAT_model\Assimilation_2016\\F_Ass_Out_' + str(
Issue_Date)
# Download new NOAA GFS data
for var in ['APCP', 'TMAX', 'TMIN']:
# Set destination folder and level
if var == 'APCP':
dst_folder = pcp_folder
level = 'surface'
elif var == 'TMAX':
dst_folder = tmax_folder
level = '2_m_above_ground'
elif var == 'TMIN':
dst_folder = tmin_folder
level = '2_m_above_ground'
if os.path.isdir(dst_folder):
# Create and set Forecast folder
forecast_folder = dst_folder + os.sep + 'Forecasts'
if not os.path.isdir(forecast_folder):
os.mkdir(forecast_folder)
# Creating log file
log_file = open(dst_folder + os.sep + "Download_log.txt", "w")
# Write current date to log file
now = date.today()
log_file.write(' run: ' + now.strftime('%Y%m%d') + '\n')
log_file.write('Data source: NOAA-GFS\n')
# Finding newest file date and move old forecasted files to forecast folder
dates = []
dirs = os.listdir(dst_folder)
for f in dirs:
if (os.path.isfile(os.path.join(dst_folder, f))):
if (var + '.tif') in f:
dates.append(
date(int(f[0:4]), int(f[4:6]), int(f[6:8])))
elif (var + '_Forecast_') in f:
shutil.copy(
os.path.join(dst_folder, f),
forecast_folder + os.sep + os.path.split(f)[1])
os.remove(os.path.join(dst_folder, f))
# Newest file date +1 or today-60days (if no files) as start date
if dates == []:
first_date = now - timedelta(days=60)
else:
first_date = max(dates) + timedelta(days=1)
log_file.write(var + ' downloading start date: ' +
first_date.strftime('%Y%m%d') + '\n')
# Downloading data
forecast_date = GetGfsClimateData.GfsForecastImport(
first_date, var, level, dst_folder, -20, 55, 40, -40,
log_file, progress)
log_file.write('Forecast date ' + var + ': ' +
forecast_date.strftime('%Y%m%d') + '\n')
log_file.close()
# Update Model climate data
# Create log file
log_file = open(logfilename, "w")
# Write current date to log file
now = date.today()
log_file.write(self.name + ' run date: ' + now.strftime('%Y%m%d') +
'\n')
# Load SWAT stations file
stations = ClimateStationsSWAT_old(climstatfilename)
progress.setConsoleInfo("Reading old climate data...")
# Getting SWAT .pcp data
pcp_juliandates, first_pcp_date, last_pcp_date, pcp_array = stations.readSWATpcpFiles(
log_file)
## numpy.savetxt(model.Path + os.sep + 'pcp_array.csv', pcp_array, delimiter=",")
## log_file.write(str(pcp_dates))
# Getting SWAT .tmp data
tmp_juliandates, first_tmp_date, last_tmp_date, tmp_max_array, tmp_min_array = stations.readSWATtmpFiles(
log_file)
## numpy.savetxt(model.Path + os.sep + 'tmp_max_array.csv', tmp_max_array, delimiter=",")
# Delete last forecast in .pcp and .tmp data if Real Time model
# Read last forecast dates from file
forecast_dates = {}
forecast_file = open(forecast_dates_file, 'r').readlines()
if not forecast_file[0].find('Forecast dates file') == -1:
for line in range(1, len(forecast_file)):
(key, val) = forecast_file[line].split()
forecast_dates[key] = val
# setting new last_dates and crop arrays
# APCP
new_last_pcp_date = datetime.strptime(
forecast_dates['APCP'], "%Y%m%d").date() - timedelta(days=1)
dif = (last_pcp_date - new_last_pcp_date).days
if dif > 0:
pcp_array = pcp_array[:-dif, :]
pcp_juliandates = pcp_juliandates[:-dif]
last_pcp_date = new_last_pcp_date
# TMP
new_last_tmp_date = datetime.strptime(
forecast_dates['TMP'], "%Y%m%d").date() - timedelta(days=1)
dif = (last_tmp_date - new_last_tmp_date).days
if dif > 0:
tmp_max_array = tmp_max_array[:-dif, :]
tmp_min_array = tmp_min_array[:-dif, :]
tmp_juliandates = tmp_juliandates[:-dif]
last_tmp_date = new_last_tmp_date
progress.setConsoleInfo("Searching for new files...")
# Getting list of new pcp data files
new_pcp_files = []
new_pcp_enddate = last_pcp_date
dirs = os.listdir(pcp_folder)
pcp_forecast_var = 'APCP_Forecast_'
pcp_var_GFS = 'APCP.tif'
pcp_var_RFE = '_rain_.tif'
for f in dirs:
if (pcp_var_GFS in f) or (pcp_var_RFE in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_pcp_date < file_date):
new_pcp_files.append(pcp_folder + os.sep + f)
# Find the last date
if new_pcp_enddate < file_date:
new_pcp_enddate = file_date
# Append forecast files for real-time
elif (pcp_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_pcp_date < file_date):
new_pcp_files.append(pcp_folder + os.sep + f)
# Find the last date
if new_pcp_enddate < file_date:
new_pcp_enddate = file_date
new_pcp_forecast_date = f.split(pcp_forecast_var)[1].split(
'.tif')[0]
# Getting list of new tmax data files
new_tmax_files = []
new_tmax_enddate = last_tmp_date
tmax_var_GFS = 'TMAX.tif'
tmax_var_ECMWF = '_TMAX_ECMWF.tif'
tmax_forecast_var = 'TMAX_Forecast_'
dirs = os.listdir(tmax_folder)
for f in dirs:
if (tmax_var_GFS in f) or (tmax_var_ECMWF in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmax_files.append(tmax_folder + os.sep + f)
# Find the last date
if new_tmax_enddate < file_date:
new_tmax_enddate = file_date
# Append forecast files for real-time
elif (tmax_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmax_files.append(tmax_folder + os.sep + f)
# Find the last date
if new_tmax_enddate < file_date:
new_tmax_enddate = file_date
new_tmax_forecast_date = f.split(
tmax_forecast_var)[1].split('.tif')[0]
# Getting list of new tmin data files
new_tmin_files = []
new_tmin_enddate = last_tmp_date
tmin_var_GFS = 'TMIN.tif'
tmin_var_ECMWF = '_TMIN_ECMWF.tif'
tmin_forecast_var = 'TMIN_Forecast_'
dirs = os.listdir(tmin_folder)
for f in dirs:
if (tmin_var_GFS in f) or (tmin_var_ECMWF in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmin_files.append(tmin_folder + os.sep + f)
# Find the last date
if new_tmin_enddate < file_date:
new_tmin_enddate = file_date
# Append forecast files for real-time
elif (tmin_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmin_files.append(tmin_folder + os.sep + f)
# Find the last date
if new_tmin_enddate < file_date:
new_tmin_enddate = file_date
new_tmin_forecast_date = f.split(
tmin_forecast_var)[1].split('.tif')[0]
## log_file.write('APCP files: ' + str(new_pcp_files) + '\n')
## log_file.write('TMAX files: ' + str(new_tmax_files) + '\n')
## log_file.write('TMIN files: ' + str(new_tmin_files) + '\n')
progress.setConsoleInfo("Extracting precipitation data...")
# Process new APCP files
if not new_pcp_files == []:
# Get new array
pcp_startdate = last_pcp_date + timedelta(days=1)
new_pcp_juliandates, new_pcp_array = ZonalStats(pcp_startdate, new_pcp_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_pcp_files, log_file, GeoAlgorithmExecutionException, None, correct_factor)
# Combine arrays
pcp_juliandates = numpy.concatenate(
(pcp_juliandates, new_pcp_juliandates), axis=0)
pcp_array = numpy.concatenate((pcp_array, new_pcp_array), axis=0)
progress.setConsoleInfo("Writing new precipitation files...")
# Write files
stations.writeSWATpcpFiles(pcp_juliandates, pcp_array, log_file)
progress.setConsoleInfo("Extracting temperature data...")
# Process Temperature files
if not (new_tmax_files == [] and new_tmin_files == []):
# Get new array
# TMAX
# Get corrections
if tmax_var_ECMWF in new_tmax_files[0]:
correct_number = -273.15
pass
else:
correct_number = None
tmp_startdate = last_tmp_date + timedelta(days=1)
new_tmax_juliandates, new_tmp_max_array = ZonalStats(tmp_startdate, new_tmax_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_tmax_files, log_file, GeoAlgorithmExecutionException, correct_number, None)
# TMIN
if tmin_var_ECMWF in new_tmin_files[0]:
correct_number = -273.15
pass
else:
correct_number = None
new_tmin_juliandates, new_tmp_min_array = ZonalStats(tmp_startdate, new_tmin_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_tmin_files, log_file, GeoAlgorithmExecutionException, correct_number, None)
# Make shure tmax and tmin have same end days
dif = (len(new_tmax_juliandates) - len(new_tmin_juliandates))
if dif > 0:
new_tmp_max_array = new_tmp_max_array[:-dif, :]
new_tmax_juliandates = new_tmax_juliandates[:-dif]
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmin_forecast_date
elif dif < 0:
new_tmp_min_array = new_tmp_min_array[:-dif, :]
new_tmin_juliandates = new_tmin_juliandates[:-dif, :]
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmax_forecast_date
else:
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmax_forecast_date
progress.setConsoleInfo("Writing new temperature files...")
# Combine arrays
# TMAX
tmp_juliandates = numpy.concatenate(
(tmp_juliandates, new_tmax_juliandates), axis=0)
tmp_max_array = numpy.concatenate(
(tmp_max_array, new_tmp_max_array), axis=0)
# TMIN
tmp_min_array = numpy.concatenate(
(tmp_min_array, new_tmp_min_array), axis=0)
# Write files
stations.writeSWATtmpFiles(tmp_juliandates, tmp_max_array,
tmp_min_array, log_file)
progress.setConsoleInfo("Update model files...")
# Updating forecast file
if model.desc['Type'] == 'RT':
if new_pcp_files == []:
new_pcp_forecast_date = forecast_dates['APCP']
forecast_file = open(forecast_dates_file, 'w')
forecast_file.write('Forecast dates file \n')
forecast_file.write('APCP ' + new_pcp_forecast_date + '\n')
forecast_file.write('TMP ' + new_tmp_forecast_date + '\n')
log_file.close()
# Run SWAT model
# Updating climate files
CSTATIONS = ClimateStationsSWAT_old(climstatfilename)
log_file = open(SRC_FOLDER + os.sep + "cstations_log.txt", "w")
last_pcp_date, last_tmp_date = CSTATIONS.writeSWATrunClimateFiles(
SRC_FOLDER, log_file)
log_file.close()
# Updating cio file
last_date = min(last_pcp_date, last_tmp_date)
if os.path.isfile(SRC_FOLDER + os.sep + "file.cio"):
cio_file = open(SRC_FOLDER + os.sep + "file.cio", "r")
cio = cio_file.readlines()
cio_file.close()
startyear = int(cio[8][0:16])
nbyears = last_date.year - startyear + 1
endjulianday = (last_date - date(last_date.year, 1, 1)).days + 1
nbyearline = str(nbyears).rjust(16) + cio[7][16:len(cio[7])]
endjdayline = str(endjulianday).rjust(
16) + cio[10][16:len(cio[10])]
cio[7] = nbyearline
cio[10] = endjdayline
cio_file = open(SRC_FOLDER + os.sep + "file.cio", "w")
cio_file.writelines(cio)
cio_file.close()
else:
raise GeoAlgorithmExecutionException('cio-file ' + SRC_FOLDER +
os.sep + "file.cio" +
' does not exist')
# Running SWAT
currpath = os.getcwd()
os.chdir(SRC_FOLDER)
runres = subprocess.call(SWAT_EXE)
os.chdir(currpath)
if runres != 0:
raise GeoAlgorithmExecutionException('SWAT run unsuccessful')
# Get startdate and enddate from SWAT file.cio
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(
date(int(SWAT_time_info[1]), 1, 1) +
timedelta(days=int(SWAT_time_info[2]) - 1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(
date(int(SWAT_time_info[1] + SWAT_time_info[4]), 1, 1))
SWAT_enddate = date2num(
date(int(SWAT_time_info[0] + SWAT_time_info[1] - 1), 1,
1)) + SWAT_time_info[3] - 1
# Prepare runoff files only
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile.txt",
ASS_FOLDER + os.sep + "Assimilationfile_f.txt")
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_q.txt",
ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt")
ASS_module1_PrepData.CreateTextFiles(NBRCH, SRC_FOLDER, ASS_FOLDER,
OUTSPECS.REACH_SKIPROWS,
SWAT_startdate, SWAT_enddate)
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_f.txt",
ASS_FOLDER + os.sep + "Assimilationfile.txt")
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt",
ASS_FOLDER + os.sep + "Assimilationfile_q.txt")
os.remove(ASS_FOLDER + os.sep + "Assimilationfile_f.txt")
os.remove(ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt")
# Run the assimilation
ASS_startdate = SWAT_startdate
ASS_enddate = SWAT_enddate
if not (os.path.isdir(Ass_Out_Folder)):
os.mkdir(Ass_Out_Folder)
src_files = os.listdir(ASS_FOLDER)
for file_name in src_files:
full_file_name = os.path.join(ASS_FOLDER, file_name)
if (os.path.isfile(full_file_name)):
shutil.copy(full_file_name, Ass_Out_Folder)
# Run the assimilation
ASS_module3_Assimilation.kf_flows(OBS_FILE, Ass_Out_Folder, NBRCH,
ASS_enddate, ASS_startdate,
SWAT_enddate, SWAT_startdate)
# Plot results
ASS_module4_Results.Results(OBS_FILE, str(Issue_Date), ASS_startdate,
ASS_enddate, Ass_Out_Folder, REACH_ID)
# Compute performance statistics
ASS_Evaluation.Results(ASS_startdate, ASS_enddate, Ass_Out_Folder,
NBRCH, REACH_ID, OBS_FILE)
def create_PEST_instruction(src_folder, obsfile, obsgroup, nreaches, tmpres):
specs = SWAT_output_format_specs()
pestspecs = SWAT_PEST_specs()
obsdata = numpy.genfromtxt(obsfile,
delimiter=',',
skiprows=0,
missing_values='NaN')
obsdata[:, 0] = obsdata[:, 0] + specs.PYEX_DATE_OFFSET
SWAT_time_info = read_SWAT_time(src_folder)
startyr = datetime.date(int(SWAT_time_info[1]), 1, 1)
startdate = startyr + datetime.timedelta(days=int(SWAT_time_info[2]) - 1)
if SWAT_time_info[4] > 0:
startdate = datetime.date(int(SWAT_time_info[1] + SWAT_time_info[4]),
1, 1)
endyear = datetime.date(int(SWAT_time_info[1] + SWAT_time_info[0] - 1), 1,
1)
enddate = endyear + datetime.timedelta(days=int(SWAT_time_info[3]) - 1)
insname = obsfile.split('.')[0] + '_' + obsfile.split('.')[1] + '.ins'
insfile = open(insname, 'w')
insfile.writelines('pif ' + pestspecs.pif + '\n')
insfile.writelines('l9\n')
obsblockname = obsfile.split('.')[0] + '_' + obsfile.split(
'.')[1] + '_observation_block.obf'
obsblockfile = open(obsblockname, 'w')
obsid = 1
if tmpres == 0:
tsteps = int((enddate - startdate).days) + 1
for i in range(0, tsteps):
currentdate = startdate + datetime.timedelta(i)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:, 0] == currentdatenum, 1]
obsw = obsdata[obsdata[:, 0] == currentdatenum, 2]
obsw = 1. / numpy.power(obsw, 2)
try:
reachid = int(obsdata[obsdata[:, 0] == currentdatenum, 3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' +
str(obsid) + ' ' + str(obs[0]) + ' ' +
str(obsw[0]) + ' ' + obsgroup + '\n')
insfile.writelines('l' + str(reachid) + ' [' + 'reach_' +
str(reachid) + '_' + str(obsid) + ']' +
str(pestspecs.flowoutinicol) + ':' +
str(pestspecs.flowoutendcol) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches - reachid) + '\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
elif tmpres == 1:
tsteps = int((enddate - startdate).days) + 1
currentyear = startdate
while (currentyear <= endyear):
if currentyear == endyear:
for m in range(0, enddate.month):
currentdate = currentyear + relativedelta(months=+m)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:, 0] == currentdatenum, 1]
obsw = obsdata[obsdata[:, 0] == currentdatenum, 2]
obsw = 1. / numpy.power(obsw, 2)
try:
reachid = int(obsdata[obsdata[:, 0] == currentdatenum,
3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' +
str(obsid) + ' ' +
str(obs[0]) + ' ' +
str(obsw[0]) + ' ' + obsgroup +
'\n')
insfile.writelines('l' + str(reachid) + ' [' +
'reach_' + str(reachid) + '_' +
str(obsid) + ']' + str(50) + ':' +
str(61) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches - reachid) +
'\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
insfile.writelines('l' + str(nreaches) + '\n')
currentyear = currentyear + relativedelta(years=+1)
else:
for m in range(0, 12):
currentdate = currentyear + relativedelta(months=+m)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:, 0] == currentdatenum, 1]
obsw = obsdata[obsdata[:, 0] == currentdatenum, 2]
obsw = 1. / numpy.power(obsw, 2)
try:
reachid = int(obsdata[obsdata[:, 0] == currentdatenum,
3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' +
str(obsid) + ' ' +
str(obs[0]) + ' ' +
str(obsw[0]) + ' ' + obsgroup +
'\n')
insfile.writelines('l' + str(reachid) + ' [' +
'reach_' + str(reachid) + '_' +
str(obsid) + ']' + str(50) + ':' +
str(61) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches - reachid) +
'\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
insfile.writelines('l' + str(nreaches) + '\n')
currentyear = currentyear + relativedelta(years=+1)
return insname, obsblockname
from read_SWAT_out import read_SWAT_time
from SWAT_output_format_specs import SWAT_output_format_specs
from ASS_utilities import ReadObsFlowsAss
import ASS_Evaluation
PREDICTION_FOLDER = 'r:\\Projects\\TigerNET\\Kavango\\Assimilation\\F_Predictions_modified' #Predictions will be stored here
SRC_FOLDER = 'r:\\Projects\\TigerNET\\Kavango\\Assimilation\\TxtInOut' #SWAT output folder
OBS_FILE = 'r:\\Projects\\TigerNET\\Kavango\\Kavango_Showcase_new\\In-situ_discharge\\Rundu.csv' #file with in-situ observations
OBS_FILE_ASS = 'r:\\Projects\\TigerNET\\Kavango\\Kavango_Showcase_new\\In-situ_discharge\\Rundu.csv'
NBRCH = 12 #number of reaches in the model
REACH_ID = 10 #reach for which in-situ data is available
OUTSPECS = SWAT_output_format_specs()
#Get startdate from SWAT file.cio and compare with startdate of assimilation to determine header in SWAT output files
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(
date(int(SWAT_time_info[1]), 1, 1) +
timedelta(days=int(SWAT_time_info[2]) - 1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(
date(int(SWAT_time_info[1] + SWAT_time_info[4]), 1, 1))
SWAT_enddate = date2num(
date(int(SWAT_time_info[0] + SWAT_time_info[1] - 1), 1,
1)) + SWAT_time_info[3] - 1
ASS_startdate = SWAT_startdate
ASS_enddate = SWAT_enddate
arraylen = 5000
count = 0
day_0_det = numpy.zeros([arraylen, 3])
def create_PEST_instruction(src_folder, obsfile, obsgroup, nreaches, tmpres):
specs = SWAT_output_format_specs()
pestspecs = SWAT_PEST_specs()
obsdata = numpy.genfromtxt(obsfile, delimiter = ',', skiprows=0, missing_values = 'NaN')
obsdata[:,0] = obsdata[:,0] + specs.PYEX_DATE_OFFSET
SWAT_time_info = read_SWAT_time(src_folder)
startyr = datetime.date(int(SWAT_time_info[1]),1,1)
startdate = startyr + datetime.timedelta(days=int(SWAT_time_info[2])-1)
if SWAT_time_info[4] > 0:
startdate = datetime.date(int(SWAT_time_info[1]+SWAT_time_info[4]),1,1)
endyear = datetime.date(int(SWAT_time_info[1]+SWAT_time_info[0]-1),1,1)
enddate = endyear + datetime.timedelta(days=int(SWAT_time_info[3])-1)
insname = obsfile.split('.')[0] + '_' + obsfile.split('.')[1] + '.ins'
insfile = open(insname,'w')
insfile.writelines('pif ' + pestspecs.pif + '\n')
insfile.writelines('l9\n')
obsblockname = obsfile.split('.')[0] + '_' + obsfile.split('.')[1] + '_observation_block.obf'
obsblockfile = open(obsblockname,'w')
obsid = 1
if tmpres == 0:
tsteps = int((enddate-startdate).days) + 1
for i in range(0,tsteps):
currentdate = startdate + datetime.timedelta(i)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:,0]==currentdatenum,1]
obsw = obsdata[obsdata[:,0]==currentdatenum,2]
obsw = 1./numpy.power(obsw,2)
try:
reachid = int(obsdata[obsdata[:,0]==currentdatenum,3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' + str(obsid) + ' ' + str(obs[0]) + ' ' + str(obsw[0]) + ' ' + obsgroup + '\n')
insfile.writelines('l' + str(reachid) + ' [' + 'reach_' + str(reachid) + '_' + str(obsid) + ']' + str(pestspecs.flowoutinicol) + ':' + str(pestspecs.flowoutendcol) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches-reachid) + '\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
elif tmpres == 1:
tsteps = int((enddate-startdate).days) + 1
currentyear = startdate
while (currentyear <= endyear):
if currentyear == endyear:
for m in range(0,enddate.month):
currentdate = currentyear + relativedelta(months=+m)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:,0]==currentdatenum,1]
obsw = obsdata[obsdata[:,0]==currentdatenum,2]
obsw = 1./numpy.power(obsw,2)
try:
reachid = int(obsdata[obsdata[:,0]==currentdatenum,3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' + str(obsid) + ' ' + str(obs[0]) + ' ' + str(obsw[0]) + ' ' + obsgroup + '\n')
insfile.writelines('l' + str(reachid) + ' [' + 'reach_' + str(reachid) + '_' + str(obsid) + ']' + str(50) + ':' + str(61) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches-reachid) + '\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
insfile.writelines('l' + str(nreaches) + '\n')
currentyear = currentyear + relativedelta(years=+1)
else:
for m in range(0,12):
currentdate = currentyear + relativedelta(months=+m)
currentdatenum = matplotlib.dates.date2num(currentdate)
obs = obsdata[obsdata[:,0]==currentdatenum,1]
obsw = obsdata[obsdata[:,0]==currentdatenum,2]
obsw = 1./numpy.power(obsw,2)
try:
reachid = int(obsdata[obsdata[:,0]==currentdatenum,3][0])
except:
pass
if ~numpy.isnan(obs):
obsblockfile.writelines('reach_' + str(reachid) + '_' + str(obsid) + ' ' + str(obs[0]) + ' ' + str(obsw[0]) + ' ' + obsgroup + '\n')
insfile.writelines('l' + str(reachid) + ' [' + 'reach_' + str(reachid) + '_' + str(obsid) + ']' + str(50) + ':' + str(61) + '\n')
if reachid < nreaches:
insfile.writelines('l' + str(nreaches-reachid) + '\n')
obsid = obsid + 1
else:
insfile.writelines('l' + str(nreaches) + '\n')
insfile.writelines('l' + str(nreaches) + '\n')
currentyear = currentyear + relativedelta(years=+1)
return insname, obsblockname
def processAlgorithm(self, progress):
progress.setConsoleInfo("Downloading new weather data...")
SRC_FOLDER = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\TxtInOut' #SWAT output folder
ASS_FOLDER = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\F_AssFolder'#storage location for assimilation input
OBS_FILE = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Kavango_Showcase_new\\In-situ_discharge\\Rundu.csv' #file with in-situ observations
pcp_folder = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\APCP'
tmax_folder = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\TMAX'
tmin_folder = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\TMIN'
forecast_dates_file = "p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\Climate_stations\\ForecastDates.txt"
logfilename = "p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\Climate_stations\\log.txt"
climstatfilename = "p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\Climate_stations\KavangoStations.txt"
subcatchmap_res = 0.01
correct_factor = 0.67
model = ModelFile("p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Kavango_Showcase_new\\ModelDescription.txt")
NBRCH = 12 #number of reaches in the model
REACH_ID = 10 #reach for which in-situ data is available
OUTSPECS = SWAT_output_format_specs()
SWAT_EXE = SRC_FOLDER + os.sep + "swat2009DtuEnv.exe"
Issue_Date = date.today()
Ass_Out_Folder = 'p:\\ACTIVE\\TigerNET 30966 PBAU\\Kavango\\Assimilation\\F_Ass_Out_' + str(Issue_Date)
# Download new NOAA GFS data
for var in ['APCP','TMAX','TMIN']:
# Set destination folder and level
if var == 'APCP':
dst_folder = pcp_folder
level = 'surface'
elif var == 'TMAX':
dst_folder = tmax_folder
level = '2_m_above_ground'
elif var == 'TMIN':
dst_folder = tmin_folder
level = '2_m_above_ground'
if os.path.isdir(dst_folder):
# Create and set Forecast folder
forecast_folder = dst_folder + os.sep + 'Forecasts'
if not os.path.isdir(forecast_folder):
os.mkdir(forecast_folder)
# Creating log file
log_file = open(dst_folder + os.sep + "Download_log.txt", "w")
# Write current date to log file
now = date.today()
log_file.write(' run: ' + now.strftime('%Y%m%d') + '\n')
log_file.write('Data source: NOAA-GFS\n')
# Finding newest file date and move old forecasted files to forecast folder
dates = []
dirs = os.listdir(dst_folder)
for f in dirs:
if ( os.path.isfile(os.path.join(dst_folder,f)) ):
if (var + '.tif') in f:
dates.append(date(int(f[0:4]),int(f[4:6]),int(f[6:8])))
elif (var + '_Forecast_') in f:
shutil.copy(os.path.join(dst_folder,f),forecast_folder + os.sep + os.path.split(f)[1])
os.remove(os.path.join(dst_folder,f))
# Newest file date +1 or today-60days (if no files) as start date
if dates == []:
first_date = now - timedelta(days=60)
else:
first_date = max(dates) + timedelta(days=1)
log_file.write(var + ' downloading start date: ' + first_date.strftime('%Y%m%d') + '\n')
# Downloading data
forecast_date = GetGfsClimateData.GfsForecastImport(first_date, var, level, dst_folder, -20, 55, 40, -40, log_file, progress)
log_file.write('Forecast date ' + var + ': ' + forecast_date.strftime('%Y%m%d') + '\n')
log_file.close()
# Update Model climate data
# Create log file
log_file = open(logfilename, "w")
# Write current date to log file
now = date.today()
log_file.write(self.name + ' run date: ' + now.strftime('%Y%m%d') + '\n')
# Load SWAT stations file
stations = ClimateStationsSWAT(climstatfilename)
progress.setConsoleInfo("Reading old climate data...")
# Getting SWAT .pcp data
pcp_juliandates, first_pcp_date, last_pcp_date, pcp_array = stations.readSWATpcpFiles(log_file)
## numpy.savetxt(model.Path + os.sep + 'pcp_array.csv', pcp_array, delimiter=",")
## log_file.write(str(pcp_dates))
# Getting SWAT .tmp data
tmp_juliandates, first_tmp_date, last_tmp_date, tmp_max_array, tmp_min_array = stations.readSWATtmpFiles(log_file)
## numpy.savetxt(model.Path + os.sep + 'tmp_max_array.csv', tmp_max_array, delimiter=",")
# Delete last forecast in .pcp and .tmp data if Real Time model
# Read last forecast dates from file
forecast_dates = {}
forecast_file=open(forecast_dates_file,'r').readlines()
if not forecast_file[0].find('Forecast dates file') == -1:
for line in range(1,len(forecast_file)):
(key, val) = forecast_file[line].split()
forecast_dates[key] = val
# setting new last_dates and crop arrays
# APCP
new_last_pcp_date = datetime.strptime(forecast_dates['APCP'], "%Y%m%d").date() - timedelta(days=1)
dif = (last_pcp_date - new_last_pcp_date).days
if dif > 0:
pcp_array = pcp_array[:-dif,:]
pcp_juliandates = pcp_juliandates[:-dif]
last_pcp_date = new_last_pcp_date
# TMP
new_last_tmp_date = datetime.strptime(forecast_dates['TMP'], "%Y%m%d").date() - timedelta(days=1)
dif = (last_tmp_date - new_last_tmp_date).days
if dif > 0:
tmp_max_array = tmp_max_array[:-dif,:]
tmp_min_array = tmp_min_array[:-dif,:]
tmp_juliandates = tmp_juliandates[:-dif]
last_tmp_date = new_last_tmp_date
progress.setConsoleInfo("Searching for new files...")
# Getting list of new pcp data files
new_pcp_files = []
new_pcp_enddate = last_pcp_date
dirs = os.listdir(pcp_folder)
pcp_forecast_var = 'APCP_Forecast_'
pcp_var_GFS = 'APCP.tif'
pcp_var_RFE = '_rain_.tif'
for f in dirs:
if (pcp_var_GFS in f) or (pcp_var_RFE in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_pcp_date < file_date):
new_pcp_files.append(pcp_folder + os.sep + f)
# Find the last date
if new_pcp_enddate < file_date:
new_pcp_enddate = file_date
# Append forecast files for real-time
elif (pcp_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_pcp_date < file_date):
new_pcp_files.append(pcp_folder + os.sep + f)
# Find the last date
if new_pcp_enddate < file_date:
new_pcp_enddate = file_date
new_pcp_forecast_date = f.split(pcp_forecast_var)[1].split('.tif')[0]
# Getting list of new tmax data files
new_tmax_files = []
new_tmax_enddate = last_tmp_date
tmax_var_GFS = 'TMAX.tif'
tmax_var_ECMWF = '_TMAX_ECMWF.tif'
tmax_forecast_var = 'TMAX_Forecast_'
dirs = os.listdir(tmax_folder)
for f in dirs:
if (tmax_var_GFS in f) or (tmax_var_ECMWF in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmax_files.append(tmax_folder + os.sep + f)
# Find the last date
if new_tmax_enddate < file_date:
new_tmax_enddate = file_date
# Append forecast files for real-time
elif (tmax_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmax_files.append(tmax_folder + os.sep + f)
# Find the last date
if new_tmax_enddate < file_date:
new_tmax_enddate = file_date
new_tmax_forecast_date = f.split(tmax_forecast_var)[1].split('.tif')[0]
# Getting list of new tmin data files
new_tmin_files = []
new_tmin_enddate = last_tmp_date
tmin_var_GFS = 'TMIN.tif'
tmin_var_ECMWF = '_TMIN_ECMWF.tif'
tmin_forecast_var = 'TMIN_Forecast_'
dirs = os.listdir(tmin_folder)
for f in dirs:
if (tmin_var_GFS in f) or (tmin_var_ECMWF in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmin_files.append(tmin_folder + os.sep + f)
# Find the last date
if new_tmin_enddate < file_date:
new_tmin_enddate = file_date
# Append forecast files for real-time
elif (tmin_forecast_var in f):
file_date = datetime.strptime(f[0:8], "%Y%m%d").date()
# Only get new files
if (last_tmp_date < file_date):
new_tmin_files.append(tmin_folder + os.sep + f)
# Find the last date
if new_tmin_enddate < file_date:
new_tmin_enddate = file_date
new_tmin_forecast_date = f.split(tmin_forecast_var)[1].split('.tif')[0]
## log_file.write('APCP files: ' + str(new_pcp_files) + '\n')
## log_file.write('TMAX files: ' + str(new_tmax_files) + '\n')
## log_file.write('TMIN files: ' + str(new_tmin_files) + '\n')
progress.setConsoleInfo("Extracting precipitation data...")
# Process new APCP files
if not new_pcp_files == []:
# Get new array
pcp_startdate = last_pcp_date + timedelta(days=1)
new_pcp_juliandates, new_pcp_array = ZonalStats(pcp_startdate, new_pcp_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_pcp_files, log_file, GeoAlgorithmExecutionException, None, correct_factor)
# Combine arrays
pcp_juliandates = numpy.concatenate((pcp_juliandates, new_pcp_juliandates), axis=0)
pcp_array = numpy.concatenate((pcp_array, new_pcp_array), axis=0)
progress.setConsoleInfo("Writing new precipitation files...")
# Write files
stations.writeSWATpcpFiles(pcp_juliandates, pcp_array, log_file)
progress.setConsoleInfo("Extracting temperature data...")
# Process Temperature files
if not (new_tmax_files == [] and new_tmin_files == []):
# Get new array
# TMAX
# Get corrections
if tmax_var_ECMWF in new_tmax_files[0]:
correct_number = -273.15
pass
else:
correct_number = None
tmp_startdate = last_tmp_date + timedelta(days=1)
new_tmax_juliandates, new_tmp_max_array = ZonalStats(tmp_startdate, new_tmax_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_tmax_files, log_file, GeoAlgorithmExecutionException, correct_number, None)
# TMIN
if tmin_var_ECMWF in new_tmin_files[0]:
correct_number = -273.15
pass
else:
correct_number = None
new_tmin_juliandates, new_tmp_min_array = ZonalStats(tmp_startdate, new_tmin_enddate, model.Path, \
model.desc['ModelName'], model.Path+os.sep+model.desc['Shapefile'], model.desc['SubbasinColumn'], \
subcatchmap_res, new_tmin_files, log_file, GeoAlgorithmExecutionException, correct_number, None)
# Make shure tmax and tmin have same end days
dif = (len(new_tmax_juliandates)-len(new_tmin_juliandates))
if dif > 0:
new_tmp_max_array = new_tmp_max_array[:-dif,:]
new_tmax_juliandates = new_tmax_juliandates[:-dif]
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmin_forecast_date
elif dif < 0:
new_tmp_min_array = new_tmp_min_array[:-dif,:]
new_tmin_juliandates = new_tmin_juliandates[:-dif,:]
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmax_forecast_date
else:
if model.desc['Type'] == 'RT':
new_tmp_forecast_date = new_tmax_forecast_date
progress.setConsoleInfo("Writing new temperature files...")
# Combine arrays
# TMAX
tmp_juliandates = numpy.concatenate((tmp_juliandates, new_tmax_juliandates), axis=0)
tmp_max_array = numpy.concatenate((tmp_max_array, new_tmp_max_array), axis=0)
# TMIN
tmp_min_array = numpy.concatenate((tmp_min_array, new_tmp_min_array), axis=0)
# Write files
stations.writeSWATtmpFiles(tmp_juliandates, tmp_max_array, tmp_min_array, log_file)
progress.setConsoleInfo("Update model files...")
# Updating forecast file
if model.desc['Type'] == 'RT':
if new_pcp_files == []:
new_pcp_forecast_date = forecast_dates['APCP']
forecast_file=open(forecast_dates_file,'w')
forecast_file.write('Forecast dates file \n')
forecast_file.write('APCP ' + new_pcp_forecast_date + '\n')
forecast_file.write('TMP ' + new_tmp_forecast_date + '\n')
log_file.close()
# Run SWAT model
# Updating climate files
CSTATIONS = ClimateStationsSWAT(climstatfilename)
log_file = open(SRC_FOLDER + os.sep + "cstations_log.txt", "w")
last_pcp_date,last_tmp_date = CSTATIONS.writeSWATrunClimateFiles(SRC_FOLDER,log_file)
log_file.close()
# Updating cio file
last_date = min(last_pcp_date,last_tmp_date)
if os.path.isfile(SRC_FOLDER + os.sep + "file.cio"):
cio_file = open(SRC_FOLDER + os.sep + "file.cio", "r")
cio=cio_file.readlines()
cio_file.close()
startyear = int(cio[8][0:16])
nbyears = last_date.year - startyear + 1
endjulianday = (last_date-date(last_date.year,1,1)).days + 1
nbyearline = str(nbyears).rjust(16) + cio[7][16:len(cio[7])]
endjdayline = str(endjulianday).rjust(16) + cio[10][16:len(cio[10])]
cio[7]=nbyearline
cio[10]=endjdayline
cio_file = open(SRC_FOLDER + os.sep + "file.cio", "w")
cio_file.writelines(cio)
cio_file.close()
else:
raise GeoAlgorithmExecutionException('cio-file ' + SRC_FOLDER + os.sep + "file.cio" + ' does not exist')
# Running SWAT
currpath = os.getcwd()
os.chdir(SRC_FOLDER)
runres = subprocess.call(SWAT_EXE)
os.chdir(currpath)
if runres != 0:
raise GeoAlgorithmExecutionException('SWAT run unsuccessful')
# Get startdate and enddate from SWAT file.cio
SWAT_time_info = read_SWAT_time(SRC_FOLDER)
SWAT_startdate = date2num(date(int(SWAT_time_info[1]),1,1) + timedelta(days=int(SWAT_time_info[2])-1))
if SWAT_time_info[4] > 0: # Account for NYSKIP>0
SWAT_startdate = date2num(date(int(SWAT_time_info[1]+SWAT_time_info[4]),1,1))
SWAT_enddate = date2num(date(int(SWAT_time_info[0]+SWAT_time_info[1]-1),1,1)) + SWAT_time_info[3]-1
# Prepare runoff files only
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile.txt",ASS_FOLDER + os.sep + "Assimilationfile_f.txt")
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_q.txt",ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt")
ASS_module1_PrepData.CreateTextFiles(NBRCH, SRC_FOLDER, ASS_FOLDER, OUTSPECS.REACH_SKIPROWS, SWAT_startdate, SWAT_enddate)
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_f.txt",ASS_FOLDER + os.sep + "Assimilationfile.txt")
shutil.copyfile(ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt",ASS_FOLDER + os.sep + "Assimilationfile_q.txt")
os.remove(ASS_FOLDER + os.sep + "Assimilationfile_f.txt")
os.remove(ASS_FOLDER + os.sep + "Assimilationfile_q_f.txt")
# Run the assimilation
ASS_startdate = SWAT_startdate
ASS_enddate = SWAT_enddate
if not(os.path.isdir(Ass_Out_Folder)):
os.mkdir(Ass_Out_Folder)
src_files = os.listdir(ASS_FOLDER)
for file_name in src_files:
full_file_name = os.path.join(ASS_FOLDER, file_name)
if (os.path.isfile(full_file_name)):
shutil.copy(full_file_name, Ass_Out_Folder)
# Run the assimilation
ASS_module3_Assimilation.kf_flows(OBS_FILE, Ass_Out_Folder, NBRCH, ASS_enddate, ASS_startdate, SWAT_enddate, SWAT_startdate)
# Plot results
ASS_module4_Results.Results(OBS_FILE, str(Issue_Date), ASS_startdate, ASS_enddate, Ass_Out_Folder, REACH_ID)
# Compute performance statistics
ASS_Evaluation.Results(ASS_startdate,ASS_enddate, Ass_Out_Folder, NBRCH, REACH_ID, OBS_FILE)
