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OSFWF_UpdateModelClimateData_old.py
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"""
***************************************************************************
OSFWF_UpdateModelClimateData.py
-------------------------------------
Copyright (C) 2014 TIGER-NET (www.tiger-net.org)
***************************************************************************
* This plugin is part of the Water Observation Information System (WOIS) *
* developed under the TIGER-NET project funded by the European Space *
* Agency as part of the long-term TIGER initiative aiming at promoting *
* the use of Earth Observation (EO) for improved Integrated Water *
* Resources Management (IWRM) in Africa. *
* *
* WOIS is a free software i.e. you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published *
* by the Free Software Foundation, either version 3 of the License, *
* or (at your option) any later version. *
* *
* WOIS is distributed in the hope that it will be useful, but WITHOUT ANY *
* WARRANTY; without even the implied warranty of MERCHANTABILITY or *
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License *
* for more details. *
* *
* You should have received a copy of the GNU General Public License along *
* with this program. If not, see <http://www.gnu.org/licenses/>. *
***************************************************************************
"""
import os
from datetime import date, timedelta, datetime
import numpy
from PyQt4 import QtGui
from processing.core.GeoAlgorithmExecutionException import GeoAlgorithmExecutionException
from processing.core.parameters import *
from SWATAlgorithm import SWATAlgorithm
from ModelFile import ModelFile
from ClimateStationsSWAT import ClimateStationsSWAT
from ZonalStats import ZonalStats
class OSFWF_UpdateModelClimateData(SWATAlgorithm):
MODEL_FILE = "MODEL_FILE"
PCP_DST_FOLDER = "PCP_DST_FOLDER"
TMAX_DST_FOLDER = "TMAX_DST_FOLDER"
TMIN_DST_FOLDER = "TMIN_DST_FOLDER"
SUBCATCH_RES = 'SUBCATCH_RES'
def __init__(self):
super(OSFWF_UpdateModelClimateData, self).__init__(__file__)
def defineCharacteristics(self):
self.name = "2 - Update model climate data (OSFWF)"
self.group = "Operational simulation and forecasting workflow (OSFWF)"
self.addParameter(ParameterFile(OSFWF_UpdateModelClimateData.MODEL_FILE, "Model description file", False, False))
self.addParameter(ParameterFile(OSFWF_UpdateModelClimateData.PCP_DST_FOLDER, "Precipitation folder", True, False))
self.addParameter(ParameterFile(OSFWF_UpdateModelClimateData.TMAX_DST_FOLDER, "Maximum temperature folder", True, False))
self.addParameter(ParameterFile(OSFWF_UpdateModelClimateData.TMIN_DST_FOLDER, "Minimum temperature folder", True, False))
self.addParameter(ParameterNumber(OSFWF_UpdateModelClimateData.SUBCATCH_RES, "Resolution of subcatchment map", 0.001, 0.5, 0.01))
def processAlgorithm(self, progress):
progress.setConsoleInfo("Loading model and data files...")
# Get inputs
model_file = str(self.getParameterValue(OSFWF_UpdateModelClimateData.MODEL_FILE))
pcp_folder = str(self.getParameterValue(OSFWF_UpdateModelClimateData.PCP_DST_FOLDER))
tmax_folder = str(self.getParameterValue(OSFWF_UpdateModelClimateData.TMAX_DST_FOLDER))
tmin_folder = str(self.getParameterValue(OSFWF_UpdateModelClimateData.TMIN_DST_FOLDER))
subcatchmap_res = float(self.getParameterValue(OSFWF_UpdateModelClimateData.SUBCATCH_RES))
# Check inputs
for folder in [pcp_folder, tmax_folder, tmin_folder]:
if not os.path.isdir(folder):
raise GeoAlgorithmExecutionException('No such directory: \"' + folder + '\" ')
if not os.path.isfile(model_file):
raise GeoAlgorithmExecutionException('No such file: \"' + model_file + '\" ')
# Load model
model = ModelFile(model_file)
# Create log file
log_file = open(model.Path + os.sep + "log.txt", "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(model.Path + os.sep + model.desc['Stations'])
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
if model.desc['Type'] == 'RT':
# Read last forecast dates from file
forecast_dates_file = model.Path + os.sep + model.desc['ForecastDateFile']
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)
if model.desc['Type'] == 'RT':
pcp_forecast_var = 'APCP_Forecast_'
else:
pcp_forecast_var = 'none'
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) and (model.desc['Type'] == 'RT'):
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) and (model.desc['Type'] == 'RT'):
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) and (model.desc['Type'] == 'RT'):
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 == []:
try:
correct_factor = float(model.desc['PcpCorrFact'])
except:
correct_factor = None
# 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()