def __init__(self, cloneMapFileName,\
pcraster_files, \
output, \
modelTime):
DynamicModel.__init__(self) #
pcr.setclone(cloneMapFileName)
self.modelTime = modelTime
self.pcraster_files = pcraster_files
# move to the input folder
os.chdir(self.pcraster_files['directory'])
self.output = output
self.output['file_name'] = vos.getFullPath(self.output['file_name'], self.output['folder'])
# object for reporting
self.netcdf_report = OutputNetcdf(cloneMapFileName, self.output['description'])
print(self.output['long_name'])
# make a netcdf file
self.netcdf_report.createNetCDF(self.output['file_name'],\
self.output['variable_name'],\
self.output['unit'],\
self.output['long_name'])
def __init__(self, cloneMapFileName,\
pcraster_files, \
modelTime, \
output, inputEPSG = None, outputEPSG = None, resample_method = None):
DynamicModel.__init__(self)
# set the clone map
self.cloneMapFileName = cloneMapFileName
pcr.setclone(self.cloneMapFileName)
# time variable/object
self.modelTime = modelTime
# output file name, folder name, etc.
self.output = output
self.output['file_name'] = vos.getFullPath(self.output['file_name'],
self.output['folder'])
# input and output projection/coordinate systems
self.inputEPSG = inputEPSG
self.outputEPSG = outputEPSG
self.resample_method = resample_method
# prepare temporary directory
self.tmpDir = output['folder'] + "/tmp/"
try:
os.makedirs(self.tmpDir)
os.system('rm -r ' + tmpDir + "/*")
except:
pass
# pcraster input files
self.pcraster_files = pcraster_files
# - the begining part of pcraster file names (e.g. "pr" for "pr000000.001")
self.pcraster_file_name = self.pcraster_files['directory']+"/"+\
self.pcraster_files['file_name']
# object for reporting
self.netcdf_report = OutputNetcdf(mapattr_dict = None,\
cloneMapFileName = cloneMapFileName,\
netcdf_format = "NETCDF3_CLASSIC",\
netcdf_zlib = False,\
netcdf_attribute_dict = None,\
netcdf_attribute_description = self.output['description'])
# make a netcdf file
self.netcdf_report.createNetCDF(self.output['file_name'],\
self.output['variable_name'],\
self.output['unit'],\
self.output['long_name'])
def __init__(self, input_files,\
output_files,\
modelTime,\
main_tmp_dir = "/dev/shm/"):
DynamicModel.__init__(self)
self.input_files = input_files
self.output_files = output_files
self.modelTime = modelTime
# main temporary directory
self.main_tmp_dir = main_tmp_dir+"/"+vos.get_random_word()
# make the temporary directory if not exist yet
try:
os.makedirs(self.main_tmp_dir)
except:
os.system('rm -r '+str(self.main_tmp_dir)+'*')
os.makedirs(self.main_tmp_dir)
# clone map for pcraster process - depend on the resolution of the basin/catchment map
pcr.setclone(self.input_files["basin30minmap"])
self.clone_map = pcr.boolean(1.0)
#
# catchment ids map
self.catchment = pcr.nominal(\
pcr.readmap(self.input_files["basin30minmap"]))
self.catchment = pcr.ifthen(pcr.scalar(self.catchment) > 0.0,\
self.catchment)
# cell area map
self.cell_area = pcr.cover(pcr.readmap(self.input_files["area30min_map"]), 0.0)
# prepare grace monthly and annual anomaly time series
self.pre_process_grace_file()
# prepare model monthly and annual anomaly time series
self.pre_process_model_file()
# prepare object for writing netcdf files:
self.output = OutputNetcdf(self.input_files["area30min_map"])
self.output.createNetCDF(self.output_files['basinscale_tws_month_anomaly']['grace'], "lwe_thickness","m")
self.output.createNetCDF(self.output_files['basinscale_tws_month_anomaly']['model'], "pcrglobwb_tws","m")
self.output.createNetCDF(self.output_files['basinscale_tws_annua_anomaly']['grace'], "lwe_thickness","m")
self.output.createNetCDF(self.output_files['basinscale_tws_annua_anomaly']['model'], "pcrglobwb_tws","m")
def __init__(self, input_files,\
output_files,\
modelTime,\
tmpDir = "/dev/shm/"):
DynamicModel.__init__(self) #
self.input_files = input_files
self.output_files = output_files
self.tmpDir = tmpDir
self.modelTime = modelTime
pcr.setclone(self.input_files["model_cell_area"])
clone_map = pcr.boolean(1)
# cell area (m2)
self.cell_area = vos.readPCRmapClone(\
self.input_files["model_cell_area"],\
self.input_files["model_cell_area"],\
self.tmpDir)
# resampling factor: ratio between target (coarse) and original (fine) resolution
self.resample_factor = round(
vos.getMapAttributes(self.input_files["one_degree_id"],'cellsize')/\
vos.getMapAttributes(self.input_files["model_cell_area"],'cellsize'))
# unique ids for upscaling to one degree resolution (grace resolution)
self.one_degree_id = pcr.nominal(\
vos.readPCRmapClone(\
self.input_files["one_degree_id"],\
self.input_files["model_cell_area"],\
self.tmpDir))
# object for reporting at coarse resolution (i.e. one arc degree - grace resolution)
self.output = OutputNetcdf(self.input_files["one_degree_id"], self.input_files["model_cell_area"])
# preparing the netcdf file at coarse resolution:
self.output.createNetCDF(self.output_files['one_degree_tws']['model'], "pcrglobwb_tws","m")
#
# edit some attributes:
attributeDictionary = {}
attributeDictionary['description'] = "One degree resolution total water storage (tws), upscaled from PCR-GLOBWB result. "
self.output.changeAtrribute(self.output_files['one_degree_tws']['model'],\
attributeDictionary)
def __init__(self, input_netcdf,\
output_netcdf,\
modelTime,\
tmpDir = "/dev/shm/"):
DynamicModel.__init__(self)
self.input_netcdf = input_netcdf
self.output_netcdf = output_netcdf
self.tmpDir = tmpDir
self.modelTime = modelTime
# a dictionary contains input clone properties (based on the input netcdf file)
#~ self.input_clone = vos.netcdfCloneAttributes(self.input_netcdf['file_name'],\
#~ np.round(self.input_netcdf['cell_resolution']*60.,1),\
#~ True)
pcr.setclone(self.input_netcdf['clone_file'])
self.input_clone = {}
self.input_clone['cellsize'] = pcr.clone().cellSize()
self.input_clone['rows'] = int(pcr.clone().nrRows())
self.input_clone['cols'] = int(pcr.clone().nrCols())
self.input_clone['xUL'] = round(pcr.clone().west(), 2)
self.input_clone['yUL'] = round(pcr.clone().north(), 2)
# resampling factor: ratio between output and input resolutions
self.resample_factor = self.output_netcdf["cell_resolution"]/\
self.input_netcdf['cell_resolution']
# clone map
if self.resample_factor > 1.0: # upscaling
# the resample factor must be a rounded value without decimal
self.resample_factor = round(self.resample_factor)
# output clone properties
self.output_netcdf['rows' ] = int(round(float(self.input_clone['rows'])/float(self.resample_factor)))
self.output_netcdf['cols' ] = int(round(float(self.input_clone['cols'])/float(self.resample_factor)))
self.output_netcdf['cellsize'] = self.output_netcdf["cell_resolution"]
self.output_netcdf['xUL' ] = self.input_clone['xUL']
self.output_netcdf['yUL' ] = self.input_clone['yUL']
# get the unique ids for the output resolution
# - use the clone for the output resolution (only for a temporary purpose)
pcr.setclone(self.output_netcdf['rows' ],
self.output_netcdf['cols' ],
self.output_netcdf['cellsize'],
self.output_netcdf['xUL' ],
self.output_netcdf['yUL' ])
# - unique_ids in a numpy object
cell_unique_ids = pcr.pcr2numpy(pcr.scalar(pcr.uniqueid(pcr.boolean(1.))),vos.MV)
# the remaining pcraster calculations are performed at the input resolution
pcr.setclone(self.input_clone['rows' ],
self.input_clone['cols' ],
self.input_clone['cellsize'],
self.input_clone['xUL' ],
self.input_clone['yUL' ])
# clone map file
self.clone_map_file = self.input_netcdf['clone_file']
# cell unique ids in a pcraster object
self.unique_ids = pcr.nominal(pcr.numpy2pcr(pcr.Scalar,
vos.regridData2FinerGrid(self.resample_factor,cell_unique_ids, vos.MV), vos.MV))
# cell area (m2)
self.cell_area = vos.readPCRmapClone(\
self.input_netcdf["cell_area"],\
self.clone_map_file,\
self.tmpDir)
else: # downscaling / resampling to smaller cell length
# all pcraster calculations are performed at the output resolution
pcr.setclone(self.output_netcdf['rows' ],
self.output_netcdf['cols' ],
self.output_netcdf['cellsize'],
self.output_netcdf['xUL' ],
self.output_netcdf['yUL' ])
# clone map file
self.clone_map_file = self.output_netcdf['clone_file']
# an object for netcdf reporting
self.output = OutputNetcdf(mapattr_dict = self.output_netcdf,\
cloneMapFileName = None,\
netcdf_format = self.output_netcdf['format'],\
netcdf_zlib = self.output_netcdf['zlib'],\
netcdf_attribute_dict = self.output_netcdf['netcdf_attribute'],\
netcdf_attribute_description = None)
# preparing the netcdf file at coarse resolution:
self.output.createNetCDF(self.output_netcdf['file_name'],\
self.output_netcdf['variable_name'],\
self.output_netcdf['variable_unit'])