def Create_Clusters_And_Connections(workspace, wbd, output, input_dir, nhru,
info, hydrobloks_info):
print "Creating and curating the covariates"
(covariates, mask) = Create_and_Curate_Covariates(wbd)
#Determine the HRUs (clustering if semidistributed; grid cell if fully distributed)
print "Computing the HRUs"
if hydrobloks_info['model_type'] == 'semi':
(cluster_ids,
nhru) = Compute_HRUs_Semidistributed_Kmeans(covariates, mask, nhru,
hydrobloks_info)
elif hydrobloks_info['model_type'] == 'full':
nhru = np.sum(mask == True)
hydrobloks_info['nhru'] = nhru
(cluster_ids, ) = Compute_HRUs_Fulldistributed(covariates, mask, nhru)
#Create the netcdf file
file_netcdf = hydrobloks_info['input_file']
hydrobloks_info['input_fp'] = nc.Dataset(file_netcdf,
'w',
format='NETCDF4')
#Retrieve some metadata
metadata = gdal_tools.retrieve_metadata(wbd['files']['mask'])
resx = metadata['resx']
#Create the dimensions (netcdf)
idate = hydrobloks_info['idate']
fdate = hydrobloks_info['fdate']
dt = hydrobloks_info['dt']
ntime = 24 * 3600 * ((fdate - idate).days + 1) / dt
nhsu = hydrobloks_info['nhru']
hydrobloks_info['input_fp'].createDimension('hsu', nhsu)
hydrobloks_info['input_fp'].createDimension('time', ntime)
#Create the groups (netcdf)
hydrobloks_info['input_fp'].createGroup('meteorology')
#Prepare the flow matrix (dynamic topmodel)
print "Calculating the flow matrix"
(flow_matrix, outlet) = Calculate_Flow_Matrix(covariates, cluster_ids,
nhru)
#Prepare the hru connections matrix (darcy clusters)
cmatrix = Calculate_HRU_Connections_Matrix(covariates, cluster_ids, nhru,
resx)
#Define the metadata
metadata = gdal_tools.retrieve_metadata(wbd['files']['ti'])
#Make the output dictionary for the basin
OUTPUT = {
'hsu': {},
'metadata': metadata,
'mask': mask,
'flow_matrix': flow_matrix,
'cmatrix': cmatrix
}
OUTPUT['outlet'] = outlet
#Remember the map of hrus
OUTPUT['hsu_map'] = cluster_ids
#Assign the model parameters
print "Assigning the model parameters"
if hydrobloks_info['model_type'] == 'semi':
OUTPUT = Assign_Parameters_Semidistributed(covariates, metadata,
hydrobloks_info, OUTPUT,
cluster_ids, mask)
elif hydrobloks_info['model_type'] == 'full':
OUTPUT = Assign_Parameters_Fulldistributed(covariates, metadata,
hydrobloks_info, OUTPUT,
cluster_ids, mask)
#Add the new number of clusters
OUTPUT['nhru'] = nhru
OUTPUT['mask'] = mask
return OUTPUT
def Create_Clusters_And_Connections(workspace,wbd,output,input_dir,nclusters,ncores,info,hydrobloks_info):
print "Creating and curating the covariates"
(covariates,mask) = Create_and_Curate_Covariates(wbd)
#Determine the HRUs (clustering if semidistributed; grid cell if fully distributed)
print "Computing the HRUs"
if hydrobloks_info['model_type'] == 'semi':
if hydrobloks_info['clustering_type'] == 'kmeans':
(cluster_ids,nclusters) = Compute_HRUs_Semidistributed_Kmeans(covariates,mask,nclusters,hydrobloks_info)
elif hydrobloks_info['clustering_type'] == 'hillslope':
(cluster_ids,nclusters) = Compute_HRUs_Semidistributed_Hillslope(covariates,mask,nclusters,hydrobloks_info)
elif hydrobloks_info['clustering_type'] == 'basin':
(cluster_ids,nclusters) = Compute_HRUs_Semidistributed_Basin(covariates,mask,nclusters,hydrobloks_info)
hydrobloks_info['nclusters'] = nclusters
elif hydrobloks_info['model_type'] == 'full':
nclusters = np.sum(mask == True)
hydrobloks_info['nclusters'] = nclusters
(cluster_ids,) = Compute_HRUs_Fulldistributed(covariates,mask,nclusters)
#Create the netcdf file
file_netcdf = hydrobloks_info['input_file']
hydrobloks_info['input_fp'] = nc.Dataset(file_netcdf, 'w', format='NETCDF4')
#Create the dimensions (netcdf)
idate = hydrobloks_info['idate']
fdate = hydrobloks_info['fdate']
dt = hydrobloks_info['dt']
ntime = 24*3600*((fdate - idate).days+1)/dt
nhsu = hydrobloks_info['nclusters']
hydrobloks_info['input_fp'].createDimension('hsu',nhsu)
hydrobloks_info['input_fp'].createDimension('time',ntime)
#Create the groups (netcdf)
hydrobloks_info['input_fp'].createGroup('meteorology')
#Prepare the flow matrix
print "Calculating the flow matrix"
(flow_matrix,outlet) = Calculate_Flow_Matrix(covariates,cluster_ids,nclusters)
#Define the metadata
metadata = gdal_tools.retrieve_metadata(wbd['files']['ti'])
#Make the output dictionary for the basin
OUTPUT = {'hsu':{},'metadata':metadata,'mask':mask,'flow_matrix':flow_matrix}
OUTPUT['outlet'] = outlet
#Determine outlet cell
#covariates['carea'][mask == False] = np.nan
#outlet_idx = np.where(covariates['carea'] == np.max(covariates['carea'][np.isnan(covariates['carea']) == 0]))
#outlet_idx = [int(outlet_idx[0]),int(outlet_idx[1])]
#OUTPUT['outlet'] = {'idx':outlet_idx,'hsu':cluster_ids[outlet_idx[0],outlet_idx[1]]}
#Remember the map of hrus
OUTPUT['hsu_map'] = cluster_ids
#Assign the model parameters
print "Assigning the model parameters"
if hydrobloks_info['model_type'] == 'semi':
OUTPUT = Assign_Parameters_Semidistributed(covariates,metadata,hydrobloks_info,OUTPUT,cluster_ids,mask)
elif hydrobloks_info['model_type'] == 'full':
OUTPUT = Assign_Parameters_Fulldistributed(covariates,metadata,hydrobloks_info,OUTPUT,cluster_ids,mask)
#Create the soil parameters file
#print "Creating the soil file"
Create_Soils_File(hydrobloks_info,OUTPUT,input_dir)
#soils_lookup = Create_Soils_File(hydrobloks_info,OUTPUT,input_dir)
#Add the new number of clusters
OUTPUT['nclusters'] = nclusters
OUTPUT['mask'] = mask
return OUTPUT
def Prepare_Model_Input_Data(hydrobloks_info):
#Prepare the info dictionary
info = {}
#Define the start/end dates
info['time_info'] = {}
info['time_info']['startdate'] = hydrobloks_info['idate']
info['time_info']['enddate'] = hydrobloks_info['fdate']
info['time_info']['dt'] = hydrobloks_info['dt']
#Define the workspace
workspace = hydrobloks_info['workspace']
#Define the model input data directory
input_dir = workspace #'%s/input' % workspace
#Read in the metadata
#file = '%s/workspace_info.pck' % workspace
#wbd = pickle.load(open(file))
#Create the dictionary to hold all of the data
output = {}
#Create the Latin Hypercube (Clustering)
nhru = hydrobloks_info['nhru']
#ncores = hydrobloks_info['ncores']
icatch = hydrobloks_info['icatch']
#Get metadata
md = gdal_tools.retrieve_metadata('%s/mask_latlon.tif' % workspace)
#Prepare the input file
wbd = {}
wbd['bbox'] = {
'minlat': md['miny'],
'maxlat': md['maxy'],
'minlon': md['minx'],
'maxlon': md['maxx'],
'res': abs(md['resx'])
}
wbd['files'] = {
'WLTSMC': '%s/theta1500_ea.tif' % workspace,
'TEXTURE_CLASS': '%s/texture_class_ea.tif' % workspace,
'cslope': '%s/cslope_ea.tif' % workspace,
'MAXSMC': '%s/thetas_ea.tif' % workspace,
'BB': '%s/bb_ea.tif' % workspace,
'DRYSMC': '%s/thetar_ea.tif' % workspace,
'fdir': '%s/fdir_ea.tif' % workspace,
'QTZ': '%s/qtz_ea.tif' % workspace,
'SATDW': '%s/dsat_ea.tif' % workspace,
'REFSMC': '%s/theta33_ea.tif' % workspace,
'mask': '%s/mask_ea.tif' % workspace,
'SATPSI': '%s/psisat_ea.tif' % workspace,
'lc': '%s/lc_ea.tif' % workspace,
'carea': '%s/carea_ea.tif' % workspace,
'ti': '%s/ti_ea.tif' % workspace,
'ndvi': '%s/ndvi_ea.tif' % workspace,
'F11': '%s/f11_ea.tif' % workspace,
'SATDK': '%s/ksat_ea.tif' % workspace,
'dem': '%s/dem_ea.tif' % workspace,
'demns': '%s/demns_ea.tif' % workspace,
'sand': '%s/sand_ea.tif' % workspace,
'clay': '%s/clay_ea.tif' % workspace,
'silt': '%s/silt_ea.tif' % workspace,
'om': '%s/om_ea.tif' % workspace,
'bare30': '%s/bare30_ea.tif' % workspace,
'water30': '%s/water30_ea.tif' % workspace,
'tree30': '%s/tree30_ea.tif' % workspace,
}
wbd['files_meteorology'] = {
'lwdown': '%s/lwdown.nc' % workspace,
'swdown': '%s/swdown.nc' % workspace,
'tair': '%s/tair.nc' % workspace,
'precip': '%s/precip.nc' % workspace,
'psurf': '%s/psurf.nc' % workspace,
'wind': '%s/wind.nc' % workspace,
'spfh': '%s/spfh.nc' % workspace,
}
#Create the clusters and their connections
output = Create_Clusters_And_Connections(workspace, wbd, output, input_dir,
nhru, info, hydrobloks_info)
#Extract the meteorological forcing
print "Preparing the meteorology"
if hydrobloks_info['model_type'] == 'semi':
Prepare_Meteorology_Semidistributed(workspace, wbd, output, input_dir,
info, hydrobloks_info)
elif hydrobloks_info['model_type'] == 'full':
Prepare_Meteorology_Fulldistributed(workspace, wbd, output, input_dir,
info, hydrobloks_info)
#Write out the files to the netcdf file
fp = hydrobloks_info['input_fp']
data = output
#Write out the metadata
grp = fp.createGroup('metadata')
grp.latitude = (wbd['bbox']['minlat'] + wbd['bbox']['maxlat']) / 2
lon = (wbd['bbox']['minlon'] + wbd['bbox']['maxlon']) / 2
if lon < 0: lon += 360
grp.longitude = lon
metadata = gdal_tools.retrieve_metadata(wbd['files']['mask'])
grp.dx = metadata['resx']
#grp.longitude = (360.0+(wbd['bbox']['minlon'] + wbd['bbox']['maxlon'])/2)
#Write the HRU mapping
#CONUS conus_albers metadata
metadata['nodata'] = -9999.0
#Save the conus_albers metadata
grp = fp.createGroup('conus_albers_mapping')
grp.createDimension('nx', metadata['nx'])
grp.createDimension('ny', metadata['ny'])
hmca = grp.createVariable('hmca', 'f4', ('ny', 'nx'))
hmca.gt = metadata['gt']
hmca.projection = metadata['projection']
hmca.description = 'HSU mapping (conus albers)'
hmca.nodata = metadata['nodata']
#Save the conus albers mapping
hsu_map = np.copy(output['hsu_map'])
hsu_map[np.isnan(hsu_map) == 1] = metadata['nodata']
hmca[:] = hsu_map
#Write out the mapping
file_ca = '%s/hsu_mapping_ea.tif' % workspace
gdal_tools.write_raster(file_ca, metadata, hsu_map)
#Map the mapping to regular lat/lon
file_ll = '%s/hsu_mapping_latlon.tif' % workspace
os.system('rm -f %s' % file_ll)
res = wbd['bbox']['res']
minlat = wbd['bbox']['minlat']
minlon = wbd['bbox']['minlon']
maxlat = wbd['bbox']['maxlat']
maxlon = wbd['bbox']['maxlon']
log = '%s/log.txt' % workspace
os.system(
'gdalwarp -tr %.16f %.16f -dstnodata %.16f -t_srs \'+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs \' -te %.16f %.16f %.16f %.16f %s %s >> %s 2>&1'
% (res, res, metadata['nodata'], minlon, minlat, maxlon, maxlat,
file_ca, file_ll, log))
#Write a map for the catchment id
file_icatch = '%s/icatch_latlon.tif' % workspace
metadata = gdal_tools.retrieve_metadata(file_ll)
metadata['nodata'] = -9999.0
tmp = gdal_tools.read_raster(file_ll)
tmp[tmp >= 0] = hydrobloks_info['icatch']
gdal_tools.write_raster(file_icatch, metadata, tmp)
#Retrieve the lat/lon metadata
#metadata = gdal_tools.retrieve_metadata(file_ll)
#metadata['nodata'] = -9999.0
#Save the lat/lon metadata
#grp = fp.createGroup('latlon_mapping')
#grp.createDimension('nlon',metadata['nx'])
#grp.createDimension('nlat',metadata['ny'])
#hmll = grp.createVariable('hmll','f4',('nlat','nlon'))
#hmll.gt = metadata['gt']
#hmll.projection = metadata['projection']
#hmll.description = 'HSU mapping (regular lat/lon)'
#hmll.nodata = metadata['nodata']
#Save the lat/lon mapping
#hsu_map = np.copy(gdal_tools.read_raster(file_ll))
#hsu_map[np.isnan(hsu_map) == 1] = metadata['nodata']
#hmll[:] = hsu_map
#Write the flow matrix
flow_matrix = output['flow_matrix']
nconnections = flow_matrix.data.size
grp = fp.createGroup('flow_matrix')
grp.createDimension('connections_columns', flow_matrix.indices.size)
grp.createDimension('connections_rows', flow_matrix.indptr.size)
grp.createVariable('data', 'f4', ('connections_columns', ))
grp.createVariable('indices', 'f4', ('connections_columns', ))
grp.createVariable('indptr', 'f4', ('connections_rows', ))
grp.variables['data'][:] = flow_matrix.data
grp.variables['indices'][:] = flow_matrix.indices
grp.variables['indptr'][:] = flow_matrix.indptr
#Write the connection matrices
#width
wmatrix = output['cmatrix']['width']
nconnections = wmatrix.data.size
grp = fp.createGroup('wmatrix')
grp.createDimension('connections_columns', wmatrix.indices.size)
grp.createDimension('connections_rows', wmatrix.indptr.size)
grp.createVariable('data', 'f4', ('connections_columns', ))
grp.createVariable('indices', 'f4', ('connections_columns', ))
grp.createVariable('indptr', 'f4', ('connections_rows', ))
grp.variables['data'][:] = wmatrix.data
grp.variables['indices'][:] = wmatrix.indices
grp.variables['indptr'][:] = wmatrix.indptr
#Write the outlet information
outlet = output['outlet']
grp = fp.createGroup('outlet')
full = grp.createGroup('full')
full.createDimension('cell', outlet['full']['hru_org'].size)
full.createVariable('i', 'i4', ('cell', ))
full.createVariable('j', 'i4', ('cell', ))
full.createVariable('hru_org', 'i4', ('cell', ))
full.createVariable('hru_dst', 'i4', ('cell', ))
full.createVariable('d8', 'i4', ('cell', ))
full.variables['i'][:] = outlet['full']['i']
full.variables['j'][:] = outlet['full']['j']
full.variables['hru_org'][:] = outlet['full']['hru_org']
full.variables['hru_dst'][:] = outlet['full']['hru_dst']
full.variables['d8'][:] = outlet['full']['d8']
summary = grp.createGroup('summary')
summary.createDimension('hru', outlet['summary']['hru_org'].size)
summary.createVariable('hru_org', 'i4', ('hru', ))
summary.createVariable('hru_dst', 'i4', ('hru', ))
summary.createVariable('counts', 'i4', ('hru', ))
summary.variables['hru_org'][:] = outlet['summary']['hru_org']
summary.variables['hru_dst'][:] = outlet['summary']['hru_dst']
summary.variables['counts'][:] = outlet['summary']['counts']
#outlet = {'full':{'i':outlet_icoord,'j':outlet_jcoord,'hru_org':outlet_hru_org,'hru_dst':outlet_hru_dst,'d8':outlet_d8},
# 'summary':{'hru_org':outlet_hru_org_summary,'hru_dst':outlet_hru_dst_summary,'counts':counts}}
#Write the model parameters
grp = fp.createGroup('parameters')
vars = [
'slope', 'area_pct', 'land_cover', 'channel', 'dem',
'soil_texture_class', 'ti', 'carea', 'area', 'BB', 'F11', 'SATPSI',
'SATDW', 'QTZ', 'WLTSMC', 'MAXSMC', 'DRYSMC', 'REFSMC', 'SATDK',
'mannings', 'm', 'psoil', 'pksat', 'sdmax'
]
for var in vars:
grp.createVariable(var, 'f4', ('hsu', ))
grp.variables[var][:] = data['hsu'][var]
#Write other metadata
#grp = fp.createGroup('metadata')
#grp.outlet_hsu = data['outlet']['hsu']
#Remove info from output
del output['hsu']
#Add in the catchment info
output['wbd'] = wbd
#Close the file
fp.close()
return output
def Prepare_Model_Input_Data(hydrobloks_info):
#Prepare the info dictionary
info = {}
#Define the start/end dates
info['time_info'] = {}
info['time_info']['startdate'] = hydrobloks_info['idate']
info['time_info']['enddate'] = hydrobloks_info['fdate']
#Define the workspace
workspace = hydrobloks_info['workspace']
#Define the model input data directory
input_dir = workspace#'%s/input' % workspace
#Read in the metadata
file = '%s/workspace_info.pck' % workspace
wbd = pickle.load(open(file))
#Create the dictionary to hold all of the data
output = {}
#Create the Latin Hypercube (Clustering)
nclusters = hydrobloks_info['nclusters']
ncores = hydrobloks_info['ncores']
icatch = hydrobloks_info['icatch']
#Prepare the input file
wbd['files'] = {
'WLTSMC':'%s/WLTSMC.tif' % workspace,
'TEXTURE_CLASS':'%s/TEXTURE_CLASS.tif' % workspace,
'cslope':'%s/cslope.tif' % workspace,
'MAXSMC':'%s/MAXSMC.tif' % workspace,
'BB':'%s/BB.tif' % workspace,
'DRYSMC':'%s/DRYSMC.tif' % workspace,
'fdir':'%s/fdir.tif' % workspace,
'QTZ':'%s/QTZ.tif' % workspace,
'SATDW':'%s/SATDW.tif' % workspace,
'REFSMC':'%s/REFSMC.tif' % workspace,
'mask':'%s/mask.tif' % workspace,
'channels':'%s/channels.tif' % workspace,
#'SATDW':'%s/SATDW.tif' % workspace,
#'REFSMC':'%s/REFSMC.tif' % workspace,
'SATPSI':'%s/SATPSI.tif' % workspace,
'nlcd':'%s/nlcd.tif' % workspace,
'carea':'%s/carea.tif' % workspace,
'ti':'%s/ti.tif' % workspace,
'ndvi':'%s/ndvi.tif' % workspace,
'F11':'%s/F11.tif' % workspace,
'SATDK':'%s/SATDK.tif' % workspace,
'dem':'%s/dem.tif' % workspace,
'demns':'%s/demns.tif' % workspace,
'strahler':'%s/strahler.tif' % workspace,
#'qbase':'%s/qbase.tif' % workspace
}
wbd['files_meteorology'] = {
'dlwrf':'%s/nldas/dlwrf/dlwrf.nc' % workspace,
'dswrf':'%s/nldas/dswrf/dswrf.nc' % workspace,
'tair':'%s/nldas/tair/tair.nc' % workspace,
'prec':'%s/nldas/prec/prec.nc' % workspace,
'pres':'%s/nldas/pres/pres.nc' % workspace,
'wind':'%s/nldas/wind/wind.nc' % workspace,
'rh':'%s/nldas/rh/rh.nc' % workspace,
'apcpsfc':'%s/stageiv/apcpsfc/apcpsfc.nc' % workspace,
}
#Create the clusters and their connections
output = Create_Clusters_And_Connections(workspace,wbd,output,input_dir,nclusters,ncores,info,hydrobloks_info)
#Extract the meteorological forcing
print "Preparing the meteorology"
if hydrobloks_info['model_type'] == 'semi':
Prepare_Meteorology_Semidistributed(workspace,wbd,output,input_dir,info,hydrobloks_info)
elif hydrobloks_info['model_type'] == 'full':
Prepare_Meteorology_Fulldistributed(workspace,wbd,output,input_dir,info,hydrobloks_info)
#Write out the files to the netcdf file
fp = hydrobloks_info['input_fp']
data = output
#Write out the metadata
grp = fp.createGroup('metadata')
grp.latitude = (wbd['bbox']['minlat'] + wbd['bbox']['maxlat'])/2
grp.longitude = (360.0+(wbd['bbox']['minlon'] + wbd['bbox']['maxlon'])/2)
#Write the HRU mapping
#CONUS conus_albers metadata
metadata = gdal_tools.retrieve_metadata(wbd['files']['mask'])
metadata['nodata'] = -9999.0
#Save the conus_albers metadata
grp = fp.createGroup('conus_albers_mapping')
grp.createDimension('nx',metadata['nx'])
grp.createDimension('ny',metadata['ny'])
hmca = grp.createVariable('hmca','f4',('ny','nx'))
hmca.gt = metadata['gt']
hmca.projection = metadata['projection']
hmca.description = 'HSU mapping (conus albers)'
hmca.nodata = metadata['nodata']
#Save the conus albers mapping
hsu_map = np.copy(output['hsu_map'])
hsu_map[np.isnan(hsu_map) == 1] = metadata['nodata']
hmca[:] = hsu_map
if hydrobloks_info['create_mask_flag'] == True:
#Write out the mapping
file_ca = '%s/hsu_mapping_conus_albers.tif' % workspace
gdal_tools.write_raster(file_ca,metadata,hsu_map)
#Map the mapping to regular lat/lon
file_ll = '%s/hsu_mapping_latlon.tif' % workspace
os.system('rm -f %s' % file_ll)
res = wbd['bbox']['res']
minlat = wbd['bbox']['minlat']
minlon = wbd['bbox']['minlon']
maxlat = wbd['bbox']['maxlat']
maxlon = wbd['bbox']['maxlon']
log = '%s/log.txt' % workspace
os.system('gdalwarp -tr %.16f %.16f -dstnodata %.16f -t_srs EPSG:4326 -s_srs EPSG:102039 -te %.16f %.16f %.16f %.16f %s %s >> %s 2>&1' % (res,res,metadata['nodata'],minlon,minlat,maxlon,maxlat,file_ca,file_ll,log))
#Write a map for the catchment id
file_icatch = '%s/icatch_latlon.tif' % workspace
metadata = gdal_tools.retrieve_metadata(file_ll)
metadata['nodata'] = -9999.0
tmp = gdal_tools.read_raster(file_ll)
tmp[tmp >= 0] = hydrobloks_info['icatch']
gdal_tools.write_raster(file_icatch,metadata,tmp)
#Add the lat/lon mapping
#Retrieve the lat/lon metadata
metadata = gdal_tools.retrieve_metadata(file_ll)
metadata['nodata'] = -9999.0
#Save the lat/lon metadata
grp = fp.createGroup('latlon_mapping')
grp.createDimension('nlon',metadata['nx'])
grp.createDimension('nlat',metadata['ny'])
hmll = grp.createVariable('hmll','f4',('nlat','nlon'))
hmll.gt = metadata['gt']
hmll.projection = metadata['projection']
hmll.description = 'HSU mapping (regular lat/lon)'
hmll.nodata = metadata['nodata']
#Save the lat/lon mapping
hsu_map = np.copy(gdal_tools.read_raster(file_ll))
hsu_map[np.isnan(hsu_map) == 1] = metadata['nodata']
hmll[:] = hsu_map
#Write the flow matrix
flow_matrix = output['flow_matrix']
nconnections = flow_matrix.data.size
grp = fp.createGroup('flow_matrix')
grp.createDimension('connections_columns',flow_matrix.indices.size)
grp.createDimension('connections_rows',flow_matrix.indptr.size)
grp.createVariable('data','f4',('connections_columns',))
grp.createVariable('indices','f4',('connections_columns',))
grp.createVariable('indptr','f4',('connections_rows',))
grp.variables['data'][:] = flow_matrix.data
grp.variables['indices'][:] = flow_matrix.indices
grp.variables['indptr'][:] = flow_matrix.indptr
#Write the outlet information
outlet = output['outlet']
grp = fp.createGroup('outlet')
full = grp.createGroup('full')
full.createDimension('cell',outlet['full']['hru_org'].size)
full.createVariable('i','i4',('cell',))
full.createVariable('j','i4',('cell',))
full.createVariable('hru_org','i4',('cell',))
full.createVariable('hru_dst','i4',('cell',))
full.createVariable('d8','i4',('cell',))
full.variables['i'][:] = outlet['full']['i']
full.variables['j'][:] = outlet['full']['j']
full.variables['hru_org'][:] = outlet['full']['hru_org']
full.variables['hru_dst'][:] = outlet['full']['hru_dst']
full.variables['d8'][:] = outlet['full']['d8']
summary = grp.createGroup('summary')
summary.createDimension('hru',outlet['summary']['hru_org'].size)
summary.createVariable('hru_org','i4',('hru',))
summary.createVariable('hru_dst','i4',('hru',))
summary.createVariable('counts','i4',('hru',))
summary.variables['hru_org'][:] = outlet['summary']['hru_org']
summary.variables['hru_dst'][:] = outlet['summary']['hru_dst']
summary.variables['counts'][:] = outlet['summary']['counts']
#outlet = {'full':{'i':outlet_icoord,'j':outlet_jcoord,'hru_org':outlet_hru_org,'hru_dst':outlet_hru_dst,'d8':outlet_d8},
# 'summary':{'hru_org':outlet_hru_org_summary,'hru_dst':outlet_hru_dst_summary,'counts':counts}}
#Write the model parameters
grp = fp.createGroup('parameters')
vars = ['slope','area_pct','land_cover','channel',
'dem','soil_texture_class','ti','carea','area',
'BB','F11','SATPSI','SATDW','QTZ',
'WLTSMC','MAXSMC','DRYSMC','REFSMC','SATDK',
'mannings','m','psoil','pksat','sdmax']
for var in vars:
grp.createVariable(var,'f4',('hsu',))
grp.variables[var][:] = data['hsu'][var]
#Write other metadata
#grp = fp.createGroup('metadata')
#grp.outlet_hsu = data['outlet']['hsu']
#Remove info from output
del output['hsu']
#Add in the catchment info
output['wbd'] = wbd
#Close the file
fp.close()
return output
