def initial(self):
#####################
# * initial section #
#####################
#-constants
# betaQ [-]: constant of kinematic wave momentum equation
self.betaQ = 0.6
#-channel LDD
self.channelLDD= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ldd(5),self.LDD)
#-channel area and storage
self.channelArea = self.channelWidth * self.channelLength
self.channelStorageCapacity= pcr.ifthenelse(self.waterBodies.distribution == 0,\
self.channelArea*self.channelDepth,pcr.scalar(0.))
#-basin outlets
self.basinOutlet = pcr.pit(self.LDD) != 0
#-read initial conditions
self.Q = clippedRead.get(self.QIniMap)
self.actualStorage = clippedRead.get(self.actualStorageIniMap)
self.actualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(self.actualStorage,self.waterBodies.distribution),0),\
self.actualStorage)
self.waterBodies.actualStorage = self.waterBodies.retrieveMapValue(
self.actualStorage)
#-update targets of average and bankful discharge
self.waterBodies.averageQ = self.waterBodies.retrieveMapValue(
self.averageQ)
self.waterBodies.bankfulQ = self.waterBodies.retrieveMapValue(
self.bankfulQ)
#-return the parameters for the kinematic wave,
# including alpha, wetted area, flood fraction, flood volume and depth
# and the corresponding land area
floodedFraction,floodedDepth,\
self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask)
self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\
self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight())
self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask)
self.landFraction = pcr.max(0., 1. - self.waterFraction)
#-update on velocity and check on Q - NOTE: does not work in case of reservoirs!
self.flowVelocity = pcr.ifthenelse(self.wettedArea > 0,
self.Q / self.wettedArea, 0.)
pcr.report(
self.flowVelocity,
pcrm.generateNameT(flowVelocityFileName,
0).replace('.000', '.ini'))
#-setting initial values for specific runoff and surface water extraction
self.landSurfaceQ = pcr.scalar(0.)
self.potWaterSurfaceQ = pcr.scalar(0.)
self.surfaceWaterExtraction = pcr.scalar(0.)
#-budget check: setting initial values for cumulative discharge and
# net cumulative input, including initial storage [m3]
self.totalDischarge = pcr.scalar(0.)
self.cumulativeDeltaStorage = pcr.catchmenttotal(
self.actualStorage, self.LDD)
def initial(self):
#####################
# * initial section #
#####################
#-constants
# betaQ [-]: constant of kinematic wave momentum equation
self.betaQ= 0.6
#-channel LDD
self.channelLDD= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ldd(5),self.LDD)
#-channel area and storage
self.channelArea= self.channelWidth*self.channelLength
self.channelStorageCapacity= pcr.ifthenelse(self.waterBodies.distribution == 0,\
self.channelArea*self.channelDepth,pcr.scalar(0.))
#-basin outlets
self.basinOutlet= pcr.pit(self.LDD) != 0
#-read initial conditions
self.Q= clippedRead.get(self.QIniMap)
self.actualStorage= clippedRead.get(self.actualStorageIniMap)
self.actualStorage= pcr.ifthenelse(self.waterBodies.distribution != 0,\
pcr.ifthenelse(self.waterBodies.location != 0,\
pcr.areatotal(self.actualStorage,self.waterBodies.distribution),0),\
self.actualStorage)
self.waterBodies.actualStorage= self.waterBodies.retrieveMapValue(self.actualStorage)
#-update targets of average and bankful discharge
self.waterBodies.averageQ= self.waterBodies.retrieveMapValue(self.averageQ)
self.waterBodies.bankfulQ= self.waterBodies.retrieveMapValue(self.bankfulQ)
#-return the parameters for the kinematic wave,
# including alpha, wetted area, flood fraction, flood volume and depth
# and the corresponding land area
floodedFraction,floodedDepth,\
self.wettedArea,self.alphaQ= self.kinAlphaComposite(self.actualStorage,self.floodplainMask)
self.wettedArea= self.waterBodies.returnMapValue(self.wettedArea,\
self.waterBodies.channelWidth+2.*self.waterBodies.updateWaterHeight())
self.waterFraction= pcr.ifthenelse(self.waterBodies.distribution == 0,\
pcr.max(self.waterFractionMask,floodedFraction),self.waterFractionMask)
self.landFraction= pcr.max(0.,1.-self.waterFraction)
#-update on velocity and check on Q - NOTE: does not work in case of reservoirs!
self.flowVelocity= pcr.ifthenelse(self.wettedArea > 0,self.Q/self.wettedArea,0.)
pcr.report(self.flowVelocity,pcrm.generateNameT(flowVelocityFileName,0).replace('.000','.ini'))
#-setting initial values for specific runoff and surface water extraction
self.landSurfaceQ= pcr.scalar(0.)
self.potWaterSurfaceQ= pcr.scalar(0.)
self.surfaceWaterExtraction= pcr.scalar(0.)
#-budget check: setting initial values for cumulative discharge and
# net cumulative input, including initial storage [m3]
self.totalDischarge= pcr.scalar(0.)
self.cumulativeDeltaStorage= pcr.catchmenttotal(self.actualStorage,self.LDD)
def main():
# output folder
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# tmp folder
tmp_folder = out_folder + "/tmp/"
if os.path.exists(tmp_folder): shutil.rmtree(tmp_folder)
os.makedirs(tmp_folder)
# set the clone map
print("set the clone map")
pcr.setclone(global_clone_map_file)
# read ldd map
print("define the ldd")
# ~ ldd_map = pcr.readmap(global_ldd_inp_file)
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(vos.readPCRmapClone(v = global_ldd_inp_file, \
cloneMapFileName = global_clone_map_file, \
tmpDir = tmp_folder, \
absolutePath = None, \
isLddMap = True, \
cover = None, \
isNomMap = False))))
# define the landmask
if landmask_map_file == None:
print("define the landmask based on the ldd input")
# ~ landmask = pcr.defined(pcr.readmap(global_ldd_inp_file))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
else:
print("define the landmask based on the input landmask_map_file")
landmask = pcr.readmap(landmask_map_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
ldd_map = pcr.lddrepair(pcr.lddrepair(pcr.ldd(ldd_map)))
landmask = pcr.defined(ldd_map)
landmask = pcr.ifthen(landmask, landmask)
# save ldd files used
# - global ldd
cmd = "cp " + str(global_ldd_inp_file) + " ."
print(cmd)
os.system(cmd)
# - ldd map that is used
pcr.report(ldd_map, "lddmap_used.map")
# make catchment map
print("make catchment map")
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
# read global subdomain file
print("read global subdomain file")
global_subdomain_map = vos.readPCRmapClone(
v=global_subdomain_file,
cloneMapFileName=global_clone_map_file,
tmpDir=tmp_folder,
absolutePath=None,
isLddMap=False,
cover=None,
isNomMap=True)
# set initial subdomain
print("assign subdomains to all catchments")
subdomains_initial = pcr.areamajority(global_subdomain_map, catchment_map)
subdomains_initial = pcr.ifthen(landmask, subdomains_initial)
pcr.aguila(subdomains_initial)
pcr.report(subdomains_initial, "global_subdomains_initial.map")
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
print("Checking all subdomains, avoid too large subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])
# clone code that will be assigned
assigned_number = 0
subdomains_final = pcr.ifthen(
pcr.scalar(subdomains_initial) < -7777, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
print(msg)
mask_selected_boolean = pcr.ifthen(subdomains_initial == nr,
pcr.boolean(1.0))
process_this_clone = False
if pcr.cellvalue(pcr.mapmaximum(pcr.scalar(mask_selected_boolean)), 1,
1)[0] > 0:
process_this_clone = True
# ~ if nr == 1: pcr.aguila(mask_selected_boolean)
# - initial check value
check_ok = True
if process_this_clone:
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
# ~ print(str(area_in_degree2))
# check whether the size of bounding box is ok
reference_area_in_degree2 = 2500.
if area_in_degree2 > 1.50 * reference_area_in_degree2:
check_ok = False
if (xmax - xmin) > 10 * (ymax - ymin): check_ok = False
# ~ # ignore checking
# ~ check_ok = True
if check_ok == True and process_this_clone == True:
msg = "Clump is not needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
if check_ok == False and process_this_clone == True:
msg = "Clump is needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# make clump
clump_ids = pcr.nominal(pcr.clump(mask_selected_boolean))
# merge clumps that are close together
clump_ids_window_majority = pcr.windowmajority(clump_ids, 10.0)
clump_ids = pcr.areamajority(clump_ids_window_majority, clump_ids)
# ~ pcr.aguila(clump_ids)
# minimimum and maximum values
min_clump_id = int(
pcr.cellvalue(pcr.mapminimum(pcr.scalar(clump_ids)), 1)[0])
max_clump_id = int(
pcr.cellvalue(pcr.mapmaximum(pcr.scalar(clump_ids)), 1)[0])
for clump_id in range(min_clump_id, max_clump_id + 1, 1):
msg = "Processing the clump %s of %s from the landmask %s" % (
str(clump_id), str(max_clump_id), str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# identify mask based on the clump
mask_selected_boolean_from_clump = pcr.ifthen(
clump_ids == pcr.nominal(clump_id), mask_selected_boolean)
mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean_from_clump,
mask_selected_boolean_from_clump)
# check whether the clump is empty
check_mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean, mask_selected_boolean_from_clump)
check_if_empty = float(
pcr.cellvalue(
pcr.mapmaximum(
pcr.scalar(
pcr.defined(
check_mask_selected_boolean_from_clump))),
1)[0])
if check_if_empty == 0.0:
msg = "Map is empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
else:
msg = "Map is NOT empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(
mask_selected_boolean_from_clump,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
# ~ # kill all aguila processes if exist
# ~ os.system('killall aguila')
pcr.aguila(subdomains_final)
print("")
print("")
print("")
print("The subdomain map is READY.")
pcr.report(subdomains_final, "global_subdomains_final.map")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
print(num_of_masks)
print("")
print("")
print("")
print("Making the clone and landmask maps for all subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
# clone and mask folders
clone_folder = out_folder + "/clone/"
if os.path.exists(clone_folder): shutil.rmtree(clone_folder)
os.makedirs(clone_folder)
mask_folder = out_folder + "/mask/"
if os.path.exists(mask_folder): shutil.rmtree(mask_folder)
os.makedirs(mask_folder)
print("")
print("")
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the subdomain %s" % (str(nr))
print(msg)
# set the global clone
pcr.setclone(global_clone_map_file)
mask_selected_boolean = pcr.ifthen(subdomains_final == nr,
pcr.boolean(1.0))
mask_selected_nominal = pcr.ifthen(subdomains_final == nr,
pcr.nominal(nr))
mask_file = "mask/mask_%s.map" % (str(nr))
pcr.report(mask_selected_nominal, mask_file)
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
print(
str(nr) + " ; " + str(area_in_degree2) + " ; " +
str((xmax - xmin)) + " ; " + str((ymax - ymin)))
# cellsize in arcdegree
cellsize = cellsize_in_arcmin / 60.
# number of rows and cols
num_rows = int(round(ymax - ymin) / cellsize)
num_cols = int(round(xmax - xmin) / cellsize)
# make the clone map using mapattr
clonemap_mask_file = "clone/clonemap_mask_%s.map" % (str(nr))
cmd = "mapattr -s -R %s -C %s -B -P yb2t -x %s -y %s -l %s %s" % (
str(num_rows), str(num_cols), str(xmin), str(ymax), str(cellsize),
clonemap_mask_file)
print(cmd)
os.system(cmd)
# set the local landmask for the clump
pcr.setclone(clonemap_mask_file)
local_mask = vos.readPCRmapClone(v = mask_file, \
cloneMapFileName = clonemap_mask_file,
tmpDir = tmp_folder, \
absolutePath = None, isLddMap = False, cover = None, isNomMap = True)
local_mask_boolean = pcr.defined(local_mask)
local_mask_boolean = pcr.ifthen(local_mask_boolean, local_mask_boolean)
pcr.report(local_mask_boolean, mask_file)
print("")
print("")
print("")
print(num_of_masks)
def generate_hydro_datasets(path, output_dir, step):
print(path)
file_name = os.path.splitext(os.path.basename(path))[0]
map_path = output_dir + '/' + file_name + '.map'
path_prefix = map_path[:-14]
if step == 'ldd':
cmd = u'gdal_translate -a_nodata -9999 -of PCRaster -ot Float32 ' + path + ' ' + map_path
print(cmd)
subprocess.call(cmd, shell=True)
# slope = pcr.slope(dem)
# pcr.report(slope, path_prefix + '_slope.map')
# pcr.setglobaloption("lddin")
if step == 'ldd':
dem = pcr.readmap(map_path)
print("Computing LDD ...")
# enable pit filling
ldd = pcr.lddcreate(dem, 9999999, 9999999, 9999999, 9999999)
pcr.report(ldd, path_prefix + '_ldd.map')
return
elif step == 'ldddem':
dem = pcr.readmap(map_path)
print("Computing LDD DEM ...")
dem_pitfilled = pcr.lddcreatedem(dem, 9999999, 9999999, 9999999,
9999999)
dem_diff = dem_pitfilled - dem
pcr.report(dem_diff, path_prefix + '_dem_pits_diff.map')
return
# print("Computing LDD without pit filling ...")
# ldd_pits = pcr.lddcreate(dem, 0, 0, 0, 0)
# pcr.report(ldd_pits, path_prefix + '_ldd_with_pits.map')
# print("Computing pits ...")
# pits = pcr.pit(ldd_pits)
# pcr.report(pits, path_prefix + '_pits.map')
if step == 'fa':
ldd = pcr.readmap(path_prefix + '_ldd.map')
print("Computing flow accumulation ...")
fa = pcr.accuflux(ldd, 1)
pcr.report(fa, path_prefix + '_fa.map')
return
if step == 'catchments':
ldd = pcr.readmap(path_prefix + '_ldd.map')
print("Delineating catchments ...")
catchments = pcr.catchment(ldd, pcr.pit(ldd))
pcr.report(catchments, path_prefix + '_catchments.map')
return
if step == 'stream_order':
ldd = pcr.readmap(path_prefix + '_ldd.map')
print("Computing stream order ...")
stream_order = pcr.streamorder(ldd)
pcr.report(stream_order, path_prefix + '_streamorder.map')
return
if step == 'stream':
ldd = pcr.readmap(path_prefix + '_ldd.map')
accuThreshold = 100
print("Computing stream ...")
stream = pcr.ifthenelse(
pcr.accuflux(ldd, 1) >= accuThreshold, pcr.boolean(1),
pcr.boolean(0))
pcr.report(stream, path_prefix + '_stream.map')
return
if step == 'height_river':
print("Computing heigh_river ...")
stream = pcr.readmap(path_prefix + '_stream.map')
dem = pcr.readmap(map_path)
height_river = pcr.ifthenelse(stream, pcr.ordinal(dem), 0)
pcr.report(height_river, path_prefix + '_height_river.map')
return
if step == 'up_elevation':
print("Computing up_elevation ...")
height_river = pcr.readmap(path_prefix + '_height_river.map')
ldd = pcr.readmap(path_prefix + '_ldd.map')
up_elevation = pcr.scalar(pcr.subcatchment(ldd, height_river))
pcr.report(up_elevation, path_prefix + '_up_elevation.map')
return
if step == 'hand':
print("Computing HAND ...")
dem = pcr.readmap(map_path)
up_elevation = pcr.readmap(path_prefix + '_up_elevation.map')
hand = pcr.max(dem - up_elevation, 0)
pcr.report(hand, path_prefix + '_hand.map')
return
if step == 'dand':
print("Computing DAND ...")
ldd = pcr.readmap(path_prefix + '_ldd.map')
stream = pcr.readmap(path_prefix + '_stream.map')
dist = pcr.ldddist(ldd, stream, 1)
pcr.report(dist, path_prefix + '_dist.map')
return
if step == 'fa_river':
print("Computing FA river ...")
fa = pcr.readmap(path_prefix + '_fa.map')
stream = pcr.readmap(path_prefix + '_stream.map')
fa_river = pcr.ifthenelse(stream, pcr.ordinal(fa), 0)
pcr.report(fa_river, path_prefix + '_fa_river.map')
return
if step == 'faand':
print("Computing FAAND ...")
fa_river = pcr.readmap(path_prefix + '_fa_river.map')
ldd = pcr.readmap(path_prefix + '_ldd.map')
up_fa = pcr.scalar(pcr.subcatchment(ldd, fa_river))
pcr.report(up_fa, path_prefix + '_faand.map')
return
def initial(self):
""" initial part of the routing module
"""
maskinfo = MaskInfo.instance()
self.var.avgdis = maskinfo.in_zero()
self.var.Beta = loadmap('beta')
self.var.InvBeta = 1 / self.var.Beta
# Inverse of beta for kinematic wave
self.var.ChanLength = loadmap('ChanLength').astype(float)
self.var.InvChanLength = 1 / self.var.ChanLength
# Inverse of channel length [1/m]
self.var.NoRoutSteps = int(
np.maximum(1, round(self.var.DtSec / self.var.DtSecChannel, 0)))
# Number of sub-steps based on value of DtSecChannel,
# or 1 if DtSec is smaller than DtSecChannel
settings = LisSettings.instance()
option = settings.options
if option['InitLisflood']:
self.var.NoRoutSteps = 1
# InitLisflood is used!
# so channel routing step is the same as the general time step
self.var.DtRouting = self.var.DtSec / self.var.NoRoutSteps
# Corresponding sub-timestep (seconds)
self.var.InvDtRouting = 1 / self.var.DtRouting
self.var.InvNoRoutSteps = 1 / float(self.var.NoRoutSteps)
# inverse for faster calculation inside the dynamic section
# -------------------------- LDD
self.var.Ldd = lddmask(loadmap('Ldd', pcr=True, lddflag=True),
self.var.MaskMap)
# Cut ldd to size of MaskMap (NEW, 29/9/2004)
# Prevents 'unsound' ldd if MaskMap covers sub-area of ldd
# Count (inverse of) upstream area for each pixel
# Needed if we want to calculate average values of variables
# upstream of gauge locations
self.var.UpArea = accuflux(self.var.Ldd, self.var.PixelAreaPcr)
# Upstream contributing area for each pixel
# Note that you might expext that values of UpArea would be identical to
# those of variable CatchArea (see below) at the outflow points.
# This is NOT actually the case, because outflow points are shifted 1
# cell in upstream direction in the calculation of CatchArea!
self.var.InvUpArea = 1 / self.var.UpArea
# Calculate inverse, so we can multiply in dynamic (faster than divide)
self.var.IsChannelPcr = boolean(loadmap('Channels', pcr=True))
self.var.IsChannel = np.bool8(compressArray(self.var.IsChannelPcr))
# Identify channel pixels
self.var.IsChannelKinematic = self.var.IsChannel.copy()
# Identify kinematic wave channel pixels
# (identical to IsChannel, unless dynamic wave is used, see below)
#self.var.IsStructureKinematic = pcraster.boolean(0)
self.var.IsStructureKinematic = np.bool8(maskinfo.in_zero())
# Map that identifies special inflow/outflow structures (reservoirs, lakes) within the
# kinematic wave channel routing. Set to (dummy) value of zero modified in reservoir and lake
# routines (if those are used)
LddChan = lddmask(self.var.Ldd, self.var.IsChannelPcr)
# ldd for Channel network
self.var.MaskMap = boolean(self.var.Ldd)
# Use boolean version of Ldd as calculation mask
# (important for correct mass balance check
# any water generated outside of Ldd won't reach
# channel anyway)
self.var.LddToChan = lddrepair(
ifthenelse(self.var.IsChannelPcr, 5, self.var.Ldd))
# Routing of runoff (incl. ground water)en
AtOutflow = boolean(pit(self.var.Ldd))
# find outlet points...
if option['dynamicWave']:
IsChannelDynamic = boolean(loadmap('ChannelsDynamic', pcr=True))
# Identify channel pixels where dynamic wave is used
self.var.IsChannelKinematic = (self.var.IsChannelPcr
== 1) & (IsChannelDynamic == 0)
# Identify (update) channel pixels where kinematic wave is used
self.var.LddKinematic = lddmask(self.var.Ldd,
self.var.IsChannelKinematic)
# Ldd for kinematic wave: ends (pit) just before dynamic stretch
LddDynamic = lddmask(self.var.Ldd, IsChannelDynamic)
# Ldd for dynamic wave
# Following statements produce an ldd network that connects the pits in
# LddKinematic to the nearest downstream dynamic wave pixel
LddToDyn = lddrepair(ifthenelse(IsChannelDynamic, 5, self.var.Ldd))
# Temporary ldd: flow paths end in dynamic pixels
PitsKinematic = cover(boolean(pit(self.var.LddKinematic)), 0)
# Define start of each flow path at pit on LddKinematic
PathKinToDyn = path(LddToDyn, PitsKinematic)
# Identify paths that connect pits in LddKinematic to dynamic wave
# pixels
LddKinToDyn = lddmask(LddToDyn, PathKinToDyn)
# Create ldd
DynWaveBoundaryCondition = boolean(pit(LddDynamic))
# NEW 12-7-2005 (experimental)
# Location of boundary condition dynamic wave
self.var.AtLastPoint = (downstream(
self.var.Ldd,
AtOutflow) == 1) & (AtOutflow != 1) & self.var.IsChannelPcr
# NEW 23-6-2005
# Dynamic wave routine gives no outflow out of pits, so we calculate this
# one cell upstream (WvD)
# (implies that most downstream cell is not taken into account in mass balance
# calculations, even if dyn wave is not used)
# Only include points that are on a channel (otherwise some small 'micro-catchments'
# are included, for which the mass balance cannot be calculated
# properly)
else:
self.var.LddKinematic = LddChan
# No dynamic wave, so kinematic ldd equals channel ldd
self.var.AtLastPoint = AtOutflow
self.var.AtLastPointC = np.bool8(
compressArray(self.var.AtLastPoint))
# assign unique identifier to each of them
maskinfo = MaskInfo.instance()
lddC = compressArray(self.var.LddKinematic)
inAr = decompress(np.arange(maskinfo.info.mapC[0], dtype="int32"))
# giving a number to each non missing pixel as id
self.var.downstruct = (compressArray(
downstream(self.var.LddKinematic, inAr))).astype("int32")
# each upstream pixel gets the id of the downstream pixel
self.var.downstruct[lddC == 5] = maskinfo.info.mapC[0]
# all pits gets a high number
#d3=np.bincount(self.var.down, weights=loadmap('AvgDis'))[:-1]
# upstream function in numpy
OutflowPoints = nominal(uniqueid(self.var.AtLastPoint))
# and assign unique identifier to each of them
self.var.Catchments = (compressArray(
catchment(self.var.Ldd, OutflowPoints))).astype(np.int32)
CatchArea = np.bincount(
self.var.Catchments,
weights=self.var.PixelArea)[self.var.Catchments]
#CatchArea = CatchArea[self.var.Catchments]
# define catchment for each outflow point
#CatchArea = areatotal(self.var.PixelArea, self.var.Catchments)
# Compute area of each catchment [m2]
# Note: in earlier versions this was calculated using the "areaarea" function,
# changed to "areatotal" in order to enable handling of grids with spatially
# variable cell areas (e.g. lat/lon grids)
self.var.InvCatchArea = 1 / CatchArea
# inverse of catchment area [1/m2]
# ************************************************************
# ***** CHANNEL GEOMETRY ************************************
# ************************************************************
self.var.ChanGrad = np.maximum(loadmap('ChanGrad'),
loadmap('ChanGradMin'))
# avoid calculation of Alpha using ChanGrad=0: this creates MV!
self.var.CalChanMan = loadmap('CalChanMan')
self.var.ChanMan = self.var.CalChanMan * loadmap('ChanMan')
# Manning's n is multiplied by ChanManCal
# enables calibration for peak timing
self.var.ChanBottomWidth = loadmap('ChanBottomWidth')
ChanDepthThreshold = loadmap('ChanDepthThreshold')
ChanSdXdY = loadmap('ChanSdXdY')
self.var.ChanUpperWidth = self.var.ChanBottomWidth + 2 * ChanSdXdY * ChanDepthThreshold
# Channel upper width [m]
self.var.TotalCrossSectionAreaBankFull = 0.5 * \
ChanDepthThreshold * (self.var.ChanUpperWidth + self.var.ChanBottomWidth)
# Area (sq m) of bank full discharge cross section [m2]
# (trapezoid area equation)
TotalCrossSectionAreaHalfBankFull = 0.5 * self.var.TotalCrossSectionAreaBankFull
# Cross-sectional area at half bankfull [m2]
# This can be used to initialise channel flow (see below)
TotalCrossSectionAreaInitValue = loadmap(
'TotalCrossSectionAreaInitValue')
self.var.TotalCrossSectionArea = np.where(
TotalCrossSectionAreaInitValue == -9999,
TotalCrossSectionAreaHalfBankFull, TotalCrossSectionAreaInitValue)
# Total cross-sectional area [m2]: if initial value in binding equals -9999 the value at half bankfull is used,
# otherwise TotalCrossSectionAreaInitValue (typically end map from previous simulation)
if option['SplitRouting']:
# in_zero = maskinfo.in_zero()
CrossSection2AreaInitValue = loadmap('CrossSection2AreaInitValue')
self.var.CrossSection2Area = np.where(
CrossSection2AreaInitValue == -9999, maskinfo.in_zero(),
CrossSection2AreaInitValue)
# cross-sectional area [m2] for 2nd line of routing: if initial value in binding equals -9999 the value is set to 0
# otherwise CrossSection2AreaInitValue (typically end map from previous simulation)
PrevSideflowInitValue = loadmap('PrevSideflowInitValue')
self.var.Sideflow1Chan = np.where(PrevSideflowInitValue == -9999,
maskinfo.in_zero(),
PrevSideflowInitValue)
# sideflow from previous run for 1st line of routing: if initial value in binding equals -9999 the value is set to 0
# otherwise PrevSideflowInitValue (typically end map from previous simulation)
# ************************************************************
# ***** CHANNEL ALPHA (KIN. WAVE)*****************************
# ************************************************************
# Following calculations are needed to calculate Alpha parameter in kinematic
# wave. Alpha currently fixed at half of bankful depth (this may change in
# future versions!)
ChanWaterDepthAlpha = np.where(self.var.IsChannel,
0.5 * ChanDepthThreshold, 0.0)
# Reference water depth for calculation of Alpha: half of bankfull
self.var.ChanWettedPerimeterAlpha = self.var.ChanBottomWidth + 2 * \
np.sqrt(np.square(ChanWaterDepthAlpha) + np.square(ChanWaterDepthAlpha * ChanSdXdY))
# Channel wetted perimeter [m](Pythagoras)
AlpTermChan = (self.var.ChanMan /
(np.sqrt(self.var.ChanGrad)))**self.var.Beta
self.var.AlpPow = 2.0 / 3.0 * self.var.Beta
self.var.ChannelAlpha = (
AlpTermChan *
(self.var.ChanWettedPerimeterAlpha**self.var.AlpPow)).astype(float)
self.var.InvChannelAlpha = 1 / self.var.ChannelAlpha
# ChannelAlpha for kinematic wave
# ************************************************************
# ***** CHANNEL INITIAL DISCHARGE ****************************
# ************************************************************
self.var.ChanM3 = self.var.TotalCrossSectionArea * self.var.ChanLength
# channel water volume [m3]
self.var.ChanIniM3 = self.var.ChanM3.copy()
self.var.ChanM3Kin = self.var.ChanIniM3.copy().astype(float)
# Initialise water volume in kinematic wave channels [m3]
self.var.ChanQKin = np.where(self.var.ChannelAlpha > 0,
(self.var.TotalCrossSectionArea /
self.var.ChannelAlpha)**self.var.InvBeta,
0).astype(float)
# Initialise discharge at kinematic wave pixels (note that InvBeta is
# simply 1/beta, computational efficiency!)
self.var.CumQ = maskinfo.in_zero()
# ininialise sum of discharge to calculate average
# ************************************************************
# ***** CHANNEL INITIAL DYNAMIC WAVE *************************
# ************************************************************
if option['dynamicWave']:
pass
# TODO !!!!!!!!!!!!!!!!!!!!
# lookchan = lookupstate(TabCrossSections, ChanCrossSections, ChanBottomLevel, self.var.ChanLength,
# DynWaveConstantHeadBoundary + ChanBottomLevel)
# ChanIniM3 = ifthenelse(AtOutflow, lookchan, ChanIniM3)
# Correct ChanIniM3 for constant head boundary in pit (only if
# dynamic wave is used)
# ChanM3Dyn = ChanIniM3
# Set volume of water in dynamic wave channel to initial value
# (note that initial condition is expressed as a state in [m3] for the dynamic wave,
# and as a rate [m3/s] for the kinematic wave (a bit confusing)
# Estimate number of iterations needed in first time step (based on Courant criterium)
# TO DO !!!!!!!!!!!!!!!!!!!!
# Potential = lookuppotential(
# TabCrossSections, ChanCrossSections, ChanBottomLevel, self.var.ChanLength, ChanM3Dyn)
# Potential
# WaterLevelDyn = Potential - ChanBottomLevel
# Water level [m above bottom level)
# WaveCelerityDyn = pcraster.sqrt(9.81 * WaterLevelDyn)
# Dynamic wave celerity [m/s]
# CourantDynamic = self.var.DtSec * \
# (WaveCelerityDyn + 2) / self.var.ChanLength
# Courant number for dynamic wave
# We don't know the water velocity at this time so
# we just guess it's 2 m/s (Odra tests show that flow velocity
# is typically much lower than wave celerity, and 2 m/s is quite
# high already so this gives a pretty conservative/safe estimate
# for DynWaveIterations)
# DynWaveIterationsTemp = max(
# 1, roundup(CourantDynamic / CourantDynamicCrit))
# DynWaveIterations = ordinal(mapmaximum(DynWaveIterationsTemp))
# Number of sub-steps needed for required numerical
# accuracy. Always greater than or equal to 1
# (otherwise division by zero!)
# TEST
# If polder option is used, we need an estimate of the initial channel discharge, but we don't know this
# for the dynamic wave pixels (since only initial state is known)! Try if this works (dyn wave flux based on zero inflow 1 iteration)
# Note that resulting ChanQ is ONLY used in the polder routine!!!
# Since we need instantaneous estimate at start of time step, a
# ChanQM3Dyn is calculated for one single one-second time step!!!
# ChanQDyn = dynwaveflux(TabCrossSections,
# ChanCrossSections,
# LddDynamic,
# ChanIniM3,
# 0.0,
# ChanBottomLevel,
# self.var.ChanMan,
# self.var.ChanLength,
# 1,
# 1,
# DynWaveBoundaryCondition)
# Compute volume and discharge in channel after dynamic wave
# ChanM3Dyn in [cu m]
# ChanQDyn in [cu m / s]
# self.var.ChanQ = ifthenelse(
# IsChannelDynamic, ChanQDyn, self.var.ChanQKin)
# Channel discharge: combine results of kinematic and dynamic wave
else:
# ***** NO DYNAMIC WAVE *************************
# Dummy code if dynamic wave is not used, in which case ChanQ equals ChanQKin
# (needed only for polder routine)
PrevDischarge = loadmap('PrevDischarge')
self.var.ChanQ = np.where(PrevDischarge == -9999,
self.var.ChanQKin, PrevDischarge)
# initialise channel discharge: cold start: equal to ChanQKin
# [m3/s]
# Initialising cumulative output variables
# These are all needed to compute the cumulative mass balance error
self.var.DischargeM3Out = maskinfo.in_zero()
# cumulative discharge at outlet [m3]
self.var.TotalQInM3 = maskinfo.in_zero()
# cumulative inflow from inflow hydrographs [m3]
#self.var.sumDis = maskinfo.in_zero()
self.var.sumDis = maskinfo.in_zero()
self.var.sumIn = maskinfo.in_zero()
def main():
# output folder (and tmp folder)
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# set the clone map
print("set the clone")
pcr.setclone(global_ldd_30min_inp_file)
# define the landmask
print("define the landmask")
# - based on the 30min input
landmask_30min = define_landmask(input_file = global_landmask_30min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30min_only.map")
# - based on the 05min input
landmask_05min = define_landmask(input_file = global_landmask_05min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_05min_only.map")
# - based on the 06min input
landmask_06min = define_landmask(input_file = global_landmask_06min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_06min_only.map")
# - based on the 30sec input
landmask_30sec = define_landmask(input_file = global_landmask_30sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30sec_only.map")
# - based on the 30sec input
landmask_03sec = define_landmask(input_file = global_landmask_03sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_03sec_only.map")
#
# - merge all landmasks
landmask = pcr.cover(landmask_30min, landmask_05min, landmask_06min,
landmask_30sec, landmask_03sec)
pcr.report(landmask, "global_landmask_extended_30min.map")
# ~ pcr.aguila(landmask)
# extend ldd
print("extend/define the ldd")
ldd_map = pcr.readmap(global_ldd_30min_inp_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
pcr.report(ldd_map, "global_ldd_extended_30min.map")
# ~ pcr.aguila(ldd_map)
# catchment map and size
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# ~ pcr.aguila(catchment_size)
# identify small islands
print("identify small islands")
# - maps of islands smaller than 15000 cells (at half arc degree resolution)
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 15000., island_map)
# ~ # - use catchments (instead of islands)
# ~ island_map = catchment_map
# ~ island_size = catchment_size
# ~ island_map = pcr.ifthen(island_size < 10000., island_map)
# - sort from the largest island
# -- take one cell per island as a representative
island_map_rep_size = pcr.ifthen(
pcr.areaorder(island_size, island_map) == 1.0, island_size)
# -- sort from the largest island
island_map_rep_ids = pcr.areaorder(
island_map_rep_size * -1.00,
pcr.ifthen(pcr.defined(island_map_rep_size), pcr.nominal(1.0)))
# -- map of smaller islands, sorted from the largest one
island_map = pcr.areamajority(pcr.nominal(island_map_rep_ids), island_map)
# identify the biggest island for every group of small islands within a certain window (arcdeg cells)
print("the biggest island for every group of small islands")
large_island_map = pcr.ifthen(
pcr.scalar(island_map) == pcr.windowminimum(pcr.scalar(island_map),
15.), island_map)
# ~ pcr.aguila(large_island_map)
# identify big catchments
print("identify large catchments")
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# ~ # - identify all large catchments with size >= 50 cells (at the resolution of 30 arcmin) = 50 x (50^2) km2 = 125000 km2
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 50, catchment_map)
# ~ # - identify all large catchments with size >= 10 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 10, catchment_map)
# ~ # - identify all large catchments with size >= 5 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 5, catchment_map)
# ~ # - identify all large catchments with size >= 20 cells (at the resolution of 30 arcmin)
# ~ large_catchment_map = pcr.ifthen(catchment_size >= 20, catchment_map)
# - identify all large catchments with size >= 25 cells (at the resolution of 30 arcmin)
large_catchment_map = pcr.ifthen(catchment_size >= 25, catchment_map)
# - give the codes that are different than islands
large_catchment_map = pcr.nominal(
pcr.scalar(large_catchment_map) +
10. * vos.getMinMaxMean(pcr.scalar(large_island_map))[1])
# merge biggest islands and big catchments
print("merge large catchments and islands")
large_catchment_and_island_map = pcr.cover(large_catchment_map,
large_island_map)
# ~ large_catchment_and_island_map = pcr.cover(large_island_map, large_catchment_map)
large_catchment_and_island_map_size = pcr.areatotal(
pcr.spatial(pcr.scalar(1.0)), large_catchment_and_island_map)
# - sort from the largest one
# -- take one cell per island as a representative
large_catchment_and_island_map_rep_size = pcr.ifthen(
pcr.areaorder(large_catchment_and_island_map_size,
large_catchment_and_island_map) == 1.0,
large_catchment_and_island_map_size)
# -- sort from the largest
large_catchment_and_island_map_rep_ids = pcr.areaorder(
large_catchment_and_island_map_rep_size * -1.00,
pcr.ifthen(pcr.defined(large_catchment_and_island_map_rep_size),
pcr.nominal(1.0)))
# -- map of largest catchments and islands, sorted from the largest one
large_catchment_and_island_map = pcr.areamajority(
pcr.nominal(large_catchment_and_island_map_rep_ids),
large_catchment_and_island_map)
# ~ pcr.report(large_catchment_and_island_map, "large_catchments_and_islands.map")
# ~ # perform cdo fillmiss2 in order to merge the small catchments to the nearest large catchments
# ~ print("spatial interpolation/extrapolation using cdo fillmiss2 to get initial subdomains")
# ~ cmd = "gdal_translate -of NETCDF large_catchments_and_islands.map large_catchments_and_islands.nc"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "cdo fillmiss2 large_catchments_and_islands.nc large_catchments_and_islands_filled.nc"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "gdal_translate -of PCRaster large_catchments_and_islands_filled.nc large_catchments_and_islands_filled.map"
# ~ print(cmd); os.system(cmd)
# ~ cmd = "mapattr -c " + global_ldd_30min_inp_file + " " + "large_catchments_and_islands_filled.map"
# ~ print(cmd); os.system(cmd)
# ~ # - initial subdomains
# ~ subdomains_initial = pcr.nominal(pcr.readmap("large_catchments_and_islands_filled.map"))
# ~ subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
# ~ pcr.aguila(subdomains_initial)
# spatial interpolation/extrapolation in order to merge the small catchments to the nearest large catchments
print("spatial interpolation/extrapolation to get initial subdomains")
field = large_catchment_and_island_map
cellID = pcr.nominal(pcr.uniqueid(pcr.defined(field)))
zoneID = pcr.spreadzone(cellID, 0, 1)
field = pcr.areamajority(field, zoneID)
subdomains_initial = field
subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
pcr.aguila(subdomains_initial)
pcr.report(subdomains_initial, "global_subdomains_30min_initial.map")
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
# ~ print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[0])))
# ~ print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[1])))
print("Checking all subdomains, avoid too large subdomains")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])
# clone code that will be assigned
assigned_number = 0
subdomains_final = pcr.ifthen(
pcr.scalar(subdomains_initial) < -7777, pcr.nominal(0))
for nr in range(1, num_of_masks + 1, 1):
msg = "Processing the landmask %s" % (str(nr))
print(msg)
mask_selected_boolean = pcr.ifthen(subdomains_initial == nr,
pcr.boolean(1.0))
# ~ if nr == 1: pcr.aguila(mask_selected_boolean)
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
# ~ print(str(area_in_degree2))
# check whether the size of bounding box is ok
# - initial check value
check_ok = True
reference_area_in_degree2 = 2500.
if area_in_degree2 > 1.50 * reference_area_in_degree2: check_ok = False
if (xmax - xmin) > 10 * (ymax - ymin): check_ok = False
if check_ok == True:
msg = "Clump is not needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(mask_selected_boolean,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
if check_ok == False:
msg = "Clump is needed."
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# make clump
clump_ids = pcr.nominal(pcr.clump(mask_selected_boolean))
# merge clumps that are close together
clump_ids_window_majority = pcr.windowmajority(clump_ids, 10.0)
clump_ids = pcr.areamajority(clump_ids_window_majority, clump_ids)
# ~ pcr.aguila(clump_ids)
# minimimum and maximum values
min_clump_id = int(
pcr.cellvalue(pcr.mapminimum(pcr.scalar(clump_ids)), 1)[0])
max_clump_id = int(
pcr.cellvalue(pcr.mapmaximum(pcr.scalar(clump_ids)), 1)[0])
for clump_id in range(min_clump_id, max_clump_id + 1, 1):
msg = "Processing the clump %s of %s from the landmask %s" % (
str(clump_id), str(max_clump_id), str(nr))
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# identify mask based on the clump
mask_selected_boolean_from_clump = pcr.ifthen(
clump_ids == pcr.nominal(clump_id), mask_selected_boolean)
mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean_from_clump,
mask_selected_boolean_from_clump)
# check whether the clump is empty
check_mask_selected_boolean_from_clump = pcr.ifthen(
mask_selected_boolean, mask_selected_boolean_from_clump)
check_if_empty = float(
pcr.cellvalue(
pcr.mapmaximum(
pcr.scalar(
pcr.defined(
check_mask_selected_boolean_from_clump))),
1)[0])
if check_if_empty == 0.0:
msg = "Map is empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
else:
msg = "Map is NOT empty !"
msg = "\n\n" + str(msg) + "\n\n"
print(msg)
# assign the clone code
assigned_number = assigned_number + 1
# update global landmask for river and land
mask_selected_nominal = pcr.ifthen(
mask_selected_boolean_from_clump,
pcr.nominal(assigned_number))
subdomains_final = pcr.cover(subdomains_final,
mask_selected_nominal)
# ~ # kill all aguila processes if exist
# ~ os.system('killall aguila')
pcr.aguila(subdomains_final)
print("")
print("")
print("")
print("The subdomain map is READY.")
pcr.report(subdomains_final, "global_subdomains_30min_final.map")
num_of_masks = int(vos.getMinMaxMean(pcr.scalar(subdomains_final))[1])
print(num_of_masks)
print("")
print("")
print("")
for nr in range(1, num_of_masks + 1, 1):
mask_selected_boolean = pcr.ifthen(subdomains_final == nr,
pcr.boolean(1.0))
xmin, ymin, xmax, ymax = boundingBox(mask_selected_boolean)
area_in_degree2 = (xmax - xmin) * (ymax - ymin)
print(
str(nr) + " ; " + str(area_in_degree2) + " ; " +
str((xmax - xmin)) + " ; " + str((ymax - ymin)))
print("")
print("")
print("")
print("Number of subdomains: " + str(num_of_masks))
print("")
print("")
print("")
# spatial extrapolation in order to cover the entire map
print("spatial interpolation/extrapolation to cover the entire map")
field = subdomains_final
cellID = pcr.nominal(pcr.uniqueid(pcr.defined(field)))
zoneID = pcr.spreadzone(cellID, 0, 1)
field = pcr.areamajority(field, zoneID)
subdomains_final_filled = field
pcr.aguila(subdomains_final_filled)
pcr.report(subdomains_final_filled,
"global_subdomains_30min_final_filled.map")
def main():
#-initialization
# MVs
MV= -999.
# minimum catchment size to process
catchmentSizeLimit= 0.0
# period of interest, start and end year
startYear= 1961
endYear= 2010
# maps
cloneMapFileName= '/data/hydroworld/PCRGLOBWB20/input30min/global/Global_CloneMap_30min.map'
lddFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/lddsound_30min.map'
cellAreaFileName= '/data/hydroworld/PCRGLOBWB20/input30min/routing/cellarea30min.map'
# set clone
pcr.setclone(cloneMapFileName)
# output
outputPath= '/scratch/rens/reservedrecharge'
percentileMapFileName= os.path.join(outputPath,'q%03d_cumsec.map')
textFileName= os.path.join(outputPath,'groundwater_environmentalflow_%d.txt')
fractionReservedRechargeMapFileName= os.path.join(outputPath,'fraction_reserved_recharge%d.map')
fractionMinimumReservedRechargeMapFileName= os.path.join(outputPath,'minimum_fraction_reserved_recharge%d.map')
# input
inputPath= '/nfsarchive/edwin-emergency-backup-DO-NOT-DELETE/rapid/edwin/05min_runs_results/2015_04_27/non_natural_2015_04_27/global/netcdf/'
# define data to be read from netCDF files
ncData= {}
variableName= 'totalRunoff'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qloc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'gwRecharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'gwRecharge_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'gwrec')
ncData[variableName]['annualAverage']= pcr.scalar(0)
variableName= 'discharge'
ncData[variableName]= {}
ncData[variableName]['fileName']= os.path.join(inputPath,'totalRunoff_monthTot_output.nc')
ncData[variableName]['fileRoot']= os.path.join(outputPath,'qc')
ncData[variableName]['annualAverage']= pcr.scalar(0)
ncData[variableName]['mapStack']= np.array([])
# percents and environmental flow condition set as percentile
percents= range(10,110,10)
environmentalFlowPercent= 10
if environmentalFlowPercent not in percents:
percents.append(environmentalFlowPercent)
percents.sort()
#-start
# obtain attributes
pcr.setclone(cloneMapFileName)
cloneSpatialAttributes= spatialAttributes(cloneMapFileName)
years= range(startYear,endYear+1)
# output path
if not os.path.isdir(outputPath):
os.makedirs(outputPath)
os.chdir(outputPath)
# compute catchments
ldd= pcr.readmap(lddFileName)
cellArea= pcr.readmap(cellAreaFileName)
catchments= pcr.catchment(ldd,pcr.pit(ldd))
fractionWater= pcr.scalar(0.0) # temporary!
lakeMask= pcr.boolean(0) # temporary!
pcr.report(catchments,os.path.join(outputPath,'catchments.map'))
maximumCatchmentID= int(pcr.cellvalue(pcr.mapmaximum(pcr.scalar(catchments)),1)[0])
# iterate over years
weight= float(len(years))**-1
for year in years:
#-echo year
print ' - processing year %d' % year
#-process data
startDate= datetime.datetime(year,1,1)
endDate= datetime.datetime(year,12,31)
timeSteps= endDate.toordinal()-startDate.toordinal()+1
dynamicIncrement= 1
for variableName in ncData.keys():
print ' extracting %s' % variableName,
ncFileIn= ncData[variableName]['fileName']
#-process data
pcrDataSet= pcrObject(variableName, ncData[variableName]['fileRoot'],\
ncFileIn,cloneSpatialAttributes, pcrVALUESCALE= pcr.Scalar, resamplingAllowed= True,\
dynamic= True, dynamicStart= startDate, dynamicEnd= endDate, dynamicIncrement= dynamicIncrement, ncDynamicDimension= 'time')
pcrDataSet.initializeFileInfo()
pcrDataSet.processFileInfo()
for fileInfo in pcrDataSet.fileProcessInfo.values()[0]:
tempFileName= fileInfo[1]
variableField= pcr.readmap(tempFileName)
variableField= pcr.ifthen(pcr.defined(ldd),pcr.cover(variableField,0))
if variableName == 'discharge':
dayNumber= int(os.path.splitext(tempFileName)[1].strip('.'))
date= datetime.date(year,1,1)+datetime.timedelta(dayNumber-1)
numberDays= calendar.monthrange(year,date.month)[1]
variableField= pcr.max(0,pcr.catchmenttotal(variableField*cellArea,ldd)/(numberDays*24*3600))
ncData[variableName]['annualAverage']+= weight*variableField
if 'mapStack' in ncData[variableName].keys():
tempArray= pcr2numpy(variableField,MV)
mask= tempArray != MV
if ncData[variableName]['mapStack'].size != 0:
ncData[variableName]['mapStack']= np.vstack((ncData[variableName]['mapStack'],tempArray[mask]))
else:
ncData[variableName]['mapStack']= tempArray[mask]
coordinates= np.zeros((ncData[variableName]['mapStack'].size,2))
pcr.setglobaloption('unitcell')
tempArray= pcr2numpy(pcr.ycoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,0]= tempArray[mask]
tempArray= pcr2numpy(pcr.xcoordinate(pcr.boolean(1))+0.5,MV)
coordinates[:,1]= tempArray[mask]
os.remove(tempFileName)
# delete object
pcrDataSet= None
del pcrDataSet
# close line on screen
print
# report annual averages
key= 'annualAverage'
ncData['discharge'][key]/= 12
for variableName in ncData.keys():
ncData[variableName][key]= pcr.max(0,ncData[variableName][key])
pcr.report(ncData[variableName][key],\
os.path.join(outputPath,'%s_%s.map' % (variableName,key)))
# remove aux.xml
for tempFileName in os.listdir(outputPath):
if 'aux.xml' in tempFileName:
os.remove(tempFileName)
# sort data
print 'sorting discharge data'
variableName= 'discharge'
key= 'mapStack'
indices= np.zeros((ncData[variableName][key].shape),np.uint)
for iCnt in xrange(ncData[variableName][key].shape[1]):
indices[:,iCnt]= ncData[variableName][key][:,iCnt].argsort(kind= 'mergesort')
ncData[variableName][key][:,iCnt]= ncData[variableName][key][:,iCnt][indices[:,iCnt]]
# extract values for percentiles
print 'returning maps'
for percent in percents:
percentile= 0.01*percent
index0= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0]))
index1= min(ncData[variableName][key].shape[0]-1,int(percentile*ncData[variableName][key].shape[0])+1)
x0= float(index0)/ncData[variableName][key].shape[0]
x1= float(index1)/ncData[variableName][key].shape[0]
if x0 <> x1:
y= ncData[variableName][key][index0,:]+(percentile-x0)*\
(ncData[variableName][key][index1,:]-ncData[variableName][key][index0,:])/(x1-x0)
else:
y= ncData[variableName][key][index0,:]
# convert a slice of the stack into an array
tempArray= np.ones((cloneSpatialAttributes.numberRows,cloneSpatialAttributes.numberCols))*MV
for iCnt in xrange(coordinates.shape[0]):
row= coordinates[iCnt,0]-1
col= coordinates[iCnt,1]-1
tempArray[row,col]= y[iCnt]
variableField= numpy2pcr(pcr.Scalar,tempArray,MV)
pcr.report(variableField,percentileMapFileName % percent)
if percent == environmentalFlowPercent:
ncData[variableName]['environmentalFlow']= variableField
tempArray= None; variableField= None
del tempArray, variableField
# process environmental flow
# initialize map of reserved recharge fraction
fractionReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
fractionMinimumReservedRechargeMap= pcr.ifthen(ncData[variableName]['environmentalFlow'] < 0,pcr.scalar(0))
textFile= open(textFileName % environmentalFlowPercent,'w')
hStr= 'Environmental flow analysis per basin, resulting in a map of renewable, exploitable recharge, for the %d%s quantile of discharge\n' % (environmentalFlowPercent,'%')
hStr+= 'Returns Q_%d/R, the fraction of reserved recharge needed to sustain fully the environental flow requirement defined as the %d percentile,\n' % (environmentalFlowPercent, environmentalFlowPercent)
hStr+= 'and Q*_%d/R, a reduced fraction that takes the availability of surface water into account\n' % environmentalFlowPercent
textFile.write(hStr)
print hStr
# create header to display on screen and write to file
# reported are: 1: ID, 2: Area, 3: average discharge, 4: environmental flow, 5: average recharge,
# 6: Q_%d/Q, 7: Q_%d/R_Avg, 8: R_Avg/Q_Avg, 9: Q*_%d/R_Avg
hStr= '%6s,%15s,%15s,%15s,%15s,%15s,%15s,%15s,%15s\n' % \
('ID','Area [km2]','Q_Avg [m3]','Q_%d [m3]' % environmentalFlowPercent ,'R_Avg [m3]','Q_%d/Q_Avg [-]' % environmentalFlowPercent,\
'Q_%d/Q_Avg [-]' % environmentalFlowPercent,'R_Avg/Q_Avg [-]','Q*_%d/Q_Avg [-]' % environmentalFlowPercent)
textFile.write(hStr)
print hStr
for catchment in xrange(1,maximumCatchmentID+1):
# create catchment mask and check whether it does not coincide with a lake
catchmentMask= catchments == catchment
catchmentSize= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,cellArea*1.e-6)),1)[0]
#~ ##~ if pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(lakeMask))),1) <> \
#~ ##~ pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,pcr.scalar(catchmentMask))),1)[0] and \
#~ ##~ catchmentSize > catchmentSizeLimit:
key= 'annualAverage'
variableName= 'discharge'
if bool(pcr.cellvalue(pcr.maptotal(pcr.ifthen((ldd == 5) & catchmentMask,\
pcr.scalar(ncData[variableName][key] > 0))),1)[0]) and catchmentSize >= catchmentSizeLimit:
# valid catchment, process
# all volumes are in m3 per year
key= 'annualAverage'
catchmentAverageDischarge= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
variableName= 'gwRecharge'
catchmentRecharge= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
(1.-fractionWater)*cellArea)),1)[0]
variableName= 'totalRunoff'
catchmentRunoff= pcr.cellvalue(pcr.maptotal(pcr.ifthen(catchmentMask,ncData[variableName][key]*\
cellArea)),1)[0]
key= 'environmentalFlow'
variableName= 'discharge'
catchmentEnvironmentalFlow= pcr.cellvalue(pcr.mapmaximum(pcr.ifthen(catchmentMask & (ldd == 5),\
ncData[variableName][key])),1)[0]*365.25*3600*24
catchmentRunoff= max(catchmentRunoff,catchmentEnvironmentalFlow)
if catchmentAverageDischarge > 0.:
fractionEnvironmentalFlow= catchmentEnvironmentalFlow/catchmentAverageDischarge
fractionGroundWaterContribution= catchmentRecharge/catchmentAverageDischarge
else:
fractionEnvironmentalFlow= 0.
fractionGroundWaterContribution= 0.
if catchmentRecharge > 0:
fractionReservedRecharge= min(1,catchmentEnvironmentalFlow/catchmentRecharge)
else:
fractionReservedRecharge= 1.0
fractionMinimumReservedRecharge= (fractionReservedRecharge+fractionGroundWaterContribution-\
fractionReservedRecharge*fractionGroundWaterContribution)*fractionReservedRecharge
#~ # echo to screen, and write to file and map
wStr= '%6s,%15.1f,%15.6g,%15.6g,%15.6g,%15.6f,%15.6f,%15.6f,%15.6f\n' % \
(catchment,catchmentSize,catchmentAverageDischarge,catchmentEnvironmentalFlow,catchmentRecharge,\
fractionEnvironmentalFlow,fractionReservedRecharge,fractionGroundWaterContribution,fractionMinimumReservedRecharge)
print wStr
textFile.write(wStr)
# update maps
fractionReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionReservedRecharge),fractionReservedRechargeMap)
fractionMinimumReservedRechargeMap= pcr.ifthenelse(catchmentMask,\
pcr.scalar(fractionMinimumReservedRecharge),fractionMinimumReservedRechargeMap)
#-report map and close text file
pcr.report(fractionReservedRechargeMap,fractionReservedRechargeMapFileName % environmentalFlowPercent)
pcr.report(fractionMinimumReservedRechargeMap,fractionMinimumReservedRechargeMapFileName % environmentalFlowPercent)
# close text file
textFile.close()
# finished
print 'all done!'
def generate_hydro_datasets(path, output_dir, step):
print(path)
file_name = os.path.splitext(os.path.basename(path))[0]
map_path = output_dir + "/" + file_name + ".map"
path_prefix = map_path[:-14]
if step == "ldd":
cmd = u"gdal_translate -a_nodata -9999 -of PCRaster -ot Float32 " + path + " " + map_path
print(cmd)
subprocess.call(cmd, shell=True)
# slope = pcr.slope(dem)
# pcr.report(slope, path_prefix + '_slope.map')
# pcr.setglobaloption("lddin")
if step == "ldd":
dem = pcr.readmap(map_path)
print("Computing LDD ...")
# enable pit filling
ldd = pcr.lddcreate(dem, 9999999, 9999999, 9999999, 9999999)
pcr.report(ldd, path_prefix + "_ldd.map")
return
elif step == "ldddem":
dem = pcr.readmap(map_path)
print("Computing LDD DEM ...")
dem_pitfilled = pcr.lddcreatedem(dem, 9999999, 9999999, 9999999, 9999999)
dem_diff = dem_pitfilled - dem
pcr.report(dem_diff, path_prefix + "_dem_pits_diff.map")
return
# print("Computing LDD without pit filling ...")
# ldd_pits = pcr.lddcreate(dem, 0, 0, 0, 0)
# pcr.report(ldd_pits, path_prefix + '_ldd_with_pits.map')
# print("Computing pits ...")
# pits = pcr.pit(ldd_pits)
# pcr.report(pits, path_prefix + '_pits.map')
if step == "fa":
ldd = pcr.readmap(path_prefix + "_ldd.map")
print("Computing flow accumulation ...")
fa = pcr.accuflux(ldd, 1)
pcr.report(fa, path_prefix + "_fa.map")
return
if step == "catchments":
ldd = pcr.readmap(path_prefix + "_ldd.map")
print("Delineating catchments ...")
catchments = pcr.catchment(ldd, pcr.pit(ldd))
pcr.report(catchments, path_prefix + "_catchments.map")
return
if step == "stream_order":
ldd = pcr.readmap(path_prefix + "_ldd.map")
print("Computing stream order ...")
stream_order = pcr.streamorder(ldd)
pcr.report(stream_order, path_prefix + "_streamorder.map")
return
if step == "stream":
ldd = pcr.readmap(path_prefix + "_ldd.map")
accuThreshold = 100
print("Computing stream ...")
stream = pcr.ifthenelse(pcr.accuflux(ldd, 1) >= accuThreshold, pcr.boolean(1), pcr.boolean(0))
pcr.report(stream, path_prefix + "_stream.map")
return
if step == "height_river":
print("Computing heigh_river ...")
stream = pcr.readmap(path_prefix + "_stream.map")
dem = pcr.readmap(map_path)
height_river = pcr.ifthenelse(stream, pcr.ordinal(dem), 0)
pcr.report(height_river, path_prefix + "_height_river.map")
return
if step == "up_elevation":
print("Computing up_elevation ...")
height_river = pcr.readmap(path_prefix + "_height_river.map")
ldd = pcr.readmap(path_prefix + "_ldd.map")
up_elevation = pcr.scalar(pcr.subcatchment(ldd, height_river))
pcr.report(up_elevation, path_prefix + "_up_elevation.map")
return
if step == "hand":
print("Computing HAND ...")
dem = pcr.readmap(map_path)
up_elevation = pcr.readmap(path_prefix + "_up_elevation.map")
hand = pcr.max(dem - up_elevation, 0)
pcr.report(hand, path_prefix + "_hand.map")
return
if step == "dand":
print("Computing DAND ...")
ldd = pcr.readmap(path_prefix + "_ldd.map")
stream = pcr.readmap(path_prefix + "_stream.map")
dist = pcr.ldddist(ldd, stream, 1)
pcr.report(dist, path_prefix + "_dist.map")
return
if step == "fa_river":
print("Computing FA river ...")
fa = pcr.readmap(path_prefix + "_fa.map")
stream = pcr.readmap(path_prefix + "_stream.map")
fa_river = pcr.ifthenelse(stream, pcr.ordinal(fa), 0)
pcr.report(fa_river, path_prefix + "_fa_river.map")
return
if step == "faand":
print("Computing FAAND ...")
fa_river = pcr.readmap(path_prefix + "_fa_river.map")
ldd = pcr.readmap(path_prefix + "_ldd.map")
up_fa = pcr.scalar(pcr.subcatchment(ldd, fa_river))
pcr.report(up_fa, path_prefix + "_faand.map")
return
28 : [28,3515]}
lddOld = pcr.readmap('/home/straa005/LDD/lddOutput/ldd_HydroSHEDS_Hydro1k_5min.map')
lddDiff = pcr.scalar(ldd) - pcr.scalar(lddOld)
pcr.report(pcr.boolean(lddDiff), 'lddDiff.map')
p
############################### main ###################################
ldd = pcr.nominal(ldd)
ldd = fillMVBoundingBox(ldd, 1, 57, 76, 26, 47)
ldd = fillMVBoundingBox(ldd, 1, 58, 62, 51, 60)
ldd = fillMVBoundingBox(ldd, 1, -108, -101, 31, 40)
ldd = pcr.lddrepair(pcr.ldd(ldd))
pits = pcr.pit(ldd)
pcr.setglobaloption('unitcell')
continentMasks = pcr.cover(mask48, pcr.windowmajority(mask48,5))
#- adjust area masks with 5 min ldd
continentMasksNew = pcr.ifthen(pcr.boolean(pcr.readmap(cloneMap)) == 0, pcr.scalar(1))
for i in areas[0:]:
print 'area mask = ', i
mask = pcr.ifthen(continentMasks == i, pcr.boolean(1))
pitsContinent = pcr.pcrand(mask, pcr.boolean(pits))
pitsContinent = pcr.nominal(pcr.uniqueid(pcr.ifthen(pitsContinent == 1, pcr.boolean(1))))
catchments = pcr.catchment(ldd, pitsContinent)
newMask = pcr.ifthen(pcr.boolean(catchments) == 1, pcr.scalar(i))
continentMasksNew = pcr.cover(continentMasksNew, newMask)
def main():
# output folder (and tmp folder)
clean_out_folder = True
if os.path.exists(out_folder):
if clean_out_folder:
shutil.rmtree(out_folder)
os.makedirs(out_folder)
else:
os.makedirs(out_folder)
os.chdir(out_folder)
os.system("pwd")
# set the clone map
print("set the clone")
pcr.setclone(global_ldd_30min_inp_file)
# define the landmask
print("define the landmask")
# - based on the 30min input
landmask_30min = define_landmask(input_file = global_landmask_30min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30min_only")
# - based on the 05min input
landmask_05min = define_landmask(input_file = global_landmask_05min_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_05min_only")
# - based on the 30sec input
landmask_30sec = define_landmask(input_file = global_landmask_30sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_30sec_only")
# - based on the 30sec input
landmask_03sec = define_landmask(input_file = global_landmask_03sec_file,\
clone_map_file = global_ldd_30min_inp_file,\
output_map_file = "landmask_03sec_only")
#
# - merge all landmasks
landmask = pcr.cover(landmask_30min, landmask_05min, landmask_30sec,
landmask_03sec)
pcr.report(landmask, "global_landmask_30min_final.map")
# ~ pcr.aguila(landmask)
# extend ldd
print("extend/define the ldd")
ldd_map = pcr.readmap(global_ldd_30min_inp_file)
ldd_map = pcr.ifthen(landmask, pcr.cover(ldd_map, pcr.ldd(5)))
pcr.report(ldd_map, "global_ldd_final.map")
# ~ pcr.aguila(ldd_map)
# identify small islands
print("identify small islands")
# - maps of islands smaller than 10000 cells (at half arc degree resolution)
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 10000., island_map)
UNTIL_THIS
# identify the biggest island for every group of small islands within the windows 10x10 arcdeg cells
# - sort from the largest catchment
catchment_pits_boolean = pcr.ifthen(
pcr.scalar(pcr.pit(ldd_map)) > 0.0, pcr.boolean(1.0))
catchment_pits_boolean = pcr.ifthen(catchment_pits_boolean,
catchment_pits_boolean)
pcr.aguila(catchment_pits_boolean)
catchment_map = pcr.nominal(
pcr.areaorder(catchment_size * -1.0,
pcr.nominal(catchment_pits_boolean)))
island_map = pcr.ifthen(island_size == pcr.windowmaximum(island_size, 10.),
island_map)
pcr.aguila(island_map)
# identify big catchments
# merge biggest islands and big catchments
# make catchment and island map
print("make catchment and island map")
# - catchment map
catchment_map = pcr.catchment(ldd_map, pcr.pit(ldd_map))
pcr.report(catchment_map, "global_catchment_not_sorted.map")
os.system("mapattr -p global_catchment_not_sorted.map")
num_of_catchments = int(vos.getMinMaxMean(pcr.scalar(catchment_map))[1])
# - maps of islands smaller than 10000 cells
island_map = pcr.ifthen(landmask, pcr.clump(pcr.defined(ldd_map)))
island_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), island_map)
island_map = pcr.ifthen(island_size < 10000., island_map)
island_map = pcr.nominal(
pcr.scalar(pcr.ifthen(landmask, pcr.clump(island_map))) +
pcr.scalar(num_of_catchments) * 100.)
pcr.aguila(island_map)
island_size = pcr.ifthen(pcr.defined(island_map), island_size)
island_map = pcr.ifthen(island_size == pcr.windowmaximum(island_size, 10.),
island_map)
pcr.aguila(island_map)
catchment_map = pcr.cover(island_map, catchment_map)
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# - calculate the size
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
# - sort from the largest catchment
catchment_pits_boolean = pcr.ifthen(
pcr.scalar(pcr.pit(ldd_map)) > 0.0, pcr.boolean(1.0))
catchment_pits_boolean = pcr.ifthen(catchment_pits_boolean,
catchment_pits_boolean)
pcr.aguila(catchment_pits_boolean)
catchment_map = pcr.nominal(
pcr.areaorder(catchment_size * -1.0,
pcr.nominal(catchment_pits_boolean)))
catchment_map = pcr.catchment(ldd_map, catchment_map)
pcr.report(catchment_map, "global_catchment_final.map")
os.system("mapattr -p global_catchment_final.map")
# - calculate the size
catchment_size = pcr.areatotal(pcr.spatial(pcr.scalar(1.0)), catchment_map)
pcr.report(catchment_size, "global_catchment_size_in_number_of_cells.map")
# number of catchments
num_of_catchments = int(vos.getMinMaxMean(pcr.scalar(catchment_map))[1])
# size of the largest catchment
catchment_size_max = vos.getMinMaxMean(catchment_size)[1]
print("")
print(str(float(catchment_size_max)))
print("")
# identify all large catchments with size >= 50 cells (at the resolution of 30 arcmin) = 50 x (50^2) km2 = 125000 km2
print("identify catchments with the minimum size of 50 cells")
catchment_map_ge_50 = pcr.ifthen(catchment_size >= 50, catchment_map)
pcr.report(catchment_map_ge_50, "global_catchment_ge_50_cells.map")
# include the island
catchment_map_ge_50 = pcr.cover(island_map, catchment_map_ge_50)
# perform cdo fillmiss2 in order to merge the small catchments to the nearest large catchments
cmd = "gdal_translate -of NETCDF global_catchment_ge_50_cells.map global_catchment_ge_50_cells.nc"
print(cmd)
os.system(cmd)
cmd = "cdo fillmiss2 global_catchment_ge_50_cells.nc global_catchment_ge_50_cells_filled.nc"
print(cmd)
os.system(cmd)
cmd = "cdo fillmiss2 global_catchment_ge_50_cells_filled.nc global_catchment_ge_50_cells_filled.nc"
print(cmd)
os.system(cmd)
cmd = "gdal_translate -of PCRaster global_catchment_ge_50_cells_filled.nc global_catchment_ge_50_cells_filled.map"
print(cmd)
os.system(cmd)
cmd = "mapattr -c " + global_ldd_30min_inp_file + " " + "global_catchment_ge_50_cells_filled.map"
print(cmd)
os.system(cmd)
# - initial subdomains
subdomains_initial = pcr.nominal(
pcr.readmap("global_catchment_ge_50_cells_filled.map"))
subdomains_initial = pcr.areamajority(subdomains_initial, catchment_map)
pcr.aguila(subdomains_initial)
# - initial subdomains clump
subdomains_initial_clump = pcr.clump(subdomains_initial)
pcr.aguila(subdomains_initial_clump)
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial))[1])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[0])))
print(str(int(vos.getMinMaxMean(pcr.scalar(subdomains_initial_clump))[1])))
# - remove temporay files (not used)
cmd = "rm global_catchment_ge_50_cells_filled*"
print(cmd)
os.system(cmd)
# clone code that will be assigned
assigned_number = 0