def snaptomap(points, mmap):
"""
Snap the points in _points_ to nearest non missing
values in _mmap_. Can be used to move gauge locations
to the nearest rivers.
Input:
- points - map with points to move
- mmap - map with points to move to
Return:
- map with shifted points
"""
points = pcr.cover(points, 0)
# Create unique id map of mmap cells
unq = pcr.nominal(pcr.cover(pcr.uniqueid(pcr.defined(mmap)), pcr.scalar(0.0)))
# Now fill holes in mmap map with lues indicating the closes mmap cell.
dist_cellid = pcr.scalar(pcr.spreadzone(unq, 0, 1))
# Get map with values at location in points with closes mmap cell
dist_cellid = pcr.ifthenelse(points > 0, dist_cellid, 0)
# Spread this out
dist_fill = pcr.spreadzone(pcr.nominal(dist_cellid), 0, 1)
# Find the new (moved) locations
npt = pcr.uniqueid(pcr.boolean(pcr.ifthen(dist_fill == unq, unq)))
# Now recreate the original value in the points maps
ptcover = pcr.spreadzone(pcr.cover(points, 0), 0, 1)
# Now get the org point value in the pt map
nptorg = pcr.ifthen(npt > 0, ptcover)
return nptorg
def snaptomap(points, mmap):
"""
Snap the points in _points_ to nearest non missing
values in _mmap_. Can be used to move gauge locations
to the nearest rivers.
Input:
- points - map with points to move
- mmap - map with points to move to
Return:
- map with shifted points
"""
points = pcr.cover(points, 0)
# Create unique id map of mmap cells
unq = pcr.nominal(pcr.cover(pcr.uniqueid(pcr.defined(mmap)), pcr.scalar(0.0)))
# Now fill holes in mmap map with lues indicating the closes mmap cell.
dist_cellid = pcr.scalar(pcr.spreadzone(unq, 0, 1))
# Get map with values at location in points with closes mmap cell
dist_cellid = pcr.ifthenelse(points > 0, dist_cellid, 0)
# Spread this out
dist_fill = pcr.spreadzone(pcr.nominal(dist_cellid), 0, 1)
# Find the new (moved) locations
npt = pcr.uniqueid(pcr.boolean(pcr.ifthen(dist_fill == unq, unq)))
# Now recreate the original value in the points maps
ptcover = pcr.spreadzone(pcr.cover(points, 0), 0, 1)
# Now get the org point value in the pt map
nptorg = pcr.ifthen(npt > 0, ptcover)
return nptorg
def subcatch_order_a(ldd, oorder):
"""
Determines subcatchments using the catchment order
This version uses the last cell BELOW order to derive the
catchments. In general you want the _b version
Input:
- ldd
- order - order to use
Output:
- map with catchment for the given streamorder
"""
outl = find_outlet(ldd)
large = pcr.subcatchment(ldd, pcr.boolean(outl))
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
dif = pcr.upstream(
ldd,
pcr.cover(
pcr.ifthen(
large,
pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts))),
),
0,
),
)
dif = pcr.cover(pcr.scalar(outl), dif) # Add catchment outlet
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(dif)))
sc = pcr.subcatchment(ldd, dif)
return sc, dif, stt
def subcatch_order_a(ldd, oorder):
"""
Determines subcatchments using the catchment order
This version uses the last cell BELOW order to derive the
catchments. In general you want the _b version
Input:
- ldd
- order - order to use
Output:
- map with catchment for the given streamorder
"""
outl = find_outlet(ldd)
large = pcr.subcatchment(ldd, pcr.boolean(outl))
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
dif = pcr.upstream(
ldd,
pcr.cover(
pcr.ifthen(
large,
pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts))),
),
0,
),
)
dif = pcr.cover(pcr.scalar(outl), dif) # Add catchment outlet
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(dif)))
sc = pcr.subcatchment(ldd, dif)
return sc, dif, stt
def subcatch_stream(ldd, stream, threshold):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
stream -- pcraster object direction, streamorder
threshold -- integer, strahler threshold, subcatchments ge threshold are
derived
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
# stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(
pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(
pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(
pcr.nominal(ldd) == 5, pcr.boolean(1),
pcr.ifthenelse(
pcr.downstream(ldd, pcr.scalar(stream_up_sum)) >
pcr.scalar(stream_ge), pcr.boolean(1), pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
return stream_ge, subcatch
def subcatch_stream(ldd, stream, threshold):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
stream -- pcraster object direction, streamorder
threshold -- integer, strahler threshold, subcatchments ge threshold are
derived
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
# stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(pcr.nominal(ldd) == 5, pcr.boolean(1), pcr.ifthenelse(pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge), pcr.boolean(1),
pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
return stream_ge, subcatch
def spatialInterpolation2PCR(fieldArray, pcrType, MV):
#-interpolates the field array to the full extent
field = pcr.numpy2pcr(pcrType, fieldArray, MV)
cellID = pcr.nominal(pcr.uniqueid(pcr.defined(field)))
zoneID = pcr.spreadzone(cellID, 0, 1)
if pcrType == pcr.Scalar:
field = pcr.areaaverage(field, zoneID)
else:
field = pcr.areamajority(field, zoneID)
return field
def interpolategauges(inputmap, method):
""""
Interpolate time series gauge data onto a grid using different methods
inputmap: map with points data for a single timestep
method: string indicating the method
inv
pol
input: inputmap, method
returns: interpolated map
"""
if method == "inv":
result = pcr.inversedistance(1, inputmap, 3, 0, 0)
elif method == "pol":
Unq = pcr.uniqueid(pcr.boolean(inputmap + 1))
result = pcr.spreadzone(pcr.ordinal(pcr.cover(Unq, 0)), 0, 1)
result = pcr.areaaverage(inputmap, result)
else:
Unq = pcr.uniqueid(pcr.boolean(inputmap + 1))
result = pcr.spreadzone(pcr.ordinal(pcr.cover(Unq, 0)), 0, 1)
result = pcr.areaaverage(inputmap, result)
return result
def interpolategauges(inputmap, method):
""""
Interpolate time series gauge data onto a grid using different methods
inputmap: map with points data for a single timestep
method: string indicating the method
inv
pol
input: inputmap, method
returns: interpolated map
"""
if method == "inv":
result = pcr.inversedistance(1, inputmap, 3, 0, 0)
elif method == "pol":
Unq = pcr.uniqueid(pcr.boolean(inputmap + 1))
result = pcr.spreadzone(pcr.ordinal(pcr.cover(Unq, 0)), 0, 1)
result = pcr.areaaverage(inputmap, result)
else:
Unq = pcr.uniqueid(pcr.boolean(inputmap + 1))
result = pcr.spreadzone(pcr.ordinal(pcr.cover(Unq, 0)), 0, 1)
result = pcr.areaaverage(inputmap, result)
return result
def pt_flow_in_river(ldd, river):
"""
Returns all points (True) that flow into the mak river (boolean map with river set to True)
:param ldd: Drainage network
:param river: Map of river (True River, False non-river)
:return ifmap: map with infrlo points into the river (True)
:return ctach: catchment of each of the inflow points
"""
dspts = pcr.downstream(ldd, pcr.cover(river, 0))
dspts = pcr.ifthenelse(pcr.cover(river, 0) == 1, 0, dspts)
catch = pcr.subcatchment(ldd, pcr.nominal(pcr.uniqueid(dspts)))
return dspts, catch
def pt_flow_in_river(ldd, river):
"""
Returns all points (True) that flow into the mak river (boolean map with river set to True)
:param ldd: Drainage network
:param river: Map of river (True River, False non-river)
:return ifmap: map with infrlo points into the river (True)
:return ctach: catchment of each of the inflow points
"""
dspts = pcr.downstream(ldd, pcr.cover(river, 0))
dspts = pcr.ifthenelse(pcr.cover(river, 0) == 1, 0, dspts)
catch = pcr.subcatchment(ldd, pcr.nominal(pcr.uniqueid(dspts)))
return dspts, catch
def premcloop(self):
self.clone = pcr.boolean(cfg.cloneString)
self.dem = pcr.scalar(cfg.dem)
self.createInstancesPremcloop()
# required for reporting as numpy
self.locations = pcr.cover(pcr.nominal(cfg.locations), 0)
pcr.report(self.locations, 'locats')
self.forestNoForest = pcr.boolean(cfg.forestNoForest)
idMap = pcr.uniqueid(self.clone)
oneLocationPerArea = pcr.areamaximum(idMap,
self.forestNoForest) == idMap
self.locationsForParameters = pcr.cover(
pcr.nominal(
pcr.scalar(pcr.ifthen(oneLocationPerArea, self.forestNoForest))
+ 1), 0)
def subcatch_order_b(
ldd, oorder, sizelimit=0, fill=False, fillcomplete=False, stoporder=0
):
"""
Determines subcatchments using the catchment order
This version tries to keep the number op upstream/downstream catchment the
small by first dederivingatchment connected to the major river(the order) given, and fill
up from there.
Input:
- ldd
- oorder - order to use
- sizelimit - smallest catchments to include, default is all (sizelimit=0) in number of cells
- if fill is set to True the higer order catchment are filled also
- if fillcomplete is set to True the whole ldd is filled with catchments.
:returns sc, dif, nldd; Subcatchment, Points, subcatchldd
"""
# outl = find_outlet(ldd)
# large = pcr.subcatchment(ldd,pcr.boolean(outl))
if stoporder == 0:
stoporder = oorder
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
maxorder = pcraster.framework.getCellValue(pcr.mapmaximum(stt), 1, 1)
dif = pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts)))
if fill:
for order in range(oorder, maxorder):
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(order)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(order), m_pts))
)
dif = pcr.uniqueid(pcr.boolean(pcr.cover(m_dif, dif)))
for myorder in range(oorder - 1, stoporder, -1):
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(myorder - 1), m_pts))
)
dif = pcr.uniqueid(
pcr.boolean(pcr.cover(pcr.ifthen(pcr.scalar(sc) == 0, m_dif), dif))
)
if fillcomplete:
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
cs, m_dif, stt = subcatch_order_a(ldd, stoporder)
dif = pcr.uniqueid(
pcr.boolean(
pcr.cover(
pcr.ifthen(pcr.scalar(sc) == 0, pcr.ordinal(m_dif)),
pcr.ordinal(dif),
)
)
)
scsize = pcr.catchmenttotal(1, ldd)
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(pcr.ifthen(scsize >= sizelimit, dif))))
sc = pcr.subcatchment(ldd, dif)
# Make pit ldd
nldd = pcr.lddrepair(pcr.ifthenelse(pcr.cover(dif, 0) > 0, 5, ldd))
return sc, dif, nldd
linescover = pcr.ifthen(lines==1,pcr.scalar(0))
pointscover = pcr.ifthen(pcr.scalar(points)==1,pcr.scalar(0))
#pcr.report(linescover,'lines.map')
#pcr.report(pointscover,'points.map')
dem = pcr.cover(dem,linescover,pointscover)
#pcr.report(dem,'dem1.map')
dem = dem + burn
#pcr.report(dem,'dem2.map')
ldd = pcr.lddcreate(dem,float("1E35"),float("1E35"),float("1E35"),float("1E35"))
else:
ldd = pcr.lddcreate(dem,burnvalue/2,float("1E35"),float("1E35"),float("1E35"))
streamorder = pcr.ordinal(pcr.streamorder(ldd))
river = pcr.boolean(pcr.ifthen(streamorder >= int(min(np.max(pcr.pcr2numpy(streamorder,-9999)),minorder)), streamorder))
outlets = pcr.ifthen(pcr.ordinal(ldd) == 5, pcr.boolean(1))
outlets = pcr.nominal(pcr.uniqueid(outlets))
catchments = pcr.nominal(pcr.catchment(ldd, outlets))
if not keepall:
catchments = pcr.nominal(pcr.ifthen(pcr.mapmaximum(pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments))) == pcr.areatotal(pcr.scalar(catchments)*0+1,pcr.nominal(catchments)),catchments))
pcr.report(ldd,ldd_map)
pcr.report(streamorder,streamorder_map)
pcr.report(river,river_map)
pcr.report(catchments,catchments_map)
if not EPSG == None:
call(('gdal_translate','-of','GTiff','-stats','-a_srs',EPSG,'-ot','Float32',catchments_map,catchments_tif))
else: call(('gdal_translate','-of','GTiff','-stats','-ot','Float32',catchments_map,catchments_tif))
wt.Raster2Pol(catchments_tif,catchshp,srs)
riversid_map = workdir + 'riverid.map'
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)
updateCatchments = selectCatchments(ldd, pits, pitsDict, areas, cloneMap)
outMask = pcr.cover(updateCatchments, continentMasksNew)
pcr.report(pcr.nominal(outMask), 'mask5min.map')
pcr.report(ldd, 'ldd5min.map')
#- create clone, ldd, and mask map for each area.
outMask = pcr.readmap('mask5min.map')
ldd = pcr.readmap('ldd5min.map')
ldd = pcr.lddrepair(pcr.ldd(pcr.ifthen(pcr.boolean(outMask) == 1, pcr.scalar(ldd))))
clone = pcr.cover(pcr.boolean(outMask), pcr.boolean(1))
pcr.report(clone, 'globalClone5min.map')
basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 0.5))
basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 0.5))
basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 1.0))
basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 1.5))
basin_map = pcr.ifthen(landmask, basin_map)
#~ pcr.aguila(basin_map)
msg = "Redefining the basin map (so that it is consistent with the ldd map used in PCR-GLOBWB):"
logger.info(msg)
# - calculate the upstream area of every pixel:
upstream_area = pcr.catchmenttotal(cell_area, ldd)
# - calculate the catchment area of every basin:
upstream_area_maximum = pcr.areamaximum(upstream_area, basin_map)
# - identify the outlet of every basin (in order to rederive the basin so that it is consistent with the ldd)
outlet = pcr.nominal(
pcr.uniqueid(
pcr.ifthen(upstream_area == upstream_area_maximum, pcr.boolean(1.0))))
# - ignoring outlets with small upstream areas
threshold = 50. * 1000. * 1000. # unit: m2
outlet = pcr.ifthen(upstream_area_maximum > threshold, outlet)
#~ pcr.aguila(outlet)
outlet = pcr.cover(outlet, pcr.nominal(0.0))
# - recalculate the basin
basin_map = pcr.nominal(pcr.subcatchment(ldd, outlet))
basin_map = pcr.clump(basin_map)
basin_map = pcr.ifthen(landmask, basin_map)
pcr.report(basin_map, "basin_map.map")
#~ pcr.aguila(basin_map)
# - calculate the basin area
basin_area = pcr.areatotal(cell_area, basin_map)
pcr.report(basin_area, "basin_area.map")
#~ pcr.aguila(basin_area)
def main(
source,
destination,
inifile,
dem_in,
rivshp,
catchshp,
gaugeshp=None,
landuse=None,
soil=None,
lai=None,
other_maps=None,
logfilename="wtools_static_maps.log",
verbose=True,
clean=True,
alltouch=False,
outlets=([], []),
):
# parse other maps into an array
if not other_maps == None:
if type(other_maps) == str:
print other_maps
other_maps = (other_maps.replace(" ", "").replace("[", "").replace(
"]", "").split(","))
source = os.path.abspath(source)
clone_map = os.path.join(source, "mask.map")
clone_shp = os.path.join(source, "mask.shp")
clone_prj = os.path.join(source, "mask.prj")
if None in (rivshp, catchshp, dem_in):
msg = """The following files are compulsory:
- DEM (raster)
- river (shape)
- catchment (shape)
"""
print(msg)
parser.print_help()
sys.exit(1)
if (inifile is not None) and (not os.path.exists(inifile)):
print "path to ini file cannot be found"
sys.exit(1)
if not os.path.exists(rivshp):
print "path to river shape cannot be found"
sys.exit(1)
if not os.path.exists(catchshp):
print "path to catchment shape cannot be found"
sys.exit(1)
if not os.path.exists(dem_in):
print "path to DEM cannot be found"
sys.exit(1)
# open a logger, dependent on verbose print to screen or not
logger, ch = wt.setlogger(logfilename, "WTOOLS", verbose)
# create directories # TODO: check if workdir is still necessary, try to
# keep in memory as much as possible
# delete old files (when the source and destination folder are different)
if np.logical_and(os.path.isdir(destination), destination is not source):
shutil.rmtree(destination)
if destination is not source:
os.makedirs(destination)
# Read mask
if not (os.path.exists(clone_map)):
logger.error(
"Clone file {:s} not found. Please run create_grid first.".format(
clone_map))
sys.exit(1)
else:
# set clone
pcr.setclone(clone_map)
# get the extent from clone.tif
xax, yax, clone, fill_value = wt.gdal_readmap(clone_map, "GTiff")
trans = wt.get_geotransform(clone_map)
extent = wt.get_extent(clone_map)
xmin, ymin, xmax, ymax = extent
zeros = np.zeros(clone.shape)
ones = pcr.numpy2pcr(pcr.Scalar, np.ones(clone.shape), -9999)
# get the projection from clone.tif
srs = wt.get_projection(clone_map)
unit_clone = srs.GetAttrValue("UNIT").lower()
# READ CONFIG FILE
# open config-file
if inifile is None:
config = ConfigParser.SafeConfigParser()
config.optionxform = str
else:
config = wt.OpenConf(inifile)
# read settings
snapgaugestoriver = wt.configget(config,
"settings",
"snapgaugestoriver",
True,
datatype="boolean")
burnalltouching = wt.configget(config,
"settings",
"burncatchalltouching",
True,
datatype="boolean")
burninorder = wt.configget(config,
"settings",
"burncatchalltouching",
False,
datatype="boolean")
verticetollerance = wt.configget(config,
"settings",
"vertice_tollerance",
0.0001,
datatype="float")
""" read parameters """
burn_outlets = wt.configget(config,
"parameters",
"burn_outlets",
10000,
datatype="int")
burn_rivers = wt.configget(config,
"parameters",
"burn_rivers",
200,
datatype="int")
burn_connections = wt.configget(config,
"parameters",
"burn_connections",
100,
datatype="int")
burn_gauges = wt.configget(config,
"parameters",
"burn_gauges",
100,
datatype="int")
minorder = wt.configget(config,
"parameters",
"riverorder_min",
3,
datatype="int")
try:
percentiles = np.array(
config.get("parameters", "statisticmaps",
"0, 100").replace(" ", "").split(","),
dtype="float",
)
except ConfigParser.NoOptionError:
percentiles = [0.0, 100.0]
# read the parameters for generating a temporary very high resolution grid
if unit_clone == "degree":
cellsize_hr = wt.configget(config,
"parameters",
"highres_degree",
0.0005,
datatype="float")
elif (unit_clone == "metre") or (unit_clone == "meter"):
cellsize_hr = wt.configget(config,
"parameters",
"highres_metre",
50,
datatype="float")
cols_hr = int((float(xmax) - float(xmin)) / cellsize_hr + 2)
rows_hr = int((float(ymax) - float(ymin)) / cellsize_hr + 2)
hr_trans = (float(xmin), cellsize_hr, float(0), float(ymax), 0,
-cellsize_hr)
clone_hr = os.path.join(destination, "clone_highres.tif")
# make a highres clone as well!
wt.CreateTif(clone_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# read staticmap locations
catchment_map = wt.configget(config, "staticmaps", "catchment",
"wflow_catchment.map")
dem_map = wt.configget(config, "staticmaps", "dem", "wflow_dem.map")
demmax_map = wt.configget(config, "staticmaps", "demmax",
"wflow_demmax.map")
demmin_map = wt.configget(config, "staticmaps", "demmin",
"wflow_demmin.map")
gauges_map = wt.configget(config, "staticmaps", "gauges",
"wflow_gauges.map")
landuse_map = wt.configget(config, "staticmaps", "landuse",
"wflow_landuse.map")
ldd_map = wt.configget(config, "staticmaps", "ldd", "wflow_ldd.map")
river_map = wt.configget(config, "staticmaps", "river", "wflow_river.map")
outlet_map = wt.configget(config, "staticmaps", "outlet",
"wflow_outlet.map")
riverlength_fact_map = wt.configget(config, "staticmaps",
"riverlength_fact",
"wflow_riverlength_fact.map")
soil_map = wt.configget(config, "staticmaps", "soil", "wflow_soil.map")
streamorder_map = wt.configget(config, "staticmaps", "streamorder",
"wflow_streamorder.map")
subcatch_map = wt.configget(config, "staticmaps", "subcatch",
"wflow_subcatch.map")
# read mask location (optional)
masklayer = wt.configget(config, "mask", "masklayer", catchshp)
# ???? empty = pcr.ifthen(ones == 0, pcr.scalar(0))
# TODO: check if extents are correct this way
# TODO: check what the role of missing values is in zeros and ones (l. 123
# in old code)
# first add a missing value to dem_in
ds = gdal.Open(dem_in, gdal.GA_Update)
RasterBand = ds.GetRasterBand(1)
fill_val = RasterBand.GetNoDataValue()
if fill_val is None:
RasterBand.SetNoDataValue(-9999)
ds = None
# reproject to clone map: see http://stackoverflow.com/questions/10454316/how-to-project-and-resample-a-grid-to-match-another-grid-with-gdal-python
# resample DEM
logger.info("Resampling dem from {:s} to {:s}".format(
os.path.abspath(dem_in), os.path.join(destination, dem_map)))
wt.gdal_warp(
dem_in,
clone_map,
os.path.join(destination, dem_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Average,
)
# retrieve amount of rows and columns from clone
# TODO: make windowstats applicable to source/target with different projections. This does not work yet.
# retrieve srs from DEM
try:
srs_dem = wt.get_projection(dem_in)
except:
logger.warning(
"No projection found in DEM, assuming WGS 1984 lat long")
srs_dem = osr.SpatialReference()
srs_dem.ImportFromEPSG(4326)
clone2dem_transform = osr.CoordinateTransformation(srs, srs_dem)
# if srs.ExportToProj4() == srs_dem.ExportToProj4():
wt.windowstats(
dem_in,
len(yax),
len(xax),
trans,
srs,
destination,
percentiles,
transform=clone2dem_transform,
logger=logger,
)
## read catchment shape-file to create catchment map
src = rasterio.open(clone_map)
shapefile = fiona.open(catchshp, "r")
catchment_shapes = [feature["geometry"] for feature in shapefile]
image = features.rasterize(catchment_shapes,
out_shape=src.shape,
all_touched=True,
transform=src.transform)
catchment_domain = pcr.numpy2pcr(pcr.Ordinal, image.copy(), 0)
## read river shape-file and create burn layer
shapefile = fiona.open(rivshp, "r")
river_shapes = [feature["geometry"] for feature in shapefile]
image = features.rasterize(river_shapes,
out_shape=src.shape,
all_touched=False,
transform=src.transform)
rivers = pcr.numpy2pcr(pcr.Nominal, image.copy(), 0)
riverdem = pcr.scalar(rivers) * pcr.readmap(
os.path.join(destination, dem_map))
pcr.setglobaloption("lddin")
riverldd = pcr.lddcreate(riverdem, 1e35, 1e35, 1e35, 1e35)
riveroutlet = pcr.cover(
pcr.ifthen(pcr.scalar(riverldd) == 5, pcr.scalar(1000)), 0)
burn_layer = pcr.cover(
(pcr.scalar(
pcr.ifthen(
pcr.streamorder(riverldd) > 1, pcr.streamorder(riverldd))) - 1)
* 1000 + riveroutlet,
0,
)
outlets_x, outlets_y = outlets
n_outlets = len(outlets_x)
logger.info("Number of outlets: {}".format(n_outlets))
if n_outlets >= 1:
outlets_map_numbered = tr.points_to_map(pcr.scalar(0), outlets_x,
outlets_y, 0.5)
outlets_map = pcr.boolean(outlets_map_numbered)
# snap outlets to closest river (max 1 cell closer to river)
outlets_map = pcr.boolean(
pcr.cover(tr.snaptomap(pcr.ordinal(outlets_map), rivers), 0))
## create ldd per catchment
logger.info("Calculating ldd")
ldddem = pcr.scalar(clone_map)
# per subcatchment, burn dem, then create modified dem that fits the ldd of the subcatchment
# this ldd dem is merged over catchments, to create a global ldd that abides to the subcatchment boundaries
for idx, shape in enumerate(catchment_shapes):
logger.info("Computing ldd for catchment " + str(idx + 1) + "/" +
str(len(catchment_shapes)))
image = features.rasterize([shape],
out_shape=src.shape,
all_touched=True,
transform=src.transform)
catchment = pcr.numpy2pcr(pcr.Scalar, image.copy(), 0)
dem_burned_catchment = (
pcr.readmap(os.path.join(destination, dem_map)) *
pcr.scalar(catchment_domain) * catchment) - burn_layer
# ldddem_catchment = pcr.lddcreatedem(
# dem_burned_catchment, 1e35, 1e35, 1e35, 1e35)
ldddem = pcr.cover(ldddem, dem_burned_catchment)
pcr.report(ldddem, os.path.join(destination, "ldddem.map"))
wflow_ldd = pcr.lddcreate(ldddem, 1e35, 1e35, 1e35, 1e35)
if n_outlets >= 1:
# set outlets to pit
wflow_ldd = pcr.ifthenelse(outlets_map, pcr.ldd(5), wflow_ldd)
wflow_ldd = pcr.lddrepair(wflow_ldd)
pcr.report(wflow_ldd, os.path.join(destination, "wflow_ldd.map"))
# compute stream order, identify river cells
streamorder = pcr.ordinal(pcr.streamorder(wflow_ldd))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
# find the minimum value in the DEM and cover missing values with a river with this value. Effect is none!! so now left out!
# mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(os.path.join(destination, dem_map)),9999999)))
# dem_resample_map = pcr.cover(os.path.join(destination, dem_map), pcr.scalar(river)*0+mindem)
# pcr.report(dem_resample_map, os.path.join(destination, dem_map))
pcr.report(streamorder, os.path.join(destination, streamorder_map))
pcr.report(river, os.path.join(destination, river_map))
# deal with your catchments
if gaugeshp == None:
logger.info("No gauges defined, using outlets instead")
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(
pcr.ifthen(pcr.scalar(wflow_ldd) == 5, pcr.boolean(1)))))
pcr.report(gauges, os.path.join(destination, gauges_map))
# TODO: Add the gauge shape code from StaticMaps.py (line 454-489)
# TODO: add river length map (see SticMaps.py, line 492-499)
# since the products here (river length fraction) are not yet used
# this is disabled for now, as it also takes a lot of computation time
if False:
# report river length
# make a high resolution empty map
dem_hr_file = os.path.join(destination, "dem_highres.tif")
burn_hr_file = os.path.join(destination, "burn_highres.tif")
demburn_hr_file = os.path.join(destination, "demburn_highres.map")
riv_hr_file = os.path.join(destination, "riv_highres.map")
wt.gdal_warp(dem_in, clone_hr, dem_hr_file)
# wt.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# open the shape layer
ds = ogr.Open(rivshp)
lyr = ds.GetLayer(0)
wt.ogr_burn(
lyr,
clone_hr,
-100,
file_out=burn_hr_file,
format="GTiff",
gdal_type=gdal.GDT_Float32,
fill_value=0,
)
# read dem and burn values and add
xax_hr, yax_hr, burn_hr, fill = wt.gdal_readmap(burn_hr_file, "GTiff")
burn_hr[burn_hr == fill] = 0
xax_hr, yax_hr, dem_hr, fill = wt.gdal_readmap(dem_hr_file, "GTiff")
dem_hr[dem_hr == fill] = np.nan
demburn_hr = dem_hr + burn_hr
demburn_hr[np.isnan(demburn_hr)] = -9999
wt.gdal_writemap(demburn_hr_file, "PCRaster", xax_hr, yax_hr,
demburn_hr, -9999.)
pcr.setclone(demburn_hr_file)
demburn_hr = pcr.readmap(demburn_hr_file)
logger.info("Calculating ldd to determine river length")
ldd_hr = pcr.lddcreate(demburn_hr, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_hr, os.path.join(destination, "ldd_hr.map"))
pcr.setglobaloption("unitcell")
riv_hr = pcr.scalar(
pcr.streamorder(ldd_hr) >= minorder) * pcr.downstreamdist(ldd_hr)
pcr.report(riv_hr, riv_hr_file)
pcr.setglobaloption("unittrue")
pcr.setclone(clone_map)
logger.info("Computing river length")
wt.windowstats(
riv_hr_file,
len(yax),
len(xax),
trans,
srs,
destination,
stat="fact",
transform=False,
logger=logger,
)
# TODO: nothing happens with the river lengths yet. Need to decide how to use these
# report outlet map
pcr.report(
pcr.ifthen(pcr.ordinal(wflow_ldd) == 5, pcr.ordinal(1)),
os.path.join(destination, outlet_map),
)
# report subcatchment map
subcatchment = pcr.subcatchment(wflow_ldd, gauges)
pcr.report(pcr.ordinal(subcatchment),
os.path.join(destination, subcatch_map))
# Report land use map
if landuse == None:
logger.info(
"No land use map used. Preparing {:s} with only ones.".format(
os.path.join(destination, landuse_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, landuse_map))
else:
logger.info("Resampling land use from {:s} to {:s}".format(
os.path.abspath(landuse),
os.path.join(destination, os.path.abspath(landuse_map)),
))
wt.gdal_warp(
landuse,
clone_map,
os.path.join(destination, landuse_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32,
)
# report soil map
if soil == None:
logger.info("No soil map used. Preparing {:s} with only ones.".format(
os.path.join(destination, soil_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, soil_map))
else:
logger.info("Resampling soil from {:s} to {:s}".format(
os.path.abspath(soil),
os.path.join(destination, os.path.abspath(soil_map)),
))
wt.gdal_warp(
soil,
clone_map,
os.path.join(destination, soil_map),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32,
)
if lai == None:
logger.info(
"No vegetation LAI maps used. Preparing default maps {:s} with only ones."
.format(os.path.join(destination, soil_map)))
pcr.report(pcr.nominal(ones), os.path.join(destination, soil_map))
else:
dest_lai = os.path.join(destination, "clim")
os.makedirs(dest_lai)
for month in range(12):
lai_in = os.path.join(lai, "LAI00000.{:03d}".format(month + 1))
lai_out = os.path.join(dest_lai,
"LAI00000.{:03d}".format(month + 1))
logger.info("Resampling vegetation LAI from {:s} to {:s}".format(
os.path.abspath(lai_in), os.path.abspath(lai_out)))
wt.gdal_warp(
lai_in,
clone_map,
lai_out,
format="PCRaster",
gdal_interp=gdalconst.GRA_Bilinear,
gdal_type=gdalconst.GDT_Float32,
)
# report soil map
if other_maps == None:
logger.info("No other maps used. Skipping other maps.")
else:
logger.info("Resampling list of other maps...")
for map_file in other_maps:
map_name = os.path.split(map_file)[1]
logger.info("Resampling a map from {:s} to {:s}".format(
os.path.abspath(map_file),
os.path.join(
destination,
os.path.splitext(os.path.basename(map_file))[0] + ".map",
),
))
wt.gdal_warp(
map_file,
clone_map,
os.path.join(
destination,
os.path.splitext(os.path.basename(map_file))[0] + ".map",
),
format="PCRaster",
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Float32,
)
if clean:
wt.DeleteList(glob.glob(os.path.join(destination, "*.xml")),
logger=logger)
wt.DeleteList(glob.glob(os.path.join(destination, "clim", "*.xml")),
logger=logger)
wt.DeleteList(glob.glob(os.path.join(destination, "*highres*")),
logger=logger)
def subcatch_stream(ldd, threshold, stream=None, min_strahler=-999, max_strahler=999, assign_edge=False, assign_existing=False, up_area=None, basin=None):
"""
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
threshold -- integer, strahler threshold, subcatchments ge threshold
are derived
stream=None -- pcraster object ordinal, stream order map (made with pcr.streamorder), if provided, stream order
map is not generated on the fly but used from this map. Useful when a subdomain within a catchment is
provided, which would cause edge effects in the stream order map
min_strahler=-999 -- integer, minimum strahler threshold of river catchments
to return
max_strahler=999 -- integer, maximum strahler threshold of river catchments
to return
assign_unique=False -- if set to True, unassigned connected areas at
the edges of the domain are assigned a unique id as well. If set
to False, edges are not assigned
assign_existing=False == if set to True, unassigned edges are assigned
to existing basins with an upstream weighting. If set to False,
edges are assigned to unique IDs, or not assigned
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
if stream is None:
stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(pcr.downstream(ldd, stream_ge) != stream_ge, pcr.boolean(1),
pcr.ifthenelse(pcr.nominal(ldd) == 5, pcr.boolean(1), pcr.ifthenelse(pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge), pcr.boolean(1),
pcr.boolean(0))))
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
# mask out areas outside basin
if basin is not None:
subcatch = pcr.ifthen(basin, subcatch)
if assign_edge:
# fill unclassified areas (in pcraster equal to zero) with a unique id, above the maximum id assigned so far
unique_edge = pcr.clump(pcr.ifthen(subcatch==0, pcr.ordinal(0)))
subcatch = pcr.ifthenelse(subcatch==0, pcr.nominal(pcr.mapmaximum(pcr.scalar(subcatch)) + pcr.scalar(unique_edge)), pcr.nominal(subcatch))
elif assign_existing:
# unaccounted areas are added to largest nearest draining basin
if up_area is None:
up_area = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), pcr.accuflux(ldd, 1))
riverid = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), subcatch)
friction = 1./pcr.scalar(pcr.spreadzone(pcr.cover(pcr.ordinal(up_area), 0), 0, 0)) # *(pcr.scalar(ldd)*0+1)
delta = pcr.ifthen(pcr.scalar(ldd)>=0, pcr.ifthen(pcr.cover(subcatch, 0)==0, pcr.spreadzone(pcr.cover(riverid, 0), 0, friction)))
subcatch = pcr.ifthenelse(pcr.boolean(pcr.cover(subcatch, 0)),
subcatch,
delta)
# finally, only keep basins with minimum and maximum river order flowing through them
strahler_subcatch = pcr.areamaximum(stream, subcatch)
subcatch = pcr.ifthen(pcr.ordinal(strahler_subcatch) >= min_strahler, pcr.ifthen(pcr.ordinal(strahler_subcatch) <= max_strahler, subcatch))
return stream_ge, pcr.ordinal(subcatch)
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'])
def main():
### Read input arguments #####
logfilename = 'wtools_static_maps.log'
parser = OptionParser()
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option('-q',
'--quiet',
dest='verbose',
default=True,
action='store_false',
help='do not print status messages to stdout')
parser.add_option('-i',
'--ini',
dest='inifile',
default=None,
help='ini file with settings for static_maps.exe')
parser.add_option('-s',
'--source',
dest='source',
default='wflow',
help='Source folder containing clone (default=./wflow)')
parser.add_option('-d',
'--destination',
dest='destination',
default='staticmaps',
help='Destination folder (default=./staticmaps)')
parser.add_option('-r',
'--river',
dest='rivshp',
default=None,
help='river network polyline layer (ESRI Shapefile)')
parser.add_option('-c',
'--catchment',
dest='catchshp',
default=None,
help='catchment polygon layer (ESRI Shapefile)')
parser.add_option('-g',
'--gauges',
dest='gaugeshp',
default=None,
help='gauge point layer (ESRI Shapefile)')
parser.add_option('-D',
'--dem',
dest='dem_in',
default=None,
help='digital elevation model (GeoTiff)')
parser.add_option('-L',
'--landuse',
dest='landuse',
default=None,
help='land use / land cover layer (GeoTiff)')
parser.add_option('-S',
'--soiltype',
dest='soil',
default=None,
help='soil type layer (GeoTiff)')
parser.add_option(
'-V',
'--vegetation',
dest='lai',
default=None,
help=
'vegetation LAI layer location (containing 12 GeoTiffs <LAI00000.XXX.tif>)'
)
parser.add_option(
'-O',
'--other_maps',
dest='other_maps',
default=None,
help=
'bracketed [] comma-separated list of paths to other maps that should be reprojected'
)
parser.add_option(
'-C',
'--clean',
dest='clean',
default=False,
action='store_true',
help='Clean the .xml files from static maps folder when finished')
parser.add_option(
'-A',
'--alltouch',
dest='alltouch',
default=False,
action='store_true',
help=
'option to burn catchments "all touching".\nUseful when catchment-size is small compared to cellsize'
)
(options, args) = parser.parse_args()
# parse other maps into an array
options.other_maps = options.other_maps.replace(' ', '').replace(
'[', '').replace(']', '').split(',')
options.source = os.path.abspath(options.source)
clone_map = os.path.join(options.source, 'mask.map')
clone_shp = os.path.join(options.source, 'mask.shp')
clone_prj = os.path.join(options.source, 'mask.prj')
if None in (options.inifile, options.rivshp, options.catchshp,
options.dem_in):
msg = """The following files are compulsory:
- ini file
- DEM (raster)
- river (shape)
- catchment (shape)
"""
print(msg)
parser.print_help()
sys.exit(1)
if not os.path.exists(options.inifile):
print 'path to ini file cannot be found'
sys.exit(1)
if not os.path.exists(options.rivshp):
print 'path to river shape cannot be found'
sys.exit(1)
if not os.path.exists(options.catchshp):
print 'path to catchment shape cannot be found'
sys.exit(1)
if not os.path.exists(options.dem_in):
print 'path to DEM cannot be found'
sys.exit(1)
# open a logger, dependent on verbose print to screen or not
logger, ch = wtools_lib.setlogger(logfilename, 'WTOOLS', options.verbose)
# create directories # TODO: check if workdir is still necessary, try to keep in memory as much as possible
# delete old files (when the source and destination folder are different)
if np.logical_and(os.path.isdir(options.destination), options.destination
is not options.source):
shutil.rmtree(options.destination)
if options.destination is not options.source:
os.makedirs(options.destination)
# Read mask
if not (os.path.exists(clone_map)):
logger.error(
'Clone file {:s} not found. Please run create_grid first.'.format(
clone_map))
sys.exit(1)
else:
# set clone
pcr.setclone(clone_map)
# get the extent from clone.tif
xax, yax, clone, fill_value = gis.gdal_readmap(clone_map, 'GTiff')
trans = wtools_lib.get_geotransform(clone_map)
extent = wtools_lib.get_extent(clone_map)
xmin, ymin, xmax, ymax = extent
zeros = np.zeros(clone.shape)
ones = pcr.numpy2pcr(pcr.Scalar, np.ones(clone.shape), -9999)
# get the projection from clone.tif
srs = wtools_lib.get_projection(clone_map)
unit_clone = srs.GetAttrValue('UNIT').lower()
### READ CONFIG FILE
# open config-file
config = wtools_lib.OpenConf(options.inifile)
# read settings
snapgaugestoriver = wtools_lib.configget(config,
'settings',
'snapgaugestoriver',
True,
datatype='boolean')
burnalltouching = wtools_lib.configget(config,
'settings',
'burncatchalltouching',
True,
datatype='boolean')
burninorder = wtools_lib.configget(config,
'settings',
'burncatchalltouching',
False,
datatype='boolean')
verticetollerance = wtools_lib.configget(config,
'settings',
'vertice_tollerance',
0.0001,
datatype='float')
''' read parameters '''
burn_outlets = wtools_lib.configget(config,
'parameters',
'burn_outlets',
10000,
datatype='int')
burn_rivers = wtools_lib.configget(config,
'parameters',
'burn_rivers',
200,
datatype='int')
burn_connections = wtools_lib.configget(config,
'parameters',
'burn_connections',
100,
datatype='int')
burn_gauges = wtools_lib.configget(config,
'parameters',
'burn_gauges',
100,
datatype='int')
minorder = wtools_lib.configget(config,
'parameters',
'riverorder_min',
3,
datatype='int')
percentiles = np.array(config.get('parameters', 'statisticmaps',
'0, 100').replace(' ', '').split(','),
dtype='float')
# read the parameters for generating a temporary very high resolution grid
if unit_clone == 'degree':
cellsize_hr = wtools_lib.configget(config,
'parameters',
'highres_degree',
0.0005,
datatype='float')
elif (unit_clone == 'metre') or (unit_clone == 'meter'):
cellsize_hr = wtools_lib.configget(config,
'parameters',
'highres_metre',
50,
datatype='float')
cols_hr = int((float(xmax) - float(xmin)) / cellsize_hr + 2)
rows_hr = int((float(ymax) - float(ymin)) / cellsize_hr + 2)
hr_trans = (float(xmin), cellsize_hr, float(0), float(ymax), 0,
-cellsize_hr)
clone_hr = os.path.join(options.destination, 'clone_highres.tif')
# make a highres clone as well!
wtools_lib.CreateTif(clone_hr, rows_hr, cols_hr, hr_trans, srs, 0)
# read staticmap locations
catchment_map = wtools_lib.configget(config, 'staticmaps', 'catchment',
'wflow_catchment.map')
dem_map = wtools_lib.configget(config, 'staticmaps', 'dem',
'wflow_dem.map')
demmax_map = wtools_lib.configget(config, 'staticmaps', 'demmax',
'wflow_demmax.map')
demmin_map = wtools_lib.configget(config, 'staticmaps', 'demmin',
'wflow_demmin.map')
gauges_map = wtools_lib.configget(config, 'staticmaps', 'gauges',
'wflow_gauges.map')
landuse_map = wtools_lib.configget(config, 'staticmaps', 'landuse',
'wflow_landuse.map')
ldd_map = wtools_lib.configget(config, 'staticmaps', 'ldd',
'wflow_ldd.map')
river_map = wtools_lib.configget(config, 'staticmaps', 'river',
'wflow_river.map')
outlet_map = wtools_lib.configget(config, 'staticmaps', 'outlet',
'wflow_outlet.map')
riverlength_fact_map = wtools_lib.configget(config, 'staticmaps',
'riverlength_fact',
'wflow_riverlength_fact.map')
soil_map = wtools_lib.configget(config, 'staticmaps', 'soil',
'wflow_soil.map')
streamorder_map = wtools_lib.configget(config, 'staticmaps', 'streamorder',
'wflow_streamorder.map')
subcatch_map = wtools_lib.configget(config, 'staticmaps', 'subcatch',
'wflow_subcatch.map')
# read mask location (optional)
masklayer = wtools_lib.configget(config, 'mask', 'masklayer',
options.catchshp)
# ???? empty = pcr.ifthen(ones == 0, pcr.scalar(0))
# TODO: check if extents are correct this way
# TODO: check what the role of missing values is in zeros and ones (l. 123 in old code)
# first add a missing value to dem_in
ds = gdal.Open(options.dem_in, gdal.GA_Update)
RasterBand = ds.GetRasterBand(1)
fill_val = RasterBand.GetNoDataValue()
if fill_val is None:
RasterBand.SetNoDataValue(-9999)
ds = None
# reproject to clone map: see http://stackoverflow.com/questions/10454316/how-to-project-and-resample-a-grid-to-match-another-grid-with-gdal-python
# resample DEM
logger.info('Resampling dem from {:s} to {:s}'.format(
os.path.abspath(options.dem_in),
os.path.join(options.destination, dem_map)))
gis.gdal_warp(options.dem_in,
clone_map,
os.path.join(options.destination, dem_map),
format='PCRaster',
gdal_interp=gdalconst.GRA_Average)
# retrieve amount of rows and columns from clone
# TODO: make windowstats applicable to source/target with different projections. This does not work yet.
# retrieve srs from DEM
try:
srs_dem = wtools_lib.get_projection(options.dem_in)
except:
logger.warning(
'No projection found in DEM, assuming WGS 1984 lat long')
srs_dem = osr.SpatialReference()
srs_dem.ImportFromEPSG(4326)
clone2dem_transform = osr.CoordinateTransformation(srs, srs_dem)
#if srs.ExportToProj4() == srs_dem.ExportToProj4():
for percentile in percentiles:
if percentile >= 100:
logger.info('computing window maximum')
percentile_dem = os.path.join(options.destination,
'wflow_dem_max.map')
elif percentile <= 0:
logger.info('computing window minimum')
percentile_dem = os.path.join(options.destination,
'wflow_dem_min.map')
else:
logger.info('computing window {:d} percentile'.format(
int(percentile)))
percentile_dem = os.path.join(
options.destination,
'wflow_dem_{:03d}.map'.format(int(percentile)))
percentile_dem = os.path.join(
options.destination,
'wflow_dem_{:03d}.map'.format(int(percentile)))
stats = wtools_lib.windowstats(options.dem_in,
len(yax),
len(xax),
trans,
srs,
percentile_dem,
percentile,
transform=clone2dem_transform,
logger=logger)
# else:
# logger.warning('Projections of DEM and clone are different. DEM statistics for different projections is not yet implemented')
"""
# burn in rivers
# first convert and clip the river shapefile
# retrieve river shape projection, if not available assume EPSG:4326
file_att = os.path.splitext(os.path.basename(options.rivshp))[0]
ds = ogr.Open(options.rivshp)
lyr = ds.GetLayerByName(file_att)
extent = lyr.GetExtent()
extent_in = [extent[0], extent[2], extent[1], extent[3]]
try:
# get spatial reference from shapefile
srs_rivshp = lyr.GetSpatialRef()
logger.info('Projection in river shapefile is {:s}'.format(srs_rivshp.ExportToProj4()))
except:
logger.warning('No projection found in {:s}, assuming WGS 1984 lat-lon'.format(options.rivshp))
srs_rivshp = osr.SpatialReference()
srs_rivshp.ImportFromEPSG(4326)
rivprojshp = os.path.join(options.destination, 'rivshp_proj.shp')
logger.info('Projecting and clipping {:s} to {:s}'.format(options.rivshp, rivprojshp))
# TODO: Line below takes a very long time to process, the bigger the shapefile, the more time. How do we deal with this?
call(('ogr2ogr','-s_srs', srs_rivshp.ExportToProj4(),'-t_srs', srs.ExportToProj4(), '-clipsrc', '{:f}'.format(xmin), '{:f}'.format(ymin), '{:f}'.format(xmax), '{:f}'.format(ymax), rivprojshp, options.rivshp))
"""
# TODO: BURNING!!
# project catchment layer to projection of clone
file_att = os.path.splitext(os.path.basename(options.catchshp))[0]
print options.catchshp
ds = ogr.Open(options.catchshp)
lyr = ds.GetLayerByName(file_att)
extent = lyr.GetExtent()
extent_in = [extent[0], extent[2], extent[1], extent[3]]
try:
# get spatial reference from shapefile
srs_catchshp = lyr.GetSpatialRef()
logger.info('Projection in catchment shapefile is {:s}'.format(
srs_catchshp.ExportToProj4()))
except:
logger.warning(
'No projection found in {:s}, assuming WGS 1984 lat-lon'.format(
options.catchshp))
srs_catchshp = osr.SpatialReference()
srs_catchshp.ImportFromEPSG(4326)
catchprojshp = os.path.join(options.destination, 'catchshp_proj.shp')
logger.info('Projecting {:s} to {:s}'.format(options.catchshp,
catchprojshp))
call(('ogr2ogr', '-s_srs', srs_catchshp.ExportToProj4(), '-t_srs',
srs.ExportToProj4(), '-clipsrc', '{:f}'.format(xmin),
'{:f}'.format(ymin), '{:f}'.format(xmax), '{:f}'.format(ymax),
catchprojshp, options.catchshp))
#
logger.info('Calculating ldd')
ldddem = pcr.readmap(os.path.join(options.destination, dem_map))
ldd_select = pcr.lddcreate(ldddem, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_select, os.path.join(options.destination, 'wflow_ldd.map'))
# compute stream order, identify river cells
streamorder = pcr.ordinal(pcr.streamorder(ldd_select))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
# find the minimum value in the DEM and cover missing values with a river with this value. Effect is none!! so now left out!
# mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(os.path.join(options.destination, dem_map)),9999999)))
# dem_resample_map = pcr.cover(os.path.join(options.destination, dem_map), pcr.scalar(river)*0+mindem)
# pcr.report(dem_resample_map, os.path.join(options.destination, dem_map))
pcr.report(streamorder, os.path.join(options.destination, streamorder_map))
pcr.report(river, os.path.join(options.destination, river_map))
# deal with your catchments
if options.gaugeshp == None:
logger.info('No gauges defined, using outlets instead')
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(
pcr.ifthen(pcr.scalar(ldd_select) == 5, pcr.boolean(1)))))
pcr.report(gauges, os.path.join(options.destination, gauges_map))
# TODO: Add the gauge shape code from StaticMaps.py (line 454-489)
# TODO: add river length map (see SticMaps.py, line 492-499)
# report river length
# make a high resolution empty map
dem_hr_file = os.path.join(options.destination, 'dem_highres.tif')
burn_hr_file = os.path.join(options.destination, 'burn_highres.tif')
demburn_hr_file = os.path.join(options.destination, 'demburn_highres.map')
riv_hr_file = os.path.join(options.destination, 'riv_highres.map')
gis.gdal_warp(options.dem_in, clone_hr, dem_hr_file)
# wtools_lib.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs, 0)
file_att = os.path.splitext(os.path.basename(options.rivshp))[0]
# open the shape layer
ds = ogr.Open(options.rivshp)
lyr = ds.GetLayerByName(file_att)
gis.ogr_burn(lyr,
clone_hr,
-100,
file_out=burn_hr_file,
format='GTiff',
gdal_type=gdal.GDT_Float32,
fill_value=0)
# read dem and burn values and add
xax_hr, yax_hr, burn_hr, fill = gis.gdal_readmap(burn_hr_file, 'GTiff')
burn_hr[burn_hr == fill] = 0
xax_hr, yax_hr, dem_hr, fill = gis.gdal_readmap(dem_hr_file, 'GTiff')
dem_hr[dem_hr == fill] = np.nan
demburn_hr = dem_hr + burn_hr
demburn_hr[np.isnan(demburn_hr)] = -9999
gis.gdal_writemap(demburn_hr_file, 'PCRaster', xax_hr, yax_hr, demburn_hr,
-9999.)
pcr.setclone(demburn_hr_file)
demburn_hr = pcr.readmap(demburn_hr_file)
ldd_hr = pcr.lddcreate(demburn_hr, 1e35, 1e35, 1e35, 1e35)
pcr.report(ldd_hr, os.path.join(options.destination, 'ldd_hr.map'))
pcr.setglobaloption('unitcell')
riv_hr = pcr.scalar(
pcr.streamorder(ldd_hr) >= minorder) * pcr.downstreamdist(ldd_hr)
pcr.report(riv_hr, riv_hr_file)
pcr.setglobaloption('unittrue')
pcr.setclone(clone_map)
logger.info('Computing river length')
#riverlength = wt.windowstats(riv_hr,clone_rows,clone_columns,clone_trans,srs_clone,resultdir,'frac',clone2dem_transform)
riverlength = wtools_lib.windowstats(riv_hr_file,
len(yax),
len(xax),
trans,
srs,
os.path.join(options.destination,
riverlength_fact_map),
stat='fact',
logger=logger)
# TODO: nothing happends with the river lengths yet. Need to decide how to use these
# report outlet map
pcr.report(pcr.ifthen(pcr.ordinal(ldd_select) == 5, pcr.ordinal(1)),
os.path.join(options.destination, outlet_map))
# report subcatchment map
subcatchment = pcr.subcatchment(ldd_select, gauges)
pcr.report(pcr.ordinal(subcatchment),
os.path.join(options.destination, subcatch_map))
# Report land use map
if options.landuse == None:
logger.info(
'No land use map used. Preparing {:s} with only ones.'.format(
os.path.join(options.destination, landuse_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, landuse_map))
else:
logger.info('Resampling land use from {:s} to {:s}'.format(
os.path.abspath(options.landuse),
os.path.join(options.destination, os.path.abspath(landuse_map))))
gis.gdal_warp(options.landuse,
clone_map,
os.path.join(options.destination, landuse_map),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32)
# report soil map
if options.soil == None:
logger.info('No soil map used. Preparing {:s} with only ones.'.format(
os.path.join(options.destination, soil_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, soil_map))
else:
logger.info('Resampling soil from {:s} to {:s}'.format(
os.path.abspath(options.soil),
os.path.join(options.destination, os.path.abspath(soil_map))))
gis.gdal_warp(options.soil,
clone_map,
os.path.join(options.destination, soil_map),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Int32)
if options.lai == None:
logger.info(
'No vegetation LAI maps used. Preparing default maps {:s} with only ones.'
.format(os.path.join(options.destination, soil_map)))
pcr.report(pcr.nominal(ones),
os.path.join(options.destination, soil_map))
else:
dest_lai = os.path.join(options.destination, 'clim')
os.makedirs(dest_lai)
for month in range(12):
lai_in = os.path.join(options.lai,
'LAI00000.{:03d}'.format(month + 1))
lai_out = os.path.join(dest_lai,
'LAI00000.{:03d}'.format(month + 1))
logger.info('Resampling vegetation LAI from {:s} to {:s}'.format(
os.path.abspath(lai_in), os.path.abspath(lai_out)))
gis.gdal_warp(lai_in,
clone_map,
lai_out,
format='PCRaster',
gdal_interp=gdalconst.GRA_Bilinear,
gdal_type=gdalconst.GDT_Float32)
# report soil map
if options.other_maps == None:
logger.info('No other maps used. Skipping other maps.')
else:
logger.info('Resampling list of other maps...')
for map_file in options.other_maps:
map_name = os.path.split(map_file)[1]
logger.info('Resampling a map from {:s} to {:s}'.format(
os.path.abspath(map_file),
os.path.join(options.destination, map_name)))
gis.gdal_warp(map_file,
clone_map,
os.path.join(options.destination, map_name),
format='PCRaster',
gdal_interp=gdalconst.GRA_Mode,
gdal_type=gdalconst.GDT_Float32)
if options.clean:
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, '*.xml')),
logger=logger)
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, 'clim', '*.xml')),
logger=logger)
wtools_lib.DeleteList(glob.glob(
os.path.join(options.destination, '*highres*')),
logger=logger)
num_of_rows_30sec = str(vos.getMapAttributesALL(output_file)["rows"] / 10.)
num_of_cols_30sec = str(vos.getMapAttributesALL(output_file)["cols"] / 10.)
x_coordinate = str(vos.getMapAttributesALL(output_file)["xUL"])
y_coordinate = str(vos.getMapAttributesALL(output_file)["yUL"])
cellsize_30sec = "0.00833333333333333333333333333333333333333333333333333333333333333333333333333333"
output_file = output_folder + "/" + tile_code + ".30sec.clo.map"
cmd = "mapattr -s -R " + num_of_rows_30sec + " -C " + num_of_cols_30sec + " -B -P yb2t -x " + x_coordinate + " -y " + y_coordinate + " -l " + cellsize_30sec + " " + output_file
print(cmd)
os.system(cmd)
# give the ids for every 30 arc sec cell (e.g. pcrcalc dem_tif_n60w180_30sec.ids.map = "nominal(uniqueid(dem_tif_n60w180_30sec.clo.map))")
input_file = output_file
output_file = output_folder + "/" + tile_code + ".30sec.ids.map"
pcr.setclone(input_file)
print("Making the clone map at 30 arcsec resolution.")
unique_ids_30sec = pcr.nominal(pcr.uniqueid(input_file))
pcr.report(unique_ids_30sec, output_file)
# - Note that this map has 30 arc sec resolution.
# resample the ids map to 3 arc sec resolution (e.g. gdalwarp -tr 0.00083333333333333333333333333333333333333 0.00083333333333333333333333333333333333333 dem_tif_n60w180_30sec.ids.map dem_tif_n60w180_30sec.ids.3sec.tif
input_file = output_file
output_file = output_folder + "/" + tile_code + ".30sec.ids.3sec.tif"
cellsize_3sec = "0.000833333333333333333333333333333333333333333333333333333333333333333333333333333"
cmd = "gdalwarp -tr " + cellsize_3sec + " " + cellsize_3sec + " " + input_file + " " + output_file
print(cmd)
os.system(cmd)
# - This still a tif file.
# convert the tif file to PCRaster map
input_file = output_file
output_file = output_folder + "/" + tile_code + ".30sec.ids.3sec.map"
def subcatch_order_b(
ldd, oorder, sizelimit=0, fill=False, fillcomplete=False, stoporder=0
):
"""
Determines subcatchments using the catchment order
This version tries to keep the number op upstream/downstream catchment the
small by first dederivingatchment connected to the major river(the order) given, and fill
up from there.
Input:
- ldd
- oorder - order to use
- sizelimit - smallest catchments to include, default is all (sizelimit=0) in number of cells
- if fill is set to True the higer order catchment are filled also
- if fillcomplete is set to True the whole ldd is filled with catchments.
:returns sc, dif, nldd; Subcatchment, Points, subcatchldd
"""
# outl = find_outlet(ldd)
# large = pcr.subcatchment(ldd,pcr.boolean(outl))
if stoporder == 0:
stoporder = oorder
stt = pcr.streamorder(ldd)
sttd = pcr.downstream(ldd, stt)
pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
maxorder = pcraster.framework.getCellValue(pcr.mapmaximum(stt), 1, 1)
dif = pcr.uniqueid(pcr.boolean(pcr.ifthen(stt == pcr.ordinal(oorder), pts)))
if fill:
for order in range(oorder, maxorder):
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(order)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(order), m_pts))
)
dif = pcr.uniqueid(pcr.boolean(pcr.cover(m_dif, dif)))
for myorder in range(oorder - 1, stoporder, -1):
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
m_pts = pcr.ifthen((pcr.scalar(sttd) - pcr.scalar(stt)) > 0.0, sttd)
m_dif = pcr.uniqueid(
pcr.boolean(pcr.ifthen(stt == pcr.ordinal(myorder - 1), m_pts))
)
dif = pcr.uniqueid(
pcr.boolean(pcr.cover(pcr.ifthen(pcr.scalar(sc) == 0, m_dif), dif))
)
if fillcomplete:
sc = pcr.subcatchment(ldd, pcr.nominal(dif))
cs, m_dif, stt = subcatch_order_a(ldd, stoporder)
dif = pcr.uniqueid(
pcr.boolean(
pcr.cover(
pcr.ifthen(pcr.scalar(sc) == 0, pcr.ordinal(m_dif)),
pcr.ordinal(dif),
)
)
)
scsize = pcr.catchmenttotal(1, ldd)
dif = pcr.ordinal(pcr.uniqueid(pcr.boolean(pcr.ifthen(scsize >= sizelimit, dif))))
sc = pcr.subcatchment(ldd, dif)
# Make pit ldd
nldd = pcr.lddrepair(pcr.ifthenelse(pcr.cover(dif, 0) > 0, 5, ldd))
return sc, dif, nldd
def main():
clone_map = "mask\mask.map"
clone_shp = "mask\mask.shp"
clone_prj = "mask\mask.prj"
workdir = "work\\"
resultdir = "staticmaps\\"
''' read commandline arguments '''
argv = sys.argv
clone_EPSG = False
try:
opts, args = getopt.getopt(argv[1:], 'i:g:p:r:c:d:l:s:CA')
except getopt.error:
print 'fout'
Usage()
sys.exit(1)
inifile = None
rivshp = None
catchshp = None
dem_in = None
landuse = None
soiltype = None
clean = False
gaugeshp = None
alltouching = False
for o, a in opts:
if o == '-i': inifile = a
if o == '-p': clone_EPSG = 'EPSG:' + a
if o == '-r': rivshp = a
if o == '-c': catchshp = a
if o == '-d': dem_in = a
if o == '-l': landuse = a
if o == '-s': soiltype = a
if o == '-C': clean = True
if o == '-g': gaugeshp = a
if o == '-A': alltouching = True
if inifile == None or rivshp == None or catchshp == None or dem_in == None:
print 'the following files are compulsory:'
print ' - ini-file'
print ' - DEM (raster)'
print ' - river (shape)'
print ' - catchment (shape)'
Usage()
sys.exit(1)
if landuse == None:
print 'no raster with landuse classifications is specified. 1 class will be applied for the entire domain'
if soiltype == None:
print 'no raster with soil classifications is specified. 1 class will be applied for the entire domain'
''' read mask '''
if not os.path.exists(clone_map):
print 'Mask not found. Make sure the file mask\mask.map exists'
print 'This file is usually created with the CreateGrid script'
sys.exit(1)
else:
pcr.setclone(clone_map)
ds = gdal.Open(clone_map, GA_ReadOnly)
clone_trans = ds.GetGeoTransform()
cellsize = clone_trans[1]
clone_rows = ds.RasterYSize
clone_columns = ds.RasterXSize
extent_mask = [
clone_trans[0], clone_trans[3] - ds.RasterYSize * cellsize,
clone_trans[0] + ds.RasterXSize * cellsize, clone_trans[3]
]
xmin, ymin, xmax, ymax = map(str, extent_mask)
ds = None
ones = pcr.scalar(pcr.readmap(clone_map))
zeros = ones * 0
empty = pcr.ifthen(ones == 0, pcr.scalar(0))
''' read projection from mask.shp '''
# TODO: check how to deal with projections (add .prj to mask.shp in creategrid)
if not os.path.exists(clone_prj):
print 'please add prj-file to mask.shp'
sys.exit(1)
if os.path.exists(clone_shp):
ds = ogr.Open(clone_shp)
file_att = os.path.splitext(os.path.basename(clone_shp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
if not spatialref == None:
srs_clone = osr.SpatialReference()
srs_clone.ImportFromWkt(spatialref.ExportToWkt())
srs_clone.AutoIdentifyEPSG()
unit_clone = False
unit_clone = srs_clone.GetAttrValue('UNIT').lower()
#clone_EPSG = 'EPSG:'+srs_clone.GetAttrValue("AUTHORITY",1)
# TODO: fix hard EPSG code below
clone_EPSG = 'EPSG:' + '4167'
print 'EPSG-code is read from mask.shp: ' + clone_EPSG
spatialref == None
if not clone_EPSG:
print 'EPSG-code cannot be read from mask.shp'
print 'please add prj-file to mask.shp or specify on command line'
print 'e.g. -p EPSG:4326 (for WGS84 lat lon projection)'
ds = None
clone_EPSG_int = int(clone_EPSG[5:len(clone_EPSG)])
''' open config-file '''
config = wt.OpenConf(inifile)
''' read settings '''
snapgaugestoriver = bool(
int(wt.configget(config, "settings", "snapgaugestoriver", "1")))
burnalltouching = bool(
int(wt.configget(config, "settings", "burncatchalltouching", "1")))
burninorder = bool(
int(wt.configget(config, "settings", "burncatchalltouching", "0")))
verticetollerance = float(
wt.configget(config, "settings", "vertice_tollerance", "0.0001"))
''' read parameters '''
burn_outlets = int(
wt.configget(config, "parameters", "burn_outlets", 10000))
burn_rivers = int(wt.configget(config, "parameters", "burn_rivers", 200))
burn_connections = int(
wt.configget(config, "parameters", "burn_connections", 100))
burn_gauges = int(wt.configget(config, "parameters", "burn_gauges", 100))
minorder = int(wt.configget(config, "parameters", "riverorder_min", 3))
exec "percentile=tr.array(" + wt.configget(config, "parameters",
"statisticmaps", [0, 100]) + ")"
if not unit_clone:
print 'failed to read unit (meter or degree) from mask projection'
unit_clone = str(wt.configget(config, "settings", "unit", 'meter'))
print 'unit read from settings: ' + unit_clone
if unit_clone == 'degree':
cellsize_hr = float(
wt.configget(config, "parameters", "highres_degree", 0.0005))
elif (unit_clone == 'metre') or (unit_clone == 'meter'):
cellsize_hr = float(
wt.configget(config, "parameters", "highres_metre", 50))
cols_hr = int((float(xmax) - float(xmin)) / cellsize_hr + 2)
rows_hr = int((float(ymax) - float(ymin)) / cellsize_hr + 2)
hr_trans = (float(xmin), cellsize_hr, float(0), float(ymax), 0,
-cellsize_hr)
''' read staticmap locations '''
catchment_map = wt.configget(config, "staticmaps", "catchment",
"wflow_catchment.map")
dem_map = wt.configget(config, "staticmaps", "dem", "wflow_dem.map")
demmax_map = wt.configget(config, "staticmaps", "demmax",
"wflow_demmax.map")
demmin_map = wt.configget(config, "staticmaps", "demmin",
"wflow_demmin.map")
gauges_map = wt.configget(config, "staticmaps", "gauges",
"wflow_gauges.map")
landuse_map = wt.configget(config, "staticmaps", "landuse",
"wflow_landuse.map")
ldd_map = wt.configget(config, "staticmaps", "ldd", "wflow_ldd.map")
river_map = wt.configget(config, "staticmaps", "river", "wflow_river.map")
outlet_map = wt.configget(config, "staticmaps", "outlet",
"wflow_outlet.map")
riverlength_fact_map = wt.configget(config, "staticmaps",
"riverlength_fact",
"wflow_riverlength_fact.map")
soil_map = wt.configget(config, "staticmaps", "soil", "wflow_soil.map")
streamorder_map = wt.configget(config, "staticmaps", "streamorder",
"wflow_streamorder.map")
subcatch_map = wt.configget(config, "staticmaps", "subcatch",
"wflow_subcatch.map")
''' read mask location (optional) '''
masklayer = wt.configget(config, "mask", "masklayer", catchshp)
''' create directories '''
if os.path.isdir(workdir):
shutil.rmtree(workdir)
os.makedirs(workdir)
if os.path.isdir(resultdir):
shutil.rmtree(resultdir)
os.makedirs(resultdir)
''' Preperation steps '''
zero_map = workdir + "zero.map"
zero_tif = workdir + "zero.tif"
pcr.report(zeros, zero_map)
# TODO: replace gdal_translate call
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, zero_tif))
pcr.setglobaloption("lddin")
''' resample DEM '''
dem_resample = workdir + "dem_resampled.tif"
ds = gdal.Open(dem_in, GA_ReadOnly)
band = ds.GetRasterBand(1)
nodata = band.GetNoDataValue()
proj = ds.GetGeoTransform()
cellsize_dem = proj[1]
''' read DEM projection '''
spatialref == None
spatialref = ds.GetProjection()
if not spatialref == None:
srs = osr.SpatialReference()
srs.ImportFromWkt(spatialref)
srs.AutoIdentifyEPSG()
dem_EPSG = 'EPSG:' + srs.GetAttrValue("AUTHORITY", 1)
print 'EPSG-code is read from ' + os.path.basename(
dem_in) + ': ' + dem_EPSG
spatialref == None
dem_EPSG_int = int(dem_EPSG[5:len(dem_EPSG)])
srs_DEM = osr.SpatialReference()
srs_DEM.ImportFromEPSG(dem_EPSG_int)
clone2dem_transform = osr.CoordinateTransformation(srs_clone, srs_DEM)
else:
dem_EPSG = clone_EPSG
print 'No projection defined for ' + os.path.basename(dem_in)
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
ds = None
print 'Resampling DEM...'
if nodata == None:
call(('gdalwarp', '-overwrite', '-t_srs', clone_prj, '-te', xmin, ymin,
xmax, ymax, '-tr', str(cellsize), str(-cellsize), '-dstnodata',
str(-9999), '-r', 'cubic', dem_in, dem_resample))
else:
call(('gdalwarp', '-overwrite', '-t_srs', clone_prj,
'-te', xmin, ymin, xmax, ymax, '-tr', str(cellsize),
str(-cellsize), '-srcnodata', str(nodata), '-dstnodata',
str(nodata), '-r', 'cubic', dem_in, dem_resample))
''' create dem.map and statistic maps '''
dem_resample_map = resultdir + dem_map
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', dem_resample, dem_resample_map))
print 'Computing DEM statistics ....'
stats = wt.windowstats(dem_in, clone_rows, clone_columns, clone_trans,
srs_clone, resultdir, percentile)
''' burn DEM '''
ds = ogr.Open(rivshp)
file_att = os.path.splitext(os.path.basename(rivshp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
# if not spatialref == None:
# srs = osr.SpatialReference()
# srs.ImportFromWkt(spatialref.ExportToWkt())
# srs.AutoIdentifyEPSG()
# rivshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
# spatialref == None
# else:
rivshp_EPSG = clone_EPSG
print 'No projection defined for ' + file_att + '.shp'
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
# strip rivers to nodes
xminc = str(float(xmin) + 0.5 * cellsize)
yminc = str(float(ymin) + 0.5 * cellsize)
xmaxc = str(float(xmax) - 0.5 * cellsize)
ymaxc = str(float(ymax) - 0.5 * cellsize)
if rivshp_EPSG == clone_EPSG:
rivclipshp = workdir + 'rivshape_clip.shp'
call(('ogr2ogr', '-s_srs', clone_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, '-clipsrc', xminc, yminc, xmaxc, ymaxc,
rivclipshp, rivshp))
else:
rivprojshp = workdir + 'rivshape_proj.shp'
rivclipshp = workdir + 'rivshape_clip.shp'
call(('ogr2ogr', '-s_srs', rivshp_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, rivprojshp, rivshp))
call(('ogr2ogr', '-s_srs', clone_EPSG, '-t_srs', clone_EPSG, '-spat',
xmin, ymin, xmax, ymax, '-clipsrc', xminc, yminc, xmaxc, ymaxc,
rivclipshp, rivprojshp))
rivshp = rivclipshp
#### BURNING BELOW ####
# TODO: check if extraction can be done within memory and retun a burn layer
shapes = wt.Reach2Nodes(rivclipshp, clone_EPSG_int,
cellsize * verticetollerance, workdir)
outlets = shapes[1]
connections = shapes[2]
outlets_att = os.path.splitext(os.path.basename(outlets))[0]
connections_att = os.path.splitext(os.path.basename(connections))[0]
dem_resample_att = os.path.splitext(os.path.basename(dem_resample))[0]
connections_tif = workdir + connections_att + ".tif"
outlets_tif = workdir + outlets_att + ".tif"
# TODO: make the burning in memory
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, connections_tif))
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, outlets_tif))
call(('gdal_rasterize', '-burn', '1', '-l', outlets_att, outlets,
outlets_tif))
call(('gdal_rasterize', '-burn', '1', '-l', connections_att, connections,
connections_tif))
# convert rivers to order
rivshp_att = os.path.splitext(os.path.basename(rivshp))[0]
rivers_tif = workdir + rivshp_att + ".tif"
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, '-ot',
'Float32', zero_map, rivers_tif))
if burninorder: # make river shape with an order attribute
OrderSHPs = wt.ReachOrder(rivshp, clone_EPSG_int,
cellsize * verticetollerance, workdir)
wt.Burn2Tif(OrderSHPs, 'order', rivers_tif)
else:
call(('gdal_rasterize', '-burn', '1', '-l', rivshp_att, rivshp,
rivers_tif))
# convert 2 maps
connections_map = workdir + connections_att + ".map"
rivers_map = workdir + rivshp_att + ".map"
outlets_map = workdir + outlets_att + ".map"
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', connections_tif, connections_map))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', rivers_tif, rivers_map))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG, '-ot',
'Float32', outlets_tif, outlets_map))
# burn the layers in DEM
outletsburn = pcr.scalar(
pcr.readmap(outlets_map)) * pcr.scalar(burn_outlets)
connectionsburn = pcr.scalar(
pcr.readmap(connections_map)) * pcr.scalar(burn_connections)
riverburn = pcr.scalar(pcr.readmap(rivers_map)) * pcr.scalar(burn_rivers)
ldddem = pcr.cover(dem_resample_map,
pcr.ifthen(riverburn > 0, pcr.scalar(0)))
ldddem = ldddem - outletsburn - connectionsburn - riverburn
ldddem = pcr.cover(ldddem, pcr.scalar(0))
pcr.report(ldddem, workdir + "dem_burn.map")
''' create ldd for multi-catchments '''
ldd = pcr.ldd(empty)
# reproject catchment shape-file
ds = ogr.Open(catchshp)
file_att = os.path.splitext(os.path.basename(catchshp))[0]
lyr = ds.GetLayerByName(file_att)
spatialref = lyr.GetSpatialRef()
# if not spatialref == None:
# srs = osr.SpatialReference()
# srs.ImportFromWkt(spatialref.ExportToWkt())
# srs.AutoIdentifyEPSG()
# catchshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
# spatialref == None
# else:
catchshp_EPSG = clone_EPSG
print 'No projection defined for ' + file_att + '.shp'
print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
if not rivshp_EPSG == clone_EPSG:
catchprojshp = workdir + 'catchshape_proj.shp'
call(('ogr2ogr', '-s_srs', catchshp_EPSG, '-t_srs', clone_ESPG,
catchprojshp, catchshp))
catchshp = catchprojshp
ds.Destroy()
ds = ogr.Open(catchshp)
file_att = os.path.splitext(os.path.basename(catchshp))[0]
lyr = ds.GetLayerByName(file_att)
fieldDef = ogr.FieldDefn("ID", ogr.OFTString)
fieldDef.SetWidth(12)
TEMP_out = Driver.CreateDataSource(workdir + "temp.shp")
if not srs == None:
TEMP_LYR = TEMP_out.CreateLayer("temp",
srs,
geom_type=ogr.wkbMultiPolygon)
else:
TEMP_LYR = TEMP_out.CreateLayer("temp", geom_type=ogr.wkbMultiPolygon)
TEMP_LYR.CreateField(fieldDef)
for i in range(lyr.GetFeatureCount()):
orgfeature = lyr.GetFeature(i)
geometry = orgfeature.geometry()
feature = ogr.Feature(TEMP_LYR.GetLayerDefn())
feature.SetGeometry(geometry)
feature.SetField("ID", str(i + 1))
TEMP_LYR.CreateFeature(feature)
TEMP_out.Destroy()
ds.Destroy
# rasterize catchment map
catchments_tif = workdir + "catchments.tif"
catchments_map = workdir + "catchments.map"
call(('gdal_translate', '-of', 'GTiff', '-a_srs', clone_EPSG, zero_map,
catchments_tif))
if alltouching:
call(('gdal_rasterize', '-at', '-a', 'ID', '-l', "temp",
workdir + 'temp.shp', catchments_tif))
else:
call(('gdal_rasterize', '-a', 'ID', '-l', "temp", workdir + 'temp.shp',
catchments_tif))
call(('gdal_translate', '-of', 'PCRaster', '-a_srs', clone_EPSG,
catchments_tif, catchments_map))
catchments = pcr.readmap(catchments_map)
riverunique = pcr.clump(pcr.nominal(pcr.ifthen(riverburn > 0, riverburn)))
rivercatch = pcr.areamajority(pcr.ordinal(catchments), riverunique)
#catchments = pcr.cover(pcr.ordinal(rivercatch),pcr.ordinal(pcr.ifthen(catchments > 0, catchments)),pcr.ordinal(0))
catchments = pcr.cover(
pcr.ifthen(catchments > 0, pcr.ordinal(catchments)),
pcr.ifthen(
riverburn > 0,
pcr.ordinal(
pcr.spreadzone(pcr.nominal(catchments),
pcr.ifthen(riverburn > 0, pcr.scalar(1)), 1))))
rivercatch_map = workdir + "catchments_river.map"
catchclip_map = workdir + "catchments_clip.map"
pcr.report(rivercatch, rivercatch_map)
pcr.report(catchments, catchclip_map)
ds = ogr.Open(workdir + "temp.shp")
lyr = ds.GetLayerByName("temp")
print 'calculating ldd'
for i in range(lyr.GetFeatureCount()):
feature = lyr.GetFeature(i)
catch = int(feature.GetField("ID"))
print "calculating ldd for catchment: " + str(i + 1) + "/" + str(
lyr.GetFeatureCount()) + "...."
ldddem_select = pcr.scalar(pcr.ifthen(catchments == catch,
catchments)) * 0 + 1 * ldddem
ldd_select = pcr.lddcreate(ldddem_select, float("1E35"), float("1E35"),
float("1E35"), float("1E35"))
ldd = pcr.cover(ldd, ldd_select)
pcr.report(ldd, resultdir + ldd_map)
ds.Destroy()
''' report stream order, river and dem '''
streamorder = pcr.ordinal(pcr.streamorder(ldd))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(dem_resample_map), 9999999)))
dem_resample_map = pcr.cover(dem_resample_map,
pcr.scalar(river) * 0 + mindem)
pcr.report(dem_resample_map, resultdir + dem_map)
pcr.report(streamorder, resultdir + streamorder_map)
pcr.report(river, resultdir + river_map)
''' deal with your catchments '''
if gaugeshp == None:
print 'No gauges defined, using outlets instead'
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(pcr.ifthen(pcr.scalar(ldd) == 5, pcr.boolean(1)))))
pcr.report(gauges, resultdir + gauges_map)
# ds = ogr.Open(gaugeshp)
# file_att = os.path.splitext(os.path.basename(gaugeshp))[0]
# lyr = ds.GetLayerByName(file_att)
# spatialref = lyr.GetSpatialRef()
## if not spatialref == None:
## srs = osr.SpatialReference()
## srs.ImportFromWkt(spatialref.ExportToWkt())
## srs.AutoIdentifyEPSG()
## gaugeshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
## spatialref == None
# #else:
# gaugeshp_EPSG = clone_EPSG
# print 'No projection defined for ' + file_att + '.shp'
# print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
#
# # reproject gauge shape if necesarry
# if not gaugeshp_EPSG == clone_EPSG:
# gaugeprojshp = workdir + 'gaugeshape_proj.shp'
# call(('ogr2ogr','-s_srs',rivshp_EPSG,'-t_srs',clone_ESPG,gaugeprojshp,gaugeshp))
# gaugeshp = gaugeprojshp
#
# file_att = os.path.splitext(os.path.basename(gaugeshp))[0]
# gaugestif = workdir + file_att + '.tif'
# gaugesmap = workdir + file_att + '.map'
# call(('gdal_translate','-of','GTiff','-a_srs',clone_EPSG,zero_map,gaugestif))
# call(('gdal_rasterize','-burn','1','-l',file_att,gaugeshp,gaugestif))
# call(('gdal_translate','-of','PCRaster','-a_srs',clone_EPSG,gaugestif,gaugesmap))
# gaugelocs = pcr.readmap(gaugesmap)
# snapgaugestoriver = True
#
# if snapgaugestoriver:
# print "Snapping gauges to river"
# gauges = pcr.uniqueid(pcr.boolean(gaugelocs))
# gauges= wt.snaptomap(pcr.ordinal(gauges),river)
#
# gaugesmap = pcr.ifthen(gauges > 0, gauges)
''' report riverlengthfrac '''
riv_hr = workdir + 'river_highres.tif'
wt.CreateTif(riv_hr, rows_hr, cols_hr, hr_trans, srs_clone, 0)
file_att = os.path.splitext(os.path.basename(rivshp))[0]
call(('gdal_rasterize', '-burn', '1', '-l', file_att, rivshp, riv_hr))
print 'Computing river length...'
#riverlength = wt.windowstats(riv_hr,clone_rows,clone_columns,clone_trans,srs_clone,resultdir,'frac',clone2dem_transform)
riverlength = wt.windowstats(riv_hr, clone_rows, clone_columns,
clone_trans, srs_clone, resultdir, 'frac')
''' report outlet map '''
pcr.report(pcr.ifthen(pcr.ordinal(ldd) == 5, pcr.ordinal(1)),
resultdir + outlet_map)
''' report map '''
catchment = pcr.ifthen(catchments > 0, pcr.ordinal(1))
pcr.report(catchment, resultdir + catchment_map)
''' report subcatchment map '''
subcatchment = pcr.subcatchment(ldd, gauges)
pcr.report(pcr.ordinal(subcatchment), resultdir + subcatch_map)
''' report landuse map '''
if landuse == None:
pcr.report(pcr.nominal(ones), resultdir + landuse_map)
else:
landuse_resample = workdir + 'landuse.tif'
landuse_map = resultdir + landuse_map
transform = wt.GetRasterTranform(landuse, srs_clone)
if not transform[0]:
call(('gdalwarp', '-overwrite', '-s_srs', clone_EPSG, '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr',
str(cellsize), str(-cellsize), '-r', 'mode', landuse,
landuse_resample))
else:
call(('gdalwarp', '-overwrite', '-s_srs', transform[1], '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr',
str(cellsize), str(-cellsize), '-r', 'mode', landuse,
landuse_resample))
call(('gdal_translate', '-of', 'PCRaster', '-ot', 'Float32',
landuse_resample, landuse_map))
landuse_work = pcr.readmap(landuse_map)
pcr.report(pcr.nominal(landuse_work), landuse_map)
''' report soil map '''
if soiltype == None:
pcr.report(pcr.nominal(ones), resultdir + soil_map)
else:
soiltype_resample = workdir + 'soiltype.tif'
soil_map = resultdir + soil_map
#transform = wt.GetRasterTranform(soiltype,srs_clone)
# if not transform[0]:
call(('gdalwarp', '-overwrite', '-s_srs', clone_EPSG, '-t_srs',
clone_EPSG, '-te', xmin, ymin, xmax, ymax, '-tr', str(cellsize),
str(-cellsize), '-r', 'mode', soiltype, soiltype_resample))
# else:
# call(('gdalwarp','-overwrite','-s_srs',transform[1],'-t_srs',clone_EPSG,'-te', xmin, ymin, xmax, ymax,'-tr',str(cellsize),str(-cellsize),'-r','mode',soiltype, soiltype_resample))
call(('gdal_translate', '-of', 'PCRaster', '-ot', 'Float32',
soiltype_resample, soil_map))
soiltype_work = pcr.readmap(soil_map)
pcr.report(pcr.nominal(soiltype_work), soil_map)
if clean:
wt.DeleteList(glob.glob(os.getcwd() + '\\' + resultdir + '/*.xml'))
vos.netcdf2PCRobjClone(inputDirectory+inputFiles,\
inputVarNames,
fulldate,
None,
cloneMapFileName)) * 0.001 # unit: bcm/year
abstraction = pcr.cover(abstraction_volume_30min, 0.0)
# use window maximum to be in the conservative side:
window_size = 1.0
abstraction = pcr.windowmaximum(abstraction, 1.0)
# covering the map with zero
pcrValue = pcr.cover(abstraction, 0.0) # unit: m/day
# the value should be higher than the previous year value
if iYear > staYear: pcrValue = pcr.max(preValue, pcrValue)
preValue = pcrValue
region_ids = pcr.uniqueid(pcr.boolean(1.0))
region_ids_masked = pcr.ifthen(landmask, region_ids)
regional_pumping_limit = pcrValue
regional_pumping_limit_masked = pcr.ifthen(landmask, regional_pumping_limit)
for iVar in range(0,len(varNames)):
var = varNames[iVar]
pcrRead = vars()[str(var)]
varField = pcr.pcr2numpy(pcrRead, vos.MV)
tssNetCDF.writePCR2NetCDF(ncFileName,var,varField,timeStamp,posCnt = index - 1)
def subcatch_stream(
ldd,
threshold,
min_strahler=-999,
max_strahler=999,
assign_edge=False,
assign_existing=False,
up_area=None,
):
"""
(From Deltares Hydrotools)
Derive catchments based upon strahler threshold
Input:
ldd -- pcraster object direction, local drain directions
threshold -- integer, strahler threshold, subcatchments ge threshold
are derived
min_strahler -- integer, minimum strahler threshold of river catchments
to return
max_strahler -- integer, maximum strahler threshold of river catchments
to return
assign_unique=False -- if set to True, unassigned connected areas at
the edges of the domain are assigned a unique id as well. If set
to False, edges are not assigned
assign_existing=False == if set to True, unassigned edges are assigned
to existing basins with an upstream weighting. If set to False,
edges are assigned to unique IDs, or not assigned
output:
stream_ge -- pcraster object, streams of strahler order ge threshold
subcatch -- pcraster object, subcatchments of strahler order ge threshold
"""
# derive stream order
stream = pcr.streamorder(ldd)
stream_ge = pcr.ifthen(stream >= threshold, stream)
stream_up_sum = pcr.ordinal(pcr.upstream(ldd, pcr.cover(pcr.scalar(stream_ge), 0)))
# detect any transfer of strahler order, to a higher strahler order.
transition_strahler = pcr.ifthenelse(
pcr.downstream(ldd, stream_ge) != stream_ge,
pcr.boolean(1),
pcr.ifthenelse(
pcr.nominal(ldd) == 5,
pcr.boolean(1),
pcr.ifthenelse(
pcr.downstream(ldd, pcr.scalar(stream_up_sum)) > pcr.scalar(stream_ge),
pcr.boolean(1),
pcr.boolean(0),
),
),
)
# make unique ids (write to file)
transition_unique = pcr.ordinal(pcr.uniqueid(transition_strahler))
# derive upstream catchment areas (write to file)
subcatch = pcr.nominal(pcr.subcatchment(ldd, transition_unique))
if assign_edge:
# fill unclassified areas (in pcraster equal to zero) with a unique id, above the maximum id assigned so far
unique_edge = pcr.clump(pcr.ifthen(subcatch == 0, pcr.ordinal(0)))
subcatch = pcr.ifthenelse(
subcatch == 0,
pcr.nominal(pcr.mapmaximum(pcr.scalar(subcatch)) + pcr.scalar(unique_edge)),
pcr.nominal(subcatch),
)
elif assign_existing:
# unaccounted areas are added to largest nearest draining basin
if up_area is None:
up_area = pcr.ifthen(
pcr.boolean(pcr.cover(stream_ge, 0)), pcr.accuflux(ldd, 1)
)
riverid = pcr.ifthen(pcr.boolean(pcr.cover(stream_ge, 0)), subcatch)
friction = 1.0 / pcr.scalar(
pcr.spreadzone(pcr.cover(pcr.ordinal(up_area), 0), 0, 0)
) # *(pcr.scalar(ldd)*0+1)
delta = pcr.ifthen(
pcr.scalar(ldd) >= 0,
pcr.ifthen(
pcr.cover(subcatch, 0) == 0,
pcr.spreadzone(pcr.cover(riverid, 0), 0, friction),
),
)
subcatch = pcr.ifthenelse(pcr.boolean(pcr.cover(subcatch, 0)), subcatch, delta)
# finally, only keep basins with minimum and maximum river order flowing through them
strahler_subcatch = pcr.areamaximum(stream, subcatch)
subcatch = pcr.ifthen(
pcr.ordinal(strahler_subcatch) >= min_strahler,
pcr.ifthen(pcr.ordinal(strahler_subcatch) <= max_strahler, subcatch),
)
return stream_ge, pcr.ordinal(subcatch)
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()
ldd = pcr.cover(ldd, ldd_select)
pcr.report(ldd, resultdir + ldd_map)
ds.Destroy()
''' report stream order, river and dem '''
streamorder = pcr.ordinal(pcr.streamorder(ldd))
river = pcr.ifthen(streamorder >= pcr.ordinal(minorder), pcr.boolean(1))
mindem = int(np.min(pcr.pcr2numpy(pcr.ordinal(dem_resample_map), 9999999)))
dem_resample_map = pcr.cover(dem_resample_map, pcr.scalar(river) * 0 + mindem)
pcr.report(dem_resample_map, resultdir + dem_map)
pcr.report(streamorder, resultdir + streamorder_map)
pcr.report(river, resultdir + river_map)
''' deal with your catchments '''
if gaugeshp == None:
print 'No gauges defined, using outlets instead'
gauges = pcr.ordinal(
pcr.uniqueid(
pcr.boolean(pcr.ifthen(pcr.scalar(ldd) == 5, pcr.boolean(1)))))
pcr.report(gauges, resultdir + gauges_map)
# ds = ogr.Open(gaugeshp)
# file_att = os.path.splitext(os.path.basename(gaugeshp))[0]
# lyr = ds.GetLayerByName(file_att)
# spatialref = lyr.GetSpatialRef()
## if not spatialref == None:
## srs = osr.SpatialReference()
## srs.ImportFromWkt(spatialref.ExportToWkt())
## srs.AutoIdentifyEPSG()
## gaugeshp_EPSG = 'EPSG:'+srs.GetAttrValue("AUTHORITY",1)
## spatialref == None
# #else:
# gaugeshp_EPSG = clone_EPSG
# print 'No projection defined for ' + file_att + '.shp'
# print 'Assumed to be the same as model projection (' + clone_EPSG + ')'
landmask = pcr.defined(pcr.readmap(clone_map))
# class map file name:
#~ class_map_file_name = "/home/sutan101/data/aqueduct_gis_layers/aqueduct_shp_from_marta/Aqueduct_States.map"
#~ class_map_file_name = "/home/sutan101/data/aqueduct_gis_layers/aqueduct_shp_from_marta/Aqueduct_GDBD.map"
#~ class_map_file_name = "/home/sutan101/data/processing_whymap/version_19september2014/major_aquifer_30min.extended.map"
#~ class_map_file_name = "/home/sutan101/data/processing_whymap/version_19september2014/major_aquifer_30min.map"
class_map_file_name = str(sys.argv[2])
class_map_default_folder = "/home/sutan101/data/aqueduct_gis_layers/aqueduct_shp_from_marta/"
if class_map_file_name == "state": class_map_file_name = class_map_default_folder + "/Aqueduct_States.map"
if class_map_file_name == "drainage_unit": class_map_file_name = class_map_default_folder + "/Aqueduct_GDBD.map"
if class_map_file_name == "aquifer": class_map_file_name = class_map_default_folder + "/why_wgs1984_BUENO.map"
if class_map_file_name == "country": class_map_file_name = "/home/sutan101/data/country_shp_from_tianyi/World_Polys_High.map"
# reading the class map
class_map = pcr.nominal(pcr.uniqueid(landmask))
if class_map_file_name != "pixel":
class_map = vos.readPCRmapClone(class_map_file_name, clone_map, tmp_directory, None, False, None, True, False)
class_map = pcr.ifthen(pcr.scalar(class_map) > 0.0, pcr.nominal(class_map))
# time selection
start_year = int(sys.argv[3])
end_year = int(sys.argv[4])
# cell_area (unit: m2)
cell_area = pcr.readmap("/data/hydroworld/PCRGLOBWB20/input5min/routing/cellsize05min.correct.map")
segment_cell_area = pcr.areatotal(cell_area, class_map)
# extent of aquifer/sedimentary basins:
sedimentary_basin = pcr.cover(pcr.scalar(pcr.readmap("/home/sutan101/data/sed_extent/sed_extent.map")), 0.0)
cell_area = sedimentary_basin * cell_area
basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 0.5)) basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 0.5)) basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 0.5)) basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 1.0)) basin_map = pcr.cover(basin_map, pcr.windowmajority(basin_map, 1.5)) basin_map = pcr.ifthen(landmask, basin_map) #~ pcr.aguila(basin_map) msg = "Redefining the basin map (so that it is consistent with the ldd map used in PCR-GLOBWB):" logger.info(msg) # - calculate the upstream area of every pixel: upstream_area = pcr.catchmenttotal(cell_area, ldd) # - calculate the catchment area of every basin: upstream_area_maximum = pcr.areamaximum(upstream_area, basin_map) # - identify the outlet of every basin (in order to rederive the basin so that it is consistent with the ldd) outlet = pcr.nominal(pcr.uniqueid(pcr.ifthen(upstream_area == upstream_area_maximum, pcr.boolean(1.0)))) # - ignoring outlets with small upstream areas threshold = 50. * 1000. * 1000. # unit: m2 outlet = pcr.ifthen(upstream_area_maximum > threshold, outlet) #~ pcr.aguila(outlet) outlet = pcr.cover(outlet, pcr.nominal(0.0)) # - recalculate the basin basin_map = pcr.nominal(pcr.subcatchment(ldd, outlet)) basin_map = pcr.clump(basin_map) basin_map = pcr.ifthen(landmask, basin_map) pcr.report(basin_map , "basin_map.map") #~ pcr.aguila(basin_map) # - calculate the basin area basin_area = pcr.areatotal(cell_area, basin_map) pcr.report(basin_area, "basin_area.map") #~ pcr.aguila(basin_area)
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")
