def _generate_harmonics(time_names, freq, prefix):
"""Generate (constant) rasters of harmonics
t0, t1, t2, ..., t_{N-1})
where t0 = 0, t_{N-1} = 2*pi
freq -- frequencies for sin() and cos() rasters
prefix -- prefix for the raster names
return list of names for the result rasters
"""
names = dict()
for i in time_names:
harm = dict()
t = get_time(len(time_names), i)
for f in freq:
sin_output = prefix + 'sin' + _time_to_name(i) + _freq_to_name(f)
grass.mapcalc("${out} = sin(${f} * ${t})", out=sin_output, t=t, f=f,
quiet=True, overwrite=grass.overwrite())
cos_output = prefix + 'cos' + _time_to_name(i) + _freq_to_name(f)
grass.mapcalc("${out} = cos(${f} * ${t})", out=cos_output, t=t, f=f,
quiet=True, overwrite=grass.overwrite())
harm[f] = dict(sin=sin_output, cos=cos_output)
names[i] = harm
return names
def main():
# User inputs
dem = options['input'] # Elevation map
out = options['friction'] # Output friction surface
slope = options['slope'] # Optional output slope
formula = options['formula'] # Formula
# Error if no friction surface is given
if not grass.find_file(dem)['name']:
grass.message(_("Input raster <%s> not found") % dem)
sys.exit()
# If output raster exists, but overwrite option isn't selected
if not grass.overwrite():
if grass.find_file(out)['name']:
grass.message(_("Output raster map <%s> already exists") % out)
sys.exit()
# Check if slope output name is specified and give error message if it needs to be overwritten but overwrite option isn't selected
if not slope:
slope = "tmp_slope_%d_" %os.getpid()
else:
if not grass.overwrite():
if grass.find_file(slope)['name']:
grass.message(_("Output raster map <%s> already exists") % slope)
sys.exit()
# Generate slope (in percent)
grass.run_command('r.slope.aspect', elevation=dem, slope=slope, format='percent')
# Choose formula
if formula == 'Hiker':
# So-called "Hiker's formula"
# Tobler W (1993) Three Presentations on Geographical Analysis and Modeling. Technical Report 93-1. California
# Gorenflo LJ and Gale N (1990) Mapping Regional Settlement in information Space. Journal of Antropological Archaeology (9): 240 - 274
expression = "$outmap = 1.0 / (( 6.0 * exp(-3.5 * abs(( $slope / 100) + 0.05 ))) * 1000)"
elif formula == 'Minetti':
# This formula is presentedy by I. Herzog who based the formula on the physiological data by Minetti.
# Herzog, I. In press. "Theory and Practice of Cost Functions." In Fusion of Cultures. Proceedings of the
# XXXVIII Conference on Computer Applications and Quantitative Methods in Archaeology, CAA 2010.
expression = "$outmap = 1337.8 * ($slope / 100)^6 + 278.19 * ($slope / 100)^5 - 517.39 * ($slope / 100)^4 - 78.199 * ($slope / 100)^3 + 93.419 * ($slope / 100)^2 + 19.825 * ($slope / 100) + 1.64"
else:
grass.message("No valid formula chosen")
sys.exit()
# Create friction surface
try:
grass.mapcalc(expression, outmap = out, slope = slope)
grass.message("Friction raster <" + out + "> complete")
except:
grass.run_command('g.remove', rast = slope)
grass.message("Unable to finish operation. Temporary slope raster deleted.")
sys.exit()
# Delete temporary slope map if necessary
if not options['slope']:
grass.run_command('g.remove', rast = slope)
grass.message("All done")
def main():
# Get the options
input = options["input"]
output = options["output"]
method = options["method"]
order = options["order"]
where = options["where"]
add_time = flags["t"]
nulls = flags["n"]
# Make sure the temporal database exists
tgis.init()
sp = tgis.open_old_stds(input, "strds")
rows = sp.get_registered_maps("id", where, order, None)
if rows:
# Create the r.series input file
filename = grass.tempfile(True)
file = open(filename, 'w')
for row in rows:
string = "%s\n" % (row["id"])
file.write(string)
file.close()
flag = ""
if len(rows) > 1000:
grass.warning(_("Processing over 1000 maps: activating -z flag of r.series which slows down processing"))
flag += "z"
if nulls:
flag += "n"
try:
grass.run_command("r.series", flags=flag, file=filename,
output=output, overwrite=grass.overwrite(),
method=method)
except CalledModuleError:
grass.fatal(_("%s failed. Check above error messages.") % 'r.series')
if not add_time:
# Create the time range for the output map
if output.find("@") >= 0:
id = output
else:
mapset = grass.gisenv()["MAPSET"]
id = output + "@" + mapset
map = sp.get_new_map_instance(id)
map.load()
map.set_temporal_extent(sp.get_temporal_extent())
# Register the map in the temporal database
if map.is_in_db():
map.update_all()
else:
map.insert()
def _initializeParameters(self, options, flags):
'''
Initialize all given and needed parameters. Get region information and
calculate boundingbox according to it
'''
self._debug("_initializeParameters", "started")
self._env = os.environ.copy()
self._env['GRASS_MESSAGE_FORMAT'] = 'gui'
g.gisenv(set="GRASS_MESSAGE_FORMAT=gui")
for key in ['url', 'coverage','output','location']:
self.params[key] = options[key].strip()
if not self.params['output']:
self.params['output'] = self.params['coverage']
if not gscript.overwrite():
result = gscript.find_file(name = self.params['output'], element = 'cell')
if result['file']:
gscript.fatal("Raster map <%s> does already exist. Choose other output name or toggle flag --o." % self.params['output'])
for key in ['password', 'username', 'version','region']:
self.params[key] = options[key]
# check if authentication information is complete
if (self.params['password'] and self.params['username'] == '') or \
(self.params['password'] == '' and self.params['username']):
gscript.fatal(_("Please insert both %s and %s parameters or none of them." % ('password', 'username')))
# configure region extent (specified name or current region)
self.params['region'] = self._getRegionParams(options['region'])
self.params['boundingbox'] = self._computeBbox(self.params['region'])
self._debug("_initializeParameters", "finished")
def run_mapcalc3d(expr):
"""Helper function to run r3.mapcalc in parallel"""
try:
gscript.run_command("r3.mapcalc", expression=expr,
overwrite=gscript.overwrite(), quiet=True)
except CalledModuleError:
exit(1)
def main():
# process command options
input = options['input']
if not gs.find_file(input)['file']:
gs.fatal(_("Raster map <%s> not found") % input)
output = options['output']
if gs.find_file(output)['file'] and not gs.overwrite():
gs.fatal(_("Output map <%s> already exists") % output)
# set aside region for internal use
gs.use_temp_region()
# subset input if desired
region = options.get('region')
if region:
if not gs.find_file(region)['file']:
gs.fatal(_("Raster map <%s> not found") % region)
gs.message("Setting region to %s" % region, flag='i')
gs.run_command('g.region', rast=region, align=input)
else:
gs.message("Using existing GRASS region", flag='i')
gs.debug('='*50)
gs.debug('\n'.join(gs.parse_command('g.region', 'p').keys()))
gs.debug('='*50)
calculate_noise(input, output)
# restore original region
gs.del_temp_region()
return None
def main():
# Get the options
input = options["input"]
output = options["output"]
type = options["type"]
# Make sure the temporal database exists
tgis.init()
#Get the current mapset to create the id of the space time dataset
mapset = grass.gisenv()["MAPSET"]
if input.find("@") >= 0:
old_id = input
else:
old_id = input + "@" + mapset
if output.find("@") >= 0:
new_id = output
else:
new_id = output + "@" + mapset
# Do not overwrite yourself
if new_id == old_id:
return
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
stds = tgis.dataset_factory(type, old_id)
if new_id.split("@")[1] != mapset:
grass.fatal(_("Space time %s dataset <%s> can not be renamed. "
"Mapset of the new identifier differs from the current "
"mapset.") % (stds.get_new_map_instance(None).get_type(),
old_id))
if stds.is_in_db(dbif=dbif) == False:
dbif.close()
grass.fatal(_("Space time %s dataset <%s> not found") % (
stds.get_new_map_instance(None).get_type(), old_id))
# Check if the new id is in the database
new_stds = tgis.dataset_factory(type, new_id)
if new_stds.is_in_db(dbif=dbif) == True and grass.overwrite() == False:
dbif.close()
grass.fatal(_("Unable to rename Space time %s dataset <%s>. Name <%s> "
"is in use, please use the overwrite flag.") % (
stds.get_new_map_instance(None).get_type(), old_id, new_id))
# Remove an already existing space time dataset
if new_stds.is_in_db(dbif=dbif) == True:
new_stds.delete(dbif=dbif)
stds.select(dbif=dbif)
stds.rename(ident=new_id, dbif=dbif)
stds.update_command_string(dbif=dbif)
def main():
"""
Compute cell areas
"""
projinfo = grass.parse_command('g.proj', flags='g')
options, flags = grass.parser()
output = options['output']
units = options['units']
# First check if output exists
if len(grass.parse_command('g.list', type='rast',
pattern=options['output'])):
if not grass.overwrite():
grass.fatal("Raster map '" + options['output'] +
"' already exists. Use '--o' to overwrite.")
# Then compute
if projinfo['units'] == 'meters':
if units == 'm2':
grass.mapcalc(output+' = nsres() * ewres()')
elif units == 'km2':
grass.mapcalc(output+' = nsres() * ewres() / 10.^6')
elif projinfo['units'] == 'degrees':
if units == 'm2':
grass.mapcalc(output+' = ( 111195. * nsres() ) * \
( ewres() * '+str(np.pi/180.)+' * 6371000. * cos(y()) )')
elif units == 'km2':
grass.mapcalc(output+' = ( 111.195 * nsres() ) * \
( ewres() * '+str(np.pi/180.)+' * 6371. * cos(y()) )')
else:
print 'Units: ', + projinfo['units'] + ' not currently supported'
def main():
first = options['first']
second = options['second']
output = options['output_prefix']
percent = options['percent']
mapset = grass.gisenv()['MAPSET']
if not grass.overwrite():
for ch in ['r','g','b']:
map = '%s.%s' % (output, ch)
if grass.find_file(map, element = 'cell', mapset = mapset)['file']:
grass.fatal(_("Raster map <%s> already exists.") % map)
percent = float(percent)
perc_inv = 100.0 - percent
frac1 = percent / 100.0
frac2 = perc_inv / 100.0
grass.message(_("Calculating the three component maps..."))
template = string.Template("$$output.$ch = if(isnull($$first), $ch#$$second, if(isnull($$second), $ch#$$first, $$frac1 * $ch#$$first + $$frac2 * $ch#$$second))")
cmd = [template.substitute(ch = ch) for ch in ['r','g','b']]
cmd = ';'.join(cmd)
grass.mapcalc(cmd,
output = output,
first = first, second = second,
frac1 = frac1, frac2 = frac2)
for ch in ['r','g','b']:
map = "%s.%s" % (output, ch)
grass.run_command('r.colors', map = map, color = 'grey255')
grass.run_command('r.support', map = map, history="",
title = "Color blend of %s and %s" % (first, second),
description = "generated by r.blend")
grass.run_command('r.support', map = map,
history = "r.blend %s channel." % ch)
grass.run_command('r.support', map = map,
history = " %d%% of %s, %d%% of %s" % (percent, first, perc_inv, second))
grass.run_command('r.support', map = map, history = "")
grass.run_command('r.support', map = map, history = os.environ['CMDLINE'])
if flags['c']:
grass.run_command('r.composite', r = '%s.r' % output,
g = '%s.g' % output, b = '%s.b' % output, output = output)
grass.run_command('r.support', map = output, history="",
title = "Color blend of %s and %s" % (first, second),
description = "generated by r.blend")
grass.run_command('r.support', map = output,
history = " %d%% of %s, %d%% of %s" % (percent, first, perc_inv, second))
grass.run_command('r.support', map = output, history = "")
grass.run_command('r.support', map = output, history = os.environ['CMDLINE'])
else:
grass.message(_("Done. Use the following command to visualize the result:"))
grass.message(_("d.rgb r=%s.r g=%s.g b=%s.b") % (output, output, output))
def run_vector_extraction(input, output, layer, type, where):
"""Helper function to run r.mapcalc in parallel"""
try:
gscript.run_command("v.extract", input=input, output=output,
layer=layer, type=type, where=where,
overwrite=gscript.overwrite(), quiet=True)
except CalledModuleError:
exit(1)
def _import_raster_maps_from_gdal(maplist, overr, exp, location, link, format_,
set_current_region=False):
impflags = ""
if overr:
impflags += "o"
if exp or location:
impflags += "e"
for row in maplist:
name = row["name"]
if format_ == "GTiff":
filename = row["filename"] + ".tif"
elif format_ == "AAIGrid":
filename = row["filename"] + ".asc"
if not overr:
impflags += "o"
try:
if link:
gscript.run_command("r.external", input=filename,
output=name,
flags=impflags,
overwrite=gscript.overwrite())
else:
gscript.run_command("r.in.gdal", input=filename,
output=name,
flags=impflags,
overwrite=gscript.overwrite())
except CalledModuleError:
gscript.fatal(_("Unable to import/link raster map <%s> from file"
" %s.") % (name, filename))
# Set the color rules if present
filename = row["filename"] + ".color"
if os.path.isfile(filename):
try:
gscript.run_command("r.colors", map=name,
rules=filename,
overwrite=gscript.overwrite())
except CalledModuleError:
gscript.fatal(_("Unable to set the color rules for "
"raster map <%s>.") % name)
# Set the computational region from the last map imported
if set_current_region is True:
gscript.run_command("g.region", raster=name)
def main():
input = options['input']
maskcats = options['maskcats']
remove = flags['r']
invert = flags['i']
if not remove and not input:
grass.fatal(_("Required parameter <input> not set"))
#check if input file exists
if not grass.find_file(input)['file'] and not remove:
grass.fatal(_("<%s> does not exist.") % input)
if not 'MASKCATS' in grass.gisenv() and not remove:
## beware: next check is made with != , not with 'is', otherwise:
#>>> grass.raster_info("basin_50K")['datatype'] is "CELL"
#False
# even if:
#>>> "CELL" is "CELL"
#True
if grass.raster_info(input)['datatype'] != "CELL":
grass.fatal(_("Raster map %s must be integer for maskcats parameter") % input)
mapset = grass.gisenv()['MAPSET']
exists = bool(grass.find_file('MASK', element = 'cell', mapset = mapset)['file'])
if remove:
if exists:
grass.run_command('g.remove', rast = 'MASK')
grass.message(_("Raster MASK removed"))
else:
grass.fatal(_("No existing MASK to remove"))
else:
if exists:
if not grass.overwrite():
grass.fatal(_("MASK already found in current mapset. Delete first or overwrite."))
else:
grass.warning(_("MASK already exists and will be overwritten"))
p = grass.feed_command('r.reclass', input = input, output = 'MASK', overwrite = True, rules = '-')
p.stdin.write("%s = 1" % maskcats)
p.stdin.close()
p.wait()
if invert:
global tmp
tmp = "r_mask_%d" % os.getpid()
grass.run_command('g.rename', rast = ('MASK',tmp), quiet = True)
grass.mapcalc("MASK=if(isnull($tmp),1,null())", tmp = tmp)
grass.run_command('g.remove', rast = tmp, quiet = True)
grass.message(_("Inverted MASK created."))
else:
grass.message(_("MASK created."))
grass.message(_("All subsequent raster operations will be limited to MASK area. ") +
"Removing or renaming raster file named MASK will " +
"restore raster operations to normal")
def main():
# Get the options
name = options["dataset"]
type = options["type"]
temporaltype = options["temporaltype"]
title = options["title"]
descr = options["description"]
semantic = options["semantictype"]
gran = options["granularity"]
# Make sure the temporal database exists
tgis.create_temporal_database()
#Get the current mapset to create the id of the space time dataset
mapset = grass.gisenv()["MAPSET"]
id = name + "@" + mapset
if type == "strds":
sp = tgis.space_time_raster_dataset(id)
if type == "str3ds":
sp = tgis.space_time_raster3d_dataset(id)
if type == "stvds":
sp = tgis.space_time_vector_dataset(id)
dbif = tgis.sql_database_interface()
dbif.connect()
if sp.is_in_db(dbif) and grass.overwrite() == False:
grass.fatal("Space time " + sp.get_new_map_instance(None).get_type() + " dataset <" + name + "> is already in the database. Use the overwrite flag.")
if sp.is_in_db(dbif) and grass.overwrite() == True:
grass.info("Overwrite space time " + sp.get_new_map_instance(None).get_type() + " dataset <" + name + "> and unregister all maps.")
sp.delete(dbif)
sp = sp.get_new_instance(id)
grass.info("Create space time " + sp.get_new_map_instance(None).get_type() + " dataset.")
sp.set_initial_values(granularity=gran, temporal_type=temporaltype, semantic_type=semantic, title=title, description=descr)
sp.insert(dbif)
dbif.close()
def main():
# Get the options
input = options["input"]
output = options["output"]
method = options["method"]
order = options["order"]
where = options["where"]
add_time = flags["t"]
nulls = flags["n"]
# Make sure the temporal database exists
tgis.init()
sp = tgis.open_old_stds(input, "strds")
rows = sp.get_registered_maps("id", where, order, None)
if rows:
# Create the r.series input file
filename = grass.tempfile(True)
file = open(filename, 'w')
for row in rows:
string = "%s\n" % (row["id"])
file.write(string)
file.close()
flag = "z"
if nulls:
flag += "n"
ret = grass.run_command("r.series", flags=flag, file=filename,
output=output, overwrite=grass.overwrite(),
method=method)
if ret == 0 and not add_time:
# Create the time range for the output map
if output.find("@") >= 0:
id = output
else:
mapset = grass.gisenv()["MAPSET"]
id = output + "@" + mapset
map = sp.get_new_map_instance(id)
map.load()
map.set_temporal_extent(sp.get_temporal_extent())
# Register the map in the temporal database
if map.is_in_db():
map.update_all()
else:
map.insert()
def registerToTemporal(basename, suffixes, mapset, start_time,
time_step, title, desc):
maps = ','.join([basename + suf + '@' + mapset for suf in suffixes])
tgis.open_new_stds(basename, type='strds',
temporaltype='absolute',
title=title, descr=desc,
semantic='mean', dbif=None,
overwrite=grass.overwrite())
tgis.register_maps_in_space_time_dataset(
type='raster', name=basename, maps=maps, start=start_time,
end=None, increment=time_step, dbif=None, interval=False)
def main():
# Input data
inraster = options["input"]
outraster = options["output"]
nsize = options["nsize"]
statistic = options["statistic"]
circular = "c" if flags["c"] else ""
# Get process id
pid = os.getpid()
# Check overwrite settings
# If output raster file exists, but overwrite option isn't selected
if not grass.overwrite():
if grass.find_file(outraster)["name"]:
grass.message(_("Output raster map <%s> already exists") % outraster)
sys.exit()
# Choose the statistic
if statistic == "Deviation from Mean":
# First, get neighbourhood mean rasters
tmp_avg = "tmp_avg_%d" % pid # Create a temporary filename
tmp_rlayers.append(tmp_avg)
proc_avg = grass.start_command(
"r.neighbors", overwrite=True, flags=circular, input=inraster, output=tmp_avg, size=nsize
)
# Get neighbourhood standard deviation rasters
tmp_stddev = "tmp_stddev_%d" % pid # Create a temporary filename
tmp_rlayers.append(tmp_stddev)
proc_stddev = grass.start_command(
"r.neighbors",
overwrite=True,
flags=circular,
method="stddev",
input=inraster,
output=tmp_stddev,
size=nsize,
)
# Wait for the processes to finish
proc_avg.wait()
proc_stddev.wait()
# Calculate Deviation from Mean
grass.mapcalc(
"$outmap = ($inraster - $avgraster) / $stddevraster",
outmap=outraster,
inraster=inraster,
avgraster=tmp_avg,
stddevraster=tmp_stddev,
)
def main(options, flags):
samples = options['samples']
res_pref = options['result_prefix']
if not os.path.isfile(samples):
sys.stderr.write("File '%s' doesn't exist.\n" % (samples, ))
sys.exit(1)
headers, outputs, inputs = get_sample_names(samples)
model = DataModel(headers, outputs, inputs, res_pref)
model.ols(overwrite=grass.overwrite())
sys.exit(0)
def open_rasters(raster_list, write=False):
for r in raster_list:
try:
if write:
if r.exist():
r.open("w", "DCELL", overwrite=grass.overwrite())
else:
r.open("w", "DCELL")
else:
r.open()
except OpenError:
grass.fatal("Can't open raster %s" % (r.name,))
def main():
# start messenger
msgr = Messenger()
# Use temporary GRASS region
grass.use_temp_region()
# reads CLI options
rast_n_file_name = options['friction']
rast_dem_name = options['dem']
rast_start_file_name = options['start_file']
rast_bc_name = options['bc']
rast_bcval_name = options['bcval']
rast_user_name = options['user_inflow']
# Load *.par file
par = Par(msgr, options['par_file'])
par.read()
# Write DEM file
par.write_dem(rast_dem_name, grass.overwrite())
# set computational region to match DEM
par.set_region_from_map(rast_dem_name)
# Write friction
par.write_n_map(rast_n_file_name, grass.overwrite())
# Write start file
par.write_start_h(rast_start_file_name, grass.overwrite())
# boundary conditions
bc = BoundaryConditions(msgr, sim_time=par.sim_time,
region=par.region)
if par.bci_file:
bci_full_path = os.path.join(par.directory, par.bci_file)
bc.read_bci(bci_full_path)
if par.bdy_file:
bdy_full_path = os.path.join(par.directory, par.bdy_file)
bc.read_bdy(bdy_full_path)
# create STDS
bc.create_stds(stds_name=rast_user_name, overwrite=grass.overwrite())
bc.create_stds(rast_bcval_name, overwrite=grass.overwrite())
# Write maps en register them in STDS
bc.populate_user_flow_stds(rast_quser_name=rast_user_name,
overwrite=grass.overwrite())
bc.populate_bc_stds(rast_bcval_name, grass.overwrite())
# write Boundary condition type map
bc.write_bctype(rast_bc_name, grass.overwrite())
# Restore original region
grass.del_temp_region()
return 0
def register_maps_in_strds(strds_name, raster_dts_lst):
'''Register given maps
'''
# create strds
strds_id = format_id(strds_name)
strds_title = "data from GPM"
strds_desc = ""
strds = tgis.open_new_stds(strds_id, 'strds', 'absolute',
strds_title, strds_desc, "mean", overwrite=grass.overwrite())
# Register the maps
tgis.register.register_map_object_list('raster', raster_dts_lst,
strds, delete_empty=True, unit=None)
def inverse_transform(settings_name, coef_prefix, result_prefix='res.'):
reader = csv.reader(open(settings_name), delimiter=',')
header = reader.next()
data_names = [coef_prefix + name for name in header[1:]]
for row in reader:
s = "%s%s = " % (result_prefix, row[0])
sums = []
for i in range(len(data_names)):
sums.append("%s*%s" % (data_names[i], row[i+1]))
s += ' + '.join(sums)
grass.mapcalc(s, overwrite=grass.overwrite(), quite=True)
def main():
# Get the options
name = options["output"]
type = options["type"]
temporaltype = options["temporaltype"]
title = options["title"]
descr = options["description"]
semantic = options["semantictype"]
tgis.init()
tgis.open_new_stds(name, type, temporaltype, title, descr,
semantic, None, grass.overwrite())
def _import_vector_maps_from_gml(maplist, overr, exp, location, link):
impflags = "o"
if exp or location:
impflags += "e"
for row in maplist:
name = row["name"]
filename = row["filename"] + ".xml"
ret = gscript.run_command("v.in.ogr", dsn=filename,
output=name, flags=impflags,
overwrite=gscript.overwrite())
if ret != 0:
gscript.fatal(_("Unable to import vector map <%s> from file "
"%s.") % (name, filename))
def main():
# Get the options
input = options["input"]
output = options["output"]
method = options["method"]
sort = options["sort"]
where = options["where"]
# Make sure the temporal database exists
tgis.create_temporal_database()
if input.find("@") >= 0:
id = input
else:
mapset = grass.gisenv()["MAPSET"]
id = input + "@" + mapset
sp = tgis.space_time_raster_dataset(id)
if sp.is_in_db() == False:
grass.fatal(_("Dataset <%s> not found in temporal database") % (id))
sp.select()
rows = sp.get_registered_maps(None, where, sort)
if rows:
inputs = ""
count = 0
for row in rows:
if count == 0:
inputs += row["id"]
else:
inputs += "," + row["id"]
count += 1
print inputs
if grass.overwrite() == True:
grass.run_command("r.series", input=inputs, output=output, overwrite=True, method=method)
else:
grass.run_command("r.series", input=inputs, output=output, overwrite=False, method=method)
def _import_raster_maps(maplist, set_current_region=False):
# We need to disable the projection check because of its
# simple implementation
impflags = "o"
for row in maplist:
name = row["name"]
filename = row["filename"] + ".pack"
ret = gscript.run_command("r.unpack", input=filename,
output=name, flags=impflags,
overwrite=gscript.overwrite(),
verbose=True)
if ret != 0:
gscript.fatal(_("Unable to unpack raster map <%s> from file "
"%s.") % (name, filename))
# Set the computational region from the last map imported
if set_current_region is True:
gscript.run_command("g.region", rast=name)
def main():
# process command options
input = options['input']
if not gs.find_file(input)['file']:
gs.fatal(_("Raster map <%s> not found") % input)
smooth = options['output']
if gs.find_file(smooth)['file'] and not gs.overwrite():
gs.fatal(_("Output map <%s> already exists") % smooth)
sd = options['sd']
try:
sd = float(sd)
except ValueError:
if not gs.find_file(sd)['file']:
gs.fatal(_("Raster map <%s> not found") % sd)
alpha = float(options['alpha'])
# set aside region for internal use
gs.use_temp_region()
# subset input if desired
region = options.get('region')
if region:
if not gs.find_file(region)['file']:
gs.fatal(_("Raster map <%s> not found") % region)
gs.message("Setting region to %s" % region, flag='i')
gs.run_command('g.region', rast=region, align=input)
else:
gs.message("Using existing GRASS region", flag='i')
gs.debug('='*50)
gs.debug('\n'.join(gs.parse_command('g.region', 'p').keys()))
gs.debug('='*50)
multiscalesmooth(input, smooth, sd, alpha)
# restore original region
gs.del_temp_region()
return None
def _generate_time(N, prefix):
"""Generate (constant) rasters of time line
t0, t1, t2, ..., t_{N-1})
where t0 = 0, t_{N-1} = 2*pi
N -- count of result rasters
prefix -- prefix for the raster names
return list of names for the result rasters
"""
assert N > 1
names = dict()
for i in range(N):
output = prefix + _time_to_name(i)
t = get_time(N, i, deg=True)
grass.mapcalc("${out} = ${t}", out=output, t=t,
quiet=True, overwrite=grass.overwrite())
names[i] = output
return names
def _import_vector_maps(maplist):
# We need to disable the projection check because of its
# simple implementation
impflags = "o"
for row in maplist:
# Separate the name from the layer
name = row["name"].split(":")[0]
# Import only unique maps
if name in imported_maps:
continue
filename = row["filename"] + ".pack"
ret = gscript.run_command("v.unpack", input=filename,
output=name, flags=impflags,
overwrite=gscript.overwrite(),
verbose=True)
if ret != 0:
gscript.fatal(_("Unable to unpack vector map <%s> from file "
"%s.") % (name, filename))
imported_maps[name] = name
def create_strds_register_maps(in_strds, out_strds, out_maps, register_null,
empty_maps, dbif):
out_id = out_strds.get_id()
if out_strds.is_in_db(dbif):
if grass.overwrite():
out_strds.delete(dbif)
out_strds = in_strds.get_new_instance(out_id)
temporal_type, semantic_type, title, description = in_strds.get_initial_values()
out_strds.set_initial_values(temporal_type, semantic_type, title,
description)
out_strds.insert(dbif)
# Register the maps in the database
count = 0
for map in out_maps.values():
count += 1
if count%10 == 0:
grass.percent(count, len(out_maps), 1)
# Read the raster map data
map.load()
# In case of a empty map continue, do not register empty maps
if not register_null:
if map.metadata.get_min() is None and \
map.metadata.get_max() is None:
empty_maps.append(map)
continue
# Insert map in temporal database
map.insert(dbif)
out_strds.register_map(map, dbif)
out_strds.update_from_registered_maps(dbif)
grass.percent(1, 1, 1)
def main():
#lazy imports
import grass.temporal as tgis
# Get the options
input = options["input"]
output = options["output"]
vector_output = options["vector_output"]
strds = options["strds"]
where = options["where"]
columns = options["columns"]
if where == "" or where == " " or where == "\n":
where = None
overwrite = grass.overwrite()
# Check the number of sample strds and the number of columns
strds_names = strds.split(",")
column_names = columns.split(",")
if len(strds_names) != len(column_names):
grass.fatal(
_("The number of columns must be equal to the number of space time raster datasets"
))
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
mapset = grass.gisenv()["MAPSET"]
out_sp = tgis.check_new_stds(output, "stvds", dbif, overwrite)
samples = []
first_strds = tgis.open_old_stds(strds_names[0], "strds", dbif)
# Single space time raster dataset
if len(strds_names) == 1:
rows = first_strds.get_registered_maps(
columns="name,mapset,start_time,end_time",
order="start_time",
dbif=dbif)
if not rows:
dbif.close()
grass.fatal(
_("Space time raster dataset <%s> is empty") % out_sp.get_id())
for row in rows:
start = row["start_time"]
end = row["end_time"]
raster_maps = [
row["name"] + "@" + row["mapset"],
]
s = Sample(start, end, raster_maps)
samples.append(s)
else:
# Multiple space time raster datasets
for name in strds_names[1:]:
dataset = tgis.open_old_stds(name, "strds", dbif)
if dataset.get_temporal_type() != first_strds.get_temporal_type():
grass.fatal(_("Temporal type of space time raster datasets must be equal\n"
"<%(a)s> of type %(type_a)s do not match <%(b)s> of type %(type_b)s"%\
{"a":first_strds.get_id(),
"type_a":first_strds.get_temporal_type(),
"b":dataset.get_id(),
"type_b":dataset.get_temporal_type()}))
mapmatrizes = tgis.sample_stds_by_stds_topology(
"strds", "strds", strds_names, strds_names[0], False, None,
"equal", False, False)
for i in range(len(mapmatrizes[0])):
isvalid = True
mapname_list = []
for mapmatrix in mapmatrizes:
entry = mapmatrix[i]
if entry["samples"]:
sample = entry["samples"][0]
name = sample.get_id()
if name is None:
isvalid = False
break
else:
mapname_list.append(name)
if isvalid:
entry = mapmatrizes[0][i]
map = entry["granule"]
start, end = map.get_temporal_extent_as_tuple()
s = Sample(start, end, mapname_list)
samples.append(s)
num_samples = len(samples)
# Get the layer and database connections of the input vector
vector_db = grass.vector.vector_db(input)
# We copy the vector table and create the new layers
if vector_db:
# Use the first layer to copy the categories from
layers = "1,"
else:
layers = ""
first = True
for layer in range(num_samples):
layer += 1
# Skip existing layer
if vector_db and layer in vector_db and \
vector_db[layer]["layer"] == layer:
continue
if first:
layers += "%i" % (layer)
first = False
else:
layers += ",%i" % (layer)
vectmap = vector_output
# We create a new vector map using the categories of the original map
try:
grass.run_command("v.category",
input=input,
layer=layers,
output=vectmap,
option="transfer",
overwrite=overwrite)
except CalledModuleError:
grass.fatal(
_("Unable to create new layers for vector map <%s>") % (vectmap))
title = _("Observaion of space time raster dataset(s) <%s>") % (strds)
description = _("Observation of space time raster dataset(s) <%s>"
" with vector map <%s>") % (strds, input)
# Create the output space time vector dataset
out_sp = tgis.open_new_stds(output, "stvds",
first_strds.get_temporal_type(),
title, description,
first_strds.get_semantic_type(), dbif,
overwrite)
dummy = out_sp.get_new_map_instance(None)
# Sample the space time raster dataset with the vector
# map at specific layer with v.what.rast
count = 1
for sample in samples:
raster_names = sample.raster_names
if len(raster_names) != len(column_names):
grass.fatal(
_("The number of raster maps in a granule must "
"be equal to the number of column names"))
# Create the columns creation string
columns_string = ""
for name, column in zip(raster_names, column_names):
# The column is by default double precision
coltype = "DOUBLE PRECISION"
# Get raster map type
raster_map = tgis.RasterDataset(name)
raster_map.load()
if raster_map.metadata.get_datatype() == "CELL":
coltype = "INT"
tmp_string = "%s %s," % (column, coltype)
columns_string += tmp_string
# Remove last comma
columns_string = columns_string[0:len(columns_string) - 1]
# Try to add a column
if vector_db and count in vector_db and vector_db[count]["table"]:
try:
grass.run_command("v.db.addcolumn",
map=vectmap,
layer=count,
column=columns_string,
overwrite=overwrite)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to add column %s to vector map <%s> "
"with layer %i") % (columns_string, vectmap, count))
else:
# Try to add a new table
grass.message("Add table to layer %i" % (count))
try:
grass.run_command("v.db.addtable",
map=vectmap,
layer=count,
columns=columns_string,
overwrite=overwrite)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to add table to vector map "
"<%s> with layer %i") % (vectmap, count))
# Call v.what.rast for each raster map
for name, column in zip(raster_names, column_names):
try:
grass.run_command("v.what.rast",
map=vectmap,
layer=count,
raster=name,
column=column,
where=where)
except CalledModuleError:
dbif.close()
grass.fatal(_("Unable to run v.what.rast for vector map <%s> "
"with layer %i and raster map <%s>") % \
(vectmap, count, str(raster_names)))
vect = out_sp.get_new_map_instance(
dummy.build_id(vectmap, mapset, str(count)))
vect.load()
start = sample.start
end = sample.end
if out_sp.is_time_absolute():
vect.set_absolute_time(start, end)
else:
vect.set_relative_time(start, end,
first_strds.get_relative_time_unit())
if vect.is_in_db(dbif):
vect.update_all(dbif)
else:
vect.insert(dbif)
out_sp.register_map(vect, dbif)
count += 1
out_sp.update_from_registered_maps(dbif)
dbif.close()
def main():
"""
Builds river segments for input to the USGS hydrologic models
PRMS and GSFLOW.
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
# I/O
streams = options["input"]
segments = options["output"]
# Hydraulic geometry
ICALC = int(options["icalc"])
# ICALC=0: Constant depth
WIDTH1 = options["width1"]
WIDTH2 = options["width2"]
# ICALC=1,2: Manning (in channel and overbank): below
# ICALC=3: Power-law relationships (following Leopold and others)
# The at-a-station default exponents are from Rhodes (1977)
CDPTH = str(float(options["cdpth"]) / 35.3146667) # cfs to m^3/s
FDPTH = options["fdpth"]
AWDTH = str(float(options["awdth"]) / 35.3146667) # cfs to m^3/s
BWDTH = options["bwdth"]
##################################################
# CHECKING DEPENDENCIES WITH OPTIONAL PARAMETERS #
##################################################
if ICALC == 3:
if CDPTH and FDPTH and AWDTH and BWDTH:
pass
else:
gscript.fatal("Missing CDPTH, FDPTH, AWDTH, and/or BWDTH. \
These are required when ICALC = 3.")
###########
# RUNNING #
###########
# New Columns for Segments
segment_columns = []
# Self ID
segment_columns.append("id integer") # segment number
segment_columns.append("ISEG integer") # segment number
segment_columns.append("NSEG integer") # segment number
# for GSFLOW
segment_columns.append(
"ICALC integer"
) # 1 for channel, 2 for channel+fp, 3 for power function
segment_columns.append(
"OUTSEG integer") # downstream segment -- tostream, renumbered
segment_columns.append("ROUGHCH double precision") # overbank roughness
segment_columns.append("ROUGHBK double precision") # in-channel roughness
segment_columns.append("WIDTH1 double precision") # overbank roughness
segment_columns.append("WIDTH2 double precision") # in-channel roughness
segment_columns.append("CDPTH double precision") # depth coeff
segment_columns.append("FDPTH double precision") # depth exp
segment_columns.append("AWDTH double precision") # width coeff
segment_columns.append("BWDTH double precision") # width exp
segment_columns.append(
"floodplain_width double precision"
) # floodplain width (8-pt approx channel + flat fp)
# The below will be all 0
segment_columns.append(
"IUPSEG varchar") # upstream segment ID number, for diversions
segment_columns.append("FLOW varchar")
segment_columns.append("RUNOFF varchar")
segment_columns.append("ETSW varchar")
segment_columns.append("PPTSW varchar")
segment_columns = ",".join(segment_columns)
# CONSIDER THE EFFECT OF OVERWRITING COLUMNS -- WARN FOR THIS
# IF MAP EXISTS ALREADY?
# Create a map to work with
g.copy(vector=(streams, segments), overwrite=gscript.overwrite())
# and add its columns
v.db_addcolumn(map=segments, columns=segment_columns)
# Produce the data table entries
##################################
colNames = np.array(gscript.vector_db_select(segments, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(segments, layer=1)["values"].values())
number_of_segments = colValues.shape[0]
cats = colValues[:, colNames == "cat"].astype(int).squeeze()
nseg = np.arange(1, len(cats) + 1)
nseg_cats = []
for i in range(len(cats)):
nseg_cats.append((nseg[i], cats[i]))
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
# id = cat (as does ISEG and NSEG)
cur.executemany("update " + segments + " set id=? where cat=?", nseg_cats)
cur.executemany("update " + segments + " set ISEG=? where cat=?",
nseg_cats)
cur.executemany("update " + segments + " set NSEG=? where cat=?",
nseg_cats)
# outseg = tostream: default is 0 if "tostream" is off-map
cur.execute("update " + segments + " set OUTSEG=0")
cur.executemany("update " + segments + " set OUTSEG=? where tostream=?",
nseg_cats)
# Hydraulic geometry selection
cur.execute("update " + segments + " set ICALC=" + str(ICALC))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
if ICALC == 0:
gscript.message("")
gscript.message("ICALC=0 (constant) not supported")
gscript.message("Continuing nonetheless.")
gscript.message("")
if ICALC == 1:
if options["width_points"] is not "":
# Can add machinery here for separate upstream and downstream widths
# But really should not vary all that much
# v.to_db(map=segments, option='start', columns='xr1,yr1')
# v.to_db(map=segments, option='end', columns='xr2,yr2')
gscript.run_command(
"v.distance",
from_=segments,
to=options["width_points"],
upload="to_attr",
to_column=options["width_points_col"],
column="WIDTH1",
)
v.db_update(map=segments, column="WIDTH2", query_column="WIDTH1")
else:
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
cur.execute("update " + segments + " set WIDTH1=" + str(WIDTH1))
cur.execute("update " + segments + " set WIDTH2=" + str(WIDTH2))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
if ICALC == 2:
# REMOVE THIS MESSAGE ONCE THIS IS INCLUDED IN INPUT-FILE BUILDER
gscript.message("")
gscript.message("ICALC=2 (8-point channel + floodplain) not supported")
gscript.message("Continuing nonetheless.")
gscript.message("")
if options["fp_width_pts"] is not "":
gscript.run_command(
"v.distance",
from_=segments,
to=options["fp_width_pts"],
upload="to_attr",
to_column=options["fp_width_pts_col"],
column="floodplain_width",
)
else:
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
cur.execute("update " + segments + " set floodplain_width=" +
str(options["fp_width_value"]))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
if ICALC == 3:
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
cur.execute("update " + segments + " set CDPTH=" + str(CDPTH))
cur.execute("update " + segments + " set FDPTH=" + str(FDPTH))
cur.execute("update " + segments + " set AWDTH=" + str(AWDTH))
cur.execute("update " + segments + " set BWDTH=" + str(BWDTH))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
# values that are 0
gscript.message("")
gscript.message("NOTICE: not currently used:")
gscript.message("IUPSEG, FLOW, RUNOFF, ETSW, and PPTSW.")
gscript.message("All set to 0.")
gscript.message("")
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
cur.execute("update " + segments + " set IUPSEG=" + str(0))
cur.execute("update " + segments + " set FLOW=" + str(0))
cur.execute("update " + segments + " set RUNOFF=" + str(0))
cur.execute("update " + segments + " set ETSW=" + str(0))
cur.execute("update " + segments + " set PPTSW=" + str(0))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
# Roughness
# ICALC=1,2: Manning (in channel)
if (options["roughch_raster"] is not "") and (options["roughch_points"]
is not ""):
gscript.fatal(
"Choose either a raster or vector or a value as Manning's n input."
)
if options["roughch_raster"] is not "":
ROUGHCH = options["roughch_raster"]
v.rast_stats(
raster=ROUGHCH,
method="average",
column_prefix="tmp",
map=segments,
flags="c",
)
# v.db_renamecolumn(map=segments, column='tmp_average,ROUGHCH', quiet=True)
v.db_update(map=segments,
column="ROUGHCH",
query_column="tmp_average",
quiet=True)
v.db_dropcolumn(map=segments, columns="tmp_average", quiet=True)
elif options["roughch_points"] is not "":
ROUGHCH = options["roughch_points"]
gscript.run_command(
"v.distance",
from_=segments,
to=ROUGHCH,
upload="to_attr",
to_column=options["roughch_pt_col"],
column="ROUGHCH",
)
else:
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
ROUGHCH = options["roughch_value"]
cur.execute("update " + segments + " set ROUGHCH=" + str(ROUGHCH))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
# ICALC=2: Manning (overbank)
if (options["roughbk_raster"] is not "") and (options["roughbk_points"]
is not ""):
gscript.fatal(
"Choose either a raster or vector or a value as Manning's n input."
)
if options["roughbk_raster"] is not "":
ROUGHBK = options["roughbk_raster"]
v.rast_stats(
raster=ROUGHBK,
method="average",
column_prefix="tmp",
map=segments,
flags="c",
)
v.db_renamecolumn(map=segments,
column="tmp_average,ROUGHBK",
quiet=True)
elif options["roughbk_points"] is not "":
ROUGHBK = options["roughbk_points"]
gscript.run_command(
"v.distance",
from_=segments,
to=ROUGHBK,
upload="to_attr",
to_column=options["roughbk_pt_col"],
column="ROUGHBK",
)
else:
segmentsTopo = VectorTopo(segments)
segmentsTopo.open("rw")
cur = segmentsTopo.table.conn.cursor()
ROUGHBK = options["roughbk_value"]
cur.execute("update " + segments + " set ROUGHBK=" + str(ROUGHBK))
segmentsTopo.table.conn.commit()
segmentsTopo.close()
def dataset_mapcalculator(inputs, output, type, expression, base, method,
nprocs=1, register_null=False, spatial=False):
"""Perform map-calculations of maps from different space time
raster/raster3d datasets, using a specific sampling method
to select temporal related maps.
A mapcalc expression must be provided to process the temporal
selected maps. Temporal operators are available in addition to
the r.mapcalc operators:
Supported operators for relative and absolute time are:
- td() - the time delta of the current interval in days
and fractions of days or the unit in case of relative time
- start_time() - The start time of the interval from the begin of
the time series in days and fractions of days or the
unit in case of relative time
- end_time() - The end time of the current interval from the begin of
the time series in days and fractions of days or the
unit in case of relative time
Supported operators for absolute time:
- start_doy() - Day of year (doy) from the start time [1 - 366]
- start_dow() - Day of week (dow) from the start time [1 - 7],
the start of the week is monday == 1
- start_year() - The year of the start time [0 - 9999]
- start_month() - The month of the start time [1 - 12]
- start_week() - Week of year of the start time [1 - 54]
- start_day() - Day of month from the start time [1 - 31]
- start_hour() - The hour of the start time [0 - 23]
- start_minute() - The minute of the start time [0 - 59]
- start_second() - The second of the start time [0 - 59]
- end_doy() - Day of year (doy) from the end time [1 - 366]
- end_dow() - Day of week (dow) from the end time [1 - 7],
the start of the week is monday == 1
- end_year() - The year of the end time [0 - 9999]
- end_month() - The month of the end time [1 - 12]
- end_week() - Week of year of the end time [1 - 54]
- end_day() - Day of month from the end time [1 - 31]
- end_hour() - The hour of the end time [0 - 23]
- end_minute() - The minute of the end time [0 - 59]
- end_second() - The minute of the end time [0 - 59]
:param inputs: The names of the input space time raster/raster3d datasets
:param output: The name of the extracted new space time raster(3d) dataset
:param type: The type of the dataset: "raster" or "raster3d"
:param expression: The r(3).mapcalc expression
:param base: The base name of the new created maps in case a
mapclac expression is provided
:param method: The method to be used for temporal sampling
:param nprocs: The number of parallel processes to be used for
mapcalc processing
:param register_null: Set this number True to register empty maps
:param spatial: Check spatial overlap
"""
# We need a database interface for fast computation
dbif = SQLDatabaseInterfaceConnection()
dbif.connect()
mapset = get_current_mapset()
msgr = get_tgis_message_interface()
input_name_list = inputs.split(",")
first_input = open_old_stds(input_name_list[0], type, dbif)
# All additional inputs in reverse sorted order to avoid
# wrong name substitution
input_name_list = input_name_list[1:]
input_name_list.sort()
input_name_list.reverse()
input_list = []
for input in input_name_list:
sp = open_old_stds(input, type, dbif)
input_list.append(copy.copy(sp))
new_sp = check_new_stds(output, type, dbif, gscript.overwrite())
# Sample all inputs by the first input and create a sample matrix
if spatial:
msgr.message(_("Starting spatio-temporal sampling..."))
else:
msgr.message(_("Starting temporal sampling..."))
map_matrix = []
id_list = []
sample_map_list = []
# First entry is the first dataset id
id_list.append(first_input.get_name())
if len(input_list) > 0:
has_samples = False
for dataset in input_list:
list = dataset.sample_by_dataset(stds=first_input,
method=method, spatial=spatial,
dbif=dbif)
# In case samples are not found
if not list and len(list) == 0:
dbif.close()
msgr.message(_("No samples found for map calculation"))
return 0
# The fist entries are the samples
map_name_list = []
if not has_samples:
for entry in list:
granule = entry["granule"]
# Do not consider gaps
if granule.get_id() is None:
continue
sample_map_list.append(granule)
map_name_list.append(granule.get_name())
# Attach the map names
map_matrix.append(copy.copy(map_name_list))
has_samples = True
map_name_list = []
for entry in list:
maplist = entry["samples"]
granule = entry["granule"]
# Do not consider gaps in the sampler
if granule.get_id() is None:
continue
if len(maplist) > 1:
msgr.warning(_("Found more than a single map in a sample "
"granule. Only the first map is used for "
"computation. Use t.rast.aggregate.ds to "
"create synchronous raster datasets."))
# Store all maps! This includes non existent maps,
# identified by id == None
map_name_list.append(maplist[0].get_name())
# Attach the map names
map_matrix.append(copy.copy(map_name_list))
id_list.append(dataset.get_name())
else:
list = first_input.get_registered_maps_as_objects(dbif=dbif)
if list is None:
dbif.close()
msgr.message(_("No maps registered in input dataset"))
return 0
map_name_list = []
for map in list:
map_name_list.append(map.get_name())
sample_map_list.append(map)
# Attach the map names
map_matrix.append(copy.copy(map_name_list))
# Needed for map registration
map_list = []
if len(map_matrix) > 0:
msgr.message(_("Starting mapcalc computation..."))
count = 0
# Get the number of samples
num = len(map_matrix[0])
# Parallel processing
proc_list = []
proc_count = 0
# For all samples
for i in range(num):
count += 1
if count % 10 == 0:
msgr.percent(count, num, 1)
# Create the r.mapcalc statement for the current time step
map_name = "{base}_{suffix}".format(base=base,
suffix=gscript.get_num_suffix(count, num))
# Remove spaces and new lines
expr = expression.replace(" ", "")
# Check that all maps are in the sample
valid_maps = True
# Replace all dataset names with their map names of the
# current time step
for j in range(len(map_matrix)):
if map_matrix[j][i] is None:
valid_maps = False
break
# Substitute the dataset name with the map name
expr = expr.replace(id_list[j], map_matrix[j][i])
# Proceed with the next sample
if not valid_maps:
continue
# Create the new map id and check if the map is already
# in the database
map_id = map_name + "@" + mapset
new_map = first_input.get_new_map_instance(map_id)
# Check if new map is in the temporal database
if new_map.is_in_db(dbif):
if gscript.overwrite():
# Remove the existing temporal database entry
new_map.delete(dbif)
new_map = first_input.get_new_map_instance(map_id)
else:
msgr.error(_("Map <%s> is already in temporal database, "
"use overwrite flag to overwrite"))
continue
# Set the time stamp
if sample_map_list[i].is_time_absolute():
start, end = sample_map_list[i].get_absolute_time()
new_map.set_absolute_time(start, end)
else:
start, end, unit = sample_map_list[i].get_relative_time()
new_map.set_relative_time(start, end, unit)
# Parse the temporal expressions
expr = _operator_parser(expr, sample_map_list[0],
sample_map_list[i])
# Add the output map name
expr = "%s=%s" % (map_name, expr)
map_list.append(new_map)
msgr.verbose(_("Apply mapcalc expression: \"%s\"") % expr)
# Start the parallel r.mapcalc computation
if type == "raster":
proc_list.append(Process(target=_run_mapcalc2d, args=(expr,)))
else:
proc_list.append(Process(target=_run_mapcalc3d, args=(expr,)))
proc_list[proc_count].start()
proc_count += 1
if proc_count == nprocs or proc_count == num or count == num:
proc_count = 0
exitcodes = 0
for proc in proc_list:
proc.join()
exitcodes += proc.exitcode
if exitcodes != 0:
dbif.close()
msgr.fatal(_("Error while mapcalc computation"))
# Empty process list
proc_list = []
# Register the new maps in the output space time dataset
msgr.message(_("Starting map registration in temporal database..."))
temporal_type, semantic_type, title, description = first_input.get_initial_values()
new_sp = open_new_stds(output, type, temporal_type, title, description,
semantic_type, dbif, gscript.overwrite())
count = 0
# collect empty maps to remove them
empty_maps = []
# Insert maps in the temporal database and in the new space time
# dataset
for new_map in map_list:
count += 1
if count % 10 == 0:
msgr.percent(count, num, 1)
# Read the map data
new_map.load()
# In case of a null map continue, do not register null maps
if new_map.metadata.get_min() is None and \
new_map.metadata.get_max() is None:
if not register_null:
empty_maps.append(new_map)
continue
# Insert map in temporal database
new_map.insert(dbif)
new_sp.register_map(new_map, dbif)
# Update the spatio-temporal extent and the metadata table entries
new_sp.update_from_registered_maps(dbif)
msgr.percent(1, 1, 1)
# Remove empty maps
if len(empty_maps) > 0:
names = ""
count = 0
for map in empty_maps:
if count == 0:
names += "%s" % (map.get_name())
else:
names += ",%s" % (map.get_name())
count += 1
if type == "raster":
gscript.run_command("g.remove", flags='f', type='raster',
name=names, quiet=True)
elif type == "raster3d":
gscript.run_command("g.remove", flags='f', type='raster_3d',
name=names, quiet=True)
dbif.close()
def main():
"""
Adds GSFLOW parameters to a set of HRU sub-basins
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
basins = options["input"]
HRU = options["output"]
slope = options["slope"]
aspect = options["aspect"]
elevation = options["elevation"]
land_cover = options["cov_type"]
soil = options["soil_type"]
################################
# CREATE HRUs FROM SUB-BASINS #
################################
g.copy(vector=(basins, HRU), overwrite=gscript.overwrite())
############################################
# ATTRIBUTE COLUMNS (IN ORDER FROM MANUAL) #
############################################
# HRU
hru_columns = []
# Self ID
hru_columns.append("id integer") # nhru
# Basic Physical Attributes (Geometry)
hru_columns.append("hru_area double precision") # acres (!!!!)
hru_columns.append(
"hru_area_m2 double precision") # [not for GSFLOW: for me!]
hru_columns.append("hru_aspect double precision") # Mean aspect [degrees]
hru_columns.append("hru_elev double precision") # Mean elevation
hru_columns.append("hru_lat double precision") # Latitude of centroid
hru_columns.append("hru_lon double precision") # Longitude of centroid
# unnecessary but why not?
hru_columns.append("hru_slope double precision") # Mean slope [percent]
# Basic Physical Attributes (Other)
# hru_columns.append('hru_type integer') # 0=inactive; 1=land; 2=lake; 3=swale; almost all will be 1
# hru_columns.append('elev_units integer') # 0=feet; 1=meters. 0=default. I think I will set this to 1 by default.
# Measured input
hru_columns.append(
"outlet_sta integer") # Index of streamflow station at basin outlet:
# station number if it has one, 0 if not
# Note that the below specify projections and note lat/lon; they really seem
# to work for any projected coordinates, with _x, _y, in meters, and _xlong,
# _ylat, in feet (i.e. they are just northing and easting). The meters and feet
# are not just simple conversions, but actually are required for different
# modules in the code, and are hence redundant but intentional.
hru_columns.append("hru_x double precision") # Easting [m]
hru_columns.append("hru_xlong double precision") # Easting [feet]
hru_columns.append("hru_y double precision") # Northing [m]
hru_columns.append("hru_ylat double precision") # Northing [feet]
# Streamflow and lake routing
hru_columns.append(
"K_coef double precision"
) # Travel time of flood wave to next downstream segment;
# this is the Muskingum storage coefficient
# 1.0 for reservoirs, diversions, and segments flowing
# out of the basin
hru_columns.append(
"x_coef double precision") # Amount of attenuation of flow wave;
# this is the Muskingum routing weighting factor
# range: 0.0--0.5; default 0.2
# 0 for all segments flowing out of the basin
hru_columns.append("hru_segment integer"
) # ID of stream segment to which flow will be routed
# this is for non-cascade routing (flow goes directly
# from HRU to stream segment)
hru_columns.append("obsin_segment integer"
) # Index of measured streamflow station that replaces
# inflow to a segment
hru_columns.append(
"cov_type integer"
) # 0=bare soil;1=grasses; 2=shrubs; 3=trees; 4=coniferous
hru_columns.append("soil_type integer") # 1=sand; 2=loam; 3=clay
# Create strings
hru_columns = ",".join(hru_columns)
# Add columns to tables
v.db_addcolumn(map=HRU, columns=hru_columns, quiet=True)
###########################
# UPDATE DATABASE ENTRIES #
###########################
colNames = np.array(gscript.vector_db_select(HRU, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(HRU, layer=1)["values"].values())
number_of_hrus = colValues.shape[0]
cats = colValues[:, colNames == "cat"].astype(int).squeeze()
rnums = colValues[:, colNames == "rnum"].astype(int).squeeze()
nhru = np.arange(1, number_of_hrus + 1)
nhrut = []
for i in range(len(nhru)):
nhrut.append((nhru[i], cats[i]))
# Access the HRUs
hru = VectorTopo(HRU)
# Open the map with topology:
hru.open("rw")
# Create a cursor
cur = hru.table.conn.cursor()
# Use it to loop across the table
cur.executemany("update " + HRU + " set id=? where cat=?", nhrut)
# Commit changes to the table
hru.table.conn.commit()
# Close the table
hru.close()
"""
# Do the same for basins <-------------- DO THIS OR SIMPLY HAVE HRUs OVERLAIN WITH GRID CELLS? IN THIS CASE, RMV AREA ADDITION TO GRAVRES
v.db_addcolumn(map=basins, columns='id int', quiet=True)
basins = VectorTopo(basins)
basins.open('rw')
cur = basins.table.conn.cursor()
cur.executemany("update basins set id=? where cat=?", nhrut)
basins.table.conn.commit()
basins.close()
"""
# if you want to append to table
# cur.executemany("update HRU(id) values(?)", nhrut) # "insert into" will add rows
# hru_columns.append('hru_area double precision')
# Acres b/c USGS
v.to_db(map=HRU,
option="area",
columns="hru_area",
units="acres",
quiet=True)
v.to_db(map=HRU,
option="area",
columns="hru_area_m2",
units="meters",
quiet=True)
# GET MEAN VALUES FOR THESE NEXT ONES, ACROSS THE BASIN
# SLOPE (and aspect)
#####################
v.rast_stats(
map=HRU,
raster=slope,
method="average",
column_prefix="tmp",
flags="c",
quiet=True,
)
v.db_update(map=HRU,
column="hru_slope",
query_column="tmp_average",
quiet=True)
# ASPECT
#########
v.db_dropcolumn(map=HRU, columns="tmp_average", quiet=True)
# Dealing with conversion from degrees (no good average) to something I can
# average -- x- and y-vectors
# Geographic coordinates, so sin=x, cos=y.... not that it matters so long
# as I am consistent in how I return to degrees
r.mapcalc("aspect_x = sin(" + aspect + ")",
overwrite=gscript.overwrite(),
quiet=True)
r.mapcalc("aspect_y = cos(" + aspect + ")",
overwrite=gscript.overwrite(),
quiet=True)
# grass.run_command('v.db.addcolumn', map=HRU, columns='aspect_x_sum double precision, aspect_y_sum double precision, ncells_in_hru integer')
v.rast_stats(
map=HRU,
raster="aspect_x",
method="sum",
column_prefix="aspect_x",
flags="c",
quiet=True,
)
v.rast_stats(
map=HRU,
raster="aspect_y",
method="sum",
column_prefix="aspect_y",
flags="c",
quiet=True,
)
hru = VectorTopo(HRU)
hru.open("rw")
cur = hru.table.conn.cursor()
cur.execute("SELECT cat,aspect_x_sum,aspect_y_sum FROM %s" % hru.name)
_arr = np.array(cur.fetchall()).astype(float)
_cat = _arr[:, 0]
_aspect_x_sum = _arr[:, 1]
_aspect_y_sum = _arr[:, 2]
aspect_angle = np.arctan2(_aspect_y_sum, _aspect_x_sum) * 180.0 / np.pi
aspect_angle[aspect_angle < 0] += 360 # all positive
aspect_angle_cat = np.vstack((aspect_angle, _cat)).transpose()
cur.executemany("update " + HRU + " set hru_aspect=? where cat=?",
aspect_angle_cat)
hru.table.conn.commit()
hru.close()
# ELEVATION
############
v.rast_stats(
map=HRU,
raster=elevation,
method="average",
column_prefix="tmp",
flags="c",
quiet=True,
)
v.db_update(map=HRU,
column="hru_elev",
query_column="tmp_average",
quiet=True)
v.db_dropcolumn(map=HRU, columns="tmp_average", quiet=True)
# CENTROIDS
############
# get x,y of centroid -- but have areas not in database table, that do have
# centroids, and having a hard time finding a good way to get rid of them!
# They have duplicate category values!
# Perhaps these are little dangles on the edges of the vectorization where
# the raster value was the same but pinched out into 1-a few cells?
# From looking at map, lots of extra centroids on area boundaries, and removing
# small areas (though threshold hard to guess) gets rid of these
hru = VectorTopo(HRU)
hru.open("rw")
hru_cats = []
hru_coords = []
for hru_i in hru:
if isinstance(hru_i, vector.geometry.Centroid):
hru_cats.append(hru_i.cat)
hru_coords.append(hru_i.coords())
hru_cats = np.array(hru_cats)
hru_coords = np.array(hru_coords)
hru.rewind()
hru_area_ids = []
for coor in hru_coords:
_area = hru.find_by_point.area(Point(coor[0], coor[1]))
hru_area_ids.append(_area)
hru_area_ids = np.array(hru_area_ids)
hru.rewind()
hru_areas = []
for _area_id in hru_area_ids:
hru_areas.append(_area_id.area())
hru_areas = np.array(hru_areas)
hru.rewind()
allcats = sorted(list(set(list(hru_cats))))
# Now create weighted mean
hru_centroid_locations = []
for cat in allcats:
hrus_with_cat = hru_cats[hru_cats == cat]
if len(hrus_with_cat) == 1:
hru_centroid_locations.append(
(hru_coords[hru_cats == cat]).squeeze())
else:
_centroids = hru_coords[hru_cats == cat]
# print _centroids
_areas = hru_areas[hru_cats == cat]
# print _areas
_x = np.average(_centroids[:, 0], weights=_areas)
_y = np.average(_centroids[:, 1], weights=_areas)
# print _x, _y
hru_centroid_locations.append(np.array([_x, _y]))
# Now upload weighted mean to database table
# allcats and hru_centroid_locations are co-indexed
index__cats = create_iterator(HRU)
cur = hru.table.conn.cursor()
for i in range(len(allcats)):
# meters
cur.execute("update " + HRU + " set hru_x=" +
str(hru_centroid_locations[i][0]) + " where cat=" +
str(allcats[i]))
cur.execute("update " + HRU + " set hru_y=" +
str(hru_centroid_locations[i][1]) + " where cat=" +
str(allcats[i]))
# feet
cur.execute("update " + HRU + " set hru_xlong=" +
str(hru_centroid_locations[i][0] * 3.28084) +
" where cat=" + str(allcats[i]))
cur.execute("update " + HRU + " set hru_ylat=" +
str(hru_centroid_locations[i][1] * 3.28084) +
" where cat=" + str(allcats[i]))
# (un)Project to lat/lon
_centroid_ll = gscript.parse_command("m.proj",
coordinates=list(
hru_centroid_locations[i]),
flags="od").keys()[0]
_lon, _lat, _z = _centroid_ll.split("|")
cur.execute("update " + HRU + " set hru_lon=" + _lon + " where cat=" +
str(allcats[i]))
cur.execute("update " + HRU + " set hru_lat=" + _lat + " where cat=" +
str(allcats[i]))
# feet -- not working.
# Probably an issue with index__cats -- maybe fix later, if needed
# But currently not a major speed issue
"""
cur.executemany("update "+HRU+" set hru_xlong=?*3.28084 where hru_x=?",
index__cats)
cur.executemany("update "+HRU+" set hru_ylat=?*3.28084 where hru_y=?",
index__cats)
"""
cur.close()
hru.table.conn.commit()
hru.close()
# ID NUMBER
############
# cur.executemany("update "+HRU+" set hru_segment=? where id=?",
# index__cats)
# Segment number = HRU ID number
v.db_update(map=HRU, column="hru_segment", query_column="id", quiet=True)
# LAND USE/COVER
############
try:
land_cover = int(land_cover)
except:
pass
if isinstance(land_cover, int):
if land_cover <= 3:
v.db_update(map=HRU,
column="cov_type",
value=land_cover,
quiet=True)
else:
sys.exit(
"WARNING: INVALID LAND COVER TYPE. CHECK INTEGER VALUES.\n"
"EXITING TO ALLOW USER TO CHANGE BEFORE RUNNING GSFLOW")
else:
# NEED TO UPDATE THIS TO MODAL VALUE!!!!
gscript.message(
"Warning: values taken from HRU centroids. Code should be updated to"
)
gscript.message("acquire modal values")
v.what_rast(map=HRU,
type="centroid",
raster=land_cover,
column="cov_type",
quiet=True)
# v.rast_stats(map=HRU, raster=land_cover, method='average', column_prefix='tmp', flags='c', quiet=True)
# v.db_update(map=HRU, column='cov_type', query_column='tmp_average', quiet=True)
# v.db_dropcolumn(map=HRU, columns='tmp_average', quiet=True)
# SOIL
############
try:
soil = int(soil)
except:
pass
if isinstance(soil, int):
if (soil > 0) and (soil <= 3):
v.db_update(map=HRU, column="soil_type", value=soil, quiet=True)
else:
sys.exit("WARNING: INVALID SOIL TYPE. CHECK INTEGER VALUES.\n"
"EXITING TO ALLOW USER TO CHANGE BEFORE RUNNING GSFLOW")
else:
# NEED TO UPDATE THIS TO MODAL VALUE!!!!
gscript.message(
"Warning: values taken from HRU centroids. Code should be updated to"
)
gscript.message("acquire modal values")
v.what_rast(map=HRU,
type="centroid",
raster=soil,
column="soil_type",
quiet=True)
def main():
"""
Builds river reaches for input to the USGS hydrologic model, GSFLOW.
These reaches link the PRMS stream segments to the MODFLOW grid cells.
"""
##################
# OPTION PARSING #
##################
options, flags = gscript.parser()
segments = options['segment_input']
grid = options['grid_input']
reaches = options['output']
elevation = options['elevation']
Smin = options['s_min']
h_stream = options['h_stream']
x1 = options['upstream_easting_column_seg']
y1 = options['upstream_northing_column_seg']
x2 = options['downstream_easting_column_seg']
y2 = options['downstream_northing_column_seg']
tostream = options['tostream_cat_column_seg']
# Hydraulic paramters
STRTHICK = options['strthick']
STRHC1 = options['strhc1']
THTS = options['thts']
THTI = options['thti']
EPS = options['eps']
UHC = options['uhc']
# Build reach maps by overlaying segments on grid
if len(gscript.find_file(segments, element='vector')['name']) > 0:
v.extract(input=segments, output='GSFLOW_TEMP__', type='line', quiet=True, overwrite=True)
v.overlay(ainput='GSFLOW_TEMP__', atype='line', binput=grid, output=reaches, operator='and', overwrite=gscript.overwrite(), quiet=True)
g.remove(type='vector', name='GSFLOW_TEMP__', quiet=True, flags='f')
else:
gscript.fatal('No vector file "'+segments+'" found.')
# Start editing database table
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
# Rename a,b columns
reachesTopo.table.columns.rename('a_'+x1, 'x1')
reachesTopo.table.columns.rename('a_'+x2, 'x2')
reachesTopo.table.columns.rename('a_'+y1, 'y1')
reachesTopo.table.columns.rename('a_'+y2, 'y2')
reachesTopo.table.columns.rename('a_NSEG', 'NSEG')
reachesTopo.table.columns.rename('a_ISEG', 'ISEG')
reachesTopo.table.columns.rename('a_stream_type', 'stream_type')
reachesTopo.table.columns.rename('a_type_code', 'type_code')
reachesTopo.table.columns.rename('a_cat', 'rnum_cat')
reachesTopo.table.columns.rename('a_'+tostream, 'tostream')
reachesTopo.table.columns.rename('a_id', 'segment_id')
reachesTopo.table.columns.rename('a_OUTSEG', 'OUTSEG')
reachesTopo.table.columns.rename('b_row', 'row')
reachesTopo.table.columns.rename('b_col', 'col')
reachesTopo.table.columns.rename('b_id', 'cell_id')
# Drop unnecessary columns
cols = reachesTopo.table.columns.names()
for col in cols:
if (col[:2] == 'a_') or (col[:2] == 'b_'):
reachesTopo.table.columns.drop(col)
# Add new columns to 'reaches'
reachesTopo.table.columns.add('KRCH', 'integer')
reachesTopo.table.columns.add('IRCH', 'integer')
reachesTopo.table.columns.add('JRCH', 'integer')
reachesTopo.table.columns.add('IREACH', 'integer')
reachesTopo.table.columns.add('RCHLEN', 'integer')
reachesTopo.table.columns.add('STRTOP', 'double precision')
reachesTopo.table.columns.add('SLOPE', 'double precision')
reachesTopo.table.columns.add('STRTHICK', 'double precision')
reachesTopo.table.columns.add('STRHC1', 'double precision')
reachesTopo.table.columns.add('THTS', 'double precision')
reachesTopo.table.columns.add('THTI', 'double precision')
reachesTopo.table.columns.add('EPS', 'double precision')
reachesTopo.table.columns.add('UHC', 'double precision')
reachesTopo.table.columns.add('xr1', 'double precision')
reachesTopo.table.columns.add('xr2', 'double precision')
reachesTopo.table.columns.add('yr1', 'double precision')
reachesTopo.table.columns.add('yr2', 'double precision')
# Commit columns before editing (necessary?)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Update some columns that can be done now
reachesTopo.open('rw')
colNames = np.array(gscript.vector_db_select(reaches, layer=1)['columns'])
colValues = np.array(gscript.vector_db_select(reaches, layer=1)['values'].values())
cats = colValues[:,colNames == 'cat'].astype(int).squeeze()
nseg = np.arange(1, len(cats)+1)
nseg_cats = []
for i in range(len(cats)):
nseg_cats.append( (nseg[i], cats[i]) )
cur = reachesTopo.table.conn.cursor()
# Hydrogeologic properties
cur.execute("update "+reaches+" set STRTHICK="+str(STRTHICK))
cur.execute("update "+reaches+" set STRHC1="+str(STRHC1))
cur.execute("update "+reaches+" set THTS="+str(THTS))
cur.execute("update "+reaches+" set THTI="+str(THTI))
cur.execute("update "+reaches+" set EPS="+str(EPS))
cur.execute("update "+reaches+" set UHC="+str(UHC))
# Grid properties
cur.execute("update "+reaches+" set KRCH=1") # Top layer: unchangable
cur.executemany("update "+reaches+" set IRCH=? where row=?", nseg_cats)
cur.executemany("update "+reaches+" set JRCH=? where col=?", nseg_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
v.to_db(map=reaches, columns='RCHLEN', option='length', quiet=True)
# Still to go after these:
# STRTOP (added with slope)
# IREACH (whole next section dedicated to this)
# SLOPE (need z_start and z_end)
# Now, the light stuff is over: time to build the reach order
v.to_db(map=reaches, option='start', columns='xr1,yr1')
v.to_db(map=reaches, option='end', columns='xr2,yr2')
# Now just sort by category, find which stream has the same xr1 and yr1 as
# x1 and y1 (or a_x1, a_y1) and then find where its endpoint matches another
# starting point and move down the line.
# v.db.select reaches col=cat,a_id,xr1,xr2 where="a_x1 = xr1"
# First, get the starting coordinates of each stream segment
# and a set of river ID's (ordered from 1...N)
colNames = np.array(gscript.vector_db_select(segments, layer=1)['columns'])
colValues = np.array(gscript.vector_db_select(segments, layer=1)['values'].values())
number_of_segments = colValues.shape[0]
segment_x1s = colValues[:,colNames == 'x1'].astype(float).squeeze()
segment_y1s = colValues[:,colNames == 'y1'].astype(float).squeeze()
segment_ids = colValues[:,colNames == 'id'].astype(float).squeeze()
# Then move back to the reaches map to produce the ordering
colNames = np.array(gscript.vector_db_select(reaches, layer=1)['columns'])
colValues = np.array(gscript.vector_db_select(reaches, layer=1)['values'].values())
reach_cats = colValues[:,colNames == 'cat'].astype(int).squeeze()
reach_x1s = colValues[:,colNames == 'xr1'].astype(float).squeeze()
reach_y1s = colValues[:,colNames == 'yr1'].astype(float).squeeze()
reach_x2s = colValues[:,colNames == 'xr2'].astype(float).squeeze()
reach_y2s = colValues[:,colNames == 'yr2'].astype(float).squeeze()
segment_ids__reach = colValues[:,colNames == 'segment_id'].astype(float).squeeze()
for segment_id in segment_ids:
reach_order_cats = []
downstream_directed = []
ssel = segment_ids == segment_id
rsel = segment_ids__reach == segment_id # selector
# Find first segment: x1y1 first here, but not necessarily later
downstream_directed.append(1)
_x_match = reach_x1s[rsel] == segment_x1s[ssel]
_y_match = reach_y1s[rsel] == segment_y1s[ssel]
_i_match = _x_match * _y_match
x1y1 = True # false if x2y2
# Find cat
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_order_cats.append(_cat)
# Get end of reach = start of next one
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
while _i_match.any():
_x_match = reach_x1s[rsel] == reach_x_end
_y_match = reach_y1s[rsel] == reach_y_end
_i_match = _x_match * _y_match
if _i_match.any():
_cat = int(reach_cats[rsel][_x_match * _y_match])
reach_x_end = float(reach_x2s[reach_cats == _cat])
reach_y_end = float(reach_y2s[reach_cats == _cat])
reach_order_cats.append(_cat)
print len(reach_order_cats), len(reach_cats[rsel])
# Reach order to database table
reach_number__reach_order_cats = []
for i in range(len(reach_order_cats)):
reach_number__reach_order_cats.append( (i+1, reach_order_cats[i]) )
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
cur = reachesTopo.table.conn.cursor()
cur.executemany("update "+reaches+" set IREACH=? where cat=?",
reach_number__reach_order_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
# TOP AND BOTTOM ARE OUT OF ORDER: SOME SEGS ARE BACKWARDS. UGH!!!!
# NEED TO GET THEM IN ORDER TO GET THE Z VALUES AT START AND END
# Compute slope and starting elevations from the elevations at the start and
# end of the reaches and the length of each reach]
gscript.message('Obtaining elevation values from raster: may take time.')
v.db_addcolumn(map=reaches, columns='zr1 double precision, zr2 double precision')
zr1 = []
zr2 = []
for i in range(len(reach_cats)):
_x = reach_x1s[i]
_y = reach_y1s[i]
_z = float(gscript.parse_command('r.what', map=elevation, coordinates=str(_x)+','+str(_y)).keys()[0].split('|')[-1])
zr1.append(_z)
_x = reach_x2s[i]
_y = reach_y2s[i]
_z = float(gscript.parse_command('r.what', map=elevation, coordinates=str(_x)+','+str(_y)).keys()[0].split('|')[-1])
zr2.append(_z)
zr1_cats = []
zr2_cats = []
for i in range(len(reach_cats)):
zr1_cats.append( (zr1[i], reach_cats[i]) )
zr2_cats.append( (zr2[i], reach_cats[i]) )
reachesTopo = VectorTopo(reaches)
reachesTopo.open('rw')
cur = reachesTopo.table.conn.cursor()
cur.executemany("update "+reaches+" set zr1=? where cat=?", zr1_cats)
cur.executemany("update "+reaches+" set zr2=? where cat=?", zr2_cats)
reachesTopo.table.conn.commit()
reachesTopo.close()
# Use these to create slope -- backwards possible on DEM!
v.db_update(map=reaches, column='SLOPE', value='(zr1 - zr2)/RCHLEN')
v.db_update(map=reaches, column='SLOPE', value=Smin, where='SLOPE <= '+str(Smin))
# srtm_local_filled_grid = srtm_local_filled @ 200m (i.e. current grid)
# resolution
# r.to.vect in=srtm_local_filled_grid out=srtm_local_filled_grid col=z type=area --o#
# NOT SURE IF IT IS BEST TO USE MEAN ELEVATION OR TOP ELEVATION!!!!!!!!!!!!!!!!!!!!!!!
v.db_addcolumn(map=reaches, columns='z_topo_mean double precision')
v.what_rast(map=reaches, raster=elevation, column='z_topo_mean')#, query_column='z')
v.db_update(map=reaches, column='STRTOP', value='z_topo_mean -'+str(h_stream), quiet=True)
def main():
repeat = int(options.pop("repeat"))
nprocs = int(options.pop("nprocs"))
subregions = options["subregions"]
tosplit = flags["d"]
# filter unused optional params
for key in list(options.keys()):
if options[key] == "":
options.pop(key)
if tosplit and "output_series" in options:
gscript.fatal(
_("Parallelization on subregion level is not supported together with <output_series> option"
))
if (not gscript.overwrite() and gscript.list_grouped(
"raster",
pattern=options["output"] + "_run1")[gscript.gisenv()["MAPSET"]]):
gscript.fatal(
_("Raster map <{r}> already exists."
" To overwrite, use the --overwrite flag").format(
r=options["output"] + "_run1"))
global TMP_RASTERS
cats = []
if tosplit:
gscript.message(_("Splitting subregions"))
cats = (gscript.read_command("r.stats", flags="n",
input=subregions).strip().splitlines())
if len(cats) < 2:
gscript.fatal(
_("Not enough subregions to split computation. Do not use -d flag."
))
mapcalcs = []
for cat in cats:
new = PREFIX + cat
TMP_RASTERS.append(new)
mapcalcs.append("{new} = if({sub} == {cat}, {sub}, null())".format(
sub=subregions, cat=cat, new=new))
pool = Pool(nprocs)
p = pool.map_async(split_subregions, mapcalcs)
try:
p.wait()
except (KeyboardInterrupt, CalledModuleError):
return
options_list = []
for i in range(repeat):
if cats:
for cat in cats:
op = options.copy()
op["random_seed"] = i + 1
if "output_series" in op:
op["output_series"] += "_run" + str(i + 1) + "_" + cat
TMP_RASTERS.append(op["output_series"])
op["output"] += "_run" + str(i + 1) + "_" + cat
op["subregions"] = PREFIX + cat
options_list.append((repeat, i + 1, cat, op))
TMP_RASTERS.append(op["output"])
else:
op = options.copy()
op["random_seed"] = i + 1
if "output_series" in op:
op["output_series"] += "_run" + str(i + 1)
op["output"] += "_run" + str(i + 1)
options_list.append((repeat, i + 1, None, op))
pool = Pool(nprocs)
p = pool.map_async(futures_process, options_list)
try:
p.wait()
except (KeyboardInterrupt, CalledModuleError):
return
if cats:
gscript.message(_("Patching subregions"))
for i in range(repeat):
patch_input = [
options["output"] + "_run" + str(i + 1) + "_" + cat
for cat in cats
]
gscript.run_command(
"r.patch",
input=patch_input,
output=options["output"] + "_run" + str(i + 1),
)
return 0
def main():
options, flags = grass.parser()
# required
elevation_input = options['elevation']
aspect_input = options['aspect']
slope_input = options['slope']
# optional
latitude = options['lat']
longitude = options['long']
linke_input = options['linke']
linke_value = options['linke_value']
albedo_input = options['albedo']
albedo_value = options['albedo_value']
horizon_basename = options['horizon_basename']
horizon_step = options['horizon_step']
# outputs
beam_rad = options['beam_rad']
diff_rad = options['diff_rad']
refl_rad = options['refl_rad']
glob_rad = options['glob_rad']
beam_rad_basename = beam_rad_basename_user = options['beam_rad_basename']
diff_rad_basename = diff_rad_basename_user = options['diff_rad_basename']
refl_rad_basename = refl_rad_basename_user = options['refl_rad_basename']
glob_rad_basename = glob_rad_basename_user = options['glob_rad_basename']
# missing output?
if not any([
beam_rad, diff_rad, refl_rad, glob_rad, beam_rad_basename,
diff_rad_basename, refl_rad_basename, glob_rad_basename
]):
grass.fatal(_("No output specified."))
start_day = int(options['start_day'])
end_day = int(options['end_day'])
day_step = int(options['day_step'])
if day_step > 1 and (beam_rad or diff_rad or refl_rad or glob_rad):
grass.fatal(
_("Day step higher then 1 would produce"
" meaningless cumulative maps."))
# check: start < end
if start_day > end_day:
grass.fatal(_("Start day is after end day."))
if day_step >= end_day - start_day:
grass.fatal(_("Day step is too big."))
step = float(options['step'])
nprocs = int(options['nprocs'])
if beam_rad and not beam_rad_basename:
beam_rad_basename = create_tmp_map_name('beam_rad')
MREMOVE.append(beam_rad_basename)
if diff_rad and not diff_rad_basename:
diff_rad_basename = create_tmp_map_name('diff_rad')
MREMOVE.append(diff_rad_basename)
if refl_rad and not refl_rad_basename:
refl_rad_basename = create_tmp_map_name('refl_rad')
MREMOVE.append(refl_rad_basename)
if glob_rad and not glob_rad_basename:
glob_rad_basename = create_tmp_map_name('glob_rad')
MREMOVE.append(glob_rad_basename)
# check for existing identical map names
if not grass.overwrite():
check_daily_map_names(beam_rad_basename,
grass.gisenv()['MAPSET'], start_day, end_day,
day_step)
check_daily_map_names(diff_rad_basename,
grass.gisenv()['MAPSET'], start_day, end_day,
day_step)
check_daily_map_names(refl_rad_basename,
grass.gisenv()['MAPSET'], start_day, end_day,
day_step)
check_daily_map_names(glob_rad_basename,
grass.gisenv()['MAPSET'], start_day, end_day,
day_step)
# check for slope/aspect
if not aspect_input or not slope_input:
params = {}
if not aspect_input:
aspect_input = create_tmp_map_name('aspect')
params.update({'aspect': aspect_input})
REMOVE.append(aspect_input)
if not slope_input:
slope_input = create_tmp_map_name('slope')
params.update({'slope': slope_input})
REMOVE.append(slope_input)
grass.info(_("Running r.slope.aspect..."))
grass.run_command('r.slope.aspect',
elevation=elevation_input,
quiet=True,
**params)
if beam_rad:
grass.mapcalc('{beam} = 0'.format(beam=beam_rad), quiet=True)
if diff_rad:
grass.mapcalc('{diff} = 0'.format(diff=diff_rad), quiet=True)
if refl_rad:
grass.mapcalc('{refl} = 0'.format(refl=refl_rad), quiet=True)
if glob_rad:
grass.mapcalc('{glob} = 0'.format(glob=glob_rad), quiet=True)
rsun_flags = ''
if flags['m']:
rsun_flags += 'm'
if flags['p']:
rsun_flags += 'p'
grass.info(_("Running r.sun in a loop..."))
count = 0
# Parallel processing
proc_list = []
proc_count = 0
suffixes_all = []
days = range(start_day, end_day + 1, day_step)
num_days = len(days)
core.percent(0, num_days, 1)
for day in days:
count += 1
core.percent(count, num_days, 10)
suffix = '_' + format_order(day)
proc_list.append(
Process(target=run_r_sun,
args=(elevation_input, aspect_input, slope_input, latitude,
longitude, linke_input, linke_value, albedo_input,
albedo_value, horizon_basename, horizon_step, day,
step, beam_rad_basename, diff_rad_basename,
refl_rad_basename, glob_rad_basename, suffix,
rsun_flags)))
proc_list[proc_count].start()
proc_count += 1
suffixes_all.append(suffix)
if proc_count == nprocs or proc_count == num_days or count == num_days:
proc_count = 0
exitcodes = 0
for proc in proc_list:
proc.join()
exitcodes += proc.exitcode
if exitcodes != 0:
core.fatal(_("Error while r.sun computation"))
# Empty process list
proc_list = []
if beam_rad:
sum_maps(beam_rad, beam_rad_basename, suffixes_all)
if diff_rad:
sum_maps(diff_rad, diff_rad_basename, suffixes_all)
if refl_rad:
sum_maps(refl_rad, refl_rad_basename, suffixes_all)
if glob_rad:
sum_maps(glob_rad, glob_rad_basename, suffixes_all)
# FIXME: how percent really works?
# core.percent(1, 1, 1)
# set color table
if beam_rad:
set_color_table([beam_rad])
if diff_rad:
set_color_table([diff_rad])
if refl_rad:
set_color_table([refl_rad])
if glob_rad:
set_color_table([glob_rad])
if not any([
beam_rad_basename_user, diff_rad_basename_user,
refl_rad_basename_user, glob_rad_basename_user
]):
return 0
# add timestamps and register to spatio-temporal raster data set
temporal = flags['t']
if temporal:
core.info(_("Registering created maps into temporal dataset..."))
import grass.temporal as tgis
def registerToTemporal(basename, suffixes, mapset, start_day, day_step,
title, desc):
"""
Register daily output maps in spatio-temporal raster data set
"""
maps = ','.join(
[basename + suf + '@' + mapset for suf in suffixes])
tgis.open_new_stds(basename,
type='strds',
temporaltype='relative',
title=title,
descr=desc,
semantic='sum',
dbif=None,
overwrite=grass.overwrite())
tgis.register_maps_in_space_time_dataset(type='rast',
name=basename,
maps=maps,
start=start_day,
end=None,
unit='days',
increment=day_step,
dbif=None,
interval=False)
# Make sure the temporal database exists
tgis.init()
mapset = grass.gisenv()['MAPSET']
if beam_rad_basename_user:
registerToTemporal(
beam_rad_basename,
suffixes_all,
mapset,
start_day,
day_step,
title="Beam irradiation",
desc="Output beam irradiation raster maps [Wh.m-2.day-1]")
if diff_rad_basename_user:
registerToTemporal(
diff_rad_basename,
suffixes_all,
mapset,
start_day,
day_step,
title="Diffuse irradiation",
desc="Output diffuse irradiation raster maps [Wh.m-2.day-1]")
if refl_rad_basename_user:
registerToTemporal(
refl_rad_basename,
suffixes_all,
mapset,
start_day,
day_step,
title="Reflected irradiation",
desc="Output reflected irradiation raster maps [Wh.m-2.day-1]")
if glob_rad_basename_user:
registerToTemporal(
glob_rad_basename,
suffixes_all,
mapset,
start_day,
day_step,
title="Total irradiation",
desc="Output total irradiation raster maps [Wh.m-2.day-1]")
# just add timestamps, don't register
else:
for i, day in enumerate(days):
if beam_rad_basename_user:
set_time_stamp(beam_rad_basename + suffixes_all[i], day=day)
if diff_rad_basename_user:
set_time_stamp(diff_rad_basename + suffixes_all[i], day=day)
if refl_rad_basename_user:
set_time_stamp(refl_rad_basename + suffixes_all[i], day=day)
if glob_rad_basename_user:
set_time_stamp(glob_rad_basename + suffixes_all[i], day=day)
# set color table for daily maps
if beam_rad_basename_user:
maps = [beam_rad_basename + suf for suf in suffixes_all]
set_color_table(maps)
if diff_rad_basename_user:
maps = [diff_rad_basename + suf for suf in suffixes_all]
set_color_table(maps)
if refl_rad_basename_user:
maps = [refl_rad_basename + suf for suf in suffixes_all]
set_color_table(maps)
if glob_rad_basename_user:
maps = [glob_rad_basename + suf for suf in suffixes_all]
set_color_table(maps)
def main():
# Get the options
input = options["input"]
output = options["output"]
strds = options["strds"]
tempwhere = options["t_where"]
where = options["where"]
methods = options["method"]
percentile = options["percentile"]
overwrite = grass.overwrite()
quiet = True
if grass.verbosity() > 2:
quiet = False
if where == "" or where == " " or where == "\n":
where = None
# Check the number of sample strds and the number of columns
strds_names = strds.split(",")
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
samples = []
first_strds = tgis.open_old_stds(strds_names[0], "strds", dbif)
# Single space time raster dataset
if len(strds_names) == 1:
granu = first_strds.get_granularity()
rows = first_strds.get_registered_maps(
"name,mapset,start_time,end_time", tempwhere, "start_time", dbif)
if not rows:
dbif.close()
grass.fatal(
_("Space time raster dataset <%s> is empty") %
first_strds.get_id())
for row in rows:
start = row["start_time"]
end = row["end_time"]
raster_maps = [
row["name"] + "@" + row["mapset"],
]
s = Sample(start, end, raster_maps, first_strds.get_name(), granu)
samples.append(s)
else:
# Multiple space time raster datasets
for name in strds_names[1:]:
dataset = tgis.open_old_stds(name, "strds", dbif)
if dataset.get_temporal_type() != first_strds.get_temporal_type():
grass.fatal(
_(
"Temporal type of space time raster "
"datasets must be equal\n<%(a)s> of type "
"%(type_a)s do not match <%(b)s> of type "
"%(type_b)s" % {
"a": first_strds.get_id(),
"type_a": first_strds.get_temporal_type(),
"b": dataset.get_id(),
"type_b": dataset.get_temporal_type(),
}))
mapmatrizes = tgis.sample_stds_by_stds_topology(
"strds",
"strds",
strds_names,
strds_names[0],
False,
None,
"equal",
False,
False,
)
# TODO check granularity for multiple STRDS
for i in range(len(mapmatrizes[0])):
isvalid = True
mapname_list = []
for mapmatrix in mapmatrizes:
entry = mapmatrix[i]
if entry["samples"]:
sample = entry["samples"][0]
name = sample.get_id()
if name is None:
isvalid = False
break
else:
mapname_list.append(name)
if isvalid:
entry = mapmatrizes[0][i]
map = entry["granule"]
start, end = map.get_temporal_extent_as_tuple()
s = Sample(start, end, mapname_list, name)
samples.append(s)
# Get the layer and database connections of the input vector
if where:
try:
grass.run_command("v.extract",
input=input,
where=where,
output=output)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to run v.extract for vector map"
" <%s> and where <%s>") % (input, where))
else:
gcopy(input, output, "vector")
msgr = Messenger()
perc_curr = 0
perc_tot = len(samples)
pymap = Vector(output)
try:
pymap.open("r")
except:
dbif.close()
grass.fatal(_("Unable to create vector map <%s>" % output))
pymap.close()
for sample in samples:
raster_names = sample.raster_names
# Call v.what.rast for each raster map
for name in raster_names:
day = sample.printDay()
column_name = "%s_%s" % (sample.strds_name, day)
try:
grass.run_command(
"v.rast.stats",
map=output,
raster=name,
column=column_name,
method=methods,
percentile=percentile,
quiet=quiet,
overwrite=overwrite,
)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to run v.what.rast for vector map"
" <%s> and raster map <%s>") % (output, name))
msgr.percent(perc_curr, perc_tot, 1)
perc_curr += 1
dbif.close()
def main():
"""
Builds a grid for the MODFLOW component of the USGS hydrologic model,
GSFLOW.
"""
options, flags = gscript.parser()
basin = options['basin']
pp = options['pour_point']
raster_input = options['raster_input']
dx = options['dx']
dy = options['dy']
grid = options['output']
mask = options['mask_output']
bc_cell = options['bc_cell']
# basin='basins_tmp_onebasin'; pp='pp_tmp'; raster_input='DEM'; raster_output='DEM_coarse'; dx=dy='500'; grid='grid_tmp'; mask='mask_tmp'
"""
# Fatal if raster input and output are not both set
_lena0 = (len(raster_input) == 0)
_lenb0 = (len(raster_output) == 0)
if _lena0 + _lenb0 == 1:
grass.fatal("You must set both raster input and output, or neither.")
"""
# Create grid -- overlaps DEM, one cell of padding
gscript.use_temp_region()
reg = gscript.region()
reg_grid_edges_sn = np.linspace(reg['s'], reg['n'], reg['rows'])
reg_grid_edges_we = np.linspace(reg['w'], reg['e'], reg['cols'])
g.region(vector=basin, ewres=dx, nsres=dy)
regnew = gscript.region()
# Use a grid ratio -- don't match exactly the desired MODFLOW resolution
grid_ratio_ns = np.round(regnew['nsres'] / reg['nsres'])
grid_ratio_ew = np.round(regnew['ewres'] / reg['ewres'])
# Get S, W, and then move the unit number of grid cells over to get N and E
# and include 3 cells of padding around the whole watershed
_s_dist = np.abs(reg_grid_edges_sn - (regnew['s'] - 3. * regnew['nsres']))
_s_idx = np.where(_s_dist == np.min(_s_dist))[0][0]
_s = float(reg_grid_edges_sn[_s_idx])
_n_grid = np.arange(_s, reg['n'] + 3 * grid_ratio_ns * reg['nsres'],
grid_ratio_ns * reg['nsres'])
_n_dist = np.abs(_n_grid - (regnew['n'] + 3. * regnew['nsres']))
_n_idx = np.where(_n_dist == np.min(_n_dist))[0][0]
_n = float(_n_grid[_n_idx])
_w_dist = np.abs(reg_grid_edges_we - (regnew['w'] - 3. * regnew['ewres']))
_w_idx = np.where(_w_dist == np.min(_w_dist))[0][0]
_w = float(reg_grid_edges_we[_w_idx])
_e_grid = np.arange(_w, reg['e'] + 3 * grid_ratio_ew * reg['ewres'],
grid_ratio_ew * reg['ewres'])
_e_dist = np.abs(_e_grid - (regnew['e'] + 3. * regnew['ewres']))
_e_idx = np.where(_e_dist == np.min(_e_dist))[0][0]
_e = float(_e_grid[_e_idx])
# Finally make the region
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
# And then make the grid
v.mkgrid(map=grid, overwrite=gscript.overwrite())
# Cell numbers (row, column, continuous ID)
v.db_addcolumn(map=grid, columns='id int', quiet=True)
colNames = np.array(gscript.vector_db_select(grid, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(grid, layer=1)['values'].values())
cats = colValues[:, colNames == 'cat'].astype(int).squeeze()
rows = colValues[:, colNames == 'row'].astype(int).squeeze()
cols = colValues[:, colNames == 'col'].astype(int).squeeze()
nrows = np.max(rows)
ncols = np.max(cols)
cats = np.ravel([cats])
_id = np.ravel([ncols * (rows - 1) + cols])
_id_cat = []
for i in range(len(_id)):
_id_cat.append((_id[i], cats[i]))
gridTopo = VectorTopo(grid)
gridTopo.open('rw')
cur = gridTopo.table.conn.cursor()
cur.executemany("update " + grid + " set id=? where cat=?", _id_cat)
gridTopo.table.conn.commit()
gridTopo.close()
# Cell area
v.db_addcolumn(map=grid, columns='area_m2', quiet=True)
v.to_db(map=grid,
option='area',
units='meters',
columns='area_m2',
quiet=True)
# Basin mask
if len(mask) > 0:
# Fine resolution region:
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
# Rasterize basin
v.to_rast(input=basin,
output=mask,
use='val',
value=1,
overwrite=gscript.overwrite(),
quiet=True)
# Coarse resolution region:
g.region(w=str(_w),
e=str(_e),
s=str(_s),
n=str(_n),
nsres=str(grid_ratio_ns * reg['nsres']),
ewres=str(grid_ratio_ew * reg['ewres']))
r.resamp_stats(input=mask,
output=mask,
method='sum',
overwrite=True,
quiet=True)
r.mapcalc(mask + ' = ' + mask + ' > 0', overwrite=True, quiet=True)
"""
# Resampled raster
if len(raster_output) > 0:
r.resamp_stats(input=raster_input, output=raster_output, method='average', overwrite=gscript.overwrite(), quiet=True)
"""
# Pour point
if len(pp) > 0:
v.db_addcolumn(map=pp,
columns=('row integer', 'col integer'),
quiet=True)
v.build(map=pp, quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='row',
query_column='row',
quiet=True)
v.what_vect(map=pp,
query_map=grid,
column='col',
query_column='col',
quiet=True)
# Next point downstream of the pour point
if len(bc_cell) > 0:
########## NEED TO USE TRUE TEMPORARY FILE ##########
# May not work with dx != dy!
v.to_rast(input=pp, output='tmp', use='val', value=1, overwrite=True)
r.buffer(input='tmp',
output='tmp',
distances=float(dx) * 1.5,
overwrite=True)
r.mapcalc('tmp = (tmp == 2) * ' + raster_input, overwrite=True)
r.drain(input=raster_input,
start_points=pp,
output='tmp2',
overwrite=True)
r.mapcalc('tmp = tmp2 * tmp', overwrite=True)
r.null(map='tmp', setnull=0)
r.to_vect(input='tmp',
output=bc_cell,
type='point',
column='z',
overwrite=gscript.overwrite(),
quiet=True)
v.db_addcolumn(map=bc_cell,
columns=('row integer', 'col integer'),
quiet=True)
v.build(map=bc_cell, quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='row', \
query_column='row', quiet=True)
v.what_vect(map=bc_cell, query_map=grid, column='col', \
query_column='col', quiet=True)
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
def main():
global TMPLOC, SRCGISRC, TGTGISRC, GISDBASE
global tile, tmpdir, in_temp, currdir, tmpregionname
in_temp = False
url = options["url"]
username = options["username"]
password = options["password"]
local = options["local"]
output = options["output"]
memory = options["memory"]
fillnulls = flags["n"]
srtmv3 = flags["2"] == 0
one = flags["1"]
dozerotile = flags["z"]
reproj_res = options["resolution"]
overwrite = grass.overwrite()
res = "00:00:03"
if srtmv3:
fillnulls = 0
if one:
res = "00:00:01"
else:
one = None
if len(local) == 0:
if len(url) == 0:
if srtmv3:
if one:
url = "https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/"
else:
url = "https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL3.003/2000.02.11/"
else:
url = "http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/"
if len(local) == 0:
local = None
# are we in LatLong location?
s = grass.read_command("g.proj", flags="j")
kv = grass.parse_key_val(s)
if fillnulls == 1 and memory <= 0:
grass.warning(
_("Amount of memory to use for interpolation must be positive, setting to 300 MB"
))
memory = "300"
# make a temporary directory
tmpdir = grass.tempfile()
grass.try_remove(tmpdir)
os.mkdir(tmpdir)
currdir = os.getcwd()
pid = os.getpid()
# change to temporary directory
os.chdir(tmpdir)
in_temp = True
if local is None:
local = tmpdir
# save region
tmpregionname = "r_in_srtm_tmp_region"
grass.run_command("g.region", save=tmpregionname, overwrite=overwrite)
# get extents
if kv["+proj"] == "longlat":
reg = grass.region()
else:
if not options["resolution"]:
grass.fatal(
_("The <resolution> must be set if the projection is not 'longlat'."
))
reg2 = grass.parse_command("g.region", flags="uplg")
north = [float(reg2["ne_lat"]), float(reg2["nw_lat"])]
south = [float(reg2["se_lat"]), float(reg2["sw_lat"])]
east = [float(reg2["ne_long"]), float(reg2["se_long"])]
west = [float(reg2["nw_long"]), float(reg2["sw_long"])]
reg = {}
if np.mean(north) > np.mean(south):
reg["n"] = max(north)
reg["s"] = min(south)
else:
reg["n"] = min(north)
reg["s"] = max(south)
if np.mean(west) > np.mean(east):
reg["w"] = max(west)
reg["e"] = min(east)
else:
reg["w"] = min(west)
reg["e"] = max(east)
# get actual location, mapset, ...
grassenv = grass.gisenv()
tgtloc = grassenv["LOCATION_NAME"]
tgtmapset = grassenv["MAPSET"]
GISDBASE = grassenv["GISDBASE"]
TGTGISRC = os.environ["GISRC"]
if kv["+proj"] != "longlat":
SRCGISRC, TMPLOC = createTMPlocation()
if options["region"] is None or options["region"] == "":
north = reg["n"]
south = reg["s"]
east = reg["e"]
west = reg["w"]
else:
west, south, east, north = options["region"].split(",")
west = float(west)
south = float(south)
east = float(east)
north = float(north)
# adjust extents to cover SRTM tiles: 1 degree bounds
tmpint = int(north)
if tmpint < north:
north = tmpint + 1
else:
north = tmpint
tmpint = int(south)
if tmpint > south:
south = tmpint - 1
else:
south = tmpint
tmpint = int(east)
if tmpint < east:
east = tmpint + 1
else:
east = tmpint
tmpint = int(west)
if tmpint > west:
west = tmpint - 1
else:
west = tmpint
if north == south:
north += 1
if east == west:
east += 1
rows = abs(north - south)
cols = abs(east - west)
ntiles = rows * cols
grass.message(_("Importing %d SRTM tiles...") % ntiles, flag="i")
counter = 1
srtmtiles = ""
valid_tiles = 0
for ndeg in range(south, north):
for edeg in range(west, east):
grass.percent(counter, ntiles, 1)
counter += 1
if ndeg < 0:
tile = "S"
else:
tile = "N"
tile = tile + "%02d" % abs(ndeg)
if edeg < 0:
tile = tile + "W"
else:
tile = tile + "E"
tile = tile + "%03d" % abs(edeg)
grass.debug("Tile: %s" % tile, debug=1)
if local != tmpdir:
gotit = import_local_tile(tile, local, pid, srtmv3, one)
else:
gotit = download_tile(tile, url, pid, srtmv3, one, username,
password)
if gotit == 1:
gotit = import_local_tile(tile, tmpdir, pid, srtmv3, one)
if gotit == 1:
grass.verbose(_("Tile %s successfully imported") % tile)
valid_tiles += 1
elif dozerotile:
# create tile with zeros
if one:
# north
if ndeg < -1:
tmpn = "%02d:59:59.5S" % (abs(ndeg) - 2)
else:
tmpn = "%02d:00:00.5N" % (ndeg + 1)
# south
if ndeg < 1:
tmps = "%02d:00:00.5S" % abs(ndeg)
else:
tmps = "%02d:59:59.5N" % (ndeg - 1)
# east
if edeg < -1:
tmpe = "%03d:59:59.5W" % (abs(edeg) - 2)
else:
tmpe = "%03d:00:00.5E" % (edeg + 1)
# west
if edeg < 1:
tmpw = "%03d:00:00.5W" % abs(edeg)
else:
tmpw = "%03d:59:59.5E" % (edeg - 1)
else:
# north
if ndeg < -1:
tmpn = "%02d:59:58.5S" % (abs(ndeg) - 2)
else:
tmpn = "%02d:00:01.5N" % (ndeg + 1)
# south
if ndeg < 1:
tmps = "%02d:00:01.5S" % abs(ndeg)
else:
tmps = "%02d:59:58.5N" % (ndeg - 1)
# east
if edeg < -1:
tmpe = "%03d:59:58.5W" % (abs(edeg) - 2)
else:
tmpe = "%03d:00:01.5E" % (edeg + 1)
# west
if edeg < 1:
tmpw = "%03d:00:01.5W" % abs(edeg)
else:
tmpw = "%03d:59:58.5E" % (edeg - 1)
grass.run_command("g.region",
n=tmpn,
s=tmps,
e=tmpe,
w=tmpw,
res=res)
grass.run_command(
"r.mapcalc",
expression="%s = 0" %
(tile + ".r.in.srtm.tmp." + str(pid)),
quiet=True,
)
grass.run_command("g.region", region=tmpregionname)
# g.list with sep = comma does not work ???
pattern = "*.r.in.srtm.tmp.%d" % pid
srtmtiles = grass.read_command("g.list",
type="raster",
pattern=pattern,
sep="newline",
quiet=True)
srtmtiles = srtmtiles.splitlines()
srtmtiles = ",".join(srtmtiles)
grass.debug("'List of Tiles: %s" % srtmtiles, debug=1)
if valid_tiles == 0:
grass.run_command("g.remove",
type="raster",
name=str(srtmtiles),
flags="f",
quiet=True)
grass.warning(_("No tiles imported"))
if local != tmpdir:
grass.fatal(
_("Please check if local folder <%s> is correct.") % local)
else:
grass.fatal(
_("Please check internet connection, credentials, and if url <%s> is correct."
) % url)
grass.run_command("g.region", raster=str(srtmtiles))
grass.message(_("Patching tiles..."))
if fillnulls == 0:
if valid_tiles > 1:
if kv["+proj"] != "longlat":
grass.run_command("r.buildvrt", input=srtmtiles, output=output)
else:
grass.run_command("r.patch", input=srtmtiles, output=output)
else:
grass.run_command("g.rename",
raster="%s,%s" % (srtmtiles, output),
quiet=True)
else:
ncells = grass.region()["cells"]
if long(ncells) > 1000000000:
grass.message(
_("%s cells to interpolate, this will take some time") %
str(ncells),
flag="i",
)
if kv["+proj"] != "longlat":
grass.run_command("r.buildvrt",
input=srtmtiles,
output=output + ".holes")
else:
grass.run_command("r.patch",
input=srtmtiles,
output=output + ".holes")
mapstats = grass.parse_command("r.univar",
map=output + ".holes",
flags="g",
quiet=True)
if mapstats["null_cells"] == "0":
grass.run_command("g.rename",
raster="%s,%s" % (output + ".holes", output),
quiet=True)
else:
grass.run_command(
"r.resamp.bspline",
input=output + ".holes",
output=output + ".interp",
se="0.0025",
sn="0.0025",
method="linear",
memory=memory,
flags="n",
)
grass.run_command(
"r.patch",
input="%s,%s" % (output + ".holes", output + ".interp"),
output=output + ".float",
flags="z",
)
grass.run_command("r.mapcalc",
expression="%s = round(%s)" %
(output, output + ".float"))
grass.run_command(
"g.remove",
type="raster",
name="%s,%s,%s" %
(output + ".holes", output + ".interp", output + ".float"),
flags="f",
quiet=True,
)
# switch to target location
if kv["+proj"] != "longlat":
os.environ["GISRC"] = str(TGTGISRC)
# r.proj
grass.message(_("Reprojecting <%s>...") % output)
kwargs = {
"location": TMPLOC,
"mapset": "PERMANENT",
"input": output,
"memory": memory,
"resolution": reproj_res,
}
if options["method"]:
kwargs["method"] = options["method"]
try:
grass.run_command("r.proj", **kwargs)
except CalledModuleError:
grass.fatal(_("Unable to to reproject raster <%s>") % output)
else:
if fillnulls != 0:
grass.run_command("g.remove",
type="raster",
pattern=pattern,
flags="f",
quiet=True)
# nice color table
grass.run_command("r.colors", map=output, color="srtm", quiet=True)
# write metadata:
tmphist = grass.tempfile()
f = open(tmphist, "w+")
f.write(os.environ["CMDLINE"])
f.close()
if srtmv3:
source1 = "SRTM V3"
else:
source1 = "SRTM V2.1"
grass.run_command(
"r.support",
map=output,
loadhistory=tmphist,
description="generated by r.in.srtm.region",
source1=source1,
source2=(local if local != tmpdir else url),
)
grass.try_remove(tmphist)
grass.message(_("Done: generated map <%s>") % output)
def main():
# lazy imports
import grass.temporal as tgis
# Get the options
inputs = options["inputs"]
output = options["output"]
type = options["type"]
# Make sure the temporal database exists
tgis.init()
#Get the current mapset to create the id of the space time dataset
mapset = grass.encode(grass.gisenv()["MAPSET"])
inputs_split = inputs.split(",")
input_ids = []
for input in inputs_split:
if input.find("@") >= 0:
input_ids.append(input)
else:
input_ids.append(input + "@" + mapset)
# Set the output name correct
if output.find("@") >= 0:
out_mapset = output.split("@")[1]
if out_mapset != mapset:
grass.fatal(
_("Output space time dataset <%s> must be located in this mapset"
) % (output))
else:
output_id = output + "@" + mapset
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
stds_list = []
first = None
for id in input_ids:
stds = tgis.open_old_stds(id, type, dbif)
if first is None:
first = stds
if first.get_temporal_type() != stds.get_temporal_type():
dbif.close()
grass.fatal(
_("Space time datasets to merge must have the same temporal type"
))
stds_list.append(stds)
# Do nothing if nothing to merge
if first is None:
dbif.close()
return
# Check if the new id is in the database
output_stds = tgis.dataset_factory(type, output_id)
output_exists = output_stds.is_in_db(dbif=dbif)
if output_exists == True and grass.overwrite() == False:
dbif.close()
grass.fatal(_("Unable to merge maps into space time %s dataset <%s> "\
"please use the overwrite flag.") % \
(stds.get_new_map_instance(None).get_type(), output_id))
if not output_exists:
output_stds = tgis.open_new_stds(output,
type,
first.get_temporal_type(),
"Merged space time dataset",
"Merged space time dataset",
"mean",
dbif=dbif,
overwrite=False)
else:
output_stds.select(dbif=dbif)
registered_output_maps = {}
# Maps that are already registered in an existing dataset
# are not registered again
if output_exists == True:
rows = output_stds.get_registered_maps(columns="id", dbif=dbif)
if rows:
for row in rows:
registered_output_maps[row["id"]] = row["id"]
for stds in stds_list:
# Avoid merging of already registered maps
if stds.get_id() != output_stds.get_id():
maps = stds.get_registered_maps_as_objects(dbif=dbif)
if maps:
for map in maps:
# Jump over already registered maps
if map.get_id() in registered_output_maps:
continue
map.select(dbif=dbif)
output_stds.register_map(map=map, dbif=dbif)
# Update the registered map list
registered_output_maps[map.get_id()] = map.get_id()
output_stds.update_from_registered_maps(dbif=dbif)
if output_exists == True:
output_stds.update_command_string(dbif=dbif)
def main():
# lazy imports
import grass.temporal as tgis
# Get the options
input = options["input"]
output = options["output"]
sampler = options["sample"]
where = options["where"]
base = options["basename"]
register_null = flags["n"]
method = options["method"]
sampling = options["sampling"]
offset = options["offset"]
nprocs = options["nprocs"]
time_suffix = options["suffix"]
type = options["type"]
topo_list = sampling.split(",")
tgis.init()
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
sp = tgis.open_old_stds(input, "strds", dbif)
sampler_sp = tgis.open_old_stds(sampler, type, dbif)
if sampler_sp.get_temporal_type() != sp.get_temporal_type():
dbif.close()
gcore.fatal(
_("Input and aggregation dataset must have "
"the same temporal type"))
# Check if intervals are present
if sampler_sp.temporal_extent.get_map_time() != "interval":
dbif.close()
gcore.fatal(
_("All registered maps of the aggregation dataset "
"must have time intervals"))
# We will create the strds later, but need to check here
tgis.check_new_stds(output, "strds", dbif, gcore.overwrite())
map_list = sp.get_registered_maps_as_objects(where=where,
order="start_time",
dbif=dbif)
if not map_list:
dbif.close()
gcore.fatal(_("Space time raster dataset <%s> is empty") % input)
granularity_list = sampler_sp.get_registered_maps_as_objects(
where=where, order="start_time", dbif=dbif)
if not granularity_list:
dbif.close()
gcore.fatal(_("Space time raster dataset <%s> is empty") % sampler)
gran = sampler_sp.get_granularity()
output_list = tgis.aggregate_by_topology(granularity_list=granularity_list,
granularity=gran,
map_list=map_list,
topo_list=topo_list,
basename=base,
time_suffix=time_suffix,
offset=offset,
method=method,
nprocs=nprocs,
spatial=None,
overwrite=gcore.overwrite())
if output_list:
temporal_type, semantic_type, title, description = sp.get_initial_values(
)
output_strds = tgis.open_new_stds(output, "strds", temporal_type,
title, description, semantic_type,
dbif, gcore.overwrite())
tgis.register_map_object_list("rast", output_list,
output_strds, register_null,
sp.get_relative_time_unit(), dbif)
# Update the raster metadata table entries with aggregation type
output_strds.set_aggregation_type(method)
output_strds.metadata.update(dbif)
dbif.close()
def main():
# Get the options
input = options["input"]
input_name = input.split('@')[0]
output = options["output"]
method = options["method"]
min_cat = None
max_cat = None
point = None
overwrite = grass.overwrite()
quiet = True
if grass.verbosity() > 2:
quiet = False
in_info = grass.vector_info(input)
# check for wild mixture of vector types
if in_info['points'] > 0 and in_info['boundaries'] > 0:
grass.fatal(
_("The input vector map contains both polygons and points,"
" cannot handle mixed types"))
pid = os.getpid()
# process points via triangulation, then exit
if in_info['points'] > 0:
point = True
layer = 1 # hardcoded for now
out_temp = '{inp}_point_tmp_{pid}'.format(inp=input_name, pid=pid)
if method == 'delaunay':
grass.message(
_("Processing point data (%d points found)...") %
in_info['points'])
grass.run_command('v.delaunay',
input=input,
layer=layer,
output=out_temp,
quiet=quiet)
grass.run_command('v.db.addtable', map=out_temp, quiet=True)
input = out_temp
in_info = grass.vector_info(input)
# process areas
if in_info['areas'] == 0 and in_info['boundaries'] == 0:
grass.fatal(_("The input vector map does not contain polygons"))
out_type = '{inp}_type_{pid}'.format(inp=input_name, pid=pid)
input_tmp = '{inp}_tmp_{pid}'.format(inp=input_name, pid=pid)
remove_names = "%s,%s" % (out_type, input_tmp)
grass.message(
_("Processing area data (%d areas found)...") % in_info['areas'])
try:
grass.run_command('v.category',
layer="2",
type='boundary',
option='add',
input=input,
out=input_tmp,
quiet=quiet)
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=input_tmp,
quiet=quiet)
grass.fatal(_("Error creating layer 2"))
try:
grass.run_command('v.db.addtable',
map=input_tmp,
layer="2",
quiet=quiet)
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=input_tmp,
quiet=quiet)
grass.fatal(_("Error creating new table for layer 2"))
try:
grass.run_command('v.to.db',
map=input_tmp,
option="sides",
columns="left,right",
layer="2",
quiet=quiet)
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=input_tmp,
quiet=quiet)
grass.fatal(_("Error populating new table for layer 2"))
try:
grass.run_command('v.type',
input=input_tmp,
output=out_type,
from_type='boundary',
to_type='line',
quiet=quiet,
layer="2")
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=remove_names,
quiet=quiet)
grass.fatal(_("Error converting polygon to line"))
report = grass.read_command('v.category',
flags='g',
input=out_type,
option='report',
quiet=quiet)
report = decode(report).split('\n')
for r in report:
if r.find('centroid') != -1:
min_cat = report[0].split()[-2]
max_cat = report[0].split()[-1]
break
if min_cat and max_cat:
try:
grass.run_command('v.edit',
map=out_type,
tool='delete',
type='centroid',
layer=2,
quiet=quiet,
cats='{mi}-{ma}'.format(mi=min_cat, ma=max_cat))
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=remove_names,
quiet=quiet)
grass.fatal(_("Error removing centroids"))
try:
try:
# TODO: fix magic numbers for layer here and there
grass.run_command('v.db.droptable',
map=out_type,
layer=1,
flags='f',
quiet=True)
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=remove_names,
quiet=quiet)
grass.fatal(_("Error removing table from layer 1"))
# TODO: when this except is happaning, it seems that never, so it seems wrong
except:
grass.warning(_("No table for layer %d" % 1))
try:
grass.run_command('v.category',
input=out_type,
option='transfer',
output=output,
layer="2,1",
quiet=quiet,
overwrite=overwrite)
except CalledModuleError:
grass.run_command('g.remove',
flags='f',
type='vector',
name=remove_names,
quiet=quiet)
grass.fatal(_("Error adding categories"))
grass.run_command('g.remove',
flags='f',
type='vector',
name=remove_names,
quiet=quiet)
if point:
grass.run_command('g.remove',
flags='f',
type='vector',
name=out_temp,
quiet=quiet)
def main():
global insert_sql
insert_sql = None
global temporary_vect
temporary_vect = None
global stats_temp_file
stats_temp_file = None
global content
content = None
global raster
raster = options['raster']
global decimals
decimals = int(options['decimals'])
global zone_map
zone_map = options['zone_map']
csvfile = options['csvfile'] if options['csvfile'] else []
separator = gscript.separator(options['separator'])
prefix = options['prefix'] if options['prefix'] else []
classes_list = options['classes_list'].split(
',') if options['classes_list'] else []
vectormap = options['vectormap'] if options['vectormap'] else []
prop = False if 'proportion' not in options['statistics'].split(
',') else True
mode = False if 'mode' not in options['statistics'].split(',') else True
# Check if input layer is CELL
if gscript.parse_command('r.info', flags='g',
map=raster)['datatype'] != 'CELL':
gscript.fatal(
_("The type of the input map 'raster' is not CELL. Please use raster with integer values"
))
if gscript.parse_command('r.info', flags='g',
map=zone_map)['datatype'] != 'CELL':
gscript.fatal(
_("The type of the input map 'zone_map' is not CELL. Please use raster with integer values"
))
# Check if 'decimals' is + and with credible value
if decimals <= 0:
gscript.fatal(_("The number of decimals should be positive"))
if decimals > 100:
gscript.fatal(_("The number of decimals should not be more than 100"))
# Adjust region to input map is flag active
if flags['r']:
gscript.use_temp_region()
gscript.run_command('g.region', raster=zone_map)
# R.STATS
tmpfile = gscript.tempfile()
try:
if flags['n']:
gscript.run_command(
'r.stats',
overwrite=True,
flags='c',
input='%s,%s' % (zone_map, raster),
output=tmpfile,
separator=separator) # Consider null values in R.STATS
else:
gscript.run_command(
'r.stats',
overwrite=True,
flags='cn',
input='%s,%s' % (zone_map, raster),
output=tmpfile,
separator=separator) # Do not consider null values in R.STATS
gscript.message(_("r.stats command finished..."))
except:
gscript.fatal(_("The execution of r.stats failed"))
# COMPUTE STATISTICS
# Open csv file and create a csv reader
rstatsfile = open(tmpfile, 'r')
reader = csv.reader(rstatsfile, delimiter=separator)
# Total pixels per category per zone
totals_dict = {}
for row in reader:
if row[0] not in totals_dict: # Will pass the condition only if the current zone ID does not exists in the dictionary
totals_dict[row[0]] = {
} # Declare a new embedded dictionnary for the current zone ID
totals_dict[row[0]][row[1]] = int(row[2])
# Delete key '*' in 'totals_dict' that could append if there are null values on the zone raster
if '*' in totals_dict:
del totals_dict['*']
# Close file
rstatsfile.close()
# Mode
if mode:
modalclass_dict = {}
for ID in totals_dict:
# The trick was found here : https://stackoverflow.com/a/268285/8013239
mode = max(totals_dict[ID].iteritems(),
key=operator.itemgetter(1))[0]
if mode == '*': # If the mode is NULL values
modalclass_dict[ID] = 'NULL'
else:
modalclass_dict[ID] = mode
# Classes proportions
if prop:
# Get list of categories to output
if classes_list: #If list of classes provided by user
class_dict = {str(a): ''
for a in classes_list
} #To be sure it's string format
else:
class_dict = {}
# Proportion of each category per zone
proportion_dict = {}
for ID in totals_dict:
proportion_dict[ID] = {}
for cl in totals_dict[ID]:
if flags['p']:
proportion_dict[ID][cl] = round(
float(totals_dict[ID][cl]) /
sum(totals_dict[ID].values()) * 100, decimals)
else:
proportion_dict[ID][cl] = round(
float(totals_dict[ID][cl]) /
sum(totals_dict[ID].values()), decimals)
if cl == '*':
class_dict['NULL'] = ''
else:
class_dict[cl] = ''
# Fill class not met in the raster with zero
for ID in proportion_dict:
for cl in class_dict:
if cl not in proportion_dict[ID].keys():
proportion_dict[ID][cl] = '{:.{}f}'.format(0, decimals)
# Get list of class sorted by value (arithmetic)
if 'NULL' in class_dict.keys():
class_list = [int(k) for k in class_dict.keys() if k != 'NULL']
class_list.sort()
class_list.append('NULL')
else:
class_list = [int(k) for k in class_dict.keys()]
class_list.sort()
gscript.verbose(_("Statistics computed..."))
# OUTPUT CONTENT
# Header
header = [
'cat',
]
if mode:
if prefix:
header.append('%s_mode' % prefix)
else:
header.append('mode')
if prop:
if prefix:
[header.append('%s_prop_%s' % (prefix, cl)) for cl in class_list]
else:
[header.append('prop_%s' % cl) for cl in class_list]
# Values
value_dict = {}
for ID in totals_dict:
value_dict[ID] = []
if mode:
value_dict[ID].append(modalclass_dict[ID])
if prop:
for cl in class_list:
value_dict[ID].append(proportion_dict[ID]['%s' % cl])
# WRITE OUTPUT
if csvfile:
outfile = open(csvfile, 'w')
writer = csv.writer(outfile, delimiter=separator)
writer.writerow(header)
csvcontent_dict = copy.deepcopy(value_dict)
[csvcontent_dict[ID].insert(0, ID) for ID in csvcontent_dict]
[csvcontent_dict[ID] for ID in csvcontent_dict]
writer.writerows(csvcontent_dict.values())
outfile.close()
if vectormap:
gscript.message(_("Creating output vector map..."))
temporary_vect = 'rzonalclasses_tmp_vect_%d' % os.getpid()
gscript.run_command('r.to.vect',
input_=zone_map,
output=temporary_vect,
type_='area',
flags='vt',
overwrite=True,
quiet=True)
insert_sql = gscript.tempfile()
fsql = open(insert_sql, 'w')
fsql.write('BEGIN TRANSACTION;\n')
if gscript.db_table_exist(temporary_vect):
if gscript.overwrite():
fsql.write('DROP TABLE %s;' % temporary_vect)
else:
gscript.fatal(
_("Table %s already exists. Use --o to overwrite") %
temporary_vect)
create_statement = 'CREATE TABLE ' + temporary_vect + ' (cat int PRIMARY KEY);\n'
fsql.write(create_statement)
for col in header[1:]:
if col.split('_')[-1] == 'mode': # Mode column should be integer
addcol_statement = 'ALTER TABLE %s ADD COLUMN %s integer;\n' % (
temporary_vect, col)
else: # Proportions column should be double precision
addcol_statement = 'ALTER TABLE %s ADD COLUMN %s double precision;\n' % (
temporary_vect, col)
fsql.write(addcol_statement)
for key in value_dict:
insert_statement = 'INSERT INTO %s VALUES (%s, %s);\n' % (
temporary_vect, key, ','.join(
[str(x) for x in value_dict[key]]))
fsql.write(insert_statement)
fsql.write('END TRANSACTION;')
fsql.close()
gscript.run_command('db.execute', input=insert_sql, quiet=True)
gscript.run_command('v.db.connect',
map_=temporary_vect,
table=temporary_vect,
quiet=True)
gscript.run_command('g.copy',
vector='%s,%s' % (temporary_vect, vectormap),
quiet=True)
def main():
options, flags = gscript.parser()
keep = flags['k']
input = options['input']
output = options['output']
category = int(options['category'])
nsize = int(options['nsize'])
maxiter = int(options['maxiter'])
animationfile = options['animationfile']
quality = int(options['quality'])
overwrite_flag = ''
if gscript.overwrite():
overwrite_flag = 't'
# keep intermediate maps
keepintmaps = False
if flags['k']:
keepintmaps = True
# to generate the animation file, intermediate files must be kept
# they will be removed at the end of the process if the 'k' flag is not set
if animationfile:
keepintmaps = True
# check if input file exists
if not gscript.find_file(input)['file']:
gscript.fatal(_("Raster map <%s> not found") % input)
# strip mapset name
in_name_strip = options['input'].split('@')
in_name = in_name_strip[0]
out_name_strip = options['output'].split('@')
out_name = out_name_strip[0]
tmp = str(os.getpid())
# maps to bootstrap the loop
# create a map containing only the category to replace and NULL
categorymap = '{}'.format(in_name) + '_bin_' + '{}'.format(tmp)
gscript.verbose(_("Category map: <%s>") % categorymap)
gscript.run_command(
'r.mapcalc',
expression="{outmap}=if({inmap}=={cat}, 1, null())".format(
outmap=categorymap, inmap=input, cat=category),
quiet=True,
overwrite='t')
# create a copy of the input map to be used as a selection map in r.neighbors,
# it will be replaced by the map with category replacement in the loop
stepmap_old = '{}'.format(in_name) + '_step_000'
gscript.run_command('g.copy',
raster='{inmap},{outmap}'.format(inmap=input,
outmap=stepmap_old),
quiet=True,
overwrite='t')
gscript.verbose(_("Category to remove: %d") % category)
gscript.verbose(_("Maxiter: %d") % maxiter)
gscript.verbose(_("Quality for animation: %d") % quality)
pixel_num = 1
iteration = 1
# iterate until no pixel of the category to be replaced is left
# or the maximum number of iterations is reached
while (pixel_num > 0) and (iteration <= maxiter):
stepmap = '{}'.format(in_name)
stepmap += '_step_'
stepmap += '{:03d}'.format(iteration)
gscript.verbose(_("Step map: <%s>") % stepmap)
# substitute pixels of the category to remove with the mode of the surrounding pixels
gscript.run_command('r.neighbors',
input=stepmap_old,
selection=categorymap,
size=nsize,
output=stepmap,
method='mode',
overwrite='true',
quiet=True)
# remove intermediate map unless the k flag is set
if keepintmaps is False:
gscript.run_command('g.remove',
type='raster',
name=stepmap_old,
flags='f',
quiet=True)
# the r.neighbors output map is the input map for the next step
stepmap_old = stepmap
# create the new map containing only the category to replace and NULL
gscript.run_command(
'r.mapcalc',
expression="{outmap}=if({inmap}=={cat},1,null())".format(
outmap=categorymap, inmap=stepmap, cat=category),
quiet=True,
overwrite='t')
# evaluate the number of the remaining pixels of the category to relace
pixel_stat = gscript.parse_command(
'r.stats',
input='{inmap}'.format(inmap=stepmap),
flags='c',
sep='=',
quiet=True)
# parse the output, if the category is not in the list raise an exception and set pixel_num = 0
try:
pixel_num = float(pixel_stat['{}'.format(category)])
except KeyError as e:
pixel_num = 0
# print(e.message)
gscript.verbose(
_("Iteration: %d Remaining pixels: %d") % (iteration, pixel_num))
iteration = iteration + 1
# the last value stopped the loop
iteration = iteration - 1
# if the loop ended before reaching pixel_num=0
if pixel_num > 0:
gscript.warning(
_("the process stopped after %d iterations with %d pixels of category %d left"
) % (iteration, pixel_num, category))
# copy the output of the last iteration to the output map
gscript.run_command('g.copy',
raster='{inmap},{outmap}'.format(inmap=stepmap,
outmap=out_name),
overwrite='{}'.format(overwrite_flag),
quiet=True)
# remove the last intermediate map unless the k flag is set
if keepintmaps is False:
gscript.run_command('g.remove',
type='raster',
name=stepmap_old,
flags='f',
quiet=True)
gscript.run_command('g.remove',
type='raster',
name=categorymap,
flags='f',
quiet=True)
# optionally create an mpeg animation of the replacement sequence
if animationfile:
gscript.message(_("Generating mpeg file %s...") % animationfile)
gscript.run_command('r.out.mpeg',
view1='{}_step_[0-9][0-9][0-9]'.format(in_name),
output='{}'.format(animationfile),
quality='{}'.format(quality),
overwrite='{}'.format(overwrite_flag))
# remove intermediate maps if they have been kept for generating the animation
# but the 'k' flag is not set
if animationfile and not flags['k']:
gscript.message(
_("Removing intermediate files after generating %s...") %
animationfile)
newiter = 0
while newiter <= iteration:
stepmap = '{}'.format(in_name)
stepmap += '_step_'
stepmap += '{:03d}'.format(newiter)
gscript.verbose(_("Removing step map: <%s>") % stepmap)
gscript.run_command('g.remove',
type='raster',
name=stepmap,
flags='f',
quiet=True)
newiter = newiter + 1
def main():
global TMPLOC, SRCGISRC, TGTGISRC, GISDBASE
global tile, tmpdir, in_temp, currdir, tmpregionname
in_temp = False
nasadem_version = options["version"]
nasadem_layer = options["layer"]
url = options["url"]
username = options["username"]
password = options["password"]
local = options["local"]
output = options["output"]
dozerotile = flags["z"]
reproj_res = options["resolution"]
overwrite = grass.overwrite()
tile = None
tmpdir = None
in_temp = None
currdir = None
tmpregionname = None
if len(local) == 0:
local = None
if len(username) == 0 or len(password) == 0:
grass.fatal(_("NASADEM download requires username and password."))
# are we in LatLong location?
s = grass.read_command("g.proj", flags="j")
kv = grass.parse_key_val(s)
# make a temporary directory
tmpdir = grass.tempfile()
grass.try_remove(tmpdir)
os.mkdir(tmpdir)
currdir = os.getcwd()
pid = os.getpid()
# change to temporary directory
os.chdir(tmpdir)
in_temp = True
# save region
tmpregionname = "r_in_nasadem_region_" + str(pid)
grass.run_command("g.region", save=tmpregionname, overwrite=overwrite)
# get extents
if kv["+proj"] == "longlat":
reg = grass.region()
if options["region"] is None or options["region"] == "":
north = reg["n"]
south = reg["s"]
east = reg["e"]
west = reg["w"]
else:
west, south, east, north = options["region"].split(",")
west = float(west)
south = float(south)
east = float(east)
north = float(north)
else:
if not options["resolution"]:
grass.fatal(
_("The <resolution> must be set if the projection is not 'longlat'.")
)
if options["region"] is None or options["region"] == "":
reg2 = grass.parse_command("g.region", flags="uplg")
north_vals = [float(reg2["ne_lat"]), float(reg2["nw_lat"])]
south_vals = [float(reg2["se_lat"]), float(reg2["sw_lat"])]
east_vals = [float(reg2["ne_long"]), float(reg2["se_long"])]
west_vals = [float(reg2["nw_long"]), float(reg2["sw_long"])]
reg = {}
if np.mean(north_vals) > np.mean(south_vals):
north = max(north_vals)
south = min(south_vals)
else:
north = min(north_vals)
south = max(south_vals)
if np.mean(west_vals) > np.mean(east_vals):
west = max(west_vals)
east = min(east_vals)
else:
west = min(west_vals)
east = max(east_vals)
# get actual location, mapset, ...
grassenv = grass.gisenv()
tgtloc = grassenv["LOCATION_NAME"]
tgtmapset = grassenv["MAPSET"]
GISDBASE = grassenv["GISDBASE"]
TGTGISRC = os.environ["GISRC"]
else:
grass.fatal(
_(
"The option <resolution> is only supported in the projection 'longlat'"
)
)
# adjust extents to cover SRTM tiles: 1 degree bounds
tmpint = int(north)
if tmpint < north:
north = tmpint + 1
else:
north = tmpint
tmpint = int(south)
if tmpint > south:
south = tmpint - 1
else:
south = tmpint
tmpint = int(east)
if tmpint < east:
east = tmpint + 1
else:
east = tmpint
tmpint = int(west)
if tmpint > west:
west = tmpint - 1
else:
west = tmpint
if north == south:
north += 1
if east == west:
east += 1
# switch to longlat location
if kv["+proj"] != "longlat":
SRCGISRC, TMPLOC = createTMPlocation()
rows = abs(north - south)
cols = abs(east - west)
ntiles = rows * cols
grass.message(_("Importing %d NASADEM tiles...") % ntiles, flag="i")
counter = 1
srtmtiles = ""
valid_tiles = 0
for ndeg in range(south, north):
for edeg in range(west, east):
grass.percent(counter, ntiles, 1)
counter += 1
if ndeg < 0:
tile = "s"
else:
tile = "n"
tile = tile + "%02d" % abs(ndeg)
if edeg < 0:
tile = tile + "w"
else:
tile = tile + "e"
tile = tile + "%03d" % abs(edeg)
grass.debug("Tile: %s" % tile, debug=1)
if local is None:
download_tile(tile, url, pid, nasadem_version, username, password)
gotit = import_local_tile(tile, local, pid, nasadem_layer)
if gotit == 1:
grass.verbose(_("Tile %s successfully imported") % tile)
valid_tiles += 1
elif dozerotile:
# create tile with zeros
# north
if ndeg < -1:
tmpn = "%02d:59:59.5S" % (abs(ndeg) - 2)
else:
tmpn = "%02d:00:00.5N" % (ndeg + 1)
# south
if ndeg < 1:
tmps = "%02d:00:00.5S" % abs(ndeg)
else:
tmps = "%02d:59:59.5N" % (ndeg - 1)
# east
if edeg < -1:
tmpe = "%03d:59:59.5W" % (abs(edeg) - 2)
else:
tmpe = "%03d:00:00.5E" % (edeg + 1)
# west
if edeg < 1:
tmpw = "%03d:00:00.5W" % abs(edeg)
else:
tmpw = "%03d:59:59.5E" % (edeg - 1)
grass.run_command("g.region", n=tmpn, s=tmps, e=tmpe, w=tmpw, res=res)
grass.run_command(
"r.mapcalc",
expression="%s = 0" % (tile + ".r.in.nasadem.tmp." + str(pid)),
quiet=True,
)
grass.run_command("g.region", region=tmpregionname)
# g.list with sep = comma does not work ???
pattern = "*.r.in.nasadem.tmp.%d" % pid
demtiles = grass.read_command(
"g.list", type="raster", pattern=pattern, sep="newline", quiet=True
)
demtiles = demtiles.splitlines()
demtiles = ",".join(demtiles)
grass.debug("'List of Tiles: %s" % demtiles, debug=1)
if valid_tiles == 0:
grass.run_command(
"g.remove", type="raster", name=str(demtiles), flags="f", quiet=True
)
grass.warning(_("No tiles imported"))
if local is not None:
grass.fatal(_("Please check if local folder <%s> is correct.") % local)
else:
grass.fatal(
_(
"Please check internet connection, credentials, and if url <%s> is correct."
)
% url
)
grass.run_command("g.region", raster=str(demtiles))
if valid_tiles > 1:
grass.message(_("Patching tiles..."))
if kv["+proj"] != "longlat":
grass.run_command("r.buildvrt", input=demtiles, output=output)
else:
grass.run_command("r.patch", input=demtiles, output=output)
grass.run_command(
"g.remove", type="raster", name=str(demtiles), flags="f", quiet=True
)
else:
grass.run_command("g.rename", raster="%s,%s" % (demtiles, output), quiet=True)
# switch to target location and repoject nasadem
if kv["+proj"] != "longlat":
os.environ["GISRC"] = str(TGTGISRC)
# r.proj
grass.message(_("Reprojecting <%s>...") % output)
kwargs = {
"location": TMPLOC,
"mapset": "PERMANENT",
"input": output,
"resolution": reproj_res,
}
if options["memory"]:
kwargs["memory"] = options["memory"]
if options["method"]:
kwargs["method"] = options["method"]
try:
grass.run_command("r.proj", **kwargs)
except CalledModuleError:
grass.fatal(_("Unable to to reproject raster <%s>") % output)
# nice color table
grass.run_command("r.colors", map=output, color="srtm", quiet=True)
# write metadata:
tmphist = grass.tempfile()
f = open(tmphist, "w+")
# hide username and password
cmdline = os.environ["CMDLINE"]
if username is not None and len(username) > 0:
cmdline = cmdline.replace("=" + username, "=xxx")
if password is not None and len(password) > 0:
cmdline = cmdline.replace("=" + password, "=xxx")
f.write(cmdline)
f.close()
source1 = nasadem_version
grass.run_command(
"r.support",
map=output,
loadhistory=tmphist,
description="generated by r.in.nasadem",
source1=source1,
source2=(local if local != tmpdir else url),
)
grass.try_remove(tmphist)
grass.message(_("Done: generated map <%s>") % output)
def main():
raster = options['raster']
maskcats = options['maskcats']
vector = options['vector']
layer = options['layer']
cats = options['cats']
where = options['where']
remove = flags['r']
invert = flags['i']
if not remove and not raster and not vector:
grass.fatal(_("Either parameter <raster> or parameter <vector> is required"))
mapset = grass.gisenv()['MAPSET']
exists = bool(grass.find_file('MASK', element='cell', mapset=mapset)['file'])
if remove:
# -> remove
if exists:
if sys.platform == 'win32':
grass.run_command('g.remove', flags='if', quiet=True,
type='raster', name='MASK')
else:
grass.run_command('g.remove', flags='f', quiet=True,
type='raster', name='MASK')
grass.message(_("Raster MASK removed"))
else:
grass.fatal(_("No existing MASK to remove"))
else:
# -> create
if exists:
if not grass.overwrite():
grass.fatal(_("MASK already found in current mapset. Delete first or overwrite."))
else:
grass.warning(_("MASK already exists and will be overwritten"))
grass.run_command('g.remove', flags='f', quiet=True,
type='raster', name='MASK')
if raster:
# check if input raster exists
if not grass.find_file(raster)['file']:
grass.fatal(_("Raster map <%s> not found") % raster)
if maskcats != '*' and not remove:
if grass.raster_info(raster)['datatype'] != "CELL":
grass.fatal(_("The raster map <%s> must be integer (CELL type) "
" in order to use the 'maskcats' parameter") % raster)
p = grass.feed_command(
'r.reclass',
input=raster,
output='MASK',
overwrite=True,
rules='-')
p.stdin.write("%s = 1" % maskcats)
p.stdin.close()
p.wait()
elif vector:
vector_name = grass.find_file(vector, 'vector')['fullname']
if not vector_name:
grass.fatal(_("Vector map <%s> not found") % vector)
# parser bug?
if len(cats) == 0:
cats = None
if len(where) == 0:
where = None
if grass.vector_info_topo(vector_name)['areas'] < 1:
grass.warning(_("No area found in vector map <%s>. "
"Creating a convex hull for MASK.") % vector_name)
global tmp_hull
tmp_hull = "tmp_hull_%d" % os.getpid()
to_rast_input = tmp_hull
# force 'flat' convex hull for 3D vector maps
try:
grass.run_command('v.hull', flags='f', quiet=True,
input=vector_name, output=tmp_hull,
layer=layer, cats=cats, where=where)
except CalledModuleError:
grass.fatal(
_("Unable to create a convex hull for vector map <%s>") %
vector_name)
else:
to_rast_input = vector_name
env = os.environ.copy()
if grass.verbosity() > 1:
env['GRASS_VERBOSE'] = '1'
grass.run_command('v.to.rast', input=to_rast_input, layer=layer,
output='MASK', use='val', val='1',
type='area', cats=cats, where=where, env=env)
if invert:
global tmp
tmp = "r_mask_%d" % os.getpid()
grass.run_command('g.rename', raster=('MASK', tmp), quiet=True)
grass.message(_("Creating inverted raster MASK..."))
grass.mapcalc("MASK = if(isnull($tmp), 1, null())", tmp=tmp)
grass.verbose(_("Inverted raster MASK created"))
else:
grass.verbose(_("Raster MASK created"))
grass.message(_("All subsequent raster operations will be limited to "
"the MASK area. Removing or renaming raster map named "
"'MASK' will restore raster operations to normal."))
def main():
options, flags = grass.parser()
elevation_input = options['elevation']
aspect_input = options['aspect']
slope_input = options['slope']
linke = options['linke']
linke_value = options['linke_value']
albedo = options['albedo']
albedo_value = options['albedo_value']
beam_rad_basename = options['beam_rad_basename']
diff_rad_basename = options['diff_rad_basename']
refl_rad_basename = options['refl_rad_basename']
glob_rad_basename = options['glob_rad_basename']
incidout_basename = options['incidout_basename']
if not any([beam_rad_basename, diff_rad_basename,
refl_rad_basename, glob_rad_basename,
incidout_basename]):
grass.fatal(_("No output specified."))
start_time = float(options['start_time'])
end_time = float(options['end_time'])
time_step = float(options['time_step'])
nprocs = int(options['nprocs'])
day = int(options['day'])
temporal = flags['t']
binary = flags['b']
binaryTmpName = 'binary'
year = int(options['year'])
if not is_grass_7() and temporal:
grass.warning(_("Flag t has effect only in GRASS 7"))
# check: start < end
if start_time > end_time:
grass.fatal(_("Start time is after end time."))
if time_step >= end_time - start_time:
grass.fatal(_("Time step is too big."))
# here we check all the days
if not grass.overwrite():
check_time_map_names(beam_rad_basename, grass.gisenv()['MAPSET'],
start_time, end_time, time_step, binary,
binaryTmpName)
check_time_map_names(diff_rad_basename, grass.gisenv()['MAPSET'],
start_time, end_time, time_step, binary,
binaryTmpName)
check_time_map_names(refl_rad_basename, grass.gisenv()['MAPSET'],
start_time, end_time, time_step, binary,
binaryTmpName)
check_time_map_names(glob_rad_basename, grass.gisenv()['MAPSET'],
start_time, end_time, time_step, binary,
binaryTmpName)
# check for slope/aspect
if not aspect_input or not slope_input:
params = {}
if not aspect_input:
aspect_input = create_tmp_map_name('aspect')
params.update({'aspect': aspect_input})
TMP.append(aspect_input)
if not slope_input:
slope_input = create_tmp_map_name('slope')
params.update({'slope': slope_input})
TMP.append(slope_input)
grass.info(_("Running r.slope.aspect..."))
grass.run_command('r.slope.aspect', elevation=elevation_input,
quiet=True, **params)
grass.info(_("Running r.sun in a loop..."))
count = 0
# Parallel processing
proc_list = []
proc_count = 0
suffixes = []
suffixes_all = []
times = list(frange(start_time, end_time, time_step))
num_times = len(times)
core.percent(0, num_times, 1)
for time in times:
count += 1
core.percent(count, num_times, 10)
suffix = '_' + format_time(time)
proc_list.append(Process(target=run_r_sun,
args=(elevation_input, aspect_input,
slope_input, day, time,
linke, linke_value,
albedo, albedo_value,
beam_rad_basename,
diff_rad_basename,
refl_rad_basename,
glob_rad_basename,
incidout_basename,
suffix,
binary, binaryTmpName)))
proc_list[proc_count].start()
proc_count += 1
suffixes.append(suffix)
suffixes_all.append(suffix)
if proc_count == nprocs or proc_count == num_times or count == num_times:
proc_count = 0
exitcodes = 0
for proc in proc_list:
proc.join()
exitcodes += proc.exitcode
if exitcodes != 0:
core.fatal(_("Error while r.sun computation"))
# Empty process list
proc_list = []
suffixes = []
# FIXME: how percent really works?
# core.percent(1, 1, 1)
# add timestamps either via temporal framework in 7 or r.timestamp in 6.x
if is_grass_7() and temporal:
core.info(_("Registering created maps into temporal dataset..."))
import grass.temporal as tgis
def registerToTemporal(basename, suffixes, mapset, start_time,
time_step, title, desc):
maps = ','.join([basename + suf + '@' + mapset for suf in suffixes])
tgis.open_new_stds(basename, type='strds',
temporaltype='absolute',
title=title, descr=desc,
semantic='mean', dbif=None,
overwrite=grass.overwrite())
tgis.register_maps_in_space_time_dataset(
type='raster', name=basename, maps=maps, start=start_time,
end=None, increment=time_step, dbif=None, interval=False)
# Make sure the temporal database exists
tgis.init()
mapset = grass.gisenv()['MAPSET']
absolute_time = datetime.datetime(year, 1, 1) + \
datetime.timedelta(days=day - 1) + \
datetime.timedelta(hours=start_time)
start = absolute_time.strftime("%Y-%m-%d %H:%M:%S")
step = datetime.timedelta(hours=time_step)
step = "%d seconds" % step.seconds
if beam_rad_basename:
registerToTemporal(beam_rad_basename, suffixes_all, mapset, start,
step, title="Beam irradiance",
desc="Output beam irradiance raster maps [Wh.m-2]")
if diff_rad_basename:
registerToTemporal(diff_rad_basename, suffixes_all, mapset, start,
step, title="Diffuse irradiance",
desc="Output diffuse irradiance raster maps [Wh.m-2]")
if refl_rad_basename:
registerToTemporal(refl_rad_basename, suffixes_all, mapset, start,
step, title="Reflected irradiance",
desc="Output reflected irradiance raster maps [Wh.m-2]")
if glob_rad_basename:
registerToTemporal(glob_rad_basename, suffixes_all, mapset, start,
step, title="Total irradiance",
desc="Output total irradiance raster maps [Wh.m-2]")
if incidout_basename:
registerToTemporal(incidout_basename, suffixes_all, mapset, start,
step, title="Incidence angle",
desc="Output incidence angle raster maps")
else:
absolute_time = datetime.datetime(year, 1, 1) + \
datetime.timedelta(days=day - 1)
for i, time in enumerate(times):
grass_time = format_grass_time(absolute_time + datetime.timedelta(hours=time))
if beam_rad_basename:
set_time_stamp(beam_rad_basename + suffixes_all[i],
time=grass_time)
if diff_rad_basename:
set_time_stamp(diff_rad_basename + suffixes_all[i],
time=grass_time)
if refl_rad_basename:
set_time_stamp(refl_rad_basename + suffixes_all[i],
time=grass_time)
if glob_rad_basename:
set_time_stamp(glob_rad_basename + suffixes_all[i],
time=grass_time)
if incidout_basename:
set_time_stamp(incidout_basename + suffixes_all[i],
time=grass_time)
if beam_rad_basename:
maps = [beam_rad_basename + suf for suf in suffixes_all]
set_color_table(maps, binary)
if diff_rad_basename:
maps = [diff_rad_basename + suf for suf in suffixes_all]
set_color_table(maps, binary)
if refl_rad_basename:
maps = [refl_rad_basename + suf for suf in suffixes_all]
set_color_table(maps, binary)
if glob_rad_basename:
maps = [glob_rad_basename + suf for suf in suffixes_all]
set_color_table(maps, binary)
if incidout_basename:
maps = [incidout_basename + suf for suf in suffixes_all]
set_color_table(maps)
def main():
"""
Links each river segment to the next downstream segment in a tributary
network by referencing its category (cat) number in a new column. "0"
means that the river exits the map.
"""
import matplotlib # required by windows
matplotlib.use("wxAGG") # required by windows
from matplotlib import pyplot as plt
options, flags = gscript.parser()
# Parsing
window = float(options["window"])
accum_mult = float(options["accum_mult"])
if options["units"] == "m2":
accum_label = "Drainage area [m$^2$]"
elif options["units"] == "km2":
accum_label = "Drainage area [km$^2$]"
elif options["units"] == "cumecs":
accum_label = "Water discharge [m$^3$ s$^{-1}$]"
elif options["units"] == "cfs":
accum_label = "Water discharge [cfs]"
else:
accum_label = "Flow accumulation [$-$]"
plots = options["plots"].split(",")
# Attributes of streams
colNames = np.array(vector_db_select(options["streams"])["columns"])
colValues = np.array(
vector_db_select(options["streams"])["values"].values())
tostream = colValues[:, colNames == "tostream"].astype(int).squeeze()
cats = colValues[:,
colNames == "cat"].astype(int).squeeze() # = "fromstream"
# We can loop over this list to get the shape of the full river network.
selected_cats = []
segment = int(options["cat"])
selected_cats.append(segment)
x = []
z = []
if options["direction"] == "downstream":
# Get network
gscript.message("Network")
while selected_cats[-1] != 0:
selected_cats.append(int(tostream[cats == selected_cats[-1]]))
x.append(selected_cats[-1])
selected_cats = selected_cats[:-1] # remove 0 at end
# Extract x points in network
data = vector.VectorTopo(
options["streams"]) # Create a VectorTopo object
data.open("r") # Open this object for reading
coords = []
_i = 0
for i in range(len(data)):
if isinstance(data.read(i + 1), vector.geometry.Line):
if data.read(i + 1).cat in selected_cats:
coords.append(data.read(i + 1).to_array())
gscript.core.percent(_i, len(selected_cats),
100.0 / len(selected_cats))
_i += 1
gscript.core.percent(1, 1, 1)
coords = np.vstack(np.array(coords))
_dx = np.diff(coords[:, 0])
_dy = np.diff(coords[:, 1])
x_downstream_0 = np.hstack((0, np.cumsum((_dx**2 + _dy**2)**0.5)))
x_downstream = x_downstream_0.copy()
elif options["direction"] == "upstream":
# terminalCATS = list(options['cat'])
# while terminalCATS:
#
print("Upstream direction not yet active!")
return
"""
# Add new lists for each successive upstream river
river_is_upstream =
while
full_river_cats
"""
# Network extraction
if options["outstream"] is not "":
selected_cats_str = list(np.array(selected_cats).astype(str))
selected_cats_csv = ",".join(selected_cats_str)
v.extract(
input=options["streams"],
output=options["outstream"],
cats=selected_cats_csv,
overwrite=gscript.overwrite(),
)
# Analysis
gscript.message("Elevation")
if options["elevation"]:
_include_z = True
DEM = RasterRow(options["elevation"])
DEM.open("r")
z = []
_i = 0
_lasti = 0
for row in coords:
z.append(DEM.get_value(Point(row[0], row[1])))
if float(_i) / len(coords) > float(_lasti) / len(coords):
gscript.core.percent(_i, len(coords), np.floor(_i - _lasti))
_lasti = _i
_i += 1
DEM.close()
z = np.array(z)
if options["window"] is not "":
x_downstream, z = moving_average(x_downstream_0, z, window)
gscript.core.percent(1, 1, 1)
else:
_include_z = False
gscript.message("Slope")
if options["slope"]:
_include_S = True
slope = RasterRow(options["slope"])
slope.open("r")
S = []
_i = 0
_lasti = 0
for row in coords:
S.append(slope.get_value(Point(row[0], row[1])))
if float(_i) / len(coords) > float(_lasti) / len(coords):
gscript.core.percent(_i, len(coords), np.floor(_i - _lasti))
_lasti = _i
_i += 1
slope.close()
S = np.array(S)
S_0 = S.copy()
if options["window"] is not "":
x_downstream, S = moving_average(x_downstream_0, S, window)
gscript.core.percent(1, 1, 1)
else:
_include_S = False
gscript.message("Accumulation")
if options["accumulation"]:
_include_A = True
accumulation = RasterRow(options["accumulation"])
accumulation.open("r")
A = []
_i = 0
_lasti = 0
for row in coords:
A.append(
accumulation.get_value(Point(row[0], row[1])) * accum_mult)
if float(_i) / len(coords) > float(_lasti) / len(coords):
gscript.core.percent(_i, len(coords), np.floor(_i - _lasti))
_lasti = _i
_i += 1
accumulation.close()
A = np.array(A)
A_0 = A.copy()
if options["window"] is not "":
x_downstream, A = moving_average(x_downstream_0, A, window)
gscript.core.percent(1, 1, 1)
else:
_include_A = False
# Plotting
if "LongProfile" in plots:
plt.figure()
plt.plot(x_downstream / 1000.0, z, "k-", linewidth=2)
plt.xlabel("Distance downstream [km]", fontsize=16)
plt.ylabel("Elevation [m]", fontsize=20)
plt.tight_layout()
if "SlopeAccum" in plots:
plt.figure()
plt.loglog(A, S, "ko", linewidth=2)
plt.xlabel(accum_label, fontsize=20)
plt.ylabel("Slope [$-$]", fontsize=20)
plt.tight_layout()
if "SlopeDistance" in plots:
plt.figure()
plt.plot(x_downstream / 1000.0, S, "k-", linewidth=2)
plt.xlabel("Distance downstream [km]", fontsize=16)
plt.ylabel("Slope [$-$]", fontsize=20)
plt.tight_layout()
if "AccumDistance" in plots:
plt.figure()
plt.plot(x_downstream / 1000.0, A, "k-", linewidth=2)
plt.xlabel("Distance downstream [km]", fontsize=16)
plt.ylabel(accum_label, fontsize=20)
plt.tight_layout()
plt.show()
# Saving data
if options["outfile_original"] is not "":
header = ["x_downstream", "E", "N"]
outfile = np.hstack((np.expand_dims(x_downstream_0, axis=1), coords))
if _include_S:
header.append("slope")
outfile = np.hstack((outfile, np.expand_dims(S_0, axis=1)))
if _include_A:
if (options["units"] == "m2") or (options["units"] == "km2"):
header.append("drainage_area_" + options["units"])
elif (options["units"] == "cumecs") or (options["units"] == "cfs"):
header.append("water_discharge_" + options["units"])
else:
header.append("flow_accumulation_arbitrary_units")
outfile = np.hstack((outfile, np.expand_dims(A_0, axis=1)))
header = np.array(header)
outfile = np.vstack((header, outfile))
np.savetxt(options["outfile_original"], outfile, "%s")
if options["outfile_smoothed"] is not "":
header = ["x_downstream", "E", "N"]
# E, N on smoothed grid
x_downstream, E = moving_average(x_downstream_0, coords[:, 0], window)
x_downstream, N = moving_average(x_downstream_0, coords[:, 1], window)
# Back to output
outfile = np.hstack((
np.expand_dims(x_downstream, axis=1),
np.expand_dims(E, axis=1),
np.expand_dims(N, axis=1),
))
if _include_S:
header.append("slope")
outfile = np.hstack((outfile, np.expand_dims(S, axis=1)))
if _include_A:
if (options["units"] == "m2") or (options["units"] == "km2"):
header.append("drainage_area_" + options["units"])
elif (options["units"] == "cumecs") or (options["units"] == "cfs"):
header.append("water_discharge_" + options["units"])
else:
header.append("flow_accumulation_arbitrary_units")
outfile = np.hstack((outfile, np.expand_dims(A, axis=1)))
header = np.array(header)
outfile = np.vstack((header, outfile))
np.savetxt(options["outfile_smoothed"], outfile, "%s")
def main():
"""
Gridded flexural isostatic solutions
"""
options, flags = grass.parser()
# if just interface description is requested, it will not get to this point
# so gflex will not be needed
# GFLEX
# try to import gflex only after we know that
# we will actually do the computation
try:
import gflex
except:
print("")
print("MODULE IMPORT ERROR.")
print("In order to run r.flexure or g.flexure, you must download and install")
print("gFlex. The most recent development version is available from")
print("https://github.com/awickert/gFlex.")
print("Installation instructions are available on the page.")
grass.fatal("Software dependency must be installed.")
# This code is for 2D flexural isostasy
flex = gflex.F2D()
# And show that it is coming from GRASS GIS
flex.grass = True
# Flags
latlon_override = flags["l"]
# Inputs
# Solution selection
flex.Method = options["method"]
if flex.Method == "FD":
flex.Solver = options["solver"]
if flex.Solver:
flex.ConvergenceTolerance = options["tolerance"]
# Always use the van Wees and Cloetingh (1994) solution type.
# It is the best.
flex.PlateSolutionType = "vWC1994"
# Parameters that are often changed for the solution
qs = options["input"]
flex.qs = garray.array(qs)
# Elastic thickness
try:
flex.Te = float(options["te"])
except:
flex.Te = garray.array(
options["te"]
) # FlexureTe is the one that is used by Flexure
flex.Te = np.array(flex.Te)
if options["te_units"] == "km":
flex.Te *= 1000
elif options["te_units"] == "m":
pass
# No "else"; shouldn't happen
flex.rho_fill = float(options["rho_fill"])
# Parameters that often stay at their default values
flex.g = float(options["g"])
flex.E = float(
options["ym"]
) # Can't just use "E" because reserved for "east", I think
flex.nu = float(options["nu"])
flex.rho_m = float(options["rho_m"])
# Solver type and iteration tolerance
flex.Solver = options["solver"]
flex.ConvergenceTolerance = float(options["tolerance"])
# Boundary conditions
flex.BC_N = options["northbc"]
flex.BC_S = options["southbc"]
flex.BC_W = options["westbc"]
flex.BC_E = options["eastbc"]
# Set verbosity
if grass.verbosity() >= 2:
flex.Verbose = True
if grass.verbosity() >= 3:
flex.Debug = True
elif grass.verbosity() == 0:
flex.Quiet = True
# First check if output exists
if len(grass.parse_command("g.list", type="rast", pattern=options["output"])):
if not grass.overwrite():
grass.fatal(
"Raster map '"
+ options["output"]
+ "' already exists. Use '--o' to overwrite."
)
# Get grid spacing from GRASS
# Check if lat/lon and proceed as directed
if grass.region_env()[6] == "3":
if latlon_override:
if flex.Verbose:
print("Latitude/longitude grid.")
print("Based on r_Earth = 6371 km")
print("Setting y-resolution [m] to 111,195 * [degrees]")
flex.dy = grass.region()["nsres"] * 111195.0
NSmid = (grass.region()["n"] + grass.region()["s"]) / 2.0
dx_at_mid_latitude = (
(3.14159 / 180.0) * 6371000.0 * np.cos(np.deg2rad(NSmid))
)
if flex.Verbose:
print(
"Setting x-resolution [m] to "
+ "%.2f" % dx_at_mid_latitude
+ " * [degrees]"
)
flex.dx = grass.region()["ewres"] * dx_at_mid_latitude
else:
grass.fatal("Need the '-l' flag to enable lat/lon solution approximation.")
# Otherwise straightforward
else:
flex.dx = grass.region()["ewres"]
flex.dy = grass.region()["nsres"]
# CALCULATE!
flex.initialize()
flex.run()
flex.finalize()
# Write to GRASS
# Create a new garray buffer and write to it
outbuffer = garray.array() # Instantiate output buffer
outbuffer[...] = flex.w
outbuffer.write(
options["output"], overwrite=grass.overwrite()
) # Write it with the desired name
# And create a nice colormap!
grass.run_command(
"r.colors", map=options["output"], color="differences", quiet=True
)
def main():
# lazy imports
import grass.temporal as tgis
# Get the options
input = options["input"]
elevation = options["elevation"]
expdir = options["directory"]
where = options["where"]
null = options["null"]
use_pdata = flags["p"]
coorcorr = flags["c"]
use_granularity = flags["g"]
# Make sure the temporal database exists
tgis.init()
if not os.path.exists(expdir):
grass.fatal(_("Export directory <%s> not found.") % expdir)
os.chdir(expdir)
sp = tgis.open_old_stds(input, "strds")
if use_granularity:
# Attention: A list of lists of maps will be returned
maps = sp.get_registered_maps_as_objects_by_granularity()
# Create a NULL map in case of granularity support
null_map = "temporary_null_map_%i" % os.getpid()
grass.mapcalc("%s = null()" % (null_map))
else:
maps = sp.get_registered_maps_as_objects(where, "start_time", None)
# To have scalar values with the same name, we need to copy the
# raster maps using a single name
map_name = "%s_%i" % (sp.base.get_name(), os.getpid())
count = 0
if maps is not None:
for map in maps:
if use_granularity:
if map and len(map) > 0:
id = map[0].get_map_id()
else:
id = map.get_map_id()
# None ids will be replaced by NULL maps
if id is None:
id = null_map
grass.run_command("g.copy",
raster="%s,%s" % (id, map_name),
overwrite=True)
out_name = "%6.6i_%s.vtk" % (count, sp.base.get_name())
mflags = ""
if use_pdata:
mflags += "p"
if coorcorr:
mflags += "c"
# Export the raster map with r.out.vtk
try:
if elevation:
grass.run_command(
"r.out.vtk",
flags=mflags,
null=null,
input=map_name,
elevation=elevation,
output=out_name,
overwrite=grass.overwrite(),
)
else:
grass.run_command(
"r.out.vtk",
flags=mflags,
null=null,
input=map_name,
output=out_name,
overwrite=grass.overwrite(),
)
except CalledModuleError:
grass.fatal(_("Unable to export raster map <%s>" % map_name))
count += 1
if use_granularity:
grass.run_command("g.remove", flags="f", type="raster", name=null_map)
grass.run_command("g.remove", flags="f", type="raster", name=map_name)
def main():
# lazy imports
import grass.temporal as tgis
from grass.pygrass.utils import copy as gcopy
from grass.pygrass.messages import Messenger
from grass.pygrass.vector import Vector
# Get the options
input = options["input"]
output = options["output"]
strds = options["strds"]
where = options["where"]
tempwhere = options["t_where"]
if output and flags['u']:
grass.fatal(_("Cannot combine 'output' option and 'u' flag"))
elif not output and not flags['u']:
grass.fatal(_("'output' option or 'u' flag must be given"))
elif not output and flags['u']:
grass.warning(
_("Attribute table of vector {name} will be updated...").format(
name=input))
if where == "" or where == " " or where == "\n":
where = None
overwrite = grass.overwrite()
quiet = True
if grass.verbosity() > 2:
quiet = False
# Check the number of sample strds and the number of columns
strds_names = strds.split(",")
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
samples = []
first_strds = tgis.open_old_stds(strds_names[0], "strds", dbif)
# Single space time raster dataset
if len(strds_names) == 1:
granu = first_strds.get_granularity()
rows = first_strds.get_registered_maps(
"name,mapset,start_time,end_time", tempwhere, "start_time", dbif)
if not rows:
dbif.close()
grass.fatal(
_("Space time raster dataset <%s> is empty") %
first_strds.get_id())
for row in rows:
start = row["start_time"]
end = row["end_time"]
raster_maps = [
row["name"] + "@" + row["mapset"],
]
s = Sample(start, end, raster_maps, first_strds.get_name(), granu)
samples.append(s)
else:
# Multiple space time raster datasets
for name in strds_names[1:]:
dataset = tgis.open_old_stds(name, "strds", dbif)
if dataset.get_temporal_type() != first_strds.get_temporal_type():
grass.fatal(
_(
"Temporal type of space time raster "
"datasets must be equal\n<%(a)s> of type "
"%(type_a)s do not match <%(b)s> of type "
"%(type_b)s" % {
"a": first_strds.get_id(),
"type_a": first_strds.get_temporal_type(),
"b": dataset.get_id(),
"type_b": dataset.get_temporal_type()
}))
mapmatrizes = tgis.sample_stds_by_stds_topology(
"strds", "strds", strds_names, strds_names[0], False, None,
"equal", False, False)
#TODO check granularity for multiple STRDS
for i in range(len(mapmatrizes[0])):
isvalid = True
mapname_list = []
for mapmatrix in mapmatrizes:
entry = mapmatrix[i]
if entry["samples"]:
sample = entry["samples"][0]
name = sample.get_id()
if name is None:
isvalid = False
break
else:
mapname_list.append(name)
if isvalid:
entry = mapmatrizes[0][i]
map = entry["granule"]
start, end = map.get_temporal_extent_as_tuple()
s = Sample(start, end, mapname_list, name)
samples.append(s)
# Get the layer and database connections of the input vector
if output:
gcopy(input, output, 'vector')
else:
output = input
msgr = Messenger()
perc_curr = 0
perc_tot = len(samples)
pymap = Vector(output)
try:
pymap.open('r')
except:
dbif.close()
grass.fatal(_("Unable to create vector map <%s>" % output))
if len(pymap.dblinks) == 0:
try:
pymap.close()
grass.run_command("v.db.addtable", map=output)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to add table <%s> to vector map <%s>" % output))
if pymap.is_open():
pymap.close()
for sample in samples:
raster_names = sample.raster_names
# Call v.what.rast for each raster map
for name in raster_names:
coltype = "DOUBLE PRECISION"
# Get raster map type
raster_map = tgis.RasterDataset(name)
raster_map.load()
if raster_map.metadata.get_datatype() == "CELL":
coltype = "INT"
day = sample.printDay()
column_name = "%s_%s" % (sample.strds_name, day)
column_string = "%s %s" % (column_name, coltype)
column_string.replace('.', '_')
try:
grass.run_command("v.db.addcolumn",
map=output,
column=column_string,
overwrite=overwrite)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to add column %s to vector map "
"<%s> ") % (column_string, output))
try:
grass.run_command("v.what.rast",
map=output,
raster=name,
column=column_name,
where=where,
quiet=quiet)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to run v.what.rast for vector map"
" <%s> and raster map <%s>") %
(output, str(raster_names)))
msgr.percent(perc_curr, perc_tot, 1)
perc_curr += 1
dbif.close()
def main():
global insert_sql
insert_sql = None
global temporary_vect
temporary_vect = None
global stats_temp_file
stats_temp_file = None
segment_map = options['map']
csvfile = options['csvfile'] if options['csvfile'] else []
vectormap = options['vectormap'] if options['vectormap'] else []
global rasters
rasters = options['rasters'].split(',') if options['rasters'] else []
area_measures = options['area_measures'].split(',') if (
options['area_measures'] and not flags['s']) else []
if area_measures:
if not gscript.find_program('r.object.geometry', '--help'):
message = _(
"You need to install the addon r.object.geometry to be able")
message += _(" to calculate area measures.\n")
message += _(
" You can install the addon with 'g.extension r.object.geometry'"
)
gscript.fatal(message)
neighborhood = True if flags['n'] else False
if neighborhood:
if not gscript.find_program('r.neighborhoodmatrix', '--help'):
message = _(
"You need to install the addon r.neighborhoodmatrix to be able"
)
message += _(" to calculate area measures.\n")
message += _(
" You can install the addon with 'g.extension r.neighborhoodmatrix'"
)
gscript.fatal(message)
raster_statistics = options['raster_statistics'].split(
',') if options['raster_statistics'] else []
separator = gscript.separator(options['separator'])
processes = int(options['processes'])
output_header = ['cat']
output_dict = collections.defaultdict(list)
raster_stat_dict = {
'zone': 0,
'min': 4,
'third_quart': 16,
'max': 5,
'sum': 12,
'null_cells': 3,
'median': 15,
'label': 1,
'first_quart': 14,
'range': 6,
'mean_of_abs': 8,
'stddev': 9,
'non_null_cells': 2,
'coeff_var': 11,
'variance': 10,
'sum_abs': 13,
'perc_90': 17,
'mean': 7
}
geometry_stat_dict = {
'cat': 0,
'area': 1,
'perimeter': 2,
'compact_square': 3,
'compact_circle': 4,
'fd': 5,
'xcoords': 6,
'ycoords': 7
}
if flags['r']:
gscript.use_temp_region()
gscript.run_command('g.region', raster=segment_map)
stats_temp_file = gscript.tempfile()
if area_measures:
gscript.message(_("Calculating geometry statistics..."))
output_header += area_measures
stat_indices = [geometry_stat_dict[x] for x in area_measures]
gscript.run_command('r.object.geometry',
input_=segment_map,
output=stats_temp_file,
overwrite=True,
quiet=True)
firstline = True
with open(stats_temp_file, 'r') as fin:
for line in fin:
if firstline:
firstline = False
continue
values = line.rstrip().split('|')
output_dict[values[0]] = [values[x] for x in stat_indices]
if rasters:
if not flags['c']:
gscript.message(_("Checking usability of raster maps..."))
for raster in rasters:
if not gscript.find_file(raster, element='cell')['name']:
gscript.message(_("Cannot find raster '%s'" % raster))
gscript.message(_("Removing this raster from list."))
rasters.remove(raster)
raster_info = gscript.parse_command('r.univar',
flags='g',
map_=raster,
quiet=True)
if len(raster_info) == 0 or int(raster_info['null_cells']) > 0:
message = 'Raster %s contains null values.\n' % raster
message += 'This can lead to errors in the calculations.\n'
message += 'Check region settings and raster extent.\n'
message += 'Possibly fill null values of raster.\n'
message += 'Removing this raster from list.'
gscript.warning(message)
while raster in rasters:
rasters.remove(raster)
continue
if len(rasters) > 0:
gscript.message(_("Calculating statistics for raster maps..."))
if len(rasters) < processes:
processes = len(rasters)
gscript.message(
_("Only one process per raster. Reduced number of processes to %i."
% processes))
stat_indices = [raster_stat_dict[x] for x in raster_statistics]
pool = Pool(processes)
func = partial(worker, segment_map, stats_temp_file)
pool.map(func, rasters)
pool.close()
pool.join()
for raster in rasters:
rastername = raster.split('@')[0]
rastername = rastername.replace('.', '_')
temp_file = stats_temp_file + '.' + rastername
output_header += [
rastername + "_" + x for x in raster_statistics
]
firstline = True
with open(temp_file, 'r') as fin:
for line in fin:
if firstline:
firstline = False
continue
values = line.rstrip().split('|')
output_dict[values[0]] = output_dict[values[0]] + [
values[x] for x in stat_indices
]
# Calculating neighborhood statistics if requested
if neighborhood:
gscript.message(_("Calculating neighborhood statistics..."))
# Add neighbordhood statistics to headers
original_nb_values = len(output_header) - 1
new_headers = ['neighbors_count']
for i in range(1, len(output_header)):
new_headers.append('%s_nbrmean' % output_header[i])
new_headers.append('%s_nbrstddev' % output_header[i])
output_header += new_headers
# Get sorted neighborhood matrix
nbr_matrix = sorted([
x.split('|')
for x in gscript.read_command('r.neighborhoodmatrix',
input_=segment_map,
flags='d',
quiet=True).splitlines()
])
# Calculate mean and stddev of neighbor values for each variable in the
# output_dict
for key, group in groupby(nbr_matrix, lambda x: x[0]):
d = {}
for i in range(original_nb_values):
d[i] = (0, 0, 0)
nbrlist = [str(x[1]) for x in group]
if len(nbrlist) > 1:
for nbr in nbrlist:
for i in range(original_nb_values):
d[i] = update(d[i], float(output_dict[nbr][i]))
output_dict[key] = output_dict[key] + [str(len(nbrlist))]
output_dict[key] = output_dict[key] + [
str(i) for sub in [finalize(x) for x in d.values()]
for i in sub
]
else:
newvalues = ['1']
nbr = nbrlist[0]
for i in range(original_nb_values):
newvalues.append(output_dict[nbr][i])
newvalues.append('0')
output_dict[key] = output_dict[key] + newvalues
message = _("Some values could not be calculated for the objects below. ")
message += _("These objects are thus not included in the results. ")
message += _("HINT: Check some of the raster maps for null values ")
message += _("and possibly fill these values with r.fillnulls.")
error_objects = []
if csvfile:
with open(csvfile, 'wb') as f:
f.write(separator.join(output_header) + "\n")
for key in output_dict:
if len(output_dict[key]) + 1 == len(output_header):
f.write(key + separator +
separator.join(output_dict[key]) + "\n")
else:
error_objects.append(key)
f.close()
if vectormap:
gscript.message(_("Creating output vector map..."))
temporary_vect = 'segmstat_tmp_vect_%d' % os.getpid()
gscript.run_command('r.to.vect',
input_=segment_map,
output=temporary_vect,
type_='area',
flags='vt',
overwrite=True,
quiet=True)
insert_sql = gscript.tempfile()
fsql = open(insert_sql, 'w')
fsql.write('BEGIN TRANSACTION;\n')
if gscript.db_table_exist(temporary_vect):
if gscript.overwrite():
fsql.write('DROP TABLE %s;' % temporary_vect)
else:
gscript.fatal(
_("Table %s already exists. Use --o to overwrite" %
temporary_vect))
create_statement = 'CREATE TABLE ' + temporary_vect + ' (cat int PRIMARY KEY);\n'
fsql.write(create_statement)
for header in output_header[1:]:
addcol_statement = 'ALTER TABLE %s ADD COLUMN %s double precision;\n' % (
temporary_vect, header)
fsql.write(addcol_statement)
for key in output_dict:
if len(output_dict[key]) + 1 == len(output_header):
sql = "INSERT INTO %s VALUES (%s, %s);\n" % (
temporary_vect, key, ",".join(output_dict[key]))
sql = sql.replace('inf', 'NULL')
sql = sql.replace('nan', 'NULL')
fsql.write(sql)
else:
if not csvfile:
error_objects.append(key)
fsql.write('END TRANSACTION;')
fsql.close()
gscript.run_command('db.execute', input=insert_sql, quiet=True)
gscript.run_command('v.db.connect',
map_=temporary_vect,
table=temporary_vect,
quiet=True)
gscript.run_command('g.copy',
vector="%s,%s" % (temporary_vect, vectormap),
quiet=True)
if error_objects:
object_string = ', '.join(error_objects[:100])
message += _(
"\n\nObjects with errors (only first 100 are shown):\n%s" %
object_string)
gscript.message(message)
def reclass(inf, outf, lim, clump, diag, les):
infile = inf
outfile = outf
lesser = les
limit = lim
clumped = clump
diagonal = diag
s = grass.read_command("g.region", flags='p')
s = decode(s)
kv = grass.parse_key_val(s, sep=':')
s = kv['projection'].strip().split()
if s == '0':
grass.fatal(_("xy-locations are not supported"))
grass.fatal(_("Need projected data with grids in meters"))
if not grass.find_file(infile)['name']:
grass.fatal(_("Raster map <%s> not found") % infile)
if clumped and diagonal:
grass.fatal(_("flags c and d are mutually exclusive"))
if clumped:
clumpfile = infile
else:
clumpfile = "%s.clump.%s" % (infile.split('@')[0], outfile)
TMPRAST.append(clumpfile)
if not grass.overwrite():
if grass.find_file(clumpfile)['name']:
grass.fatal(_("Temporary raster map <%s> exists") % clumpfile)
if diagonal:
grass.message(
_("Generating a clumped raster file including "
"diagonal neighbors..."))
grass.run_command('r.clump',
flags='d',
input=infile,
output=clumpfile)
else:
grass.message(_("Generating a clumped raster file ..."))
grass.run_command('r.clump', input=infile, output=clumpfile)
if lesser:
grass.message(
_("Generating a reclass map with area size less than "
"or equal to %f hectares...") % limit)
else:
grass.message(
_("Generating a reclass map with area size greater "
"than or equal to %f hectares...") % limit)
recfile = outfile + '.recl'
TMPRAST.append(recfile)
sflags = 'aln'
if grass.raster_info(infile)['datatype'] in ('FCELL', 'DCELL'):
sflags += 'i'
p1 = grass.pipe_command('r.stats',
flags=sflags,
input=(clumpfile, infile),
sep=';')
p2 = grass.feed_command('r.reclass',
input=clumpfile,
output=recfile,
rules='-')
rules = ''
for line in p1.stdout:
f = decode(line).rstrip(os.linesep).split(';')
if len(f) < 5:
continue
hectares = float(f[4]) * 0.0001
if lesser:
test = hectares <= limit
else:
test = hectares >= limit
if test:
rules += "%s = %s %s\n" % (f[0], f[2], f[3])
if rules:
p2.stdin.write(encode(rules))
p1.wait()
p2.stdin.close()
p2.wait()
if p2.returncode != 0:
if lesser:
grass.fatal(
_("No areas of size less than or equal to %f "
"hectares found.") % limit)
else:
grass.fatal(
_("No areas of size greater than or equal to %f "
"hectares found.") % limit)
grass.mapcalc("$outfile = $recfile", outfile=outfile, recfile=recfile)
def main():
try:
import sklearn
import joblib
if sklearn.__version__ < "0.20":
gs.fatal(
"Package python3-scikit-learn 0.20 or newer is not installed")
except ImportError:
gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
# parser options
group = options["group"]
output = options["output"]
model_load = options["load_model"]
probability = flags["p"]
prob_only = flags["z"]
chunksize = int(options["chunksize"])
# remove @ from output in case overwriting result
if "@" in output:
output = output.split("@")[0]
# check probabilities=True if prob_only=True
if prob_only is True and probability is False:
gs.fatal("Need to set probabilities=True if prob_only=True")
# reload fitted model and training data
estimator, y, class_labels = joblib.load(model_load)
# define RasterStack
stack = RasterStack(group=group)
# perform raster prediction
region = Region()
row_incr = math.ceil(chunksize / region.cols)
# do not read by increments if increment > n_rows
if row_incr >= region.rows:
row_incr = None
# prediction
if prob_only is False:
gs.message("Predicting classification/regression raster...")
stack.predict(
estimator=estimator,
output=output,
height=row_incr,
overwrite=gs.overwrite(),
)
if probability is True:
gs.message("Predicting class probabilities...")
stack.predict_proba(
estimator=estimator,
output=output,
class_labels=np.unique(y),
overwrite=gs.overwrite(),
height=row_incr,
)
# assign categories for classification map
if class_labels and prob_only is False:
rules = []
for val, lab in class_labels.items():
rules.append(",".join([str(val), str(lab)]))
rules = "\n".join(rules)
rules_file = string_to_rules(rules)
r.category(map=output, rules=rules_file, separator="comma")
def main(options, flags):
# Get the options
input = options["input"]
output = options["output"]
where = options["where"]
base = options["basename"]
nprocs = int(options["nprocs"])
step = options["step"]
levels = options["levels"]
minlevel = options["minlevel"]
maxlevel = options["maxlevel"]
cut = options["cut"]
register_null = flags["n"]
t_flag = flags["t"]
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
overwrite = gscript.overwrite()
sp = tgis.open_old_stds(input, "strds", dbif)
maps = sp.get_registered_maps_as_objects(where=where, dbif=dbif)
if not maps:
dbif.close()
gscript.warning(_("Space time raster dataset <%s> is empty") % sp.get_id())
return
# Check the new stvds
new_sp = tgis.check_new_stds(output, "stvds", dbif=dbif,
overwrite=overwrite)
# Setup the flags
flags = ""
if t_flag is True:
flags += "t"
# Configure the r.to.vect module
contour_module = pymod.Module("r.contour", input="dummy",
output="dummy", run_=False,
finish_=False, flags=flags,
overwrite=overwrite,
quiet=True)
if step:
contour_module.inputs.step = float(step)
if minlevel:
contour_module.inputs.minlevel = float(minlevel)
if maxlevel:
contour_module.inputs.maxlevel = float(maxlevel)
if levels:
contour_module.inputs.levels = levels.split(",")
if cut:
contour_module.inputs.cut = int(cut)
# The module queue for parallel execution, except if attribute tables should
# be created. Then force single process use
if t_flag is False:
if nprocs > 1:
nprocs = 1
gscript.warning(_("The number of parellel r.contour processes was "\
"reduced to 1 because of the table attribute "\
"creation"))
process_queue = pymod.ParallelModuleQueue(int(nprocs))
count = 0
num_maps = len(maps)
new_maps = []
# run r.to.vect all selected maps
for map in maps:
count += 1
map_name = "%s_%i" % (base, count)
new_map = tgis.open_new_map_dataset(map_name, None, type="vector",
temporal_extent=map.get_temporal_extent(),
overwrite=overwrite, dbif=dbif)
new_maps.append(new_map)
mod = copy.deepcopy(contour_module)
mod(input=map.get_id(), output=new_map.get_id())
sys.stderr.write(mod.get_bash() + "\n")
process_queue.put(mod)
if count%10 == 0:
gscript.percent(count, num_maps, 1)
# Wait for unfinished processes
process_queue.wait()
# Open the new space time vector dataset
ttype, stype, title, descr = sp.get_initial_values()
new_sp = tgis.open_new_stds(output, "stvds", ttype, title,
descr, stype, dbif, overwrite)
# collect empty maps to remove them
num_maps = len(new_maps)
empty_maps = []
# Register the maps in the database
count = 0
for map in new_maps:
count += 1
if count%10 == 0:
gscript.percent(count, num_maps, 1)
# Do not register empty maps
try:
if map.load() is not True:
continue
except FatalError:
continue
if map.metadata.get_number_of_primitives() == 0:
if not register_null:
empty_maps.append(map)
continue
# Insert map in temporal database
map.insert(dbif)
new_sp.register_map(map, dbif)
# Update the spatio-temporal extent and the metadata table entries
new_sp.update_from_registered_maps(dbif)
gscript.percent(1, 1, 1)
# Remove empty maps
if len(empty_maps) > 0:
names = ""
count = 0
for map in empty_maps:
if count == 0:
count += 1
names += "%s" % (map.get_name())
else:
names += ",%s" % (map.get_name())
gscript.run_command("g.remove", flags='f', type='vector', name=names,
quiet=True)
dbif.close()
def main():
if not hasNumPy:
grass.fatal(_("Required dependency NumPy not found. Exiting."))
sharpen = options["method"] # sharpening algorithm
ms1_orig = options["blue"] # blue channel
ms2_orig = options["green"] # green channel
ms3_orig = options["red"] # red channel
pan_orig = options["pan"] # high res pan channel
out = options["output"] # prefix for output RGB maps
bits = options["bitdepth"] # bit depth of image channels
bladjust = flags["l"] # adjust blue channel
sproc = flags["s"] # serial processing
rescale = flags["r"] # rescale to spread pixel values to entire 0-255 range
# Checking bit depth
bits = float(bits)
if bits < 2 or bits > 30:
grass.warning(_("Bit depth is outside acceptable range"))
return
outb = grass.core.find_file("%s_blue" % out)
outg = grass.core.find_file("%s_green" % out)
outr = grass.core.find_file("%s_red" % out)
if (
outb["name"] != "" or outg["name"] != "" or outr["name"] != ""
) and not grass.overwrite():
grass.warning(
_(
"Maps with selected output prefix names already exist."
" Delete them or use overwrite flag"
)
)
return
pid = str(os.getpid())
# convert input image channels to 8 bit for processing
ms1 = "tmp%s_ms1" % pid
ms2 = "tmp%s_ms2" % pid
ms3 = "tmp%s_ms3" % pid
pan = "tmp%s_pan" % pid
if not rescale:
if bits == 8:
grass.message(_("Using 8bit image channels"))
if sproc:
# serial processing
grass.run_command(
"g.copy",
raster="%s,%s" % (ms1_orig, ms1),
quiet=True,
overwrite=True,
)
grass.run_command(
"g.copy",
raster="%s,%s" % (ms2_orig, ms2),
quiet=True,
overwrite=True,
)
grass.run_command(
"g.copy",
raster="%s,%s" % (ms3_orig, ms3),
quiet=True,
overwrite=True,
)
grass.run_command(
"g.copy",
raster="%s,%s" % (pan_orig, pan),
quiet=True,
overwrite=True,
)
else:
# parallel processing
pb = grass.start_command(
"g.copy",
raster="%s,%s" % (ms1_orig, ms1),
quiet=True,
overwrite=True,
)
pg = grass.start_command(
"g.copy",
raster="%s,%s" % (ms2_orig, ms2),
quiet=True,
overwrite=True,
)
pr = grass.start_command(
"g.copy",
raster="%s,%s" % (ms3_orig, ms3),
quiet=True,
overwrite=True,
)
pp = grass.start_command(
"g.copy",
raster="%s,%s" % (pan_orig, pan),
quiet=True,
overwrite=True,
)
pb.wait()
pg.wait()
pr.wait()
pp.wait()
else:
grass.message(_("Converting image chanels to 8bit for processing"))
maxval = pow(2, bits) - 1
if sproc:
# serial processing
grass.run_command(
"r.rescale",
input=ms1_orig,
from_="0,%f" % maxval,
output=ms1,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=ms2_orig,
from_="0,%f" % maxval,
output=ms2,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=ms3_orig,
from_="0,%f" % maxval,
output=ms3,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=pan_orig,
from_="0,%f" % maxval,
output=pan,
to="0,255",
quiet=True,
overwrite=True,
)
else:
# parallel processing
pb = grass.start_command(
"r.rescale",
input=ms1_orig,
from_="0,%f" % maxval,
output=ms1,
to="0,255",
quiet=True,
overwrite=True,
)
pg = grass.start_command(
"r.rescale",
input=ms2_orig,
from_="0,%f" % maxval,
output=ms2,
to="0,255",
quiet=True,
overwrite=True,
)
pr = grass.start_command(
"r.rescale",
input=ms3_orig,
from_="0,%f" % maxval,
output=ms3,
to="0,255",
quiet=True,
overwrite=True,
)
pp = grass.start_command(
"r.rescale",
input=pan_orig,
from_="0,%f" % maxval,
output=pan,
to="0,255",
quiet=True,
overwrite=True,
)
pb.wait()
pg.wait()
pr.wait()
pp.wait()
else:
grass.message(_("Rescaling image chanels to 8bit for processing"))
min_ms1 = int(grass.raster_info(ms1_orig)["min"])
max_ms1 = int(grass.raster_info(ms1_orig)["max"])
min_ms2 = int(grass.raster_info(ms2_orig)["min"])
max_ms2 = int(grass.raster_info(ms2_orig)["max"])
min_ms3 = int(grass.raster_info(ms3_orig)["min"])
max_ms3 = int(grass.raster_info(ms3_orig)["max"])
min_pan = int(grass.raster_info(pan_orig)["min"])
max_pan = int(grass.raster_info(pan_orig)["max"])
maxval = pow(2, bits) - 1
if sproc:
# serial processing
grass.run_command(
"r.rescale",
input=ms1_orig,
from_="%f,%f" % (min_ms1, max_ms1),
output=ms1,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=ms2_orig,
from_="%f,%f" % (min_ms2, max_ms2),
output=ms2,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=ms3_orig,
from_="%f,%f" % (min_ms3, max_ms3),
output=ms3,
to="0,255",
quiet=True,
overwrite=True,
)
grass.run_command(
"r.rescale",
input=pan_orig,
from_="%f,%f" % (min_pan, max_pan),
output=pan,
to="0,255",
quiet=True,
overwrite=True,
)
else:
# parallel processing
pb = grass.start_command(
"r.rescale",
input=ms1_orig,
from_="%f,%f" % (min_ms1, max_ms1),
output=ms1,
to="0,255",
quiet=True,
overwrite=True,
)
pg = grass.start_command(
"r.rescale",
input=ms2_orig,
from_="%f,%f" % (min_ms2, max_ms2),
output=ms2,
to="0,255",
quiet=True,
overwrite=True,
)
pr = grass.start_command(
"r.rescale",
input=ms3_orig,
from_="%f,%f" % (min_ms3, max_ms3),
output=ms3,
to="0,255",
quiet=True,
overwrite=True,
)
pp = grass.start_command(
"r.rescale",
input=pan_orig,
from_="%f,%f" % (min_pan, max_pan),
output=pan,
to="0,255",
quiet=True,
overwrite=True,
)
pb.wait()
pg.wait()
pr.wait()
pp.wait()
# get PAN resolution:
kv = grass.raster_info(map=pan)
nsres = kv["nsres"]
ewres = kv["ewres"]
panres = (nsres + ewres) / 2
# clone current region
grass.use_temp_region()
grass.run_command("g.region", res=panres, align=pan)
# Select sharpening method
grass.message(_("Performing pan sharpening with hi res pan image: %f" % panres))
if sharpen == "brovey":
brovey(pan, ms1, ms2, ms3, out, pid, sproc)
elif sharpen == "ihs":
ihs(pan, ms1, ms2, ms3, out, pid, sproc)
elif sharpen == "pca":
pca(pan, ms1, ms2, ms3, out, pid, sproc)
# Could add other sharpening algorithms here, e.g. wavelet transformation
grass.message(_("Assigning grey equalized color tables to output images..."))
# equalized grey scales give best contrast
grass.message(_("setting pan-sharpened channels to equalized grey scale"))
for ch in ["red", "green", "blue"]:
grass.run_command(
"r.colors", quiet=True, map="%s_%s" % (out, ch), flags="e", color="grey"
)
# Landsat too blue-ish because panchromatic band less sensitive to blue
# light, so output blue channed can be modified
if bladjust:
grass.message(_("Adjusting blue channel color table..."))
blue_colors = ["0 0 0 0\n5% 0 0 0\n67% 255 255 255\n100% 255 255 255"]
# these previous colors are way too blue for landsat
# blue_colors = ['0 0 0 0\n10% 0 0 0\n20% 200 200 200\n40% 230 230 230\n67% 255 255 255\n100% 255 255 255']
bc = grass.feed_command("r.colors", quiet=True, map="%s_blue" % out, rules="-")
bc.stdin.write(grass.encode("\n".join(blue_colors)))
bc.stdin.close()
# output notice
grass.verbose(_("The following pan-sharpened output maps have been generated:"))
for ch in ["red", "green", "blue"]:
grass.verbose(_("%s_%s") % (out, ch))
grass.verbose(_("To visualize output, run: g.region -p raster=%s_red" % out))
grass.verbose(_("d.rgb r=%s_red g=%s_green b=%s_blue" % (out, out, out)))
grass.verbose(
_("If desired, combine channels into a single RGB map with 'r.composite'.")
)
grass.verbose(_("Channel colors can be rebalanced using i.colors.enhance."))
# write cmd history:
for ch in ["red", "green", "blue"]:
grass.raster_history("%s_%s" % (out, ch))
# create a group with the three outputs
# grass.run_command('i.group', group=out,
# input="{n}_red,{n}_blue,{n}_green".format(n=out))
# Cleanup
grass.message(_("cleaning up temp files"))
try:
grass.run_command(
"g.remove", flags="f", type="raster", pattern="tmp%s*" % pid, quiet=True
)
except:
""
def main():
global vrtfile, tmpfile
infile = options['input']
rast = options['output']
also = flags['a']
# check for gdalinfo (just to check if installation is complete)
if not gscript.find_program('gdalinfo', '--help'):
gscript.fatal(_("'gdalinfo' not found, install GDAL tools first "
"(http://www.gdal.org)"))
pid = str(os.getpid())
tmpfile = gscript.tempfile()
# let's go
spotdir = os.path.dirname(infile)
spotname = gscript.basename(infile, 'hdf')
if rast:
name = rast
else:
name = spotname
if not gscript.overwrite() and gscript.find_file(name)['file']:
gscript.fatal(_("<%s> already exists. Aborting.") % name)
# still a ZIP file? (is this portable?? see the r.in.srtm script for
# ideas)
if infile.lower().endswith('.zip'):
gscript.fatal(_("Please extract %s before import.") % infile)
try:
p = gscript.Popen(['file', '-ib', infile], stdout=gscript.PIPE)
s = p.communicate()[0]
if s == "application/x-zip":
gscript.fatal(_("Please extract %s before import.") % infile)
except:
pass
# create VRT header for NDVI
projfile = os.path.join(spotdir, "0001_LOG.TXT")
vrtfile = tmpfile + '.vrt'
# first process the NDVI:
gscript.try_remove(vrtfile)
create_VRT_file(projfile, vrtfile, infile)
# let's import the NDVI map...
gscript.message(_("Importing SPOT VGT NDVI map..."))
try:
gscript.run_command('r.in.gdal', input=vrtfile, output=name)
except CalledModuleError:
gscript.fatal(_("An error occurred. Stop."))
gscript.message(_("Imported SPOT VEGETATION NDVI map <%s>.") % name)
#################
# http://www.vgt.vito.be/faq/FAQS/faq19.html
# What is the relation between the digital number and the real NDVI ?
# Real NDVI =coefficient a * Digital Number + coefficient b
# = a * DN +b
#
# Coefficient a = 0.004
# Coefficient b = -0.1
# clone current region
# switch to a temporary region
gscript.use_temp_region()
gscript.run_command('g.region', raster=name, quiet=True)
gscript.message(_("Remapping digital numbers to NDVI..."))
tmpname = "%s_%s" % (name, pid)
gscript.mapcalc("$tmpname = 0.004 * $name - 0.1", tmpname=tmpname, name=name)
gscript.run_command('g.remove', type='raster', name=name, quiet=True,
flags='f')
gscript.run_command('g.rename', raster=(tmpname, name), quiet=True)
# write cmd history:
gscript.raster_history(name)
# apply color table:
gscript.run_command('r.colors', map=name, color='ndvi', quiet=True)
##########################
# second, optionally process the SM quality map:
# SM Status Map
# http://nieuw.vgt.vito.be/faq/FAQS/faq22.html
# Data about
# Bit NR 7: Radiometric quality for B0 coded as 0 if bad and 1 if good
# Bit NR 6: Radiometric quality for B2 coded as 0 if bad and 1 if good
# Bit NR 5: Radiometric quality for B3 coded as 0 if bad and 1 if good
# Bit NR 4: Radiometric quality for MIR coded as 0 if bad and 1 if good
# Bit NR 3: land code 1 or water code 0
# Bit NR 2: ice/snow code 1 , code 0 if there is no ice/snow
# Bit NR 1: 0 0 1 1
# Bit NR 0: 0 1 0 1
# clear shadow uncertain cloud
#
# Note:
# pos 7 6 5 4 3 2 1 0 (bit position)
# 128 64 32 16 8 4 2 1 (values for 8 bit)
#
#
# Bit 4-7 should be 1: their sum is 240
# Bit 3 land code, should be 1, sum up to 248 along with higher bits
# Bit 2 ice/snow code
# Bit 0-1 should be 0
#
# A good map threshold: >= 248
if also:
gscript.message(_("Importing SPOT VGT NDVI quality map..."))
gscript.try_remove(vrtfile)
qname = spotname.replace('NDV', 'SM')
qfile = os.path.join(spotdir, qname)
create_VRT_file(projfile, vrtfile, qfile)
# let's import the SM quality map...
smfile = name + '.sm'
try:
gscript.run_command('r.in.gdal', input=vrtfile, output=smfile)
except CalledModuleError:
gscript.fatal(_("An error occurred. Stop."))
# some of the possible values:
rules = [r + '\n' for r in ['8 50 50 50',
'11 70 70 70',
'12 90 90 90',
'60 grey',
'155 blue',
'232 violet',
'235 red',
'236 brown',
'248 orange',
'251 yellow',
'252 green']]
gscript.write_command('r.colors', map=smfile, rules='-', stdin=rules)
gscript.message(_("Imported SPOT VEGETATION SM quality map <%s>.") %
smfile)
gscript.message(_("Note: A snow map can be extracted by category "
"252 (d.rast %s cat=252)") % smfile)
gscript.message("")
gscript.message(_("Filtering NDVI map by Status Map quality layer..."))
filtfile = "%s_filt" % name
gscript.mapcalc("$filtfile = if($smfile % 4 == 3 || "
"($smfile / 16) % 16 == 0, null(), $name)",
filtfile=filtfile, smfile=smfile, name=name)
gscript.run_command('r.colors', map=filtfile, color='ndvi', quiet=True)
gscript.message(_("Filtered SPOT VEGETATION NDVI map <%s>.") %
filtfile)
# write cmd history:
gscript.raster_history(smfile)
gscript.raster_history(filtfile)
gscript.message(_("Done."))
def main():
# lazy imports
import grass.temporal as tgis
# Get the options
input = options["input"]
output = options["output"]
method = options["method"]
quantile = options["quantile"]
order = options["order"]
where = options["where"]
add_time = flags["t"]
nulls = flags["n"]
# Check if number of methods and output maps matches
if 'quantile' in method:
len_method = len(method.split(',')) - 1
else:
len_method = len(method.split(','))
if (len(list(filter(None, quantile.split(',')))) + len_method) != len(
output.split(',')):
grass.fatal(
_('Number requested methods and output maps do not match.'))
# Make sure the temporal database exists
tgis.init()
sp = tgis.open_old_stds(input, "strds")
rows = sp.get_registered_maps("id", where, order, None)
if rows:
# Create the r.series input file
filename = grass.tempfile(True)
file = open(filename, 'w')
for row in rows:
string = "%s\n" % (row["id"])
file.write(string)
file.close()
flag = ""
if len(rows) > 1000:
grass.warning(
_("Processing over 1000 maps: activating -z flag of r.series which slows down processing"
))
flag += "z"
if nulls:
flag += "n"
try:
grass.run_command("r.series",
flags=flag,
file=filename,
output=output,
overwrite=grass.overwrite(),
method=method,
quantile=quantile)
except CalledModuleError:
grass.fatal(
_("%s failed. Check above error messages.") % 'r.series')
if not add_time:
# We need to set the temporal extent from the subset of selected maps
maps = sp.get_registered_maps_as_objects(where=where,
order=order,
dbif=None)
first_map = maps[0]
last_map = maps[-1]
start_a, end_a = first_map.get_temporal_extent_as_tuple()
start_b, end_b = last_map.get_temporal_extent_as_tuple()
if end_b is None:
end_b = start_b
if first_map.is_time_absolute():
extent = tgis.AbsoluteTemporalExtent(start_time=start_a,
end_time=end_b)
else:
extent = tgis.RelativeTemporalExtent(
start_time=start_a,
end_time=end_b,
unit=first_map.get_relative_time_unit())
for out_map in output.split(','):
# Create the time range for the output map
if out_map.find("@") >= 0:
id = out_map
else:
mapset = grass.gisenv()["MAPSET"]
id = out_map + "@" + mapset
map = sp.get_new_map_instance(id)
map.load()
map.set_temporal_extent(extent=extent)
# Register the map in the temporal database
if map.is_in_db():
map.update_all()
else:
map.insert()
def main():
if not hasNumPy:
grass.fatal(_("Required dependency NumPy not found. Exiting."))
sharpen = options['method'] # sharpening algorithm
ms1 = options['blue'] # blue channel
ms2 = options['green'] # green channel
ms3 = options['red'] # red channel
pan = options['pan'] # high res pan channel
out = options['output'] # prefix for output RGB maps
bladjust = flags['l'] # adjust blue channel
sproc = flags['s'] # serial processing
outb = grass.core.find_file('%s_blue' % out)
outg = grass.core.find_file('%s_green' % out)
outr = grass.core.find_file('%s_red' % out)
if (outb['name'] != '' or outg['name'] != '' or outr['name'] != '') and not grass.overwrite():
grass.warning(_('Maps with selected output prefix names already exist.'
' Delete them or use overwrite flag'))
return
pid = str(os.getpid())
# get PAN resolution:
kv = grass.raster_info(map=pan)
nsres = kv['nsres']
ewres = kv['ewres']
panres = (nsres + ewres) / 2
# clone current region
grass.use_temp_region()
grass.run_command('g.region', res=panres, align=pan)
grass.message(_("Performing pan sharpening with hi res pan image: %f" % panres))
if sharpen == "brovey":
grass.verbose(_("Using Brovey algorithm"))
# pan/intensity histogram matching using linear regression
outname = 'tmp%s_pan1' % pid
panmatch1 = matchhist(pan, ms1, outname)
outname = 'tmp%s_pan2' % pid
panmatch2 = matchhist(pan, ms2, outname)
outname = 'tmp%s_pan3' % pid
panmatch3 = matchhist(pan, ms3, outname)
outr = '%s_red' % out
outg = '%s_green' % out
outb = '%s_blue' % out
# calculate brovey transformation
grass.message(_("Calculating Brovey transformation..."))
if sproc:
# serial processing
e = '''eval(k = "$ms1" + "$ms2" + "$ms3")
"$outr" = 1.0 * "$ms3" * "$panmatch3" / k
"$outg" = 1.0 * "$ms2" * "$panmatch2" / k
"$outb" = 1.0 * "$ms1" * "$panmatch1" / k'''
grass.mapcalc(e, outr=outr, outg=outg, outb=outb,
panmatch1=panmatch1, panmatch2=panmatch2,
panmatch3=panmatch3, ms1=ms1, ms2=ms2, ms3=ms3,
overwrite=True)
else:
# parallel processing
pb = grass.mapcalc_start('%s_blue = (1.0 * %s * %s) / (%s + %s + %s)' %
(out, ms1, panmatch1, ms1, ms2, ms3),
overwrite=True)
pg = grass.mapcalc_start('%s_green = (1.0 * %s * %s) / (%s + %s + %s)' %
(out, ms2, panmatch2, ms1, ms2, ms3),
overwrite=True)
pr = grass.mapcalc_start('%s_red = (1.0 * %s * %s) / (%s + %s + %s)' %
(out, ms3, panmatch3, ms1, ms2, ms3),
overwrite=True)
pb.wait()
pg.wait()
pr.wait()
# Cleanup
grass.run_command('g.remove', flags='f', quiet=True, type='raster',
name='%s,%s,%s' % (panmatch1, panmatch2, panmatch3))
elif sharpen == "ihs":
grass.verbose(_("Using IHS<->RGB algorithm"))
# transform RGB channels into IHS color space
grass.message(_("Transforming to IHS color space..."))
grass.run_command('i.rgb.his', overwrite=True,
red=ms3,
green=ms2,
blue=ms1,
hue="tmp%s_hue" % pid,
intensity="tmp%s_int" % pid,
saturation="tmp%s_sat" % pid)
# pan/intensity histogram matching using linear regression
target = "tmp%s_int" % pid
outname = "tmp%s_pan_int" % pid
panmatch = matchhist(pan, target, outname)
# substitute pan for intensity channel and transform back to RGB color space
grass.message(_("Transforming back to RGB color space and sharpening..."))
grass.run_command('i.his.rgb', overwrite=True,
hue="tmp%s_hue" % pid,
intensity="%s" % panmatch,
saturation="tmp%s_sat" % pid,
red="%s_red" % out,
green="%s_green" % out,
blue="%s_blue" % out)
# Cleanup
grass.run_command('g.remove', flags='f', quiet=True, type='raster',
name=panmatch)
elif sharpen == "pca":
grass.verbose(_("Using PCA/inverse PCA algorithm"))
grass.message(_("Creating PCA images and calculating eigenvectors..."))
# initial PCA with RGB channels
pca_out = grass.read_command('i.pca', quiet=True, rescale='0,0',
input='%s,%s,%s' % (ms1, ms2, ms3),
output='tmp%s.pca' % pid)
if len(pca_out) < 1:
grass.fatal(_("Input has no data. Check region settings."))
b1evect = []
b2evect = []
b3evect = []
for l in pca_out.replace('(', ',').replace(')', ',').splitlines():
b1evect.append(float(l.split(',')[1]))
b2evect.append(float(l.split(',')[2]))
b3evect.append(float(l.split(',')[3]))
# inverse PCA with hi res pan channel substituted for principal component 1
pca1 = 'tmp%s.pca.1' % pid
pca2 = 'tmp%s.pca.2' % pid
pca3 = 'tmp%s.pca.3' % pid
b1evect1 = b1evect[0]
b1evect2 = b1evect[1]
b1evect3 = b1evect[2]
b2evect1 = b2evect[0]
b2evect2 = b2evect[1]
b2evect3 = b2evect[2]
b3evect1 = b3evect[0]
b3evect2 = b3evect[1]
b3evect3 = b3evect[2]
outname = 'tmp%s_pan' % pid
panmatch = matchhist(pan, ms1, outname)
grass.message(_("Performing inverse PCA ..."))
stats1 = grass.parse_command("r.univar", map=ms1, flags='g',
parse=(grass.parse_key_val,
{'sep': '='}))
stats2 = grass.parse_command("r.univar", map=ms2, flags='g',
parse=(grass.parse_key_val,
{'sep': '='}))
stats3 = grass.parse_command("r.univar", map=ms3, flags='g',
parse=(grass.parse_key_val,
{'sep': '='}))
b1mean = float(stats1['mean'])
b2mean = float(stats2['mean'])
b3mean = float(stats3['mean'])
if sproc:
# serial processing
e = '''eval(k = "$ms1" + "$ms2" + "$ms3")
"$outr" = 1.0 * "$ms3" * "$panmatch3" / k
"$outg" = 1.0 * "$ms2" * "$panmatch2" / k
"$outb" = 1.0* "$ms1" * "$panmatch1" / k'''
outr = '%s_red' % out
outg = '%s_green' % out
outb = '%s_blue' % out
cmd1 = "$outb = (1.0 * $panmatch * $b1evect1) + ($pca2 * $b2evect1) + ($pca3 * $b3evect1) + $b1mean"
cmd2 = "$outg = (1.0 * $panmatch * $b1evect2) + ($pca2 * $b2evect1) + ($pca3 * $b3evect2) + $b2mean"
cmd3 = "$outr = (1.0 * $panmatch * $b1evect3) + ($pca2 * $b2evect3) + ($pca3 * $b3evect3) + $b3mean"
cmd = '\n'.join([cmd1, cmd2, cmd3])
grass.mapcalc(cmd, outb=outb, outg=outg, outr=outr,
panmatch=panmatch, pca2=pca2, pca3=pca3,
b1evect1=b1evect1, b2evect1=b2evect1, b3evect1=b3evect1,
b1evect2=b1evect2, b2evect2=b2evect2, b3evect2=b3evect2,
b1evect3=b1evect3, b2evect3=b2evect3, b3evect3=b3evect3,
b1mean=b1mean, b2mean=b2mean, b3mean=b3mean,
overwrite=True)
else:
# parallel processing
pb = grass.mapcalc_start('%s_blue = (%s * %f) + (%s * %f) + (%s * %f) + %f'
% (out, panmatch, b1evect1, pca2,
b2evect1, pca3, b3evect1, b1mean),
overwrite=True)
pg = grass.mapcalc_start('%s_green = (%s * %f) + (%s * %f) + (%s * %f) + %f'
% (out, panmatch, b1evect2, pca2,
b2evect2, pca3, b3evect2, b2mean),
overwrite=True)
pr = grass.mapcalc_start('%s_red = (%s * %f) + (%s * %f) + (%s * ''%f) + %f'
% (out, panmatch, b1evect3, pca2,
b2evect3, pca3, b3evect3, b3mean),
overwrite=True)
pr.wait()
pg.wait()
pb.wait()
# Cleanup
grass.run_command('g.remove', flags='f', quiet=True, type="raster",
pattern='tmp%s*,%s' % (pid, panmatch))
# Could add other sharpening algorithms here, e.g. wavelet transformation
grass.message(_("Assigning grey equalized color tables to output images..."))
# equalized grey scales give best contrast
for ch in ['red', 'green', 'blue']:
grass.run_command('r.colors', quiet=True, map="%s_%s" % (out, ch),
flags="e", color='grey')
# Landsat too blue-ish because panchromatic band less sensitive to blue
# light, so output blue channed can be modified
if bladjust:
grass.message(_("Adjusting blue channel color table..."))
rules = grass.tempfile()
colors = open(rules, 'w')
colors.write('5 0 0 0\n20 200 200 200\n40 230 230 230\n67 255 255 255 \n')
colors.close()
grass.run_command('r.colors', map="%s_blue" % out, rules=rules)
os.remove(rules)
# output notice
grass.verbose(_("The following pan-sharpened output maps have been generated:"))
for ch in ['red', 'green', 'blue']:
grass.verbose(_("%s_%s") % (out, ch))
grass.verbose(_("To visualize output, run: g.region -p raster=%s_red" % out))
grass.verbose(_("d.rgb r=%s_red g=%s_green b=%s_blue" % (out, out, out)))
grass.verbose(_("If desired, combine channels into a single RGB map with 'r.composite'."))
grass.verbose(_("Channel colors can be rebalanced using i.colors.enhance."))
# write cmd history:
for ch in ['red', 'green', 'blue']:
grass.raster_history("%s_%s" % (out, ch))
# create a group with the three output
grass.run_command('i.group', group=out,
input="{n}_red,{n}_blue,{n}_green".format(n=out))
# Cleanup
grass.run_command('g.remove', flags="f", type="raster",
pattern="tmp%s*" % pid, quiet=True)
def main():
# Get the options
input = options["input"]
strds = options["strds"]
where = options["where"]
column = options["column"]
method = options["method"]
tempwhere = options["t_where"]
sampling = options["sampling"]
if where == "" or where == " " or where == "\n":
where = None
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
sp = tgis.open_old_stds(input, "stvds", dbif)
strds_sp = tgis.open_old_stds(strds, "strds", dbif)
if strds_sp.get_temporal_type() != sp.get_temporal_type():
dbif.close()
grass.fatal(
_("Input and aggregation dataset must "
"have the same temporal type"))
# Check if intervals are present in the sample dataset
if sp.get_temporal_type() == "absolute":
map_time = sp.absolute_time.get_map_time()
else:
map_time = sp.relative_time.get_map_time()
if map_time != "interval":
dbif.close()
grass.fatal(
_("All registered maps of the space time vector "
"dataset must have time intervals"))
rows = sp.get_registered_maps("name,layer,mapset,start_time,end_time",
tempwhere, "start_time", dbif)
if not rows:
dbif.close()
grass.fatal(_("Space time vector dataset <%s> is empty") % sp.get_id())
# Sample the raster dataset with the vector dataset and run v.what.rast
for row in rows:
start = row["start_time"]
end = row["end_time"]
vectmap = row["name"] + "@" + row["mapset"]
layer = row["layer"]
raster_maps = tgis.collect_map_names(strds_sp, dbif, start, end,
sampling)
aggreagated_map_name = None
if raster_maps:
# Aggregation
if method != "disabled" and len(raster_maps) > 1:
# Generate the temporary map name
aggreagated_map_name = "aggreagated_map_name_" + \
str(os.getpid())
new_map = tgis.aggregate_raster_maps(raster_maps,
aggreagated_map_name,
start, end, 0, method,
False, dbif)
aggreagated_map_name = aggreagated_map_name + "_0"
if new_map is None:
continue
# We overwrite the raster_maps list
raster_maps = (new_map.get_id(), )
for rastermap in raster_maps:
if column:
col_name = column
else:
# Create a new column with the SQL compliant
# name of the sampled raster map
col_name = rastermap.split("@")[0].replace(".", "_")
coltype = "DOUBLE PRECISION"
# Get raster type
rasterinfo = raster.raster_info(rastermap)
if rasterinfo["datatype"] == "CELL":
coltype = "INT"
try:
if layer:
grass.run_command("v.db.addcolumn",
map=vectmap,
layer=layer,
column="%s %s" % (col_name, coltype),
overwrite=grass.overwrite())
else:
grass.run_command("v.db.addcolumn",
map=vectmap,
column="%s %s" % (col_name, coltype),
overwrite=grass.overwrite())
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to add column %s to vector map <%s>") %
(col_name, vectmap))
# Call v.what.rast
try:
if layer:
grass.run_command("v.what.rast",
map=vectmap,
layer=layer,
raster=rastermap,
column=col_name,
where=where)
else:
grass.run_command("v.what.rast",
map=vectmap,
raster=rastermap,
column=col_name,
where=where)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to run v.what.rast for vector map "
"<%s> and raster map <%s>") % (vectmap, rastermap))
if aggreagated_map_name:
try:
grass.run_command("g.remove",
flags='f',
type='raster',
name=aggreagated_map_name)
except CalledModuleError:
dbif.close()
grass.fatal(
_("Unable to remove raster map <%s>") %
(aggreagated_map_name))
# Use the first map in case a column names was provided
if column:
break
dbif.close()
