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:
gscript.fatal("You must set both raster input and output, or neither.")
"""
# Fatal if bc_cell set but mask and grid are false
if bc_cell != '':
if (mask == '') or (pp == ''):
gscript.fatal(
'Mask and pour point must be set to define b.c. cell')
# Create grid -- overlaps DEM, three cells 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('tmp' + ' = ' + mask + ' > 0', overwrite=True, quiet=True)
g.rename(raster=('tmp', mask), overwrite=True, quiet=True)
r.null(map=mask, null=0, quiet=True)
# Add mask location (1 vs 0) in the MODFLOW grid
v.db_addcolumn(map=grid,
columns='basinmask double precision',
quiet=True)
v.what_rast(map=grid, type='centroid', raster=mask, column='basinmask')
"""
# 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
# Requires pp (always) and mask (sometimes)
# Dependency set above w/ gscript.fatal
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('tmp2 = if(tmp==2,1,null()) * ' + raster_input,
overwrite=True)
g.rename(raster=('tmp2', 'tmp'), overwrite=True, quiet=True)
#r.mapcalc('tmp = if(isnull('+raster_input+',0,(tmp == 2)))', overwrite=True)
#g.region(rast='tmp')
#r.null(map=raster_input,
r.drain(input=raster_input,
start_points=pp,
output='tmp2',
overwrite=True)
r.mapcalc('tmp3 = tmp2 * tmp', overwrite=True, quiet=True)
g.rename(raster=('tmp3', 'tmp'), overwrite=True, quiet=True)
#r.null(map='tmp', setnull=0) # Not necessary: center point removed above
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',
'x double precision', 'y double precision'),
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)
v.to_db(map=bc_cell, option='coor', columns=('x,y'))
# Find out if this is diagonal: finite difference works only N-S, W-E
colNames = np.array(gscript.vector_db_select(pp, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(pp, layer=1)['values'].values())
pp_row = int(colValues[:, colNames == 'row'].astype(int).squeeze())
pp_col = int(colValues[:, colNames == 'col'].astype(int).squeeze())
colNames = np.array(
gscript.vector_db_select(bc_cell, layer=1)['columns'])
colValues = np.array(
gscript.vector_db_select(bc_cell, layer=1)['values'].values())
bc_row = int(colValues[:, colNames == 'row'].astype(int).squeeze())
bc_col = int(colValues[:, colNames == 'col'].astype(int).squeeze())
# Also get x and y while we are at it: may be needed later
bc_x = float(colValues[:, colNames == 'x'].astype(float).squeeze())
bc_y = float(colValues[:, colNames == 'y'].astype(float).squeeze())
if (bc_row != pp_row) and (bc_col != pp_col):
# If not diagonal, two possible locations that are adjacent
# to the pour point
_col1, _row1 = str(bc_col), str(pp_row)
_col2, _row2 = str(pp_col), str(bc_row)
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(grid,
layer=1,
where='(row == ' + _row1 +
') AND (col ==' + _col1 + ')',
columns='basinmask')
_ismask_1 = int(_ismask_1['values'].values()[0][0])
_ismask_2 = gscript.vector_db_select(grid,
layer=1,
where='(row == ' + _row2 +
') AND (col ==' + _col2 + ')',
columns='basinmask')
_ismask_2 = int(_ismask_2['values'].values()[0][0])
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
'All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu')
# Otherwise, those that keep those that are not covered by basin
# mask and set ...
# ... wait, do we want the point that touches as few interior
# cells as possible?
# maybe just try setting both and seeing what happens for now!
else:
# Get dx and dy
dx = gscript.region()['ewres']
dy = gscript.region()['nsres']
# Build tool to handle multiple b.c. cells?
bcvect = vector.Vector(bc_cell)
bcvect.open('rw')
_cat_i = 2
if not _ismask_1:
# _x should always be bc_x, but writing generalized code
_x = bc_x + dx * (int(_col1) - bc_col) # col 1 at w edge
_y = bc_y - dy * (int(_row1) - bc_row) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row1, _col1, _x, _y),
)
bcvect.table.conn.commit()
_cat_i += 1
if not _ismask_2:
# _y should always be bc_y, but writing generalized code
_x = bc_x + dx * (int(_col2) - bc_col) # col 1 at w edge
_y = bc_y - dy * (int(_row2) - bc_row) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row2, _col2, _x, _y),
)
bcvect.table.conn.commit()
# Build database table and vector geometry
bcvect.build()
bcvect.close()
g.region(n=reg['n'],
s=reg['s'],
w=reg['w'],
e=reg['e'],
nsres=reg['nsres'],
ewres=reg['ewres'])
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():
"""
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:
gscript.fatal("You must set both raster input and output, or neither.")
"""
# Fatal if bc_cell set but mask and grid are false
if bc_cell != "":
if (mask == "") or (pp == ""):
gscript.fatal(
"Mask and pour point must be set to define b.c. cell")
# Create grid -- overlaps DEM, three cells of padding
g.region(raster=raster_input, ewres=dx, nsres=dy)
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"] - 3.0 * 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"] + 3.0 * 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"] - 3.0 * 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"] + 3.0 * 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 double precision", 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("tmp" + " = " + mask + " > 0", overwrite=True, quiet=True)
g.rename(raster=("tmp", mask), overwrite=True, quiet=True)
r.null(map=mask, null=0, quiet=True)
# Add mask location (1 vs 0) in the MODFLOW grid
v.db_addcolumn(map=grid,
columns="basinmask double precision",
quiet=True)
v.what_rast(map=grid, type="centroid", raster=mask, column="basinmask")
"""
# 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
# Requires pp (always) and mask (sometimes)
# Dependency set above w/ gscript.fatal
# g.region(raster='DEM')
# dx = gscript.region()['ewres']
# dy = gscript.region()['nsres']
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("tmp2 = if(tmp==2,1,null()) * " + raster_input,
overwrite=True)
# r.mapcalc('tmp = if(isnull('+raster_input+',0,(tmp == 2)))', overwrite=True)
# g.region(rast='tmp')
# r.null(map=raster_input,
# g.region(raster=raster_input)
# r.resample(input=raster_input, output='tmp3', overwrite=True)
r.resamp_stats(input=raster_input,
output="tmp3",
method="minimum",
overwrite=True)
r.drain(input="tmp3", start_points=pp, output="tmp", overwrite=True)
# 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='tmp2', output='tmp3', overwrite=True)
# g.rename(raster=('tmp3','tmp2'), overwrite=True, quiet=True)
r.mapcalc("tmp3 = tmp2 * tmp", overwrite=True, quiet=True)
g.rename(raster=("tmp3", "tmp"), overwrite=True, quiet=True)
# r.null(map='tmp', setnull=0) # Not necessary: center point removed above
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",
"x double precision",
"y double precision",
),
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)
v.to_db(map=bc_cell, option="coor", columns=("x,y"))
# Of the candidates, the pour point is the closest one
# v.db_addcolumn(map=bc_cell, columns=('dist_to_pp double precision'), quiet=True)
# v.distance(from_=bc_cell, to=pp, upload='dist', column='dist_to_pp')
# Find out if this is diagonal: finite difference works only N-S, W-E
colNames = np.array(gscript.vector_db_select(pp, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(pp, layer=1)["values"].values())
pp_row = colValues[:, colNames == "row"].astype(int).squeeze()
pp_col = colValues[:, colNames == "col"].astype(int).squeeze()
colNames = np.array(
gscript.vector_db_select(bc_cell, layer=1)["columns"])
colValues = np.array(
gscript.vector_db_select(bc_cell, layer=1)["values"].values())
bc_row = colValues[:, colNames == "row"].astype(int).squeeze()
bc_col = colValues[:, colNames == "col"].astype(int).squeeze()
# Also get x and y while we are at it: may be needed later
bc_x = colValues[:, colNames == "x"].astype(float).squeeze()
bc_y = colValues[:, colNames == "y"].astype(float).squeeze()
if (bc_row != pp_row).all() and (bc_col != pp_col).all():
if bc_row.ndim > 0:
if len(bc_row) > 1:
for i in range(len(bc_row)):
"""
UNTESTED!!!!
And probably unimportant -- having 2 cells with river
going through them is most likely going to happen with
two adjacent cells -- so a side and a corner
"""
_col1, _row1 = str(bc_col[i]), str(pp_row[i])
_col2, _row2 = str(pp_col[i]), str(bc_row[i])
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row1 + ") AND (col ==" +
_col1 + ")",
columns="basinmask",
)
_ismask_1 = int(_ismask_1["values"].values()[0][0])
_ismask_2 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row2 + ") AND (col ==" +
_col2 + ")",
columns="basinmask",
)
_ismask_2 = int(_ismask_2["values"].values()[0][0])
# check if either of these is the other point
"""
NOT DOING THIS YET -- HAVEN'T THOUGHT THROUGH IF
ACTUALLY NECESSARY. (And this is an edge case anyway)
"""
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
"All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu"
)
# If not diagonal, two possible locations that are adjacent
# to the pour point
_col1, _row1 = str(bc_col), str(pp_row)
_col2, _row2 = str(pp_col), str(bc_row)
# Check if either of these is covered by the basin mask
_ismask_1 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row1 + ") AND (col ==" + _col1 + ")",
columns="basinmask",
)
_ismask_1 = int(_ismask_1["values"].values()[0][0])
_ismask_2 = gscript.vector_db_select(
grid,
layer=1,
where="(row == " + _row2 + ") AND (col ==" + _col2 + ")",
columns="basinmask",
)
_ismask_2 = int(_ismask_2["values"].values()[0][0])
# If both covered by mask, error
if _ismask_1 and _ismask_2:
gscript.fatal(
"All possible b.c. cells covered by basin mask.\n\
Contact the developer: awickert (at) umn(.)edu")
# Otherwise, those that keep those that are not covered by basin
# mask and set ...
# ... wait, do we want the point that touches as few interior
# cells as possible?
# maybe just try setting both and seeing what happens for now!
else:
# Get dx and dy
# dx = gscript.region()['ewres']
# dy = gscript.region()['nsres']
# Build tool to handle multiple b.c. cells?
bcvect = vector.Vector(bc_cell)
bcvect.open("rw")
_cat_i = 2
if _ismask_1 != 0:
# _x should always be bc_x, but writing generalized code
_x = bc_x + float(dx) * (int(_col1) - bc_col
) # col 1 at w edge
_y = bc_y - float(dy) * (int(_row1) - bc_row
) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row1, _col1, _x, _y),
)
bcvect.table.conn.commit()
_cat_i += 1
if _ismask_2 != 0:
# _y should always be bc_y, but writing generalized code
_x = bc_x + float(dx) * (int(_col2) - bc_col
) # col 1 at w edge
_y = bc_y - float(dy) * (int(_row2) - bc_row
) # row 1 at n edge
point0 = Point(_x, _y)
bcvect.write(
point0,
cat=_cat_i,
attrs=(None, _row2, _col2, _x, _y),
)
bcvect.table.conn.commit()
# Build database table and vector geometry
bcvect.build()
bcvect.close()
g.region(
n=reg["n"],
s=reg["s"],
w=reg["w"],
e=reg["e"],
nsres=reg["nsres"],
ewres=reg["ewres"],
)
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]) )
# There is no reason that I have to upload both of these to the attribute
# table, but why not?
vname = 'reaches'
vect = VectorTopo(vname)
vect.open('rw')
cur = vect.table.conn.cursor()
cur.executemany("update "+vname+" set zr1=? where cat=?", zr1_cats)
cur.executemany("update "+vname+" set zr2=? where cat=?", zr2_cats)
cur.executemany("update segments set OUTSEG=? where tostream=?", nseg_cats)
vect.table.conn.commit()
vect.close()
# Use these to create slope
Smin = '0.001' # MINIMUM 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 <= 0')
# 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#
h_stream='1' # meter
v.db_addcolumn(map='reaches', columns='z_topo_mean double precision')
v.what_vect(map='reaches', query_map='srtm_local_filled_grid', column='z_topo_mean', query_column='z')
v.db_update(map='reaches', column='STRTOP', value='z_topo_mean -'+h_stream)