def __init__(self, points_xy=None, align_to_region=True, xmin=None,
xmax=None, ymin=None, ymax=None):
if points_xy is not None:
points = np.array(points_xy)
self.xmin = np.min(points[:,0])
self.xmax = np.max(points[:,0])
self.ymin = np.min(points[:,1])
self.ymax = np.max(points[:,1])
else:
self.xmin = xmin
self.ymin = ymin
self.xmax = xmax
self.ymax = ymax
if align_to_region is not None:
reg = region.Region()
self.xmin = np.floor( (self.xmin - reg.get_bbox().west) /
reg.ewres ) * \
reg.ewres + reg.get_bbox().west
self.ymin = np.floor( (self.ymin - reg.get_bbox().south ) /
reg.nsres ) * \
reg.nsres + reg.get_bbox().south
self.xmax = np.ceil( (self.xmax - reg.get_bbox().east ) /
reg.ewres ) * \
reg.ewres + reg.get_bbox().east
self.ymax = np.ceil( (self.ymax - reg.get_bbox().north ) /
reg.nsres ) * \
reg.nsres + reg.get_bbox().north
self.bbox = Bbox()
self.bbox.north = self.ymax
self.bbox.south = self.ymin
self.bbox.west = self.xmin
self.bbox.east = self.xmax
def __init__(self, reg=region.Region(), default=False, prec=4, meta=None):
self.c_region = ctypes.pointer(libgis.Cell_head())
self.corners = self.llcorner(reg)
self.prec = prec
self.meta = meta
if default:
self.get_default()
else:
self.get_current()
def main(options, flags):
# Get the options
points = options["points"]
strds = options["strds"]
output = options["output"]
where = options["where"]
order = options["order"]
column = options["column"]
separator = options["separator"]
coordinates = options["coordinates"]
# Setup separator
if separator == "pipe":
separator = "|"
if separator == "comma":
separator = ","
if separator == "space":
separator = " "
if separator == "tab":
separator = "\t"
if separator == "newline":
separator = "\n"
use_cats = False
write_header = flags["n"]
use_raster_region = flags["r"]
overwrite = gscript.overwrite()
if points and coordinates:
gscript.fatal(_("points and coordinates are mutually exclusive"))
if not points and not coordinates:
gscript.fatal(_("You must specify points or coordinates"))
# Make sure the temporal database exists
tgis.init()
# We need a database interface
dbif = tgis.SQLDatabaseInterfaceConnection()
dbif.connect()
sp = tgis.open_old_stds(strds, "strds", dbif)
maps = sp.get_registered_maps_as_objects(where=where, order=order,
dbif=dbif)
dbif.close()
if not maps:
gscript.fatal(_("Space time raster dataset <%s> is empty") % sp.get_id())
# The list of sample points
p_list = []
if not coordinates:
# Check if the chosen header column is in the vector map
vname = points
vmapset= ""
if "@" in points:
vname, vmapset = points.split("@")
v = pyvect.VectorTopo(vname, vmapset)
v.open("r")
col_index = 0
if v.exist() is False:
gscript.fatal(_("Vector map <%s> does not exist" %(points)))
if not v.table:
use_cats = True
gscript.warning(_("Vector map <%s> does not have an attribute table, using cats as header column." %(points)))
if v.table and column not in v.table.columns:
gscript.fatal(_("Vector map <%s> has no column named %s" %(points, column)))
if use_cats is False:
col_index = list(v.table.columns.names()).index(column)
# Create the point list
for line in v:
if line.gtype == libvect.GV_POINT:
if use_cats is False:
p = SamplePoint(line.x, line.y, line.cat, line.attrs.values()[col_index])
elif use_cats is True:
p = SamplePoint(line.x, line.y, line.cat)
p_list.append(p)
v.close()
else:
# Convert the coordinates into sample points
coord_list = coordinates.split(",")
use_cats = True
count = 0
cat = 1
while count < len(coord_list):
x = coord_list[count]
count += 1
y = coord_list[count]
count += 1
p = SamplePoint(float(x), float(y), cat)
p_list.append(p)
cat += 1
if output:
out_file = open(output, "w")
else:
out_file = sys.stdout
# Write the header
if write_header:
out_file.write("start_time")
out_file.write(separator)
out_file.write("end_time")
out_file.write(separator)
count = 0
for p in p_list:
count += 1
if use_cats is True:
out_file.write(str(p.cat))
else:
out_file.write(str(p.column))
if count != len(p_list):
out_file.write(separator)
out_file.write("\n")
# Sorting the points by y-coordinate to make use of the single row cache and read direction
sorted_p_list = sorted(p_list, key=SamplePointComparisonY)
# Sample the raster maps
num = 0
for map in maps:
num += 1
sys.stderr.write("Sample map <%s> number %i out of %i\n" %(map.get_name(), num, len(maps)))
start, end = map.get_temporal_extent_as_tuple()
out_file.write(str(start))
out_file.write(separator)
if not end:
out_file.write(str(start))
else:
out_file.write(str(end))
out_file.write(separator)
r = pyrast.RasterRow(map.get_name(), map.get_mapset())
if r.exist() is False:
gscript.fatal(_("Raster map <%s> does not exist" %(map.get_id())))
region = None
if use_raster_region is True:
r.set_region_from_rast()
region = pyregion.Region()
region.from_rast(map.get_name())
# Open the raster layer after the region settings
r.open("r")
# Sample the raster maps with the sorted points
for p in sorted_p_list:
p.value = r.get_value(point=p, region=region)
# Write the point values from the original list
count = 0
for p in p_list:
count += 1
out_file.write(str(p.value))
if count != len(p_list):
out_file.write(separator)
out_file.write("\n")
r.close()
out_file.close()
data.open('r') # Open this object for reading
segments = []
for cat in selected_cats:
points_with_cat = data.cat(cat_id=cat, vtype='lines')[0]
subcoords = []
for point in points_with_cat:
subcoords.append([point.x, point.y])
segments.append(rn.Segment(_id=cat, to_ids=tostream[cats == cat]))
segments[-1].set_EastingNorthing(ENarray=subcoords)
segments[-1].calc_x_from_EastingNorthing()
data.close()
net = rn.Network(segments)
bbox = BoundingBox(points_xy=net.segments_xy_flattened())
reg = region.Region()
reg.set_bbox(bbox.bbox)
reg.write()
DEM = garray.array()
DEM.read(options['elevation'])
DEM = np.flipud(DEM)
# nearest or linear?
x = np.arange(reg.west + reg.ewres / 2., reg.east, reg.ewres)
y = np.arange(reg.south + reg.nsres / 2., reg.north, reg.nsres)
itp = RegularGridInterpolator((x, y), DEM.transpose(), method='nearest')
for segment in net.segment_list:
segment.set_z(itp(segment.EastingNorthing))
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.
"""
# Parsing inside function
_cat = int(options['cat'])
overwrite_flag = gscript.overwrite()
elevation = options['elevation']
if elevation == '': elevation = None
slope = options['slope']
if slope == '': slope = None
accumulation = options['accumulation']
if accumulation == '': accumulation = None
direction = options['direction']
if direction == '': direction = None
streams = options['streams']
if streams == '': streams = None
outstream = options['outstream']
if outstream == '': outstream = None
outfile = options['outfile']
if outfile == '': outfile = None
# !!!!!!!!!!!!!!!!!
# ADD SWITCHES TO INDIVIDUALLY SMOOTH SLOPE, ACCUM, ETC.
# !!!!!!!!!!!!!!!!!
try:
window = float(options['window'])
except:
window = None
try:
dx_target = float(options['dx_target'])
except:
dx_target = None
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(streams)['columns'])
colValues = np.array(vector_db_select(streams)['values'].values())
warnings.warn('tostream is not generalized')
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 = _cat
selected_cats.append(segment)
# Get all cats in network
data = vector.VectorTopo(streams) # Create a VectorTopo object
data.open('r') # Open this object for reading
if direction == 'downstream':
gscript.message("Extracting drainage pathway...",)
# Get 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
gscript.message("Done.")
elif direction == 'upstream':
gscript.message("Extracting drainage network...",)
# GENERALIZE COLUMN NAME!!!!!!!!
tostream_col = np.where(np.array(data.table.columns.names())
== 'tostream')[0][0]
terminalCats = [_cat]
terminal_x_values = [0]
netcats = []
net_tocats = []
while len(terminalCats) > 0:
for cat in terminalCats:
netcats.append(cat)
# ALSO UNADVISABLE NAME -- NEED TO GET TOSTREAM, GENERALIZED
#print data.table_to_dict()
colnum = np.where( np.array(data.table.columns.names())
== 'tostream')[0][0]
net_tocats.append(data.table_to_dict()[cat][colnum])
oldcats = terminalCats
terminalCats = []
for cat in oldcats:
terminalCats += list(cats[tostream == cat])
#data.close()
netcats = np.array(netcats)
net_tocats = np.array(net_tocats)
selected_cats = netcats
gscript.message("Done.")
segments = []
for cat in selected_cats:
points_with_cat = data.cat(cat_id=cat, vtype='lines')[0]
subcoords = []
for point in points_with_cat:
subcoords.append([point.x, point.y])
segments.append( rn.Segment(_id=cat, to_ids=tostream[cats == cat]) )
segments[-1].set_EastingNorthing(ENarray=subcoords)
segments[-1].calc_x_from_EastingNorthing()
# x grid spacing
#print segments[-1].Easting[-1], segments[-1].Northing[-1]
#print segments[-1].EastingNorthing[-1]
#print ""
if dx_target is not None:
dx_target = float(dx_target)
segments[-1].set_target_dx_downstream(dx_target)
segments[-1].densify_x_E_N()
data.close()
net = rn.Network(segments)
bbox = BoundingBox(points_xy=net.segments_xy_flattened())
reg_to_revert = region.Region()
reg = region.Region() # to limit region for computational efficiency
reg.set_bbox(bbox.bbox)
reg.write()
# Network extraction
if outstream:
selected_cats_str = list(np.array(selected_cats).astype(str))
selected_cats_csv = ','.join(selected_cats_str)
v.extract( input=streams, output=outstream, \
cats=selected_cats_csv, overwrite=overwrite_flag )
# All coordinates
coords = net.segments_xy_flattened()
#x_downstream =
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# UPDATE !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"""
##### FIND RIGHT SPOT TO ADD CLASS STUFF HERE/BELOW ####
# Extract x points in network
data = vector.VectorTopo(streams) # Create a VectorTopo object
data.open('r') # Open this object for reading
coords = []
_i = 0
for i in range(len(data)):
if type(data.read(i+1)) is 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./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)**.5)))
x_downstream = x_downstream_0.copy()
data.close()
"""
# TEMPORARY!!!!
#x_downstream = get_xEN()
#x_downstream_0 = x_downstream[0]
# Analysis
# Downstream distances -- 0 at mouth
net.compute_x_in_network()
# Elevation
if elevation:
gscript.message("Elevation")
_include_z = True
# Load DEM
griddata = garray.array()
griddata.read(elevation)
griddata = np.flipud(griddata)
# Interpolate: nearest or linear?
x = np.arange(reg.west + reg.ewres/2., reg.east, reg.ewres)
y = np.arange(reg.south + reg.nsres/2., reg.north, reg.nsres)
itp = RegularGridInterpolator( (x, y), griddata.transpose(),
method='nearest')
_i = 0
_lasti = 0
_nexti = 0
for segment in net.segment_list:
try:
segment.set_z( itp(segment.EastingNorthing) )
except:
print segment.EastingNorthing
print np.vstack((segment.Easting_original, segment.Northing_original)).transpose()
sys.exit()
if _i > _nexti:
gscript.core.percent( _i, len(net.segment_list), np.floor(_i - _lasti))
_nexti = float(_nexti) + len(net.segment_list)/10.
if _nexti > len(net.segment_list):
_nexti = len(net.segment_list) - 1
_lasti = _i
_i += 1
gscript.core.percent(1, 1, 1)
del griddata
#warnings.warn('Need to handle window in network')
#gscript.core.percent(1, 1, 1)
else:
_include_z = False
# Slope
if slope:
gscript.message("Slope")
_include_S = True
_slope = RasterRow(slope)
_slope.open('r')
_i = 0
_lasti = 0
_nexti = 0
for segment in net.segment_list:
sen = segment.EastingNorthing # all E,N
S = []
for row in sen:
#try:
S.append(_slope.get_value(Point(row[0], row[1])))
#except:
# print "ERROR"
if _i > _nexti:
gscript.core.percent(_i, len(coords), np.floor(_i - _lasti))
_nexti = float(_nexti) + len(coords)/10.
if _nexti > len(coords):
_nexti = len(coords) - 1
_lasti = _i
_i += 1
# MAKE SETTER FOR THIS!!!!
segment.channel_slope = np.array(S)
if window is not None:
pass
#net.smooth_window()
#_x_downstream, _S = moving_average(x_downstream_0, S, window)
_slope.close()
S = np.array(S)
S_0 = S.copy()
gscript.core.percent(1, 1, 1)
else:
_include_S = False
# Accumulation / drainage area
if accumulation:
gscript.message("Accumulation")
_include_A = True
accumulation = RasterRow(accumulation)
accumulation.open('r')
_i = 0
_lasti = 0
_nexti = 0
for segment in net.segment_list:
A = []
sen = segment.EastingNorthing # all E,N
for row in sen:
A.append(accumulation.get_value(Point(row[0], row[1]))
* accum_mult)
if _i > _nexti:
gscript.core.percent(_i, len(coords), np.floor(_i - _lasti))
_nexti = float(_nexti) + len(coords)/10.
if _nexti > len(coords):
_nexti = len(coords) - 1
_lasti = _i
_i += 1
# MAKE SETTER FOR THIS!!!!
segment.channel_flow_accumulation = np.array(A)
accumulation.close()
A = np.array(A)
A_0 = A.copy()
"""
if window is not None:
_x_downstream, A = moving_average(x_downstream_0, A, window)
"""
gscript.core.percent(1, 1, 1)
else:
_include_A = False
# Revert to original region
reg_to_revert
# Smoothing
if window is not None:
net.smooth_window(window)
# Plotting
if 'LongProfile' in plots:
plt.figure()
if window:
for segment in net.segment_list:
plt.plot(segment.x/1000., segment.z_smoothed, 'k-', linewidth=2)
else:
for segment in net.segment_list:
plt.plot(segment.x/1000., segment.z, 'k-', linewidth=2)
#plt.plot(x_downstream/1000., z, 'k-', linewidth=2)
plt.xlabel('Distance from mouth [km]', fontsize=16)
plt.ylabel('Elevation [m]', fontsize=16)
plt.tight_layout()
if 'SlopeAccum' in plots:
plt.figure()
if window:
for segment in net.segment_list:
_y_points = segment.channel_slope_smoothed[
segment.channel_flow_accumulation_smoothed > 0
]
_x_points = segment.channel_flow_accumulation_smoothed[
segment.channel_flow_accumulation_smoothed > 0
]
plt.loglog(_x_points, _y_points, 'k.', alpha=.5)
else:
for segment in net.segment_list:
_y_points = segment.channel_slope[
segment.channel_flow_accumulation > 0
]
_x_points = segment.channel_flow_accumulation[
segment.channel_flow_accumulation > 0
]
plt.loglog(_x_points, _y_points, 'k.', alpha=.5)
plt.xlabel(accum_label, fontsize=16)
plt.ylabel('Slope [$-$]', fontsize=16)
plt.tight_layout()
if 'SlopeDistance' in plots:
plt.figure()
if window:
for segment in net.segment_list:
plt.plot(segment.x/1000., segment.channel_slope_smoothed,
'k-', linewidth=2)
else:
for segment in net.segment_list:
plt.plot(segment.x/1000., segment.channel_slope,
'k-', linewidth=2)
plt.xlabel('Distance downstream [km]', fontsize=16)
plt.ylabel('Slope [$-$]', fontsize=20)
plt.tight_layout()
if 'AccumDistance' in plots:
plt.figure()
for segment in net.segment_list:
_x_points = segment.x[segment.channel_flow_accumulation > 0]
_y_points = segment.channel_flow_accumulation[
segment.channel_flow_accumulation > 0
]
plt.plot(_x_points/1000., _y_points, 'k.', alpha=.5)
plt.xlabel('Distance downstream [km]', fontsize=16)
plt.ylabel(accum_label, fontsize=16)
plt.tight_layout()
plt.show()
# Saving data -- will need to update for more complex data structures!
if outfile:
net.compute_profile_from_starting_segment()
_outfile = np.vstack((net.long_profile_header, net.long_profile_output))
np.savetxt(outfile, _outfile, '%s')
else:
pass
#print net.accum_from_headwaters[1] - net.slope_from_headwaters[1]
"""
for segment in net.segment_list:
print segment.channel_flow_accumulation_smoothed
print segment.channel_slope_smoothed
print segment.channel_flow_accumulation_smoothed - \
segment.channel_slope_smoothed
"""
"""