def test_contains_point(self):
"""Test contain_point method"""
area = Area(v_id=1, c_mapinfo=self.c_mapinfo)
p = Point(0.5, 0.5)
bbox = Bbox(4.0, 0.0, 4.0, 0.0)
self.assertTrue(area.contains_point(p, bbox))
self.assertTrue(area.contains_point(p))
def get_bbox(reg, row, col, width, height, overlap):
"""Return a Bbox
:param reg: a Region object to split
:type reg: Region object
:param row: the number of row
:type row: int
:param col: the number of row
:type col: int
:param width: the width of tiles
:type width: int
:param height: the width of tiles
:type height: int
:param overlap: the value of overlap between tiles
:type overlap: int
"""
north = reg.north - (row * height - overlap) * reg.nsres
south = reg.north - ((row + 1) * height + overlap) * reg.nsres
east = reg.west + ((col + 1) * width + overlap) * reg.ewres
west = reg.west + (col * width - overlap) * reg.ewres
return Bbox(
north=north if north <= reg.north else reg.north,
south=south if south >= reg.south else reg.south,
east=east if east <= reg.east else reg.east,
west=west if west >= reg.west else reg.west,
)
def bbox(self):
"""Return the BBox of the vecor map
"""
bbox = Bbox()
if libvect.Vect_get_map_box(self.c_mapinfo, bbox.c_bbox) == 0:
raise GrassError("I can not find the Bbox.")
return bbox
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 select_by_bbox(self, bbox):
"""Return the BBox of the vector map
"""
# TODO replace with bbox if bbox else Bbox() ??
bbox = Bbox()
if libvect.Vect_get_map_box(self.c_mapinfo, bbox.c_bbox) == 0:
raise GrassError("I can not find the Bbox.")
return bbox
def update_lines(line, alist, cur=None, sql=None):
"""Update lines using only the boundary"""
to_up = []
bbox = Bbox()
aline = Line()
for area in alist:
bbox = area.bbox(bbox)
if (intersects(area.get_points(aline), line)) or (area.contain_pnt(
line[0], bbox)):
to_up.append((line.cat, area.cat))
if (cur is not None) and (sql is not None):
cur.executemany(sql, to_up)
return to_up
def get_bbox(self):
"""Return a Bbox object with the extension of the region. ::
>>> reg = Region()
>>> reg.get_bbox()
Bbox(228500.0, 215000.0, 645000.0, 630000.0)
..
"""
from grass.pygrass.vector.basic import Bbox
return Bbox(north=self.north, south=self.south,
east=self.east, west=self.west,
top=self.top, bottom=self.bottom)
def _get_vector_features_as_wkb_list(lock, conn, data):
"""Return vector layer features as wkb list
supported feature types:
point, centroid, line, boundary, area
:param lock: A multiprocessing.Lock instance
:param conn: A multiprocessing.Pipe instance used to send True or False
:param data: The list of data entries [function_id,name,mapset,extent,
feature_type, field]
"""
wkb_list = None
try:
name = data[1]
mapset = data[2]
extent = data[3]
feature_type = data[4]
field = data[5]
bbox = None
mapset = utils.get_mapset_vector(name, mapset)
if not mapset:
raise ValueError("Unable to find vector map <%s>" % (name))
layer = VectorTopo(name, mapset)
if layer.exist() is True:
if extent is not None:
bbox = Bbox(
north=extent["north"],
south=extent["south"],
east=extent["east"],
west=extent["west"],
)
layer.open("r")
if feature_type.lower() == "area":
wkb_list = layer.areas_to_wkb_list(bbox=bbox, field=field)
else:
wkb_list = layer.features_to_wkb_list(
bbox=bbox, feature_type=feature_type, field=field)
layer.close()
finally:
# Send even if an exception was raised.
conn.send(wkb_list)
def main():
"""Do the main processing
"""
# Parse input options:
patch_map = options['input']
patches = patch_map.split('@')[0]
patches_mapset = patch_map.split('@')[1] if len(
patch_map.split('@')) > 1 else None
pop_proxy = options['pop_proxy']
layer = options['layer']
costs = options['costs']
cutoff = float(options['cutoff'])
border_dist = int(options['border_dist'])
conefor_dir = options['conefor_dir']
memory = int(options['memory'])
# Parse output options:
prefix = options['prefix']
edge_map = '{}_edges'.format(prefix)
vertex_map = '{}_vertices'.format(prefix)
shortest_paths = '{}_shortest_paths'.format(prefix)
# Parse flags:
p_flag = flags['p']
t_flag = flags['t']
r_flag = flags['r']
dist_flags = 'kn' if flags['k'] else 'n'
lin_cat = 1
zero_dist = None
folder = grass.tempdir()
if not os.path.exists(folder):
os.makedirs(folder)
# Setup counter for progress message
counter = 0
# Check if location is lat/lon (only in lat/lon geodesic distance
# measuring is supported)
if grass.locn_is_latlong():
grass.verbose("Location is lat/lon: Geodesic distance \
measure is used")
# Check if prefix is legal GRASS name
if not grass.legal_name(prefix):
grass.fatal('{} is not a legal name for GRASS \
maps.'.format(prefix))
if prefix[0].isdigit():
grass.fatal('Tables names starting with a digit are not SQL \
compliant.'.format(prefix))
# Check if output maps not already exists or could be overwritten
for output in [edge_map, vertex_map, shortest_paths]:
if grass.db.db_table_exist(output) and not grass.overwrite():
grass.fatal('Vector map <{}> already exists'.format(output))
# Check if input has required attributes
in_db_connection = grass.vector.vector_db(patch_map)
if not int(layer) in in_db_connection.keys():
grass.fatal('No attribute table connected vector map {} at \
layer {}.'.format(patches, layer))
#Check if cat column exists
pcols = grass.vector.vector_columns(patch_map, layer=layer)
#Check if cat column exists
if not 'cat' in pcols.keys():
grass.fatal('Cannot find the reqired column cat in vector map \
{}.'.format(patches))
#Check if pop_proxy column exists
if not pop_proxy in pcols.keys():
grass.fatal('Cannot find column {} in vector map \
{}'.format(pop_proxy, patches))
#Check if pop_proxy column is numeric type
if not pcols[pop_proxy]['type'] in ['INTEGER', 'REAL', 'DOUBLE PRECISION']:
grass.fatal('Column {} is of type {}. Only numeric types \
(integer or double precision) \
allowed!'.format(pop_proxy, pcols[pop_proxy]['type']))
#Check if pop_proxy column does not contain values <= 0
pop_vals = np.fromstring(grass.read_command('v.db.select',
flags='c',
map=patches,
columns=pop_proxy,
nv=-9999).rstrip('\n'),
dtype=float,
sep='\n')
if np.min(pop_vals) <= 0:
grass.fatal('Column {} contains values <= 0 or NULL. Neither \
values <= 0 nor NULL allowed!}'.format(pop_proxy))
##############################################
# Use pygrass region instead of grass.parse_command !?!
start_reg = grass.parse_command('g.region', flags='ugp')
max_n = start_reg['n']
min_s = start_reg['s']
max_e = start_reg['e']
min_w = start_reg['w']
# cost_nsres = reg['nsres']
# cost_ewres = reg['ewres']
# Rasterize patches
# http://www.gdal.org/gdal_tutorial.html
# http://geoinformaticstutorial.blogspot.no/2012/11/convert-
# shapefile-to-raster-with-gdal.html
if t_flag:
# Rasterize patches with "all-touched" mode using GDAL
# Read region-settings (not needed canuse max_n, min_s, max_e,
# min_w nsres, ewres...
prast = os.path.join(folder, 'patches_rast.tif')
# Check if GDAL-GRASS plugin is installed
if ogr.GetDriverByName('GRASS'):
#With GDAL-GRASS plugin
#Locate file for patch vector map
pfile = grass.parse_command('g.findfile',
element='vector',
file=patches,
mapset=patches_mapset)['file']
pfile = os.path.join(pfile, 'head')
else:
# Without GDAL-GRASS-plugin
grass.warning("Cannot find GDAL-GRASS plugin. Consider \
installing it in order to save time for \
all-touched rasterisation")
pfile = os.path.join(folder, 'patches_vect.gpkg')
# Export patch vector map to temp-file in a GDAL-readable
# format (shp)
grass.run_command('v.out.ogr',
flags='m',
quiet=True,
input=patch_map,
type='area',
layer=layer,
output=pfile,
lco='GEOMETRY_NAME=geom')
# Rasterize vector map with all-touched option
os.system('gdal_rasterize -l {} -at -tr {} {} \
-te {} {} {} {} -ot Uint32 -a cat \
{} {} -q'.format(patches, start_reg['ewres'],
start_reg['nsres'], start_reg['w'],
start_reg['s'], start_reg['e'],
start_reg['n'], pfile, prast))
if not ogr.GetDriverByName('GRASS'):
# Remove vector temp-file
os.remove(os.path.join(folder, 'patches_vect.gpkg'))
# Import rasterized patches
grass.run_command('r.external',
flags='o',
quiet=True,
input=prast,
output='{}_patches_pol'.format(TMP_PREFIX))
else:
# Simple rasterisation (only area)
# in G 7.6 also with support for 'centroid'
if float(grass.version()['version'][:3]) >= 7.6:
conv_types = ['area', 'centroid']
else:
conv_types = ['area']
grass.run_command('v.to.rast',
quiet=True,
input=patches,
use='cat',
type=conv_types,
output='{}_patches_pol'.format(TMP_PREFIX))
# Extract boundaries from patch raster map
grass.run_command('r.mapcalc',
expression='{p}_patches_boundary=if(\
{p}_patches_pol,\
if((\
(isnull({p}_patches_pol[-1,0])||| \
{p}_patches_pol[-1,0]!={p}_patches_pol)||| \
(isnull({p}_patches_pol[0,1])||| \
{p}_patches_pol[0,1]!={p}_patches_pol)||| \
(isnull({p}_patches_pol[1,0])||| \
{p}_patches_pol[1,0]!={p}_patches_pol)||| \
(isnull({p}_patches_pol[0,-1])||| \
{p}_patches_pol[0,-1]!={p}_patches_pol)), \
{p}_patches_pol,null()), null())'.format(p=TMP_PREFIX),
quiet=True)
rasterized_cats = grass.read_command(
'r.category',
separator='newline',
map='{p}_patches_boundary'.format(p=TMP_PREFIX)).replace(
'\t', '').strip('\n')
rasterized_cats = list(
map(int, set([x for x in rasterized_cats.split('\n') if x != ''])))
#Init output vector maps if they are requested by user
network = VectorTopo(edge_map)
network_columns = [(u'cat', 'INTEGER PRIMARY KEY'), (u'from_p', 'INTEGER'),
(u'to_p', 'INTEGER'), (u'min_dist', 'DOUBLE PRECISION'),
(u'dist', 'DOUBLE PRECISION'),
(u'max_dist', 'DOUBLE PRECISION')]
network.open('w', tab_name=edge_map, tab_cols=network_columns)
vertex = VectorTopo(vertex_map)
vertex_columns = [
(u'cat', 'INTEGER PRIMARY KEY'),
(pop_proxy, 'DOUBLE PRECISION'),
]
vertex.open('w', tab_name=vertex_map, tab_cols=vertex_columns)
if p_flag:
# Init cost paths file for start-patch
grass.run_command('v.edit',
quiet=True,
map=shortest_paths,
tool='create')
grass.run_command('v.db.addtable',
quiet=True,
map=shortest_paths,
columns="cat integer,\
from_p integer,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision")
start_region_bbox = Bbox(north=float(max_n),
south=float(min_s),
east=float(max_e),
west=float(min_w))
vpatches = VectorTopo(patches, mapset=patches_mapset)
vpatches.open('r', layer=int(layer))
###Loop through patches
vpatch_ids = np.array(vpatches.features_to_wkb_list(
feature_type="centroid", bbox=start_region_bbox),
dtype=[('vid', 'uint32'), ('cat', 'uint32'),
('geom', '|S10')])
cats = set(vpatch_ids['cat'])
n_cats = len(cats)
if n_cats < len(vpatch_ids['cat']):
grass.verbose('At least one MultiPolygon found in patch map.\n \
Using average coordinates of the centroids for \
visual representation of the patch.')
for cat in cats:
if cat not in rasterized_cats:
grass.warning('Patch {} has not been rasterized and will \
therefore not be treated as part of the \
network. Consider using t-flag or change \
resolution.'.format(cat))
continue
grass.verbose("Calculating connectivity-distances for patch \
number {}".format(cat))
# Filter
from_vpatch = vpatch_ids[vpatch_ids['cat'] == cat]
# Get patch ID
if from_vpatch['vid'].size == 1:
from_centroid = Centroid(v_id=int(from_vpatch['vid']),
c_mapinfo=vpatches.c_mapinfo)
from_x = from_centroid.x
from_y = from_centroid.y
# Get centroid
if not from_centroid:
continue
else:
xcoords = []
ycoords = []
for f_p in from_vpatch['vid']:
from_centroid = Centroid(v_id=int(f_p),
c_mapinfo=vpatches.c_mapinfo)
xcoords.append(from_centroid.x)
ycoords.append(from_centroid.y)
# Get centroid
if not from_centroid:
continue
from_x = np.average(xcoords)
from_y = np.average(ycoords)
# Get BoundingBox
from_bbox = grass.parse_command('v.db.select',
map=patch_map,
flags='r',
where='cat={}'.format(cat))
attr_filter = vpatches.table.filters.select(pop_proxy)
attr_filter = attr_filter.where("cat={}".format(cat))
proxy_val = vpatches.table.execute().fetchone()
# Prepare start patch
start_patch = '{}_patch_{}'.format(TMP_PREFIX, cat)
reclass_rule = grass.encode('{} = 1\n* = NULL'.format(cat))
recl = grass.feed_command(
'r.reclass',
quiet=True,
input='{}_patches_boundary'.format(TMP_PREFIX),
output=start_patch,
rules='-')
recl.stdin.write(reclass_rule)
recl.stdin.close()
recl.wait()
# Check if patch was rasterised (patches smaller raster resolution and close to larger patches may not be rasterised)
#start_check = grass.parse_command('r.info', flags='r', map=start_patch)
#start_check = grass.parse_command('r.univar', flags='g', map=start_patch)
#print(start_check)
"""if start_check['min'] != '1':
grass.warning('Patch {} has not been rasterized and will \
therefore not be treated as part of the \
network. Consider using t-flag or change \
resolution.'.format(cat))
grass.run_command('g.remove', flags='f', vector=start_patch,
raster=start_patch, quiet=True)
grass.del_temp_region()
continue"""
# Prepare stop patches
############################################
reg = grass.parse_command('g.region',
flags='ug',
quiet=True,
raster=start_patch,
n=float(from_bbox['n']) + float(cutoff),
s=float(from_bbox['s']) - float(cutoff),
e=float(from_bbox['e']) + float(cutoff),
w=float(from_bbox['w']) - float(cutoff),
align='{}_patches_pol'.format(TMP_PREFIX))
north = reg['n'] if max_n > reg['n'] else max_n
south = reg['s'] if min_s < reg['s'] else min_s
east = reg['e'] if max_e < reg['e'] else max_e
west = reg['w'] if min_w > reg['w'] else min_w
# Set region to patch search radius
grass.use_temp_region()
grass.run_command('g.region',
quiet=True,
n=north,
s=south,
e=east,
w=west,
align='{}_patches_pol'.format(TMP_PREFIX))
# Create buffer around start-patch as a mask
# for cost distance analysis
grass.run_command('r.buffer',
quiet=True,
input=start_patch,
output='MASK',
distances=cutoff)
grass.run_command('r.mapcalc',
quiet=True,
expression='{pf}_patch_{p}_neighbours_contur=\
if({pf}_patches_boundary=={p},\
null(),\
{pf}_patches_boundary)'.format(
pf=TMP_PREFIX, p=cat))
grass.run_command('r.mask', flags='r', quiet=True)
# Calculate cost distance
cost_distance_map = '{}_patch_{}_cost_dist'.format(prefix, cat)
grass.run_command('r.cost',
flags=dist_flags,
quiet=True,
overwrite=True,
input=costs,
output=cost_distance_map,
start_rast=start_patch,
memory=memory)
#grass.run_command('g.region', flags='up')
# grass.raster.raster_history(cost_distance_map)
cdhist = History(cost_distance_map)
cdhist.clear()
cdhist.creator = os.environ['USER']
cdhist.write()
# History object cannot modify description
grass.run_command('r.support',
map=cost_distance_map,
description='Generated by r.connectivity.distance',
history=os.environ['CMDLINE'])
# Export distance at boundaries
maps = '{0}_patch_{1}_neighbours_contur,{2}_patch_{1}_cost_dist'
maps = maps.format(TMP_PREFIX, cat, prefix),
connections = grass.encode(
grass.read_command('r.stats',
flags='1ng',
quiet=True,
input=maps,
separator=';').rstrip('\n'))
if connections:
con_array = np.genfromtxt(BytesIO(connections),
delimiter=';',
dtype=None,
names=['x', 'y', 'cat', 'dist'])
else:
grass.warning('No connections for patch {}'.format(cat))
# Write centroid to vertex map
vertex.write(Point(from_x, from_y), cat=int(cat), attrs=proxy_val)
vertex.table.conn.commit()
# Remove temporary map data
grass.run_command('g.remove',
quiet=True,
flags='f',
type=['raster', 'vector'],
pattern="{}*{}*".format(TMP_PREFIX, cat))
grass.del_temp_region()
continue
#Find closest points on neigbour patches
to_cats = set(np.atleast_1d(con_array['cat']))
to_coords = []
for to_cat in to_cats:
connection = con_array[con_array['cat'] == to_cat]
connection.sort(order=['dist'])
pixel = border_dist if len(
connection) > border_dist else len(connection) - 1
# closest_points_x = connection['x'][pixel]
# closest_points_y = connection['y'][pixel]
closest_points_to_cat = to_cat
closest_points_min_dist = connection['dist'][0]
closest_points_dist = connection['dist'][pixel]
closest_points_max_dist = connection['dist'][-1]
to_patch_ids = vpatch_ids[vpatch_ids['cat'] == int(to_cat)]['vid']
if len(to_patch_ids) == 1:
to_centroid = Centroid(v_id=to_patch_ids,
c_mapinfo=vpatches.c_mapinfo)
to_x = to_centroid.x
to_y = to_centroid.y
elif len(to_patch_ids) >= 1:
xcoords = []
ycoords = []
for t_p in to_patch_ids:
to_centroid = Centroid(v_id=int(t_p),
c_mapinfo=vpatches.c_mapinfo)
xcoords.append(to_centroid.x)
ycoords.append(to_centroid.y)
# Get centroid
if not to_centroid:
continue
to_x = np.average(xcoords)
to_y = np.average(ycoords)
to_coords.append('{},{},{},{},{},{}'.format(
connection['x'][0], connection['y'][0], to_cat,
closest_points_min_dist, closest_points_dist,
closest_points_max_dist))
#Save edges to network dataset
if closest_points_dist <= 0:
zero_dist = 1
# Write data to network
network.write(Line([(from_x, from_y), (to_x, to_y)]),
cat=lin_cat,
attrs=(
cat,
int(closest_points_to_cat),
closest_points_min_dist,
closest_points_dist,
closest_points_max_dist,
))
network.table.conn.commit()
lin_cat = lin_cat + 1
# Save closest points and shortest paths through cost raster as
# vector map (r.drain limited to 1024 points) if requested
if p_flag:
grass.verbose('Extracting shortest paths for patch number \
{}...'.format(cat))
points_n = len(to_cats)
tiles = int(points_n / 1024.0)
rest = points_n % 1024
if not rest == 0:
tiles = tiles + 1
tile_n = 0
while tile_n < tiles:
tile_n = tile_n + 1
#Import closest points for start-patch in 1000er blocks
sp = grass.feed_command('v.in.ascii',
flags='nr',
overwrite=True,
quiet=True,
input='-',
stderr=subprocess.PIPE,
output="{}_{}_cp".format(
TMP_PREFIX, cat),
separator=",",
columns="x double precision,\
y double precision,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision")
sp.stdin.write(grass.encode("\n".join(to_coords)))
sp.stdin.close()
sp.wait()
# Extract shortest paths for start-patch in chunks of
# 1024 points
cost_paths = "{}_{}_cost_paths".format(TMP_PREFIX, cat)
start_points = "{}_{}_cp".format(TMP_PREFIX, cat)
grass.run_command('r.drain',
overwrite=True,
quiet=True,
input=cost_distance_map,
output=cost_paths,
drain=cost_paths,
start_points=start_points)
grass.run_command('v.db.addtable',
map=cost_paths,
quiet=True,
columns="cat integer,\
from_p integer,\
to_p integer,\
dist_min double precision,\
dist double precision,\
dist_max double precision")
grass.run_command('v.db.update',
map=cost_paths,
column='from_p',
value=cat,
quiet=True)
grass.run_command('v.distance',
quiet=True,
from_=cost_paths,
to=start_points,
upload='to_attr',
column='to_p',
to_column='to_p')
grass.run_command('v.db.join',
quiet=True,
map=cost_paths,
column='to_p',
other_column='to_p',
other_table=start_points,
subset_columns='dist_min,dist,dist_max')
#grass.run_command('v.info', flags='c',
# map=cost_paths)
grass.run_command('v.patch',
flags='ae',
overwrite=True,
quiet=True,
input=cost_paths,
output=shortest_paths)
# Remove temporary map data
grass.run_command('g.remove',
quiet=True,
flags='f',
type=['raster', 'vector'],
pattern="{}*{}*".format(TMP_PREFIX, cat))
# Remove temporary map data for patch
if r_flag:
grass.run_command('g.remove',
flags='f',
type='raster',
name=cost_distance_map,
quiet=True)
vertex.write(Point(from_x, from_y), cat=int(cat), attrs=proxy_val)
vertex.table.conn.commit()
# Print progress message
grass.percent(i=int((float(counter) / n_cats) * 100), n=100, s=3)
# Update counter for progress message
counter = counter + 1
if zero_dist:
grass.warning('Some patches are directly adjacent to others. \
Minimum distance set to 0.0000000001')
# Close vector maps and build topology
network.close()
vertex.close()
# Add vertex attributes
# grass.run_command('v.db.addtable', map=vertex_map)
# grass.run_command('v.db.join', map=vertex_map, column='cat',
# other_table=in_db_connection[int(layer)]['table'],
# other_column='cat', subset_columns=pop_proxy,
# quiet=True)
# Add history and meta data to produced maps
grass.run_command('v.support',
flags='h',
map=edge_map,
person=os.environ['USER'],
cmdhist=os.environ['CMDLINE'])
grass.run_command('v.support',
flags='h',
map=vertex_map,
person=os.environ['USER'],
cmdhist=os.environ['CMDLINE'])
if p_flag:
grass.run_command('v.support',
flags='h',
map=shortest_paths,
person=os.environ['USER'],
cmdhist=os.environ['CMDLINE'])
# Output also Conefor files if requested
if conefor_dir:
query = """SELECT p_from, p_to, avg(dist) FROM
(SELECT
CASE
WHEN from_p > to_p THEN to_p
ELSE from_p END AS p_from,
CASE
WHEN from_p > to_p THEN from_p
ELSE to_p END AS p_to,
dist
FROM {}) AS x
GROUP BY p_from, p_to""".format(edge_map)
with open(os.path.join(conefor_dir, 'undirected_connection_file'),
'w') as edges:
edges.write(
grass.read_command('db.select', sql=query, separator=' '))
with open(os.path.join(conefor_dir, 'directed_connection_file'),
'w') as edges:
edges.write(
grass.read_command('v.db.select',
map=edge_map,
separator=' ',
flags='c'))
with open(os.path.join(conefor_dir, 'node_file'), 'w') as nodes:
nodes.write(
grass.read_command('v.db.select',
map=vertex_map,
separator=' ',
flags='c'))
def do_it_all(global_vars, target_pts_np):
"""Conduct weighted and parametrised partial viewshed and cummulate it with
the previous partial viewsheds
:param target_pts_np: Array of target points in global coordinate system
:type target_pts_np: ndarray
:return: 2D array of weighted parametrised cummulative viewshed
:rtype: ndarray
"""
# Set counter
counter = 1
# Get variables out of global_vars dictionary
reg = global_vars["region"]
exp_range = global_vars["range"]
flagstring = global_vars["flagstring"]
r_dsm = global_vars["r_dsm"]
v_elevation = global_vars["observer_elevation"]
refr_coeff = global_vars["refr_coeff"]
memory = global_vars["memory"]
parametrise_viewshed = global_vars["param_viewshed"]
dsm_type = global_vars["dsm_type"]
b_1 = global_vars["b_1"]
cores = global_vars["cores"]
tempname = global_vars["tempname"]
# Create empty viewshed
np_cum = np.empty((reg.rows, reg.cols), dtype=np.single)
np_cum[:] = np.nan
tmp_vs = "{}_{}".format(tempname, os.getpid())
for target_pnt in target_pts_np:
# Display a progress info message
grass.percent(counter, len(target_pts_np), 1)
grass.verbose("Processing point {i} ({p:.1%})".format(
i=int(target_pnt[0]), p=counter / len(target_pts_np)))
# Global coordinates and attributes of target point T
t_glob = target_pnt[1:]
# ======================================================================
# 1. Set local computational region: +/- exp_range from target point
# ======================================================================
# compute position of target point within a pixel
delta_n = math.ceil(
(t_glob[1] - reg.south) / reg.nsres) * reg.nsres - (t_glob[1] -
reg.south)
delta_s = (t_glob[1] - reg.south) - math.floor(
(t_glob[1] - reg.south) / reg.nsres) * reg.nsres
delta_e = math.ceil(
(t_glob[0] - reg.west) / reg.ewres) * reg.ewres - (t_glob[0] -
reg.west)
delta_w = (t_glob[0] - reg.west) - math.floor(
(t_glob[0] - reg.west) / reg.ewres) * reg.ewres
# ensure that local region doesn't exceed global region
loc_reg_n = min(t_glob[1] + exp_range + delta_n, reg.north)
loc_reg_s = max(t_glob[1] - exp_range - delta_s, reg.south)
loc_reg_e = min(t_glob[0] + exp_range + delta_e, reg.east)
loc_reg_w = max(t_glob[0] - exp_range - delta_w, reg.west)
# pygrass sets region for pygrass tasks
lreg = deepcopy(reg)
lreg.set_bbox(Bbox(loc_reg_n, loc_reg_s, loc_reg_e, loc_reg_w))
lreg.set_raster_region()
# Create processing environment with region information
c_env = os.environ.copy()
c_env["GRASS_REGION"] = grass.region_env(n=loc_reg_n,
s=loc_reg_s,
e=loc_reg_e,
w=loc_reg_w)
lreg_shape = [lreg.rows, lreg.cols]
# ======================================================================
# 2. Calculate binary viewshed and convert to numpy
# ======================================================================
vs = grass.pipe_command(
"r.viewshed",
flags="b" + flagstring,
input=r_dsm,
output=tmp_vs,
coordinates="{},{}".format(t_glob[0], t_glob[1]),
observer_elevation=0.0,
target_elevation=v_elevation,
max_distance=exp_range,
refraction_coeff=refr_coeff,
memory=int(round(memory / cores)),
quiet=True,
overwrite=True,
env=c_env,
)
vs.communicate()
# Workaround for https://github.com/OSGeo/grass/issues/1436
clean_temp(vs.pid)
# Read viewshed into numpy with single precision and replace NoData
np_viewshed = raster2numpy(tmp_vs).astype(np.single)
np_viewshed[np_viewshed == -2147483648] = np.nan
# ======================================================================
# 3. Prepare local coordinates and attributes of target point T
# ======================================================================
# Calculate how much of rows/cols of local region lies
# outside global region
o_1 = [
max(t_glob[1] + exp_range + reg.nsres / 2 - reg.north, 0),
max(reg.west - (t_glob[0] - exp_range - reg.ewres / 2), 0),
]
t_loc = np.append(
np.array([
exp_range / reg.nsres + 0.5 - o_1[0] / reg.nsres,
exp_range / reg.ewres + 0.5 - o_1[1] / reg.ewres,
]),
t_glob[2:],
)
# ======================================================================
# 4. Parametrise viewshed
# ======================================================================
np_viewshed = parametrise_viewshed(
lreg_shape,
t_loc,
np_viewshed,
reg,
exp_range,
r_dsm,
dsm_type,
v_elevation,
b_1,
).astype(np.single)
# ======================================================================
# 5. Cummulate viewsheds
# ======================================================================
# Determine position of local parametrised viewshed within
# global cummulative viewshed
o_2 = [
int(round((reg.north - loc_reg_n) / reg.nsres)), # NS (rows)
int(round((loc_reg_w - reg.west) / reg.ewres)), # EW (cols)
]
# Add local parametrised viewshed to global cummulative viewshed
# replace nans with 0 in processed regions, keep nan where both are nan
all_nan = np.all(
np.isnan([
np_cum[o_2[0]:o_2[0] + lreg_shape[0],
o_2[1]:o_2[1] + lreg_shape[1]],
np_viewshed,
]),
axis=0,
)
np_cum[o_2[0]:o_2[0] + lreg_shape[0],
o_2[1]:o_2[1] + lreg_shape[1]] = np.nansum(
[
np_cum[o_2[0]:o_2[0] + lreg_shape[0],
o_2[1]:o_2[1] + lreg_shape[1]],
np_viewshed,
],
axis=0,
)
np_cum[o_2[0]:o_2[0] + lreg_shape[0],
o_2[1]:o_2[1] + lreg_shape[1]][all_nan] = np.nan
counter += 1
return np_cum
