def euclidean_distance_fields(prefix, region, overwrite=False):
"""
Generate euclidean distance fields from map corner and centre coordinates
Parameters
----------
prefix : str
Name to use as prefix to save distance maps
region : grass.pygrass.gis.region.Region
Region
overwrite : bool
Whether to overwrite existing maps
"""
point_topleft = Point(region.west + region.ewres / 2,
region.north - region.nsres / 2)
point_topright = Point(region.east - region.ewres / 2,
region.north - region.nsres / 2)
point_lowerleft = Point(region.west + region.ewres / 2,
region.south + region.nsres / 2)
point_lowerright = Point(region.east - region.ewres / 2,
region.south + region.nsres / 2)
point_centre = Point(
region.west + (region.east - region.west) / 2,
region.south + (region.north - region.south) / 2,
)
points = {
"topleft": point_topleft,
"topright": point_topright,
"lowerleft": point_lowerleft,
"lowerright": point_lowerright,
"centre": point_centre,
}
for name, p in points.items():
point_name = "_".join([prefix, name])
vect = VectorTopo(name=point_name)
vect.open(
mode="w",
tab_name=point_name,
tab_cols=[("cat", "INTEGER PRIMARY KEY"), ("name", "TEXT")],
)
vect.write(p, ("point", ))
vect.table.conn.commit()
vect.close()
gvect.to_rast(
input=point_name,
type="point",
use="val",
output=point_name,
overwrite=overwrite,
)
grast.grow_distance(point_name,
distance="distance_to_" + point_name,
overwrite=overwrite)
g.remove(name=point_name, type="raster", flags="f")
g.remove(name=point_name, type="raster", flags="f")
# Define region
grass.run_command('g.region', vect = i, res = '00:00:00.9',
align = map_for_define_region, flags = 'p')
# Use vector as a mask
grass.run_command('r.mask', vector = i, cat = 2, overwrite = True) # Mask for the buffer for individual i
# Cut maps
for mm in maps:
expr = i+'_'+mm+' = '+mm+'@PERMANENT'
print expr
r.mapcalc(expr, overwrite = True)
# distance from roads
r.grow_distance(input = i+'_'+'gROADS_v1_americas_rast',
distance = i+'_'+'gROADS_v1_dist_from_roads_meters_exp', metric = 'geodesic',
flags = 'm', overwrite = True)
# road density
size = 9990/30 # 10 km = 333 pixels
r.neighbors(input = i+'_'+'gROADS_v1_americas_rast',
output = i+'_'+'gROADS_v1_road_density_pixels_per_100km2', method = 'sum',
size = size, overwrite = True)
r.mapcalc(i+'_'+'gROADS_v1_road_density_km_per_100km2_exp = '+i+'_'+'gROADS_v1_road_density_pixels_per_100km2 * 30 / 1000', overwrite = True)
# Hansen bin
r.mapcalc(i+'_Neotropic_Hansen_percenttreecoverd_2000_wgs84_gt0_binary = if(Neotropic_Hansen_percenttreecoverd_2000_wgs84 > 0, 1, 0)', overwrite = True)
# Hansen correspondent year
expr = i+'_Neotropic_Hansen_percenttreecoverd_2000_wgs84_gt0_binary_year_exp = if('+i+'_Neotropical_Hansen_treecoverlossperyear_wgs84_2017 > 0 && '+ \
i+'_Neotropical_Hansen_treecoverlossperyear_wgs84_2017 < '+str(yy)+', 0, '+i+'_Neotropic_Hansen_percenttreecoverd_2000_wgs84_gt0_binary)'
export=False,
dirout='')
maps_forest[56]
# patch size
patch_size(input_maps=maps_forest, remove_trash=True, export=False)
# distance to roads
road_maps = grass.list_grouped(type='raster', pattern='*roads*')['PERMANENT']
road_maps = [i for i in road_maps if 'dist' not in i]
for i in road_maps:
g.region(raster=i, flags='p')
r.grow_distance(input=i, distance=i + '_road_dist_m', overwrite=True)
# distance to water
water_maps1 = grass.list_grouped(type='raster',
pattern='*Drainage*')['PERMANENT']
water_maps2 = grass.list_grouped(type='raster',
pattern='*Water_presence*')['PERMANENT']
water_maps = water_maps1 + water_maps2
water_maps = [i for i in water_maps if 'dist' not in i]
for i in water_maps:
print i
g.region(raster=i, flags='p')
def compute_attractiveness(raster,
metric,
constant,
kappa,
alpha,
mask=None,
output_name=None):
"""
Compute a raster map whose values follow an (euclidean) distance function
( {constant} + {kappa} ) / ( {kappa} + exp({alpha} * {distance}) ), where:
Source: http://publications.jrc.ec.europa.eu/repository/bitstream/JRC87585/lb-na-26474-en-n.pd
Parameters
----------
constant : 1
kappa :
A constant named 'K'
alpha :
A constant named 'a'
distance :
A distance map based on the input raster
score :
A score term to multiply the distance function
mask :
Optional raster MASK which is inverted to selectively exclude non-NULL
cells from distance related computations.
output_name :
Name to pass to temporary_filename() to create a temporary map name
Returns
-------
tmp_output :
A temporary proximity to features raster map.
Examples
--------
...
"""
distance_terms = [
str(raster),
str(metric),
"distance",
str(constant),
str(kappa),
str(alpha),
]
if score:
grass.debug(_(
"Score for attractiveness equation: {s}".format(s=score)))
distance_terms += str(score)
# tmp_distance = temporary_filename('_'.join(distance_terms))
tmp_distance = temporary_filename(filename="_".join([raster, metric]))
r.grow_distance(input=raster,
distance=tmp_distance,
metric=metric,
quiet=True,
overwrite=True)
if mask:
msg = "Inverted masking to exclude non-NULL cells "
msg += "from distance related computations based on '{mask}'"
msg = msg.format(mask=mask)
grass.verbose(_(msg))
r.mask(raster=mask, flags="i", overwrite=True, quiet=True)
# FIXME: use a parameters dictionary, avoid conditionals
if score:
distance_function = build_distance_function(
constant=constant,
kappa=kappa,
alpha=alpha,
variable=tmp_distance,
score=score,
)
# FIXME: use a parameters dictionary, avoid conditionals
if not score:
distance_function = build_distance_function(constant=constant,
kappa=kappa,
alpha=alpha,
variable=tmp_distance)
# temporary maps will be removed
if output_name:
tmp_distance_map = temporary_filename(filename=output_name)
else:
basename = "_".join([raster, "attractiveness"])
tmp_distance_map = temporary_filename(filename=basename)
distance_function = EQUATION.format(result=tmp_distance_map,
expression=distance_function)
msg = "* Distance function: {f}".format(f=distance_function)
grass.verbose(_(msg))
grass.mapcalc(distance_function, overwrite=True)
r.null(map=tmp_distance_map, null=0) # Set NULLs to 0
compress_status = grass.read_command("r.compress",
flags="g",
map=tmp_distance_map)
grass.verbose(_(
"* Compress status: {s}".format(s=compress_status))) # REMOVEME
return tmp_distance_map