def worker(cmd):
window, src = cmd
reg = Region()
old_reg = deepcopy(reg)
try:
# update region
reg.north = dict(window)['north']
reg.south = dict(window)['south']
reg.west = dict(window)['west']
reg.east = dict(window)['east']
reg.set_current()
reg.write()
reg.set_raster_region()
# read raster data
with RasterRow(src) as rs:
arr = np.asarray(rs)
except:
pass
finally:
# reset region
old_reg.write()
reg.set_raster_region()
return(arr)
def test_resampling_to_numpy(self):
region = Region()
region.ewres = 0.1
region.nsres = 0.1
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 400)
region.ewres = 1
region.nsres = 1
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 40)
region.ewres = 5
region.nsres = 5
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 8)
def test_resampling_to_numpy(self):
region = Region()
region.ewres = 0.1
region.nsres = 0.1
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 400)
region.ewres = 1
region.nsres = 1
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 40)
region.ewres = 5
region.nsres = 5
region.adjust()
region.set_raster_region()
a = raster2numpy(self.name)
self.assertEqual(len(a), 8)
def main():
"""Do the main work"""
# set numpy printing options
np.set_printoptions(formatter={"float": lambda x: "{0:0.2f}".format(x)})
# ==========================================================================
# Input data
# ==========================================================================
# Required
r_output = options["output"]
r_dsm = options["input"]
dsm_type = grass.parse_command("r.info", map=r_dsm, flags="g")["datatype"]
# Test if DSM exist
gfile_dsm = grass.find_file(name=r_dsm, element="cell")
if not gfile_dsm["file"]:
grass.fatal("Raster map <{}> not found".format(r_dsm))
# Exposure settings
v_source = options["sampling_points"]
r_source = options["source"]
source_cat = options["sourcecat"]
r_weights = options["weights"]
# test if source vector map exist and contains points
if v_source:
gfile_vsource = grass.find_file(name=v_source, element="vector")
if not gfile_vsource["file"]:
grass.fatal("Vector map <{}> not found".format(v_source))
if not grass.vector.vector_info_topo(v_source, layer=1)["points"] > 0:
grass.fatal("Vector map <{}> does not contain any points.".format(
v_source))
if r_source:
gfile_rsource = grass.find_file(name=r_source, element="cell")
if not gfile_rsource["file"]:
grass.fatal("Raster map <{}> not found".format(r_source))
# if source_cat is set, check that r_source is CELL
source_datatype = grass.parse_command("r.info",
map=r_source,
flags="g")["datatype"]
if source_cat != "*" and source_datatype != "CELL":
grass.fatal(
"The raster map <%s> must be integer (CELL type) in order to \
use the 'sourcecat' parameter" % r_source)
if r_weights:
gfile_weights = grass.find_file(name=r_weights, element="cell")
if not gfile_weights["file"]:
grass.fatal("Raster map <{}> not found".format(r_weights))
# Viewshed settings
range_inp = float(options["range"])
v_elevation = float(options["observer_elevation"])
b_1 = float(options["b1_distance"])
pfunction = options["function"]
refr_coeff = float(options["refraction_coeff"])
flagstring = ""
if flags["r"]:
flagstring += "r"
if flags["c"]:
flagstring += "c"
# test values
if v_elevation < 0.0:
grass.fatal("Observer elevation must be larger than or equal to 0.0.")
if range_inp <= 0.0 and range_inp != -1:
grass.fatal("Exposure range must be larger than 0.0.")
if pfunction == "Fuzzy_viewshed" and range_inp == -1:
grass.fatal("Exposure range cannot be \
infinity for fuzzy viewshed approch.")
if pfunction == "Fuzzy_viewshed" and b_1 > range_inp:
grass.fatal("Exposure range must be larger than radius around \
the viewpoint where clarity is perfect.")
# Sampling settings
source_sample_density = float(options["sample_density"])
seed = options["seed"]
if not seed: # if seed is not set, set it to process number
seed = os.getpid()
# Optional
cores = int(options["nprocs"])
memory = int(options["memory"])
# ==========================================================================
# Region settings
# ==========================================================================
# check that location is not in lat/long
if grass.locn_is_latlong():
grass.fatal("The analysis is not available for lat/long coordinates.")
# get comp. region parameters
reg = Region()
# check that NSRES equals EWRES
if abs(reg.ewres - reg.nsres) > 1e-6:
grass.fatal("Variable north-south and east-west 2D grid resolution \
is not supported")
# adjust exposure range as a multiplicate of region resolution
# if infinite, set exposure range to the max of region size
if range_inp != -1:
multiplicate = math.floor(range_inp / reg.nsres)
exp_range = multiplicate * reg.nsres
else:
range_inf = max(reg.north - reg.south, reg.east - reg.west)
multiplicate = math.floor(range_inf / reg.nsres)
exp_range = multiplicate * reg.nsres
if RasterRow("MASK", Mapset().name).exist():
grass.warning("Current MASK is temporarily renamed.")
unset_mask()
# ==========================================================================
# Random sample exposure source with target points T
# ==========================================================================
if v_source:
# go for using input vector map as sampling points
v_source_sample = v_source
grass.verbose("Using sampling points from input vector map")
else:
# go for sampling
# min. distance between samples set to half of region resolution
# (issue in r.random.cells)
sample_distance = reg.nsres / 2
v_source_sample = sample_raster_with_points(
r_source,
source_cat,
source_sample_density,
sample_distance,
"{}_rand_pts_vect".format(TEMPNAME),
seed,
)
# ==========================================================================
# Get coordinates and attributes of target points T
# ==========================================================================
# Prepare a list of maps to extract attributes from
# DSM values
attr_map_list = [r_dsm]
if pfunction in ["Solid_angle", "Visual_magnitude"]:
grass.verbose("Precomputing parameter maps...")
# Precompute values A, B, C, D for solid angle function
# using moving window [row, col]
if pfunction == "Solid_angle":
r_a_z = "{}_A_z".format(TEMPNAME)
r_b_z = "{}_B_z".format(TEMPNAME)
r_c_z = "{}_C_z".format(TEMPNAME)
r_d_z = "{}_D_z".format(TEMPNAME)
expr = ";".join([
"$outmap_A = ($inmap[0, 0] + \
$inmap[0, -1] + \
$inmap[1, -1] + \
$inmap[1, 0]) / 4",
"$outmap_B = ($inmap[-1, 0] + \
$inmap[-1, -1] + \
$inmap[0, -1] + \
$inmap[0, 0]) / 4",
"$outmap_C = ($inmap[-1, 1] + \
$inmap[-1, 0] + \
$inmap[0, 0] + \
$inmap[0, 1]) / 4",
"$outmap_D = ($inmap[0, 1] + \
$inmap[0, 0] + \
$inmap[1, 0] + \
$inmap[1, 1]) / 4",
])
grass.mapcalc(
expr,
inmap=r_dsm,
outmap_A=r_a_z,
outmap_B=r_b_z,
outmap_C=r_c_z,
outmap_D=r_d_z,
overwrite=True,
quiet=grass.verbosity() <= 1,
)
attr_map_list.extend([r_a_z, r_b_z, r_c_z, r_d_z])
# Precompute values slopes in e-w direction, n-s direction
# as atan(dz/dx) (e-w direction), atan(dz/dy) (n-s direction)
# using moving window [row, col]
elif pfunction == "Visual_magnitude":
r_slope_ew = "{}_slope_ew".format(TEMPNAME)
r_slope_ns = "{}_slope_ns".format(TEMPNAME)
expr = ";".join([
"$outmap_ew = atan((sqrt(2) * $inmap[-1, 1] + \
2 * $inmap[0, 1] + \
sqrt(2) * $inmap[1, 1] - \
sqrt(2) * $inmap[-1, -1] - \
2 * $inmap[0, -1] - \
sqrt(2) * $inmap[1, -1]) / \
(8 * $w_ew))",
"$outmap_ns = atan((sqrt(2) * $inmap[-1, -1] + \
2 * $inmap[-1, 0] + \
sqrt(2) * $inmap[-1, 1] - \
sqrt(2) * $inmap[1, -1] - \
2 * $inmap[1, 0] - \
sqrt(2) * $inmap[1, 1]) / \
(8 * $w_ns))",
])
grass.mapcalc(
expr,
inmap=r_dsm,
outmap_ew=r_slope_ew,
outmap_ns=r_slope_ns,
w_ew=reg.ewres,
w_ns=reg.nsres,
overwrite=True,
quiet=grass.verbosity() <= 1,
)
attr_map_list.extend([r_slope_ew, r_slope_ns])
# Use viewshed weights if provided
if r_weights:
attr_map_list.append(r_weights)
# Extract attribute values
target_pts_grass = grass.read_command(
"r.what",
flags="v",
map=attr_map_list,
points=v_source_sample,
separator="|",
null_value="*",
quiet=True,
)
# columns to use depending on parametrization function
usecols = list(range(0, 4 + len(attr_map_list)))
usecols.remove(3) # skip 3rd column - site_name
# convert coordinates and attributes of target points T to numpy array
target_pts_np = txt2numpy(
target_pts_grass,
sep="|",
names=None,
null_value="*",
usecols=usecols,
structured=False,
)
# if one point only - 0D array which cannot be used in iteration
if target_pts_np.ndim == 1:
target_pts_np = target_pts_np.reshape(1, -1)
target_pts_np = target_pts_np[~np.isnan(target_pts_np).any(axis=1)]
no_points = target_pts_np.shape[0]
# if viewshed weights not set by flag - set weight to 1 for all pts
if not r_weights:
weights_np = np.ones((no_points, 1))
target_pts_np = np.hstack((target_pts_np, weights_np))
grass.debug("target_pts_np: {}".format(target_pts_np))
# ==========================================================================
# Calculate weighted parametrised cummulative viewshed
# by iterating over target points T
# ==========================================================================
grass.verbose("Calculating partial viewsheds...")
# Parametrisation function
if pfunction == "Solid_angle":
parametrise_viewshed = solid_angle_reverse
elif pfunction == "Distance_decay":
parametrise_viewshed = distance_decay_reverse
elif pfunction == "Fuzzy_viewshed":
parametrise_viewshed = fuzzy_viewshed_reverse
elif pfunction == "Visual_magnitude":
parametrise_viewshed = visual_magnitude_reverse
else:
parametrise_viewshed = binary
# Collect variables that will be used in do_it_all() into a dictionary
global_vars = {
"region": reg,
"range": exp_range,
"param_viewshed": parametrise_viewshed,
"observer_elevation": v_elevation,
"b_1": b_1,
"memory": memory,
"refr_coeff": refr_coeff,
"flagstring": flagstring,
"r_dsm": r_dsm,
"dsm_type": dsm_type,
"cores": cores,
"tempname": TEMPNAME,
}
# Split target points to chunks for each core
target_pnts = np.array_split(target_pts_np, cores)
# Combine each chunk with dictionary
combo = list(zip(itertools.repeat(global_vars), target_pnts))
# Calculate partial cummulative viewshed
with Pool(cores) as pool:
np_sum = pool.starmap(do_it_all, combo)
pool.close()
pool.join()
# We should probably use nansum here?
all_nan = np.all(np.isnan(np_sum), axis=0)
np_sum = np.nansum(np_sum, axis=0, dtype=np.single)
np_sum[all_nan] = np.nan
grass.verbose("Writing final result and cleaning up...")
# Restore original computational region
reg.read()
reg.set_current()
reg.set_raster_region()
# Convert numpy array of cummulative viewshed to raster
numpy2raster(np_sum, mtype="FCELL", rastname=r_output, overwrite=True)
# Remove temporary files and reset mask if needed
cleanup()
# Set raster history to output raster
grass.raster_history(r_output, overwrite=True)
grass.run_command(
"r.support",
overwrite=True,
map=r_output,
title="Visual exposure index as {}".format(pfunction.replace("_",
" ")),
description="generated by r.viewshed.exposure",
units="Index value",
quiet=True,
)
