def scatter_1D(self, positions, values, field, location, add_info):
if self.root == True:
positions, pos_ind = self.mesh.sortPositionsByMeshes(positions,
return_ind=[],
surface=True)
values = [values[i] for i in pos_ind]
add_info = [add_info[i] for i in pos_ind]
location = self.mesh.sortIndexByMeshes(location, shift=False)
pos = positions.pop(0)
val = values.pop(0)
info = add_info.pop(0)
loc = location.pop(0)
for child in self.children:
loc_i = copy.copy(location[0])
child.scatter_1D(positions, values, field, location, add_info)
array_1D = location_indexes_inv(loc_i, store=False)
nonzero = numpy.flatnonzero(field[array_1D])
array_1D = array_1D[nonzero]
pos = numpy.append(pos,
child.mesh.getPosition(
child.mesh.location_sat[nonzero]),
axis=0)
val = numpy.append(val, field[array_1D])
info = numpy.append(info, child.mesh.direction_sat[nonzero])
if len(self.children) > 0:
field[location_indexes_inv(loc, store=False)] *= 0
self.scatter_1D_aux(pos, val, field, loc, info)
def scatter_1D_aux(self,
positions,
values,
field,
general_location,
add_info,
prec=10**(-c.INDEX_PREC)):
#Creating 1-1 connection between local satellite nodes and general nodes
dic = {
loc_i: gen_i
for loc_i, gen_i in zip(self.mesh.location_sat, general_location)
}
#Getting mesh coordinates
mc = self.mesh.getIndex(positions)
index = mc.astype(int)
array = self.mesh.indexToArray(index)
#Calcualting array indexes with respect to the whole domain
array_gen = [dic[i] for i in array]
#Getting the correct indexes for the field
array_1D = location_indexes_inv(array_gen, store=False)
horizontal = numpy.equal(add_info % 2, 0)
vertical = numpy.logical_not(horizontal)
dindex = numpy.where(horizontal, mc[:, 0] - index[:, 0],
mc[:, 1] - index[:, 1])
filter_1D = dindex > prec
#Adapting horizontal and vertical such that n_index works for inner boundaries as well as outer boundaries
#mask = numpy.ones_like(add_info, dtype = numpy.bool_)
#for boundary in self.mesh.boundaries:
# if boundary.type == Inner_2D_Rectangular.type:
# mask[numpy.flatnonzero(numpy.isin(array, boundary.location))] = False
#horizontal = numpy.logical_or(horizontal, mask)
#vertical = numpy.logical_and(vertical, numpy.logical_not(mask))
#n_index = numpy.where(horizontal, array_1D+1, array_1D+2)
#n_index = numpy.where(numpy.logical_and(vertical, array_1D == 0), n_index-2+self.mesh.nxsat, n_index)
#n_index = numpy.where(numpy.logical_and(vertical, array_1D == len(self.mesh.location_sat)-1-self.mesh.nxsat), n_index-2+self.mesh.nxsat, n_index)
# Finding the other node involved
temp_location_indexes_inv = {
locations: indexes[0]
for indexes, locations in numpy.ndenumerate(self.mesh.location_sat)
}
loc_ind = numpy.asarray(
[temp_location_indexes_inv.get(ind_i) for ind_i in array],
dtype=numpy.uint16)
n_index = [
self.mesh.adjacent_sat[loc_ind[i]][add_info[i]]
for i in range(len(array_1D))
]
n_index = location_indexes_inv(
[dic[self.mesh.location_sat[ind]] for ind in n_index], store=False)
#scatter process
numpy.add.at(field, array_1D, (1 - dindex) * values)
numpy.add.at(field, n_index[filter_1D],
dindex[filter_1D] * values[filter_1D])
def scatter_1D(self,
positions,
values,
field,
location,
add_info,
prec=10**(-c.INDEX_PREC)):
#Getting mesh coordinates
mc = self.mesh.getIndex(positions)
index = mc.astype(int)
array = self.mesh.indexToArray(index)
array_1D = location_indexes_inv(array, store=False)
horizontal = numpy.equal(add_info % 2, 0)
vertical = numpy.logical_not(horizontal)
dindex = numpy.where(horizontal, mc[:, 0] - index[:, 0],
mc[:, 1] - index[:, 1])
#n_index = numpy.where(horizontal, array_1D+1, array_1D+2)
#n_index = numpy.where(numpy.logical_and(vertical, array_1D == 0), n_index-2+self.mesh.nxsat, n_index)
#n_index = numpy.where(numpy.logical_and(vertical, array_1D == len(location)-1-self.mesh.nxsat), n_index-2+self.mesh.nxsat, n_index)
n_index = numpy.asarray([
self.mesh.adjacent_sat[array_1D[i]][add_info[i]]
for i in range(len(array_1D))
],
dtype=numpy.uint16)
filter_i = dindex > prec
values = values * numpy.ones_like(
(dindex)) if type(values) != numpy.ndarray else values
numpy.add.at(field, array_1D, (1 - dindex) * values)
numpy.add.at(field, n_index[filter_i],
dindex[filter_i] * values[filter_i])
def createDistributionAtBorder(self,
location,
part_solver,
species,
delta_n,
prec=1e-5):
add_rand = numpy.random.rand(len(location))
#This needs to be generalized later
#NOTE: Modified(2021/02/14) with no backward compatibility
local_loc = location_indexes_inv(location, store=False)
mpf_new = delta_n * self.areas[local_loc]
#Treating borders
mpf_new /= numpy.where(
numpy.max(part_solver.pic.mesh.volumes) /
part_solver.pic.mesh.volumes[location] > 3, 2, 1)
#Computing number of particles created
mpf_new = mpf_new / species.spwt + species.mesh_values.residuals[
location] + add_rand
mp_new = mpf_new.astype(int)
species.mesh_values.residuals[location] = mpf_new - mp_new
#Assigning positions
pos_1 = numpy.repeat(part_solver.pic.mesh.getPosition(location),
mp_new,
axis=0)
random = numpy.random.rand(numpy.shape(pos_1)[0])
random += numpy.where(random == 0, 1e-3, 0)
hit_1 = numpy.repeat(self.directions[local_loc], mp_new)
ind_b = numpy.flatnonzero(hit_1 == 0)
ind_l = numpy.flatnonzero(hit_1 == 3)
ind_r = numpy.flatnonzero(hit_1 == 1)
ind_t = numpy.flatnonzero(hit_1 == 2)
#Bottom
shifts = numpy.where(
numpy.abs(pos_1[ind_b, 0] - self.xmin) < prec,
random[ind_b] * part_solver.pic.mesh.dx / 2,
(random[ind_b] - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[ind_b, 1] - self.xmax) < prec,
random[ind_b] * part_solver.pic.mesh.dx / 2, 0)
pos_1[ind_b, 0] += shifts
#Left
shifts = numpy.where(
numpy.abs(pos_1[ind_l, 1] - self.ymin) < prec,
random[ind_l] * part_solver.pic.mesh.dy / 2,
(random[ind_l] - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[ind_l, 1] - self.ymax) < prec,
random[ind_l] * part_solver.pic.mesh.dy / 2, 0)
pos_1[ind_l, 1] += shifts
#Right
shifts = numpy.where(
numpy.abs(pos_1[ind_r, 1] - self.ymin) < prec,
random[ind_r] * part_solver.pic.mesh.dy / 2,
(random[ind_r] - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[ind_r, 1] - self.ymax) < prec,
random[ind_r] * part_solver.pic.mesh.dy / 2, 0)
pos_1[ind_r, 1] += shifts
#Top
shifts = numpy.where(
numpy.abs(pos_1[ind_t, 0] - self.xmin) < prec,
random[ind_t] * part_solver.pic.mesh.dx / 2,
(random[ind_t] - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[ind_t, 1] - self.xmax) < prec,
random[ind_t] * part_solver.pic.mesh.dx / 2, 0)
pos_1[ind_t, 0] += shifts
repeats = numpy.ones(numpy.shape(hit_1)[0], dtype=numpy.uint8)
return (numpy.append(pos_1, hit_1[:, None], axis=1), ), repeats
def createDistributionAtBorder(self,
location,
part_solver,
species,
delta_n,
prec=1e-5):
add_rand = numpy.random.rand(len(location))
#NOTE: this should be generalized
if self.material != 'space':
local_loc = location_indexes_inv(location, store=False)
else:
c = 0
local_loc = []
for index, loc in numpy.ndenumerate(self.location):
if loc == location[c]:
local_loc.append(index[0])
c += 1
local_loc = numpy.asarray(local_loc, dtype='uint32')
mpf_new = delta_n * self.areas[local_loc]
mpf_new = mpf_new / species.spwt + species.mesh_values.residuals[
location] + add_rand
mp_new = mpf_new.astype(int)
species.mesh_values.residuals[location] = mpf_new - mp_new
#Assigning positions
pos_1 = numpy.repeat(part_solver.pic.mesh.getPosition(location),
mp_new,
axis=0)
random = numpy.random.rand(numpy.shape(pos_1)[0])
#Bottom
if self.directions[local_loc[0]] == 0:
shifts = numpy.where(
numpy.abs(pos_1[:, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[:, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[:, 0] += shifts
hit_1 = numpy.zeros_like(pos_1[:, 1], dtype=numpy.uint8)[:, None]
#Left
elif self.directions[local_loc[0]] == 3:
shifts = numpy.where(
numpy.abs(pos_1[:, 1] - self.ymin) < prec,
random * part_solver.pic.mesh.dy / 2,
(random - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[:, 1] - self.ymax) < prec,
random * part_solver.pic.mesh.dy / 2, 0)
pos_1[:, 1] += shifts
hit_1 = 3 * numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:,
None]
#Right
elif self.directions[local_loc[0]] == 1:
shifts = numpy.where(
numpy.abs(pos_1[:, 1] - self.ymin) < prec,
random * part_solver.pic.mesh.dy / 2,
(random - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[:, 1] - self.ymax) < prec,
random * part_solver.pic.mesh.dy / 2, 0)
pos_1[:, 1] += shifts
hit_1 = numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:, None]
#Top
else:
shifts = numpy.where(
numpy.abs(pos_1[:, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[:, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[:, 0] += shifts
hit_1 = 2 * numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:,
None]
repeats = numpy.ones(numpy.shape(hit_1)[0], dtype=numpy.uint8)
return (numpy.append(pos_1, hit_1, axis=1), ), repeats
def createDistributionAtBorder(self,
location,
part_solver,
species,
delta_n,
prec=1e-5):
#This needs to be generalized later
#NOTE: Modified(2021/02/14) with no backward compatibility
local_loc = location_indexes_inv(location, store=False)
mpf_new = delta_n * self.areas[local_loc]
#Treating borders
y = (numpy.arange(part_solver.pic.mesh.nPoints) // part_solver.pic.
mesh.nx) * part_solver.pic.mesh.dy + part_solver.pic.mesh.ymin
if part_solver.pic.mesh.ymin == 0.0:
y[:part_solver.pic.mesh.nx] = part_solver.pic.mesh.dy / 8
dv = 2 * numpy.pi * y * part_solver.pic.mesh.dy * part_solver.pic.mesh.dx
mpf_new /= numpy.where(
numpy.abs(dv[location] / part_solver.pic.mesh.volumes[location] -
2) > 1e-3, 2, 1)
#Computing number of particles created
add_rand = numpy.random.rand(len(location))
mpf_new = mpf_new / species.spwt + species.mesh_values.residuals[
location] + add_rand
mp_new = mpf_new.astype(int)
species.mesh_values.residuals[location] = mpf_new - mp_new
#Assigning positions
pos = part_solver.pic.mesh.getPosition(location)
pos_1 = numpy.repeat(pos, mp_new, axis=0)
hit_1 = self.directions[local_loc]
ind_b = hit_1 == 0
ind_l = hit_1 == 3
ind_r = hit_1 == 1
ind_t = hit_1 == 2
#Bottom
random = numpy.random.rand(numpy.sum(mp_new[ind_b]))
random += numpy.where(random == 0, 1e-3, 0)
ind = numpy.repeat(ind_b, mp_new)
shifts = numpy.where(
numpy.abs(pos_1[ind, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[ind, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[ind, 0] += shifts
#Left
rmin = numpy.where(pos[ind_l, 1] == self.ymin, pos[ind_l, 1],
pos[ind_l, 1] - part_solver.pic.mesh.dy / 2)
rmax = numpy.where(pos[ind_l, 1] == self.ymax, pos[ind_l, 1],
pos[ind_l, 1] + part_solver.pic.mesh.dy / 2)
pos_1[numpy.repeat(ind_l, mp_new),
1] = cmt.randomYPositions_2D_cm(mp_new[ind_l], rmin, rmax)
#Right
rmin = numpy.where(pos[ind_r, 1] == self.ymin, pos[ind_r, 1],
pos[ind_r, 1] - part_solver.pic.mesh.dy / 2)
rmax = numpy.where(pos[ind_r, 1] == self.ymax, pos[ind_r, 1],
pos[ind_r, 1] + part_solver.pic.mesh.dy / 2)
pos_1[numpy.repeat(ind_r, mp_new),
1] = cmt.randomYPositions_2D_cm(mp_new[ind_r], rmin, rmax)
#Top
random = numpy.random.rand(numpy.sum(mp_new[ind_t]))
random += numpy.where(random == 0, 1e-3, 0)
ind = numpy.repeat(ind_t, mp_new)
shifts = numpy.where(
numpy.abs(pos_1[ind, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[ind, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[ind, 0] += shifts
hit_1 = numpy.repeat(hit_1, mp_new)
repeats = numpy.ones(numpy.shape(hit_1)[0], dtype=numpy.uint8)
return (numpy.append(pos_1, hit_1[:, None], axis=1), ), repeats
def createDistributionAtBorder(self,
location,
part_solver,
species,
delta_n,
prec=1e-5):
add_rand = numpy.random.rand(len(location))
local_loc = location_indexes_inv(location, store=False)
mpf_new = delta_n * part_solver.pic.mesh.area_sat[local_loc]
mpf_new = mpf_new / species.spwt + species.mesh_values.residuals[
location] + add_rand
mp_new = mpf_new.astype(int)
species.mesh_values.residuals[location] = mpf_new - mp_new
#Assigning positions
pos_1 = numpy.repeat(part_solver.pic.mesh.getPosition(location),
mp_new,
axis=0)
random = numpy.random.rand(numpy.shape(pos_1)[0])
#Bottom
if self.directions[local_loc[0]] == 0:
shifts = numpy.where(
numpy.abs(pos_1[:, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[:, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[:, 0] += shifts
hit_1 = numpy.zeros_like(pos_1[:, 1], dtype=numpy.uint8)[:, None]
#Left
elif self.directions[local_loc[0]] == 3:
shifts = numpy.where(
numpy.abs(pos_1[:, 1] - self.ymin) < prec,
random * part_solver.pic.mesh.dy / 2,
(random - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[:, 1] - self.ymax) < prec,
random * part_solver.pic.mesh.dy / 2, 0)
pos_1[:, 1] += shifts
hit_1 = 3 * numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:,
None]
#Right
elif self.directions[local_loc[0]] == 1:
shifts = numpy.where(
numpy.abs(pos_1[:, 1] - self.ymin) < prec,
random * part_solver.pic.mesh.dy / 2,
(random - 0.5) * part_solver.pic.mesh.dy)
shifts -= numpy.where(
numpy.abs(pos_1[:, 1] - self.ymax) < prec,
random * part_solver.pic.mesh.dy / 2, 0)
pos_1[:, 1] += shifts
hit_1 = numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:, None]
#Top
else:
shifts = numpy.where(
numpy.abs(pos_1[:, 0] - self.xmin) < prec,
random * part_solver.pic.mesh.dx / 2,
(random - 0.5) * part_solver.pic.mesh.dx)
shifts -= numpy.where(
numpy.abs(pos_1[:, 0] - self.xmax) < prec,
random * part_solver.pic.mesh.dx / 2, 0)
pos_1[:, 0] += shifts
hit_1 = 2 * numpy.ones_like(pos_1[:, 1], dtype=numpy.uint8)[:,
None]
repeats = numpy.ones(numpy.shape(hit_1)[0], dtype=numpy.uint8)
return (numpy.append(pos_1, hit_1, axis=1), ), repeats