def find_mean_cloudlet_velocity(cloudlets,
u, v, w,
MC):
dx, dy, dz, dt = MC['dx'], MC['dy'], MC['dz'], MC['dt']
ug, vg = MC['ug'], MC['vg']
for cloudlet in cloudlets:
if len(cloudlet['condensed']) > 0:
K, J, I = index_to_zyx( cloudlet['condensed'], MC )
# find the mean motion of the cloudlet
u_mean = u[K, J, I].mean()-ug
v_mean = v[K, J, I].mean()-vg
w_mean = w[K, J, I].mean()
cloudlet['u_condensed'] = round(u_mean*dt/dx)
cloudlet['v_condensed'] = round(v_mean*dt/dy)
cloudlet['w_condensed'] = round(w_mean*dt/dz)
else:
cloudlet['u_condensed'] = 0.
cloudlet['v_condensed'] = 0.
cloudlet['w_condensed'] = 0.
K, J, I = index_to_zyx( cloudlet['plume'], MC )
# find the mean motion of the cloudlet
u_mean = u[K, J, I].mean()-ug
v_mean = v[K, J, I].mean()-vg
w_mean = w[K, J, I].mean()
cloudlet['u_plume'] = round(u_mean*dt/dx)
cloudlet['v_plume'] = round(v_mean*dt/dy)
cloudlet['w_plume'] = round(w_mean*dt/dz)
return cloudlets
def calc_env(index, shellindex, edgeindex, MC):
if len(shellindex) > 0:
K_J_I = index_to_zyx(shellindex, MC)
n = 6
for i in range(n):
# Find all the points just outside the clouds
stacklist = [K_J_I, ]
for item in ((0, -1, 0), (0, 1, 0),
(0, 0, -1), (0, 0, 1)):
stacklist.append( K_J_I + numpy.array(item)[:, numpy.newaxis] )
maskindex = numpy.hstack(stacklist)
maskindex[1, :] = maskindex[1, :] % MC['ny']
maskindex[2, :] = maskindex[2, :] % MC['nx']
maskindex = numpy.unique( zyx_to_index(maskindex[0, :],
maskindex[1, :],
maskindex[2, :],
MC) )
# From the expanded mask, select the points outside the cloud
envindex = numpy.setdiff1d(maskindex, index, assume_unique = True)
K_J_I = index_to_zyx(envindex, MC)
# Select the points within 4 grid cells of cloud
r = calc_radii(envindex, edgeindex, MC)
mask = r < 4.5
envindex = envindex[mask]
else:
envindex = []
return envindex
def find_mean_cloudlet_velocity(cloudlets, u, v, w, MC):
dx, dy, dz, dt = MC['dx'], MC['dy'], MC['dz'], MC['dt']
ug, vg = MC['ug'], MC['vg']
for cloudlet in cloudlets:
if len(cloudlet['condensed']) > 0:
K, J, I = index_to_zyx(cloudlet['condensed'], MC)
# find the mean motion of the cloudlet
u_mean = u[K, J, I].mean() - ug
v_mean = v[K, J, I].mean() - vg
w_mean = w[K, J, I].mean()
cloudlet['u_condensed'] = round(u_mean * dt / dx)
cloudlet['v_condensed'] = round(v_mean * dt / dy)
cloudlet['w_condensed'] = round(w_mean * dt / dz)
else:
cloudlet['u_condensed'] = 0.
cloudlet['v_condensed'] = 0.
cloudlet['w_condensed'] = 0.
K, J, I = index_to_zyx(cloudlet['plume'], MC)
# find the mean motion of the cloudlet
u_mean = u[K, J, I].mean() - ug
v_mean = v[K, J, I].mean() - vg
w_mean = w[K, J, I].mean()
cloudlet['u_plume'] = round(u_mean * dt / dx)
cloudlet['v_plume'] = round(v_mean * dt / dy)
cloudlet['w_plume'] = round(w_mean * dt / dz)
return cloudlets
def calc_env(index, shellindex, edgeindex, MC):
if len(shellindex) > 0:
K_J_I = index_to_zyx(shellindex, MC)
n = 6
for i in range(n):
# Find all the points just outside the clouds
stacklist = [
K_J_I,
]
for item in ((0, -1, 0), (0, 1, 0), (0, 0, -1), (0, 0, 1)):
stacklist.append(K_J_I + numpy.array(item)[:, numpy.newaxis])
maskindex = numpy.hstack(stacklist)
maskindex[1, :] = maskindex[1, :] % MC['ny']
maskindex[2, :] = maskindex[2, :] % MC['nx']
maskindex = numpy.unique(
zyx_to_index(maskindex[0, :], maskindex[1, :], maskindex[2, :],
MC))
# From the expanded mask, select the points outside the cloud
envindex = numpy.setdiff1d(maskindex, index, assume_unique=True)
K_J_I = index_to_zyx(envindex, MC)
# Select the points within 4 grid cells of cloud
r = calc_radii(envindex, edgeindex, MC)
mask = r < 4.5
envindex = envindex[mask]
else:
envindex = []
return envindex
def calc_com(mask, MC):
ny, nx = MC['ny'], MC['nx']
pts = index_to_zyx( mask, MC )
z = pts[0,:].astype(float).mean()
# Correct Center of Mass for reentrant domain
y1 = pts[1,:].astype(float)
x1 = pts[2,:].astype(float)
y2 = (y1 < ny/2.)*y1 + (y1>= ny/2.)*(y1 - ny)
x2 = (x1 < nx/2.)*x1 + (x1>= nx/2.)*(x1 - nx)
y1m = y1.mean()
y2m = y2.mean()
x1m = x1.mean()
x2m = x2.mean()
if numpy.var(y2 - y2m) > numpy.var(y1 - y1m):
y = y1m
else:
y = (y2m + .5)%ny - .5
if numpy.var(y2 - y2m) > numpy.var(y1 - y1m):
y = y1m
else:
y = (y2m + .5)%ny - .5
if numpy.var(x2 - x2m) > numpy.var(x1 - x1m):
x = x1m
else:
x = (x2m + .5)%nx - .5
return numpy.array(([z], [y], [x]))
def calc_com(mask, MC):
ny, nx = MC['ny'], MC['nx']
pts = index_to_zyx(mask, MC)
z = pts[0, :].astype(float).mean()
# Correct Center of Mass for reentrant domain
y1 = pts[1, :].astype(float)
x1 = pts[2, :].astype(float)
y2 = (y1 < ny / 2.) * y1 + (y1 >= ny / 2.) * (y1 - ny)
x2 = (x1 < nx / 2.) * x1 + (x1 >= nx / 2.) * (x1 - nx)
y1m = y1.mean()
y2m = y2.mean()
x1m = x1.mean()
x2m = x2.mean()
if numpy.var(y2 - y2m) > numpy.var(y1 - y1m):
y = y1m
else:
y = (y2m + .5) % ny - .5
if numpy.var(y2 - y2m) > numpy.var(y1 - y1m):
y = y1m
else:
y = (y2m + .5) % ny - .5
if numpy.var(x2 - x2m) > numpy.var(x1 - x1m):
x = x1m
else:
x = (x2m + .5) % nx - .5
return numpy.array(([z], [y], [x]))
def save_text_file(clouds, t, MC):
count = 0
for id in clouds:
for point_type in clouds[id]:
count = count + len(clouds[id][point_type])
recarray = numpy.zeros(count,
dtype=[('id', 'i4'), ('type', 'a14'), ('x', 'i4'),
('y', 'i4'), ('z', 'i4')])
count = 0
for id in clouds:
for point_type in clouds[id]:
data = clouds[id][point_type]
n = len(data)
if n == 0: continue
recarray['id'][count:n + count] = id
recarray['type'][count:n + count] = point_type
z, y, x = index_to_zyx(data, MC)
recarray['x'][count:n + count] = x
recarray['y'][count:n + count] = y
recarray['z'][count:n + count] = z
count = count + n
recarray.tofile(open('output/clouds_at_time_%08g.txt' % t, 'w'), '\r\n')
def advect_indexes(indexes, u, v, w, MC):
K_J_I = index_to_zyx(indexes, MC)
K_J_I[0, :] = K_J_I[0, :] - w
K_J_I[1, :] = K_J_I[1, :] - v
K_J_I[2, :] = K_J_I[2, :] - u
K_J_I[0, K_J_I[0, :] >= MC['nz']] = MC['nz'] - 1
K_J_I[0, K_J_I[0, :] < 0] = 0
K_J_I[1, :] = K_J_I[1, :] % MC['ny']
K_J_I[2, :] = K_J_I[2, :] % MC['nx']
advected_indexes = zyx_to_index(K_J_I[0, :], K_J_I[1, :], K_J_I[2, :], MC)
return advected_indexes
def advect_indexes(indexes, u, v, w, MC):
K_J_I = index_to_zyx(indexes, MC)
K_J_I[0, :] = K_J_I[0, :] - w
K_J_I[1, :] = K_J_I[1, :] - v
K_J_I[2, :] = K_J_I[2, :] - u
K_J_I[0, K_J_I[0, :] >= MC['nz']] = MC['nz']-1
K_J_I[0, K_J_I[0, :] < 0] = 0
K_J_I[1, :] = K_J_I[1, :] % MC['ny']
K_J_I[2, :] = K_J_I[2, :] % MC['nx']
advected_indexes = zyx_to_index(K_J_I[0,:], K_J_I[1,:], K_J_I[2,:], MC)
return advected_indexes
def save_text_file(clouds, t, MC):
count = 0
for id in clouds:
for point_type in clouds[id]:
count = count + len(clouds[id][point_type])
recarray = numpy.zeros(count, dtype=[('id', 'i4'),('type', 'a14'),('x','i4'),('y', 'i4'), ('z', 'i4')])
count = 0
for id in clouds:
for point_type in clouds[id]:
data = clouds[id][point_type]
n = len(data)
if n == 0: continue
recarray['id'][count:n + count] = id
recarray['type'][count:n + count] = point_type
z, y, x = index_to_zyx(data, MC)
recarray['x'][count:n + count] = x
recarray['y'][count:n + count] = y
recarray['z'][count:n + count] = z
count = count + n
recarray.tofile(open('output/clouds_at_time_%08g.txt' % t, 'w'), '\r\n')
