def __init__(self,
inlet_pa,
dest_pa,
inletinfo,
kernel,
dim,
active_stages=[1],
callback=None,
ghost_pa=None):
"""An API to add/delete particle when moving between inlet-fluid
Parameters
----------
inlet_pa : particle_array
particle array for inlet
dest_pa : particle_array
particle_array of the fluid
inletinfo : InletInfo instance
contains information fo inlet
kernel : Kernel instance
Kernel to be used for computations
dim : int
dimension of the problem
active_stages : list
stages of integrator at which update should be active
callback : function
callback after the update function
ghost_pa : particle_array
particle_array of the ghost_inlet
"""
self.inlet_pa = inlet_pa
self.dest_pa = dest_pa
self.ghost_pa = ghost_pa
self.callback = callback
self.dim = dim
self.kernel = kernel
self.inletinfo = inletinfo
self.x = self.y = self.z = 0.0
self.xn = self.yn = self.zn = 0.0
self.length = 0.0
self.dx = 0.0
self.active_stages = active_stages
self.io_eval = None
self._init = False
cfg = get_config()
self.gpu = cfg.use_opencl or cfg.use_cuda
def __init__(self,
outlet_pa,
source_pa,
outletinfo,
kernel,
dim,
active_stages=[1],
callback=None,
ghost_pa=None):
"""An API to add/delete particle when moving between fluid-outlet
Parameters
----------
outlet_pa : particle_array
particle array for outlet
source_pa : particle_array
particle_array of the fluid
ghost_pa : particle_array
particle_array of the outlet ghost
outletinfo : OutletInfo instance
contains information fo outlet
kernel : Kernel instance
Kernel to be used for computations
dim : int
dimnesion of the problem
active_stages : list
stages of integrator at which update should be active
callback : function
callback after the update function
"""
self.outlet_pa = outlet_pa
self.source_pa = source_pa
self.ghost_pa = ghost_pa
self.dim = dim
self.kernel = kernel
self.outletinfo = outletinfo
self.x = self.y = self.z = 0.0
self.xn = self.yn = self.zn = 0.0
self.length = 0.0
self.callback = callback
self.active_stages = active_stages
self.io_eval = None
self._init = False
self.props_to_copy = None
cfg = get_config()
self.gpu = cfg.use_opencl or cfg.use_cuda
def setup(backend, openmp=False):
get_config().use_openmp = openmp
e = Elementwise(axpb, backend=backend)
return e
"""Shows the use of annotate without any type information.
The type information is extracted from the arguments passed
and the function is annotated and compiled at runtime.
"""
from compyle.api import annotate, Elementwise, wrap, get_config, declare
import numpy as np
from numpy import sin
@annotate
def axpb(i, x, y, a, b):
xi = x[i]
y[i] = a * sin(xi) + b
x = np.linspace(0, 1, 10000)
y = np.zeros_like(x)
a = 2.0
b = 3.0
backend = 'opencl'
get_config().use_openmp = True
x, y = wrap(x, y, backend=backend)
e = Elementwise(axpb, backend=backend)
e(x, y, a, b)
p.add_argument('-n',
action='store',
type=int,
dest='n',
default=512,
help='Number of grid points in y.')
p.add_argument('--show',
action='store_true',
dest='show',
default=False,
help='Show animation (requires mayavi)')
p.add_argument('--gif',
action='store_true',
default=False,
help='Make a gif animation (requires imageio)')
cfg = get_config()
cfg.suppress_warnings = True
o = p.parse_args()
julia = elementwise(julia)
ny = o.n
nx = int(4 * ny // 3)
x, y = np.mgrid[-2:2:nx * 1j, -1.5:1.5:ny * 1j]
x, y = x.ravel(), y.ravel()
z = np.zeros_like(x)
x, y, z = wrap(x, y, z)
timer(x, y, z)
if o.show:
plot(x, y, z, nx, ny)
if o.gif: