diff --git a/hysop/backend/host/python/operator/penalization.py b/hysop/backend/host/python/operator/penalization.py
index d1aa890e322e6e19b1e0d3c49f7c9836bcddd5ec..d851c436d631b783a511ad6cf5d6a1802a74545d 100644
--- a/hysop/backend/host/python/operator/penalization.py
+++ b/hysop/backend/host/python/operator/penalization.py
@@ -39,11 +39,11 @@ class PythonPenalizeVorticity(HostOperator):
@debug
def __new__(cls, obstacles, variables,
- velocity, vorticity,
- dt, coeff=None, ubar=None, formulation=None, **kwds):
+ velocity, vorticity,
+ dt, coeff=None, ubar=None, formulation=None, **kwds):
return super(PythonPenalizeVorticity, cls).__new__(cls,
- input_fields=None, output_fields=None,
- input_params=None, **kwds)
+ input_fields=None, output_fields=None,
+ input_params=None, **kwds)
@debug
def __init__(self, obstacles, variables,
@@ -252,3 +252,105 @@ class PythonPenalizeVorticity(HostOperator):
# Y direction
self.tmp = self.stencil[1](a=self.tmp_v[0], out=self.tmp, axis=0)
self.W[0][...] += -self.tmp
+
+
+class PythonPenalizeVelocity(HostOperator, CommonPenalization):
+ """
+
+ """
+ __default_method = {
+ SpaceDiscretization: SpaceDiscretization.FDC4
+ }
+
+ __available_methods = {
+ SpaceDiscretization: (InstanceOf(SpaceDiscretization), InstanceOf(int))
+ }
+
+ @classmethod
+ def default_method(cls):
+ dm = super(PythonPenalizeVelocity, cls).default_method()
+ dm.update(cls.__default_method)
+ return dm
+
+ @classmethod
+ def available_methods(cls):
+ am = super(PythonPenalizeVelocity, cls).available_methods()
+ am.update(cls.__available_methods)
+ return am
+
+ @debug
+ def __new__(cls, obstacles, variables, velocity,
+ dt, coeff=None, ubar=None, formulation=None, **kwds):
+ return super(PythonPenalizeVelocity, cls).__new__(
+ cls, input_fields=None, output_fields=None, input_params=None, **kwds)
+
+ @debug
+ def __init__(self, obstacles, variables,
+ velocity,
+ dt, coeff=None, ubar=None, formulation=None, **kwds):
+ check_instance(velocity, Field)
+ check_instance(variables, dict, keys=Field,
+ values=CartesianTopologyDescriptors)
+
+ input_fields = {velocity: variables[velocity], }
+ output_fields = {velocity: variables[velocity]}
+ input_params = {dt.name: dt}
+ for o in (obstacles.values() if isinstance(obstacles, dict) else obstacles):
+ input_fields[o] = variables[o]
+
+ self.velocity = velocity
+ self.dt = dt
+ super(PythonPenalizeVelocity, self).__init__(
+ input_fields=input_fields, output_fields=output_fields,
+ input_params=input_params,
+ obstacles=obstacles, velocity=velocity, dt=dt, coeff=coeff, ubar=ubar, formulation=formulation, **kwds)
+
+ @debug
+ def discretize(self):
+ if self.discretized:
+ return
+ super(PythonPenalizeVelocity, self).discretize()
+ self.dvelocity = self.input_discrete_tensor_fields[self.velocity]
+
+ dv = self.dvelocity
+ self.dobstacles = {}
+ for c, o in self.obstacles.items():
+ o_df = self.input_discrete_fields[o]
+ self.dobstacles[c] = o_df.data[0]
+
+ self.v_ghost_exchanger = dv.build_ghost_exchanger()
+ if self.v_ghost_exchanger is None:
+ def _dummy_func():
+ pass
+ self.v_ghost_exchanger = _dummy_func
+
+ @debug
+ def get_work_properties(self):
+ requests = super(PythonPenalizeVelocity, self).get_work_properties()
+ buffers = MemoryRequest.empty_like(
+ a=self.dvelocity.buffers[0], nb_components=1, backend=self.dvelocity.backend)
+ requests.push_mem_request('Wtmp', buffers)
+ return requests
+
+ def setup(self, work):
+ super(PythonPenalizeVelocity, self).setup(work=work)
+ Wtmp = work.get_buffer(self, 'Wtmp', handle=True)
+ self.tmp = Wtmp[-1]
+
+ @classmethod
+ def supported_dimensions(cls):
+ return (3, 2)
+
+ @classmethod
+ def supports_mpi(cls):
+ return True
+
+ @op_apply
+ def apply(self, **kwds):
+ super(PythonPenalizeVelocity, self).apply(**kwds)
+
+ # Penalize velocity
+ self._compute_penalization()
+ for ubar, v in zip(self._ubar()[::-1], self.dvelocity.buffers):
+ v[...] += (v - ubar) * self.tmp
+ self.v_ghost_exchanger()
diff --git a/hysop/operator/dummy.py b/hysop/operator/dummy.py
index eac94502e6d23ca14ea7d535784e4af754d38aa0..272db67d8ce06e28c6f04bd03bb72f3cda3703fb 100644
--- a/hysop/operator/dummy.py
+++ b/hysop/operator/dummy.py
@@ -16,8 +16,8 @@ class PythonDummy(HostOperator):
@debug
def __new__(cls, variables, **kwds):
- return super(PythonDummy, cls).__new__(cls,
- input_fields=None, output_fields=None, **kwds)
+ return super(PythonDummy, cls).__new__(
+ cls, input_fields=None, output_fields=None, **kwds)
@debug
def __init__(self, variables, **kwds):
@@ -49,10 +49,49 @@ class PythonDummy(HostOperator):
return True
+class OpenClDummy(OpenClOperator):
+
+ @debug
+ def __new__(cls, variables, **kwds):
+ return super(OpenClDummy, cls).__new__(
+ cls,
+ input_fields=None, output_fields=None, **kwds)
+
+ @debug
+ def __init__(self, variables, **kwds):
+ check_instance(variables, dict, keys=Field,
+ values=CartesianTopologyDescriptors)
+ input_fields = variables
+ output_fields = variables
+ super(OpenClDummy, self).__init__(
+ input_fields=input_fields, output_fields=output_fields,
+ **kwds)
+
+ @debug
+ def get_field_requirements(self):
+ requirements = super(OpenClDummy, self).get_field_requirements()
+ for (is_input, reqs) in requirements.iter_requirements():
+ if (reqs is None):
+ continue
+ (field, td, req) = reqs
+ req.axes = ((0, 1, 2), )
+ return requirements
+
+ @op_apply
+ def apply(self, **kwds):
+ super(OpenClDummy, self).apply(**kwds)
+ # Here doing nothing
+
+ @classmethod
+ def supports_mpi(cls):
+ return True
+
+
class Dummy(ComputationalGraphNodeFrontend):
__implementations = {
Implementation.PYTHON: PythonDummy
+ Implementation.OPENCL: OpenClDummy
}
@classmethod
@@ -62,4 +101,3 @@ class Dummy(ComputationalGraphNodeFrontend):
@classmethod
def default_implementation(cls):
return Implementation.PYTHON
-