diff --git a/HySoP/hysop/fields/vector.py b/HySoP/hysop/fields/vector.py
index cce238fc58f12183ea3e06fbba6d3ea033fbf7a4..aa0087f73681254d1bc1f9ec80e6c7dfd4851ae6 100644
--- a/HySoP/hysop/fields/vector.py
+++ b/HySoP/hysop/fields/vector.py
@@ -90,7 +90,10 @@ class VectorField(DiscreteField):
"""
if formula is not None:
print 'initializing', self.name, '...'
- v_formula = np.vectorize(formula)
+ if isinstance(formula, np.lib.function_base.vectorize):
+ v_formula = formula
+ else:
+ v_formula = np.vectorize(formula)
if self.dimension == 3:
self.data[0][...], self.data[1][...], self.data[2][...] = \
v_formula(*(self.topology.mesh.coords + args))
diff --git a/HySoP/hysop/gpu/gpu_discrete.py b/HySoP/hysop/gpu/gpu_discrete.py
index a0155d9bee89bb3851de6a131dc630b6dbe29dcb..f87c87a82feb9746b14d227591013c70905c1c1e 100644
--- a/HySoP/hysop/gpu/gpu_discrete.py
+++ b/HySoP/hysop/gpu/gpu_discrete.py
@@ -103,7 +103,7 @@ class GPUVectorField(VectorField, GPUDiscreteField):
print vfield.mem_size / (1024 ** 2), "MB)"
@debug
- def initialize(self, formula=None):
+ def initialize(self, formula=None, *args):
"""
GPU data initialization.
Performs the initialization from different ways if device not already
@@ -111,59 +111,58 @@ class GPUVectorField(VectorField, GPUDiscreteField):
- with an OpenCL kernel,
- with a python formula (as VectorField) and the copy data to device.
"""
- if not self.data_on_device:
- if self.initialization_kernel is None:
- VectorField.initialize(self, formula)
- if self.contains_data:
- if self.dimension == 2:
- self.data[0] = np.asarray(
- self.data[0],
- dtype=self.precision, order=ORDER)
- self.data[1] = np.asarray(
- self.data[1],
- dtype=self.precision, order=ORDER)
- else:
- self.data[0] = np.asarray(
- self.data[0],
- dtype=self.precision, order=ORDER)
- self.data[1] = np.asarray(
- self.data[1],
- dtype=self.precision, order=ORDER)
- self.data[2] = np.asarray(
- self.data[2],
- dtype=self.precision, order=ORDER)
- data, time = self.toDevice()
- if CL_PROFILE:
- print "Transfers : ", self.mem_size,
- print "Bytes transfered at ",
- print "{0:.3f} GBytes/sec".format(
- (self.mem_size * 1e-9) / time)
- else:
- coord_min = np.ones(4, dtype=self.precision)
- mesh_size = np.ones(4, dtype=self.precision)
- coord_min[0:self.dimension] = np.asarray(
- self.topology.mesh.origin,
- dtype=self.precision)
- mesh_size[0:self.dimension] = np.asarray(
- self.topology.mesh.space_step,
- dtype=self.precision)
+ if self.initialization_kernel is None:
+ VectorField.initialize(self, formula, *args)
+ if self.contains_data:
if self.dimension == 2:
- evt = self.initialization_kernel(self.gpu_data[0],
- self.gpu_data[1],
- coord_min,
- mesh_size)
+ self.data[0] = np.asarray(
+ self.data[0],
+ dtype=self.precision, order=ORDER)
+ self.data[1] = np.asarray(
+ self.data[1],
+ dtype=self.precision, order=ORDER)
else:
- evt = self.initialization_kernel(self.gpu_data[0],
- self.gpu_data[1],
- self.gpu_data[2],
- coord_min,
- mesh_size)
+ self.data[0] = np.asarray(
+ self.data[0],
+ dtype=self.precision, order=ORDER)
+ self.data[1] = np.asarray(
+ self.data[1],
+ dtype=self.precision, order=ORDER)
+ self.data[2] = np.asarray(
+ self.data[2],
+ dtype=self.precision, order=ORDER)
+ data, time = self.toDevice()
if CL_PROFILE:
- self.queue.finish()
- cTime = (evt.profile.end - evt.profile.start) * 1e-9
- print "Computing : ", cTime, "sec"
- self.contains_data = False
- self.data_on_device = True
+ print "Transfers : ", self.mem_size,
+ print "Bytes transfered at ",
+ print "{0:.3f} GBytes/sec".format(
+ (self.mem_size * 1e-9) / time)
+ else:
+ coord_min = np.ones(4, dtype=self.precision)
+ mesh_size = np.ones(4, dtype=self.precision)
+ coord_min[0:self.dimension] = np.asarray(
+ self.topology.mesh.origin,
+ dtype=self.precision)
+ mesh_size[0:self.dimension] = np.asarray(
+ self.topology.mesh.space_step,
+ dtype=self.precision)
+ if self.dimension == 2:
+ evt = self.initialization_kernel(self.gpu_data[0],
+ self.gpu_data[1],
+ coord_min,
+ mesh_size)
+ else:
+ evt = self.initialization_kernel(self.gpu_data[0],
+ self.gpu_data[1],
+ self.gpu_data[2],
+ coord_min,
+ mesh_size)
+ if CL_PROFILE:
+ self.queue.finish()
+ cTime = (evt.profile.end - evt.profile.start) * 1e-9
+ print "Computing : ", cTime, "sec"
+ self.contains_data = False
+ self.data_on_device = True
def toDevice(self):
"""
@@ -315,7 +314,7 @@ class GPUScalarField(ScalarField, GPUDiscreteField):
print sfield.mem_size / (1024 ** 2), "MB)"
@debug
- def initialize(self, formula=None):
+ def initialize(self, formula=None, *args):
"""
GPU data initialization.
Performs the initialization from different ways if device not already
@@ -323,36 +322,35 @@ class GPUScalarField(ScalarField, GPUDiscreteField):
- with an OpenCL kernel,
- with a python formula (as VectorField) and the copy data to device.
"""
- if not self.data_on_device:
- if self.initialization_kernel is None:
- ScalarField.initialize(self, formula)
- if self.contains_data:
- self.data = np.asarray(self.data,
- dtype=self.precision, order=ORDER)
- data, time = self.toDevice()
- if CL_PROFILE:
- print "Transfers : ", self.mem_size,
- print "Bytes transfered at ",
- print "{0:.3f} GBytes/sec".format(
- (self.mem_size * 1e-9) / time)
- else:
- coord_min = np.ones(4, dtype=self.precision)
- mesh_size = np.ones(4, dtype=self.precision)
- coord_min[0:self.dimension] = np.asarray(
- self.topology.mesh.origin,
- dtype=self.precision)
- mesh_size[0:self.dimension] = np.asarray(
- self.topology.mesh.space_step,
- dtype=self.precision)
- evt = self.initialization_kernel(self.gpu_data,
- coord_min,
- mesh_size)
+ if self.initialization_kernel is None:
+ ScalarField.initialize(self, formula, *args)
+ if self.contains_data:
+ self.data = np.asarray(self.data,
+ dtype=self.precision, order=ORDER)
+ data, time = self.toDevice()
if CL_PROFILE:
- self.queue.finish()
- cTime = (evt.profile.end - evt.profile.start) * 1e-9
- print "Computing : ", cTime, "sec"
- self.contains_data = False
- self.data_on_device = True
+ print "Transfers : ", self.mem_size,
+ print "Bytes transfered at ",
+ print "{0:.3f} GBytes/sec".format(
+ (self.mem_size * 1e-9) / time)
+ else:
+ coord_min = np.ones(4, dtype=self.precision)
+ mesh_size = np.ones(4, dtype=self.precision)
+ coord_min[0:self.dimension] = np.asarray(
+ self.topology.mesh.origin,
+ dtype=self.precision)
+ mesh_size[0:self.dimension] = np.asarray(
+ self.topology.mesh.space_step,
+ dtype=self.precision)
+ evt = self.initialization_kernel(self.gpu_data,
+ coord_min,
+ mesh_size)
+ if CL_PROFILE:
+ self.queue.finish()
+ cTime = (evt.profile.end - evt.profile.start) * 1e-9
+ print "Computing : ", cTime, "sec"
+ self.contains_data = False
+ self.data_on_device = True
def toDevice(self):
"""
diff --git a/HySoP/hysop/operator/discrete/analytic.py b/HySoP/hysop/operator/discrete/analytic.py
index 31aeef6e63baa2ca76fb5be64e1eb9a6b4138065..575c9fd094603ebb1bd16798c06ec39d10b73783 100644
--- a/HySoP/hysop/operator/discrete/analytic.py
+++ b/HySoP/hysop/operator/discrete/analytic.py
@@ -3,7 +3,7 @@
Apply user-defined formula to a set of discrete variables.
"""
-from parmepy.constants import np, debug, ORDER
+from parmepy.constants import np, debug
from discrete import DiscreteOperator
from parmepy.mpi import MPI
@@ -39,31 +39,7 @@ class Analytic_D(DiscreteOperator):
def apply(self, t, dt, ite):
self.compute_time = MPI.Wtime()
for df in self.variables:
- datatype = df.data[0].dtype
- if df.isVector:
- if df.topology.dim == 3:
- df.data[0][...], df.data[1][...], df.data[2][...] = \
- self.vformula(*(
- df.topology.mesh.coords + (t, dt, ite)))
- df.data[0][...] = np.asarray(df.data[0],
- dtype=datatype, order=ORDER)
- df.data[1][...] = np.asarray(df.data[1],
- dtype=datatype, order=ORDER)
- df.data[2][...] = np.asarray(df.data[2],
- dtype=datatype, order=ORDER)
- elif df.topology.dim == 2:
- df.data[0][...], df.data[1][...] = \
- self.vformula(*(
- df.topology.mesh.coords + (t, dt, ite)))
- df.data[0][...] = np.asarray(df.data[0],
- dtype=datatype, order=ORDER)
- df.data[1][...] = np.asarray(df.data[1],
- dtype=datatype, order=ORDER)
- else:
- df.data[...] = \
- self.vformula(*(df.topology.mesh.coords + (t, dt, ite)))
- df.data[...] = np.asarray(df.data,
- dtype=datatype, order=ORDER)
+ df.initialize(self.vformula, t, dt, ite)
for df in self.output:
df.contains_data = True