def test_save_load_octree(): np.random.seed(int(0x4d3d3d3)) pos = np.random.normal(0.5, scale=0.05, size=(NPART, 3)) * (DRE - DLE) + DLE octree = ParticleOctreeContainer((1, 1, 1), DLE, DRE) octree.n_ref = 32 for i in range(3): np.clip(pos[:, i], DLE[i], DRE[i], pos[:, i]) # Convert to integers pos = np.floor((pos - DLE) / dx).astype("uint64") morton = get_morton_indices(pos) morton.sort() octree.add(morton) octree.finalize() saved = octree.save_octree() loaded = OctreeContainer.load_octree(saved) always = AlwaysSelector(None) ir1 = octree.ires(always) ir2 = loaded.ires(always) yield assert_equal, ir1, ir2 fc1 = octree.fcoords(always) fc2 = loaded.fcoords(always) yield assert_equal, fc1, fc2 fw1 = octree.fwidth(always) fw2 = loaded.fwidth(always) yield assert_equal, fw1, fw2
def test_save_load_octree(): np.random.seed(int(0x4d3d3d3)) pos = np.random.normal(0.5, scale=0.05, size=(NPART,3)) * (DRE-DLE) + DLE octree = ParticleOctreeContainer((1, 1, 1), DLE, DRE) octree.n_ref = 32 for i in range(3): np.clip(pos[:,i], DLE[i], DRE[i], pos[:,i]) # Convert to integers pos = np.floor((pos - DLE)/dx).astype("uint64") morton = get_morton_indices(pos) morton.sort() octree.add(morton) octree.finalize() saved = octree.save_octree() loaded = OctreeContainer.load_octree(saved) always = AlwaysSelector(None) ir1 = octree.ires(always) ir2 = loaded.ires(always) yield assert_equal, ir1, ir2 fc1 = octree.fcoords(always) fc2 = loaded.fcoords(always) yield assert_equal, fc1, fc2 fw1 = octree.fwidth(always) fw2 = loaded.fwidth(always) yield assert_equal, fw1, fw2
def test_add_particles_random(): np.random.seed(int(0x4d3d3d3)) pos = np.random.normal(0.5, scale=0.05, size=(NPART, 3)) * (DRE - DLE) + DLE # Now convert to integers for i in range(3): np.clip(pos[:, i], DLE[i], DRE[i], pos[:, i]) # Convert to integers pos = np.floor((pos - DLE) / dx).astype("uint64") morton = get_morton_indices(pos) morton.sort() for ndom in [1, 2, 4, 8]: octree = ParticleOctreeContainer((1, 1, 1), DLE, DRE) octree.n_ref = 32 for dom, split in enumerate(np.array_split(morton, ndom)): octree.add(split) octree.finalize() # This visits every oct. tc = octree.recursively_count() total_count = np.zeros(len(tc), dtype="int32") for i in sorted(tc): total_count[i] = tc[i] yield assert_equal, octree.nocts, total_count.sum() # This visits every cell -- including those covered by octs. #for dom in range(ndom): # level_count += octree.count_levels(total_count.size-1, dom, mask) yield assert_equal, total_count, [1, 8, 64, 64, 256, 536, 1856, 1672]
def test_add_particles_random(): np.random.seed(int(0x4d3d3d3)) pos = np.random.normal(0.5, scale=0.05, size=(NPART,3)) * (DRE-DLE) + DLE # Now convert to integers for i in range(3): np.clip(pos[:,i], DLE[i], DRE[i], pos[:,i]) # Convert to integers pos = np.floor((pos - DLE)/dx).astype("uint64") morton = get_morton_indices(pos) morton.sort() for ndom in [1, 2, 4, 8]: octree = ParticleOctreeContainer((1, 1, 1), DLE, DRE) octree.n_ref = 32 for dom, split in enumerate(np.array_split(morton, ndom)): octree.add(split) octree.finalize() # This visits every oct. tc = octree.recursively_count() total_count = np.zeros(len(tc), dtype="int32") for i in sorted(tc): total_count[i] = tc[i] yield assert_equal, octree.nocts, total_count.sum() # This visits every cell -- including those covered by octs. #for dom in range(ndom): # level_count += octree.count_levels(total_count.size-1, dom, mask) yield assert_equal, total_count, [1, 8, 64, 64, 256, 536, 1856, 1672]
def to_octree(self, over_refine_factor=1, dims=(1, 1, 1), n_ref=64): mi = self.morton mi.sort() eps = np.finfo(self.dtype).eps LE = self.min(axis=0) LE -= np.abs(LE) * eps RE = self.max(axis=0) RE += np.abs(RE) * eps octree = ParticleOctreeContainer(dims, LE, RE, over_refine=over_refine_factor) octree.n_ref = n_ref octree.add(mi) octree.finalize() return octree
def to_octree(self, over_refine_factor = 1, dims = (1,1,1), n_ref = 64): mi = self.morton mi.sort() eps = np.finfo(self.dtype).eps LE = self.min(axis=0) LE -= np.abs(LE) * eps RE = self.max(axis=0) RE += np.abs(RE) * eps octree = ParticleOctreeContainer(dims, LE, RE, over_refine = over_refine_factor) octree.n_ref = n_ref octree.add(mi) octree.finalize() return octree
def particle_operation(self, positions, fields=None, method=None, nneighbors=64, kernel_name='cubic'): r"""Operate on particles, in a particle-against-particle fashion. This uses the octree indexing system to call a "smoothing" operation (defined in yt/geometry/particle_smooth.pyx) that expects to be called in a particle-by-particle fashion. For instance, the canonical example of this would be to compute the Nth nearest neighbor, or to compute the density for a given particle based on some kernel operation. Many of the arguments to this are identical to those used in the smooth and deposit functions. Note that the `fields` argument must not be empty, as these fields will be modified in place. Parameters ---------- positions : array_like (Nx3) The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles. fields : list of arrays All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc. One of these will likely be modified in place. method : string This is the "method name" which will be looked up in the `particle_smooth` namespace as `methodname_smooth`. nneighbors : int, default 64 The number of neighbors to examine during the process. kernel_name : string, default 'cubic' This is the name of the smoothing kernel to use. Current supported kernel names include `cubic`, `quartic`, `quintic`, `wendland2`, `wendland4`, and `wendland6`. Returns ------- Nothing. """ # Here we perform our particle deposition. positions.convert_to_units("code_length") morton = compute_morton(positions[:, 0], positions[:, 1], positions[:, 2], self.ds.domain_left_edge, self.ds.domain_right_edge) morton.sort() particle_octree = ParticleOctreeContainer([1, 1, 1], self.ds.domain_left_edge, self.ds.domain_right_edge, over_refine=1) particle_octree.n_ref = nneighbors * 2 particle_octree.add(morton) particle_octree.finalize() pdom_ind = particle_octree.domain_ind(self.selector) if fields is None: fields = [] cls = getattr(particle_smooth, "%s_smooth" % method, None) if cls is None: raise YTParticleDepositionNotImplemented(method) nz = self.nz mdom_ind = self.domain_ind nvals = (nz, nz, nz, (mdom_ind >= 0).sum()) op = cls(nvals, len(fields), nneighbors, kernel_name) op.initialize() mylog.debug("Smoothing %s particles into %s Octs", positions.shape[0], nvals[-1]) op.process_particles(particle_octree, pdom_ind, positions, fields, self.domain_id, self._domain_offset, self.ds.periodicity, self.ds.geometry) vals = op.finalize() if vals is None: return if isinstance(vals, list): vals = [np.asfortranarray(v) for v in vals] else: vals = np.asfortranarray(vals) return vals
def smooth(self, positions, fields=None, index_fields=None, method=None, create_octree=False, nneighbors=64, kernel_name='cubic'): r"""Operate on the mesh, in a particle-against-mesh fashion, with non-local input. This uses the octree indexing system to call a "smoothing" operation (defined in yt/geometry/particle_smooth.pyx) that can take input from several (non-local) particles and construct some value on the mesh. The canonical example is to conduct a smoothing kernel operation on the mesh. Parameters ---------- positions : array_like (Nx3) The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles. fields : list of arrays All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc. index_fields : list of arrays All of the fields defined on the mesh that may be used as input to the operation. method : string This is the "method name" which will be looked up in the `particle_smooth` namespace as `methodname_smooth`. Current methods include `volume_weighted`, `nearest`, `idw`, `nth_neighbor`, and `density`. create_octree : bool Should we construct a new octree for indexing the particles? In cases where we are applying an operation on a subset of the particles used to construct the mesh octree, this will ensure that we are able to find and identify all relevant particles. nneighbors : int, default 64 The number of neighbors to examine during the process. kernel_name : string, default 'cubic' This is the name of the smoothing kernel to use. Current supported kernel names include `cubic`, `quartic`, `quintic`, `wendland2`, `wendland4`, and `wendland6`. Returns ------- List of fortran-ordered, mesh-like arrays. """ # Here we perform our particle deposition. positions.convert_to_units("code_length") if create_octree: morton = compute_morton(positions[:, 0], positions[:, 1], positions[:, 2], self.ds.domain_left_edge, self.ds.domain_right_edge) morton.sort() particle_octree = ParticleOctreeContainer( [1, 1, 1], self.ds.domain_left_edge, self.ds.domain_right_edge, over_refine=self._oref) # This should ensure we get everything within one neighbor of home. particle_octree.n_ref = nneighbors * 2 particle_octree.add(morton) particle_octree.finalize() pdom_ind = particle_octree.domain_ind(self.selector) else: particle_octree = self.oct_handler pdom_ind = self.domain_ind if fields is None: fields = [] if index_fields is None: index_fields = [] cls = getattr(particle_smooth, "%s_smooth" % method, None) if cls is None: raise YTParticleDepositionNotImplemented(method) nz = self.nz mdom_ind = self.domain_ind nvals = (nz, nz, nz, (mdom_ind >= 0).sum()) op = cls(nvals, len(fields), nneighbors, kernel_name) op.initialize() mylog.debug("Smoothing %s particles into %s Octs", positions.shape[0], nvals[-1]) # Pointer operations within 'process_octree' require arrays to be # contiguous cf. https://bitbucket.org/yt_analysis/yt/issues/1079 fields = [np.ascontiguousarray(f, dtype="float64") for f in fields] op.process_octree(self.oct_handler, mdom_ind, positions, self.fcoords, fields, self.domain_id, self._domain_offset, self.ds.periodicity, index_fields, particle_octree, pdom_ind, self.ds.geometry) # If there are 0s in the smoothing field this will not throw an error, # but silently return nans for vals where dividing by 0 # Same as what is currently occurring, but suppressing the div by zero # error. with np.errstate(invalid='ignore'): vals = op.finalize() if vals is None: return if isinstance(vals, list): vals = [np.asfortranarray(v) for v in vals] else: vals = np.asfortranarray(vals) return vals
def particle_operation(self, positions, fields = None, method = None, nneighbors = 64): r"""Operate on particles, in a particle-against-particle fashion. This uses the octree indexing system to call a "smoothing" operation (defined in yt/geometry/particle_smooth.pyx) that expects to be called in a particle-by-particle fashion. For instance, the canonical example of this would be to compute the Nth nearest neighbor, or to compute the density for a given particle based on some kernel operation. Many of the arguments to this are identical to those used in the smooth and deposit functions. Note that the `fields` argument must not be empty, as these fields will be modified in place. Parameters ---------- positions : array_like (Nx3) The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles. fields : list of arrays All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc. One of these will likely be modified in place. method : string This is the "method name" which will be looked up in the `particle_smooth` namespace as `methodname_smooth`. nneighbors : int, default 64 The number of neighbors to examine during the process. Returns ------- Nothing. """ # Here we perform our particle deposition. positions.convert_to_units("code_length") morton = compute_morton( positions[:,0], positions[:,1], positions[:,2], self.ds.domain_left_edge, self.ds.domain_right_edge) morton.sort() particle_octree = ParticleOctreeContainer([1, 1, 1], self.ds.domain_left_edge, self.ds.domain_right_edge, over_refine = 1) particle_octree.n_ref = nneighbors * 2 particle_octree.add(morton) particle_octree.finalize() pdom_ind = particle_octree.domain_ind(self.selector) if fields is None: fields = [] cls = getattr(particle_smooth, "%s_smooth" % method, None) if cls is None: raise YTParticleDepositionNotImplemented(method) nz = self.nz mdom_ind = self.domain_ind nvals = (nz, nz, nz, (mdom_ind >= 0).sum()) op = cls(nvals, len(fields), nneighbors) op.initialize() mylog.debug("Smoothing %s particles into %s Octs", positions.shape[0], nvals[-1]) op.process_particles(particle_octree, pdom_ind, positions, fields, self.domain_id, self._domain_offset, self.ds.periodicity, self.ds.geometry) vals = op.finalize() if vals is None: return if isinstance(vals, list): vals = [np.asfortranarray(v) for v in vals] else: vals = np.asfortranarray(vals) return vals
def smooth(self, positions, fields = None, index_fields = None, method = None, create_octree = False, nneighbors = 64): r"""Operate on the mesh, in a particle-against-mesh fashion, with non-local input. This uses the octree indexing system to call a "smoothing" operation (defined in yt/geometry/particle_smooth.pyx) that can take input from several (non-local) particles and construct some value on the mesh. The canonical example is to conduct a smoothing kernel operation on the mesh. Parameters ---------- positions : array_like (Nx3) The positions of all of the particles to be examined. A new indexed octree will be constructed on these particles. fields : list of arrays All the necessary fields for computing the particle operation. For instance, this might include mass, velocity, etc. index_fields : list of arrays All of the fields defined on the mesh that may be used as input to the operation. method : string This is the "method name" which will be looked up in the `particle_smooth` namespace as `methodname_smooth`. Current methods include `volume_weighted`, `nearest`, `idw`, `nth_neighbor`, and `density`. create_octree : bool Should we construct a new octree for indexing the particles? In cases where we are applying an operation on a subset of the particles used to construct the mesh octree, this will ensure that we are able to find and identify all relevant particles. nneighbors : int, default 64 The number of neighbors to examine during the process. Returns ------- List of fortran-ordered, mesh-like arrays. """ # Here we perform our particle deposition. positions.convert_to_units("code_length") if create_octree: morton = compute_morton( positions[:,0], positions[:,1], positions[:,2], self.ds.domain_left_edge, self.ds.domain_right_edge) morton.sort() particle_octree = ParticleOctreeContainer([1, 1, 1], self.ds.domain_left_edge, self.ds.domain_right_edge, over_refine = self._oref) # This should ensure we get everything within one neighbor of home. particle_octree.n_ref = nneighbors * 2 particle_octree.add(morton) particle_octree.finalize() pdom_ind = particle_octree.domain_ind(self.selector) else: particle_octree = self.oct_handler pdom_ind = self.domain_ind if fields is None: fields = [] if index_fields is None: index_fields = [] cls = getattr(particle_smooth, "%s_smooth" % method, None) if cls is None: raise YTParticleDepositionNotImplemented(method) nz = self.nz mdom_ind = self.domain_ind nvals = (nz, nz, nz, (mdom_ind >= 0).sum()) op = cls(nvals, len(fields), nneighbors) op.initialize() mylog.debug("Smoothing %s particles into %s Octs", positions.shape[0], nvals[-1]) op.process_octree(self.oct_handler, mdom_ind, positions, self.fcoords, fields, self.domain_id, self._domain_offset, self.ds.periodicity, index_fields, particle_octree, pdom_ind, self.ds.geometry) # If there are 0s in the smoothing field this will not throw an error, # but silently return nans for vals where dividing by 0 # Same as what is currently occurring, but suppressing the div by zero # error. with np.errstate(invalid='ignore'): vals = op.finalize() if vals is None: return if isinstance(vals, list): vals = [np.asfortranarray(v) for v in vals] else: vals = np.asfortranarray(vals) return vals