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Improving grid.vicinity_offset performance optionally with numba

This commit is contained in:
Daniel Otto de Mentock 2022-12-10 07:22:22 +00:00 committed by Martin Diehl
parent 4400c83da3
commit e03ad0db11
1 changed files with 23 additions and 7 deletions
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30
python/damask/_grid.py
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@ -19,6 +19,14 @@ from . import Rotation
from . import Table from . import Table
from . import Colormap from . import Colormap
from ._typehints import FloatSequence, IntSequence, IntCollection, NumpyRngSeed from ._typehints import FloatSequence, IntSequence, IntCollection, NumpyRngSeed
try:
import numba as nb # type: ignore
except ImportError:
nb = False
def numba_njit_wrapper(**kwargs):
return (lambda function: nb.njit(function) if nb else function)
class Grid: class Grid:
""" """
@ -1129,7 +1137,7 @@ class Grid:
""" """
def most_frequent(stencil: np.ndarray, def most_frequent(stencil: np.ndarray,
selection: Union[None,set], selection: Union[None,set],
rng): rng: np.random.Generator):
me = stencil[stencil.size//2] me = stencil[stencil.size//2]
if selection is None or me in selection: if selection is None or me in selection:
unique, counts = np.unique(stencil,return_counts=True) unique, counts = np.unique(stencil,return_counts=True)
@ -1289,19 +1297,27 @@ class Grid:
Updated grid-based geometry. Updated grid-based geometry.
""" """
def tainted_neighborhood(stencil: np.ndarray, selection: Union[None,set]): @numba_njit_wrapper()
def tainted_neighborhood(stencil: np.ndarray,
selection: Optional[np.ndarray] = None):
me = stencil[stencil.size//2] me = stencil[stencil.size//2]
return np.any(stencil != me if selection is None else if selection is None:
np.in1d(stencil,np.array(list(selection - {me})))) return np.any(stencil != me)
elif not len(selection)==0:
for stencil_item in stencil:
for selection_item in selection:
if stencil_item==selection_item and selection_item!=me:
return True
return False
d = np.floor(distance).astype(np.int64) d = np.floor(distance).astype(np.int64)
ext = np.linspace(-d,d,1+2*d,dtype=float), ext = np.linspace(-d,d,1+2*d,dtype=float),
xx,yy,zz = np.meshgrid(ext,ext,ext) xx,yy,zz = np.meshgrid(ext,ext,ext)
footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8 footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8
offset_ = np.nanmax(self.material)+1 if offset is None else offset offset_ = np.nanmax(self.material)+1 if offset is None else offset
selection_ = None if selection is None else \ selection_ = None if selection is None else \
set(self.material.flatten()) - set(util.aslist(selection)) if invert_selection else \ np.array(list(set(self.material.flatten()) - set(util.aslist(selection)))) if invert_selection else \
set(self.material.flatten()) & set(util.aslist(selection)) np.array(list(set(self.material.flatten()) & set(util.aslist(selection))))
mask = ndimage.generic_filter(self.material, mask = ndimage.generic_filter(self.material,
tainted_neighborhood, tainted_neighborhood,
footprint=footprint, footprint=footprint,