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DAMASK_EICMD
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hide imported modules in autocompletion

This commit is contained in:
Martin Diehl 2021-05-06 15:00:03 +02:00
parent 18dfc9e54d
commit 8626f6c047
1 changed files with 5 additions and 5 deletions
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10
python/damask/seeds.py
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@ -3,8 +3,8 @@
from scipy import spatial as _spatial from scipy import spatial as _spatial
import numpy as _np import numpy as _np
from . import util from . import util as _util
from . import grid_filters from . import grid_filters as _grid_filters
def from_random(size,N_seeds,cells=None,rng_seed=None): def from_random(size,N_seeds,cells=None,rng_seed=None):
@ -34,7 +34,7 @@ def from_random(size,N_seeds,cells=None,rng_seed=None):
if cells is None: if cells is None:
coords = rng.random((N_seeds,3)) * size coords = rng.random((N_seeds,3)) * size
else: else:
grid_coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F') grid_coords = _grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \ coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
+ _np.broadcast_to(size/cells,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving cells + _np.broadcast_to(size/cells,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving cells
@ -73,7 +73,7 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=
s = 1 s = 1
i = 0 i = 0
progress = util._ProgressBar(N_seeds+1,'',50) progress = _util._ProgressBar(N_seeds+1,'',50)
while s < N_seeds: while s < N_seeds:
candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3)) candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \ tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
@ -120,7 +120,7 @@ def from_grid(grid,selection=None,invert=False,average=False,periodic=True):
material = grid.material.reshape((-1,1),order='F') material = grid.material.reshape((-1,1),order='F')
mask = _np.full(grid.cells.prod(),True,dtype=bool) if selection is None else \ mask = _np.full(grid.cells.prod(),True,dtype=bool) if selection is None else \
_np.isin(material,selection,invert=invert).flatten() _np.isin(material,selection,invert=invert).flatten()
coords = grid_filters.coordinates0_point(grid.cells,grid.size).reshape(-1,3,order='F') coords = _grid_filters.coordinates0_point(grid.cells,grid.size).reshape(-1,3,order='F')
if not average: if not average:
return (coords[mask],material[mask]) return (coords[mask],material[mask])