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[skip ci] added parameter description; shortened hybrid_IA pre-loop logic

This commit is contained in:
Philip Eisenlohr 2020-09-29 11:15:10 -04:00
parent 8d204ea445
commit 31f86c28f4
2 changed files with 9 additions and 16 deletions
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14
python/damask/_rotation.py
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@ -665,9 +665,10 @@ class Rotation:
Parameters
----------
fname : file, str, or pathlib.Path
ODF file containing normalized probability (labeled 'intensity')
on a grid in Euler space (labeled 'euler').
weights : numpy.ndarray of shape (n)
Texture intensity values (probability density or volume fraction) at Euler grid points.
Eulers : numpy.ndarray of shape (n,3)
Grid coordinates in Euler space at which weights are defined.
N : integer, optional
Number of discrete orientations to be sampled from the given ODF.
Defaults to 500.
@ -680,10 +681,6 @@ class Rotation:
A seed to initialize the BitGenerator. Defaults to None, i.e. unpredictable entropy
will be pulled from the OS.
Notes
-----
Explain here the different things that need to be considered
"""
def _dg(eu,deg):
"""Return infinitesimal Euler space volume of bin(s)."""
@ -694,9 +691,8 @@ class Rotation:
dg = 1.0 if fractions else _dg(Eulers,degrees)
dV_V = dg * np.maximum(0.0,weights.squeeze())
orientations = Rotation.from_Eulers(Eulers[util.hybrid_IA(dV_V,N,seed)],degrees)
return orientations
return Rotation.from_Eulers(Eulers[util.hybrid_IA(dV_V,N,seed)],degrees)
@staticmethod

9
python/damask/util.py
View File

@ -189,20 +189,17 @@ def execution_stamp(class_name,function_name=None):
def hybrid_IA(dist,N,seed=None):
rng = np.random.default_rng(seed)
N_opt_samples = max(np.count_nonzero(dist),N) # random subsampling if too little samples requested
N_opt_samples,N_inv_samples = (max(np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
while (not np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
repeats = np.rint(scale*dist).astype(int)
N_inv_samples = np.sum(repeats)
while (not np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
if N_inv_samples < N_opt_samples else \
(scale_,0.5*(scale_ + scale), 1.0)
repeats = np.rint(scale*dist).astype(int)
N_inv_samples = np.sum(repeats)
return np.repeat(np.arange(len(dist)),repeats)[rng.permutation(N_inv_samples)[:N]]
return np.repeat(np.arange(len(dist)),repeats)[np.random.default_rng(seed).permutation(N_inv_samples)[:N]]
####################################################################################################