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DAMASK_EICMD
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This commit is contained in:
Martin Diehl 2022-11-25 07:00:15 +01:00
parent f8844285d7
commit 77be2c0d4c
1 changed files with 10 additions and 4 deletions
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14
python/damask/util.py
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@ -416,7 +416,7 @@ def project_equal_area(vector: _np.ndarray,
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2] -shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
def hybrid_IA(dist: _np.ndarray, def hybrid_IA(dist: _FloatSequence,
N: int, N: int,
rng_seed: _NumpyRngSeed = None) -> _np.ndarray: rng_seed: _NumpyRngSeed = None) -> _np.ndarray:
""" """
@ -425,19 +425,25 @@ def hybrid_IA(dist: _np.ndarray,
Parameters Parameters
---------- ----------
dist : numpy.ndarray dist : numpy.ndarray
Distribution to be approximated Distribution to be approximated.
N : int N : int
Number of samples to draw. Number of samples to draw.
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
A seed to initialize the BitGenerator. Defaults to None. A seed to initialize the BitGenerator. Defaults to None.
If None, then fresh, unpredictable entropy will be pulled from the OS. If None, then fresh, unpredictable entropy will be pulled from the OS.
Returns
-------
hist : numpy.ndarray, shape (N)
Integer approximation of the distribution.
""" """
N_opt_samples,N_inv_samples = (max(_np.count_nonzero(dist),N),0) # random subsampling if too little samples requested N_opt_samples = max(_np.count_nonzero(dist),N) # random subsampling if too little samples requested
N_inv_samples = 0
scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0) 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): while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
repeats = _np.rint(scale*dist).astype(_np.int64) repeats = _np.rint(scale*_np.array(dist)).astype(_np.int64)
N_inv_samples = _np.sum(repeats) N_inv_samples = _np.sum(repeats)
scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \ scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
if N_inv_samples < N_opt_samples else \ if N_inv_samples < N_opt_samples else \