clearly distinguish seeds for tessellation and seeds for RNG
This commit is contained in:
Martin Diehl
parent
1541ac0add
commit
fd8743af5e
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@ -1233,6 +1233,7 @@ class Orientation(Rotation):
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array([[ 0.000, 0.000, 0.000],
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[ 0.577, -0.000, 0.816],
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[ 0.000, 0.000, 0.000]])
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"""
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d = self.to_frame(uvw=self.kinematics[mode]['direction'],with_symmetry=False)
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p = self.to_frame(hkl=self.kinematics[mode]['plane'] ,with_symmetry=False)
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@ -682,7 +682,7 @@ class Rotation:
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@staticmethod
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def from_random(shape = None,
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seed = None,
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rng_seed = None,
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**kwargs):
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"""
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Draw random rotation.
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@ -694,12 +694,12 @@ class Rotation:
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shape : tuple of ints, optional
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Shape of the sample. Defaults to None which gives a
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single rotation
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seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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"""
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rng = np.random.default_rng(seed)
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rng = np.random.default_rng(rng_seed)
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r = rng.random(3 if shape is None else tuple(shape)+(3,) if hasattr(shape, '__iter__') else (shape,3))
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A = np.sqrt(r[...,2])
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@ -718,7 +718,7 @@ class Rotation:
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N = 500,
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degrees = True,
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fractions = True,
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seed = None,
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rng_seed = None,
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**kwargs):
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"""
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Sample discrete values from a binned ODF.
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@ -737,7 +737,7 @@ class Rotation:
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fractions : boolean, optional
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ODF values correspond to volume fractions, not probability density.
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Defaults to True.
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seed: {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed: {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None, i.e. unpredictable entropy
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will be pulled from the OS.
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@ -768,7 +768,7 @@ class Rotation:
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dg = 1.0 if fractions else _dg(Eulers,degrees)
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dV_V = dg * np.maximum(0.0,weights.squeeze())
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return Rotation.from_Eulers(Eulers[util.hybrid_IA(dV_V,N,seed)],degrees)
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return Rotation.from_Eulers(Eulers[util.hybrid_IA(dV_V,N,rng_seed)],degrees)
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@staticmethod
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@ -776,7 +776,7 @@ class Rotation:
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sigma,
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N = 500,
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degrees = True,
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seed = None,
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rng_seed = None,
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**kwargs):
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"""
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Calculate set of rotations with Gaussian distribution around center.
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@ -791,12 +791,12 @@ class Rotation:
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Number of samples, defaults to 500.
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degrees : boolean, optional
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sigma is given in degrees.
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seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None, i.e. unpredictable entropy
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will be pulled from the OS.
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"""
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rng = np.random.default_rng(seed)
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rng = np.random.default_rng(rng_seed)
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sigma = np.radians(sigma) if degrees else sigma
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u,Theta = (rng.random((N,2)) * 2.0 * np.array([1,np.pi]) - np.array([1.0, 0])).T
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omega = abs(rng.normal(scale=sigma,size=N))
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@ -813,7 +813,7 @@ class Rotation:
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sigma = 0.0,
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N = 500,
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degrees = True,
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seed = None,
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rng_seed = None,
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**kwargs):
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"""
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Calculate set of rotations with Gaussian distribution around direction.
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@ -831,12 +831,12 @@ class Rotation:
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Number of samples, defaults to 500.
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degrees : boolean, optional
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sigma, alpha, and beta are given in degrees.
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seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None, i.e. unpredictable entropy
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will be pulled from the OS.
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"""
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rng = np.random.default_rng(seed)
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rng = np.random.default_rng(rng_seed)
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sigma_,alpha_,beta_ = map(np.radians,(sigma,alpha,beta)) if degrees else (sigma,alpha,beta)
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d_cr = np.array([np.sin(alpha_[0])*np.cos(alpha_[1]), np.sin(alpha_[0])*np.sin(alpha_[1]), np.cos(alpha_[0])])
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@ -5,7 +5,7 @@ from . import util
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from . import grid_filters
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def from_random(size,N_seeds,grid=None,seed=None):
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def from_random(size,N_seeds,grid=None,rng_seed=None):
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"""
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Random seeding in space.
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@ -18,12 +18,12 @@ def from_random(size,N_seeds,grid=None,seed=None):
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grid : numpy.ndarray of shape (3), optional.
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If given, ensures that all seeds initiate one grain if using a
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standard Voronoi tessellation.
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seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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"""
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rng = _np.random.default_rng(seed)
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rng = _np.random.default_rng(rng_seed)
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if grid is None:
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coords = rng.random((N_seeds,3)) * size
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else:
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@ -34,7 +34,7 @@ def from_random(size,N_seeds,grid=None,seed=None):
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return coords
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def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,seed=None):
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def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,rng_seed=None):
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"""
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Seeding in space according to a Poisson disc distribution.
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@ -50,12 +50,12 @@ def from_Poisson_disc(size,N_seeds,N_candidates,distance,periodic=True,seed=None
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Minimum acceptable distance to other seeds.
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periodic : boolean, optional
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Calculate minimum distance for periodically repeated grid.
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seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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"""
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rng = _np.random.default_rng(seed)
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rng = _np.random.default_rng(rng_seed)
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coords = _np.empty((N_seeds,3))
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coords[0] = rng.random(3) * size
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@ -42,7 +42,7 @@ def srepr(arg,glue = '\n'):
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arg : iterable
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Items to join.
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glue : str, optional
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Defaults to \n.
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Glue used for joining operation. Defaults to \n.
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"""
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if (not hasattr(arg, "strip") and
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@ -163,7 +163,15 @@ def show_progress(iterable,N_iter=None,prefix='',bar_length=50):
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def scale_to_coprime(v):
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"""Scale vector to co-prime (relatively prime) integers."""
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"""
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Scale vector to co-prime (relatively prime) integers.
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Parameters
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----------
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v : numpy.ndarray of shape (:)
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Vector to scale.
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"""
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MAX_DENOMINATOR = 1000000
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def get_square_denominator(x):
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@ -214,7 +222,21 @@ def execution_stamp(class_name,function_name=None):
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return f'damask.{class_name}{_function_name} v{version} ({now})'
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def hybrid_IA(dist,N,seed=None):
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def hybrid_IA(dist,N,rng_seed=None):
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"""
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Hybrid integer approximation.
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Parameters
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----------
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dist : numpy.ndarray
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Distribution to be approximated
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N : int
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Number of samples to draw.
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rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
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A seed to initialize the BitGenerator. Defaults to None.
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If None, then fresh, unpredictable entropy will be pulled from the OS.
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"""
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N_opt_samples,N_inv_samples = (max(np.count_nonzero(dist),N),0) # random subsampling if too little samples requested
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scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
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@ -225,7 +247,7 @@ def hybrid_IA(dist,N,seed=None):
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if N_inv_samples < N_opt_samples else \
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(scale_,0.5*(scale_ + scale), 1.0)
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return np.repeat(np.arange(len(dist)),repeats)[np.random.default_rng(seed).permutation(N_inv_samples)[:N]]
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return np.repeat(np.arange(len(dist)),repeats)[np.random.default_rng(rng_seed).permutation(N_inv_samples)[:N]]
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def shapeshifter(fro,to,mode='left',keep_ones=False):
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@ -18,6 +18,7 @@ def patch_damask_version(monkeypatch):
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"""Set damask.version for reproducible tests results."""
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monkeypatch.setattr(damask, 'version', patched_version)
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patched_date = datetime.datetime(2019, 11, 2, 11, 58, 0)
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@pytest.fixture
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def patch_datetime_now(monkeypatch):
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@ -29,6 +30,7 @@ def patch_datetime_now(monkeypatch):
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monkeypatch.setattr(datetime, 'datetime', mydatetime)
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@pytest.fixture
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def patch_execution_stamp(monkeypatch):
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"""Set damask.util.execution_stamp for reproducible tests results."""
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@ -38,6 +40,7 @@ def patch_execution_stamp(monkeypatch):
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monkeypatch.setattr(damask.util, 'execution_stamp', execution_stamp)
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@pytest.fixture
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def patch_plt_show(monkeypatch):
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def _None(block=None):
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@ -50,16 +53,19 @@ def pytest_addoption(parser):
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action="store_true",
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default=False)
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@pytest.fixture
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def update(request):
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"""Store current results as new reference results."""
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return request.config.getoption("--update")
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@pytest.fixture
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def reference_dir_base():
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"""Directory containing reference results."""
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return Path(__file__).parent/'reference'
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@pytest.fixture
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def set_of_quaternions():
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"""A set of n random rotations."""
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@ -125,9 +125,9 @@ class TestOrientation:
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def test_from_fiber_component(self):
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r = Rotation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2),
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sigma=0.0,N=1,seed=0)
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sigma=0.0,N=1,rng_seed=0)
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assert np.all(Orientation.from_fiber_component(alpha=np.zeros(2),beta=np.zeros(2),
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sigma=0.0,N=1,seed=0,lattice='triclinic').quaternion
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sigma=0.0,N=1,rng_seed=0,lattice='triclinic').quaternion
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== r.quaternion)
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@pytest.mark.parametrize('kwargs',[
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@ -175,8 +175,8 @@ class TestOrientation:
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@pytest.mark.parametrize('lattice',Orientation.crystal_families)
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@pytest.mark.parametrize('N',[1,8,32])
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def test_disorientation(self,lattice,N):
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o = Orientation.from_random(lattice=lattice,shape=N,seed=0)
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p = Orientation.from_random(lattice=lattice,shape=N,seed=1)
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o = Orientation.from_random(lattice=lattice,shape=N)
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p = Orientation.from_random(lattice=lattice,shape=N)
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d,ops = o.disorientation(p,return_operators=True)
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@ -198,8 +198,8 @@ class TestOrientation:
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(None,None),
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])
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def test_disorientation_blending(self,lattice,a,b):
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o = Orientation.from_random(lattice=lattice,shape=a,seed=0)
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p = Orientation.from_random(lattice=lattice,shape=b,seed=1)
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o = Orientation.from_random(lattice=lattice,shape=a)
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p = Orientation.from_random(lattice=lattice,shape=b)
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blend = util.shapeblender(o.shape,p.shape)
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for loc in np.random.randint(0,blend,(10,len(blend))):
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assert o[tuple(loc[:len(o.shape)])].disorientation(p[tuple(loc[-len(p.shape):])]) \
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@ -214,7 +214,7 @@ class TestOrientation:
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@pytest.mark.parametrize('lattice',Orientation.crystal_families)
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@pytest.mark.parametrize('shape',[(1),(2,3),(4,3,2)])
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def test_reduced_vectorization(self,lattice,shape):
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o = Orientation.from_random(lattice=lattice,shape=shape,seed=0)
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o = Orientation.from_random(lattice=lattice,shape=shape)
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for r, theO in zip(o.reduced.flatten(),o.flatten()):
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assert r == theO.reduced
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@ -223,7 +223,7 @@ class TestOrientation:
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@pytest.mark.parametrize('vector',np.array([[1,0,0],[1,2,3],[-1,1,-1]]))
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@pytest.mark.parametrize('proper',[True,False])
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def test_to_SST_vectorization(self,lattice,shape,vector,proper):
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o = Orientation.from_random(lattice=lattice,shape=shape,seed=0)
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o = Orientation.from_random(lattice=lattice,shape=shape)
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for r, theO in zip(o.to_SST(vector=vector,proper=proper).reshape((-1,3)),o.flatten()):
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assert np.allclose(r,theO.to_SST(vector=vector,proper=proper))
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@ -232,7 +232,7 @@ class TestOrientation:
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@pytest.mark.parametrize('vector',np.array([[1,0,0],[1,2,3],[-1,1,-1]]))
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@pytest.mark.parametrize('proper',[True,False])
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def test_IPF_color_vectorization(self,lattice,shape,vector,proper):
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o = Orientation.from_random(lattice=lattice,shape=shape,seed=0)
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o = Orientation.from_random(lattice=lattice,shape=shape)
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poles = o.to_SST(vector=vector,proper=proper)
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for r, theO in zip(o.IPF_color(poles,proper=proper).reshape((-1,3)),o.flatten()):
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assert np.allclose(r,theO.IPF_color(theO.to_SST(vector=vector,proper=proper),proper=proper))
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@ -245,7 +245,7 @@ class TestOrientation:
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(None,(3,)),
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])
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def test_to_SST_blending(self,lattice,a,b):
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o = Orientation.from_random(lattice=lattice,shape=a,seed=0)
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o = Orientation.from_random(lattice=lattice,shape=a)
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v = np.random.random(b+(3,))
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blend = util.shapeblender(o.shape,b)
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for loc in np.random.randint(0,blend,(10,len(blend))):
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@ -778,7 +778,7 @@ class TestRotation:
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assert r.shape == shape
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def test_equal(self):
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assert Rotation.from_random(seed=1) == Rotation.from_random(seed=1)
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assert Rotation.from_random(rng_seed=1) == Rotation.from_random(rng_seed=1)
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def test_inversion(self):
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r = Rotation.from_random()
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