def test_1V():
obj = test()
obj.single_mode_evolution = False
f_generalized = MB_dist(obj.q1_center, obj.q2_center, obj.p1, obj.p2,
obj.p3, 1)
C_f_hat_generalized = 2 * af.fft2(
collision_operator.BGK(
f_generalized, obj.q1_center, obj.q2_center, obj.p1, obj.p2,
obj.p3, obj.compute_moments,
obj.physical_system.params)) / (obj.N_q2 * obj.N_q1)
# Background
C_f_hat_generalized[0, 0, :] = 0
# Finding the indices of the mode excited:
i_q1_max = np.unravel_index(
af.imax(af.abs(C_f_hat_generalized))[1],
(obj.N_q1, obj.N_q2, obj.N_p1 * obj.N_p2 * obj.N_p3),
order='F')[0]
i_q2_max = np.unravel_index(
af.imax(af.abs(C_f_hat_generalized))[1],
(obj.N_q1, obj.N_q2, obj.N_p1 * obj.N_p2 * obj.N_p3),
order='F')[1]
obj.p1 = np.array(af.reorder(obj.p1, 1, 2, 3, 0))
obj.p2 = np.array(af.reorder(obj.p2, 1, 2, 3, 0))
obj.p3 = np.array(af.reorder(obj.p3, 1, 2, 3, 0))
delta_f_hat = 0.01 * (1 / (2 * np.pi))**(1 / 2) \
* np.exp(-0.5 * obj.p1**2)
obj.single_mode_evolution = True
C_f_hat_single_mode = collision_operator.linearized_BGK(
delta_f_hat, obj.p1, obj.p2, obj.p3, obj.compute_moments,
obj.physical_system.params)
assert (af.mean(
af.abs(
af.flat(C_f_hat_generalized[i_q1_max, i_q2_max]) -
af.to_array(C_f_hat_single_mode.flatten()))) < 1e-14)
def argmax(a: ndarray,
axis: tp.Optional[int] = None,
out: tp.Optional[ndarray] = None) -> ndarray:
if out:
raise ValueError('out != None is not supported')
val, idx = af.imax(a._af_array, dim=axis)
return _wrap_af_array(idx)
def argmax(self,
axis: tp.Optional[int] = None,
out: tp.Optional['ndarray'] = None):
"""
Return indices of the maximum values along the given axis.
"""
val, idx = af.imax(self._af_array, dim=axis)
return _wrap_af_array(idx)
def predict_proba1(self, X):
near_locs, near_dists = af.vision.nearest_neighbour(X, self._data, self._dim, \
self._num_nearest, self._match_type)
weights = self._get_neighbor_weights(near_dists)
top_labels = af.moddims(self._labels[near_locs], \
get_dims(near_locs)[0], get_dims(near_locs)[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
probs, _ = af.imax(accum_weights, dim=0)
return probs.T
def _predict_proba(self, query, train_feats, train_labels, k, dist_type,
weight_by_dist):
near_locs, near_dists = af.vision.nearest_neighbour(query, train_feats, 1, \
k, dist_type)
weights = self._get_neighbor_weights(near_dists, weight_by_dist, k)
top_labels = af.moddims(train_labels[near_locs], \
near_locs.dims()[0], near_locs.dims()[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
probs, _ = af.imax(accum_weights, dim=0)
return probs.T
def argmax(self, axis=None):
if axis is None:
return self.flat.argmax(axis=0)
if not isinstance(axis, numbers.Number):
raise TypeError('an integer is required for the axis')
val, idx = arrayfire.imax(self.d_array, pu.c2f(self.shape, axis))
shape = list(self.shape)
shape.pop(axis)
if(len(shape)):
return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)
else:
return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)[()]
def argmax(self, axis=None):
if axis is None:
return self.flat.argmax(axis=0)
if not isinstance(axis, numbers.Number):
raise TypeError('an integer is required for the axis')
val, idx = arrayfire.imax(self.d_array, pu.c2f(self.shape, axis))
shape = list(self.shape)
shape.pop(axis)
if (len(shape)):
return ndarray(shape, dtype=pu.typemap(idx.dtype()), af_array=idx)
else:
return ndarray(shape, dtype=pu.typemap(idx.dtype()),
af_array=idx)[()]
def predict(self, X):
near_locs, near_dists = af.vision.nearest_neighbour(X, self._data, self._dim, \
self._num_nearest, self._match_type)
weights = self._get_neighbor_weights(near_dists)
top_labels = af.moddims(self._labels[near_locs], \
get_dims(near_locs)[0], get_dims(near_locs)[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
_, max_weight_locs = af.imax(accum_weights, dim=0)
pred_idxs = af.range(get_dims(accum_weights)[1]) * get_dims(
accum_weights)[0] + max_weight_locs.T
top_labels_flat = af.flat(top_labels)
pred_classes = top_labels_flat[pred_idxs]
return pred_classes
def _predict(self, query, train_feats, train_labels, k, dist_type,
weight_by_dist):
near_locs, near_dists = af.vision.nearest_neighbour(query, train_feats, 1, \
k, dist_type)
weights = self._get_neighbor_weights(near_dists, weight_by_dist, k)
top_labels = af.moddims(train_labels[near_locs], \
near_locs.dims()[0], near_locs.dims()[1])
accum_weights = af.scan_by_key(
top_labels, weights) # reduce by key would be more ideal
_, max_weight_locs = af.imax(accum_weights, dim=0)
pred_idxs = af.range(accum_weights.dims()
[1]) * accum_weights.dims()[0] + max_weight_locs.T
top_labels_flat = af.flat(top_labels)
pred_classes = top_labels_flat[pred_idxs]
return pred_classes
def predict(self, X):
probs = self.predict_proba(X)
_, classes = af.imax(probs, 1)
return classes
def accuracy(predicted, target):
_, tlabels = af.imax(target, 1)
_, plabels = af.imax(predicted, 1)
return 100 * af.count(plabels == tlabels) / tlabels.elements()
def predict_class(X, Weights):
probs = predict_prob(X, Weights)
_, classes = af.imax(probs, 1)
return classes
def _predict(self, X: af.Array, Weights: af.Array) -> af.Array:
probs = self._predict_proba(X, Weights)
_, classes = af.imax(probs, 1)
classes = classes + self._label_offset
return classes
def _initialize(self, params):
"""
Called when the solver object is declared. This function is
used to initialize the distribution function, and the field
quantities using the options as provided by the user. The
quantities are then mapped to the fourier basis by taking FFTs.
The independant modes are then evolved by using the linear
solver.
"""
# af.broadcast(function, *args) performs batched operations on
# function(*args):
f = af.broadcast(self.physical_system.initial_conditions.\
initialize_f, self.q1_center, self.q2_center,
self.p1, self.p2, self.p3, params
)
# Taking FFT:
f_hat = af.fft2(f)
# Since (k_q1, k_q2) = (0, 0) will give the background distribution:
# The division by (self.N_q1 * self.N_q2) is performed since the FFT
# at (0, 0) returns (amplitude * (self.N_q1 * self.N_q2))
self.f_background = af.abs(f_hat[0, 0, :])/ (self.N_q1 * self.N_q2)
# Calculating derivatives of the background distribution function:
self._calculate_dfdp_background()
# Scaling Appropriately:
# Except the case of (0, 0) the FFT returns
# (0.5 * amplitude * (self.N_q1 * self.N_q2)):
f_hat = 2 * f_hat / (self.N_q1 * self.N_q2)
if(self.single_mode_evolution == True):
# Background
f_hat[0, 0, :] = 0
# Finding the indices of the perturbation introduced:
i_q1_max = np.unravel_index(af.imax(af.abs(f_hat))[1],
(self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3
),order = 'F'
)[0]
i_q2_max = np.unravel_index(af.imax(af.abs(f_hat))[1],
(self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3
),order = 'F'
)[1]
# Taking sum to get a scalar value:
params.k_q1 = af.sum(self.k_q1[i_q1_max, i_q2_max])
params.k_q2 = af.sum(self.k_q2[i_q1_max, i_q2_max])
self.f_background = np.array(self.f_background).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self.p1 = np.array(self.p1).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self.p2 = np.array(self.p2).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self.p3 = np.array(self.p3).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self._A_q1 = np.array(self._A_q1).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self._A_q2 = np.array(self._A_q2).\
reshape(self.N_p1, self.N_p2, self.N_p3)
params.rho_background = self.compute_moments('density',
self.f_background
)
params.p1_bulk_background = self.compute_moments('mom_p1_bulk',
self.f_background
) / params.rho_background
params.p2_bulk_background = self.compute_moments('mom_p2_bulk',
self.f_background
) / params.rho_background
params.p3_bulk_background = self.compute_moments('mom_p3_bulk',
self.f_background
) / params.rho_background
params.T_background = ((1 / params.p_dim) * \
self.compute_moments('energy',
self.f_background
)
- params.rho_background *
params.p1_bulk_background**2
- params.rho_background *
params.p2_bulk_background**2
- params.rho_background *
params.p3_bulk_background**2
) / params.rho_background
self.dfdp1_background = np.array(self.dfdp1_background).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self.dfdp2_background = np.array(self.dfdp2_background).\
reshape(self.N_p1, self.N_p2, self.N_p3)
self.dfdp3_background = np.array(self.dfdp3_background).\
reshape(self.N_p1, self.N_p2, self.N_p3)
# Unable to recover pert_real and pert_imag accurately from the input.
# There seems to be some error in recovering these quantities which
# is dependant upon the resolution. Using user-defined quantities instead
delta_f_hat = params.pert_real * self.f_background \
+ params.pert_imag * self.f_background * 1j
self.Y = np.array([delta_f_hat])
compute_electrostatic_fields(self)
self.Y = np.array([delta_f_hat,
self.delta_E1_hat, self.delta_E2_hat, self.delta_E3_hat,
self.delta_B1_hat, self.delta_B2_hat, self.delta_B3_hat
]
)
else:
# Using a vector Y to evolve the system:
self.Y = af.constant(0, self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
7, dtype = af.Dtype.c64
)
# Assigning the 0th indice along axis 3 to the f_hat:
self.Y[:, :, :, 0] = f_hat
# Initializing the EM field quantities:
self.E3_hat = af.constant(0, self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
dtype = af.Dtype.c64
)
self.B1_hat = af.constant(0, self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
dtype = af.Dtype.c64
)
self.B2_hat = af.constant(0, self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
dtype = af.Dtype.c64
)
self.B3_hat = af.constant(0, self.N_q1, self.N_q2,
self.N_p1 * self.N_p2 * self.N_p3,
dtype = af.Dtype.c64
)
# Initializing EM fields using Poisson Equation:
if(self.physical_system.params.fields_initialize == 'electrostatic' or
self.physical_system.params.fields_initialize == 'fft'
):
compute_electrostatic_fields(self)
# If option is given as user-defined:
elif(self.physical_system.params.fields_initialize == 'user-defined'):
E1, E2, E3 = \
self.physical_system.initial_conditions.initialize_E(self.q1_center, self.q2_center, self.physical_system.params)
B1, B2, B3 = \
self.physical_system.initial_conditions.initialize_B(self.q1_center, self.q2_center, self.physical_system.params)
# Scaling Appropriately
self.E1_hat = af.tile(2 * af.fft2(E1) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
self.E2_hat = af.tile(2 * af.fft2(E2) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
self.E3_hat = af.tile(2 * af.fft2(E3) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
self.B1_hat = af.tile(2 * af.fft2(B1) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
self.B2_hat = af.tile(2 * af.fft2(B2) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
self.B3_hat = af.tile(2 * af.fft2(B3) / (self.N_q1 * self.N_q2), 1, 1, self.N_p1 * self.N_p2 * self.N_p3)
else:
raise NotImplementedError('Method invalid/not-implemented')
# Assigning other indices along axis 3 to be
# the EM field quantities:
self.Y[:, :, :, 1] = self.E1_hat
self.Y[:, :, :, 2] = self.E2_hat
self.Y[:, :, :, 3] = self.E3_hat
self.Y[:, :, :, 4] = self.B1_hat
self.Y[:, :, :, 5] = self.B2_hat
self.Y[:, :, :, 6] = self.B3_hat
af.eval(self.Y)
return
def argmax(arr, axis=None):
val, idx = af.imax(arr, axis)
return idx
