def phase_lagrange_matrices(self, given_set_name, eval_set_name, sparse=False):
"""
Compute the matrices mapping values at some nodes to values and derivatives at new nodes.
Parameters
----------
given_set_name : str
Name of the set of nodes with which to perform the interpolation.
eval_set_name : str
Name of the set of nodes at which to evaluate the values and derivatives.
sparse : bool
If True, the returned matrix will be in scipy CSR sparse format. Otherwise, it is
returned as a dense numpy.array.
Returns
-------
ndarray[num_eval_set, num_given_set]
Matrix containing the values at the new nodes.
ndarray[num_eval_set, num_given_set]
Matrix containing the time derivatives at the new nodes.
Notes
-----
The values are mapped using the equation:
.. math::
x_{eval} = \\left[ L \\right] x_{given}
And the derivatives are mapped with the equation:
.. math::
\\dot{x}_{eval} = \\left[ D \\right] x_{given} \\frac{d \\tau}{dt}
"""
L_blocks = []
D_blocks = []
for iseg in range(self.num_segments):
i1, i2 = self.subset_segment_indices[given_set_name][iseg, :]
indices = self.subset_node_indices[given_set_name][i1:i2]
nodes_given = self.node_stau[indices]
i1, i2 = self.subset_segment_indices[eval_set_name][iseg, :]
indices = self.subset_node_indices[eval_set_name][i1:i2]
nodes_eval = self.node_stau[indices]
L_block, D_block = lagrange_matrices(nodes_given, nodes_eval)
L_blocks.append(L_block)
D_blocks.append(D_block)
L = block_diag(*L_blocks)
D = block_diag(*D_blocks)
if sparse:
L = sp.csr_matrix(L)
D = sp.csr_matrix(D)
return L, D
def setup(self):
"""
Define the independent variables as output variables.
"""
igd = self.options['input_grid_data']
ogd = self.options['output_grid_data']
output_subset = self.options['output_subset']
if ogd is None:
ogd = igd
self.input_num_nodes = igd.num_nodes
self.output_num_nodes = ogd.subset_num_nodes[output_subset]
# Build the interpolation matrix which maps from the input grid to the output grid.
# Rather than a single phase-wide interpolating polynomial, map each segment.
# To do this, find the nodes in the output grid which fall in each segment of the input
# grid. Then build a Lagrange interpolating polynomial for that segment
L_blocks = []
output_nodes_ptau = ogd.node_ptau[
ogd.subset_node_indices[output_subset]].tolist()
for iseg in range(igd.num_segments):
i1, i2 = igd.segment_indices[iseg]
iptau_segi = igd.node_ptau[i1:i2]
istau_segi = igd.node_stau[i1:i2]
# The indices of the output grid that fall within this segment of the input grid
if ogd is igd:
optau_segi = iptau_segi
else:
ptau_hi = igd.segment_ends[iseg + 1]
if iseg < igd.num_segments - 1:
idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]
else:
idxs_in_iseg = np.arange(len(output_nodes_ptau))
optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]
# Remove the captured nodes so we don't accidentally include them again
output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]
# # Now get the output nodes which fall in iseg in iseg's segment tau space.
ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (
iptau_segi[-1] - iptau_segi[0]) - 1
# Create the interpolation matrix and add it to the blocks
L, _ = lagrange_matrices(istau_segi, ostau_segi)
L_blocks.append(L)
self.interpolation_matrix = block_diag(*L_blocks)
for (name, kwargs) in self._timeseries_outputs:
units = kwargs['units']
desc = kwargs['units']
shape = kwargs['shape']
self._add_output_configure(name, units, shape, desc)
def phase_lagrange_matrices(self, given_set_name, eval_set_name):
"""
Compute the matrices mapping values at some nodes to values and derivatives at new nodes.
Parameters
----------
given_set_name : str
Name of the set of nodes with which to perform the interpolation.
eval_set_name : str
Name of the set of nodes at which to evaluate the values and derivatives.
Returns
-------
ndarray[num_eval_set, num_given_set]
Matrix that yields the values at the new nodes.
ndarray[num_eval_set, num_given_set]
Matrix that yields the time derivatives at the new nodes.
Notes
-----
The values are mapped using the equation:
.. math::
x_{eval} = \\left[ L \\right] x_{given}
And the derivatives are mapped with the equation:
.. math::
\\dot{x}_{eval} = \\left[ D \\right] x_{given} \\frac{d \\tau}{dt}
"""
L_blocks = []
D_blocks = []
for iseg in range(self.num_segments):
i1, i2 = self.subset_segment_indices[given_set_name][iseg, :]
indices = self.subset_node_indices[given_set_name][i1:i2]
nodes_given = self.node_stau[indices]
i1, i2 = self.subset_segment_indices[eval_set_name][iseg, :]
indices = self.subset_node_indices[eval_set_name][i1:i2]
nodes_eval = self.node_stau[indices]
L_block, D_block = lagrange_matrices(nodes_given, nodes_eval)
L_blocks.append(L_block)
D_blocks.append(D_block)
L = block_diag(*L_blocks)
D = block_diag(*D_blocks)
return L, D
axes[0],
color='b',
scale=.25)
plot_tangent(time[state_disc_idxs],
vy[state_disc_idxs],
vydot[state_disc_idxs],
axes[1],
color='b',
scale=.25)
if i == 0:
plt.savefig('lgr_animation_1.png')
# Plot the interpolating polynomials
t_dense = np.linspace(time[0], time[-1], 100)
L, D = lagrange_matrices(time[state_disc_idxs], t_dense)
y_dense = L.dot(y[state_disc_idxs])
vy_dense = L.dot(vy[state_disc_idxs])
axes[0].plot(t_dense, y_dense, ms=None, ls=':')
axes[1].plot(t_dense, vy_dense, ms=None, ls=':')
if i == 0:
plt.savefig('lgr_animation_2.png')
# # Plot the values and rates at the collocation nodes
# tau_s, _ = lgr(3, include_endpoint=True)
# tau_disc = tau_s
# tau_col = tau_s[:-1]
# L, D = lagrange_matrices(tau_disc, tau_col)
#
def setup(self):
"""
Define the independent variables as output variables.
"""
igd = self.options['input_grid_data']
ogd = self.options['output_grid_data']
output_subset = self.options['output_subset']
if ogd is None:
ogd = igd
input_num_nodes = igd.num_nodes
output_num_nodes = ogd.subset_num_nodes[output_subset]
# Build the interpolation matrix which maps from the input grid to the output grid.
# Rather than a single phase-wide interpolating polynomial, map each segment.
# To do this, find the nodes in the output grid which fall in each segment of the input
# grid. Then build a Lagrange interpolating polynomial for that segment
L_blocks = []
output_nodes_ptau = ogd.node_ptau[ogd.subset_node_indices[output_subset]].tolist()
for iseg in range(igd.num_segments):
i1, i2 = igd.segment_indices[iseg]
iptau_segi = igd.node_ptau[i1:i2]
istau_segi = igd.node_stau[i1:i2]
# The indices of the output grid that fall within this segment of the input grid
if ogd is igd:
optau_segi = iptau_segi
else:
ptau_hi = igd.segment_ends[iseg+1]
if iseg < igd.num_segments - 1:
idxs_in_iseg = np.where(output_nodes_ptau <= ptau_hi)[0]
else:
idxs_in_iseg = np.arange(len(output_nodes_ptau))
optau_segi = np.asarray(output_nodes_ptau)[idxs_in_iseg]
# Remove the captured nodes so we don't accidentally include them again
output_nodes_ptau = output_nodes_ptau[len(idxs_in_iseg):]
# # Now get the output nodes which fall in iseg in iseg's segment tau space.
ostau_segi = 2.0 * (optau_segi - iptau_segi[0]) / (iptau_segi[-1] - iptau_segi[0]) - 1
# Create the interpolation matrix and add it to the blocks
L, _ = lagrange_matrices(istau_segi, ostau_segi)
L_blocks.append(L)
self.interpolation_matrix = block_diag(*L_blocks)
for (name, kwargs) in self._timeseries_outputs:
input_kwargs = {k: kwargs[k] for k in ('units', 'desc')}
input_name = 'input_values:{0}'.format(name)
shape = kwargs['shape']
self.add_input(input_name,
shape=(input_num_nodes,) + shape,
**input_kwargs)
output_name = name
output_kwargs = {k: kwargs[k] for k in ('units', 'desc')}
output_kwargs['shape'] = (output_num_nodes,) + kwargs['shape']
self.add_output(output_name, **output_kwargs)
self._vars.append((input_name, output_name, shape))
size = np.prod(shape)
val_jac = np.zeros((output_num_nodes, size, input_num_nodes, size))
for i in range(size):
val_jac[:, i, :, i] = self.interpolation_matrix
val_jac = val_jac.reshape((output_num_nodes * size, input_num_nodes * size),
order='C')
val_jac_rows, val_jac_cols = np.where(val_jac != 0)
rs, cs = val_jac_rows, val_jac_cols
self.declare_partials(of=output_name,
wrt=input_name,
rows=rs, cols=cs, val=val_jac[rs, cs])
