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dmdbase.py
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dmdbase.py
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"""
Base module for the DMD: `fit` method must be implemented in inherited classes
"""
from __future__ import division
import warnings
from builtins import object
from builtins import range
from os.path import splitext
import matplotlib as mpl
import numpy as np
from past.utils import old_div
mpl.rcParams['figure.max_open_warning'] = 0
import matplotlib.pyplot as plt
from .dmdoperator import DMDOperator
class DMDBase(object):
"""
Dynamic Mode Decomposition base class.
:param svd_rank: the rank for the truncation; If 0, the method computes the
optimal rank and uses it for truncation; if positive interger, the
method uses the argument for the truncation; if float between 0 and 1,
the rank is the number of the biggest singular values that are needed
to reach the 'energy' specified by `svd_rank`; if -1, the method does
not compute truncation.
:type svd_rank: int or float
:param int tlsq_rank: rank truncation computing Total Least Square. Default
is 0, that means no truncation.
:param bool exact: flag to compute either exact DMD or projected DMD.
Default is False.
:param opt: If True, amplitudes are computed like in optimized DMD (see
:func:`~dmdbase.DMDBase._compute_amplitudes` for reference). If
False, amplitudes are computed following the standard algorithm. If
`opt` is an integer, it is used as the (temporal) index of the snapshot
used to compute DMD modes amplitudes (following the standard algorithm).
The reconstruction will generally be better in time instants near the
chosen snapshot; however increasing `opt` may lead to wrong results when
the system presents small eigenvalues. For this reason a manual
selection of the number of eigenvalues considered for the analyisis may
be needed (check `svd_rank`). Also setting `svd_rank` to a value between
0 and 1 may give better results. Default is False.
:type opt: bool or int
:param rescale_mode: Scale Atilde as shown in
10.1016/j.jneumeth.2015.10.010 (section 2.4) before computing its
eigendecomposition. None means no rescaling, 'auto' means automatic
rescaling using singular values, otherwise the scaling factors.
:type rescale_mode: {'auto'} or None or numpy.ndarray
:param bool forward_backward: If True, the low-rank operator is computed
like in fbDMD (reference: https://arxiv.org/abs/1507.02264). Default is
False.
:cvar dict original_time: dictionary that contains information about the
time window where the system is sampled:
- `t0` is the time of the first input snapshot;
- `tend` is the time of the last input snapshot;
- `dt` is the delta time between the snapshots.
:cvar dict dmd_time: dictionary that contains information about the time
window where the system is reconstructed:
- `t0` is the time of the first approximated solution;
- `tend` is the time of the last approximated solution;
- `dt` is the delta time between the approximated solutions.
"""
def __init__(self, svd_rank=0, tlsq_rank=0, exact=False, opt=False,
rescale_mode=None, forward_backward=False):
self._Atilde = DMDOperator(svd_rank=svd_rank, exact=exact,
rescale_mode=rescale_mode, forward_backward=forward_backward)
self._tlsq_rank = tlsq_rank
self.original_time = None
self.dmd_time = None
self._opt = opt
self._b = None # amplitudes
self._snapshots = None
self._snapshots_shape = None
@property
def opt(self):
return self._opt
@property
def tlsq_rank(self):
return self._tlsq_rank
@property
def svd_rank(self):
return self.operator._svd_rank
@property
def rescale_mode(self):
return self.operator._rescale_mode
@property
def exact(self):
return self.operator._exact
@property
def forward_backward(self):
return self.operator._forward_backward
@property
def dmd_timesteps(self):
"""
Get the timesteps of the reconstructed states.
:return: the time intervals of the original snapshots.
:rtype: numpy.ndarray
"""
return np.arange(self.dmd_time['t0'],
self.dmd_time['tend'] + self.dmd_time['dt'],
self.dmd_time['dt'])
@property
def original_timesteps(self):
"""
Get the timesteps of the original snapshot.
:return: the time intervals of the original snapshots.
:rtype: numpy.ndarray
"""
return np.arange(self.original_time['t0'],
self.original_time['tend'] + self.original_time['dt'],
self.original_time['dt'])
@property
def modes(self):
"""
Get the matrix containing the DMD modes, stored by column.
:return: the matrix containing the DMD modes.
:rtype: numpy.ndarray
"""
return self.operator.modes
@property
def atilde(self):
"""
Get the reduced Koopman operator A, called A tilde.
:return: the reduced Koopman operator A.
:rtype: numpy.ndarray
"""
return self.operator.as_numpy_array
@property
def operator(self):
"""
Get the instance of DMDOperator.
:return: the instance of DMDOperator
:rtype: DMDOperator
"""
return self._Atilde
@property
def eigs(self):
"""
Get the eigenvalues of A tilde.
:return: the eigenvalues from the eigendecomposition of `atilde`.
:rtype: numpy.ndarray
"""
return self.operator.eigenvalues
def _translate_eigs_exponent(self, tpow):
"""
Transforms the exponent of the eigenvalues in the dynamics formula
according to the selected value of `self.opt` (check the documentation
for `opt` in :func:`__init__ <dmdbase.DMDBase.__init__>`).
:param tpow: the exponent(s) of Sigma in the original DMD formula.
:type tpow: int or np.ndarray
:return: the exponent(s) adjusted according to `self.opt`
:rtype: int or np.ndarray
"""
if isinstance(self.opt, bool):
amplitudes_snapshot_index = 0
else:
amplitudes_snapshot_index = self.opt
if amplitudes_snapshot_index < 0:
# we take care of negative indexes: -n becomes T - n
return tpow - (self.snapshots.shape[1] + amplitudes_snapshot_index)
else:
return tpow - amplitudes_snapshot_index
@property
def dynamics(self):
"""
Get the time evolution of each mode.
.. math::
\\mathbf{x}(t) \\approx
\\sum_{k=1}^{r} \\boldsymbol{\\phi}_{k} \\exp \\left( \\omega_{k} t \\right) b_{k} =
\\sum_{k=1}^{r} \\boldsymbol{\\phi}_{k} \\left( \\lambda_{k} \\right)^{\\left( t / \\Delta t \\right)} b_{k}
:return: the matrix that contains all the time evolution, stored by
row.
:rtype: numpy.ndarray
"""
temp = np.outer(self.eigs, np.ones(self.dmd_timesteps.shape[0]))
tpow = old_div(self.dmd_timesteps - self.original_time['t0'],
self.original_time['dt'])
# The new formula is x_(k+j) = \Phi \Lambda^k \Phi^(-1) x_j.
# Since j is fixed, for a given snapshot "u" we have the following
# formula:
# x_u = \Phi \Lambda^{u-j} \Phi^(-1) x_j
# Therefore tpow must be scaled appropriately.
tpow = self._translate_eigs_exponent(tpow)
return (np.power(temp, tpow) * self._b[:, None])
@property
def reconstructed_data(self):
"""
Get the reconstructed data.
:return: the matrix that contains the reconstructed snapshots.
:rtype: numpy.ndarray
"""
return self.modes.dot(self.dynamics)
@property
def snapshots(self):
"""
Get the original input data.
:return: the matrix that contains the original snapshots.
:rtype: numpy.ndarray
"""
return self._snapshots
@property
def frequency(self):
"""
Get the amplitude spectrum.
:return: the array that contains the frequencies of the eigenvalues.
:rtype: numpy.ndarray
"""
return np.log(self.eigs).imag / (2 * np.pi * self.original_time['dt'])
@property
def amplitudes(self):
"""
Get the coefficients that minimize the error between the original
system and the reconstructed one. For futher information, see
`dmdbase._compute_amplitudes`.
:return: the array that contains the amplitudes coefficient.
:rtype: numpy.ndarray
"""
return self._b
def fit(self, X):
"""
Abstract method to fit the snapshots matrices.
Not implemented, it has to be implemented in subclasses.
"""
raise NotImplementedError(
'Subclass must implement abstract method {}.fit'.format(
self.__class__.__name__))
@staticmethod
def _col_major_2darray(X):
"""
Private method that takes as input the snapshots and stores them into a
2D matrix, by column. If the input data is already formatted as 2D
array, the method saves it, otherwise it also saves the original
snapshots shape and reshapes the snapshots.
:param X: the input snapshots.
:type X: int or numpy.ndarray
:return: the 2D matrix that contains the flatten snapshots, the shape
of original snapshots.
:rtype: numpy.ndarray, tuple
"""
# If the data is already 2D ndarray
if isinstance(X, np.ndarray) and X.ndim == 2:
snapshots = X
snapshots_shape = None
else:
input_shapes = [np.asarray(x).shape for x in X]
if len(set(input_shapes)) != 1:
raise ValueError('Snapshots have not the same dimension.')
snapshots_shape = input_shapes[0]
snapshots = np.transpose([np.asarray(x).flatten() for x in X])
# check condition number of the data passed in
cond_number = np.linalg.cond(snapshots)
if cond_number > 10e4:
warnings.warn(
"Input data matrix X has condition number {}. "
"Consider preprocessing data, passing in augmented data matrix, or regularization methods."
.format(cond_number))
return snapshots, snapshots_shape
def _compute_amplitudes(self):
"""
Compute the amplitude coefficients. If `self.opt` is False the
amplitudes are computed by minimizing the error between the modes and
the first snapshot; if `self.opt` is True the amplitudes are computed by
minimizing the error between the modes and all the snapshots, at the
expense of bigger computational cost.
This method uses the class variables self._snapshots (for the
snapshots), self.modes and self.eigs.
:return: the amplitudes array
:rtype: numpy.ndarray
References for optimal amplitudes:
Jovanovic et al. 2014, Sparsity-promoting dynamic mode decomposition,
https://hal-polytechnique.archives-ouvertes.fr/hal-00995141/document
"""
if isinstance(self.opt, bool) and self.opt:
# compute the vandermonde matrix
omega = old_div(np.log(self.eigs), self.original_time['dt'])
vander = np.exp(
np.multiply(*np.meshgrid(omega, self.dmd_timesteps))).T
# perform svd on all the snapshots
U, s, V = np.linalg.svd(self._snapshots, full_matrices=False)
P = np.multiply(np.dot(self.modes.conj().T, self.modes),
np.conj(np.dot(vander,
vander.conj().T)))
tmp = np.linalg.multi_dot([U, np.diag(s), V]).conj().T
q = np.conj(np.diag(np.linalg.multi_dot([vander, tmp, self.modes])))
# b optimal
a = np.linalg.solve(P, q)
else:
if isinstance(self.opt, bool):
amplitudes_snapshot_index = 0
else:
amplitudes_snapshot_index = self.opt
a = np.linalg.lstsq(self.modes,
self._snapshots.T[amplitudes_snapshot_index],
rcond=None)[0]
return a
def plot_eigs(self,
show_axes=True,
show_unit_circle=True,
figsize=(8, 8),
title=''):
"""
Plot the eigenvalues.
:param bool show_axes: if True, the axes will be showed in the plot.
Default is True.
:param bool show_unit_circle: if True, the circle with unitary radius
and center in the origin will be showed. Default is True.
:param tuple(int,int) figsize: tuple in inches defining the figure
size. Default is (8, 8).
:param str title: title of the plot.
"""
if self.eigs is None:
raise ValueError('The eigenvalues have not been computed.'
'You have to perform the fit method.')
plt.figure(figsize=figsize)
plt.title(title)
plt.gcf()
ax = plt.gca()
points, = ax.plot(self.eigs.real,
self.eigs.imag,
'bo',
label='Eigenvalues')
# set limits for axis
limit = np.max(np.ceil(np.absolute(self.eigs)))
ax.set_xlim((-limit, limit))
ax.set_ylim((-limit, limit))
plt.ylabel('Imaginary part')
plt.xlabel('Real part')
if show_unit_circle:
unit_circle = plt.Circle((0., 0.),
1.,
color='green',
fill=False,
label='Unit circle',
linestyle='--')
ax.add_artist(unit_circle)
# Dashed grid
gridlines = ax.get_xgridlines() + ax.get_ygridlines()
for line in gridlines:
line.set_linestyle('-.')
ax.grid(True)
ax.set_aspect('equal')
# x and y axes
if show_axes:
ax.annotate('',
xy=(np.max([limit * 0.8, 1.]), 0.),
xytext=(np.min([-limit * 0.8, -1.]), 0.),
arrowprops=dict(arrowstyle="->"))
ax.annotate('',
xy=(0., np.max([limit * 0.8, 1.])),
xytext=(0., np.min([-limit * 0.8, -1.])),
arrowprops=dict(arrowstyle="->"))
# legend
if show_unit_circle:
ax.add_artist(
plt.legend([points, unit_circle],
['Eigenvalues', 'Unit circle'],
loc=1))
else:
ax.add_artist(plt.legend([points], ['Eigenvalues'], loc=1))
plt.show()
def plot_modes_2D(self,
index_mode=None,
filename=None,
x=None,
y=None,
order='C',
figsize=(8, 8)):
"""
Plot the DMD Modes.
:param index_mode: the index of the modes to plot. By default, all
the modes are plotted.
:type index_mode: int or sequence(int)
:param str filename: if specified, the plot is saved at `filename`.
:param numpy.ndarray x: domain abscissa.
:param numpy.ndarray y: domain ordinate
:param order: read the elements of snapshots using this index order,
and place the elements into the reshaped array using this index
order. It has to be the same used to store the snapshot. 'C' means
to read/ write the elements using C-like index order, with the last
axis index changing fastest, back to the first axis index changing
slowest. 'F' means to read / write the elements using Fortran-like
index order, with the first index changing fastest, and the last
index changing slowest. Note that the 'C' and 'F' options take no
account of the memory layout of the underlying array, and only
refer to the order of indexing. 'A' means to read / write the
elements in Fortran-like index order if a is Fortran contiguous in
memory, C-like order otherwise.
:type order: {'C', 'F', 'A'}, default 'C'.
:param tuple(int,int) figsize: tuple in inches defining the figure
size. Default is (8, 8).
"""
if self.modes is None:
raise ValueError('The modes have not been computed.'
'You have to perform the fit method.')
if x is None and y is None:
if self._snapshots_shape is None:
raise ValueError(
'No information about the original shape of the snapshots.')
if len(self._snapshots_shape) != 2:
raise ValueError(
'The dimension of the input snapshots is not 2D.')
# If domain dimensions have not been passed as argument,
# use the snapshots dimensions
if x is None and y is None:
x = np.arange(self._snapshots_shape[0])
y = np.arange(self._snapshots_shape[1])
xgrid, ygrid = np.meshgrid(x, y)
if index_mode is None:
index_mode = list(range(self.modes.shape[1]))
elif isinstance(index_mode, int):
index_mode = [index_mode]
if filename:
basename, ext = splitext(filename)
for idx in index_mode:
fig = plt.figure(figsize=figsize)
fig.suptitle('DMD Mode {}'.format(idx))
real_ax = fig.add_subplot(1, 2, 1)
imag_ax = fig.add_subplot(1, 2, 2)
mode = self.modes.T[idx].reshape(xgrid.shape, order=order)
real = real_ax.pcolor(xgrid,
ygrid,
mode.real,
cmap='jet',
vmin=mode.real.min(),
vmax=mode.real.max())
imag = imag_ax.pcolor(xgrid,
ygrid,
mode.imag,
vmin=mode.imag.min(),
vmax=mode.imag.max())
fig.colorbar(real, ax=real_ax)
fig.colorbar(imag, ax=imag_ax)
real_ax.set_aspect('auto')
imag_ax.set_aspect('auto')
real_ax.set_title('Real')
imag_ax.set_title('Imag')
# padding between elements
plt.tight_layout(pad=2.)
if filename:
plt.savefig('{0}.{1}{2}'.format(basename, idx, ext))
plt.close(fig)
if not filename:
plt.show()
def plot_snapshots_2D(self,
index_snap=None,
filename=None,
x=None,
y=None,
order='C',
figsize=(8, 8)):
"""
Plot the snapshots.
:param index_snap: the index of the snapshots to plot. By default, all
the snapshots are plotted.
:type index_snap: int or sequence(int)
:param str filename: if specified, the plot is saved at `filename`.
:param numpy.ndarray x: domain abscissa.
:param numpy.ndarray y: domain ordinate
:param order: read the elements of snapshots using this index order,
and place the elements into the reshaped array using this index
order. It has to be the same used to store the snapshot. 'C' means
to read/ write the elements using C-like index order, with the last
axis index changing fastest, back to the first axis index changing
slowest. 'F' means to read / write the elements using Fortran-like
index order, with the first index changing fastest, and the last
index changing slowest. Note that the 'C' and 'F' options take no
account of the memory layout of the underlying array, and only
refer to the order of indexing. 'A' means to read / write the
elements in Fortran-like index order if a is Fortran contiguous in
memory, C-like order otherwise.
:type order: {'C', 'F', 'A'}, default 'C'.
:param tuple(int,int) figsize: tuple in inches defining the figure
size. Default is (8, 8).
"""
if self._snapshots is None:
raise ValueError('Input snapshots not found.')
if x is None and y is None:
if self._snapshots_shape is None:
raise ValueError(
'No information about the original shape of the snapshots.')
if len(self._snapshots_shape) != 2:
raise ValueError(
'The dimension of the input snapshots is not 2D.')
# If domain dimensions have not been passed as argument,
# use the snapshots dimensions
if x is None and y is None:
x = np.arange(self._snapshots_shape[0])
y = np.arange(self._snapshots_shape[1])
xgrid, ygrid = np.meshgrid(x, y)
if index_snap is None:
index_snap = list(range(self._snapshots.shape[1]))
elif isinstance(index_snap, int):
index_snap = [index_snap]
if filename:
basename, ext = splitext(filename)
for idx in index_snap:
fig = plt.figure(figsize=figsize)
fig.suptitle('Snapshot {}'.format(idx))
snapshot = (self._snapshots.T[idx].real.reshape(xgrid.shape,
order=order))
contour = plt.pcolor(xgrid,
ygrid,
snapshot,
vmin=snapshot.min(),
vmax=snapshot.max())
fig.colorbar(contour)
if filename:
plt.savefig('{0}.{1}{2}'.format(basename, idx, ext))
plt.close(fig)
if not filename:
plt.show()