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# timeresp.py - time-domain simulation routines.
#
# Initial author: Eike Welk
# Creation date: 12 May 2011
#
# Modified: Sawyer B. Fuller (minster@uw.edu) to add discrete-time
# capability and better automatic time vector creation
# Date: June 2020
#
# Modified by Ilhan Polat to improve automatic time vector creation
# Date: August 17, 2020
#
# Modified by Richard Murray to add TimeResponseData class
# Date: August 2021
#
# Use `git shortlog -n -s statesp.py` for full list of contributors
"""Time domain simulation routines.
This module contains a collection of functions that are used to
compute time-domain simulations of LTI systems.
Arguments to time-domain simulations include a time vector, an input
vector (when needed), and an initial condition vector. The most
general function for simulating LTI systems the
`forced_response` function, which has the form::
t, y = forced_response(sys, T, U, X0)
where `T` is a vector of times at which the response should be
evaluated, `U` is a vector of inputs (one for each time point) and
`X0` is the initial condition for the system.
See :ref:`time-series-convention` for more information on how time
series data are represented.
"""
import warnings
from copy import copy
import numpy as np
import scipy as sp
from numpy import einsum, maximum, minimum
from scipy.linalg import eig, eigvals, matrix_balance, norm
from . import config
from .exception import pandas_check
from .iosys import NamedSignal, isctime, isdtime
from .timeplot import time_response_plot
__all__ = ['forced_response', 'step_response', 'step_info',
'initial_response', 'impulse_response', 'TimeResponseData',
'TimeResponseList']
class TimeResponseData:
"""A class for returning time responses.
This class maintains and manipulates the data corresponding to the
temporal response of an input/output system. It is used as the return
type for time domain simulations (step response, input/output response,
etc).
A time response consists of a time vector, an output vector, and
optionally an input vector and/or state vector. Inputs and outputs can
be 1D (scalar input/output) or 2D (vector input/output).
A time response can be stored for multiple input signals (called traces),
with the output and state indexed by the trace number. This allows for
input/output response matrices, which is mainly useful for impulse and
step responses for linear systems. For multi-trace responses, the same
time vector must be used for all traces.
Time responses are accessed through either the raw data, stored as `t`,
`y`, `x`, `u`, or using a set of properties `time`, `outputs`,
`states`, `inputs`. When accessing time responses via their
properties, squeeze processing is applied so that (by default)
single-input, single-output systems will have the output and input
indices suppressed. This behavior is set using the `squeeze` parameter.
Attributes
----------
t : 1D array
Time values of the input/output response(s). This attribute is
normally accessed via the `time` property.
y : 2D or 3D array
Output response data, indexed either by output index and time (for
single trace responses) or output, trace, and time (for multi-trace
responses). These data are normally accessed via the `outputs`
property, which performs squeeze processing.
x : 2D or 3D array, or None
State space data, indexed either by output number and time (for single
trace responses) or output, trace, and time (for multi-trace
responses). If no state data are present, value is `None`. These
data are normally accessed via the `states` property, which
performs squeeze processing.
u : 2D or 3D array, or None
Input signal data, indexed either by input index and time (for single
trace responses) or input, trace, and time (for multi-trace
responses). If no input data are present, value is `None`. These
data are normally accessed via the `inputs` property, which
performs squeeze processing.
squeeze : bool, optional
By default, if a system is single-input, single-output (SISO)
then the outputs (and inputs) are returned as a 1D array
(indexed by time) and if a system is multi-input or
multi-output, then the outputs are returned as a 2D array
(indexed by output and time) or a 3D array (indexed by output,
trace, and time). If `squeeze=True`, access to the output
response will remove single-dimensional entries from the shape
of the inputs and outputs even if the system is not SISO. If
`squeeze=False`, the output is returned as a 2D or 3D array
(indexed by the output [if multi-input], trace [if multi-trace]
and time) even if the system is SISO. The default value can be
set using config.defaults['control.squeeze_time_response'].
transpose : bool, optional
If True, transpose all input and output arrays (for backward
compatibility with MATLAB and `scipy.signal.lsim`). Default
value is False.
issiso : bool, optional
Set to `True` if the system generating the data is single-input,
single-output. If passed as `None` (default), the input data
will be used to set the value.
ninputs, noutputs, nstates : int
Number of inputs, outputs, and states of the underlying system.
input_labels, output_labels, state_labels : array of str
Names for the input, output, and state variables.
success : bool, optional
If `False`, result may not be valid (see
`input_output_response`). Defaults to `True`.
message : str, optional
Informational message if `success` is `False`.
sysname : str, optional
Name of the system that created the data.
params : dict, optional
If system is a nonlinear I/O system, set parameter values.
plot_inputs : bool, optional
Whether or not to plot the inputs by default (can be overridden in
the plot() method)
ntraces : int, optional
Number of independent traces represented in the input/output
response. If ntraces is 0 (default) then the data represents a
single trace with the trace index surpressed in the data.
title : str, optional
Set the title to use when plotting.
trace_labels : array of string, optional
Labels to use for traces (set to sysname it ntraces is 0)
trace_types : array of string, optional
Type of trace. Currently only 'step' is supported, which controls
the way in which the signal is plotted.
Notes
-----
The responses for individual elements of the time response can be
accessed using integers, slices, or lists of signal offsets or the
names of the appropriate signals::
sys = ct.rss(4, 2, 1)
resp = ct.initial_response(sys, X0=[1, 1, 1, 1])
plt.plot(resp.time, resp.outputs['y[0]'])
In the case of multi-trace data, the responses should be indexed using
the output signal name (or offset) and the input signal name (or
offset)::
sys = ct.rss(4, 2, 2, strictly_proper=True)
resp = ct.step_response(sys)
plt.plot(resp.time, resp.outputs[['y[0]', 'y[1]'], 'u[0]'].T)
For backward compatibility with earlier versions of python-control,
this class has an `__iter__` method that allows it to be assigned to
a tuple with a variable number of elements. This allows the following
patterns to work::
t, y = step_response(sys)
t, y, x = step_response(sys, return_x=True)
Similarly, the class has `__getitem__` and `__len__` methods that
allow the return value to be indexed:
* response[0]: returns the time vector
* response[1]: returns the output vector
* response[2]: returns the state vector
When using this (legacy) interface, the state vector is not affected
by the `squeeze` parameter.
The default settings for `return_x`, `squeeze` and `transpose`
can be changed by calling the class instance and passing new values::
response(tranpose=True).input
See `TimeResponseData.__call__` for more information.
"""
#
# Class attributes
#
# These attributes are defined as class attributes so that they are
# documented properly. They are "overwritten" in __init__.
#
#: Squeeze processing parameter.
#:
#: By default, if a system is single-input, single-output (SISO)
#: then the inputs and outputs are returned as a 1D array (indexed
#: by time) and if a system is multi-input or multi-output, then
#: the inputs are returned as a 2D array (indexed by input and
#: time) and the outputs are returned as either a 2D array (indexed
#: by output and time) or a 3D array (indexed by output, trace, and
#: time). If squeeze=True, access to the output response will
#: remove single-dimensional entries from the shape of the inputs
#: and outputs even if the system is not SISO. If squeeze=False,
#: keep the input as a 2D or 3D array (indexed by the input (if
#: multi-input), trace (if single input) and time) and the output
#: as a 3D array (indexed by the output, trace, and time) even if
#: the system is SISO. The default value can be set using
#: config.defaults['control.squeeze_time_response'].
#:
#: :meta hide-value:
squeeze = None
def __init__(
self, time, outputs, states=None, inputs=None, issiso=None,
output_labels=None, state_labels=None, input_labels=None,
title=None, transpose=False, return_x=False, squeeze=None,
multi_trace=False, trace_labels=None, trace_types=None,
plot_inputs=True, sysname=None, params=None, success=True,
message=None
):
"""Create an input/output time response object.
Parameters
----------
time : 1D array
Time values of the output. Ignored if None.
outputs : ndarray
Output response of the system. This can either be a 1D array
indexed by time (for SISO systems or MISO systems with a specified
input), a 2D array indexed by output and time (for MIMO systems
with no input indexing, such as initial_response or forced
response) or trace and time (for SISO systems with multiple
traces), or a 3D array indexed by output, trace, and time (for
multi-trace input/output responses).
states : array, optional
Individual response of each state variable. This should be a 2D
array indexed by the state index and time (for single trace
systems) or a 3D array indexed by state, trace, and time.
inputs : array, optional
Inputs used to generate the output. This can either be a 1D
array indexed by time (for SISO systems or MISO/MIMO systems
with a specified input), a 2D array indexed either by input and
time (for a multi-input system) or trace and time (for a
single-input, multi-trace response), or a 3D array indexed by
input, trace, and time.
title : str, optonal
Title of the data set (used as figure title in plotting).
squeeze : bool, optional
By default, if a system is single-input, single-output (SISO)
then the inputs and outputs are returned as a 1D array (indexed
by time) and if a system is multi-input or multi-output, then
the inputs are returned as a 2D array (indexed by input and
time) and the outputs are returned as either a 2D array (indexed
by output and time) or a 3D array (indexed by output, trace, and
time). If squeeze=True, access to the output response will
remove single-dimensional entries from the shape of the inputs
and outputs even if the system is not SISO. If squeeze=False,
keep the input as a 2D or 3D array (indexed by the input (if
multi-input), trace (if single input) and time) and the output
as a 3D array (indexed by the output, trace, and time) even if
the system is SISO. The default value can be set using
config.defaults['control.squeeze_time_response'].
Other Parameters
----------------
input_labels, output_labels, state_labels: array of str, optional
Optional labels for the inputs, outputs, and states, given as a
list of strings matching the appropriate signal dimension.
sysname : str, optional
Name of the system that created the data.
transpose : bool, optional
If True, transpose all input and output arrays (for backward
compatibility with MATLAB and `scipy.signal.lsim`).
Default value is False.
return_x : bool, optional
If True, return the state vector when enumerating result by
assigning to a tuple (default = False).
plot_inputs : bool, optional
Whether or not to plot the inputs by default (can be overridden
in the plot() method)
multi_trace : bool, optional
If `True`, then 2D input array represents multiple traces. For
a MIMO system, the `input` attribute should then be set to
indicate which trace is being specified. Default is `False`.
success : bool, optional
If `False`, result may not be valid (see
`input_output_response`).
message : str, optional
Informational message if `success` is `False`.
"""
#
# Process and store the basic input/output elements
#
# Time vector
self.t = np.atleast_1d(time)
if self.t.ndim != 1:
raise ValueError("Time vector must be 1D array")
self.title = title
self.sysname = sysname
self.params = params
#
# Output vector (and number of traces)
#
self.y = np.array(outputs)
if self.y.ndim == 3:
multi_trace = True
self.noutputs = self.y.shape[0]
self.ntraces = self.y.shape[1]
elif multi_trace and self.y.ndim == 2:
self.noutputs = 1
self.ntraces = self.y.shape[0]
elif not multi_trace and self.y.ndim == 2:
self.noutputs = self.y.shape[0]
self.ntraces = 0
elif not multi_trace and self.y.ndim == 1:
self.noutputs = 1
self.ntraces = 0
# Reshape the data to be 2D for consistency
self.y = self.y.reshape(self.noutputs, -1)
else:
raise ValueError("Output vector is the wrong shape")
# Check and store labels, if present
self.output_labels = _process_labels(
output_labels, "output", self.noutputs)
# Make sure time dimension of output is the right length
if self.t.shape[-1] != self.y.shape[-1]:
raise ValueError("Output vector does not match time vector")
#
# State vector (optional)
#
# If present, the shape of the state vector should be consistent
# with the multi-trace nature of the data.
#
if states is None:
self.x = None
self.nstates = 0
else:
self.x = np.array(states)
self.nstates = self.x.shape[0]
# Make sure the shape is OK
if multi_trace and \
(self.x.ndim != 3 or self.x.shape[1] != self.ntraces) or \
not multi_trace and self.x.ndim != 2 :
raise ValueError("State vector is the wrong shape")
# Make sure time dimension of state is the right length
if self.t.shape[-1] != self.x.shape[-1]:
raise ValueError("State vector does not match time vector")
# Check and store labels, if present
self.state_labels = _process_labels(
state_labels, "state", self.nstates)
#
# Input vector (optional)
#
# If present, the shape and dimensions of the input vector should be
# consistent with the trace count computed above.
#
if inputs is None:
self.u = None
self.ninputs = 0
self.plot_inputs = False
else:
self.u = np.array(inputs)
self.plot_inputs = plot_inputs
# Make sure the shape is OK and figure out the nuumber of inputs
if multi_trace and self.u.ndim == 3 and \
self.u.shape[1] == self.ntraces:
self.ninputs = self.u.shape[0]
elif multi_trace and self.u.ndim == 2 and \
self.u.shape[0] == self.ntraces:
self.ninputs = 1
elif not multi_trace and self.u.ndim == 2 and \
self.ntraces == 0:
self.ninputs = self.u.shape[0]
elif not multi_trace and self.u.ndim == 1:
self.ninputs = 1
# Reshape the data to be 2D for consistency
self.u = self.u.reshape(self.ninputs, -1)
else:
raise ValueError("Input vector is the wrong shape")
# Make sure time dimension of output is the right length
if self.t.shape[-1] != self.u.shape[-1]:
raise ValueError("Input vector does not match time vector")
# Check and store labels, if present
self.input_labels = _process_labels(
input_labels, "input", self.ninputs)
# Check and store trace labels, if present
self.trace_labels = _process_labels(
trace_labels, "trace", self.ntraces)
self.trace_types = trace_types
# Figure out if the system is SISO
if issiso is None:
# Figure out based on the data
if self.ninputs == 1:
issiso = (self.noutputs == 1)
elif self.ninputs > 1:
issiso = False
else:
# Missing input data => can't resolve
raise ValueError("Can't determine if system is SISO")
elif issiso is True and (self.ninputs > 1 or self.noutputs > 1):
raise ValueError("Keyword `issiso` does not match data")
# Set the value to be used for future processing
self.issiso = issiso
# Keep track of whether to squeeze inputs, outputs, and states
if not (squeeze is True or squeeze is None or squeeze is False):
raise ValueError("Unknown squeeze value")
self.squeeze = squeeze
# Keep track of whether to transpose for MATLAB/scipy.signal
self.transpose = transpose
# Store legacy keyword values (only needed for legacy interface)
self.return_x = return_x
# Information on the whether the simulation result may be incorrect
self.success = success
self.message = message
def __call__(self, **kwargs):
"""Change value of processing keywords.
Calling the time response object will create a copy of the object and
change the values of the keywords used to control the `outputs`,
`states`, and `inputs` properties.
Parameters
----------
squeeze : bool, optional
If squeeze=True, access to the output response will remove
single-dimensional entries from the shape of the inputs, outputs,
and states even if the system is not SISO. If squeeze=False, keep
the input as a 2D or 3D array (indexed by the input (if
multi-input), trace (if single input) and time) and the output and
states as a 3D array (indexed by the output/state, trace, and
time) even if the system is SISO.
transpose : bool, optional
If True, transpose all input and output arrays (for backward
compatibility with MATLAB and `scipy.signal.lsim`).
Default value is False.
return_x : bool, optional
If True, return the state vector when enumerating result by
assigning to a tuple (default = False).
input_labels, output_labels, state_labels: array of str
Labels for the inputs, outputs, and states, given as a
list of strings matching the appropriate signal dimension.
"""
# Make a copy of the object
response = copy(self)
# Update any keywords that we were passed
response.transpose = kwargs.pop('transpose', self.transpose)
response.squeeze = kwargs.pop('squeeze', self.squeeze)
response.return_x = kwargs.pop('return_x', self.return_x)
# Check for new labels
input_labels = kwargs.pop('input_labels', None)
if input_labels is not None:
response.input_labels = _process_labels(
input_labels, "input", response.ninputs)
output_labels = kwargs.pop('output_labels', None)
if output_labels is not None:
response.output_labels = _process_labels(
output_labels, "output", response.noutputs)
state_labels = kwargs.pop('state_labels', None)
if state_labels is not None:
response.state_labels = _process_labels(
state_labels, "state", response.nstates)
# Make sure there were no extraneous keywords
if kwargs:
raise TypeError("unrecognized keywords: ", str(kwargs))
return response
@property
def time(self):
"""Time vector.
Time values of the input/output response(s).
:type: 1D array"""
return self.t
# Getter for output (implements squeeze processing)
@property
def outputs(self):
"""Time response output vector.
Output response of the system, indexed by either the output and time
(if only a single input is given) or the output, trace, and time
(for multiple traces). See `TimeResponseData.squeeze` for a
description of how this can be modified using the `squeeze` keyword.
Input and output signal names can be used to index the data in
place of integer offsets, with the input signal names being used to
access multi-input data.
:type: 1D, 2D, or 3D array
"""
# TODO: move to __init__ to avoid recomputing each time?
y = _process_time_response(
self.y, issiso=self.issiso,
transpose=self.transpose, squeeze=self.squeeze)
return NamedSignal(y, self.output_labels, self.input_labels)
# Getter for states (implements squeeze processing)
@property
def states(self):
"""Time response state vector.
Time evolution of the state vector, indexed indexed by either the
state and time (if only a single trace is given) or the state, trace,
and time (for multiple traces). See `TimeResponseData.squeeze`
for a description of how this can be modified using the `squeeze`
keyword.
Input and output signal names can be used to index the data in
place of integer offsets, with the input signal names being used to
access multi-input data.
:type: 2D or 3D array
"""
# TODO: move to __init__ to avoid recomputing each time?
x = _process_time_response(
self.x, transpose=self.transpose,
squeeze=self.squeeze, issiso=False)
# Special processing for SISO case: always retain state index
if self.issiso and self.ntraces == 1 and x.ndim == 3 and \
self.squeeze is not False:
# Single-input, single-output system with single trace
x = x[:, 0, :]
return NamedSignal(x, self.state_labels, self.input_labels)
# Getter for inputs (implements squeeze processing)
@property
def inputs(self):
"""Time response input vector.
Input(s) to the system, indexed by input (optiona), trace (optional),
and time. If a 1D vector is passed, the input corresponds to a
scalar-valued input. If a 2D vector is passed, then it can either
represent multiple single-input traces or a single multi-input trace.
The optional `multi_trace` keyword should be used to disambiguate
the two. If a 3D vector is passed, then it represents a multi-trace,
multi-input signal, indexed by input, trace, and time.
Input and output signal names can be used to index the data in
place of integer offsets, with the input signal names being used to
access multi-input data.
See `TimeResponseData.squeeze` for a description of how the
dimensions of the input vector can be modified using the `squeeze`
keyword.
:type: 1D or 2D array
"""
# TODO: move to __init__ to avoid recomputing each time?
if self.u is None:
return None
u = _process_time_response(
self.u, issiso=self.issiso,
transpose=self.transpose, squeeze=self.squeeze)
return NamedSignal(u, self.input_labels, self.input_labels)
# Getter for legacy state (implements non-standard squeeze processing)
# TODO: remove when no longer needed
@property
def _legacy_states(self):
"""Time response state vector (legacy version).
Time evolution of the state vector, indexed indexed by either the
state and time (if only a single trace is given) or the state,
trace, and time (for multiple traces).
The `legacy_states` property is not affected by the `squeeze` keyword
and hence it will always have these dimensions.
:type: 2D or 3D array
"""
if self.x is None:
return None
elif self.ninputs == 1 and self.noutputs == 1 and \
self.ntraces == 1 and self.x.ndim == 3:
# Single-input, single-output system with single trace
x = self.x[:, 0, :]
else:
# Return the full set of data
x = self.x
# Transpose processing
if self.transpose:
x = np.transpose(x, np.roll(range(x.ndim), 1))
return x
# Implement iter to allow assigning to a tuple
def __iter__(self):
if not self.return_x:
return iter((self.time, self.outputs))
return iter((self.time, self.outputs, self._legacy_states))
# Implement (thin) getitem to allow access via legacy indexing
def __getitem__(self, index):
# See if we were passed a slice
if isinstance(index, slice):
if (index.start is None or index.start == 0) and index.stop == 2:
return (self.time, self.outputs)
# Otherwise assume we were passed a single index
if index == 0:
return self.time
if index == 1:
return self.outputs
if index == 2:
return self._legacy_states
raise IndexError
# Implement (thin) len to emulate legacy testing interface
def __len__(self):
return 3 if self.return_x else 2
# Convert to pandas
def to_pandas(self):
"""Convert response data to pandas data frame.
Creates a pandas data frame using the input, output, and state labels
for the time response. The column labels are given by the input and
output (and state, when present) labels, with time labeled by 'time'
and traces (for multi-trace responses) labeled by 'trace'.
"""
if not pandas_check():
raise ImportError("pandas not installed")
import pandas
# Create a dict for setting up the data frame
data = {'time': np.tile(
self.time, self.ntraces if self.ntraces > 0 else 1)}
if self.ntraces > 0:
data['trace'] = np.hstack([
np.full(self.time.size, label) for label in self.trace_labels])
if self.ninputs > 0:
data.update(
{name: self.u[i].reshape(-1)
for i, name in enumerate(self.input_labels)})
if self.noutputs > 0:
data.update(
{name: self.y[i].reshape(-1)
for i, name in enumerate(self.output_labels)})
if self.nstates > 0:
data.update(
{name: self.x[i].reshape(-1)
for i, name in enumerate(self.state_labels)})
return pandas.DataFrame(data)
# Plot data
def plot(self, *args, **kwargs):
"""Plot the time response data objects.
This method calls `time_response_plot`, passing all arguments
and keywords.
"""
return time_response_plot(self, *args, **kwargs)
#
# Time response data list class
#
# This class is a subclass of list that adds a plot() method, enabling
# direct plotting from routines returning a list of TimeResponseData
# objects.
#
class TimeResponseList(list):
"""List of TimeResponseData objects with plotting capability.
This class consists of a list of `TimeResponseData` objects.
It is a subclass of the Python `list` class, with a `plot` method that
plots the individual `TimeResponseData` objects.
"""
def plot(self, *args, **kwargs):
from .ctrlplot import ControlPlot
lines = None
label = kwargs.pop('label', [None] * len(self))
for i, response in enumerate(self):
cplt = TimeResponseData.plot(
response, *args, label=label[i], **kwargs)
if lines is None:
lines = cplt.lines
else:
# Append the lines in the new plot to previous lines
for row in range(cplt.lines.shape[0]):
for col in range(cplt.lines.shape[1]):
lines[row, col] += cplt.lines[row, col]
return ControlPlot(lines, cplt.axes, cplt.figure)
# Process signal labels
def _process_labels(labels, signal, length):
"""Process time response signal labels.
Parameters
----------
labels : list of str or dict
Description of the labels for the signal. This can be a list of
strings or a dict giving the index of each signal (used in iosys).
signal : str
Name of the signal being processed (for error messages).
length : int
Number of labels required.
Returns
-------
labels : list of str
List of labels.
"""
if labels is None or len(labels) == 0:
return None
# See if we got passed a dictionary (from iosys)
if isinstance(labels, dict):
# Form inverse dictionary
ivd = {v: k for k, v in labels.items()}
try:
# Turn into a list
labels = [ivd[n] for n in range(len(labels))]
except KeyError:
raise ValueError("Name dictionary for %s is incomplete" % signal)
# Convert labels to a list
if isinstance(labels, str):
labels = [labels]
else:
labels = list(labels)
# Make sure the signal list is the right length and type
if len(labels) != length:
raise ValueError("List of %s labels is the wrong length" % signal)
elif not all([isinstance(label, str) for label in labels]):
raise ValueError("List of %s labels must all be strings" % signal)
return labels
# Helper function for checking array-like parameters
def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False,
transpose=False):
"""Helper function for checking array_like parameters.
* Check type and shape of `in_obj`.
* Convert `in_obj` to an array if necessary.
* Change shape of `in_obj` according to parameter `squeeze`.
* If `in_obj` is a scalar (number) it is converted to an array with
a legal shape, that is filled with the scalar value.
The function raises an exception when it detects an error.
Parameters
----------
in_obj : array like object
The array or matrix which is checked.
legal_shapes : list of tuple
A list of shapes that in_obj can legally have.
The special value "any" means that there can be any
number of elements in a certain dimension.
* `(2, 3)` describes an array with 2 rows and 3 columns
* `(2, "any")` describes an array with 2 rows and any number of
columns
err_msg_start : str
String that is prepended to the error messages, when this function
raises an exception. It should be used to identify the argument which
is currently checked.
squeeze : bool
If True, all dimensions with only one element are removed from the
array. If False the array's shape is unmodified.
For example:
`array([[1,2,3]])` is converted to `array([1, 2, 3])`
transpose : bool, optional
If True, assume that 2D input arrays are transposed from the standard
format. Used to convert MATLAB-style inputs to our format.
Returns
-------
out_array : array
The checked and converted contents of `in_obj`.
"""
# convert nearly everything to an array.
out_array = np.asarray(in_obj)
if (transpose):
out_array = np.transpose(out_array)
# Test element data type, elements must be numbers
legal_kinds = set(("i", "f", "c")) # integer, float, complex
if out_array.dtype.kind not in legal_kinds:
err_msg = "Wrong element data type: '{d}'. Array elements " \
"must be numbers.".format(d=str(out_array.dtype))
raise TypeError(err_msg_start + err_msg)
# If array is zero dimensional (in_obj is scalar):
# create array with legal shape filled with the original value.
if out_array.ndim == 0:
for s_legal in legal_shapes:
# search for shape that does not contain the special symbol any.
if "any" in s_legal:
continue
the_val = out_array[()]
out_array = np.empty(s_legal, 'd')
out_array.fill(the_val)
break
# Test shape
def shape_matches(s_legal, s_actual):
"""Test if two shape tuples match"""
# Array must have required number of dimensions
if len(s_legal) != len(s_actual):
return False
# All dimensions must contain required number of elements. Joker: "all"
for n_legal, n_actual in zip(s_legal, s_actual):
if n_legal == "any":
continue
if n_legal != n_actual:
return False
return True
# Iterate over legal shapes, and see if any matches out_array's shape.
for s_legal in legal_shapes:
if shape_matches(s_legal, out_array.shape):
break
else:
legal_shape_str = " or ".join([str(s) for s in legal_shapes])
err_msg = "Wrong shape (rows, columns): {a}. Expected: {e}." \
.format(e=legal_shape_str, a=str(out_array.shape))
raise ValueError(err_msg_start + err_msg)
# Convert shape
if squeeze:
out_array = np.squeeze(out_array)
# We don't want zero dimensional arrays
if out_array.shape == tuple():
out_array = out_array.reshape((1,))
return out_array
# Forced response of a linear system
def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None,
interpolate=False, return_x=None, squeeze=None):
"""Compute the output of a linear system given the input.
As a convenience for parameters `U`, `X0`:
Numbers (scalars) are converted to constant arrays with the correct shape.
The correct shape is inferred from arguments `sys` and `T`.
For information on the **shape** of parameters `U`, `T`, `X0` and
return values `T`, `yout`, `xout`, see :ref:`time-series-convention`.
Parameters
----------
sysdata : I/O system or list of I/O systems
I/O system(s) for which forced response is computed.
T : array_like, optional for discrete LTI `sys`
Time steps at which the input is defined; values must be evenly spaced.
If None, `U` must be given and `len(U)` time steps of sys.dt are
simulated. If sys.dt is None or True (undetermined time step), a time
step of 1.0 is assumed.
U : array_like or float, optional
Input array giving input at each time `T`.
If `U` is None or 0, `T` must be given, even for discrete
time systems. In this case, for continuous time systems, a direct
calculation of the matrix exponential is used, which is faster than the
general interpolating algorithm used otherwise.
X0 : array_like or float, default=0.
Initial condition.
params : dict, optional
If system is a nonlinear I/O system, set parameter values.
transpose : bool, default=False
If True, transpose all input and output arrays (for backward
compatibility with MATLAB and `scipy.signal.lsim`).
interpolate : bool, default=False
If True and system is a discrete time system, the input will
be interpolated between the given time steps and the output
will be given at system sampling rate. Otherwise, only return
the output at the times given in `T`. No effect on continuous
time simulations.
return_x : bool, default=None
Used if the time response data is assigned to a tuple:
* If False, return only the time and output vectors.
* If True, also return the the state vector.