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	from typing import Type
	from sympy import Interval, numer, Rational, solveset
	from sympy.core.add import Add
	from sympy.core.basic import Basic
	from sympy.core.containers import Tuple
	from sympy.core.evalf import EvalfMixin
	from sympy.core.expr import Expr
	from sympy.core.function import expand
	from sympy.core.logic import fuzzy_and
	from sympy.core.mul import Mul
	from sympy.core.numbers import I, pi, oo
	from sympy.core.power import Pow
	from sympy.core.singleton import S
	from sympy.core.symbol import Dummy, Symbol
	from sympy.functions import Abs
	from sympy.core.sympify import sympify, _sympify
	from sympy.matrices import Matrix, ImmutableMatrix, ImmutableDenseMatrix, eye, ShapeError, zeros
	from sympy.functions.elementary.exponential import (exp, log)
	from sympy.matrices.expressions import MatMul, MatAdd
	from sympy.polys import Poly, rootof
	from sympy.polys.polyroots import roots
	from sympy.polys.polytools import (cancel, degree)
	from sympy.series import limit
	from sympy.utilities.misc import filldedent

	from mpmath.libmp.libmpf import prec_to_dps

	__all__ = ['TransferFunction', 'Series', 'MIMOSeries', 'Parallel', 'MIMOParallel',
	'Feedback', 'MIMOFeedback', 'TransferFunctionMatrix', 'StateSpace', 'gbt', 'bilinear', 'forward_diff', 'backward_diff',
	'phase_margin', 'gain_margin']

	def _roots(poly, var):
	""" like roots, but works on higher-order polynomials. """
	r = roots(poly, var, multiple=True)
	n = degree(poly)
	if len(r) != n:
	r = [rootof(poly, var, k) for k in range(n)]
	return r

	def gbt(tf, sample_per, alpha):
	r"""
	Returns falling coefficients of H(z) from numerator and denominator.

	Explanation
	===========

	Where H(z) is the corresponding discretized transfer function,
	discretized with the generalised bilinear transformation method.
	H(z) is obtained from the continuous transfer function H(s)
	by substituting $s(z) = \frac{z-1}{T(\alpha z + (1-\alpha))}$ into H(s), where T is the
	sample period.
	Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
	as [a, b], [c, d].

	Examples
	========

	>>> from sympy.physics.control.lti import TransferFunction, gbt
	>>> from sympy.abc import s, L, R, T

	>>> tf = TransferFunction(1, s*L + R, s)
	>>> numZ, denZ = gbt(tf, T, 0.5)
	>>> numZ
	[T/(2(L + RT/2)), T/(2(L + RT/2))]
	>>> denZ
	[1, (-L + RT/2)/(L + RT/2)]

	>>> numZ, denZ = gbt(tf, T, 0)
	>>> numZ
	[T/L]
	>>> denZ
	[1, (-L + R*T)/L]

	>>> numZ, denZ = gbt(tf, T, 1)
	>>> numZ
	[T/(L + R*T), 0]
	>>> denZ
	[1, -L/(L + R*T)]

	>>> numZ, denZ = gbt(tf, T, 0.3)
	>>> numZ
	[3T/(10(L + 3RT/10)), 7T/(10(L + 3RT/10))]
	>>> denZ
	[1, (-L + 7RT/10)/(L + 3RT/10)]

	References
	==========

	.. [1] https://www.polyu.edu.hk/ama/profile/gfzhang/Research/ZCC09_IJC.pdf
	"""
	if not tf.is_SISO:
	raise NotImplementedError("Not implemented for MIMO systems.")

	T = sample_per # and sample period T
	s = tf.var
	z = s # dummy discrete variable z

	np = tf.num.as_poly(s).all_coeffs()
	dp = tf.den.as_poly(s).all_coeffs()
	alpha = Rational(alpha).limit_denominator(1000)

	# The next line results from multiplying H(z) with z^N/z^N
	N = max(len(np), len(dp)) - 1
	num = Add([ T(N-i) c * (z-1)*i (alpha * z + 1 - alpha)**(N-i) for c, i in zip(np[::-1], range(len(np))) ])
	den = Add([ T(N-i) c * (z-1)*i (alpha * z + 1 - alpha)**(N-i) for c, i in zip(dp[::-1], range(len(dp))) ])

	num_coefs = num.as_poly(z).all_coeffs()
	den_coefs = den.as_poly(z).all_coeffs()

	para = den_coefs[0]
	num_coefs = [coef/para for coef in num_coefs]
	den_coefs = [coef/para for coef in den_coefs]

	return num_coefs, den_coefs

	def bilinear(tf, sample_per):
	r"""
	Returns falling coefficients of H(z) from numerator and denominator.

	Explanation
	===========

	Where H(z) is the corresponding discretized transfer function,
	discretized with the bilinear transform method.
	H(z) is obtained from the continuous transfer function H(s)
	by substituting $s(z) = \frac{2}{T}\frac{z-1}{z+1}$ into H(s), where T is the
	sample period.
	Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
	as [a, b], [c, d].

	Examples
	========

	>>> from sympy.physics.control.lti import TransferFunction, bilinear
	>>> from sympy.abc import s, L, R, T

	>>> tf = TransferFunction(1, s*L + R, s)
	>>> numZ, denZ = bilinear(tf, T)
	>>> numZ
	[T/(2(L + RT/2)), T/(2(L + RT/2))]
	>>> denZ
	[1, (-L + RT/2)/(L + RT/2)]
	"""
	return gbt(tf, sample_per, S.Half)

	def forward_diff(tf, sample_per):
	r"""
	Returns falling coefficients of H(z) from numerator and denominator.

	Explanation
	===========

	Where H(z) is the corresponding discretized transfer function,
	discretized with the forward difference transform method.
	H(z) is obtained from the continuous transfer function H(s)
	by substituting $s(z) = \frac{z-1}{T}$ into H(s), where T is the
	sample period.
	Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
	as [a, b], [c, d].

	Examples
	========

	>>> from sympy.physics.control.lti import TransferFunction, forward_diff
	>>> from sympy.abc import s, L, R, T

	>>> tf = TransferFunction(1, s*L + R, s)
	>>> numZ, denZ = forward_diff(tf, T)
	>>> numZ
	[T/L]
	>>> denZ
	[1, (-L + R*T)/L]
	"""
	return gbt(tf, sample_per, S.Zero)

	def backward_diff(tf, sample_per):
	r"""
	Returns falling coefficients of H(z) from numerator and denominator.

	Explanation
	===========

	Where H(z) is the corresponding discretized transfer function,
	discretized with the backward difference transform method.
	H(z) is obtained from the continuous transfer function H(s)
	by substituting $s(z) = \frac{z-1}{Tz}$ into H(s), where T is the
	sample period.
	Coefficients are falling, i.e. $H(z) = \frac{az+b}{cz+d}$ is returned
	as [a, b], [c, d].

	Examples
	========

	>>> from sympy.physics.control.lti import TransferFunction, backward_diff
	>>> from sympy.abc import s, L, R, T

	>>> tf = TransferFunction(1, s*L + R, s)
	>>> numZ, denZ = backward_diff(tf, T)
	>>> numZ
	[T/(L + R*T), 0]
	>>> denZ
	[1, -L/(L + R*T)]
	"""
	return gbt(tf, sample_per, S.One)

	def phase_margin(system):
	r"""
	Returns the phase margin of a continuous time system.
	Only applicable to Transfer Functions which can generate valid bode plots.

	Raises
	======

	NotImplementedError
	When time delay terms are present in the system.

	ValueError
	When a SISO LTI system is not passed.

	When more than one free symbol is present in the system.
	The only variable in the transfer function should be
	the variable of the Laplace transform.

	Examples
	========

	>>> from sympy.physics.control import TransferFunction, phase_margin
	>>> from sympy.abc import s

	>>> tf = TransferFunction(1, s*3 + 2s**2 + s, s)
	>>> phase_margin(tf)
	180(-pi + atan((-1 + (-218(1/3)/(9 + sqrt(93))(1/3) + 12*(1/3)(9 + sqrt(93))(1/3))2/36)/(-12*(1/3)(9 + sqrt(93))*(1/3)/3 + 218*(1/3)/(3(9 + sqrt(93))**(1/3)))))/pi + 180
	>>> phase_margin(tf).n()
	21.3863897518751

	>>> tf1 = TransferFunction(s3, s2 + 5*s, s)
	>>> phase_margin(tf1)
	-180 + 180(atan(sqrt(2)(-51/10 - sqrt(101)/10)sqrt(1 + sqrt(101))/(2(sqrt(101)/2 + 51/2))) + pi)/pi
	>>> phase_margin(tf1).n()
	-25.1783920627277

	>>> tf2 = TransferFunction(1, s + 1, s)
	>>> phase_margin(tf2)
	-180

	See Also
	========

	gain_margin

	References
	==========

	.. [1] https://en.wikipedia.org/wiki/Phase_margin

	"""
	from sympy.functions import arg

	if not isinstance(system, SISOLinearTimeInvariant):
	raise ValueError("Margins are only applicable for SISO LTI systems.")

	_w = Dummy("w", real=True)
	repl = I*_w
	expr = system.to_expr()
	len_free_symbols = len(expr.free_symbols)
	if expr.has(exp):
	raise NotImplementedError("Margins for systems with Time delay terms are not supported.")
	elif len_free_symbols > 1:
	raise ValueError("Extra degree of freedom found. Make sure"
	" that there are no free symbols in the dynamical system other"
	" than the variable of Laplace transform.")

	w_expr = expr.subs({system.var: repl})

	mag = 20*log(Abs(w_expr), 10)
	mag_sol = list(solveset(mag, _w, Interval(0, oo, left_open=True)))

	if (len(mag_sol) == 0):
	pm = S(-180)
	else:
	wcp = mag_sol[0]
	pm = ((arg(w_expr)*S(180)/pi).subs({_w:wcp}) + S(180)) % 360

	if(pm >= 180):
	pm = pm - 360

	return pm

	def gain_margin(system):
	r"""
	Returns the gain margin of a continuous time system.
	Only applicable to Transfer Functions which can generate valid bode plots.

	Raises
	======

	NotImplementedError
	When time delay terms are present in the system.

	ValueError
	When a SISO LTI system is not passed.

	When more than one free symbol is present in the system.
	The only variable in the transfer function should be
	the variable of the Laplace transform.

	Examples
	========

	>>> from sympy.physics.control import TransferFunction, gain_margin
	>>> from sympy.abc import s

	>>> tf = TransferFunction(1, s*3 + 2s**2 + s, s)
	>>> gain_margin(tf)
	20*log(2)/log(10)
	>>> gain_margin(tf).n()
	6.02059991327962

	>>> tf1 = TransferFunction(s3, s2 + 5*s, s)
	>>> gain_margin(tf1)
	oo

	See Also
	========

	phase_margin

	References
	==========

	https://en.wikipedia.org/wiki/Bode_plot

	"""
	if not isinstance(system, SISOLinearTimeInvariant):
	raise ValueError("Margins are only applicable for SISO LTI systems.")

	_w = Dummy("w", real=True)
	repl = I*_w
	expr = system.to_expr()
	len_free_symbols = len(expr.free_symbols)
	if expr.has(exp):
	raise NotImplementedError("Margins for systems with Time delay terms are not supported.")
	elif len_free_symbols > 1:
	raise ValueError("Extra degree of freedom found. Make sure"
	" that there are no free symbols in the dynamical system other"
	" than the variable of Laplace transform.")

	w_expr = expr.subs({system.var: repl})

	mag = 20*log(Abs(w_expr), 10)
	phase = w_expr
	phase_sol = list(solveset(numer(phase.as_real_imag()[1].cancel()),_w, Interval(0, oo, left_open = True)))

	if (len(phase_sol) == 0):
	gm = oo
	else:
	wcg = phase_sol[0]
	gm = -mag.subs({_w:wcg})

	return gm

	class LinearTimeInvariant(Basic, EvalfMixin):
	"""A common class for all the Linear Time-Invariant Dynamical Systems."""

	_clstype: Type

	# Users should not directly interact with this class.
	def __new__(cls, system, *kwargs):
	if cls is LinearTimeInvariant:
	raise NotImplementedError('The LTICommon class is not meant to be used directly.')
	return super(LinearTimeInvariant, cls).__new__(cls, system, *kwargs)

	@classmethod
	def _check_args(cls, args):
	if not args:
	raise ValueError("At least 1 argument must be passed.")
	if not all(isinstance(arg, cls._clstype) for arg in args):
	raise TypeError(f"All arguments must be of type {cls._clstype}.")
	var_set = {arg.var for arg in args}
	if len(var_set) != 1:
	raise ValueError(filldedent(f"""
	All transfer functions should use the same complex variable
	of the Laplace transform. {len(var_set)} different
	values found."""))

	@property
	def is_SISO(self):
	"""Returns `True` if the passed LTI system is SISO else returns False."""
	return self._is_SISO


	class SISOLinearTimeInvariant(LinearTimeInvariant):
	"""A common class for all the SISO Linear Time-Invariant Dynamical Systems."""
	# Users should not directly interact with this class.
	_is_SISO = True


	class MIMOLinearTimeInvariant(LinearTimeInvariant):
	"""A common class for all the MIMO Linear Time-Invariant Dynamical Systems."""
	# Users should not directly interact with this class.
	_is_SISO = False


	SISOLinearTimeInvariant._clstype = SISOLinearTimeInvariant
	MIMOLinearTimeInvariant._clstype = MIMOLinearTimeInvariant


	def _check_other_SISO(func):
	def wrapper(args, *kwargs):
	if not isinstance(args[-1], SISOLinearTimeInvariant):
	return NotImplemented
	else:
	return func(args, *kwargs)
	return wrapper


	def _check_other_MIMO(func):
	def wrapper(args, *kwargs):
	if not isinstance(args[-1], MIMOLinearTimeInvariant):
	return NotImplemented
	else:
	return func(args, *kwargs)
	return wrapper


	class TransferFunction(SISOLinearTimeInvariant):
	r"""
	A class for representing LTI (Linear, time-invariant) systems that can be strictly described
	by ratio of polynomials in the Laplace transform complex variable. The arguments
	are ``num``, ``den``, and ``var``, where ``num`` and ``den`` are numerator and
	denominator polynomials of the ``TransferFunction`` respectively, and the third argument is
	a complex variable of the Laplace transform used by these polynomials of the transfer function.
	``num`` and ``den`` can be either polynomials or numbers, whereas ``var``
	has to be a :py:class:`~.Symbol`.

	Explanation
	===========

	Generally, a dynamical system representing a physical model can be described in terms of Linear
	Ordinary Differential Equations like -

	$\small{b_{m}y^{\left(m\right)}+b_{m-1}y^{\left(m-1\right)}+\dots+b_{1}y^{\left(1\right)}+b_{0}y=
	a_{n}x^{\left(n\right)}+a_{n-1}x^{\left(n-1\right)}+\dots+a_{1}x^{\left(1\right)}+a_{0}x}$

	Here, $x$ is the input signal and $y$ is the output signal and superscript on both is the order of derivative
	(not exponent). Derivative is taken with respect to the independent variable, $t$. Also, generally $m$ is greater
	than $n$.

	It is not feasible to analyse the properties of such systems in their native form therefore, we use
	mathematical tools like Laplace transform to get a better perspective. Taking the Laplace transform
	of both the sides in the equation (at zero initial conditions), we get -

	$\small{\mathcal{L}[b_{m}y^{\left(m\right)}+b_{m-1}y^{\left(m-1\right)}+\dots+b_{1}y^{\left(1\right)}+b_{0}y]=
	\mathcal{L}[a_{n}x^{\left(n\right)}+a_{n-1}x^{\left(n-1\right)}+\dots+a_{1}x^{\left(1\right)}+a_{0}x]}$

	Using the linearity property of Laplace transform and also considering zero initial conditions
	(i.e. $\small{y(0^{-}) = 0}$, $\small{y'(0^{-}) = 0}$ and so on), the equation
	above gets translated to -

	$\small{b_{m}\mathcal{L}[y^{\left(m\right)}]+\dots+b_{1}\mathcal{L}[y^{\left(1\right)}]+b_{0}\mathcal{L}[y]=
	a_{n}\mathcal{L}[x^{\left(n\right)}]+\dots+a_{1}\mathcal{L}[x^{\left(1\right)}]+a_{0}\mathcal{L}[x]}$

	Now, applying Derivative property of Laplace transform,

	$\small{b_{m}s^{m}\mathcal{L}[y]+\dots+b_{1}s\mathcal{L}[y]+b_{0}\mathcal{L}[y]=
	a_{n}s^{n}\mathcal{L}[x]+\dots+a_{1}s\mathcal{L}[x]+a_{0}\mathcal{L}[x]}$

	Here, the superscript on $s$ is exponent. Note that the zero initial conditions assumption, mentioned above, is very important
	and cannot be ignored otherwise the dynamical system cannot be considered time-independent and the simplified equation above
	cannot be reached.

	Collecting $\mathcal{L}[y]$ and $\mathcal{L}[x]$ terms from both the sides and taking the ratio
	$\frac{ \mathcal{L}\left\{y\right\} }{ \mathcal{L}\left\{x\right\} }$, we get the typical rational form of transfer
	function.

	The numerator of the transfer function is, therefore, the Laplace transform of the output signal
	(The signals are represented as functions of time) and similarly, the denominator
	of the transfer function is the Laplace transform of the input signal. It is also a convention
	to denote the input and output signal's Laplace transform with capital alphabets like shown below.

	$H(s) = \frac{Y(s)}{X(s)} = \frac{ \mathcal{L}\left\{y(t)\right\} }{ \mathcal{L}\left\{x(t)\right\} }$

	$s$, also known as complex frequency, is a complex variable in the Laplace domain. It corresponds to the
	equivalent variable $t$, in the time domain. Transfer functions are sometimes also referred to as the Laplace
	transform of the system's impulse response. Transfer function, $H$, is represented as a rational
	function in $s$ like,

	$H(s) =\ \frac{a_{n}s^{n}+a_{n-1}s^{n-1}+\dots+a_{1}s+a_{0}}{b_{m}s^{m}+b_{m-1}s^{m-1}+\dots+b_{1}s+b_{0}}$

	Parameters
	==========

	num : Expr, Number
	The numerator polynomial of the transfer function.
	den : Expr, Number
	The denominator polynomial of the transfer function.
	var : Symbol
	Complex variable of the Laplace transform used by the
	polynomials of the transfer function.

	Raises
	======

	TypeError
	When ``var`` is not a Symbol or when ``num`` or ``den`` is not a
	number or a polynomial.
	ValueError
	When ``den`` is zero.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction(s + a, s**2 + s + 1, s)
	>>> tf1
	TransferFunction(a + s, s**2 + s + 1, s)
	>>> tf1.num
	a + s
	>>> tf1.den
	s**2 + s + 1
	>>> tf1.var
	s
	>>> tf1.args
	(a + s, s**2 + s + 1, s)

	Any complex variable can be used for ``var``.

	>>> tf2 = TransferFunction(ap3 - ap*2 + sp, p + a**2, p)
	>>> tf2
	TransferFunction(ap3 - ap*2 + ps, a**2 + p, p)
	>>> tf3 = TransferFunction((p + 3)(p - 1), (p - 1)(p + 5), p)
	>>> tf3
	TransferFunction((p - 1)(p + 3), (p - 1)(p + 5), p)

	To negate a transfer function the ``-`` operator can be prepended:

	>>> tf4 = TransferFunction(-a + s, p**2 + s, p)
	>>> -tf4
	TransferFunction(a - s, p**2 + s, p)
	>>> tf5 = TransferFunction(s*4 - 2s*3 + 5s + 4, s + 4, s)
	>>> -tf5
	TransferFunction(-s*4 + 2s*3 - 5s - 4, s + 4, s)

	You can use a float or an integer (or other constants) as numerator and denominator:

	>>> tf6 = TransferFunction(1/2, 4, s)
	>>> tf6.num
	0.500000000000000
	>>> tf6.den
	4
	>>> tf6.var
	s
	>>> tf6.args
	(0.5, 4, s)

	You can take the integer power of a transfer function using the ``**`` operator:

	>>> tf7 = TransferFunction(s + a, s - a, s)
	>>> tf7**3
	TransferFunction((a + s)3, (-a + s)3, s)
	>>> tf7**0
	TransferFunction(1, 1, s)
	>>> tf8 = TransferFunction(p + 4, p - 3, p)
	>>> tf8**-1
	TransferFunction(p - 3, p + 4, p)

	Addition, subtraction, and multiplication of transfer functions can form
	unevaluated ``Series`` or ``Parallel`` objects.

	>>> tf9 = TransferFunction(s + 1, s**2 + s + 1, s)
	>>> tf10 = TransferFunction(s - p, s + 3, s)
	>>> tf11 = TransferFunction(4s2 + 2s - 4, s - 1, s)
	>>> tf12 = TransferFunction(1 - s, s**2 + 4, s)
	>>> tf9 + tf10
	Parallel(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(-p + s, s + 3, s))
	>>> tf10 - tf11
	Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(-4s2 - 2s + 4, s - 1, s))
	>>> tf9 * tf10
	Series(TransferFunction(s + 1, s**2 + s + 1, s), TransferFunction(-p + s, s + 3, s))
	>>> tf10 - (tf9 + tf12)
	Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(-s - 1, s2 + s + 1, s), TransferFunction(s - 1, s2 + 4, s))
	>>> tf10 - (tf9 * tf12)
	Parallel(TransferFunction(-p + s, s + 3, s), Series(TransferFunction(-1, 1, s), TransferFunction(s + 1, s2 + s + 1, s), TransferFunction(1 - s, s2 + 4, s)))
	>>> tf11 * tf10 * tf9
	Series(TransferFunction(4s2 + 2s - 4, s - 1, s), TransferFunction(-p + s, s + 3, s), TransferFunction(s + 1, s**2 + s + 1, s))
	>>> tf9 * tf11 + tf10 * tf12
	Parallel(Series(TransferFunction(s + 1, s*2 + s + 1, s), TransferFunction(4s*2 + 2s - 4, s - 1, s)), Series(TransferFunction(-p + s, s + 3, s), TransferFunction(1 - s, s**2 + 4, s)))
	>>> (tf9 + tf12) * (tf10 + tf11)
	Series(Parallel(TransferFunction(s + 1, s2 + s + 1, s), TransferFunction(1 - s, s2 + 4, s)), Parallel(TransferFunction(-p + s, s + 3, s), TransferFunction(4s2 + 2s - 4, s - 1, s)))

	These unevaluated ``Series`` or ``Parallel`` objects can convert into the
	resultant transfer function using ``.doit()`` method or by ``.rewrite(TransferFunction)``.

	>>> ((tf9 + tf10) * tf12).doit()
	TransferFunction((1 - s)((-p + s)(s*2 + s + 1) + (s + 1)(s + 3)), (s + 3)(s2 + 4)(s**2 + s + 1), s)
	>>> (tf9 * tf10 - tf11 * tf12).rewrite(TransferFunction)
	TransferFunction(-(1 - s)(s + 3)(s*2 + s + 1)(4s2 + 2s - 4) + (-p + s)(s - 1)(s + 1)(s2 + 4), (s - 1)(s + 3)(s2 + 4)(s**2 + s + 1), s)

	See Also
	========

	Feedback, Series, Parallel

	References
	==========

	.. [1] https://en.wikipedia.org/wiki/Transfer_function
	.. [2] https://en.wikipedia.org/wiki/Laplace_transform

	"""
	def __new__(cls, num, den, var):
	num, den = _sympify(num), _sympify(den)

	if not isinstance(var, Symbol):
	raise TypeError("Variable input must be a Symbol.")

	if den == 0:
	raise ValueError("TransferFunction cannot have a zero denominator.")

	if (((isinstance(num, (Expr, TransferFunction, Series, Parallel)) and num.has(Symbol)) or num.is_number) and
	((isinstance(den, (Expr, TransferFunction, Series, Parallel)) and den.has(Symbol)) or den.is_number)):
	return super(TransferFunction, cls).__new__(cls, num, den, var)

	else:
	raise TypeError("Unsupported type for numerator or denominator of TransferFunction.")

	@classmethod
	def from_rational_expression(cls, expr, var=None):
	r"""
	Creates a new ``TransferFunction`` efficiently from a rational expression.

	Parameters
	==========

	expr : Expr, Number
	The rational expression representing the ``TransferFunction``.
	var : Symbol, optional
	Complex variable of the Laplace transform used by the
	polynomials of the transfer function.

	Raises
	======

	ValueError
	When ``expr`` is of type ``Number`` and optional parameter ``var``
	is not passed.

	When ``expr`` has more than one variables and an optional parameter
	``var`` is not passed.
	ZeroDivisionError
	When denominator of ``expr`` is zero or it has ``ComplexInfinity``
	in its numerator.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy.physics.control.lti import TransferFunction
	>>> expr1 = (s + 5)/(3s2 + 2s + 1)
	>>> tf1 = TransferFunction.from_rational_expression(expr1)
	>>> tf1
	TransferFunction(s + 5, 3s2 + 2s + 1, s)
	>>> expr2 = (ap3 - ap*2 + sp)/(p + a**2) # Expr with more than one variables
	>>> tf2 = TransferFunction.from_rational_expression(expr2, p)
	>>> tf2
	TransferFunction(ap3 - ap*2 + ps, a**2 + p, p)

	In case of conflict between two or more variables in a expression, SymPy will
	raise a ``ValueError``, if ``var`` is not passed by the user.

	>>> tf = TransferFunction.from_rational_expression((a + as)/(s*2 + s + 1))
	Traceback (most recent call last):
	...
	ValueError: Conflicting values found for positional argument `var` ({a, s}). Specify it manually.

	This can be corrected by specifying the ``var`` parameter manually.

	>>> tf = TransferFunction.from_rational_expression((a + as)/(s*2 + s + 1), s)
	>>> tf
	TransferFunction(as + a, s*2 + s + 1, s)

	``var`` also need to be specified when ``expr`` is a ``Number``

	>>> tf3 = TransferFunction.from_rational_expression(10, s)
	>>> tf3
	TransferFunction(10, 1, s)

	"""
	expr = _sympify(expr)
	if var is None:
	_free_symbols = expr.free_symbols
	_len_free_symbols = len(_free_symbols)
	if _len_free_symbols == 1:
	var = list(_free_symbols)[0]
	elif _len_free_symbols == 0:
	raise ValueError(filldedent("""
	Positional argument `var` not found in the
	TransferFunction defined. Specify it manually."""))
	else:
	raise ValueError(filldedent("""
	Conflicting values found for positional argument `var` ({}).
	Specify it manually.""".format(_free_symbols)))

	_num, _den = expr.as_numer_denom()
	if _den == 0 or _num.has(S.ComplexInfinity):
	raise ZeroDivisionError("TransferFunction cannot have a zero denominator.")
	return cls(_num, _den, var)

	@classmethod
	def from_coeff_lists(cls, num_list, den_list, var):
	r"""
	Creates a new ``TransferFunction`` efficiently from a list of coefficients.

	Parameters
	==========

	num_list : Sequence
	Sequence comprising of numerator coefficients.
	den_list : Sequence
	Sequence comprising of denominator coefficients.
	var : Symbol
	Complex variable of the Laplace transform used by the
	polynomials of the transfer function.

	Raises
	======

	ZeroDivisionError
	When the constructed denominator is zero.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction
	>>> num = [1, 0, 2]
	>>> den = [3, 2, 2, 1]
	>>> tf = TransferFunction.from_coeff_lists(num, den, s)
	>>> tf
	TransferFunction(s*2 + 2, 3s*3 + 2s*2 + 2s + 1, s)

	# Create a Transfer Function with more than one variable
	>>> tf1 = TransferFunction.from_coeff_lists([p, 1], [2*p, 0, 4], s)
	>>> tf1
	TransferFunction(ps + 1, 2ps*2 + 4, s)

	"""
	num_list = num_list[::-1]
	den_list = den_list[::-1]
	num_var_powers = [var**i for i in range(len(num_list))]
	den_var_powers = [var**i for i in range(len(den_list))]

	_num = sum(coeff * var_power for coeff, var_power in zip(num_list, num_var_powers))
	_den = sum(coeff * var_power for coeff, var_power in zip(den_list, den_var_powers))

	if _den == 0:
	raise ZeroDivisionError("TransferFunction cannot have a zero denominator.")

	return cls(_num, _den, var)

	@classmethod
	def from_zpk(cls, zeros, poles, gain, var):
	r"""
	Creates a new ``TransferFunction`` from given zeros, poles and gain.

	Parameters
	==========

	zeros : Sequence
	Sequence comprising of zeros of transfer function.
	poles : Sequence
	Sequence comprising of poles of transfer function.
	gain : Number, Symbol, Expression
	A scalar value specifying gain of the model.
	var : Symbol
	Complex variable of the Laplace transform used by the
	polynomials of the transfer function.

	Examples
	========

	>>> from sympy.abc import s, p, k
	>>> from sympy.physics.control.lti import TransferFunction
	>>> zeros = [1, 2, 3]
	>>> poles = [6, 5, 4]
	>>> gain = 7
	>>> tf = TransferFunction.from_zpk(zeros, poles, gain, s)
	>>> tf
	TransferFunction(7(s - 3)(s - 2)(s - 1), (s - 6)(s - 5)*(s - 4), s)

	# Create a Transfer Function with variable poles and zeros
	>>> tf1 = TransferFunction.from_zpk([p, k], [p + k, p - k], 2, s)
	>>> tf1
	TransferFunction(2(-k + s)(-p + s), (-k - p + s)*(k - p + s), s)

	# Complex poles or zeros are acceptable
	>>> tf2 = TransferFunction.from_zpk([0], [1-1j, 1+1j, 2], -2, s)
	>>> tf2
	TransferFunction(-2s, (s - 2)(s - 1.0 - 1.0I)(s - 1.0 + 1.0*I), s)

	"""
	num_poly = 1
	den_poly = 1
	for zero in zeros:
	num_poly *= var - zero
	for pole in poles:
	den_poly *= var - pole

	return cls(gain*num_poly, den_poly, var)

	@property
	def num(self):
	"""
	Returns the numerator polynomial of the transfer function.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction
	>>> G1 = TransferFunction(s*2 + ps + 3, s - 4, s)
	>>> G1.num
	ps + s*2 + 3
	>>> G2 = TransferFunction((p + 5)(p - 3), (p - 3)(p + 1), p)
	>>> G2.num
	(p - 3)*(p + 5)

	"""
	return self.args[0]

	@property
	def den(self):
	"""
	Returns the denominator polynomial of the transfer function.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction
	>>> G1 = TransferFunction(s + 4, p*3 - 2p + 4, s)
	>>> G1.den
	p*3 - 2p + 4
	>>> G2 = TransferFunction(3, 4, s)
	>>> G2.den
	4

	"""
	return self.args[1]

	@property
	def var(self):
	"""
	Returns the complex variable of the Laplace transform used by the polynomials of
	the transfer function.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G1.var
	p
	>>> G2 = TransferFunction(0, s - 5, s)
	>>> G2.var
	s

	"""
	return self.args[2]

	def _eval_subs(self, old, new):
	arg_num = self.num.subs(old, new)
	arg_den = self.den.subs(old, new)
	argnew = TransferFunction(arg_num, arg_den, self.var)
	return self if old == self.var else argnew

	def _eval_evalf(self, prec):
	return TransferFunction(
	self.num._eval_evalf(prec),
	self.den._eval_evalf(prec),
	self.var)

	def _eval_simplify(self, **kwargs):
	tf = cancel(Mul(self.num, 1/self.den, evaluate=False), expand=False).as_numer_denom()
	num_, den_ = tf[0], tf[1]
	return TransferFunction(num_, den_, self.var)

	def _eval_rewrite_as_StateSpace(self, *args):
	"""
	Returns the equivalent space space model of the transfer function model.
	The state space model will be returned in the controllable cannonical form.

	Unlike the space state to transfer function model conversion, the transfer function
	to state space model conversion is not unique. There can be multiple state space
	representations of a given transfer function model.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control import TransferFunction, StateSpace
	>>> tf = TransferFunction(s2 + 1, s3 + 2*s + 10, s)
	>>> tf.rewrite(StateSpace)
	StateSpace(Matrix([
	[ 0, 1, 0],
	[ 0, 0, 1],
	[-10, -2, 0]]), Matrix([
	[0],
	[0],
	[1]]), Matrix([[1, 0, 1]]), Matrix([[0]]))

	"""
	if not self.is_proper:
	raise ValueError("Transfer Function must be proper.")

	num_poly = Poly(self.num, self.var)
	den_poly = Poly(self.den, self.var)
	n = den_poly.degree()

	num_coeffs = num_poly.all_coeffs()
	den_coeffs = den_poly.all_coeffs()
	diff = n - num_poly.degree()
	num_coeffs = [0]*diff + num_coeffs

	a = den_coeffs[1:]
	a_mat = Matrix([[(-1)*coefficient/den_coeffs[0] for coefficient in reversed(a)]])
	vert = zeros(n-1, 1)
	mat = eye(n-1)
	A = vert.row_join(mat)
	A = A.col_join(a_mat)

	B = zeros(n, 1)
	B[n-1] = 1

	i = n
	C = []
	while(i > 0):
	C.append(num_coeffs[i] - den_coeffs[i]*num_coeffs[0])
	i -= 1
	C = Matrix([C])

	D = Matrix([num_coeffs[0]])

	return StateSpace(A, B, C, D)

	def expand(self):
	"""
	Returns the transfer function with numerator and denominator
	in expanded form.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> G1 = TransferFunction((a - s)2, (s2 + a)**2, s)
	>>> G1.expand()
	TransferFunction(a*2 - 2as + s2, a2 + 2as2 + s*4, s)
	>>> G2 = TransferFunction((p + 3b)(p - b), (p - b)(p + 2b), p)
	>>> G2.expand()
	TransferFunction(-3b2 + 2bp + p2, -2b*2 + bp + p**2, p)

	"""
	return TransferFunction(expand(self.num), expand(self.den), self.var)

	def dc_gain(self):
	"""
	Computes the gain of the response as the frequency approaches zero.

	The DC gain is infinite for systems with pure integrators.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction(s + 3, s**2 - 9, s)
	>>> tf1.dc_gain()
	-1/3
	>>> tf2 = TransferFunction(p2, p - 3 + p3, p)
	>>> tf2.dc_gain()
	0
	>>> tf3 = TransferFunction(ap*2 - b, s + b, s)
	>>> tf3.dc_gain()
	(ap*2 - b)/b
	>>> tf4 = TransferFunction(1, s, s)
	>>> tf4.dc_gain()
	oo

	"""
	m = Mul(self.num, Pow(self.den, -1, evaluate=False), evaluate=False)
	return limit(m, self.var, 0)

	def poles(self):
	"""
	Returns the poles of a transfer function.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction((p + 3)(p - 1), (p - 1)(p + 5), p)
	>>> tf1.poles()
	[-5, 1]
	>>> tf2 = TransferFunction((1 - s)2, (s2 + 1)**2, s)
	>>> tf2.poles()
	[I, I, -I, -I]
	>>> tf3 = TransferFunction(s*2, as + p, s)
	>>> tf3.poles()
	[-p/a]

	"""
	return _roots(Poly(self.den, self.var), self.var)

	def zeros(self):
	"""
	Returns the zeros of a transfer function.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction((p + 3)(p - 1), (p - 1)(p + 5), p)
	>>> tf1.zeros()
	[-3, 1]
	>>> tf2 = TransferFunction((1 - s)2, (s2 + 1)**2, s)
	>>> tf2.zeros()
	[1, 1]
	>>> tf3 = TransferFunction(s*2, as + p, s)
	>>> tf3.zeros()
	[0, 0]

	"""
	return _roots(Poly(self.num, self.var), self.var)

	def eval_frequency(self, other):
	"""
	Returns the system response at any point in the real or complex plane.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy.physics.control.lti import TransferFunction
	>>> from sympy import I
	>>> tf1 = TransferFunction(1, s*2 + 2s + 1, s)
	>>> omega = 0.1
	>>> tf1.eval_frequency(I*omega)
	1/(0.99 + 0.2*I)
	>>> tf2 = TransferFunction(s*2, as + p, s)
	>>> tf2.eval_frequency(2)
	4/(2*a + p)
	>>> tf2.eval_frequency(I*2)
	-4/(2Ia + p)
	"""
	arg_num = self.num.subs(self.var, other)
	arg_den = self.den.subs(self.var, other)
	argnew = TransferFunction(arg_num, arg_den, self.var).to_expr()
	return argnew.expand()

	def is_stable(self):
	"""
	Returns True if the transfer function is asymptotically stable; else False.

	This would not check the marginal or conditional stability of the system.

	Examples
	========

	>>> from sympy.abc import s, p, a
	>>> from sympy import symbols
	>>> from sympy.physics.control.lti import TransferFunction
	>>> q, r = symbols('q, r', negative=True)
	>>> tf1 = TransferFunction((1 - s)2, (s + 1)2, s)
	>>> tf1.is_stable()
	True
	>>> tf2 = TransferFunction((1 - p)2, (s2 + 1)**2, s)
	>>> tf2.is_stable()
	False
	>>> tf3 = TransferFunction(4, q*s - r, s)
	>>> tf3.is_stable()
	False
	>>> tf4 = TransferFunction(p + 1, ap - s*2, p)
	>>> tf4.is_stable() is None # Not enough info about the symbols to determine stability
	True

	"""
	return fuzzy_and(pole.as_real_imag()[0].is_negative for pole in self.poles())

	def __add__(self, other):
	if isinstance(other, (TransferFunction, Series)):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	return Parallel(self, other)
	elif isinstance(other, Parallel):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	arg_list = list(other.args)
	return Parallel(self, *arg_list)
	else:
	raise ValueError("TransferFunction cannot be added with {}.".
	format(type(other)))

	def __radd__(self, other):
	return self + other

	def __sub__(self, other):
	if isinstance(other, (TransferFunction, Series)):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	return Parallel(self, -other)
	elif isinstance(other, Parallel):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	arg_list = [-i for i in list(other.args)]
	return Parallel(self, *arg_list)
	else:
	raise ValueError("{} cannot be subtracted from a TransferFunction."
	.format(type(other)))

	def __rsub__(self, other):
	return -self + other

	def __mul__(self, other):
	if isinstance(other, (TransferFunction, Parallel)):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	return Series(self, other)
	elif isinstance(other, Series):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	arg_list = list(other.args)
	return Series(self, *arg_list)
	else:
	raise ValueError("TransferFunction cannot be multiplied with {}."
	.format(type(other)))

	__rmul__ = __mul__

	def __truediv__(self, other):
	if isinstance(other, TransferFunction):
	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	return Series(self, TransferFunction(other.den, other.num, self.var))
	elif (isinstance(other, Parallel) and len(other.args
	) == 2 and isinstance(other.args[0], TransferFunction)
	and isinstance(other.args[1], (Series, TransferFunction))):

	if not self.var == other.var:
	raise ValueError(filldedent("""
	Both TransferFunction and Parallel should use the
	same complex variable of the Laplace transform."""))
	if other.args[1] == self:
	# plant and controller with unit feedback.
	return Feedback(self, other.args[0])
	other_arg_list = list(other.args[1].args) if isinstance(
	other.args[1], Series) else other.args[1]
	if other_arg_list == other.args[1]:
	return Feedback(self, other_arg_list)
	elif self in other_arg_list:
	other_arg_list.remove(self)
	else:
	return Feedback(self, Series(*other_arg_list))

	if len(other_arg_list) == 1:
	return Feedback(self, *other_arg_list)
	else:
	return Feedback(self, Series(*other_arg_list))
	else:
	raise ValueError("TransferFunction cannot be divided by {}.".
	format(type(other)))

	__rtruediv__ = __truediv__

	def __pow__(self, p):
	p = sympify(p)
	if not p.is_Integer:
	raise ValueError("Exponent must be an integer.")
	if p is S.Zero:
	return TransferFunction(1, 1, self.var)
	elif p > 0:
	num_, den_ = self.nump, self.denp
	else:
	p = abs(p)
	num_, den_ = self.denp, self.nump

	return TransferFunction(num_, den_, self.var)

	def __neg__(self):
	return TransferFunction(-self.num, self.den, self.var)

	@property
	def is_proper(self):
	"""
	Returns True if degree of the numerator polynomial is less than
	or equal to degree of the denominator polynomial, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction(bs2 + p2 - ap + s, b - p**2, s)
	>>> tf1.is_proper
	False
	>>> tf2 = TransferFunction(p*2 - 4p, p*3 + 3p + 2, p)
	>>> tf2.is_proper
	True

	"""
	return degree(self.num, self.var) <= degree(self.den, self.var)

	@property
	def is_strictly_proper(self):
	"""
	Returns True if degree of the numerator polynomial is strictly less
	than degree of the denominator polynomial, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf1.is_strictly_proper
	False
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tf2.is_strictly_proper
	True

	"""
	return degree(self.num, self.var) < degree(self.den, self.var)

	@property
	def is_biproper(self):
	"""
	Returns True if degree of the numerator polynomial is equal to
	degree of the denominator polynomial, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf1.is_biproper
	True
	>>> tf2 = TransferFunction(p**2, p + a, p)
	>>> tf2.is_biproper
	False

	"""
	return degree(self.num, self.var) == degree(self.den, self.var)

	def to_expr(self):
	"""
	Converts a ``TransferFunction`` object to SymPy Expr.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction
	>>> from sympy import Expr
	>>> tf1 = TransferFunction(s, as*2 + 1, s)
	>>> tf1.to_expr()
	s/(as*2 + 1)
	>>> isinstance(_, Expr)
	True
	>>> tf2 = TransferFunction(1, (p + 3b)(b - p), p)
	>>> tf2.to_expr()
	1/((b - p)(3b + p))
	>>> tf3 = TransferFunction((s - 2)(s - 3), (s - 1)(s - 2)*(s - 3), s)
	>>> tf3.to_expr()
	((s - 3)(s - 2))/(((s - 3)(s - 2)*(s - 1)))

	"""

	if self.num != 1:
	return Mul(self.num, Pow(self.den, -1, evaluate=False), evaluate=False)
	else:
	return Pow(self.den, -1, evaluate=False)


	def _flatten_args(args, _cls):
	temp_args = []
	for arg in args:
	if isinstance(arg, _cls):
	temp_args.extend(arg.args)
	else:
	temp_args.append(arg)
	return tuple(temp_args)


	def _dummify_args(_arg, var):
	dummy_dict = {}
	dummy_arg_list = []

	for arg in _arg:
	_s = Dummy()
	dummy_dict[_s] = var
	dummy_arg = arg.subs({var: _s})
	dummy_arg_list.append(dummy_arg)

	return dummy_arg_list, dummy_dict


	class Series(SISOLinearTimeInvariant):
	r"""
	A class for representing a series configuration of SISO systems.

	Parameters
	==========

	args : SISOLinearTimeInvariant
	SISO systems in a series configuration.
	evaluate : Boolean, Keyword
	When passed ``True``, returns the equivalent
	``Series(*args).doit()``. Set to ``False`` by default.

	Raises
	======

	ValueError
	When no argument is passed.

	``var`` attribute is not same for every system.
	TypeError
	Any of the passed ``*args`` has unsupported type

	A combination of SISO and MIMO systems is
	passed. There should be homogeneity in the
	type of systems passed, SISO in this case.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Series, Parallel
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tf3 = TransferFunction(p**2, p + s, s)
	>>> S1 = Series(tf1, tf2)
	>>> S1
	Series(TransferFunction(ap2 + bs, -p + s, s), TransferFunction(s3 - 2, s4 + 5*s + 6, s))
	>>> S1.var
	s
	>>> S2 = Series(tf2, Parallel(tf3, -tf1))
	>>> S2
	Series(TransferFunction(s3 - 2, s4 + 5s + 6, s), Parallel(TransferFunction(p2, p + s, s), TransferFunction(-ap*2 - bs, -p + s, s)))
	>>> S2.var
	s
	>>> S3 = Series(Parallel(tf1, tf2), Parallel(tf2, tf3))
	>>> S3
	Series(Parallel(TransferFunction(ap2 + bs, -p + s, s), TransferFunction(s3 - 2, s4 + 5s + 6, s)), Parallel(TransferFunction(s3 - 2, s4 + 5s + 6, s), TransferFunction(p**2, p + s, s)))
	>>> S3.var
	s

	You can get the resultant transfer function by using ``.doit()`` method:

	>>> S3 = Series(tf1, tf2, -tf3)
	>>> S3.doit()
	TransferFunction(-p*2(s*3 - 2)(ap2 + bs), (-p + s)(p + s)(s*4 + 5s + 6), s)
	>>> S4 = Series(tf2, Parallel(tf1, -tf3))
	>>> S4.doit()
	TransferFunction((s*3 - 2)(-p*2(-p + s) + (p + s)(ap*2 + bs)), (-p + s)(p + s)(s*4 + 5s + 6), s)

	Notes
	=====

	All the transfer functions should use the same complex variable
	``var`` of the Laplace transform.

	See Also
	========

	MIMOSeries, Parallel, TransferFunction, Feedback

	"""
	def __new__(cls, *args, evaluate=False):

	args = _flatten_args(args, Series)
	cls._check_args(args)
	obj = super().__new__(cls, *args)

	return obj.doit() if evaluate else obj

	@property
	def var(self):
	"""
	Returns the complex variable used by all the transfer functions.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, Series, Parallel
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G2 = TransferFunction(p, 4 - p, p)
	>>> G3 = TransferFunction(0, p**4 - 1, p)
	>>> Series(G1, G2).var
	p
	>>> Series(-G3, Parallel(G1, G2)).var
	p

	"""
	return self.args[0].var

	def doit(self, **hints):
	"""
	Returns the resultant transfer function obtained after evaluating
	the transfer functions in series configuration.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Series
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> Series(tf2, tf1).doit()
	TransferFunction((s*3 - 2)(ap2 + bs), (-p + s)(s4 + 5s + 6), s)
	>>> Series(-tf1, -tf2).doit()
	TransferFunction((2 - s*3)(-ap2 - bs), (-p + s)(s4 + 5s + 6), s)

	"""

	_num_arg = (arg.doit().num for arg in self.args)
	_den_arg = (arg.doit().den for arg in self.args)
	res_num = Mul(*_num_arg, evaluate=True)
	res_den = Mul(*_den_arg, evaluate=True)
	return TransferFunction(res_num, res_den, self.var)

	def _eval_rewrite_as_TransferFunction(self, args, *kwargs):
	return self.doit()

	@_check_other_SISO
	def __add__(self, other):

	if isinstance(other, Parallel):
	arg_list = list(other.args)
	return Parallel(self, *arg_list)

	return Parallel(self, other)

	__radd__ = __add__

	@_check_other_SISO
	def __sub__(self, other):
	return self + (-other)

	def __rsub__(self, other):
	return -self + other

	@_check_other_SISO
	def __mul__(self, other):

	arg_list = list(self.args)
	return Series(*arg_list, other)

	def __truediv__(self, other):
	if isinstance(other, TransferFunction):
	return Series(*self.args, TransferFunction(other.den, other.num, other.var))
	elif isinstance(other, Series):
	tf_self = self.rewrite(TransferFunction)
	tf_other = other.rewrite(TransferFunction)
	return tf_self / tf_other
	elif (isinstance(other, Parallel) and len(other.args) == 2
	and isinstance(other.args[0], TransferFunction) and isinstance(other.args[1], Series)):

	if not self.var == other.var:
	raise ValueError(filldedent("""
	All the transfer functions should use the same complex variable
	of the Laplace transform."""))
	self_arg_list = set(self.args)
	other_arg_list = set(other.args[1].args)
	res = list(self_arg_list ^ other_arg_list)
	if len(res) == 0:
	return Feedback(self, other.args[0])
	elif len(res) == 1:
	return Feedback(self, *res)
	else:
	return Feedback(self, Series(*res))
	else:
	raise ValueError("This transfer function expression is invalid.")

	def __neg__(self):
	return Series(TransferFunction(-1, 1, self.var), self)

	def to_expr(self):
	"""Returns the equivalent ``Expr`` object."""
	return Mul(*(arg.to_expr() for arg in self.args), evaluate=False)

	@property
	def is_proper(self):
	"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is less than or equal to degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Series
	>>> tf1 = TransferFunction(bs2 + p2 - ap + s, b - p**2, s)
	>>> tf2 = TransferFunction(p*2 - 4p, p*3 + 3s + 2, s)
	>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
	>>> S1 = Series(-tf2, tf1)
	>>> S1.is_proper
	False
	>>> S2 = Series(tf1, tf2, tf3)
	>>> S2.is_proper
	True

	"""
	return self.doit().is_proper

	@property
	def is_strictly_proper(self):
	"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is strictly less than degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Series
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s2 + 5*s + 6, s)
	>>> tf3 = TransferFunction(1, s**2 + s + 1, s)
	>>> S1 = Series(tf1, tf2)
	>>> S1.is_strictly_proper
	False
	>>> S2 = Series(tf1, tf2, tf3)
	>>> S2.is_strictly_proper
	True

	"""
	return self.doit().is_strictly_proper

	@property
	def is_biproper(self):
	r"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is equal to degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Series
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(p, s**2, s)
	>>> tf3 = TransferFunction(s**2, 1, s)
	>>> S1 = Series(tf1, -tf2)
	>>> S1.is_biproper
	False
	>>> S2 = Series(tf2, tf3)
	>>> S2.is_biproper
	True

	"""
	return self.doit().is_biproper


	def _mat_mul_compatible(*args):
	"""To check whether shapes are compatible for matrix mul."""
	return all(args[i].num_outputs == args[i+1].num_inputs for i in range(len(args)-1))


	class MIMOSeries(MIMOLinearTimeInvariant):
	r"""
	A class for representing a series configuration of MIMO systems.

	Parameters
	==========

	args : MIMOLinearTimeInvariant
	MIMO systems in a series configuration.
	evaluate : Boolean, Keyword
	When passed ``True``, returns the equivalent
	``MIMOSeries(*args).doit()``. Set to ``False`` by default.

	Raises
	======

	ValueError
	When no argument is passed.

	``var`` attribute is not same for every system.

	``num_outputs`` of the MIMO system is not equal to the
	``num_inputs`` of its adjacent MIMO system. (Matrix
	multiplication constraint, basically)
	TypeError
	Any of the passed ``*args`` has unsupported type

	A combination of SISO and MIMO systems is
	passed. There should be homogeneity in the
	type of systems passed, MIMO in this case.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import MIMOSeries, TransferFunctionMatrix
	>>> from sympy import Matrix, pprint
	>>> mat_a = Matrix([[5*s], [5]]) # 2 Outputs 1 Input
	>>> mat_b = Matrix([[5, 1/(6s*2)]]) # 1 Output 2 Inputs
	>>> mat_c = Matrix([[1, s], [5/s, 1]]) # 2 Outputs 2 Inputs
	>>> tfm_a = TransferFunctionMatrix.from_Matrix(mat_a, s)
	>>> tfm_b = TransferFunctionMatrix.from_Matrix(mat_b, s)
	>>> tfm_c = TransferFunctionMatrix.from_Matrix(mat_c, s)
	>>> MIMOSeries(tfm_c, tfm_b, tfm_a)
	MIMOSeries(TransferFunctionMatrix(((TransferFunction(1, 1, s), TransferFunction(s, 1, s)), (TransferFunction(5, s, s), TransferFunction(1, 1, s)))), TransferFunctionMatrix(((TransferFunction(5, 1, s), TransferFunction(1, 6s2, s)),)), TransferFunctionMatrix(((TransferFunction(5s, 1, s),), (TransferFunction(5, 1, s),))))
	>>> pprint(_, use_unicode=False) # For Better Visualization
	[5*s] [1 s]
	[---] [5 1 ] [- -]
	[ 1 ] [- ----] [1 1]
	[ ] [1 2] [ ]
	[ 5 ] [ 6*s ]{t} [5 1]
	[ - ] [- -]
	[ 1 ]{t} [s 1]{t}
	>>> MIMOSeries(tfm_c, tfm_b, tfm_a).doit()
	TransferFunctionMatrix(((TransferFunction(150s4 + 25s, 6s3, s), TransferFunction(150s*4 + 5s, 6s2, s)), (TransferFunction(150s*3 + 25, 6s*3, s), TransferFunction(150s*3 + 5, 6s**2, s))))
	>>> pprint(_, use_unicode=False) # (2 Inputs -A-> 2 Outputs) -> (2 Inputs -B-> 1 Output) -> (1 Input -C-> 2 Outputs) is equivalent to (2 Inputs -Series Equivalent-> 2 Outputs).
	[ 4 4 ]
	[150s + 25s 150s + 5s]
	[------------- ------------]
	[ 3 2 ]
	[ 6s 6s ]
	[ ]
	[ 3 3 ]
	[ 150s + 25 150s + 5 ]
	[ ----------- ---------- ]
	[ 3 2 ]
	[ 6s 6s ]{t}

	Notes
	=====

	All the transfer function matrices should use the same complex variable ``var`` of the Laplace transform.

	``MIMOSeries(A, B)`` is not equivalent to ``AB``. It is always in the reverse order, that is ``BA``.

	See Also
	========

	Series, MIMOParallel

	"""
	def __new__(cls, *args, evaluate=False):

	cls._check_args(args)

	if _mat_mul_compatible(*args):
	obj = super().__new__(cls, *args)

	else:
	raise ValueError(filldedent("""
	Number of input signals do not match the number
	of output signals of adjacent systems for some args."""))

	return obj.doit() if evaluate else obj

	@property
	def var(self):
	"""
	Returns the complex variable used by all the transfer functions.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, MIMOSeries, TransferFunctionMatrix
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G2 = TransferFunction(p, 4 - p, p)
	>>> G3 = TransferFunction(0, p**4 - 1, p)
	>>> tfm_1 = TransferFunctionMatrix([[G1, G2, G3]])
	>>> tfm_2 = TransferFunctionMatrix([[G1], [G2], [G3]])
	>>> MIMOSeries(tfm_2, tfm_1).var
	p

	"""
	return self.args[0].var

	@property
	def num_inputs(self):
	"""Returns the number of input signals of the series system."""
	return self.args[0].num_inputs

	@property
	def num_outputs(self):
	"""Returns the number of output signals of the series system."""
	return self.args[-1].num_outputs

	@property
	def shape(self):
	"""Returns the shape of the equivalent MIMO system."""
	return self.num_outputs, self.num_inputs

	def doit(self, cancel=False, **kwargs):
	"""
	Returns the resultant transfer function matrix obtained after evaluating
	the MIMO systems arranged in a series configuration.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, MIMOSeries, TransferFunctionMatrix
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tfm1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf2]])
	>>> tfm2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf1]])
	>>> MIMOSeries(tfm2, tfm1).doit()
	TransferFunctionMatrix(((TransferFunction(2(-p + s)(s*3 - 2)(ap2 + bs)(s4 + 5s + 6), (-p + s)*2(s*4 + 5s + 6)2, s), TransferFunction((-p + s)2(s3 - 2)(ap2 + bs) + (-p + s)(ap*2 + bs)*2(s*4 + 5s + 6), (-p + s)*3(s*4 + 5s + 6), s)), (TransferFunction((-p + s)(s3 - 2)2(s*4 + 5s + 6) + (s*3 - 2)(ap2 + bs)(s4 + 5s + 6)*2, (-p + s)(s*4 + 5s + 6)*3, s), TransferFunction(2(s*3 - 2)(ap2 + bs), (-p + s)(s4 + 5s + 6), s))))

	"""
	_arg = (arg.doit()._expr_mat for arg in reversed(self.args))

	if cancel:
	res = MatMul(*_arg, evaluate=True)
	return TransferFunctionMatrix.from_Matrix(res, self.var)

	_dummy_args, _dummy_dict = _dummify_args(_arg, self.var)
	res = MatMul(*_dummy_args, evaluate=True)
	temp_tfm = TransferFunctionMatrix.from_Matrix(res, self.var)
	return temp_tfm.subs(_dummy_dict)

	def _eval_rewrite_as_TransferFunctionMatrix(self, args, *kwargs):
	return self.doit()

	@_check_other_MIMO
	def __add__(self, other):

	if isinstance(other, MIMOParallel):
	arg_list = list(other.args)
	return MIMOParallel(self, *arg_list)

	return MIMOParallel(self, other)

	__radd__ = __add__

	@_check_other_MIMO
	def __sub__(self, other):
	return self + (-other)

	def __rsub__(self, other):
	return -self + other

	@_check_other_MIMO
	def __mul__(self, other):

	if isinstance(other, MIMOSeries):
	self_arg_list = list(self.args)
	other_arg_list = list(other.args)
	return MIMOSeries(other_arg_list, self_arg_list) # A*B = MIMOSeries(B, A)

	arg_list = list(self.args)
	return MIMOSeries(other, *arg_list)

	def __neg__(self):
	arg_list = list(self.args)
	arg_list[0] = -arg_list[0]
	return MIMOSeries(*arg_list)


	class Parallel(SISOLinearTimeInvariant):
	r"""
	A class for representing a parallel configuration of SISO systems.

	Parameters
	==========

	args : SISOLinearTimeInvariant
	SISO systems in a parallel arrangement.
	evaluate : Boolean, Keyword
	When passed ``True``, returns the equivalent
	``Parallel(*args).doit()``. Set to ``False`` by default.

	Raises
	======

	ValueError
	When no argument is passed.

	``var`` attribute is not same for every system.
	TypeError
	Any of the passed ``*args`` has unsupported type

	A combination of SISO and MIMO systems is
	passed. There should be homogeneity in the
	type of systems passed.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Parallel, Series
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tf3 = TransferFunction(p**2, p + s, s)
	>>> P1 = Parallel(tf1, tf2)
	>>> P1
	Parallel(TransferFunction(ap2 + bs, -p + s, s), TransferFunction(s3 - 2, s4 + 5*s + 6, s))
	>>> P1.var
	s
	>>> P2 = Parallel(tf2, Series(tf3, -tf1))
	>>> P2
	Parallel(TransferFunction(s3 - 2, s4 + 5s + 6, s), Series(TransferFunction(p2, p + s, s), TransferFunction(-ap*2 - bs, -p + s, s)))
	>>> P2.var
	s
	>>> P3 = Parallel(Series(tf1, tf2), Series(tf2, tf3))
	>>> P3
	Parallel(Series(TransferFunction(ap2 + bs, -p + s, s), TransferFunction(s3 - 2, s4 + 5s + 6, s)), Series(TransferFunction(s3 - 2, s4 + 5s + 6, s), TransferFunction(p**2, p + s, s)))
	>>> P3.var
	s

	You can get the resultant transfer function by using ``.doit()`` method:

	>>> Parallel(tf1, tf2, -tf3).doit()
	TransferFunction(-p*2(-p + s)(s4 + 5s + 6) + (-p + s)(p + s)(s*3 - 2) + (p + s)(ap2 + bs)(s4 + 5s + 6), (-p + s)(p + s)(s*4 + 5s + 6), s)
	>>> Parallel(tf2, Series(tf1, -tf3)).doit()
	TransferFunction(-p*2(ap2 + bs)(s4 + 5s + 6) + (-p + s)(p + s)(s*3 - 2), (-p + s)(p + s)(s4 + 5s + 6), s)

	Notes
	=====

	All the transfer functions should use the same complex variable
	``var`` of the Laplace transform.

	See Also
	========

	Series, TransferFunction, Feedback

	"""
	def __new__(cls, *args, evaluate=False):

	args = _flatten_args(args, Parallel)
	cls._check_args(args)
	obj = super().__new__(cls, *args)

	return obj.doit() if evaluate else obj

	@property
	def var(self):
	"""
	Returns the complex variable used by all the transfer functions.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, Parallel, Series
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G2 = TransferFunction(p, 4 - p, p)
	>>> G3 = TransferFunction(0, p**4 - 1, p)
	>>> Parallel(G1, G2).var
	p
	>>> Parallel(-G3, Series(G1, G2)).var
	p

	"""
	return self.args[0].var

	def doit(self, **hints):
	"""
	Returns the resultant transfer function obtained after evaluating
	the transfer functions in parallel configuration.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Parallel
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> Parallel(tf2, tf1).doit()
	TransferFunction((-p + s)(s3 - 2) + (ap*2 + bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s)
	>>> Parallel(-tf1, -tf2).doit()
	TransferFunction((2 - s*3)(-p + s) + (-ap2 - bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s)

	"""

	_arg = (arg.doit().to_expr() for arg in self.args)
	res = Add(*_arg).as_numer_denom()
	return TransferFunction(*res, self.var)

	def _eval_rewrite_as_TransferFunction(self, args, *kwargs):
	return self.doit()

	@_check_other_SISO
	def __add__(self, other):

	self_arg_list = list(self.args)
	return Parallel(*self_arg_list, other)

	__radd__ = __add__

	@_check_other_SISO
	def __sub__(self, other):
	return self + (-other)

	def __rsub__(self, other):
	return -self + other

	@_check_other_SISO
	def __mul__(self, other):

	if isinstance(other, Series):
	arg_list = list(other.args)
	return Series(self, *arg_list)

	return Series(self, other)

	def __neg__(self):
	return Series(TransferFunction(-1, 1, self.var), self)

	def to_expr(self):
	"""Returns the equivalent ``Expr`` object."""
	return Add(*(arg.to_expr() for arg in self.args), evaluate=False)

	@property
	def is_proper(self):
	"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is less than or equal to degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Parallel
	>>> tf1 = TransferFunction(bs2 + p2 - ap + s, b - p**2, s)
	>>> tf2 = TransferFunction(p*2 - 4p, p*3 + 3s + 2, s)
	>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
	>>> P1 = Parallel(-tf2, tf1)
	>>> P1.is_proper
	False
	>>> P2 = Parallel(tf2, tf3)
	>>> P2.is_proper
	True

	"""
	return self.doit().is_proper

	@property
	def is_strictly_proper(self):
	"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is strictly less than degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Parallel
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
	>>> P1 = Parallel(tf1, tf2)
	>>> P1.is_strictly_proper
	False
	>>> P2 = Parallel(tf2, tf3)
	>>> P2.is_strictly_proper
	True

	"""
	return self.doit().is_strictly_proper

	@property
	def is_biproper(self):
	"""
	Returns True if degree of the numerator polynomial of the resultant transfer
	function is equal to degree of the denominator polynomial of
	the same, else False.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Parallel
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(p**2, p + s, s)
	>>> tf3 = TransferFunction(s, s**2 + s + 1, s)
	>>> P1 = Parallel(tf1, -tf2)
	>>> P1.is_biproper
	True
	>>> P2 = Parallel(tf2, tf3)
	>>> P2.is_biproper
	False

	"""
	return self.doit().is_biproper


	class MIMOParallel(MIMOLinearTimeInvariant):
	r"""
	A class for representing a parallel configuration of MIMO systems.

	Parameters
	==========

	args : MIMOLinearTimeInvariant
	MIMO Systems in a parallel arrangement.
	evaluate : Boolean, Keyword
	When passed ``True``, returns the equivalent
	``MIMOParallel(*args).doit()``. Set to ``False`` by default.

	Raises
	======

	ValueError
	When no argument is passed.

	``var`` attribute is not same for every system.

	All MIMO systems passed do not have same shape.
	TypeError
	Any of the passed ``*args`` has unsupported type

	A combination of SISO and MIMO systems is
	passed. There should be homogeneity in the
	type of systems passed, MIMO in this case.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunctionMatrix, MIMOParallel
	>>> from sympy import Matrix, pprint
	>>> expr_1 = 1/s
	>>> expr_2 = s/(s**2-1)
	>>> expr_3 = (2 + s)/(s**2 - 1)
	>>> expr_4 = 5
	>>> tfm_a = TransferFunctionMatrix.from_Matrix(Matrix([[expr_1, expr_2], [expr_3, expr_4]]), s)
	>>> tfm_b = TransferFunctionMatrix.from_Matrix(Matrix([[expr_2, expr_1], [expr_4, expr_3]]), s)
	>>> tfm_c = TransferFunctionMatrix.from_Matrix(Matrix([[expr_3, expr_4], [expr_1, expr_2]]), s)
	>>> MIMOParallel(tfm_a, tfm_b, tfm_c)
	MIMOParallel(TransferFunctionMatrix(((TransferFunction(1, s, s), TransferFunction(s, s2 - 1, s)), (TransferFunction(s + 2, s2 - 1, s), TransferFunction(5, 1, s)))), TransferFunctionMatrix(((TransferFunction(s, s2 - 1, s), TransferFunction(1, s, s)), (TransferFunction(5, 1, s), TransferFunction(s + 2, s2 - 1, s)))), TransferFunctionMatrix(((TransferFunction(s + 2, s2 - 1, s), TransferFunction(5, 1, s)), (TransferFunction(1, s, s), TransferFunction(s, s2 - 1, s)))))
	>>> pprint(_, use_unicode=False) # For Better Visualization
	[ 1 s ] [ s 1 ] [s + 2 5 ]
	[ - ------] [------ - ] [------ - ]
	[ s 2 ] [ 2 s ] [ 2 1 ]
	[ s - 1] [s - 1 ] [s - 1 ]
	[ ] + [ ] + [ ]
	[s + 2 5 ] [ 5 s + 2 ] [ 1 s ]
	[------ - ] [ - ------] [ - ------]
	[ 2 1 ] [ 1 2 ] [ s 2 ]
	[s - 1 ]{t} [ s - 1]{t} [ s - 1]{t}
	>>> MIMOParallel(tfm_a, tfm_b, tfm_c).doit()
	TransferFunctionMatrix(((TransferFunction(s*2 + s(2s + 2) - 1, s(s*2 - 1), s), TransferFunction(2s*2 + 5s(s2 - 1) - 1, s(s2 - 1), s)), (TransferFunction(s2 + s(s + 2) + 5s(s2 - 1) - 1, s(s*2 - 1), s), TransferFunction(5s*2 + 2s - 3, s**2 - 1, s))))
	>>> pprint(_, use_unicode=False)
	[ 2 2 / 2 \ ]
	[ s + s(2s + 2) - 1 2s + 5s*\s - 1/ - 1]
	[ -------------------- -----------------------]
	[ / 2 \ / 2 \ ]
	[ s\s - 1/ s\s - 1/ ]
	[ ]
	[ 2 / 2 \ 2 ]
	[s + s(s + 2) + 5s\s - 1/ - 1 5s + 2*s - 3 ]
	[--------------------------------- -------------- ]
	[ / 2 \ 2 ]
	[ s*\s - 1/ s - 1 ]{t}

	Notes
	=====

	All the transfer function matrices should use the same complex variable
	``var`` of the Laplace transform.

	See Also
	========

	Parallel, MIMOSeries

	"""
	def __new__(cls, *args, evaluate=False):

	args = _flatten_args(args, MIMOParallel)

	cls._check_args(args)

	if any(arg.shape != args[0].shape for arg in args):
	raise TypeError("Shape of all the args is not equal.")

	obj = super().__new__(cls, *args)

	return obj.doit() if evaluate else obj

	@property
	def var(self):
	"""
	Returns the complex variable used by all the systems.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOParallel
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G2 = TransferFunction(p, 4 - p, p)
	>>> G3 = TransferFunction(0, p**4 - 1, p)
	>>> G4 = TransferFunction(p2, p2 - 1, p)
	>>> tfm_a = TransferFunctionMatrix([[G1, G2], [G3, G4]])
	>>> tfm_b = TransferFunctionMatrix([[G2, G1], [G4, G3]])
	>>> MIMOParallel(tfm_a, tfm_b).var
	p

	"""
	return self.args[0].var

	@property
	def num_inputs(self):
	"""Returns the number of input signals of the parallel system."""
	return self.args[0].num_inputs

	@property
	def num_outputs(self):
	"""Returns the number of output signals of the parallel system."""
	return self.args[0].num_outputs

	@property
	def shape(self):
	"""Returns the shape of the equivalent MIMO system."""
	return self.num_outputs, self.num_inputs

	def doit(self, **hints):
	"""
	Returns the resultant transfer function matrix obtained after evaluating
	the MIMO systems arranged in a parallel configuration.

	Examples
	========

	>>> from sympy.abc import s, p, a, b
	>>> from sympy.physics.control.lti import TransferFunction, MIMOParallel, TransferFunctionMatrix
	>>> tf1 = TransferFunction(ap2 + bs, s - p, s)
	>>> tf2 = TransferFunction(s3 - 2, s4 + 5*s + 6, s)
	>>> tfm_1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
	>>> tfm_2 = TransferFunctionMatrix([[tf2, tf1], [tf1, tf2]])
	>>> MIMOParallel(tfm_1, tfm_2).doit()
	TransferFunctionMatrix(((TransferFunction((-p + s)(s3 - 2) + (ap*2 + bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s), TransferFunction((-p + s)(s3 - 2) + (ap*2 + bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s)), (TransferFunction((-p + s)(s3 - 2) + (ap*2 + bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s), TransferFunction((-p + s)(s3 - 2) + (ap*2 + bs)(s4 + 5s + 6), (-p + s)(s4 + 5s + 6), s))))

	"""
	_arg = (arg.doit()._expr_mat for arg in self.args)
	res = MatAdd(*_arg, evaluate=True)
	return TransferFunctionMatrix.from_Matrix(res, self.var)

	def _eval_rewrite_as_TransferFunctionMatrix(self, args, *kwargs):
	return self.doit()

	@_check_other_MIMO
	def __add__(self, other):

	self_arg_list = list(self.args)
	return MIMOParallel(*self_arg_list, other)

	__radd__ = __add__

	@_check_other_MIMO
	def __sub__(self, other):
	return self + (-other)

	def __rsub__(self, other):
	return -self + other

	@_check_other_MIMO
	def __mul__(self, other):

	if isinstance(other, MIMOSeries):
	arg_list = list(other.args)
	return MIMOSeries(*arg_list, self)

	return MIMOSeries(other, self)

	def __neg__(self):
	arg_list = [-arg for arg in list(self.args)]
	return MIMOParallel(*arg_list)


	class Feedback(TransferFunction):
	r"""
	A class for representing closed-loop feedback interconnection between two
	SISO input/output systems.

	The first argument, ``sys1``, is the feedforward part of the closed-loop
	system or in simple words, the dynamical model representing the process
	to be controlled. The second argument, ``sys2``, is the feedback system
	and controls the fed back signal to ``sys1``. Both ``sys1`` and ``sys2``
	can either be ``Series`` or ``TransferFunction`` objects.

	Parameters
	==========

	sys1 : Series, TransferFunction
	The feedforward path system.
	sys2 : Series, TransferFunction, optional
	The feedback path system (often a feedback controller).
	It is the model sitting on the feedback path.

	If not specified explicitly, the sys2 is
	assumed to be unit (1.0) transfer function.
	sign : int, optional
	The sign of feedback. Can either be ``1``
	(for positive feedback) or ``-1`` (for negative feedback).
	Default value is `-1`.

	Raises
	======

	ValueError
	When ``sys1`` and ``sys2`` are not using the
	same complex variable of the Laplace transform.

	When a combination of ``sys1`` and ``sys2`` yields
	zero denominator.

	TypeError
	When either ``sys1`` or ``sys2`` is not a ``Series`` or a
	``TransferFunction`` object.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> plant = TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> controller = TransferFunction(5*s - 10, s + 7, s)
	>>> F1 = Feedback(plant, controller)
	>>> F1
	Feedback(TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s), TransferFunction(5*s - 10, s + 7, s), -1)
	>>> F1.var
	s
	>>> F1.args
	(TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s), TransferFunction(5*s - 10, s + 7, s), -1)

	You can get the feedforward and feedback path systems by using ``.sys1`` and ``.sys2`` respectively.

	>>> F1.sys1
	TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> F1.sys2
	TransferFunction(5*s - 10, s + 7, s)

	You can get the resultant closed loop transfer function obtained by negative feedback
	interconnection using ``.doit()`` method.

	>>> F1.doit()
	TransferFunction((s + 7)(s2 - 4s + 2)(3s*2 + 7s - 3), ((s + 7)(s2 - 4s + 2) + (5s - 10)(3s2 + 7s - 3))(s2 - 4s + 2), s)
	>>> G = TransferFunction(2s2 + 5s + 1, s*2 + 2s + 3, s)
	>>> C = TransferFunction(5*s + 10, s + 10, s)
	>>> F2 = Feedback(G*C, TransferFunction(1, 1, s))
	>>> F2.doit()
	TransferFunction((s + 10)(5s + 10)(s2 + 2s + 3)(2s*2 + 5s + 1), (s + 10)((s + 10)(s*2 + 2s + 3) + (5s + 10)(2s2 + 5s + 1))(s2 + 2s + 3), s)

	To negate a ``Feedback`` object, the ``-`` operator can be prepended:

	>>> -F1
	Feedback(TransferFunction(-3s2 - 7s + 3, s*2 - 4s + 2, s), TransferFunction(10 - 5*s, s + 7, s), -1)
	>>> -F2
	Feedback(Series(TransferFunction(-1, 1, s), TransferFunction(2s2 + 5s + 1, s*2 + 2s + 3, s), TransferFunction(5*s + 10, s + 10, s)), TransferFunction(-1, 1, s), -1)

	See Also
	========

	MIMOFeedback, Series, Parallel

	"""
	def __new__(cls, sys1, sys2=None, sign=-1):
	if not sys2:
	sys2 = TransferFunction(1, 1, sys1.var)

	if not (isinstance(sys1, (TransferFunction, Series, Feedback))
	and isinstance(sys2, (TransferFunction, Series, Feedback))):
	raise TypeError("Unsupported type for `sys1` or `sys2` of Feedback.")

	if sign not in [-1, 1]:
	raise ValueError(filldedent("""
	Unsupported type for feedback. `sign` arg should
	either be 1 (positive feedback loop) or -1
	(negative feedback loop)."""))

	if Mul(sys1.to_expr(), sys2.to_expr()).simplify() == sign:
	raise ValueError("The equivalent system will have zero denominator.")

	if sys1.var != sys2.var:
	raise ValueError(filldedent("""
	Both `sys1` and `sys2` should be using the
	same complex variable."""))

	return super(TransferFunction, cls).__new__(cls, sys1, sys2, _sympify(sign))

	@property
	def sys1(self):
	"""
	Returns the feedforward system of the feedback interconnection.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> plant = TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> controller = TransferFunction(5*s - 10, s + 7, s)
	>>> F1 = Feedback(plant, controller)
	>>> F1.sys1
	TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> G = TransferFunction(2s2 + 5s + 1, p*2 + 2p + 3, p)
	>>> C = TransferFunction(5*p + 10, p + 10, p)
	>>> P = TransferFunction(1 - s, p + 2, p)
	>>> F2 = Feedback(TransferFunction(1, 1, p), GCP)
	>>> F2.sys1
	TransferFunction(1, 1, p)

	"""
	return self.args[0]

	@property
	def sys2(self):
	"""
	Returns the feedback controller of the feedback interconnection.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> plant = TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> controller = TransferFunction(5*s - 10, s + 7, s)
	>>> F1 = Feedback(plant, controller)
	>>> F1.sys2
	TransferFunction(5*s - 10, s + 7, s)
	>>> G = TransferFunction(2s2 + 5s + 1, p*2 + 2p + 3, p)
	>>> C = TransferFunction(5*p + 10, p + 10, p)
	>>> P = TransferFunction(1 - s, p + 2, p)
	>>> F2 = Feedback(TransferFunction(1, 1, p), GCP)
	>>> F2.sys2
	Series(TransferFunction(2s2 + 5s + 1, p*2 + 2p + 3, p), TransferFunction(5*p + 10, p + 10, p), TransferFunction(1 - s, p + 2, p))

	"""
	return self.args[1]

	@property
	def var(self):
	"""
	Returns the complex variable of the Laplace transform used by all
	the transfer functions involved in the feedback interconnection.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> plant = TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> controller = TransferFunction(5*s - 10, s + 7, s)
	>>> F1 = Feedback(plant, controller)
	>>> F1.var
	s
	>>> G = TransferFunction(2s2 + 5s + 1, p*2 + 2p + 3, p)
	>>> C = TransferFunction(5*p + 10, p + 10, p)
	>>> P = TransferFunction(1 - s, p + 2, p)
	>>> F2 = Feedback(TransferFunction(1, 1, p), GCP)
	>>> F2.var
	p

	"""
	return self.sys1.var

	@property
	def sign(self):
	"""
	Returns the type of MIMO Feedback model. ``1``
	for Positive and ``-1`` for Negative.
	"""
	return self.args[2]

	@property
	def num(self):
	"""
	Returns the numerator of the closed loop feedback system.
	"""
	return self.sys1

	@property
	def den(self):
	"""
	Returns the denominator of the closed loop feedback model.
	"""
	unit = TransferFunction(1, 1, self.var)
	arg_list = list(self.sys1.args) if isinstance(self.sys1, Series) else [self.sys1]
	if self.sign == 1:
	return Parallel(unit, -Series(self.sys2, *arg_list))
	return Parallel(unit, Series(self.sys2, *arg_list))

	@property
	def sensitivity(self):
	"""
	Returns the sensitivity function of the feedback loop.

	Sensitivity of a Feedback system is the ratio
	of change in the open loop gain to the change in
	the closed loop gain.

	.. note::
	This method would not return the complementary
	sensitivity function.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> C = TransferFunction(5*p + 10, p + 10, p)
	>>> P = TransferFunction(1 - p, p + 2, p)
	>>> F_1 = Feedback(P, C)
	>>> F_1.sensitivity
	1/((1 - p)(5p + 10)/((p + 2)*(p + 10)) + 1)

	"""

	return 1/(1 - self.signself.sys1.to_expr()self.sys2.to_expr())

	def doit(self, cancel=False, expand=False, **hints):
	"""
	Returns the resultant transfer function obtained by the
	feedback interconnection.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> plant = TransferFunction(3s2 + 7s - 3, s*2 - 4s + 2, s)
	>>> controller = TransferFunction(5*s - 10, s + 7, s)
	>>> F1 = Feedback(plant, controller)
	>>> F1.doit()
	TransferFunction((s + 7)(s2 - 4s + 2)(3s*2 + 7s - 3), ((s + 7)(s2 - 4s + 2) + (5s - 10)(3s2 + 7s - 3))(s2 - 4s + 2), s)
	>>> G = TransferFunction(2s2 + 5s + 1, s*2 + 2s + 3, s)
	>>> F2 = Feedback(G, TransferFunction(1, 1, s))
	>>> F2.doit()
	TransferFunction((s*2 + 2s + 3)(2s*2 + 5s + 1), (s*2 + 2s + 3)(3s*2 + 7s + 4), s)

	Use kwarg ``expand=True`` to expand the resultant transfer function.
	Use ``cancel=True`` to cancel out the common terms in numerator and
	denominator.

	>>> F2.doit(cancel=True, expand=True)
	TransferFunction(2s2 + 5s + 1, 3s2 + 7s + 4, s)
	>>> F2.doit(expand=True)
	TransferFunction(2s4 + 9s*3 + 17s*2 + 17s + 3, 3s4 + 13s*3 + 27s*2 + 29s + 12, s)

	"""
	arg_list = list(self.sys1.args) if isinstance(self.sys1, Series) else [self.sys1]
	# F_n and F_d are resultant TFs of num and den of Feedback.
	F_n, unit = self.sys1.doit(), TransferFunction(1, 1, self.sys1.var)
	if self.sign == -1:
	F_d = Parallel(unit, Series(self.sys2, *arg_list)).doit()
	else:
	F_d = Parallel(unit, -Series(self.sys2, *arg_list)).doit()

	_resultant_tf = TransferFunction(F_n.num * F_d.den, F_n.den * F_d.num, F_n.var)

	if cancel:
	_resultant_tf = _resultant_tf.simplify()

	if expand:
	_resultant_tf = _resultant_tf.expand()

	return _resultant_tf

	def _eval_rewrite_as_TransferFunction(self, num, den, sign, **kwargs):
	return self.doit()

	def to_expr(self):
	"""
	Converts a ``Feedback`` object to SymPy Expr.

	Examples
	========

	>>> from sympy.abc import s, a, b
	>>> from sympy.physics.control.lti import TransferFunction, Feedback
	>>> from sympy import Expr
	>>> tf1 = TransferFunction(a+s, 1, s)
	>>> tf2 = TransferFunction(b+s, 1, s)
	>>> fd1 = Feedback(tf1, tf2)
	>>> fd1.to_expr()
	(a + s)/((a + s)*(b + s) + 1)
	>>> isinstance(_, Expr)
	True
	"""

	return self.doit().to_expr()

	def __neg__(self):
	return Feedback(-self.sys1, -self.sys2, self.sign)


	def _is_invertible(a, b, sign):
	"""
	Checks whether a given pair of MIMO
	systems passed is invertible or not.
	"""
	_mat = eye(a.num_outputs) - sign(a.doit()._expr_mat)(b.doit()._expr_mat)
	_det = _mat.det()

	return _det != 0


	class MIMOFeedback(MIMOLinearTimeInvariant):
	r"""
	A class for representing closed-loop feedback interconnection between two
	MIMO input/output systems.

	Parameters
	==========

	sys1 : MIMOSeries, TransferFunctionMatrix
	The MIMO system placed on the feedforward path.
	sys2 : MIMOSeries, TransferFunctionMatrix
	The system placed on the feedback path
	(often a feedback controller).
	sign : int, optional
	The sign of feedback. Can either be ``1``
	(for positive feedback) or ``-1`` (for negative feedback).
	Default value is `-1`.

	Raises
	======

	ValueError
	When ``sys1`` and ``sys2`` are not using the
	same complex variable of the Laplace transform.

	Forward path model should have an equal number of inputs/outputs
	to the feedback path outputs/inputs.

	When product of ``sys1`` and ``sys2`` is not a square matrix.

	When the equivalent MIMO system is not invertible.

	TypeError
	When either ``sys1`` or ``sys2`` is not a ``MIMOSeries`` or a
	``TransferFunctionMatrix`` object.

	Examples
	========

	>>> from sympy import Matrix, pprint
	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunctionMatrix, MIMOFeedback
	>>> plant_mat = Matrix([[1, 1/s], [0, 1]])
	>>> controller_mat = Matrix([[10, 0], [0, 10]]) # Constant Gain
	>>> plant = TransferFunctionMatrix.from_Matrix(plant_mat, s)
	>>> controller = TransferFunctionMatrix.from_Matrix(controller_mat, s)
	>>> feedback = MIMOFeedback(plant, controller) # Negative Feedback (default)
	>>> pprint(feedback, use_unicode=False)
	/ [1 1] [10 0 ] \-1 [1 1]
	\| [- -] [-- - ] \| [- -]
	\| [1 s] [1 1 ] \| [1 s]
	\|I + [ ] [ ] \| [ ]
	\| [0 1] [0 10] \| [0 1]
	\| [- -] [- --] \| [- -]
	\ [1 1]{t} [1 1 ]{t}/ [1 1]{t}

	To get the equivalent system matrix, use either ``doit`` or ``rewrite`` method.

	>>> pprint(feedback.doit(), use_unicode=False)
	[1 1 ]
	[-- -----]
	[11 121*s]
	[ ]
	[0 1 ]
	[- -- ]
	[1 11 ]{t}

	To negate the ``MIMOFeedback`` object, use ``-`` operator.

	>>> neg_feedback = -feedback
	>>> pprint(neg_feedback.doit(), use_unicode=False)
	[-1 -1 ]
	[--- -----]
	[11 121*s]
	[ ]
	[ 0 -1 ]
	[ - --- ]
	[ 1 11 ]{t}

	See Also
	========

	Feedback, MIMOSeries, MIMOParallel

	"""
	def __new__(cls, sys1, sys2, sign=-1):
	if not (isinstance(sys1, (TransferFunctionMatrix, MIMOSeries))
	and isinstance(sys2, (TransferFunctionMatrix, MIMOSeries))):
	raise TypeError("Unsupported type for `sys1` or `sys2` of MIMO Feedback.")

	if sys1.num_inputs != sys2.num_outputs or \
	sys1.num_outputs != sys2.num_inputs:
	raise ValueError(filldedent("""
	Product of `sys1` and `sys2` must
	yield a square matrix."""))

	if sign not in (-1, 1):
	raise ValueError(filldedent("""
	Unsupported type for feedback. `sign` arg should
	either be 1 (positive feedback loop) or -1
	(negative feedback loop)."""))

	if not _is_invertible(sys1, sys2, sign):
	raise ValueError("Non-Invertible system inputted.")
	if sys1.var != sys2.var:
	raise ValueError(filldedent("""
	Both `sys1` and `sys2` should be using the
	same complex variable."""))

	return super().__new__(cls, sys1, sys2, _sympify(sign))

	@property
	def sys1(self):
	r"""
	Returns the system placed on the feedforward path of the MIMO feedback interconnection.

	Examples
	========

	>>> from sympy import pprint
	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
	>>> tf1 = TransferFunction(s2 + s + 1, s2 - s + 1, s)
	>>> tf2 = TransferFunction(1, s, s)
	>>> tf3 = TransferFunction(1, 1, s)
	>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
	>>> sys2 = TransferFunctionMatrix([[tf3, tf3], [tf3, tf2]])
	>>> F_1 = MIMOFeedback(sys1, sys2, 1)
	>>> F_1.sys1
	TransferFunctionMatrix(((TransferFunction(s2 + s + 1, s2 - s + 1, s), TransferFunction(1, s, s)), (TransferFunction(1, s, s), TransferFunction(s2 + s + 1, s2 - s + 1, s))))
	>>> pprint(_, use_unicode=False)
	[ 2 ]
	[s + s + 1 1 ]
	[---------- - ]
	[ 2 s ]
	[s - s + 1 ]
	[ ]
	[ 2 ]
	[ 1 s + s + 1]
	[ - ----------]
	[ s 2 ]
	[ s - s + 1]{t}

	"""
	return self.args[0]

	@property
	def sys2(self):
	r"""
	Returns the feedback controller of the MIMO feedback interconnection.

	Examples
	========

	>>> from sympy import pprint
	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
	>>> tf1 = TransferFunction(s2, s3 - s + 1, s)
	>>> tf2 = TransferFunction(1, s, s)
	>>> tf3 = TransferFunction(1, 1, s)
	>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
	>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
	>>> F_1 = MIMOFeedback(sys1, sys2)
	>>> F_1.sys2
	TransferFunctionMatrix(((TransferFunction(s2, s3 - s + 1, s), TransferFunction(1, 1, s)), (TransferFunction(1, 1, s), TransferFunction(1, s, s))))
	>>> pprint(_, use_unicode=False)
	[ 2 ]
	[ s 1]
	[---------- -]
	[ 3 1]
	[s - s + 1 ]
	[ ]
	[ 1 1]
	[ - -]
	[ 1 s]{t}

	"""
	return self.args[1]

	@property
	def var(self):
	r"""
	Returns the complex variable of the Laplace transform used by all
	the transfer functions involved in the MIMO feedback loop.

	Examples
	========

	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
	>>> tf1 = TransferFunction(p, 1 - p, p)
	>>> tf2 = TransferFunction(1, p, p)
	>>> tf3 = TransferFunction(1, 1, p)
	>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
	>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
	>>> F_1 = MIMOFeedback(sys1, sys2, 1) # Positive feedback
	>>> F_1.var
	p

	"""
	return self.sys1.var

	@property
	def sign(self):
	r"""
	Returns the type of feedback interconnection of two models. ``1``
	for Positive and ``-1`` for Negative.
	"""
	return self.args[2]

	@property
	def sensitivity(self):
	r"""
	Returns the sensitivity function matrix of the feedback loop.

	Sensitivity of a closed-loop system is the ratio of change
	in the open loop gain to the change in the closed loop gain.

	.. note::
	This method would not return the complementary
	sensitivity function.

	Examples
	========

	>>> from sympy import pprint
	>>> from sympy.abc import p
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
	>>> tf1 = TransferFunction(p, 1 - p, p)
	>>> tf2 = TransferFunction(1, p, p)
	>>> tf3 = TransferFunction(1, 1, p)
	>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf2, tf1]])
	>>> sys2 = TransferFunctionMatrix([[tf1, tf3], [tf3, tf2]])
	>>> F_1 = MIMOFeedback(sys1, sys2, 1) # Positive feedback
	>>> F_2 = MIMOFeedback(sys1, sys2) # Negative feedback
	>>> pprint(F_1.sensitivity, use_unicode=False)
	[ 4 3 2 5 4 2 ]
	[- p + 3p - 4p + 3p - 1 p - 2p + 3p - 3p + 1 ]
	[---------------------------- -----------------------------]
	[ 4 3 2 5 4 3 2 ]
	[ p + 3p - 8p + 8p - 3 p + 3p - 8p + 8p - 3*p]
	[ ]
	[ 4 3 2 3 2 ]
	[ p - p - p + p 3p - 6p + 4*p - 1 ]
	[ -------------------------- -------------------------- ]
	[ 4 3 2 4 3 2 ]
	[ p + 3p - 8p + 8p - 3 p + 3p - 8p + 8p - 3 ]
	>>> pprint(F_2.sensitivity, use_unicode=False)
	[ 4 3 2 5 4 2 ]
	[p - 3p + 2p + p - 1 p - 2p + 3p - 3*p + 1]
	[------------------------ --------------------------]
	[ 4 3 5 4 2 ]
	[ p - 3p + 2p - 1 p - 3p + 2p - p ]
	[ ]
	[ 4 3 2 4 3 ]
	[ p - p - p + p 2p - 3p + 2*p - 1 ]
	[ ------------------- --------------------- ]
	[ 4 3 4 3 ]
	[ p - 3p + 2p - 1 p - 3p + 2p - 1 ]

	"""
	_sys1_mat = self.sys1.doit()._expr_mat
	_sys2_mat = self.sys2.doit()._expr_mat

	return (eye(self.sys1.num_inputs) - \
	self.sign_sys1_mat_sys2_mat).inv()

	def doit(self, cancel=True, expand=False, **hints):
	r"""
	Returns the resultant transfer function matrix obtained by the
	feedback interconnection.

	Examples
	========

	>>> from sympy import pprint
	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, MIMOFeedback
	>>> tf1 = TransferFunction(s, 1 - s, s)
	>>> tf2 = TransferFunction(1, s, s)
	>>> tf3 = TransferFunction(5, 1, s)
	>>> tf4 = TransferFunction(s - 1, s, s)
	>>> tf5 = TransferFunction(0, 1, s)
	>>> sys1 = TransferFunctionMatrix([[tf1, tf2], [tf3, tf4]])
	>>> sys2 = TransferFunctionMatrix([[tf3, tf5], [tf5, tf5]])
	>>> F_1 = MIMOFeedback(sys1, sys2, 1)
	>>> pprint(F_1, use_unicode=False)
	/ [ s 1 ] [5 0] \-1 [ s 1 ]
	\| [----- - ] [- -] \| [----- - ]
	\| [1 - s s ] [1 1] \| [1 - s s ]
	\|I - [ ] [ ] \| [ ]
	\| [ 5 s - 1] [0 0] \| [ 5 s - 1]
	\| [ - -----] [- -] \| [ - -----]
	\ [ 1 s ]{t} [1 1]{t}/ [ 1 s ]{t}
	>>> pprint(F_1.doit(), use_unicode=False)
	[ -s s - 1 ]
	[------- ----------- ]
	[6s - 1 s(6*s - 1) ]
	[ ]
	[5s - 5 (s - 1)(6*s + 24)]
	[------- ------------------]
	[6s - 1 s(6*s - 1) ]{t}

	If the user wants the resultant ``TransferFunctionMatrix`` object without
	canceling the common factors then the ``cancel`` kwarg should be passed ``False``.

	>>> pprint(F_1.doit(cancel=False), use_unicode=False)
	[ s*(s - 1) s - 1 ]
	[ ----------------- ----------- ]
	[ (1 - s)(6s - 1) s(6s - 1) ]
	[ ]
	[s(25s - 25) + 5(1 - s)(6s - 1) s(s - 1)(6s - 1) + s(25s - 25)]
	[----------------------------------- -----------------------------------]
	[ (1 - s)(6s - 1) 2 ]
	[ s (6s - 1) ]{t}

	If the user wants the expanded form of the resultant transfer function matrix,
	the ``expand`` kwarg should be passed as ``True``.

	>>> pprint(F_1.doit(expand=True), use_unicode=False)
	[ -s s - 1 ]
	[------- -------- ]
	[6*s - 1 2 ]
	[ 6*s - s ]
	[ ]
	[ 2 ]
	[5s - 5 6s + 18*s - 24]
	[------- ----------------]
	[6*s - 1 2 ]
	[ 6*s - s ]{t}

	"""
	_mat = self.sensitivity * self.sys1.doit()._expr_mat

	_resultant_tfm = _to_TFM(_mat, self.var)

	if cancel:
	_resultant_tfm = _resultant_tfm.simplify()

	if expand:
	_resultant_tfm = _resultant_tfm.expand()

	return _resultant_tfm

	def _eval_rewrite_as_TransferFunctionMatrix(self, sys1, sys2, sign, **kwargs):
	return self.doit()

	def __neg__(self):
	return MIMOFeedback(-self.sys1, -self.sys2, self.sign)


	def _to_TFM(mat, var):
	"""Private method to convert ImmutableMatrix to TransferFunctionMatrix efficiently"""
	to_tf = lambda expr: TransferFunction.from_rational_expression(expr, var)
	arg = [[to_tf(expr) for expr in row] for row in mat.tolist()]
	return TransferFunctionMatrix(arg)


	class TransferFunctionMatrix(MIMOLinearTimeInvariant):
	r"""
	A class for representing the MIMO (multiple-input and multiple-output)
	generalization of the SISO (single-input and single-output) transfer function.

	It is a matrix of transfer functions (``TransferFunction``, SISO-``Series`` or SISO-``Parallel``).
	There is only one argument, ``arg`` which is also the compulsory argument.
	``arg`` is expected to be strictly of the type list of lists
	which holds the transfer functions or reducible to transfer functions.

	Parameters
	==========

	arg : Nested ``List`` (strictly).
	Users are expected to input a nested list of ``TransferFunction``, ``Series``
	and/or ``Parallel`` objects.

	Examples
	========

	.. note::
	``pprint()`` can be used for better visualization of ``TransferFunctionMatrix`` objects.

	>>> from sympy.abc import s, p, a
	>>> from sympy import pprint
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, Series, Parallel
	>>> tf_1 = TransferFunction(s + a, s**2 + s + 1, s)
	>>> tf_2 = TransferFunction(p*4 - 3p + 2, s + p, s)
	>>> tf_3 = TransferFunction(3, s + 2, s)
	>>> tf_4 = TransferFunction(-a + p, 9*s - 9, s)
	>>> tfm_1 = TransferFunctionMatrix([[tf_1], [tf_2], [tf_3]])
	>>> tfm_1
	TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s),), (TransferFunction(p4 - 3*p + 2, p + s, s),), (TransferFunction(3, s + 2, s),)))
	>>> tfm_1.var
	s
	>>> tfm_1.num_inputs
	1
	>>> tfm_1.num_outputs
	3
	>>> tfm_1.shape
	(3, 1)
	>>> tfm_1.args
	(((TransferFunction(a + s, s2 + s + 1, s),), (TransferFunction(p4 - 3*p + 2, p + s, s),), (TransferFunction(3, s + 2, s),)),)
	>>> tfm_2 = TransferFunctionMatrix([[tf_1, -tf_3], [tf_2, -tf_1], [tf_3, -tf_2]])
	>>> tfm_2
	TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(p4 - 3p + 2, p + s, s), TransferFunction(-a - s, s2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-p4 + 3p - 2, p + s, s))))
	>>> pprint(tfm_2, use_unicode=False) # pretty-printing for better visualization
	[ a + s -3 ]
	[ ---------- ----- ]
	[ 2 s + 2 ]
	[ s + s + 1 ]
	[ ]
	[ 4 ]
	[p - 3*p + 2 -a - s ]
	[------------ ---------- ]
	[ p + s 2 ]
	[ s + s + 1 ]
	[ ]
	[ 4 ]
	[ 3 - p + 3*p - 2]
	[ ----- --------------]
	[ s + 2 p + s ]{t}

	TransferFunctionMatrix can be transposed, if user wants to switch the input and output transfer functions

	>>> tfm_2.transpose()
	TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s), TransferFunction(p4 - 3p + 2, p + s, s), TransferFunction(3, s + 2, s)), (TransferFunction(-3, s + 2, s), TransferFunction(-a - s, s2 + s + 1, s), TransferFunction(-p4 + 3p - 2, p + s, s))))
	>>> pprint(_, use_unicode=False)
	[ 4 ]
	[ a + s p - 3*p + 2 3 ]
	[---------- ------------ ----- ]
	[ 2 p + s s + 2 ]
	[s + s + 1 ]
	[ ]
	[ 4 ]
	[ -3 -a - s - p + 3*p - 2]
	[ ----- ---------- --------------]
	[ s + 2 2 p + s ]
	[ s + s + 1 ]{t}

	>>> tf_5 = TransferFunction(5, s, s)
	>>> tf_6 = TransferFunction(5s, (2 + s*2), s)
	>>> tf_7 = TransferFunction(5, (s(2 + s*2)), s)
	>>> tf_8 = TransferFunction(5, 1, s)
	>>> tfm_3 = TransferFunctionMatrix([[tf_5, tf_6], [tf_7, tf_8]])
	>>> tfm_3
	TransferFunctionMatrix(((TransferFunction(5, s, s), TransferFunction(5s, s2 + 2, s)), (TransferFunction(5, s(s**2 + 2), s), TransferFunction(5, 1, s))))
	>>> pprint(tfm_3, use_unicode=False)
	[ 5 5*s ]
	[ - ------]
	[ s 2 ]
	[ s + 2]
	[ ]
	[ 5 5 ]
	[---------- - ]
	[ / 2 \ 1 ]
	[s*\s + 2/ ]{t}
	>>> tfm_3.var
	s
	>>> tfm_3.shape
	(2, 2)
	>>> tfm_3.num_outputs
	2
	>>> tfm_3.num_inputs
	2
	>>> tfm_3.args
	(((TransferFunction(5, s, s), TransferFunction(5s, s2 + 2, s)), (TransferFunction(5, s(s**2 + 2), s), TransferFunction(5, 1, s))),)

	To access the ``TransferFunction`` at any index in the ``TransferFunctionMatrix``, use the index notation.

	>>> tfm_3[1, 0] # gives the TransferFunction present at 2nd Row and 1st Col. Similar to that in Matrix classes
	TransferFunction(5, s(s*2 + 2), s)
	>>> tfm_3[0, 0] # gives the TransferFunction present at 1st Row and 1st Col.
	TransferFunction(5, s, s)
	>>> tfm_3[:, 0] # gives the first column
	TransferFunctionMatrix(((TransferFunction(5, s, s),), (TransferFunction(5, s(s*2 + 2), s),)))
	>>> pprint(_, use_unicode=False)
	[ 5 ]
	[ - ]
	[ s ]
	[ ]
	[ 5 ]
	[----------]
	[ / 2 \]
	[s*\s + 2/]{t}
	>>> tfm_3[0, :] # gives the first row
	TransferFunctionMatrix(((TransferFunction(5, s, s), TransferFunction(5s, s*2 + 2, s)),))
	>>> pprint(_, use_unicode=False)
	[5 5*s ]
	[- ------]
	[s 2 ]
	[ s + 2]{t}

	To negate a transfer function matrix, ``-`` operator can be prepended:

	>>> tfm_4 = TransferFunctionMatrix([[tf_2], [-tf_1], [tf_3]])
	>>> -tfm_4
	TransferFunctionMatrix(((TransferFunction(-p*4 + 3p - 2, p + s, s),), (TransferFunction(a + s, s**2 + s + 1, s),), (TransferFunction(-3, s + 2, s),)))
	>>> tfm_5 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, -tf_1]])
	>>> -tfm_5
	TransferFunctionMatrix(((TransferFunction(-a - s, s2 + s + 1, s), TransferFunction(-p4 + 3p - 2, p + s, s)), (TransferFunction(-3, s + 2, s), TransferFunction(a + s, s*2 + s + 1, s))))

	``subs()`` returns the ``TransferFunctionMatrix`` object with the value substituted in the expression. This will not
	mutate your original ``TransferFunctionMatrix``.

	>>> tfm_2.subs(p, 2) # substituting p everywhere in tfm_2 with 2.
	TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(12, s + 2, s), TransferFunction(-a - s, s2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-12, s + 2, s))))
	>>> pprint(_, use_unicode=False)
	[ a + s -3 ]
	[---------- ----- ]
	[ 2 s + 2 ]
	[s + s + 1 ]
	[ ]
	[ 12 -a - s ]
	[ ----- ----------]
	[ s + 2 2 ]
	[ s + s + 1]
	[ ]
	[ 3 -12 ]
	[ ----- ----- ]
	[ s + 2 s + 2 ]{t}
	>>> pprint(tfm_2, use_unicode=False) # State of tfm_2 is unchanged after substitution
	[ a + s -3 ]
	[ ---------- ----- ]
	[ 2 s + 2 ]
	[ s + s + 1 ]
	[ ]
	[ 4 ]
	[p - 3*p + 2 -a - s ]
	[------------ ---------- ]
	[ p + s 2 ]
	[ s + s + 1 ]
	[ ]
	[ 4 ]
	[ 3 - p + 3*p - 2]
	[ ----- --------------]
	[ s + 2 p + s ]{t}

	``subs()`` also supports multiple substitutions.

	>>> tfm_2.subs({p: 2, a: 1}) # substituting p with 2 and a with 1
	TransferFunctionMatrix(((TransferFunction(s + 1, s2 + s + 1, s), TransferFunction(-3, s + 2, s)), (TransferFunction(12, s + 2, s), TransferFunction(-s - 1, s2 + s + 1, s)), (TransferFunction(3, s + 2, s), TransferFunction(-12, s + 2, s))))
	>>> pprint(_, use_unicode=False)
	[ s + 1 -3 ]
	[---------- ----- ]
	[ 2 s + 2 ]
	[s + s + 1 ]
	[ ]
	[ 12 -s - 1 ]
	[ ----- ----------]
	[ s + 2 2 ]
	[ s + s + 1]
	[ ]
	[ 3 -12 ]
	[ ----- ----- ]
	[ s + 2 s + 2 ]{t}

	Users can reduce the ``Series`` and ``Parallel`` elements of the matrix to ``TransferFunction`` by using
	``doit()``.

	>>> tfm_6 = TransferFunctionMatrix([[Series(tf_3, tf_4), Parallel(tf_3, tf_4)]])
	>>> tfm_6
	TransferFunctionMatrix(((Series(TransferFunction(3, s + 2, s), TransferFunction(-a + p, 9s - 9, s)), Parallel(TransferFunction(3, s + 2, s), TransferFunction(-a + p, 9s - 9, s))),))
	>>> pprint(tfm_6, use_unicode=False)
	[-a + p 3 -a + p 3 ]
	[-------*----- ------- + -----]
	[9s - 9 s + 2 9s - 9 s + 2]{t}
	>>> tfm_6.doit()
	TransferFunctionMatrix(((TransferFunction(-3a + 3p, (s + 2)(9s - 9), s), TransferFunction(27s + (-a + p)(s + 2) - 27, (s + 2)(9s - 9), s)),))
	>>> pprint(_, use_unicode=False)
	[ -3a + 3p 27s + (-a + p)(s + 2) - 27]
	[----------------- ----------------------------]
	[(s + 2)(9s - 9) (s + 2)(9s - 9) ]{t}
	>>> tf_9 = TransferFunction(1, s, s)
	>>> tf_10 = TransferFunction(1, s**2, s)
	>>> tfm_7 = TransferFunctionMatrix([[Series(tf_9, tf_10), tf_9], [tf_10, Parallel(tf_9, tf_10)]])
	>>> tfm_7
	TransferFunctionMatrix(((Series(TransferFunction(1, s, s), TransferFunction(1, s2, s)), TransferFunction(1, s, s)), (TransferFunction(1, s2, s), Parallel(TransferFunction(1, s, s), TransferFunction(1, s**2, s)))))
	>>> pprint(tfm_7, use_unicode=False)
	[ 1 1 ]
	[---- - ]
	[ 2 s ]
	[s*s ]
	[ ]
	[ 1 1 1]
	[ -- -- + -]
	[ 2 2 s]
	[ s s ]{t}
	>>> tfm_7.doit()
	TransferFunctionMatrix(((TransferFunction(1, s3, s), TransferFunction(1, s, s)), (TransferFunction(1, s2, s), TransferFunction(s2 + s, s3, s))))
	>>> pprint(_, use_unicode=False)
	[1 1 ]
	[-- - ]
	[ 3 s ]
	[s ]
	[ ]
	[ 2 ]
	[1 s + s]
	[-- ------]
	[ 2 3 ]
	[s s ]{t}

	Addition, subtraction, and multiplication of transfer function matrices can form
	unevaluated ``Series`` or ``Parallel`` objects.

	- For addition and subtraction:
	All the transfer function matrices must have the same shape.

	- For multiplication (C = A * B):
	The number of inputs of the first transfer function matrix (A) must be equal to the
	number of outputs of the second transfer function matrix (B).

	Also, use pretty-printing (``pprint``) to analyse better.

	>>> tfm_8 = TransferFunctionMatrix([[tf_3], [tf_2], [-tf_1]])
	>>> tfm_9 = TransferFunctionMatrix([[-tf_3]])
	>>> tfm_10 = TransferFunctionMatrix([[tf_1], [tf_2], [tf_4]])
	>>> tfm_11 = TransferFunctionMatrix([[tf_4], [-tf_1]])
	>>> tfm_12 = TransferFunctionMatrix([[tf_4, -tf_1, tf_3], [-tf_2, -tf_4, -tf_3]])
	>>> tfm_8 + tfm_10
	MIMOParallel(TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p*4 - 3p + 2, p + s, s),), (TransferFunction(-a - s, s2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s),), (TransferFunction(p*4 - 3p + 2, p + s, s),), (TransferFunction(-a + p, 9*s - 9, s),))))
	>>> pprint(_, use_unicode=False)
	[ 3 ] [ a + s ]
	[ ----- ] [ ---------- ]
	[ s + 2 ] [ 2 ]
	[ ] [ s + s + 1 ]
	[ 4 ] [ ]
	[p - 3*p + 2] [ 4 ]
	[------------] + [p - 3*p + 2]
	[ p + s ] [------------]
	[ ] [ p + s ]
	[ -a - s ] [ ]
	[ ---------- ] [ -a + p ]
	[ 2 ] [ ------- ]
	[ s + s + 1 ]{t} [ 9*s - 9 ]{t}
	>>> -tfm_10 - tfm_8
	MIMOParallel(TransferFunctionMatrix(((TransferFunction(-a - s, s2 + s + 1, s),), (TransferFunction(-p4 + 3p - 2, p + s, s),), (TransferFunction(a - p, 9s - 9, s),))), TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),), (TransferFunction(-p*4 + 3p - 2, p + s, s),), (TransferFunction(a + s, s**2 + s + 1, s),))))
	>>> pprint(_, use_unicode=False)
	[ -a - s ] [ -3 ]
	[ ---------- ] [ ----- ]
	[ 2 ] [ s + 2 ]
	[ s + s + 1 ] [ ]
	[ ] [ 4 ]
	[ 4 ] [- p + 3*p - 2]
	[- p + 3*p - 2] + [--------------]
	[--------------] [ p + s ]
	[ p + s ] [ ]
	[ ] [ a + s ]
	[ a - p ] [ ---------- ]
	[ ------- ] [ 2 ]
	[ 9*s - 9 ]{t} [ s + s + 1 ]{t}
	>>> tfm_12 * tfm_8
	MIMOSeries(TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p*4 - 3p + 2, p + s, s),), (TransferFunction(-a - s, s*2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(-a + p, 9s - 9, s), TransferFunction(-a - s, s2 + s + 1, s), TransferFunction(3, s + 2, s)), (TransferFunction(-p4 + 3p - 2, p + s, s), TransferFunction(a - p, 9s - 9, s), TransferFunction(-3, s + 2, s)))))
	>>> pprint(_, use_unicode=False)
	[ 3 ]
	[ ----- ]
	[ -a + p -a - s 3 ] [ s + 2 ]
	[ ------- ---------- -----] [ ]
	[ 9*s - 9 2 s + 2] [ 4 ]
	[ s + s + 1 ] [p - 3*p + 2]
	[ ] *[------------]
	[ 4 ] [ p + s ]
	[- p + 3*p - 2 a - p -3 ] [ ]
	[-------------- ------- -----] [ -a - s ]
	[ p + s 9*s - 9 s + 2]{t} [ ---------- ]
	[ 2 ]
	[ s + s + 1 ]{t}
	>>> tfm_12 * tfm_8 * tfm_9
	MIMOSeries(TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),),)), TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p*4 - 3p + 2, p + s, s),), (TransferFunction(-a - s, s*2 + s + 1, s),))), TransferFunctionMatrix(((TransferFunction(-a + p, 9s - 9, s), TransferFunction(-a - s, s2 + s + 1, s), TransferFunction(3, s + 2, s)), (TransferFunction(-p4 + 3p - 2, p + s, s), TransferFunction(a - p, 9s - 9, s), TransferFunction(-3, s + 2, s)))))
	>>> pprint(_, use_unicode=False)
	[ 3 ]
	[ ----- ]
	[ -a + p -a - s 3 ] [ s + 2 ]
	[ ------- ---------- -----] [ ]
	[ 9*s - 9 2 s + 2] [ 4 ]
	[ s + s + 1 ] [p - 3*p + 2] [ -3 ]
	[ ] [------------] [-----]
	[ 4 ] [ p + s ] [s + 2]{t}
	[- p + 3*p - 2 a - p -3 ] [ ]
	[-------------- ------- -----] [ -a - s ]
	[ p + s 9*s - 9 s + 2]{t} [ ---------- ]
	[ 2 ]
	[ s + s + 1 ]{t}
	>>> tfm_10 + tfm_8*tfm_9
	MIMOParallel(TransferFunctionMatrix(((TransferFunction(a + s, s2 + s + 1, s),), (TransferFunction(p4 - 3p + 2, p + s, s),), (TransferFunction(-a + p, 9s - 9, s),))), MIMOSeries(TransferFunctionMatrix(((TransferFunction(-3, s + 2, s),),)), TransferFunctionMatrix(((TransferFunction(3, s + 2, s),), (TransferFunction(p*4 - 3p + 2, p + s, s),), (TransferFunction(-a - s, s**2 + s + 1, s),)))))
	>>> pprint(_, use_unicode=False)
	[ a + s ] [ 3 ]
	[ ---------- ] [ ----- ]
	[ 2 ] [ s + 2 ]
	[ s + s + 1 ] [ ]
	[ ] [ 4 ]
	[ 4 ] [p - 3*p + 2] [ -3 ]
	[p - 3p + 2] + [------------] [-----]
	[------------] [ p + s ] [s + 2]{t}
	[ p + s ] [ ]
	[ ] [ -a - s ]
	[ -a + p ] [ ---------- ]
	[ ------- ] [ 2 ]
	[ 9*s - 9 ]{t} [ s + s + 1 ]{t}

	These unevaluated ``Series`` or ``Parallel`` objects can convert into the
	resultant transfer function matrix using ``.doit()`` method or by
	``.rewrite(TransferFunctionMatrix)``.

	>>> (-tfm_8 + tfm_10 + tfm_8*tfm_9).doit()
	TransferFunctionMatrix(((TransferFunction((a + s)(s + 2)3 - 3(s + 2)*2(s*2 + s + 1) - 9(s + 2)(s2 + s + 1), (s + 2)3(s*2 + s + 1), s),), (TransferFunction((p + s)(-3p4 + 9p - 6), (p + s)*2(s + 2), s),), (TransferFunction((-a + p)(s + 2)(s2 + s + 1)2 + (a + s)(s + 2)(9s - 9)(s*2 + s + 1) + (3a + 3s)(9s - 9)(s*2 + s + 1), (s + 2)(9s - 9)(s2 + s + 1)2, s),)))
	>>> (-tfm_12 * -tfm_8 * -tfm_9).rewrite(TransferFunctionMatrix)
	TransferFunctionMatrix(((TransferFunction(3(-3a + 3p)(p + s)(s + 2)(s2 + s + 1)2 + 3(-3a - 3s)(p + s)(s + 2)(9s - 9)(s*2 + s + 1) + 3(a + s)(s + 2)2(9s - 9)(-p*4 + 3p - 2)(s2 + s + 1), (p + s)(s + 2)*3(9s - 9)(s2 + s + 1)2, s),), (TransferFunction(3(-a + p)(p + s)(s + 2)2(-p*4 + 3p - 2)(s2 + s + 1) + 3(3a + 3s)(p + s)2(s + 2)(9s - 9) + 3(p + s)(s + 2)(9s - 9)(-3p*4 + 9p - 6)(s2 + s + 1), (p + s)2(s + 2)*3(9s - 9)(s**2 + s + 1), s),)))

	See Also
	========

	TransferFunction, MIMOSeries, MIMOParallel, Feedback

	"""
	def __new__(cls, arg):

	expr_mat_arg = []
	try:
	var = arg[0][0].var
	except TypeError:
	raise ValueError(filldedent("""
	`arg` param in TransferFunctionMatrix should
	strictly be a nested list containing TransferFunction
	objects."""))
	for row in arg:
	temp = []
	for element in row:
	if not isinstance(element, SISOLinearTimeInvariant):
	raise TypeError(filldedent("""
	Each element is expected to be of
	type `SISOLinearTimeInvariant`."""))

	if var != element.var:
	raise ValueError(filldedent("""
	Conflicting value(s) found for `var`. All TransferFunction
	instances in TransferFunctionMatrix should use the same
	complex variable in Laplace domain."""))

	temp.append(element.to_expr())
	expr_mat_arg.append(temp)

	if isinstance(arg, (tuple, list, Tuple)):
	# Making nested Tuple (sympy.core.containers.Tuple) from nested list or nested Python tuple
	arg = Tuple((Tuple(r, sympify=False) for r in arg), sympify=False)

	obj = super(TransferFunctionMatrix, cls).__new__(cls, arg)
	obj._expr_mat = ImmutableMatrix(expr_mat_arg)
	return obj

	@classmethod
	def from_Matrix(cls, matrix, var):
	"""
	Creates a new ``TransferFunctionMatrix`` efficiently from a SymPy Matrix of ``Expr`` objects.

	Parameters
	==========

	matrix : ``ImmutableMatrix`` having ``Expr``/``Number`` elements.
	var : Symbol
	Complex variable of the Laplace transform which will be used by the
	all the ``TransferFunction`` objects in the ``TransferFunctionMatrix``.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunctionMatrix
	>>> from sympy import Matrix, pprint
	>>> M = Matrix([[s, 1/s], [1/(s+1), s]])
	>>> M_tf = TransferFunctionMatrix.from_Matrix(M, s)
	>>> pprint(M_tf, use_unicode=False)
	[ s 1]
	[ - -]
	[ 1 s]
	[ ]
	[ 1 s]
	[----- -]
	[s + 1 1]{t}
	>>> M_tf.elem_poles()
	[[[], [0]], [[-1], []]]
	>>> M_tf.elem_zeros()
	[[[0], []], [[], [0]]]

	"""
	return _to_TFM(matrix, var)

	@property
	def var(self):
	"""
	Returns the complex variable used by all the transfer functions or
	``Series``/``Parallel`` objects in a transfer function matrix.

	Examples
	========

	>>> from sympy.abc import p, s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix, Series, Parallel
	>>> G1 = TransferFunction(p*2 + 2p + 4, p - 6, p)
	>>> G2 = TransferFunction(p, 4 - p, p)
	>>> G3 = TransferFunction(0, p**4 - 1, p)
	>>> G4 = TransferFunction(s + 1, s**2 + s + 1, s)
	>>> S1 = Series(G1, G2)
	>>> S2 = Series(-G3, Parallel(G2, -G1))
	>>> tfm1 = TransferFunctionMatrix([[G1], [G2], [G3]])
	>>> tfm1.var
	p
	>>> tfm2 = TransferFunctionMatrix([[-S1, -S2], [S1, S2]])
	>>> tfm2.var
	p
	>>> tfm3 = TransferFunctionMatrix([[G4]])
	>>> tfm3.var
	s

	"""
	return self.args[0][0][0].var

	@property
	def num_inputs(self):
	"""
	Returns the number of inputs of the system.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
	>>> G1 = TransferFunction(s + 3, s**2 - 3, s)
	>>> G2 = TransferFunction(4, s**2, s)
	>>> G3 = TransferFunction(p2 + s2, p - 3, s)
	>>> tfm_1 = TransferFunctionMatrix([[G2, -G1, G3], [-G2, -G1, -G3]])
	>>> tfm_1.num_inputs
	3

	See Also
	========

	num_outputs

	"""
	return self._expr_mat.shape[1]

	@property
	def num_outputs(self):
	"""
	Returns the number of outputs of the system.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunctionMatrix
	>>> from sympy import Matrix
	>>> M_1 = Matrix([[s], [1/s]])
	>>> TFM = TransferFunctionMatrix.from_Matrix(M_1, s)
	>>> print(TFM)
	TransferFunctionMatrix(((TransferFunction(s, 1, s),), (TransferFunction(1, s, s),)))
	>>> TFM.num_outputs
	2

	See Also
	========

	num_inputs

	"""
	return self._expr_mat.shape[0]

	@property
	def shape(self):
	"""
	Returns the shape of the transfer function matrix, that is, ``(# of outputs, # of inputs)``.

	Examples
	========

	>>> from sympy.abc import s, p
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
	>>> tf1 = TransferFunction(p2 - 1, s4 + s**3 - p, p)
	>>> tf2 = TransferFunction(1 - p, p*2 - 3p + 7, p)
	>>> tf3 = TransferFunction(3, 4, p)
	>>> tfm1 = TransferFunctionMatrix([[tf1, -tf2]])
	>>> tfm1.shape
	(1, 2)
	>>> tfm2 = TransferFunctionMatrix([[-tf2, tf3], [tf1, -tf1]])
	>>> tfm2.shape
	(2, 2)

	"""
	return self._expr_mat.shape

	def __neg__(self):
	neg = -self._expr_mat
	return _to_TFM(neg, self.var)

	@_check_other_MIMO
	def __add__(self, other):

	if not isinstance(other, MIMOParallel):
	return MIMOParallel(self, other)
	other_arg_list = list(other.args)
	return MIMOParallel(self, *other_arg_list)

	@_check_other_MIMO
	def __sub__(self, other):
	return self + (-other)

	@_check_other_MIMO
	def __mul__(self, other):

	if not isinstance(other, MIMOSeries):
	return MIMOSeries(other, self)
	other_arg_list = list(other.args)
	return MIMOSeries(*other_arg_list, self)

	def __getitem__(self, key):
	trunc = self._expr_mat.__getitem__(key)
	if isinstance(trunc, ImmutableMatrix):
	return _to_TFM(trunc, self.var)
	return TransferFunction.from_rational_expression(trunc, self.var)

	def transpose(self):
	"""Returns the transpose of the ``TransferFunctionMatrix`` (switched input and output layers)."""
	transposed_mat = self._expr_mat.transpose()
	return _to_TFM(transposed_mat, self.var)

	def elem_poles(self):
	"""
	Returns the poles of each element of the ``TransferFunctionMatrix``.

	.. note::
	Actual poles of a MIMO system are NOT the poles of individual elements.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
	>>> tf_1 = TransferFunction(3, (s + 1), s)
	>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
	>>> tf_3 = TransferFunction(s + 3, s*2 + 3s + 2, s)
	>>> tf_4 = TransferFunction(s + 2, s*2 + 5s - 10, s)
	>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
	>>> tfm_1
	TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)(s + 2), s)), (TransferFunction(s + 3, s2 + 3s + 2, s), TransferFunction(s + 2, s*2 + 5s - 10, s))))
	>>> tfm_1.elem_poles()
	[[[-1], [-2, -1]], [[-2, -1], [-5/2 + sqrt(65)/2, -sqrt(65)/2 - 5/2]]]

	See Also
	========

	elem_zeros

	"""
	return [[element.poles() for element in row] for row in self.doit().args[0]]

	def elem_zeros(self):
	"""
	Returns the zeros of each element of the ``TransferFunctionMatrix``.

	.. note::
	Actual zeros of a MIMO system are NOT the zeros of individual elements.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
	>>> tf_1 = TransferFunction(3, (s + 1), s)
	>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
	>>> tf_3 = TransferFunction(s + 3, s*2 + 3s + 2, s)
	>>> tf_4 = TransferFunction(s*2 - 9s + 20, s*2 + 5s - 10, s)
	>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
	>>> tfm_1
	TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)(s + 2), s)), (TransferFunction(s + 3, s2 + 3s + 2, s), TransferFunction(s*2 - 9s + 20, s*2 + 5s - 10, s))))
	>>> tfm_1.elem_zeros()
	[[[], [-6]], [[-3], [4, 5]]]

	See Also
	========

	elem_poles

	"""
	return [[element.zeros() for element in row] for row in self.doit().args[0]]

	def eval_frequency(self, other):
	"""
	Evaluates system response of each transfer function in the ``TransferFunctionMatrix`` at any point in the real or complex plane.

	Examples
	========

	>>> from sympy.abc import s
	>>> from sympy.physics.control.lti import TransferFunction, TransferFunctionMatrix
	>>> from sympy import I
	>>> tf_1 = TransferFunction(3, (s + 1), s)
	>>> tf_2 = TransferFunction(s + 6, (s + 1)*(s + 2), s)
	>>> tf_3 = TransferFunction(s + 3, s*2 + 3s + 2, s)
	>>> tf_4 = TransferFunction(s*2 - 9s + 20, s*2 + 5s - 10, s)
	>>> tfm_1 = TransferFunctionMatrix([[tf_1, tf_2], [tf_3, tf_4]])
	>>> tfm_1
	TransferFunctionMatrix(((TransferFunction(3, s + 1, s), TransferFunction(s + 6, (s + 1)(s + 2), s)), (TransferFunction(s + 3, s2 + 3s + 2, s), TransferFunction(s*2 - 9s + 20, s*2 + 5s - 10, s))))
	>>> tfm_1.eval_frequency(2)
	Matrix([
	[ 1, 2/3],
	[5/12, 3/2]])
	>>> tfm_1.eval_frequency(I*2)
	Matrix([
	[ 3/5 - 6*I/5, -I],
	[3/20 - 11I/20, -101/74 + 23I/74]])
	"""
	mat = self._expr_mat.subs(self.var, other)
	return mat.expand()

	def _flat(self):
	"""Returns flattened list of args in TransferFunctionMatrix"""
	return [elem for tup in self.args[0] for elem in tup]

	def _eval_evalf(self, prec):
	"""Calls evalf() on each transfer function in the transfer function matrix"""
	dps = prec_to_dps(prec)
	mat = self._expr_mat.applyfunc(lambda a: a.evalf(n=dps))
	return _to_TFM(mat, self.var)

	def _eval_simplify(self, **kwargs):
	"""Simplifies the transfer function matrix"""
	simp_mat = self._expr_mat.applyfunc(lambda a: cancel(a, expand=False))
	return _to_TFM(simp_mat, self.var)

	def expand(self, **hints):
	"""Expands the transfer function matrix"""
	expand_mat = self._expr_mat.expand(**hints)
	return _to_TFM(expand_mat, self.var)

	class StateSpace(LinearTimeInvariant):
	r"""
	State space model (ssm) of a linear, time invariant control system.

	Represents the standard state-space model with A, B, C, D as state-space matrices.
	This makes the linear control system:
	(1) x'(t) = A * x(t) + B * u(t); x in R^n , u in R^k
	(2) y(t) = C * x(t) + D * u(t); y in R^m
	where u(t) is any input signal, y(t) the corresponding output, and x(t) the system's state.

	Parameters
	==========

	A : Matrix
	The State matrix of the state space model.
	B : Matrix
	The Input-to-State matrix of the state space model.
	C : Matrix
	The State-to-Output matrix of the state space model.
	D : Matrix
	The Feedthrough matrix of the state space model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace

	The easiest way to create a StateSpaceModel is via four matrices:

	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> StateSpace(A, B, C, D)
	StateSpace(Matrix([
	[1, 2],
	[1, 0]]), Matrix([
	[1],
	[1]]), Matrix([[0, 1]]), Matrix([[0]]))


	One can use less matrices. The rest will be filled with a minimum of zeros:

	>>> StateSpace(A, B)
	StateSpace(Matrix([
	[1, 2],
	[1, 0]]), Matrix([
	[1],
	[1]]), Matrix([[0, 0]]), Matrix([[0]]))


	See Also
	========

	TransferFunction, TransferFunctionMatrix

	References
	==========
	.. [1] https://en.wikipedia.org/wiki/State-space_representation
	.. [2] https://in.mathworks.com/help/control/ref/ss.html

	"""
	def __new__(cls, A=None, B=None, C=None, D=None):
	if A is None:
	A = zeros(1)
	if B is None:
	B = zeros(A.rows, 1)
	if C is None:
	C = zeros(1, A.cols)
	if D is None:
	D = zeros(C.rows, B.cols)

	A = _sympify(A)
	B = _sympify(B)
	C = _sympify(C)
	D = _sympify(D)

	if (isinstance(A, ImmutableDenseMatrix) and isinstance(B, ImmutableDenseMatrix) and
	isinstance(C, ImmutableDenseMatrix) and isinstance(D, ImmutableDenseMatrix)):
	# Check State Matrix is square
	if A.rows != A.cols:
	raise ShapeError("Matrix A must be a square matrix.")

	# Check State and Input matrices have same rows
	if A.rows != B.rows:
	raise ShapeError("Matrices A and B must have the same number of rows.")

	# Check Ouput and Feedthrough matrices have same rows
	if C.rows != D.rows:
	raise ShapeError("Matrices C and D must have the same number of rows.")

	# Check State and Ouput matrices have same columns
	if A.cols != C.cols:
	raise ShapeError("Matrices A and C must have the same number of columns.")

	# Check Input and Feedthrough matrices have same columns
	if B.cols != D.cols:
	raise ShapeError("Matrices B and D must have the same number of columns.")

	obj = super(StateSpace, cls).__new__(cls, A, B, C, D)
	obj._A = A
	obj._B = B
	obj._C = C
	obj._D = D

	# Determine if the system is SISO or MIMO
	num_outputs = D.rows
	num_inputs = D.cols
	if num_inputs == 1 and num_outputs == 1:
	obj._is_SISO = True
	obj._clstype = SISOLinearTimeInvariant
	else:
	obj._is_SISO = False
	obj._clstype = MIMOLinearTimeInvariant

	return obj

	else:
	raise TypeError("A, B, C and D inputs must all be sympy Matrices.")

	@property
	def state_matrix(self):
	"""
	Returns the state matrix of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.state_matrix
	Matrix([
	[1, 2],
	[1, 0]])

	"""
	return self._A

	@property
	def input_matrix(self):
	"""
	Returns the input matrix of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.input_matrix
	Matrix([
	[1],
	[1]])

	"""
	return self._B

	@property
	def output_matrix(self):
	"""
	Returns the output matrix of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.output_matrix
	Matrix([[0, 1]])

	"""
	return self._C

	@property
	def feedforward_matrix(self):
	"""
	Returns the feedforward matrix of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.feedforward_matrix
	Matrix([[0]])

	"""
	return self._D

	@property
	def num_states(self):
	"""
	Returns the number of states of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.num_states
	2

	"""
	return self._A.rows

	@property
	def num_inputs(self):
	"""
	Returns the number of inputs of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.num_inputs
	1

	"""
	return self._D.cols

	@property
	def num_outputs(self):
	"""
	Returns the number of outputs of the model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[1, 2], [1, 0]])
	>>> B = Matrix([1, 1])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.num_outputs
	1

	"""
	return self._D.rows

	def _eval_evalf(self, prec):
	"""
	Returns state space model where numerical expressions are evaluated into floating point numbers.
	"""
	dps = prec_to_dps(prec)
	return StateSpace(
	self._A.evalf(n = dps),
	self._B.evalf(n = dps),
	self._C.evalf(n = dps),
	self._D.evalf(n = dps))

	def _eval_rewrite_as_TransferFunction(self, *args):
	"""
	Returns the equivalent Transfer Function of the state space model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import TransferFunction, StateSpace
	>>> A = Matrix([[-5, -1], [3, -1]])
	>>> B = Matrix([2, 5])
	>>> C = Matrix([[1, 2]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.rewrite(TransferFunction)
	[[TransferFunction(12s + 59, s2 + 6s + 8, s)]]

	"""
	s = Symbol('s')
	n = self._A.shape[0]
	I = eye(n)
	G = self._C(sI - self._A).solve(self._B) + self._D
	G = G.simplify()
	to_tf = lambda expr: TransferFunction.from_rational_expression(expr, s)
	tf_mat = [[to_tf(expr) for expr in sublist] for sublist in G.tolist()]
	return tf_mat

	def __add__(self, other):
	"""
	Add two State Space systems (parallel connection).

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A1 = Matrix([[1]])
	>>> B1 = Matrix([[2]])
	>>> C1 = Matrix([[-1]])
	>>> D1 = Matrix([[-2]])
	>>> A2 = Matrix([[-1]])
	>>> B2 = Matrix([[-2]])
	>>> C2 = Matrix([[1]])
	>>> D2 = Matrix([[2]])
	>>> ss1 = StateSpace(A1, B1, C1, D1)
	>>> ss2 = StateSpace(A2, B2, C2, D2)
	>>> ss1 + ss2
	StateSpace(Matrix([
	[1, 0],
	[0, -1]]), Matrix([
	[ 2],
	[-2]]), Matrix([[-1, 1]]), Matrix([[0]]))

	"""
	# Check for scalars
	if isinstance(other, (int, float, complex, Symbol)):
	A = self._A
	B = self._B
	C = self._C
	D = self._D.applyfunc(lambda element: element + other)

	else:
	# Check nature of system
	if not isinstance(other, StateSpace):
	raise ValueError("Addition is only supported for 2 State Space models.")
	# Check dimensions of system
	elif ((self.num_inputs != other.num_inputs) or (self.num_outputs != other.num_outputs)):
	raise ShapeError("Systems with incompatible inputs and outputs cannot be added.")

	m1 = (self._A).row_join(zeros(self._A.shape[0], other._A.shape[-1]))
	m2 = zeros(other._A.shape[0], self._A.shape[-1]).row_join(other._A)

	A = m1.col_join(m2)
	B = self._B.col_join(other._B)
	C = self._C.row_join(other._C)
	D = self._D + other._D

	return StateSpace(A, B, C, D)

	def __radd__(self, other):
	"""
	Right add two State Space systems.

	Examples
	========

	>>> from sympy.physics.control import StateSpace
	>>> s = StateSpace()
	>>> 5 + s
	StateSpace(Matrix([[0]]), Matrix([[0]]), Matrix([[0]]), Matrix([[5]]))

	"""
	return self + other

	def __sub__(self, other):
	"""
	Subtract two State Space systems.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A1 = Matrix([[1]])
	>>> B1 = Matrix([[2]])
	>>> C1 = Matrix([[-1]])
	>>> D1 = Matrix([[-2]])
	>>> A2 = Matrix([[-1]])
	>>> B2 = Matrix([[-2]])
	>>> C2 = Matrix([[1]])
	>>> D2 = Matrix([[2]])
	>>> ss1 = StateSpace(A1, B1, C1, D1)
	>>> ss2 = StateSpace(A2, B2, C2, D2)
	>>> ss1 - ss2
	StateSpace(Matrix([
	[1, 0],
	[0, -1]]), Matrix([
	[ 2],
	[-2]]), Matrix([[-1, -1]]), Matrix([[-4]]))

	"""
	return self + (-other)

	def __rsub__(self, other):
	"""
	Right subtract two tate Space systems.

	Examples
	========

	>>> from sympy.physics.control import StateSpace
	>>> s = StateSpace()
	>>> 5 - s
	StateSpace(Matrix([[0]]), Matrix([[0]]), Matrix([[0]]), Matrix([[5]]))

	"""
	return other + (-self)

	def __neg__(self):
	"""
	Returns the negation of the state space model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-5, -1], [3, -1]])
	>>> B = Matrix([2, 5])
	>>> C = Matrix([[1, 2]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> -ss
	StateSpace(Matrix([
	[-5, -1],
	[ 3, -1]]), Matrix([
	[2],
	[5]]), Matrix([[-1, -2]]), Matrix([[0]]))

	"""
	return StateSpace(self._A, self._B, -self._C, -self._D)

	def __mul__(self, other):
	"""
	Multiplication of two State Space systems (serial connection).

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-5, -1], [3, -1]])
	>>> B = Matrix([2, 5])
	>>> C = Matrix([[1, 2]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss*5
	StateSpace(Matrix([
	[-5, -1],
	[ 3, -1]]), Matrix([
	[2],
	[5]]), Matrix([[5, 10]]), Matrix([[0]]))

	"""
	# Check for scalars
	if isinstance(other, (int, float, complex, Symbol)):
	A = self._A
	B = self._B
	C = self._C.applyfunc(lambda element: element*other)
	D = self._D.applyfunc(lambda element: element*other)

	else:
	# Check nature of system
	if not isinstance(other, StateSpace):
	raise ValueError("Multiplication is only supported for 2 State Space models.")
	# Check dimensions of system
	elif self.num_inputs != other.num_outputs:
	raise ShapeError("Systems with incompatible inputs and outputs cannot be multiplied.")

	m1 = (other._A).row_join(zeros(other._A.shape[0], self._A.shape[1]))
	m2 = (self._B * other._C).row_join(self._A)

	A = m1.col_join(m2)
	B = (other._B).col_join(self._B * other._D)
	C = (self._D * other._C).row_join(self._C)
	D = self._D * other._D

	return StateSpace(A, B, C, D)

	def __rmul__(self, other):
	"""
	Right multiply two tate Space systems.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-5, -1], [3, -1]])
	>>> B = Matrix([2, 5])
	>>> C = Matrix([[1, 2]])
	>>> D = Matrix([0])
	>>> ss = StateSpace(A, B, C, D)
	>>> 5*ss
	StateSpace(Matrix([
	[-5, -1],
	[ 3, -1]]), Matrix([
	[10],
	[25]]), Matrix([[1, 2]]), Matrix([[0]]))

	"""
	if isinstance(other, (int, float, complex, Symbol)):
	A = self._A
	C = self._C
	B = self._B.applyfunc(lambda element: element*other)
	D = self._D.applyfunc(lambda element: element*other)
	return StateSpace(A, B, C, D)
	else:
	return self*other

	def __repr__(self):
	A_str = self._A.__repr__()
	B_str = self._B.__repr__()
	C_str = self._C.__repr__()
	D_str = self._D.__repr__()

	return f"StateSpace(\n{A_str},\n\n{B_str},\n\n{C_str},\n\n{D_str})"


	def append(self, other):
	"""
	Returns the first model appended with the second model. The order is preserved.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A1 = Matrix([[1]])
	>>> B1 = Matrix([[2]])
	>>> C1 = Matrix([[-1]])
	>>> D1 = Matrix([[-2]])
	>>> A2 = Matrix([[-1]])
	>>> B2 = Matrix([[-2]])
	>>> C2 = Matrix([[1]])
	>>> D2 = Matrix([[2]])
	>>> ss1 = StateSpace(A1, B1, C1, D1)
	>>> ss2 = StateSpace(A2, B2, C2, D2)
	>>> ss1.append(ss2)
	StateSpace(Matrix([
	[1, 0],
	[0, -1]]), Matrix([
	[2, 0],
	[0, -2]]), Matrix([
	[-1, 0],
	[ 0, 1]]), Matrix([
	[-2, 0],
	[ 0, 2]]))

	"""
	n = self.num_states + other.num_states
	m = self.num_inputs + other.num_inputs
	p = self.num_outputs + other.num_outputs

	A = zeros(n, n)
	B = zeros(n, m)
	C = zeros(p, n)
	D = zeros(p, m)

	A[:self.num_states, :self.num_states] = self._A
	A[self.num_states:, self.num_states:] = other._A
	B[:self.num_states, :self.num_inputs] = self._B
	B[self.num_states:, self.num_inputs:] = other._B
	C[:self.num_outputs, :self.num_states] = self._C
	C[self.num_outputs:, self.num_states:] = other._C
	D[:self.num_outputs, :self.num_inputs] = self._D
	D[self.num_outputs:, self.num_inputs:] = other._D
	return StateSpace(A, B, C, D)

	def observability_matrix(self):
	"""
	Returns the observability matrix of the state space model:
	[C, C * A^1, C * A^2, .. , C * A^(n-1)]; A in R^(n x n), C in R^(m x k)

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> ob = ss.observability_matrix()
	>>> ob
	Matrix([
	[0, 1],
	[1, 0]])

	References
	==========
	.. [1] https://in.mathworks.com/help/control/ref/statespacemodel.obsv.html

	"""
	n = self.num_states
	ob = self._C
	for i in range(1,n):
	ob = ob.col_join(self._C * self._A**i)

	return ob

	def observable_subspace(self):
	"""
	Returns the observable subspace of the state space model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> ob_subspace = ss.observable_subspace()
	>>> ob_subspace
	[Matrix([
	[0],
	[1]]), Matrix([
	[1],
	[0]])]

	"""
	return self.observability_matrix().columnspace()

	def is_observable(self):
	"""
	Returns if the state space model is observable.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.is_observable()
	True

	"""
	return self.observability_matrix().rank() == self.num_states

	def controllability_matrix(self):
	"""
	Returns the controllability matrix of the system:
	[B, A * B, A^2 * B, .. , A^(n-1) * B]; A in R^(n x n), B in R^(n x m)

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.controllability_matrix()
	Matrix([
	[0.5, -0.75],
	[ 0, 0.5]])

	References
	==========
	.. [1] https://in.mathworks.com/help/control/ref/statespacemodel.ctrb.html

	"""
	co = self._B
	n = self._A.shape[0]
	for i in range(1, n):
	co = co.row_join(((self._A)*i) self._B)

	return co

	def controllable_subspace(self):
	"""
	Returns the controllable subspace of the state space model.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> co_subspace = ss.controllable_subspace()
	>>> co_subspace
	[Matrix([
	[0.5],
	[ 0]]), Matrix([
	[-0.75],
	[ 0.5]])]

	"""
	return self.controllability_matrix().columnspace()

	def is_controllable(self):
	"""
	Returns if the state space model is controllable.

	Examples
	========

	>>> from sympy import Matrix
	>>> from sympy.physics.control import StateSpace
	>>> A = Matrix([[-1.5, -2], [1, 0]])
	>>> B = Matrix([0.5, 0])
	>>> C = Matrix([[0, 1]])
	>>> D = Matrix([1])
	>>> ss = StateSpace(A, B, C, D)
	>>> ss.is_controllable()
	True

	"""
	return self.controllability_matrix().rank() == self.num_states