Dec 31, 2016 · Dec 27, 2016 · Dec 27, 2016 · Dec 27, 2016 · Dec 27, 2016 · Dec 27, 2016
diff --git a/README.rst b/README.rst
 (see below).  Note that in order to install slycot, you will need a FORTRAN
 compiler on your machine.  The Slycot wrapper can be found at:

 https://github.com/jgoppert/Slycot
 https://github.com/python-control/Slycot

 Installation
 ============
diff --git a/control/__init__.py b/control/__init__.py
 from .ctrlutil import *
 from .frdata import *
 from .canonical import *
 from .robust import *
 from .config import *

 # Exceptions
 from .exception import *
diff --git a/control/config.py b/control/config.py

 # Set defaults to match MATLAB
 def use_matlab_defaults():
    """
    Use MATLAB compatible configuration settings
        * Bode plots plot gain in dB, phase in degrees, frequency in Hertz
    """
    # Bode plot defaults
    global bode_dB; bode_dB = True
    global bode_deg; bode_deg = True
    global bode_Hz; bode_Hz = True

 # Set defaults to match FBS (Astrom and Murray)
 def use_fbs_defaults():
    """
    Use `Astrom and Murray <http://fbsbook.org>`_ compatible settings
        * Bode plots plot gain in powers of ten, phase in degrees,
          frequency in Hertz
    """
    # Bode plot defaults
    global bode_dB; bode_dB = False
    global bode_deg; bode_deg = True
diff --git a/control/dtime.py b/control/dtime.py
    Ts : real
        Sampling period
    method : string
        Method to use for conversion: 'matched' (default), 'tustin', 'zoh'
        Method to use for conversion: 'matched', 'tustin', 'zoh' (default)

    Returns
    -------
diff --git a/control/frdata.py b/control/frdata.py
                    sys.__class__)

 def frd(*args):
 '''
 """
    Construct a Frequency Response Data model, or convert a system

    frd models store the (measured) frequency response of a system.
    See Also
    --------
    ss, tf
 '''
 """
    return FRD(*args)
diff --git a/control/timeresp.py b/control/timeresp.py
 # timeresp.py - time-domain simulation routes
 """
 Time domain simulation.

 This file contains a collection of functions that calculate
 time responses for linear systems.

 .. _time-series-convention:

 Convention for Time Series
 --------------------------

 This is a convention for function arguments and return values that
 represent time series: sequences of values that change over time. It
 is used throughout the library, for example in the functions
 :func:`forced_response`, :func:`step_response`, :func:`impulse_response`,
 and :func:`initial_response`.

 .. note::
    This convention is different from the convention used in the library
    :mod:`scipy.signal`. In Scipy's convention the meaning of rows and columns
    is interchanged.  Thus, all 2D values must be transposed when they are
    used with functions from :mod:`scipy.signal`.

 Types:

    * **Arguments** can be **arrays**, **matrices**, or **nested lists**.
    * **Return values** are **arrays** (not matrices).

 The time vector is either 1D, or 2D with shape (1, n)::

      T = [[t1,     t2,     t3,     ..., tn    ]]

 Input, state, and output all follow the same convention. Columns are different
 points in time, rows are different components. When there is only one row, a
 1D object is accepted or returned, which adds convenience for SISO systems::

      U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)]
           [u2(t1), u2(t2), u2(t3), ..., u2(tn)]
           ...
           ...
           [ui(t1), ui(t2), ui(t3), ..., ui(tn)]]
 """Time domain simulation.

      Same for X, Y
 This file contains a collection of functions that calculate time
 responses for linear systems.

 So, U[:,2] is the system's input at the third point intime; and U[1] or U[1,:]
 is the sequence of values for the system's second input.
 See doc/conventions.rst#time-series-conventions_ for more information
 on how time series data are represented.

 The initial conditions are either 1D, or 2D with shape (j, 1)::

     X0 = [[x1]
           [x2]
           ...
           ...
           [xj]]

 As all simulation functions return *arrays*, plotting is convenient::

    t, y = step(sys)
    plot(t, y)

 The output of a MIMO system can be plotted like this::

    t, y, x = lsim(sys, u, t)
    plot(t, y[0], label='y_0')
    plot(t, y[1], label='y_1')

 The convention also works well with the state space form of linear systems. If
 ``D`` is the feedthrough *matrix* of a linear system, and ``U`` is its input
 (*matrix* or *array*), then the feedthrough part of the system's response,
 can be computed like this::

    ft = D * U

 ----------------------------------------------------------------
 """

 """Copyright (c) 2011 by California Institute of Technology
        If True, transpose all input and output arrays (for backward
        compatibility with MATLAB and scipy.signal.lsim)

    return_x: bool
        If True, return the state vector (default = False).

    Returns
    -------
    T: array
        If True, transpose all input and output arrays (for backward
        compatibility with MATLAB and scipy.signal.lsim)

    return_x: bool
        If True, return the state vector (default = False).

    Returns
    -------
    T: array
        If True, transpose all input and output arrays (for backward
        compatibility with MATLAB and scipy.signal.lsim)

    return_x: bool
        If True, return the state vector (default = False).

    Returns
    -------
    T: array
diff --git a/doc/control.rst b/doc/control.rst

    ss
    tf
    frd
    rss
    drss

 System interconnections
 =======================
 .. autosummary::
   :toctree: generated/

   append
   connect
   feedback
   negate
   parallel
   series

 Frequency domain plotting
 =========================

 .. autosummary::
    :toctree: generated/

    bode
    bode_plot
    nyquist
    nyquist_plot
    gangof4
    gangof4_plot
    nichols
    nichols_plot

 Time domain simulation
    step_response
    phase_plot

 .. _time-series-convention:

 Convention for Time Series
 --------------------------

 This is a convention for function arguments and return values that
 represent time series: sequences of values that change over time. It
 is used throughout the library, for example in the functions
 :func:`forced_response`, :func:`step_response`, :func:`impulse_response`,
 and :func:`initial_response`.

 .. note::
    This convention is different from the convention used in the library
    :mod:`scipy.signal`. In Scipy's convention the meaning of rows and columns
    is interchanged.  Thus, all 2D values must be transposed when they are
    used with functions from :mod:`scipy.signal`.

 Types:

    * **Arguments** can be **arrays**, **matrices**, or **nested lists**.
    * **Return values** are **arrays** (not matrices).

 The time vector is either 1D, or 2D with shape (1, n)::

      T = [[t1,     t2,     t3,     ..., tn    ]]

 Input, state, and output all follow the same convention. Columns are different
 points in time, rows are different components. When there is only one row, a
 1D object is accepted or returned, which adds convenience for SISO systems::

      U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)]
           [u2(t1), u2(t2), u2(t3), ..., u2(tn)]
           ...
           ...
           [ui(t1), ui(t2), ui(t3), ..., ui(tn)]]

      Same for X, Y

 So, U[:,2] is the system's input at the third point in time; and U[1] or U[1,:]
 is the sequence of values for the system's second input.

 The initial conditions are either 1D, or 2D with shape (j, 1)::

     X0 = [[x1]
           [x2]
           ...
           ...
           [xj]]

 As all simulation functions return *arrays*, plotting is convenient::

    t, y = step(sys)
    plot(t, y)

 The output of a MIMO system can be plotted like this::

    t, y, x = lsim(sys, u, t)
    plot(t, y[0], label='y_0')
    plot(t, y[1], label='y_1')

 The convention also works well with the state space form of linear systems. If
 ``D`` is the feedthrough *matrix* of a linear system, and ``U`` is its input
 (*matrix* or *array*), then the feedthrough part of the system's response,
 can be computed like this::

    ft = D * U


 Block diagram algebra
 =====================
 .. autosummary::
    :toctree: generated/

    acker
    h2syn
    hinfsyn
    lqr
    place

    unwrap
    db2mag
    mag2db
    damp
    isctime
    isdtime
    issiso
    issys
    pade
    sample_system
    canonical_form
    observable_form
    reachable_form
    ss2tf
    ssdata
Original file line number	Diff line number	Diff line change
Expand Up		@@ -41,7 +41,7 @@ functionality is limited or absent, and installation of slycot is recommended
		(see below). Note that in order to install slycot, you will need a FORTRAN
		compiler on your machine. The Slycot wrapper can be found at:

		https://github.com/jgoppert/Slycot
		https://github.com/python-control/Slycot

		Installation
		============
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -65,6 +65,8 @@
		from .ctrlutil import *
		from .frdata import *
		from .canonical import *
		from .robust import *
		from .config import *

		# Exceptions
		from .exception import *
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -16,13 +16,22 @@

		# Set defaults to match MATLAB
		def use_matlab_defaults():
		"""
		Use MATLAB compatible configuration settings
		* Bode plots plot gain in dB, phase in degrees, frequency in Hertz
		"""
		# Bode plot defaults
		global bode_dB; bode_dB = True
		global bode_deg; bode_deg = True
		global bode_Hz; bode_Hz = True

		# Set defaults to match FBS (Astrom and Murray)
		def use_fbs_defaults():
		"""
		Use `Astrom and Murray <http://fbsbook.org>`_ compatible settings
		* Bode plots plot gain in powers of ten, phase in degrees,
		frequency in Hertz
		"""
		# Bode plot defaults
		global bode_dB; bode_dB = False
		global bode_deg; bode_deg = True
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -65,7 +65,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None):
		Ts : real
		Sampling period
		method : string
		Method to use for conversion: 'matched' (default), 'tustin', 'zoh'
		Method to use for conversion: 'matched', 'tustin', 'zoh' (default)

		Returns
		-------
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -469,7 +469,7 @@ def _convertToFRD(sys, omega, inputs=1, outputs=1):
		sys.__class__)

		def frd(*args):
		'''
		"""
		Construct a Frequency Response Data model, or convert a system

		frd models store the (measured) frequency response of a system.
Expand DownExpand Up		@@ -501,5 +501,5 @@ def frd(*args):
		See Also
		--------
		ss, tf
		'''
		"""
		return FRD(*args)
Original file line number	Diff line number	Diff line change
		@@ -1,78 +1,12 @@
		# timeresp.py - time-domain simulation routes
		"""
		Time domain simulation.

		This file contains a collection of functions that calculate
		time responses for linear systems.

		.. _time-series-convention:

		Convention for Time Series
		--------------------------

		This is a convention for function arguments and return values that
		represent time series: sequences of values that change over time. It
		is used throughout the library, for example in the functions
		:func:`forced_response`, :func:`step_response`, :func:`impulse_response`,
		and :func:`initial_response`.

		.. note::
		This convention is different from the convention used in the library
		:mod:`scipy.signal`. In Scipy's convention the meaning of rows and columns
		is interchanged. Thus, all 2D values must be transposed when they are
		used with functions from :mod:`scipy.signal`.

		Types:

		* Arguments can be arrays, matrices, or nested lists.
		* Return values are arrays (not matrices).

		The time vector is either 1D, or 2D with shape (1, n)::

		T = [[t1, t2, t3, ..., tn ]]

		Input, state, and output all follow the same convention. Columns are different
		points in time, rows are different components. When there is only one row, a
		1D object is accepted or returned, which adds convenience for SISO systems::

		U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)]
		[u2(t1), u2(t2), u2(t3), ..., u2(tn)]
		...
		...
		[ui(t1), ui(t2), ui(t3), ..., ui(tn)]]
		"""Time domain simulation.

		Same for X, Y
		This file contains a collection of functions that calculate time
		responses for linear systems.

		So, U[:,2] is the system's input at the third point intime; and U[1] or U[1,:]
		is the sequence of values for the system's second input.
		See doc/conventions.rst#time-series-conventions_ for more information
		on how time series data are represented.

		The initial conditions are either 1D, or 2D with shape (j, 1)::

		X0 = [[x1]
		[x2]
		...
		...
		[xj]]

		As all simulation functions return arrays, plotting is convenient::

		t, y = step(sys)
		plot(t, y)

		The output of a MIMO system can be plotted like this::

		t, y, x = lsim(sys, u, t)
		plot(t, y[0], label='y_0')
		plot(t, y[1], label='y_1')

		The convention also works well with the state space form of linear systems. If
		``D`` is the feedthrough matrix of a linear system, and ``U`` is its input
		(matrix or array), then the feedthrough part of the system's response,
		can be computed like this::

		ft = D * U

		----------------------------------------------------------------
		"""

		"""Copyright (c) 2011 by California Institute of Technology
Expand DownExpand Up		@@ -453,6 +387,9 @@ def step_response(sys, T=None, X0=0., input=None, output=None,
		If True, transpose all input and output arrays (for backward
		compatibility with MATLAB and scipy.signal.lsim)

		return_x: bool
		If True, return the state vector (default = False).

		Returns
		-------
		T: array
Expand DownExpand Up		@@ -529,6 +466,9 @@ def initial_response(sys, T=None, X0=0., input=0, output=None,
		If True, transpose all input and output arrays (for backward
		compatibility with MATLAB and scipy.signal.lsim)

		return_x: bool
		If True, return the state vector (default = False).

		Returns
		-------
		T: array
Expand DownExpand Up		@@ -599,6 +539,9 @@ def impulse_response(sys, T=None, X0=0., input=0, output=None,
		If True, transpose all input and output arrays (for backward
		compatibility with MATLAB and scipy.signal.lsim)

		return_x: bool
		If True, return the state vector (default = False).

		Returns
		-------
		T: array
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -17,22 +17,31 @@ System creation

		ss
		tf
		frd
		rss
		drss

		System interconnections
		=======================
		.. autosummary::
		:toctree: generated/

		append
		connect
		feedback
		negate
		parallel
		series

		Frequency domain plotting
		=========================

		.. autosummary::
		:toctree: generated/

		bode
		bode_plot
		nyquist
		nyquist_plot
		gangof4
		gangof4_plot
		nichols
		nichols_plot

		Time domain simulation
Expand All		@@ -47,74 +56,6 @@ Time domain simulation
		step_response
		phase_plot

		.. _time-series-convention:

		Convention for Time Series
		--------------------------

		This is a convention for function arguments and return values that
		represent time series: sequences of values that change over time. It
		is used throughout the library, for example in the functions
		:func:`forced_response`, :func:`step_response`, :func:`impulse_response`,
		and :func:`initial_response`.

		.. note::
		This convention is different from the convention used in the library
		:mod:`scipy.signal`. In Scipy's convention the meaning of rows and columns
		is interchanged. Thus, all 2D values must be transposed when they are
		used with functions from :mod:`scipy.signal`.

		Types:

		* Arguments can be arrays, matrices, or nested lists.
		* Return values are arrays (not matrices).

		The time vector is either 1D, or 2D with shape (1, n)::

		T = [[t1, t2, t3, ..., tn ]]

		Input, state, and output all follow the same convention. Columns are different
		points in time, rows are different components. When there is only one row, a
		1D object is accepted or returned, which adds convenience for SISO systems::

		U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)]
		[u2(t1), u2(t2), u2(t3), ..., u2(tn)]
		...
		...
		[ui(t1), ui(t2), ui(t3), ..., ui(tn)]]

		Same for X, Y

		So, U[:,2] is the system's input at the third point in time; and U[1] or U[1,:]
		is the sequence of values for the system's second input.

		The initial conditions are either 1D, or 2D with shape (j, 1)::

		X0 = [[x1]
		[x2]
		...
		...
		[xj]]

		As all simulation functions return arrays, plotting is convenient::

		t, y = step(sys)
		plot(t, y)

		The output of a MIMO system can be plotted like this::

		t, y, x = lsim(sys, u, t)
		plot(t, y[0], label='y_0')
		plot(t, y[1], label='y_1')

		The convention also works well with the state space form of linear systems. If
		``D`` is the feedthrough matrix of a linear system, and ``U`` is its input
		(matrix or array), then the feedthrough part of the system's response,
		can be computed like this::

		ft = D * U


		Block diagram algebra
		=====================
		.. autosummary::
Expand DownExpand Up		@@ -160,6 +101,8 @@ Control system synthesis
		:toctree: generated/

		acker
		h2syn
		hinfsyn
		lqr
		place

Expand All		@@ -183,12 +126,15 @@ Utility functions and conversions
		unwrap
		db2mag
		mag2db
		damp
		isctime
		isdtime
		issiso
		issys
		pade
		sample_system
		canonical_form
		observable_form
		reachable_form
		ss2tf
		ssdata
Expand Down