ControlCorp
diff --git a/‎README.rst
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diff --git a/‎doc/conventions.rst
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diff --git a/‎doc/intro.rst
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@@ -22,14 +22,17 @@ Python Control Systems Library
 The Python Control Systems Library is a Python module that implements basic
 operations for analysis and design of feedback control systems.
 
-
 Have a go now!
-==============
+--------------
 Try out the examples in the examples folder using the binder service.
 
 .. image:: https://mybinder.org/badge_logo.svg
  :target: https://mybinder.org/v2/gh/python-control/python-control/HEAD
 
+The package can also be installed on Google Colab using the commands::
+
+  !pip install control
+  import control as ct
 
 Features
 --------
@@ -44,17 +47,16 @@ Features
 - Nonlinear systems: optimization-based control, describing functions, differential flatness
 
 Links
-=====
+-----
 
 - Project home page: http://python-control.org
 - Source code repository: https://github.com/python-control/python-control
 - Documentation: http://python-control.readthedocs.org/
 - Issue tracker: https://github.com/python-control/python-control/issues
 - Mailing list: http://sourceforge.net/p/python-control/mailman/
 
-
 Dependencies
-============
+------------
 
 The package requires numpy, scipy, and matplotlib.  In addition, some routines
 use a module called slycot, that 
10000
is a Python wrapper around some FORTRAN
@@ -64,6 +66,7 @@ functionality is limited or absent, and installation of slycot is recommended
 
 https://github.com/python-control/Slycot
 
+
 Installation
 ============
 
@@ -73,7 +76,7 @@ Conda and conda-forge
 The easiest way to get started with the Control Systems library is
 using `Conda <https://conda.io>`_.
 
-The Control Systems library has been packages for the `conda-forge
+The Control Systems library has packages available using the `conda-forge
 <https://conda-forge.org>`_ Conda channel, and as of Slycot version
 0.3.4, binaries for that package are available for 64-bit Windows,
 OSX, and Linux.
@@ -83,6 +86,10 @@ conda environment, run::
 
   conda install -c conda-forge control slycot
 
+Mixing packages from conda-forge and the default conda channel can
+sometimes cause problems with dependencies, so it is usually best to
+instally NumPy, SciPy, and Matplotlib from conda-forge as well.
+
 Pip
 ---
 
@@ -92,7 +99,8 @@ To install using pip::
   pip install control
 
 If you install Slycot using pip you'll need a development environment
-(e.g., Python development files, C and Fortran compilers).
+(e.g., Python development files, C and Fortran compilers).  Pip
+installation can be particularly complicated for Windows.
 
 Installing from source
 ----------------------
@@ -106,11 +114,13 @@ toplevel `python-control` directory::
 Article and Citation Information
 ================================
 
-An `article <https://ieeexplore.ieee.org/abstract/document/9683368>`_ about the library is available on IEEE Explore. If the Python Control Systems Library helped you in your research, please cite::
+An `article <https://ieeexplore.ieee.org/abstract/document/9683368>`_ about
+the library is available on IEEE Explore. If the Python Control Systems Library helped you in your research, please cite::
 
   @inproceedings{python-control2021,
     title={The Python Control Systems Library (python-control)},
+    author={Fuller, Sawyer and Greiner, Ben and Moore, Jason and
+            Murray, Richard and van Paassen, Ren{\'e} and Yorke, Rory},
     booktitle={60th IEEE Conference on Decision and Control (CDC)},
     pages={4875--4881},
     year={2021},
 
@@ -6,8 +6,11 @@
 Library conventions
 *******************
 
-The python-control library uses a set of standard conventions for the way
-that different types of standard information used by the library.
+The python-control library uses a set of standard conventions for the
+way that different types of standard information used by the library.
+Throughout this manual, we assume the `control` package has been
+imported as `ct`.
+
 
 LTI system representation
 =========================
@@ -29,7 +32,7 @@ of linear time-invariant (LTI) systems:
 
 where u is the input, y is the output, and x is the state.
 
-To create a state space system, use the :func:`ss` function:
+To create a state space system, use the :func:`ss` function::
 
   sys = ct.ss(A, B, C, D)
 
@@ -51,7 +54,7 @@ transfer functions
 where n is generally greater than or equal to m (for a proper transfer
 function).
 
-To create a transfer function, use the :func:`tf` function:
+To create a transfer function, use the :func:`tf` function::
 
   sys = ct.tf(num, den)
 
@@ -77,7 +80,7 @@ performed.
 The FRD class is also used as the return type for the
 :func:`frequency_response` function (and the equivalent method for the
 :class:`StateSpace` and :class:`TransferFunction` classes).  This
-object can be assigned to a tuple using
+object can be assigned to a tuple using::
 
     mag, phase, omega = response
 
@@ -91,7 +94,7 @@ is not SISO or `squeeze` is False, the array is 3D, indexed by the
 output, input, and frequency.  If `squeeze` is True then
 single-dimensional axes are removed.  The processing of the `squeeze`
 keyword can be changed by calling the response function with a new
-argument:
+argument::
 
     mag, phase, omega = response(squeeze=False)
 
@@ -101,10 +104,10 @@ Discrete time systems
 A discrete time system is created by specifying a nonzero 'timebase', dt.
 The timebase argument can be given when a system is constructed:
 
-* dt = 0: continuous time system (default)
-* dt > 0: discrete time system with sampling period 'dt'
-* dt = True: discrete time with unspecified sampling period
-* dt = None: no timebase specified
+* `dt = 0`: continuous time system (default)
+* `dt > 0`: discrete time system with sampling period 'dt'
+* `dt = True`: discrete time with unspecified sampling period
+* `dt = None`: no timebase specified
 
 Only the :class:`StateSpace`, :class:`TransferFunction`, and
 :class:`InputOutputSystem` classes allow explicit representation of
@@ -119,8 +122,8 @@ result will have the timebase of the latter system. For continuous time
 systems, the :func:`sample_system` function or the :meth:`StateSpace.sample`
 and :meth:`TransferFunction.sample` methods can be used to create a discrete
 time system from a continuous time system.  See
-:ref:`utility-and-conversions`. The default value of 'dt' can be changed by
-changing the value of ``control.config.defaults['control.default_dt']``.
+:ref:`utility-and-conversions`. The default value of `dt` can be changed by
+changing the value of `control.config.defaults['control.default_dt']`.
 
 Conversion between representations
 ----------------------------------
@@ -165,10 +168,9 @@ points in time, rows are different components::
            ...
            [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.
+(and similarly 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.
 
 When there is only one row, a 1D object is accepted or returned, which adds
 convenience for SISO systems:
@@ -185,8 +187,10 @@ Functions that return time responses (e.g., :func:`forced_response`,
 :func:`impulse_response`, :func:`input_output_response`,
 :func:`initial_response`, and :func:`step_response`) return a
 :class:`TimeResponseData` object that contains the data for the time
-response.  These data can be accessed via the ``time``, ``outputs``,
-``states`` and ``inputs`` properties::
+response.  These data can be accessed via the
+:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`,
+:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs`
+properties::
 
     sys = ct.rss(4, 1, 1)
     response = ct.step_response(sys)
@@ -213,13 +217,13 @@ The output of a MIMO LTI system can be plotted like this::
     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 (2D array) of a linear system,
-and ``U`` is its input (array), then the feedthrough part of the system's
+systems. If `D` is the feedthrough matrix (2D array) of a linear system,
+and `U` is its input (array), then the feedthrough part of the system's
 response, can be computed like this::
 
     ft = D @ U
 
-Finally, the `to_pandas()` function can be used to create a pandas dataframe:
+Finally, the `to_pandas()` function can be used to create a pandas dataframe::
 
     df = response.to_pandas()
 
@@ -242,16 +246,12 @@ for various types of plots and establishing the underlying representation for
 state space matrices.
 
 To set the default value of a configuration variable, set the appropriate
-element of the `control.config.defaults` dictionary:
-
-.. code-block:: python
+element of the `control.config.defaults` dictionary::
 
     ct.config.defaults['module.parameter'] = value
 
 The `~control.config.set_defaults` function can also be used to set multiple
-configuration parameters at the same time:
-
-.. code-block:: python
+configuration parameters at the same time::
 
     ct.config.set_defaults('module', param1=val1, param2=val2, ...]
 
 
@@ -31,23 +31,43 @@ some thing to keep in mind:
 * You must include commas in vectors.  So [1 2 3] must be [1, 2, 3].
 * Functions that return multiple arguments use tuples.  
 * You cannot use braces for collections; use tuples instead.
+* Time series data have time as the final index (see
+  :ref:`time-series-convention`).
 
 Installation
 ============
 
-The `python-control` package can be installed using pip, conda or the
-standard setuptools mechanisms.  The package requires `numpy`_ and
-`scipy`_, and the plotting routines require `matplotlib
-<https://matplotlib.org>`_.  In addition, some routines require the `slycot
-<https://github.com/python-control/Slycot>`_ library in order to implement
-more advanced features (including some MIMO functionality).
+The `python-control` package can be installed using conda or pip.  The
+package requires `NumPy`_ and `SciPy`_, and the plotting routines
+require `Matplotlib <https://matplotlib.org>`_.  In addition, some
+routines require the `Slycot
+<https://github.com/python-control/Slycot>`_ library in order to
+implement more advanced features (including some MIMO functionality).
 
+For users with the Anaconda distribution of Python, the following
+command can be used::
+
+  conda install -c conda-forge control slycot
+
+This installs `slycot` and `python-control` from conda-forge, including the
+`openblas` package.  NumPy, SciPy, and Matplotlib will also be installed if
+they are not already present.
+
+.. note::
+   Mixing packages from conda-forge and the default conda channel
+   can sometimes cause problems with dependencies, so it is usually best to
+   instally NumPy, SciPy, and Matplotlib from conda-forge as well.)
 
 To install using pip::
 
   pip install slycot   # optional
   pip install control
 
+.. note::
+   If you install Slycot using pip you'll need a development
+   environment (e.g., Python development files, C and Fortran compilers).
+   Pip installation can be particularly complicated for Windows.
+
 Many parts of `python-control` will work without `slycot`, but some
 functionality is limited or absent, and installation of `slycot` is
 recommended. Users can check to insure that slycot is installed
@@ -56,36 +76,22 @@ correctly by running the command::
   python -c "import slycot"
 
 and verifying that no error message appears. More information on the 
-slycot package can be obtained from the `slycot project page
+Slycot package can be obtained from the `Slycot project page
 <https://github.com/python-control/Slycot>`_.
 
-For users with the Anaconda distribution of Python, the following
-commands can be used::
-
-  conda install numpy scipy matplotlib    # if not yet installed
-  conda install -c conda-forge control slycot
-
-This installs `slycot` and `python-control` from conda-forge, including the
-`openblas` package.
-
-Alternatively, to use setuptools, first `download the source
+Alternatively, to install from source, first `download the source
 <https://github.com/python-control/python-control/releases>`_ and unpack it.
 To install in your home directory, use::
 
-  python setup.py install --user
-
-or to install for all users (on Linux or Mac OS)::
-
-  python setup.py build
-  sudo python setup.py install
+  pip install .
 
 Getting started
 ===============
 
 There are two different ways to use the package.  For the default interface
 described in :ref:`function-ref`, simply import the control package as follows::
 
-    >>> import control
+    >>> import control as ct
 
 If you want to have a MATLAB-like environment, use the :ref:`matlab-module`::