Merge pull request #643 from basicmachines/master

Various docstring edits + fixed plot legends on cruise control example

control/bdalg.py

@@ -263,7 +263,7 @@ def append(*sys):
 
     Parameters
     ----------
-    sys1, sys2, ..., sysn: StateSpace or Transferfunction
+    sys1, sys2, ..., sysn: StateSpace or TransferFunction
         LTI systems to combine
 
 
@@ -275,7 +275,7 @@ def append(*sys):
 
     Examples
     --------
-    >>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]]", [[6., 8]], [[9.]])
+    >>> sys1 = ss([[1., -2], [3., -4]], [[5.], [7]], [[6., 8]], [[9.]])
     >>> sys2 = ss([[-1.]], [[1.]], [[1.]], [[0.]])
     >>> sys = append(sys1, sys2)
 
@@ -299,7 +299,7 @@ def connect(sys, Q, inputv, outputv):
 
     Parameters
     ----------
-    sys : StateSpace Transferfunction
+    sys : StateSpace or TransferFunction
         System to be connected
     Q : 2D array
         Interconnection matrix. First column gives the input to be connected.

control/iosys.py

@@ -131,7 +131,7 @@ def __init__(self, inputs=None, outputs=None, states=None, params={},
                  name=None, **kwargs):
         """Create an input/output system.
 
-        The InputOutputSystem contructor is used to create an input/output
+        The InputOutputSystem constructor is used to create an input/output
         object with the core information required for all input/output
         systems.  Instances of this class are normally created by one of the
         input/output subclasses: :class:`~control.LinearICSystem`,
@@ -661,7 +661,7 @@ def copy(self, newname=None):
 class LinearIOSystem(InputOutputSystem, StateSpace):
     """Input/output representation of a linear (state space) system.
 
-    This class is used to implementat a system that is a linear state
+    This class is used to implement a system that is a linear state
     space system (defined by the StateSpace system object).
 
     Parameters
@@ -1675,7 +1675,7 @@ def find_eqpt(sys, x0, u0=[], y0=None, t=0, params={},
               return_y=False, return_result=False, **kw):
     """Find the equilibrium point for an input/output system.
 
-    Returns the value of an equlibrium point given the initial state and
+    Returns the value of an equilibrium point given the initial state and
     either input value or desired output value for the equilibrium point.
 
     Parameters
@@ -1926,15 +1926,15 @@ def linearize(sys, xeq, ueq=[], t=0, params={}, **kw):
 
     This function computes the linearization of an input/output system at a
     given state and input value and returns a :class:`~control.StateSpace`
-    object.  The eavaluation point need not be an equilibrium point.
+    object.  The evaluation point need not be an equilibrium point.
 
     Parameters
     ----------
     sys : InputOutputSystem
         The system to be linearized
     xeq : array
         The state at which the linearization will be evaluated (does not need
-        to be an equlibrium state).
+        to be an equilibrium state).
     ueq : array
         The input at which the linearization will be evaluated (does not need
         to correspond to an equlibrium state).
@@ -2055,7 +2055,7 @@ def interconnect(syslist, connections=None, inplist=[], outlist=[],
         ('sys', 'sig') are also recognized.
 
         Similarly, each output-spec should describe an output signal from one
-        of the susystems.  The lowest level representation is a tuple of the
+        of the subsystems.  The lowest level representation is a tuple of the
         form `(subsys_i, out_j, gain)`.  The input will be constructed by
         summing the listed outputs after multiplying by the gain term.  If the
         gain term is omitted, it is assumed to be 1.  If the system has a

control/optimal.py

@@ -25,7 +25,7 @@ class OptimalControlProblem():
     """Description of a finite horizon, optimal control problem.
 
     The `OptimalControlProblem` class holds all of the information required to
-    specify and optimal control problem: the system dynamics, cost function,
+    specify an optimal control problem: the system dynamics, cost function,
     and constraints.  As much as possible, the information used to specify an
     optimal control problem matches the notation and terminology of the SciPy
     `optimize.minimize` module, with the hope that this makes it easier to
@@ -94,13 +94,13 @@ class OptimalControlProblem():
     The `_cost_function` method takes the information computes the cost of the
     trajectory generated by the proposed input.  It does this by calling a
     user-defined function for the integral_cost given the current states and
-    inputs at each point along the trajetory and then adding the value of a
+    inputs at each point along the trajectory and then adding the value of a
     user-defined terminal cost at the final pint in the trajectory.
 
     The `_constraint_function` method evaluates the constraint functions along
     the trajectory generated by the proposed input.  As in the case of the
     cost function, the constraints are evaluated at the state and input along
-    each point on the trjectory.  This information is compared against the
+    each point on the trajectory.  This information is compared against the
     constraint upper and lower bounds.  The constraint function is processed
     in the class initializer, so that it only needs to be computed once.
 
@@ -567,7 +567,7 @@ def _process_initial_guess(self, initial_guess):
     #
     # Initially guesses from the user are passed as input vectors as a
     # function of time, but internally we store the guess in terms of the
-    # basis coefficients.  We do this by solving a least squares probelm to
+    # basis coefficients.  We do this by solving a least squares problem to
     # find coefficients that match the input functions at the time points (as
     # much as possible, if the problem is under-determined).
     #
@@ -880,7 +880,7 @@ def solve_ocp(
         Function that returns the terminal cost given the current state
         and input.  Called as terminal_cost(x, u).
 
-    terminal_constraint : list of tuples, optional
+    terminal_constraints : list of tuples, optional
         List of constraints that should hold at the end of the trajectory.
         Same format as `constraints`.
 
@@ -914,7 +914,7 @@ def solve_ocp(
     res : OptimalControlResult
         Bundle object with the results of the optimal control problem.
 
-    res.success: bool
+    res.success : bool
         Boolean flag indicating whether the optimization was successful.
 
     res.time : array
@@ -982,7 +982,7 @@ def create_mpc_iosystem(
         Function that returns the terminal cost given the current state
         and input.  Called as terminal_cost(x, u).
 
-    terminal_constraint : list of tuples, optional
+    terminal_constraints : list of tuples, optional
         List of constraints that should hold at the end of the trajectory.
         Same format as `constraints`.
 
@@ -992,7 +992,7 @@ def create_mpc_iosystem(
     Returns
     -------
     ctrl : InputOutputSystem
-        An I/O system taking the currrent state of the model system and
+        An I/O system taking the current state of the model system and
         returning the current input to be applied that minimizes the cost
         function while satisfying the constraints.
 
@@ -1039,9 +1039,9 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
     R : 2D array_like
         Weighting matrix for input cost.  Dimensions must match system input.
     x0 : 1D array
-        Nomimal value of the system state (for which cost should be zero).
+        Nominal value of the system state (for which cost should be zero).
     u0 : 1D array
-        Nomimal value of the system input (for which cost should be zero).
+        Nominal value of the system input (for which cost should be zero).
 
     Returns
     -------
@@ -1082,7 +1082,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0):
 # As in the cost function evaluation, the main "trick" in creating a constrain
 # on the state or input is to properly evaluate the constraint on the stacked
 # state and input vector at the current time point.  The constraint itself
-# will be called at each poing along the trajectory (or the endpoint) via the
+# will be called at each point along the trajectory (or the endpoint) via the
 # constrain_function() method.
 #
 # Note that these functions to not actually evaluate the constraint, they
@@ -1250,7 +1250,7 @@ def input_range_constraint(sys, lb, ub):
 def output_poly_constraint(sys, A, b):
     """Create output constraint from polytope
 
-    Creates a linear constraint on the system ouput of the form A y <= b that
+    Creates a linear constraint on the system output of the form A y <= b that
     can be used as an optimal control constraint (trajectory or terminal).
 
     Parameters

control/statefbk.py

@@ -277,9 +277,9 @@ def lqe(A, G, C, QN, RN, NN=None):
 
     .. math:: x_e = A x_e + B u + L(y - C x_e - D u)
 
-    produces a state estimate that x_e that minimizes the expected squared
-    error using the sensor measurements y. The noise cross-correlation `NN`
-    is set to zero when omitted.
+    produces a state estimate x_e that minimizes the expected squared error
+    using the sensor measurements y. The noise cross-correlation `NN` is
+    set to zero when omitted.
 
     Parameters
     ----------
@@ -617,7 +617,7 @@ def gram(sys, type):
     if type not in ['c', 'o', 'cf', 'of']:
         raise ValueError("That type is not supported!")
 
-    # TODO: Check for continous or discrete, only continuous supported for now
+    # TODO: Check for continuous or discrete, only continuous supported for now
         # if isCont():
         #    dico = 'C'
         # elif isDisc():