address @slivingston comments + pycodestyle updates

Author: murrayrm

control/config.py

@@ -1,7 +1,7 @@
 # config.py - package defaults
 # RMM, 4 Nov 2012
 #
-# TODO: add ability to read/write configuration files (ala matplotlib)
+# TODO: add ability to read/write configuration files (a la matplotlib)
 
 """Functions to access default parameter values.
 
@@ -390,7 +390,7 @@ def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True):
     Use this function to handle a legacy keyword that has been renamed.
     This function pops the old keyword off of the kwargs dictionary and
     issues a warning.  If both the old and new keyword are present, a
-    ControlArgument exception is raised.
+    `ControlArgument` exception is raised.
 
     Parameters
     ----------
@@ -442,7 +442,7 @@ def _process_param(name, defval, kwargs, alias_mapping, sigval=None):
     aliases and legacy aliases::
 
        alias_mapping = {
-            'argument_name_1', (['alias', ...], ['legacy', ...]),
+            'argument_name_1': (['alias', ...], ['legacy', ...]),
             ...}
 
     If `param` is a named keyword in the function signature with default
@@ -452,7 +452,7 @@ def _process_param(name, defval, kwargs, alias_mapping, sigval=None):
         param = _process_param('param', defval, kwargs, function_aliases)
 
     If `param` is a variable keyword argument (in `kwargs`), `defval` can
-    be pssed as either None or the default value to use if `param` is not
+    be passed as either None or the default value to use if `param` is not
     present in `kwargs`.
 
     Parameters
@@ -462,7 +462,7 @@ def _process_param(name, defval, kwargs, alias_mapping, sigval=None):
     defval : object or dict
         Default value for the parameter.
     kwargs : dict
-        Dictionary of varaible keyword arguments.
+        Dictionary of variable keyword arguments.
     alias_mapping : dict
         Dictionary providing aliases and legacy names.
     sigval : object, optional
@@ -478,7 +478,7 @@ def _process_param(name, defval, kwargs, alias_mapping, sigval=None):
     Raises
     ------
     TypeError
-        If multiple keyword aliased are used for the same parameter.
+        If multiple keyword aliases are used for the same parameter.
 
     Warns
     -----
@@ -527,7 +527,7 @@ def _process_kwargs(kwargs, alias_mapping):
     a tuple consisting of valid aliases and legacy aliases::
 
        alias_mapping = {
-            'argument_name_1', (['alias', ...], ['legacy', ...]),
+            'argument_name_1': (['alias', ...], ['legacy', ...]),
             ...}
 
     If an alias is present in the dictionary of keywords, it will be used

control/optimal.py

@@ -49,6 +49,19 @@
     'optimal.solve_ivp_options': {},
 }
 
+# Parameter and keyword aliases
+_optimal_aliases = {
+    # param:                  ([alias, ...],                [legacy, ...])
+    'integral_cost':          (['trajectory_cost', 'cost'], []),
+    'initial_state':          (['x0', 'X0'],                []),
+    'initial_input':          (['u0', 'U0'],                []),
+    'final_state':            (['xf'],                      []),
+    'final_input':            (['uf'],                      []),
+    'initial_time':           (['T0'],                      []),
+    'trajectory_constraints': (['constraints'],             []),
+    'return_states':          (['return_x'],                []),
+}
+
 
 class OptimalControlProblem():
     """Description of a finite horizon, optimal control problem.
@@ -334,7 +347,8 @@ def _cost_function(self, coeffs):
 
             # Integrate the cost
             costs = np.array(costs)
-           # Approximate the integral using trapezoidal rule
+
+            # Approximate the integral using trapezoidal rule
             cost = np.sum(0.5 * (costs[:-1] + costs[1:]) * dt)
 
         else:
@@ -1018,20 +1032,6 @@ def __init__(
         self.states = response.states
 
 
-# Parameter and keyword aliases
-_optimal_aliases = {
-    # param:                  ([alias, ...],                [legacy, ...])
-    'integral_cost':          (['trajectory_cost', 'cost'], []),
-    'initial_state':          (['x0', 'X0'],                []),
-    'initial_input':          (['u0', 'U0'],                []),
-    'final_state':            (['xf'],                      []),
-    'final_input':            (['uf'],                      []),
-    'initial_time':           (['T0'],                      []),
-    'trajectory_constraints': (['constraints'],             []),
-    'return_states':          (['return_x'],                []),
-}
-
-
 # Compute the input for a nonlinear, (constrained) optimal control problem
 def solve_optimal_trajectory(
         sys, timepts, initial_state=None, integral_cost=None,
@@ -1183,7 +1183,8 @@ def solve_optimal_trajectory(
             kwargs['minimize_method'] = method
         else:
             if kwargs.get('trajectory_method'):
-                raise ValueError("'trajectory_method' specified more than once")
+                raise ValueError(
+                    "'trajectory_method' specified more than once")
             warnings.warn(
                 "'method' parameter is deprecated; assuming trajectory_method",
                 FutureWarning)
@@ -1819,14 +1820,14 @@ def compute_estimate(
         return OptimalEstimationResult(
             self, res, squeeze=squeeze, print_summary=print_summary)
 
-
     #
     # Create an input/output system implementing an moving horizon estimator
     #
     # This function creates an input/output system that has internal state
     # xhat, u, v, y for all previous time points.  When the system update
     # function is called,
     #
+
     def create_mhe_iosystem(
             self, estimate_labels=None, measurement_labels=None,
             control_labels=None, inputs=None, outputs=None, **kwargs):
@@ -2490,6 +2491,7 @@ def _evaluate_output_range_constraint(x, u):
     # Return a nonlinear constraint object based on the polynomial
     return (opt.NonlinearConstraint, _evaluate_output_range_constraint, lb, ub)
 
+
 #
 # Create a constraint on the disturbance input
 #
@@ -2534,6 +2536,7 @@ def disturbance_range_constraint(sys, lb, ub):
 # Utility functions
 #
 
+
 #
 # Process trajectory constraints
 #
@@ -2545,6 +2548,7 @@ def disturbance_range_constraint(sys, lb, ub):
 # internal representation (currently a tuple with the constraint type as the
 # first element.
 #
+
 def _process_constraints(clist, name):
     if clist is None:
         clist = []
@@ -2560,7 +2564,7 @@ def _process_constraints(clist, name):
         if isinstance(constraint, tuple):
             # Original style of constraint
             ctype, fun, lb, ub = constraint
-            if not ctype in [opt.LinearConstraint, opt.NonlinearConstraint]:
+            if ctype not in [opt.LinearConstraint, opt.NonlinearConstraint]:
                 raise TypeError(f"unknown {name} constraint type {ctype}")
             constraint_list.append(constraint)
         elif isinstance(constraint, opt.LinearConstraint):

control/timeresp.py

@@ -54,6 +54,21 @@
            'initial_response', 'impulse_response', 'TimeResponseData',
            'TimeResponseList']
 
+# Dictionary of aliases for time response commands
+_timeresp_aliases = {
+    # param:            ([alias, ...], [legacy, ...])
+    'timepts':          (['T'],        []),
+    'inputs':           (['U'],        ['u']),
+    'outputs':          (['Y'],        ['y']),
+    'initial_state':    (['X0'],       ['x0']),
+    'final_output':     (['yfinal'],   []),
+    'return_states':    (['return_x'], []),
+    'evaluation_times': (['t_eval'],   []),
+    'timepts_num':      (['T_num'],    []),
+    'input_indices':    (['input'],    []),
+    'output_indices':   (['output'],   []),
+}
+
 
 class TimeResponseData:
     """Input/output system time response data.
@@ -344,7 +359,7 @@ def __init__(
             # Make sure the shape is OK
             if multi_trace and \
                (self.x.ndim != 3 or self.x.shape[1] != self.ntraces) or \
-               not multi_trace and self.x.ndim != 2 :
+               not multi_trace and self.x.ndim != 2:
                 raise ValueError("State vector is the wrong shape")
 
             # Make sure time dimension of state is the right length
@@ -701,6 +716,7 @@ def plot(self, *args, **kwargs):
         """
         return time_response_plot(self, *args, **kwargs)
 
+
 #
 # Time response data list class
 #
@@ -739,20 +755,6 @@ def plot(self, *args, **kwargs):
                         lines[row, col] += cplt.lines[row, col]
         return ControlPlot(lines, cplt.axes, cplt.figure)
 
-# Dictionary of aliases for time response commands
-_timeresp_aliases = {
-    # param:            ([alias, ...], [legacy, ...])
-    'timepts':          (['T'],        []),
-    'inputs':           (['U'],        ['u']),
-    'outputs':          (['Y'],        ['y']),
-    'initial_state':    (['X0'],       ['x0']),
-    'final_output':     (['yfinal'],   []),
-    'return_states':    (['return_x'], []),
-    'evaluation_times': (['t_eval'],   []),
-    'timepts_num':      (['T_num'],    []),
-    'input_indices':    (['input'],    []),
-    'output_indices':   (['output'],   []),
-}
 
 # Process signal labels
 def _process_labels(labels, signal, length):
@@ -991,8 +993,8 @@ def forced_response(
         True, the array is 1D (indexed by time).  If the system is not SISO or
         `squeeze` is False, the array is 2D (indexed by output and time).
     resp.states : array
-        Time evolution of the state vector, represented as a 2D array indexed by
-        state and time.
+        Time evolution of the state vector, represented as a 2D array
+        indexed by state and time.
     resp.inputs : array
         Input(s) to the system, indexed by input and time.
 
@@ -1428,7 +1430,8 @@ def step_response(
     output = _process_param(
         'output_indices', output_indices, kwargs, _timeresp_aliases)
     return_x = _process_param(
-        'return_states', return_states, kwargs, _timeresp_aliases, sigval=False)
+        'return_states', return_states, kwargs, _timeresp_aliases,
+        sigval=False)
     T_num = _process_param(
         'timepts_num', timepts_num, kwargs, _timeresp_aliases)
 
@@ -1839,7 +1842,8 @@ def initial_response(
     output = _process_param(
         'output_indices', output_indices, kwargs, _timeresp_aliases)
     return_x = _process_param(
-        'return_states', return_states, kwargs, _timeresp_aliases, sigval=False)
+        'return_states', return_states, kwargs, _timeresp_aliases,
+        sigval=False)
     T_num = _process_param(
         'timepts_num', timepts_num, kwargs, _timeresp_aliases)
 
@@ -1972,7 +1976,8 @@ def impulse_response(
     output = _process_param(
         'output_indices', output_indices, kwargs, _timeresp_aliases)
     return_x = _process_param(
-        'return_states', return_states, kwargs, _timeresp_aliases, sigval=False)
+        'return_states', return_states, kwargs, _timeresp_aliases,
+        sigval=False)
     T_num = _process_param(
         'timepts_num', timepts_num, kwargs, _timeresp_aliases)
 

doc/develop.rst

@@ -305,7 +305,7 @@ Parameter aliases
 -----------------
 
 As described above, parameter names are generally longer strings that
-describe the purpose fo the paramater.  Similar to `matplotlib` (e.g.,
+describe the purpose of the parameter.  Similar to `matplotlib` (e.g.,
 the use of `lw` as an alias for `linewidth`), some commonly used
 parameter names can be specified using an "alias" that allows the use
 of a shorter key.
@@ -321,11 +321,11 @@ variable by replacing aliases with the full key::
   _process_kwargs(kwargs, aliases)
 
 The values for named parameters can then be assigned to a local
-variable using a call to :func:`~config.process_param` of the form::
+variable using a call to :func:`~config._process_param` of the form::
 
-  var = _process_kwargs('param', param, kwargs, aliases)
+  var = _process_param('param', param, kwargs, aliases)
 
-where 'param` is the named parameter used in the function signature
+where `param` is the named parameter used in the function signature
 and var is the local variable in the function (may also be `param`,
 but doesn't have to be).
 
@@ -356,7 +356,7 @@ The alias mapping is a dictionary that returns a tuple consisting of
 valid aliases and legacy aliases::
 
   alias_mapping = {
-      'argument_name_1', (['alias', ...], ['legacy', ...]),
+      'argument_name_1': (['alias', ...], ['legacy', ...]),
        ...}
 
 If an alias is present in the dictionary of keywords, it will be used