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murrayrmmurrayrm
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PEP8 cleanup
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‎control/optimal.py

Lines changed: 19 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -20,6 +20,7 @@
2020

2121
__all__ = ['find_optimal_input']
2222

23+
2324
class OptimalControlProblem():
2425
"""Description of a finite horizon, optimal control problem
2526
@@ -124,7 +125,6 @@ def __init__(
124125
else:
125126
self.terminal_constraints = terminal_constraints
126127

127-
128128
#
129129
# Compute and store constraints
130130
#
@@ -197,7 +197,7 @@ def __init__(
197197
initial_guess = np.broadcast_to(
198198
initial_guess.reshape(-1, 1),
199199
(self.system.ninputs, self.time_vector.size))
200-
except:
200+
except ValueError:
201201
raise ValueError("initial guess is the wrong shape")
202202

203203
elif initial_guess.shape != \
@@ -222,7 +222,6 @@ def __init__(
222222
if log:
223223
logging.info("New optimal control problem initailized")
224224

225-
226225
#
227226
# Cost function
228227
#
@@ -253,7 +252,7 @@ def _cost_function(self, inputs):
253252
else:
254253
if self.log:
255254
logging.debug("calling input_output_response from state\n"
256-
+ str(x))
255+
+ str(x))
257256
logging.debug("initial input[0:3] =\n" + str(inputs[:, 0:3]))
258257

259258
# Simulate the system to get the state
@@ -266,7 +265,7 @@ def _cost_function(self, inputs):
266265

267266
if self.log:
268267
logging.debug("input_output_response returned states\n"
269-
+ str(states))
268+
+ str(states))
270269

271270
# Trajectory cost
272271
# TODO: vectorize
@@ -293,7 +292,7 @@ def _cost_function(self, inputs):
293292

294293
# Terminal cost
295294
if self.terminal_cost is not None:
296-
cost += self.terminal_cost(states[:,-1], inputs[:,-1])
295+
cost += self.terminal_cost(states[:, -1], inputs[:, -1])
297296

298297
# Update statistics
299298
self.cost_evaluations += 1
@@ -307,7 +306,6 @@ def _cost_function(self, inputs):
307306
# Return the total cost for this input sequence
308307
return cost
309308

310-
311309
#
312310
# Constraints
313311
#
@@ -368,7 +366,7 @@ def _constraint_function(self, inputs):
368366
else:
369367
if self.log:
370368
logging.debug("calling input_output_response from state\n"
371-
+ str(x))
369+
+ str(x))
372370
logging.debug("initial input[0:3] =\n" + str(inputs[:, 0:3]))
373371

374372
# Simulate the system to get the state
@@ -390,9 +388,9 @@ def _constraint_function(self, inputs):
390388
elif type == opt.LinearConstraint:
391389
# `fun` is the A matrix associated with the polytope...
392390
value.append(
393-
np.dot(fun, np.hstack([states[:,i], inputs[:,i]])))
391+
np.dot(fun, np.hstack([states[:, i], inputs[:, i]])))
394392
elif type == opt.NonlinearConstraint:
395-
value.append(fun(states[:,i], inputs[:,i]))
393+
value.append(fun(states[:, i], inputs[:, i]))
396394
else:
397395
raise TypeError("unknown constraint type %s" %
398396
constraint[0])
@@ -405,9 +403,9 @@ def _constraint_function(self, inputs):
405403
continue
406404
elif type == opt.LinearConstraint:
407405
value.append(
408-
np.dot(fun, np.hstack([states[:,i], inputs[:,i]])))
406+
np.dot(fun, np.hstack([states[:, i], inputs[:, i]])))
409407
elif type == opt.NonlinearConstraint:
410-
value.append(fun(states[:,i], inputs[:,i]))
408+
value.append(fun(states[:, i], inputs[:, i]))
411409
else:
412410
raise TypeError("unknown constraint type %s" %
413411
constraint[0])
@@ -448,7 +446,7 @@ def _eqconst_function(self, inputs):
448446
else:
449447
if self.log:
450448
logging.debug("calling input_output_response from state\n"
451-
+ str(x))
449+
+ str(x))
452450
logging.debug("initial input[0:3] =\n" + str(inputs[:, 0:3]))
453451

454452
# Simulate the system to get the state
@@ -461,7 +459,7 @@ def _eqconst_function(self, inputs):
461459

462460
if self.log:
463461
logging.debug("input_output_response returned states\n"
464-
+ str(states))
462+
+ str(states))
465463

466464
# Evaluate the constraint function along the trajectory
467465
value = []
@@ -474,9 +472,9 @@ def _eqconst_function(self, inputs):
474472
elif type == opt.LinearConstraint:
475473
# `fun` is the A matrix associated with the polytope...
476474
value.append(
477-
np.dot(fun, np.hstack([states[:,i], inputs[:,i]])))
475+
np.dot(fun, np.hstack([states[:, i], inputs[:, i]])))
478476
elif type == opt.NonlinearConstraint:
479-
value.append(fun(states[:,i], inputs[:,i]))
477+
value.append(fun(states[:, i], inputs[:, i]))
480478
else:
481479
raise TypeError("unknown constraint type %s" %
482480
constraint[0])
@@ -489,9 +487,9 @@ def _eqconst_function(self, inputs):
489487
continue
490488
elif type == opt.LinearConstraint:
491489
value.append(
492-
np.dot(fun, np.hstack([states[:,i], inputs[:,i]])))
490+
np.dot(fun, np.hstack([states[:, i], inputs[:, i]])))
493491
elif type == opt.NonlinearConstraint:
494-
value.append(fun(states[:,i], inputs[:,i]))
492+
value.append(fun(states[:, i], inputs[:, i]))
495493
else:
496494
raise TypeError("unknown constraint type %s" %
497495
constraint[0])
@@ -523,7 +521,7 @@ def _eqconst_function(self, inputs):
523521
#
524522
def _reset_statistics(self, log=False):
525523
"""Reset counters for keeping track of statistics"""
526-
self.log=log
524+
self.log = log
527525
self.cost_evaluations, self.cost_process_time = 0, 0
528526
self.constraint_evaluations, self.constraint_process_time = 0, 0
529527
self.eqconst_evaluations, self.eqconst_process_time = 0, 0
@@ -555,13 +553,13 @@ def _create_mpc_iosystem(self, dt=True):
555553
def _update(t, x, u, params={}):
556554
inputs = x.reshape((self.system.ninputs, self.time_vector.size))
557555
self.initial_guess = np.hstack(
558-
[inputs[:,1:], inputs[:,-1:]]).reshape(-1)
556+
[inputs[:, 1:], inputs[:, -1:]]).reshape(-1)
559557
res = self.compute_trajectory(u, print_summary=False)
560558
return res.inputs.reshape(-1)
561559

562560
def _output(t, x, u, params={}):
563561
inputs = x.reshape((self.system.ninputs, self.time_vector.size))
564-
return inputs[:,0]
562+
return inputs[:, 0]
565563

566564
return ct.NonlinearIOSystem(
567565
_update, _output, dt=dt,

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