Residual imports

Author: mrmundt

doc/OnlineDocs/contribution_guide.rst

@@ -413,7 +413,7 @@ provides an example of how this can be done, including a directory
 for plugins and package tests.  For example, this package can be
 imported as a subpackage of ``pyomo.contrib``::
 
-    from pyomo.environ import *
+    import pyomo.environ as pyo
     from pyomo.contrib.example import a
 
     # Print the value of 'a' defined by this package

doc/OnlineDocs/explanation/analysis/sensitivity_toolbox.rst

@@ -31,7 +31,7 @@ Here :math:`x_1`, :math:`x_2`, and :math:`x_3` are the decision variables while
 .. doctest:: python
 
     # Import Pyomo and the sensitivity toolbox
-    >>> from pyomo.environ import *
+    >>> import pyomo.environ as pyo
     >>> from pyomo.contrib.sensitivity_toolbox.sens import sensitivity_calculation
 
     # Create a concrete model

doc/OnlineDocs/explanation/modeling/dae.rst

@@ -69,8 +69,8 @@ concrete Pyomo model:
 .. doctest::
 
    Required imports
-   >>> from pyomo.environ import *
-   >>> from pyomo.dae import *
+   >>> import pyomo.environ as pyo
+   >>> from pyomo.dae import ContinuousSet
 
    >>> model = pyo.ConcreteModel()
 
@@ -96,10 +96,10 @@ abstract Pyomo model using the example data file.
 .. doctest::
 
    Required imports
-   >>> from pyomo.environ import *
-   >>> from pyomo.dae import *
+   >>> import pyomo.environ as pyo
+   >>> from pyomo.dae import ContinuousSet
 
-   >>> model = AbstractModel()
+   >>> model = pyo.AbstractModel()
 
    The ContinuousSet below will be initialized using the points
    in the data file when a model instance is created.
@@ -151,7 +151,7 @@ argument. Any keyword argument that is valid for a Pyomo
 .. doctest::
 
    Required imports
-   >>> from pyomo.environ import *
+   >>> import pyomo.environ as pyo
    >>> from pyomo.dae import ContinuousSet, DerivativeVar
 
    >>> model = pyo.ConcreteModel()
@@ -198,7 +198,7 @@ an ordinary or partial differential equation.
 .. doctest::
 
    Required imports
-   >>> from pyomo.environ import *
+   >>> fimport pyomo.environ as pyo
    >>> from pyomo.dae import ContinuousSet, DerivativeVar, Integral
 
    >>> model = pyo.ConcreteModel()
@@ -803,7 +803,7 @@ We begin by formulating the model using pyomo.DAE
     >>> m.theta[0].fix(3.14 - 0.1)
 
     >>> def _diffeq1(m, t):
-    ...     return m.domegadt[t] == -m.b * m.omega[t] - m.c * sin(m.theta[t])
+    ...     return m.domegadt[t] == -m.b * m.omega[t] - m.c * pyo.sin(m.theta[t])
     >>> m.diffeq1 = pyo.Constraint(m.t, rule=_diffeq1)
 
     >>> def _diffeq2(m, t):
@@ -886,7 +886,7 @@ example.
 
     >>> def _diffeq1(m, t):
     ...    return m.domegadt[t] == -m.b[t] * m.omega[t] - \
-    ...                             m.c[t] * sin(m.theta[t])
+    ...                             m.c[t] * pyo.sin(m.theta[t])
     >>> m.diffeq1 = pyo.Constraint(m.t, rule=_diffeq1)
 
     >>> def _diffeq2(m, t):

doc/OnlineDocs/explanation/modeling/network.rst

@@ -45,8 +45,8 @@ concrete Pyomo model:
 
 .. doctest::
 
-    >>> from pyomo.environ import *
-    >>> from pyomo.network import *
+    >>> import pyomo.environ as pyo
+    >>> from pyomo.network import Port
     >>> m = pyo.ConcreteModel()
     >>> m.x = pyo.Var()
     >>> m.y = pyo.Var(['a', 'b']) # can be indexed
@@ -79,8 +79,8 @@ concrete Pyomo model:
 
 .. doctest::
 
-    >>> from pyomo.environ import *
-    >>> from pyomo.network import *
+    >>> import pyomo.environ as pyo
+    >>> from pyomo.network import Port, Arc
     >>> m = pyo.ConcreteModel()
     >>> m.x = pyo.Var()
     >>> m.y = pyo.Var(['a', 'b'])
@@ -139,8 +139,8 @@ the arcs on a model:
 
 .. doctest::
 
-    >>> from pyomo.environ import *
-    >>> from pyomo.network import *
+    >>> import pyomo.environ as pyo
+    >>> from pyomo.network import Port, Arc
     >>> m = pyo.ConcreteModel()
     >>> m.x = pyo.Var()
     >>> m.y = pyo.Var(['a', 'b'])
@@ -301,8 +301,8 @@ class:
 .. doctest::
     :skipif: not __import__("pyomo.network").network.decomposition.imports_available
 
-    >>> from pyomo.environ import *
-    >>> from pyomo.network import *
+    >>> import pyomo.environ as pyo
+    >>> from pyomo.network import Port, Arc, SequentialDecomposition
     >>> m = pyo.ConcreteModel()
     >>> m.unit1 = pyo.Block()
     >>> m.unit1.x = pyo.Var()

doc/OnlineDocs/explanation/modeling_utils/preprocessing.rst

@@ -40,7 +40,7 @@ transformation on a concrete Pyomo model:
 
 .. doctest::
 
-    >>> from pyomo.environ import *
+    >>> import pyomo.environ as pyo
     >>> m = pyo.ConcreteModel()
     >>> m.v1 = pyo.Var(initialize=1, bounds=(1, 8))
     >>> m.v2 = pyo.Var(initialize=2, bounds=(0, 3))
@@ -67,7 +67,7 @@ Explicit pyo.Constraints to Variable Bounds
 
 .. doctest::
 
-    >>> from pyomo.environ import *
+    >>> import pyomo.environ as pyo
     >>> m = pyo.ConcreteModel()
     >>> m.v1 = pyo.Var(initialize=1)
     >>> m.v2 = pyo.Var(initialize=2)

doc/OnlineDocs/explanation/solvers/gdpopt.rst

@@ -63,8 +63,8 @@ An example that includes the modeling approach may be found below.
   :skipif: not glpk_available
 
   Required imports
-  >>> from pyomo.environ import *
-  >>> from pyomo.gdp import *
+  >>> import pyomo.environ as pyo
+  >>> from pyomo.gdp import Disjunct, Disjunction
 
   Create a simple model
   >>> model = pyo.ConcreteModel(name='LOA example')
@@ -80,7 +80,7 @@ An example that includes the modeling approach may be found below.
   >>> model.fix_y.c = pyo.Constraint(expr=model.y == 0)
 
   >>> model.d = Disjunction(expr=[model.fix_x, model.fix_y])
-  >>> model.objective = pyo.Objective(expr=model.x + 0.1*model.y, sense=minimize)
+  >>> model.objective = pyo.Objective(expr=model.x + 0.1*model.y, sense=pyo.minimize)
 
   Solve the model using GDPopt
   >>> results = pyo.SolverFactory('gdpopt.loa').solve(
@@ -158,14 +158,14 @@ To use the GDPopt-LBB solver, define your Pyomo GDP model as usual:
   :skipif: not baron_available
 
   Required imports
-  >>> from pyomo.environ import *
+  >>> import pyomo.environ as pyo
   >>> from pyomo.gdp import Disjunct, Disjunction
 
   Create a simple model
   >>> m = pyo.ConcreteModel()
   >>> m.x1 = pyo.Var(bounds = (0,8))
   >>> m.x2 = pyo.Var(bounds = (0,8))
-  >>> m.obj = pyo.Objective(expr=m.x1 + m.x2, sense=minimize)
+  >>> m.obj = pyo.Objective(expr=m.x1 + m.x2, sense=pyo.minimize)
   >>> m.y1 = Disjunct()
   >>> m.y2 = Disjunct()
   >>> m.y1.c1 = pyo.Constraint(expr=m.x1 >= 2)

doc/OnlineDocs/explanation/solvers/mindtpy.rst

@@ -72,18 +72,18 @@ An example which includes the modeling approach may be found below.
 .. doctest::
 
   Required imports
-  >>> from pyomo.environ import *
+  >>> import pyomo.environ as pyo
 
   Create a simple model
   >>> model = pyo.ConcreteModel()
 
   >>> model.x = pyo.Var(bounds=(1.0,10.0),initialize=5.0)
-  >>> model.y = pyo.Var(within=Binary)
+  >>> model.y = pyo.Var(within=pyo.Binary)
 
   >>> model.c1 = pyo.Constraint(expr=(model.x-4.0)**2 - model.x <= 50.0*(1-model.y))
-  >>> model.c2 = pyo.Constraint(expr=model.x*log(model.x)+5.0 <= 50.0*(model.y))
+  >>> model.c2 = pyo.Constraint(expr=model.x*pyo.log(model.x)+5.0 <= 50.0*(model.y))
 
-  >>> model.objective = pyo.Objective(expr=model.x, sense=minimize)
+  >>> model.objective = pyo.Objective(expr=model.x, sense=pyo.minimize)
 
   Solve the model using MindtPy
   >>> pyo.SolverFactory('mindtpy').solve(model, mip_solver='glpk', nlp_solver='ipopt') # doctest: +SKIP
@@ -140,7 +140,7 @@ A usage example for LP/NLP based branch-and-bound algorithm is as follows:
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='OA',
   ...                                    mip_solver='cplex_persistent',  # or 'gurobi_persistent'
   ...                                    nlp_solver='ipopt',
@@ -160,7 +160,7 @@ A usage example for regularized OA is as follows:
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='OA',
   ...                                    mip_solver='cplex',
   ...                                    nlp_solver='ipopt',
@@ -181,7 +181,7 @@ A usage example for OA with solution pool is as follows:
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='OA',
   ...                                    mip_solver='cplex_persistent',
   ...                                    nlp_solver='ipopt',
@@ -202,7 +202,7 @@ A usage example for Feasibility Pump as the initialization strategy is as follow
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='OA',
   ...                                    init_strategy='FP',
   ...                                    mip_solver='cplex',
@@ -215,7 +215,7 @@ A usage example for Feasibility Pump as the decomposition strategy is as follows
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='FP',
   ...                                    mip_solver='cplex',
   ...                                    nlp_solver='ipopt',
@@ -282,7 +282,7 @@ A usage example for GOA is as follows:
 
 .. code::
 
-  >>> pyo.pyo.SolverFactory('mindtpy').solve(model,
+  >>> pyo.SolverFactory('mindtpy').solve(model,
   ...                                    strategy='GOA',
   ...                                    mip_solver='cplex',
   ...                                    nlp_solver='baron')

doc/OnlineDocs/explanation/solvers/multistart.rst

@@ -14,7 +14,7 @@ To use the multistart solver, define your Pyomo model as usual:
 .. doctest::
 
   Required import
-  >>> from pyomo.environ import *
+  >>> import pyomo.environ as pyo
 
   Create a simple model
   >>> m = pyo.ConcreteModel()

doc/OnlineDocs/explanation/solvers/z3_interface.rst

@@ -19,7 +19,7 @@ To use the sat solver define your pyomo model as usual:
 .. doctest::
 
   Required import
-  >>> from pyomo.environ import *
+  >>> import pyomo.environ as pyo
   >>> from pyomo.contrib.satsolver.satsolver import SMTSatSolver
 
   Create a simple model

doc/OnlineDocs/howto/abstract_models/data/raw_dicts.rst

@@ -14,12 +14,12 @@ components, the required data dictionary maps the implicit index
 
  .. doctest::
 
-    >>> from pyomo.environ import *
-    >>> m = AbstractModel()
-    >>> m.I = Set()
-    >>> m.p = Param()
-    >>> m.q = Param(m.I)
-    >>> m.r = Param(m.I, m.I, default=0)
+    >>> import pyomo.environ as pyo
+    >>> m = pyo.AbstractModel()
+    >>> m.I = pyo.Set()
+    >>> m.p = pyo.Param()
+    >>> m.q = pyo.Param(m.I)
+    >>> m.r = pyo.Param(m.I, m.I, default=0)
     >>> data = {None: {
     ...     'I': {None: [1,2,3]},
     ...     'p': {None: 100},

doc/OnlineDocs/howto/interrogating.rst

@@ -27,7 +27,7 @@ can be accessed using its ``value`` member. For example, suppose the
 model contains a variable named ``quant`` that is a singleton (has no
 indexes) and suppose further that the name of the instance object is
 ``instance``. Then the value of this variable can be accessed using
-``pyo.value(instance.quant)``. Variables with indexes can be referenced
+``pyo.pyo.value(instance.quant)``. Variables with indexes can be referenced
 by supplying the index.
 
 Consider the following very simple example, which is similar to the
@@ -54,13 +54,13 @@ the following code snippet displays all variables and their values:
    >>> for v in instance.component_objects(pyo.Var, active=True):
    ...     print("Variable",v)  # doctest: +SKIP
    ...     for index in v:
-   ...         print ("   ",index, pyo.value(v[index]))  # doctest: +SKIP
+   ...         print ("   ",index, pyo.pyo.value(v[index]))  # doctest: +SKIP
 
 
 Alternatively,
 
    >>> for v in instance.component_data_objects(pyo.Var, active=True):
-   ...     print(v, pyo.value(v))  # doctest: +SKIP
+   ...     print(v, pyo.pyo.value(v))  # doctest: +SKIP
 
 This code could be improved by checking to see if the variable is not
 indexed (i.e., the only index value is ``None``), then the code could
@@ -96,7 +96,7 @@ of every Parameter in a model:
    ...     nametoprint = str(str(parmobject.name))
    ...     print ("Parameter ", nametoprint)  # doctest: +SKIP
    ...     for index in parmobject:
-   ...         vtoprint = pyo.value(parmobject[index])
+   ...         vtoprint = pyo.pyo.value(parmobject[index])
    ...         print ("   ",index, vtoprint)  # doctest: +SKIP
 
 
@@ -136,7 +136,7 @@ an instance, duals can be accessed in the following fashion.
    :language: python
 
 The following snippet will only work, of course, if there is a
-constraint with the name ``AxbConstraint`` that has and index, which is
+constraint with the name ``Axbpyo.Constraint`` that has and index, which is
 the string ``Film``.
 
 .. literalinclude:: /src/scripting/driveabs2_Access_one_dual.spy
@@ -145,7 +145,7 @@ the string ``Film``.
 Here is a complete example that relies on the file ``abstract2.py`` to
 provide the model and the file ``abstract2.dat`` to provide the
 data. Note that the model in ``abstract2.py`` does contain a constraint
-named ``AxbConstraint`` and ``abstract2.dat`` does specify an index for
+named ``Axbpyo.Constraint`` and ``abstract2.dat`` does specify an index for
 it named ``Film``.
 
 .. literalinclude:: /src/scripting/driveabs2.spy