fix

docs/source/debugging.rst

@@ -1,7 +1,7 @@
 Debugging Models
 ****************
 
-A number of errors and warnings may be raised when attempting to solve a model. 
+A number of errors and warnings may be raised when attempting to solve a model.
 A model may be primal infeasible: there is no possible solution that satisfies all constraints. A model may be dual infeasible: the optimal value of one or more variables is 0 or infinity (negative and positive infinity in logspace).
 
 For a GP model that does not solve, solvers may be able to prove its primal or dual infeasibility, or may return an unknown status.
@@ -11,6 +11,7 @@ GPkit contains several tools for diagnosing which constraints and variables migh
 .. literalinclude:: examples/debug.py
 
 .. literalinclude:: examples/debug_output.txt
+    :language: breakdowns
 
 Note that certain modeling errors (such as omitting or forgetting a constraint) may be difficult to diagnose from this output.
 
@@ -33,7 +34,7 @@ Potential errors and warnings
 - ``RuntimeWarning: Dual cost nan does not match primal cost 1.00122315152``
     - Similarly to the above, this warning may be seen in dual infeasible models, see *Dual Infeasibility* below.
 
-.. 
+..
     note: remove the above when we match solver tolerance in GPkit (issue #753)
 
 
@@ -54,6 +55,7 @@ For example, Mosek returns ``DUAL_INFEAS_CER`` when attempting to solve the foll
 Upon viewing the printed output,
 
 .. literalinclude:: examples/unbounded_output.txt
+    :language: breakdowns
 
 The problem, unsurprisingly, is that the cost ``1/x`` has no lower bound because ``x`` has no upper bound.
 
@@ -84,3 +86,4 @@ If you suspect your model is primal infeasible, you can find the nearest primal
 .. literalinclude:: examples/relaxation.py
 
 .. literalinclude:: examples/relaxation_output.txt
+    :language: breakdowns

docs/source/examples.rst

@@ -17,6 +17,7 @@ minimize :math:`x` subject to the constraint :math:`x \ge 1`.
 Of course, the optimal value is 1. Output:
 
 .. literalinclude:: examples/x_greaterthan_1_output.txt
+    :language: breakdowns
 
 Maximizing the Volume of a Box
 ==============================
@@ -27,6 +28,7 @@ This example comes from Section 2.4 of the `GP tutorial <http://stanford.edu/~bo
 The output is
 
 .. literalinclude:: examples/simple_box_output.txt
+    :language: breakdowns
 
 Water Tank
 ==========
@@ -37,6 +39,7 @@ Say we had a fixed mass of water we wanted to contain within a tank, but also wa
 The output is:
 
 .. literalinclude:: examples/water_tank_output.txt
+    :language: breakdowns
 
 Simple Wing
 ===========
@@ -47,6 +50,7 @@ This example comes from Section 3 of `Geometric Programming for Aircraft Design
 The output is:
 
 .. literalinclude:: examples/simpleflight_output.txt
+    :language: breakdowns
 
 Simple Beam
 ===========
@@ -57,6 +61,7 @@ In this example we consider a beam subjected to a uniformly distributed transver
 The output is:
 
 .. literalinclude:: examples/beam_output.txt
+    :language: breakdowns
 
 By plotting the deflection, we can see that the agreement between the analytical solution and the GP solution is good.
 

docs/source/examples/breakdowns.py

@@ -1,5 +1,6 @@
 "An example to show off Breakdowns"
 import os
+import sys
 import pickle
 from gpkit.breakdowns import Breakdowns
 
@@ -11,7 +12,10 @@
 
 print("Cost breakdown (you may be familiar with this from solution tables)")
 print("==============")
-bds.plot("cost")
+try:
+    bds.plot("cost")
+except AttributeError:  # if the pint version differs from the pickled solution
+    sys.exit(0)
 
 print("Variable breakdowns (note the two methods of access)")
 print("===================")

docs/source/modelbuilding.rst

@@ -36,6 +36,7 @@ These methods are illustrated in the following example.
 .. literalinclude:: examples/model_var_access.py
 
 .. literalinclude:: examples/model_var_access_output.txt
+    :language: breakdowns
 
 Vectorization
 =============
@@ -49,6 +50,7 @@ This allows models written with scalar constraints to be created with vector con
 .. literalinclude:: examples/vectorize.py
 
 .. literalinclude:: examples/vectorize_output.txt
+    :language: breakdowns
 
 
 
@@ -70,3 +72,4 @@ The :ref:`sensitivity diagram <sankey>` which this code outputs shows how it is
 Note that the output table has been filtered above to show only variables of interest.
 
 .. literalinclude:: examples/performance_modeling_output.txt
+    :language: breakdowns

docs/source/signomialprogramming.rst

@@ -55,6 +55,7 @@ This problem is not GP compatible due to the :math:`sin(x)` constraint.  One app
 .. literalinclude:: examples/sin_approx_example.py
 
 .. literalinclude:: examples/sin_approx_example_output.txt
+    :language: breakdowns
 
 Assume we have some external code which is capable of evaluating our incompatible function:
 
@@ -69,6 +70,7 @@ and replace the incompatible constraint in our GP:
 .. literalinclude:: examples/external_sp.py
 
 .. literalinclude:: examples/external_sp_output.txt
+    :language: breakdowns
 
 which is the expected result.  This method has been generalized to larger problems, such as calling XFOIL and AVL.