Merge branch 'master' into issue1088

docs/source/advancedcommands.rst

@@ -1,6 +1,65 @@
 Advanced Commands
 *****************
 
+.. _migp:
+
+Choice Variables
+================
+If MOSEK 9 is installed, GPkit supports discretized free variables with the ``mosek_conif`` solver.
+Choice variables are free in the sense of having multiple possible choices, but discretized in the
+sense of having a limited set of possible solutions.
+
+.. literalinclude:: examples/migp.py
+
+If solved with the mosek_conif solver, the script above will print::
+
+    Optimal Cost
+    ------------
+     [ 1.5       2.15      2.8       3.22     ... ]
+
+    ~~~~~~~~
+    WARNINGS
+    ~~~~~~~~
+    No Dual Solution
+    ----------------
+    This model has the discretized choice variables [x] and hence no dual
+    solution. You can fix those variables to their optimal value and get
+    sensitivities to the resulting continuous problem by updating your model's
+    substitions with `sol["choicevariables"]`.
+    ~~~~~~~~
+
+    Swept Variables
+    ---------------
+    numerator : [ 0.5
+                  1.15
+                  1.8
+                  2.45
+                  3.1
+                  3.75
+                  4.4
+                  5.05
+                  5.7
+                  6.35
+                  7         ]
+
+    Free Variables
+    --------------
+    x : [ 1
+          1
+          1
+          2
+          2
+          2
+          2
+          2
+          2
+          3
+          3         ]
+
+Note that the optimal values for ``x`` are discretized, clicking from 1
+to 2 to 3 during the sweep, and that the solution has no dual variables.
+
+
 Derived Variables
 =================
 

docs/source/examples/migp.py

@@ -0,0 +1,10 @@
+import numpy as np
+from gpkit import *
+
+x = Variable("x", choices=range(1,4))
+num = Variable("numerator", np.linspace(0.5, 7, 11))
+
+m = Model(x + num/x)
+sol = m.solve(verbosity=0)
+
+print(sol.table())

docs/source/examples/migp_output.txt

@@ -0,0 +1,59 @@
+
+Optimal Cost
+------------
+ [ 1.41      2.14      2.68      3.13     ... ]
+
+~~~~~~~~
+WARNINGS
+~~~~~~~~
+Freed Choice Variables
+----------------------
+This model has the discretized choice variables [x], but since the 'cvxopt' solver doesn't support discretization they were treated as continuous variables.
+~~~~~~~~
+
+Swept Variables
+---------------
+numerator : [ 0.5
+              1.15
+              1.8
+              2.45
+              3.1
+              3.75
+              4.4
+              5.05
+              5.7
+              6.35
+              7         ]
+
+Free Variables
+--------------
+x : [ 0.707
+      1.07
+      1.34
+      1.57
+      1.76
+      1.94
+      2.1
+      2.25
+      2.39
+      2.52
+      2.65      ]
+
+Variable Sensitivities
+----------------------
+numerator : [ +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5
+              +0.5      ]
+
+Most Sensitive Constraints (in last sweep)
+------------------------------------------
+(none)
+

docs/source/examples/performance_modeling.py

@@ -242,9 +242,14 @@ def setup(self):
 sol = M.solve(verbosity=0)
 print(sol.diff("solution.pkl", showvars=vars_of_interest, sortbymodel=False))
 
-# this will only make an image when run in jupyter notebook
-# from gpkit.interactive.sankey import Sankey
-from gpkit.interactive.sankey import Sankey
-variablesankey = Sankey(sol, M).diagram(AC.wing.A)
-sankey = Sankey(sol, M).diagram(width=1200, height=400, maxlinks=30)
-sankey  # pylint: disable=pointless-statement
+try:
+    from gpkit.interactive.sankey import Sankey
+    variablesankey = Sankey(sol, M).diagram(AC.wing.A)
+    sankey = Sankey(sol, M).diagram(width=1200, height=400, maxlinks=30)
+    # the line below shows an interactive graph if run in jupyter notebook
+    sankey  # pylint: disable=pointless-statement
+except (ImportError, ModuleNotFoundError):
+    print("Making Sankey diagrams requires the ipysankeywidget package")
+
+from gpkit.interactive.references import referencesplot
+referencesplot(M, openimmediately=False)

docs/source/examples/relaxation_output.txt

@@ -150,7 +150,7 @@ Relaxed Constants
 Model Sensitivities (sorts models in sections below)
 -------------------
   +2.0 : Relax2.OriginalValues
- <1E-8 : Relax2
+ <1e-8 : Relax2
 
 Free Variables
 --------------

docs/source/examples/simple_sp.py

@@ -17,3 +17,7 @@
 # full interim solutions are available
 print("x values of each GP solve (note convergence)")
 print(", ".join("%.5f" % sol["freevariables"][x] for sol in m.program.results))
+
+# use x0 to give the solution, reducing number of GPs needed
+m.localsolve(verbosity=0, x0={x: 0.9, y:0.1})
+assert len(m.program.results) == 2

docs/source/installation.rst

@@ -4,42 +4,44 @@ Installation
 ************
 
 1. If you are on Mac or Windows, we recommend installing `Anaconda <http://www.continuum.io/downloads>`_. Alternatively, `install pip and create a virtual environment <https://packaging.python.org/guides/installing-using-pip-and-virtualenv/>`_.
-2. (optional) Install the MOSEK solver as directed below
-3. Run ``pip install gpkit`` in the appropriate terminal or command prompt.
-4. Open a Python prompt and run ``import gpkit`` to finish installation and run unit tests.
+2. (optional) Install the MOSEK 9 solver with ``pip install Mosek``, then a license as described below
+3. (optional) Install the MOSEK 8 solver as described below
+4. Run ``pip install gpkit`` in the appropriate terminal or command prompt.
+5. Open a Python prompt and run ``import gpkit`` to finish installation and run unit tests.
 
 If you encounter any bugs please email ``gpkit@mit.edu``
 or `raise a GitHub issue <http://github.com/convexengineering/gpkit/issues/new>`_.
 
 
-Installing MOSEK
-================
-GPkit interfaces with two off the shelf solvers: cvxopt, and MOSEK.
+Installing MOSEK 8
+==================
+GPkit interfaces with two off the shelf solvers: cvxopt, and MOSEK (versions 8 and 9).
 Cvxopt is open source and installed by default; MOSEK requires a commercial licence or (free)
-academic license.
+academic license. In MOSEK version 8 GPkit uses the command-line interface ``mskexpopt`` solver, while
+in MOSEK 9 it uses the more active exponential-cone interface (and hence supports :ref:`migp`).
 
 Mac OS X
   - If ``which gcc`` does not return anything, install the `Apple Command Line Tools <https://developer.apple.com/downloads/index.action?=command%20line%20tools>`_.
   - Download `MOSEK 8 <https://www.mosek.com/downloads/>`_, then:
       - Move the ``mosek`` folder to your home directory
       - Follow `these steps for Mac <http://docs.mosek.com/7.0/toolsinstall/Mac_OS_X_installation.html>`_.
-      - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``~/mosek/``
+  - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``~/mosek/``
 
 Linux
   - Download `MOSEK 8 <https://www.mosek.com/downloads/>`_, then:
       - Move the ``mosek`` folder to your home directory
       - Follow `these steps for Linux <http://docs.mosek.com/7.0/toolsinstall/Linux_UNIX_installation_instructions.html>`_.
-      - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``~/mosek/``
+  - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``~/mosek/``
 
 Windows
+    - Make sure ``gcc`` is on your system path.
+        - To do this, type ``gcc`` into a command prompt.
+        - If you get ``executable not found``, then install the 64-bit version (x86_64 installer architecture dropdown option) with GCC version 6.4.0 or older of `mingw <http://sourceforge.net/projects/mingw-w64/>`_.
+        - In an Anaconda command prompt (or equivalent), run ``cd C:\Program Files\mingw-w64\x86_64-6.4.0-posix-seh-rt_v5-rev0\`` (or whatever corresponds to the correct installation directory; note that if mingw is in ``Program Files (x86)`` instead of ``Program Files`` you've installed the 32-bit version by mistake)
+        - Run ``mingw-w64`` to add it to your executable path. For step 3 of the install process you'll need to run ``pip install gpkit`` from this prompt.
     - Download `MOSEK 8 <https://www.mosek.com/downloads/>`_, then:
         - Follow `these steps for Windows <http://docs.mosek.com/7.0/toolsinstall/Windows_installation.html>`_.
-        - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``C:\Users\(your_username)\mosek\``
-        - Make sure ``gcc`` is on your system path.
-            - To do this, type ``gcc`` into a command prompt.
-            - If you get ``executable not found``, then install the 64-bit version (x86_64 installer architecture dropdown option) with GCC version 6.4.0 or older of `mingw <http://sourceforge.net/projects/mingw-w64/>`_.
-            - In an Anaconda command prompt (or equivalent), run ``cd C:\Program Files\mingw-w64\x86_64-6.4.0-posix-seh-rt_v5-rev0\`` (or whatever corresponds to the correct installation directory; note that if mingw is in ``Program Files (x86)`` instead of ``Program Files`` you've installed the 32-bit version by mistake)
-            - Run ``mingw-w64`` to add it to your executable path. For step 3 of the install process you'll need to run ``pip install gpkit`` from this prompt.
+    - Request an `academic license file <http://license.mosek.com/academic>`_ and put it in ``C:\Users\(your_username)\mosek\``
 
 Debugging your installation
 ===========================

docs/source/signomialprogramming.rst

@@ -33,7 +33,7 @@ Example Usage
 
 When using the ``localsolve`` method, the ``reltol`` argument specifies the relative tolerance of the solver: that is, by what percent does the solution have to improve between iterations? If any iteration improves less than that amount, the solver stops and returns its value.
 
-If you wish to start the local optimization at a particular point :math:`x_k`, however, you may do so by putting that position (a dictionary formatted as you would a substitution) as the ``xk`` argument.
+If you wish to start the local optimization at a particular point :math:`x_0`, however, you may do so by putting that position (a dictionary formatted as you would a substitution) as the ``x0`` argument.
 
 .. _sgp:
 

docs/source/visint.rst

@@ -1,5 +1,45 @@
 Visualization and Interaction
 *****************************
+
+Variable Reference Plots
+========================
+
+Code in this section uses the `CE solar model <https://github.com/convexengineering/solar/tree/gpkitdocs>`_.
+
+.. code:: python
+
+    from solar.solar import *
+    Vehicle = Aircraft(Npod=3, sp=True)
+    M = Mission(Vehicle, latitude=[20])
+    M.cost = M[M.aircraft.Wtotal]
+    sol = M.localsolve()
+
+    from gpkit.interactive.references import referencesplot
+    referencesplot(M, openimmediately=True)
+
+Running the code above will produce two files in your working directory:
+``referencesplot.html`` and ``referencesplot.json``, and (unless you specify
+``openimmediately=False``) open the former in your web browser,
+showing you something like this:
+
+.. figure:: figures/referencesplot.png
+
+`Click to see the interactive version of this plot. <https://web.mit.edu/eburn/www/referencesplot/referencesplot.html>`_
+
+When a model's name is hovered over its connections are highlighted, showing in
+red the other models it imports variables from to use in its constraints and in
+blue the models that import variables from it.
+
+By default connections are shown with equal width ("Unweighted").
+When "Global Sensitivities" is selected, connection width is proportional to
+the sensitivity of all variables in that connection to the importing model,
+corresponding exactly to how much the model's cost would decrease if those variables were relaxed
+in only that importing model. This can give a sense of which connections are vital to
+the overall model. When "Normalized Sensitivities"  is selected, that
+global weight is divided by the weight of all variables in the importing model,
+giving a sense of which connections are vital to each subsystem.
+
+
 .. _sankey:
 Sensitivity Diagrams
 ====================
@@ -9,9 +49,9 @@ Requirements
 -  Jupyter Notebook
 -  `ipysankeywidget <https://github.com/ricklupton/ipysankeywidget>`__
     - Note that you'll need to activate these widgets on Jupyter by runnning
-    
+
       - ``jupyter nbextension enable --py --sys-prefix widgetsnbextension``
-      
+
       - ``jupyter nbextension enable --py --sys-prefix ipysankeywidget``
 
 Example
@@ -25,10 +65,10 @@ Code in this section uses the `CE solar model <https://github.com/convexengineer
     Vehicle = Aircraft(Npod=3, sp=True)
     M = Mission(Vehicle, latitude=[20])
     M.cost = M[M.aircraft.Wtotal]
-    sol = M.localsolve("mosek_cli")
+    sol = M.localsolve()
 
     from gpkit.interactive.sankey import Sankey
-    
+
 Once the code above has been run in a Jupyter notebook, the code below will create interactive hierarchies of your model's sensitivities, like so:
 
 .. figure:: figures/Mission.gif

fulltests.sh

@@ -1,3 +1,4 @@
-python -c "from gpkit.tests import run; run()" 
+python -c "from gpkit.tests import run; run()"
 rm *.pkl
 rm solution.*
+rm referencesplot.*

gpkit/constraints/bounded.py

@@ -3,7 +3,7 @@
 import numpy as np
 from .. import Variable
 from .set import ConstraintSet
-from ..small_scripts import appendsolwarning
+from ..small_scripts import appendsolwarning, initsolwarning
 
 
 def varkey_bounds(varkeys, lower, upper):
@@ -77,6 +77,7 @@ def process_result(self, result):
     def check_boundaries(self, result):
         "Creates (and potentially prints) a dictionary of unbounded variables."
         out = defaultdict(set)
+        initsolwarning(result, "Arbitrarily Bounded Variables")
         for i, varkey in enumerate(self.bound_varkeys):
             value = result["variables"][varkey]
             c_senss = [result["sensitivities"]["constraints"].get(c, 0)