Methods exist to facilitate creating, solving, and plotting the results of a single-variable sweep (see :ref:`Sweeps` for details). Example usage is as follows:
.. literalinclude:: examples/plot_sweep1d.py
Which results in:
- jupyter notebook
- ipysankeywidget
Code in this section uses the CE solar model
from solar.solar import *
Vehicle = Aircraft(Npod=3, sp=True)
M = Mission(Vehicle, latitude=[20])
M.cost = M[M.aircraft.Wtotal]
sol = M.localsolve("mosek_cli")
from gpkit.interactive.sankey import Sankey
Sankey(sol, M, "SolarMission").diagram(M.aircraft.Wtotal, left=210, right=130)
Sankey
diagrams can be used to
visualize sensitivity structure in a model. A blue flow from a constraint to its parent
indicates that the sensitivity of the chosen variable (or of making the
constraint easier, if no variable is given) is negative; that
is, the objective of the overall model would improve if that variable's
value were increased in that constraint alone. Red indicates a
positive sensitivity: the objective and the the constraint 'want' that
variable's value decreased. Gray flows indicate a sensitivity whose
absolute value is below 1e-2, i.e. a constraint that is inactive for
that variable. Where equal red and blue flows meet, they cancel each
other out to gray.
In a Sankey diagram of a variable, the variable is on the left with its final sensitivity; to the right of it are all constraints that variable is in.
Free variables have an overall sensitivity of 0, so this visualization shows how the various pressures on that variable in all its constraints cancel each other out; this can get quite complex, as in this diagram of the pressures on wingspan:
Sankey(sol, M, "SolarMission").diagram(M.aircraft.b, right=180)
Fixed variables can have a nonzero overall sensitivity. Sankey diagrams can how that sensitivity comes together:
Sankey(sol, M, "SolarMission").diagram(M.variables_byname("tmin")[0], right=160, left=10)
Note that the only difference between free and fixed variabels from this perspective
is their final sensitivity; for example Nprop, the number of propellers on the
plane, has almost zero sensitivity, much like the wingspan b, above.
Sankey(sol, M, "SolarMission").diagram(M.variables_byname("tmin")[0], right=160, left=10)
When created without a variable, the diagram shows the sensitivity of every named model to becoming locally easier. Because derivatives are additive, these sensitivities are too: a model's sensitivity is equal to the sum of its constraints' sensitivities. Gray lines in this diagram indicate models without any tight constraints.
Sankey(sol, M, "SolarMission").diagram(height=600)
| Code | Result |
|---|---|
s = Sankey(M) |
Creates Sankey object of a given model |
s.diagram(vars) |
Creates the diagram in a way Jupyter knows how to present |
d = s.diagram() |
Don't do this! Captures output, preventing Jupyter from seeing it. |
s.diagram(width=...) |
Sets width in pixels. Same for height. |
s.diagram(left=...) |
Sets left (top, right, bottom) margin in pixels. Use if text is being cut off. |