[docs] switch to PlotlyJS

jump-dev · Dec 7, 2023 · e868731 · e868731
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Showing 15 changed files with 29 additions and 13 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -23,6 +23,7 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 MultiObjectiveAlgorithms = "0327d340-17cd-11ea-3e99-2fd5d98cecda"
 PATHSolver = "f5f7c340-0bb3-5c69-969a-41884d311d1b"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
@@ -53,6 +54,7 @@ Literate = "2.8"
 MathOptInterface = "=1.23.0"
 MultiObjectiveAlgorithms = "=1.3.1"
 PATHSolver = "=1.7.0"
+PlotlyJS = "0.18"
 Plots = "1"
 SCS = "=2.0.0"
 SQLite = "1"

diff --git a/docs/src/tutorials/algorithms/cutting_stock_column_generation.jl b/docs/src/tutorials/algorithms/cutting_stock_column_generation.jl
@@ -16,6 +16,7 @@ import HiGHS
 import Plots
 import SparseArrays
 import Test  #src
+Plots.plotlyjs()
 
 # ## Background
 

diff --git a/docs/src/tutorials/algorithms/tsp_lazy_constraints.jl b/docs/src/tutorials/algorithms/tsp_lazy_constraints.jl
@@ -34,6 +34,7 @@ using JuMP
 import GLPK
 import Random
 import Plots
+Plots.plotlyjs()
 
 # ## [Mathematical Formulation](@id tsp_model)
 

diff --git a/docs/src/tutorials/applications/power_systems.jl b/docs/src/tutorials/applications/power_systems.jl
@@ -20,6 +20,7 @@ import DataFrames
 import HiGHS
 import Plots
 import StatsPlots
+Plots.plotlyjs()
 
 # ## Economic dispatch
 

diff --git a/docs/src/tutorials/applications/two_stage_stochastic.jl b/docs/src/tutorials/applications/two_stage_stochastic.jl
@@ -16,6 +16,7 @@ import HiGHS
 import Plots
 import StatsPlots
 import Statistics
+Plots.plotlyjs()
 
 # ## Background
 

diff --git a/docs/src/tutorials/conic/ellipse_approx.jl b/docs/src/tutorials/conic/ellipse_approx.jl
@@ -52,6 +52,7 @@ import Plots
 import Random
 import SCS
 import Test  #src
+Plots.plotlyjs()
 
 # ## Data
 

diff --git a/docs/src/tutorials/conic/min_ellipse.jl b/docs/src/tutorials/conic/min_ellipse.jl
@@ -46,6 +46,7 @@ import LinearAlgebra
 import Plots
 import SCS
 import Test  #src
+Plots.plotlyjs()
 
 # ## Data
 

diff --git a/docs/src/tutorials/conic/simple_examples.jl b/docs/src/tutorials/conic/simple_examples.jl
@@ -16,6 +16,7 @@ import Plots
 import Random
 import SCS
 import Test
+Plots.plotlyjs()
 
 # ## Maximum cut via SDP
 
@@ -32,7 +33,7 @@ import Test
 # where ``L`` is the weighted graph Laplacian and ``e`` is a vector of ones.
 # For more details, see:
 #
-# Goemans, M. X., & Williamson, D. P. (1995). 
+# Goemans, M. X., & Williamson, D. P. (1995).
 # [_Improved approximation algorithms for maximum cut and satisfiability problems
 # using semidefinite programming._](https://doi.org/10.1145/227683.227684)
 # Journal of the ACM (JACM), 42(6), 1115-1145.
@@ -122,7 +123,7 @@ S, T = solve_max_cut_sdp([0 1 5 0; 1 0 0 9; 5 0 0 2; 0 9 2 0])
 # Given a set of points ``a_1, \ldots, a_m``  in ``\mathbb{R}^n``, allocate them to ``k`` clusters.
 #
 # For more details, see:
-# 
+#
 # Peng, J., & Wei, Y. (2007).
 # [_Approximating k-means-type clustering via semidefinite programming_](https://doi.org/10.1137/050641983).
 # SIAM Journal on Optimization, 18(1), 186-205.
@@ -179,7 +180,7 @@ example_k_means_clustering()
 # \begin{bmatrix}
 #       1   &  ρ_{AB} &  ρ_{AC} \\
 #      ρ_{AB} &  1    &  ρ_{BC}  \\
-#      ρ_{AC} &  ρ_{BC} &  1   
+#      ρ_{AC} &  ρ_{BC} &  1
 # \end{bmatrix} \succeq 0
 # ```
 
@@ -250,16 +251,16 @@ example_correlation_problem()
 # ```math
 #     D[a, b]^2 \leq Q[a, a] + Q[b, b] - 2 Q[a, b] \leq c^2 \; D[a, b]^2
 # ```
-# for all edges ``(a, b)`` in the graph and minimize ``c^2``, 
+# for all edges ``(a, b)`` in the graph and minimize ``c^2``,
 # which gives us the SDP formulation below.
 # Since we may choose any point to be the origin, we fix the first vertex at 0.
 #
 # For more details, see:
 #
 # J. Matoušek (2002), [_Lectures on discrete geometry_](https://doi.org/10.1007/978-1-4613-0039-7),
 # Springer, pp. 378-379
-# 
-#  N. Linial (2002), 
+#
+#  N. Linial (2002),
 # _[Finite metric spaces--combinatorics, geometry and algorithms](https://arxiv.org/abs/math/0304466)_,
 # Proceedings of the ICM, Vol. 3, 573-586
 
@@ -318,11 +319,11 @@ example_minimum_distortion()
 # Consider the pentagon graph:
 # ```raw
 #      [5]
-#     /   \    
-#    /     \     
+#     /   \
+#    /     \
 #  [1]     [4]
 #   |       |
-#   |       |  
+#   |       |
 #  [2] --- [3]
 # ```
 # with five vertices and edges. Its Lovász number is known to be precisely
@@ -334,7 +335,7 @@ example_minimum_distortion()
 # matrix with entries ``(i,j)`` and ``(j,i)`` equal to 1, with all other entries 0.
 # Let  ``E`` be the graph's edge set; in this example, ``E`` contains
 # (1,2), (2,3), (3,4), (4,5), (5,1)
-# and their transposes. The Lovász number can be computed from the program 
+# and their transposes. The Lovász number can be computed from the program
 # ```math
 # \begin{align}
 #     \text{max}      & \quad    J • X & \\
@@ -351,8 +352,8 @@ example_minimum_distortion()
 # A course in convexity,
 # American Mathematical Society (Vol. 54), pp. 182-185.
 #
-# Knuth, D. E. (1994), 
-# [_The sandwich theorem_](https://doi.org/10.37236%2F1193), 
+# Knuth, D. E. (1994),
+# [_The sandwich theorem_](https://doi.org/10.37236%2F1193),
 # Electronic Journal of Combinatorics, Volume 1, Issue 1, A1.
 
 function example_theta_problem()
@@ -389,7 +390,7 @@ example_theta_problem()
 # Closed-form expressions for the optimal value are available.
 # For more details, see:
 
-# Bertsimas, D., Gupta, V., & Kallus, N. (2018). 
+# Bertsimas, D., Gupta, V., & Kallus, N. (2018).
 # [_Data-driven robust optimization._](https://doi.org/10.1007/s10107-017-1125-8)
 # Mathematical Programming, 167, 235-292.
 

diff --git a/docs/src/tutorials/linear/facility_location.jl b/docs/src/tutorials/linear/facility_location.jl
@@ -16,6 +16,7 @@ import HiGHS
 import LinearAlgebra
 import Plots
 import Random
+Plots.plotlyjs()
 
 # ## Uncapacitated facility location
 

diff --git a/docs/src/tutorials/linear/factory_schedule.jl b/docs/src/tutorials/linear/factory_schedule.jl
@@ -24,6 +24,7 @@ import DataFrames
 import HiGHS
 import StatsPlots
 import Test  #src
+Plots.plotlyjs()
 
 # ## Formulation
 

diff --git a/docs/src/tutorials/linear/multi_objective_knapsack.jl b/docs/src/tutorials/linear/multi_objective_knapsack.jl
@@ -18,6 +18,7 @@ import HiGHS
 import MultiObjectiveAlgorithms as MOA
 import Plots
 import Test  #hide
+Plots.plotlyjs()
 
 # [MultiObjectiveAlgorithms.jl](@ref) is a package which implements a variety of
 # algorithms for solving multi-objective optimization problems. Because it is a

diff --git a/docs/src/tutorials/linear/piecewise_linear.jl b/docs/src/tutorials/linear/piecewise_linear.jl
@@ -13,6 +13,7 @@
 using JuMP
 import HiGHS
 import Plots
+Plots.plotlyjs()
 
 # ## Minimizing a convex function (outer approximation)
 

diff --git a/docs/src/tutorials/nonlinear/classifiers.jl b/docs/src/tutorials/nonlinear/classifiers.jl
@@ -29,6 +29,7 @@ import LinearAlgebra
 import Plots
 import Random
 import Test #src
+Plots.plotlyjs()
 
 # ## Data and visualisation
 

diff --git a/docs/src/tutorials/nonlinear/portfolio.jl b/docs/src/tutorials/nonlinear/portfolio.jl
@@ -40,6 +40,7 @@ import MultiObjectiveAlgorithms as MOA
 import Plots
 import Statistics
 import StatsPlots
+Plots.plotlyjs()
 
 # ## Formulation
 

diff --git a/docs/src/tutorials/nonlinear/rocket_control.jl b/docs/src/tutorials/nonlinear/rocket_control.jl
@@ -17,6 +17,7 @@
 using JuMP
 import Ipopt
 import Plots
+Plots.plotlyjs()
 
 # ## Overview