feat: improved run.jl script

Project.toml

@@ -4,6 +4,7 @@ authors = ["Jaan Tollander de Balsch"]
 version = "0.2.0"
 
 [deps]
+ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [compat]

README.md

@@ -51,20 +51,16 @@ The [`src/nxn.mzn`](./src/nxn.mzn) file contains the MiniZinc constraint program
 
 The [`src/plots.jl`](./src/plots.jl) file contains the plotting code.
 
-Each `nxn.sh` shell script inside [`scripts`](./scripts/) contains the command for running the model for the grid of given size.
+Use [`script/run.jl`](./scripts/run.jl) Julia script for running the model for the grid of given size.
+
+Use [`script/plot.jl`](./scripts/plot.jl) Julia script to plot results.
 
 The [`results`](./results/) directory contains the output from the shell scripts and the generated SVG plots for each grid size and taxicab distance in format `<grid>/<distance>/<id>.svg`.
 
 
 ## Instructions
-We can begin by installing MiniZinc and adding it to our PATH environment variable. The, we can run the appropriate shell file from the `src` directory and write the output to `results/3x3.txt` file. For example:
-
-```bash
-./scripts/3x3.sh > results/3x3.txt
-```
-
-Alternatively, we can run the MiniZinc model directly.
+We can begin by installing MiniZinc and adding it to our PATH environment variable. The, we can run the appropriate shell file from the `script` directory and write the output to `results/3x3.txt` file. For example:
 
 ```bash
-minizinc src/nxn.mzn -s -a --solver gecode -D "n=3;"
+julia scripts/run.jl -n 3 --minizinc `which minizinc`
 ```

scripts/run.jl

@@ -1,13 +1,40 @@
 using LockPatternComplexity
+using ArgParse
 
-n = 4  # 2, 4, 6
-N, xs, ys, ds = line_types(n)
-minizinc = "/home/jaan/bin/MiniZincIDE-2.5.5-bundle-linux-x86_64/bin/minizinc"
-cmd = `$minizinc src/nxn_gen.mzn 
-       -D "n=$n;N=$N;xs=$(string(xs));ys=$(string(ys));ds=$(string(ds));"
-       --solver chuffed
-       --statistics 
-       --all-solutions
-       --two-pass`
-
-run(cmd)
+function data(n::Int)
+    N, xs, ys, ds = line_types(n)
+    return "n=$n;N=$N;xs=$(xs);ys=$(ys);ds=$(ds);"
+end
+
+function command(n::Int, minizinc, solver, flags)::Cmd
+    d = data(n)
+    return `$minizinc src/nxn.mzn -D $d --solver $solver $flags`
+end
+
+s = ArgParseSettings()
+
+@add_arg_table s begin
+    "-n"
+    help = "grid size"
+    arg_type = Int
+    default = 3
+    "--minizinc"
+    help = "full path to MiniZinc. Use command `which minizinc` to find it out."
+    arg_type = String
+    default = "/home/jaan/bin/MiniZincIDE-2.5.5-bundle-linux-x86_64/bin/minizinc"
+end
+
+args = parse_args(s)
+n = args["n"]
+minizinc = args["minizinc"]
+
+function cmd(n)
+    n == 2 && return command(2, minizinc, "gecode", ["--all-solutions", "--statistics"])
+    n == 3 && return command(3, minizinc, "gecode", ["--all-solutions", "--statistics"])
+    n == 4 && return command(4, minizinc, "chuffed", ["--all-solutions", "--statistics", "--two-pass"])
+    n == 5 && return command(5, minizinc, "chuffed", ["--all-solutions", "--statistics", "--two-pass"])
+    n == 6 && return command(6, minizinc, "chuffed", ["--all-solutions", "--statistics", "--two-pass"])
+    error("")
+end
+
+run(cmd(n))

src/nxn.mzn

@@ -33,30 +33,22 @@ array[1..m-1] of var int: dx = [x[p[i]] - x[p[i+1]] | i in 1..m-1];
 array[1..m-1] of var int: dy = [y[p[i]] - y[p[i+1]] | i in 1..m-1];
 
 % Maximum complexity constraints.
-% Enforce that there exists one of each line type.
 predicate line(int: x1, int: y1, int: k) = exists(i in 1..m-1, c in 1..k)(
     (dx[i] = c*x1 /\ dy[i] = c*y1) \/ (dx[i] = -c*x1 /\ dy[i] = -c*y1)
 );
 
-% Greatest common divisor
-function int: gcd(int: a, int: b) = max(
-    [i | i in 1..min(a, b) where (a mod i) = 0 /\ (b mod i) = 0]
-);
-
-constraint line(0, 1, n-1);
-constraint line(1, 0, n-1);
-constraint line(1, 1, n-1);
-constraint line(1, -1, n-1);
-constraint forall(x1 in 1..n-2, y1 in (x1+1)..n-1 where gcd(x1, y1)=1)(
-    let {int: d = (n-1) div y1} in
-    line(x1, y1, d) /\
-    line(y1, x1, d) /\
-    line(x1, -y1, d) /\
-    line(y1, -x1, d)
-);
+int: N;
+array[1..N] of int: xs;
+array[1..N] of int: ys;
+array[1..N] of int: ds;
+constraint count(i in 1..N)(line(xs[i], ys[i], ds[i])) = min(N, m-1);
+% constraint forall(i in 1..N)(line(xs[i], ys[i], ds[i]));
 
-% Maximize distance
+% Find max complexity patterns
 solve satisfy;
-
-% Print output
 output ["pattern=\(p)", "\n", "distance=\(sum(i in 1..m-1)(abs(dx[i]) + abs(dy[i])))"];
+
+% Maximize distance
+% var int: distance = sum(i in 1..m-1)(abs(dx[i]) + abs(dy[i]));
+% solve maximize distance;
+% output ["pattern=\(p)", "\n", "distance=\(distance)"];