add adaptive binary

gen.sage

@@ -153,11 +153,12 @@ class ResultantsCtx(Name, System):
         self.n_ctx = IntegerCtx(n, default=3, message="Dimension ?")
         self.d_ctx = IntegerCtx(d, default=3, message="Degree ?")
 
-        dense_ctx = DenseCtx([self.d_ctx.value for _ in range(self.n_ctx.value)])
+        dense_ctx = DenseCtx(
+            [self.d_ctx.value for _ in range(self.n_ctx.value)])
         self.ring = dense_ctx.ring
         variables = list(map(str, self.ring.gens()))
         self.system = dense_ctx.system
-        
+
         assert "y" not in variables
         Cpy = PolynomialRing(CC, names=["y"] + variables)
         f = Cpy.random_element(degree=self.d_ctx.value, terms=1000)
@@ -304,7 +305,8 @@ class LinearHomotopyCtx(Name, Problem):
         self.target_ctx = TargetCtx(target_type=target_type, default="dense")
         self.start = URootsCtx(
             degrees=self.target_ctx.target.get_degrees(), compute_fiber=True)
-        self.name = "linear" / self.target_ctx.target.name / Path(str(self.start.name).replace("uroots-", ""))
+        self.name = "linear" / self.target_ctx.target.name / \
+            Path(str(self.start.name).replace("uroots-", ""))
 
         # compute homotopy
         try:

run_entries.py

@@ -37,6 +37,6 @@ for i, pkg_name in enumerate(pkg_list):
         print(f"Data {j + 1}/{len(data_list)}")
         pkg_path = (Path("packages") / Path(f"{pkg_name}")).with_suffix(".py")
         data_name = Path(data_path)
-        if not ((Path("benchmarks") / data_name / pkg_name).exists() and args.n):
+        if not ((Path("benchmarks") / data_name / pkg_name / "info.json").exists() and args.n):
             subprocess.run(["python3", "runtest.py", str(pkg_path), str(("data" / Path(data_path)).with_suffix(".json"))] + ["--timeout", str(args.timeout)]*(
                 args.timeout != None) + ["--mem", str(args.mem)]*(args.mem != None) + ["--perf"]*args.perf)

synthesize.py

@@ -175,7 +175,10 @@ result_stats = {
     "minsteps": lambda res: steps_format(min([int(p) for p in res["steplist"] if p is not None])),
     "maxsteps": lambda res: steps_format(max([int(p) for p in res["steplist"] if p is not None])),
     "q1steps": lambda res: steps_format(round(numpy.quantile([int(p) for p in res["steplist"] if p is not None], 0.25), 1)),
-    "q3steps": lambda res: steps_format(round(numpy.quantile([int(p) for p in res["steplist"] if p is not None], 0.75), 1))
+    "q3steps": lambda res: steps_format(round(numpy.quantile([int(p) for p in res["steplist"] if p is not None], 0.75), 1)),
+    "maxprec": lambda res: str(max([int(p) for p in res["maxpreclist"]])),
+    "meanprec": lambda res: str(numpy.mean([int(p) for p in res["meanpreclist"]])),
+    "wmeanprec": lambda res: str(numpy.mean([int(p) for p in res["weightedmeanpreclist"]])),
 }
 
 def _table(data, branch, node):
@@ -200,7 +203,7 @@ def _table(data, branch, node):
     # Packages handling
     if key in ["homotopycontinuation", "algpath", "macaulay2", "sirocco", "adaptive"]:
         dir = f"benchmarks/{data}/{key}/"
-        if not os.path.exists(dir):
+        if not (Path(dir) / "info.json").exists():
             return [{"str": "not benchmarked", "w": len(get_leaves(node))}]
         
         info_dict = json.load(open(f"{dir}info.json"))