diff --git a/data/linear/sumpowlin-factorized-3-10-10/random-100/1.json b/data/linear/sumpowlin-factorized-3-10-10/random-100/1.json
new file mode 100644
index 0000000000000000000000000000000000000000..ac22bc009fd5c8b92347014c86131c1580c3e645
--- /dev/null
+++ b/data/linear/sumpowlin-factorized-3-10-10/random-100/1.json
@@ -0,0 +1,525 @@
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index 0000000000000000000000000000000000000000..d9ef4d04156e26d291544d8eef1c0120a103a6da
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index 0000000000000000000000000000000000000000..edf92cd1e43590d199762e8b8f50b0196478d983
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+ ],
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+ ],
+ [
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+ "0.913545457642601 - 0.406736643075800*I",
+ "0.309016994374947 + 0.951056516295154*I"
+ ]
+ ]
+}
\ No newline at end of file
diff --git a/synthesize.py b/synthesize.sage
similarity index 87%
rename from synthesize.py
rename to synthesize.sage
index 0e80059f0fcfcfa0da22ecc0765298d4ee55eb54..272838425773adb71483cdf588e7f4776fab9161 100644
--- a/synthesize.py
+++ b/synthesize.sage
@@ -113,7 +113,7 @@ def _header(header_dict):
for child in header_dict["children"]:
res = fusion(res, _header(child))
if res == []:
- res.append([{"str": header_dict["val"], "w": 1}])
+ res.append([{"str": header_dict["val"], "w": int(1)}])
else:
res.append([{"str": header_dict["val"], "w": len(res[0])}])
return res
@@ -136,16 +136,29 @@ def get_leaves(D):
return L
def degrees(data):
- name = data.split("_")[1].split("-")[0]
- auxi = [int(d) for d in data.split("_")[1].split("-")[1:]]
- if name == "dense":
- return auxi
- elif name == "structured":
- return [auxi[1] for _ in range(auxi[0])]
- elif name == "katsura":
- return [1] + [2 for _ in range(auxi[0])]
- else:
+ data_path = (Path("data") / Path(data)).with_suffix(".json")
+ if not data_path.exists():
+ return [-1]
+
+ data_file = open(data_path, "r")
+ try:
+ d = json.load(data_file)
+ except:
return [-1]
+
+
+ A = PolynomialRing(CC, names = d["parameters"] + d["variables"])
+ t = A.gens()[0]
+ return [A(f).subs(t = 0).degree() for f in d["system"]]
+
+def instructions(data, derivative = False):
+ function = "f" if not derivative else "df"
+ for pkg in ["algpath", "adaptive"]:
+ pkg_out = Path("benchmarks") / Path(data) / Path(pkg) / Path("out.json")
+ if pkg_out.exists():
+ return json.load(open(str(pkg_out), "r"))["instructions"][function]
+ return -1
+
#. Formulas for the different statistics to computes
data_stats = {
@@ -154,8 +167,8 @@ data_stats = {
"dimension": lambda data: len(json.load(open(f"data/{data}.json", "r"))["system"]),
"max deg": lambda data: int(max(degrees(data))),
"paths": lambda data: len(json.load(open(f"data/{data}.json", "r"))["fiber"]),
- "f": lambda data: json.load(open(f"benchmarks/{data}/algpath/out.json", "r"))["instructions"]["f"],
- "df": lambda data: json.load(open(f"benchmarks/{data}/algpath/out.json", "r"))["instructions"]["df"],
+ "f": lambda data: instructions(data),
+ "df": lambda data: instructions(data, derivative=True),
"bezout": lambda data: str(numpy.prod(degrees(data))),
"medmean hc algpath": lambda data: '{0:.2f}'.format(float(result_stats["medsteps"]["f"](json.load(open(f"benchmarks/{data}/algpath/out.json", "r")), data))/float(result_stats["medsteps"]["f"](json.load(open(f"benchmarks/{data}/homotopycontinuation/out.json", "r")), data))),
"htype": lambda data: "N" if data.split("_")[0] == "newton" else ("T" if data.split("_")[0] == "linear" else "?")
@@ -189,18 +202,18 @@ def _table(data, branch, node):
# Data stats
if key in data_stats:
try:
- return [{"str": data_stats[key](data), "w": 1}]
+ return [{"str": str(data_stats[key](data)), "w": int(1)}]
except:
- return [{"str": "", "w": 1}]
+ return [{"str": "", "w": int(1)}]
# Local stats
if key in result_stats:
pkg_name = branch[0]
results = json.load(open(f"benchmarks/{data}/{pkg_name}/out.json", "r"))
try:
- return [{"str": result_stats[key](results), "w": 1}]
+ return [{"str": str(result_stats[key](results)), "w": int(1)}]
except:
- return [{"str": "", "w": 1}]
+ return [{"str": "", "w": int(1)}]
# Packages handling
if key in ["homotopycontinuation", "algpath", "macaulay2", "sirocco", "adaptive"]: