Datasets:
File size: 4,235 Bytes
e7a6bab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | import csv
import argparse
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
K = 1.0
BOUNDARY_EPS = 0.02
def load_csv(path):
with open(path, newline="", encoding="utf-8") as f:
return list(csv.DictReader(f))
def index_by_id(rows):
index = {}
for row in rows:
sid = str(row.get("scenario_id", "")).strip()
if not sid:
raise ValueError("Blank or missing scenario_id detected")
if sid in index:
raise ValueError(f"Duplicate scenario_id detected: {sid}")
index[sid] = row
return index
def parse_binary(value, field_name, sid):
try:
parsed = int(value)
except ValueError as e:
raise ValueError(f"Invalid {field_name} for {sid}: {value}") from e
if parsed not in (0, 1):
raise ValueError(f"Invalid {field_name} for {sid}: {parsed}")
return parsed
def compute_surfaces(pressure, buffer, lag, coupling):
s1 = buffer - (pressure * coupling) - (K * lag)
s2 = buffer - (pressure * (coupling ** 2)) - (K * lag)
s3 = buffer - (pressure * coupling) - (K * (lag ** 2))
return {
"baseline_surface": s1,
"coupling_surface": s2,
"lag_surface": s3,
}
def plot_projection(pred_path, truth_path):
preds = load_csv(pred_path)
truth = load_csv(truth_path)
pred_map = index_by_id(preds)
truth_map = index_by_id(truth)
if set(pred_map.keys()) != set(truth_map.keys()):
raise ValueError("scenario_id mismatch between prediction and truth files")
plt.figure(figsize=(10, 8))
for sid in sorted(truth_map):
truth_row = truth_map[sid]
pred_row = pred_map[sid]
pressure = float(truth_row["pressure"])
buffer = float(truth_row["buffer"])
lag = float(truth_row["lag"])
coupling = float(truth_row["coupling"])
pred = parse_binary(pred_row["prediction"], "prediction", sid)
label = parse_binary(truth_row["label_stable"], "label_stable", sid)
surfaces = compute_surfaces(pressure, buffer, lag, coupling)
manifold_margin = min(surfaces.values())
active_surface = min(surfaces, key=surfaces.get)
x = pressure * coupling
y = buffer
if abs(manifold_margin) <= BOUNDARY_EPS:
color = "orange"
marker = "s"
elif manifold_margin < 0:
color = "red"
marker = "x"
else:
color = "green"
marker = "o"
if label == 0 and pred == 1:
color = "purple"
marker = "*"
elif label == 1 and pred == 0:
color = "blue"
marker = "D"
size = 110
edgecolor = "black" if active_surface == "coupling_surface" else "none"
linewidth = 1.2 if active_surface == "coupling_surface" else 0.0
plt.scatter(
x,
y,
c=color,
marker=marker,
s=size,
edgecolors=edgecolor,
linewidths=linewidth,
)
plt.xlabel("Pressure × Coupling")
plt.ylabel("Buffer Capacity")
plt.title("Clarus Stability Manifold Projection (2D View)")
plt.grid(True)
plt.tight_layout()
legend_elements = [
Line2D([0], [0], marker="o", color="w", label="Stable region", markerfacecolor="green", markersize=10),
Line2D([0], [0], marker="x", color="red", label="Collapse region", linestyle="None", markersize=10),
Line2D([0], [0], marker="s", color="w", label="Near boundary", markerfacecolor="orange", markersize=10),
Line2D([0], [0], marker="*", color="w", label="False rescue", markerfacecolor="purple", markersize=12),
Line2D([0], [0], marker="D", color="w", label="False collapse", markerfacecolor="blue", markersize=10),
Line2D([0], [0], marker="o", color="black", label="Coupling surface active", markerfacecolor="white", markersize=9),
]
plt.legend(handles=legend_elements)
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pred", required=True)
parser.add_argument("--truth", required=True)
args = parser.parse_args()
plot_projection(args.pred, args.truth) |