diff --git a/crates/applications/stacked_cores/scripts/extract_meas.py b/crates/applications/stacked_cores/scripts/extract_meas.py
index 22e6c0e..1d85e0c 100755
--- a/crates/applications/stacked_cores/scripts/extract_meas.py
+++ b/crates/applications/stacked_cores/scripts/extract_meas.py
@@ -46,6 +46,16 @@ class ParameterizedMeas:
     def add_meas(self, params: tuple, meas_rows: list):
         self.meas[tuple(params)] = meas_rows
 
+    def all_rows(self) -> list:
+        # this is just `sum(self.meas.values())` but python is a idiot
+        rows = []
+        for mrows in self.meas.values():
+            rows.extend(mrows)
+        return rows
+
+    def runs(self) -> list:
+        return self.meas.values()
+
     def __repr__(self) -> str:
         meas_entries = "\n".join(
             f"    {format_float_tuple(k)}: {indented(format_list(v))}," for (k, v) in sorted(self.meas.items())
diff --git a/crates/applications/stacked_cores/scripts/plot_52xx.py b/crates/applications/stacked_cores/scripts/plot_52xx.py
new file mode 100755
index 0000000..9faf792
--- /dev/null
+++ b/crates/applications/stacked_cores/scripts/plot_52xx.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+
+from math import sqrt
+
+import plotly.express as px
+from pandas import DataFrame
+
+from stacked_cores_52xx import *
+
+def plot(name: str, x_name: str, y_series: list):
+    df = DataFrame(data={ x_name: sweep_1d(len(y_series)), "y": y_series })
+    fig = px.line(df, x=x_name, y="y", title=name)
+    fig.show()
+
+def eval_series(meas: 'ParameterizedMeas', points: list, y_idx: int = -1):
+    return [sample_all(meas, *p)[y_idx] for p in points]
+
+def sample_all(meas: 'ParameterizedMeas', a0: float, a1: float) -> tuple:
+    runs = [extract_transfer_from_meas_rows(r) for r in meas.runs()]
+    distances = [(distance_to(m, (a0, a1)), m) for m in runs]
+
+    return weighted_sum_of_neighbors_by_inv_distance(distances)
+
+def extract_transfer_from_meas_rows(meas_rows: list) -> tuple:
+    return (meas_rows[0].m[0], meas_rows[0].m[1], meas_rows[1].m[2], meas_rows[2].m[3])
+
+def interpolate(meas: 'ParameterizedMeas', a0: float, a1: float) -> tuple:
+    """
+    this interpolates a point among four neighboring points in 2d.
+    the implementation only supports 2d, but the technique is extendable to N dim.
+    """
+    rows = [r.m for r in meas.all_rows()]
+    distances = [(distance_to(m, (a0, a1)), m) for m in rows]
+    # a0_below_dist, a0_below_val = min(d for d in distances if d[1][0] <= a0)
+    # a0_above_dist, a0_above_val = min(d for d in distances if d[1][0] >= a0)
+    # a1_below_dist, a1_below_val = min(d for d in distances if d[1][1] <= a1)
+    # a1_above_dist, a1_above_val = min(d for d in distances if d[1][1] >= a1)
+    a0_below = min((d for d in distances if d[1][0] <= a0), default=None)
+    a0_above = min((d for d in distances if d[1][0] >= a0), default=None)
+    a1_below = min((d for d in distances if d[1][1] <= a1), default=None)
+    a1_above = min((d for d in distances if d[1][1] >= a1), default=None)
+
+    neighbors = [a for a in [a0_below, a0_above, a1_below, a1_above] if a is not None]
+    return weighted_sum_of_neighbors_by_inv_distance(neighbors)
+
+def weighted_sum_of_neighbors_by_inv_distance(neighbors: list) -> tuple:
+    """
+    each neighbor is (distance, value).
+    return a weighted sum of these neighbors, where lower-distance neighbors are more strongly weighted.
+    """
+    D = sum(a[0] for a in neighbors)
+    weight_n = lambda n: 1/max(n[0], 1e-3)  # non-normalized weight for neighbor
+    W = sum(weight_n(n) for n in neighbors)
+    weighted_n = lambda n: weighted(n[1], weight_n(n)/W)  # normalized weighted contribution for neighbor
+    return element_sum([weighted_n(n) for n in neighbors])
+
+def weighted_sum_of_neighbors(neighbors: list) -> tuple:
+    """
+    each neighbor is (distance, value).
+    return a weighted sum of these neighbors, where lower-distance neighbors are more strongly weighted.
+    """
+    D = sum(a[0] for a in neighbors)
+    weight_n = lambda n: D - n[0]  # non-normalized weight for neighbor
+    W = sum(weight_n(n) for n in neighbors)
+    weighted_n = lambda n: weighted(n[1], weight_n(n)/W)  # normalized weighted contribution for neighbor
+    return element_sum([weighted_n(n) for n in neighbors])
+
+def distance_to(p0: tuple, p1: tuple) -> float:
+    return sqrt(sum((x0-x1)*(x0-x1) for (x0, x1) in zip(p0, p1)))
+
+def element_sum(lists: list) -> list:
+    elems = lists[0]
+    for l in lists[1:]:
+        for i, e in enumerate(l):
+            elems[i] += e
+    return elems
+
+def weighted(l: list, scale: float) -> list:
+    return [e*scale for e in l]
+
+
+or_gates = DB
+
+sweep_1d = lambda points=101: [unit_to_m(x/(points-1)) for x in range(points)]
+sweep_a0 = lambda a1, points=101: [(unit_to_m(x/(points-1)), a1) for x in range(points)]
+sweep_a1 = lambda a0, points=101: [(a0, unit_to_m(x/(points-1))) for x in range(points)]
+
+unit_to_m = lambda u: -17000 + 34000 * u
+
+for name, meas in sorted(or_gates.items()):
+    trace = eval_series(meas, sweep_a1(-17000))
+    plot(f"{name}", "a1", trace)