app: stacked_cores: 52-xx: add some facilities for plotting 52-xx or gate runs

it's primitive; not the *best* results

crates/applications/stacked_cores/scripts/extract_meas.py

@@ -33,11 +33,11 @@ def format_float_tuple(t: tuple) -> str:
 def format_list(l: list) -> str:
     if len(l) == 0: return "[]"
     if len(l) == 1: return f"{l}"
-    formatted_elems = [f"\t{e}" for e in l]
+    formatted_elems = [f"    {e}," for e in l]
     return "\n".join(["["] + formatted_elems + ["]"])
 
 def indented(s: str) -> str:
-    return s.replace('\n', '\n\t')
+    return s.replace('\n', '\n    ')
 
 class ParameterizedMeas:
     def __init__(self, meas = None):
@@ -46,9 +46,19 @@ 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"\t{format_float_tuple(k)}: {indented(format_list(v))}," for (k, v) in sorted(self.meas.items())
+            f"    {format_float_tuple(k)}: {indented(format_list(v))}," for (k, v) in sorted(self.meas.items())
         )
         return f"ParameterizedMeas({{\n{meas_entries}\n}})"
 

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)

crates/applications/stacked_cores/scripts/stacked_cores_52xx.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+
+import os
+
+from extract_meas import extract_parameterized_meas, indented
+
+from stacked_cores_52xx_db import DB
+
+def extract_stems(dirlist: list) -> list:
+    stems = set()
+    TERM = "-drive-"
+    for d in dirlist:
+        print(d)
+        if not d.startswith("52-"): continue
+        if TERM not in d: continue
+        stem = d[:d.find(TERM) + len(TERM)]
+        stems.add(stem)
+
+    return stems
+
+
+def compute_db():
+    here, _ = os.path.split(__file__)
+    toplevel_out = f"{here}/../../../../out/applications/stacked_cores"
+    stems = extract_stems(os.listdir(toplevel_out))
+    return {
+        s: extract_parameterized_meas(os.path.join(toplevel_out, s), [2e-9, 4e-9, 8e-9])
+        for s in stems
+    }
+
+def dump(path: str, db: dict):
+    with open(path, "w") as f:
+        f.write("from extract_meas import MeasRow, ParameterizedMeas\n\n")
+        f.write("DB = {")
+        for k, v in sorted(db.items()):
+            f.write(indented(f"\n{k!r}: {v},"))
+        f.write("\n}")
+
+def update_db():
+    db = compute_db()
+    dump("stacked_cores_52xx_db.py", db)
+
+if __name__ == '__main__': update_db()

crates/applications/stacked_cores/scripts/stacked_cores_52xx_db.py

@@ -0,0 +1,228 @@
+from extract_meas import MeasRow, ParameterizedMeas
+
+DB = {
+    '52-or--0.0004rad-1000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-2e10-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-16877, -16904, -16285, -16878]),
+            MeasRow(4e-09, [-28630, -28441,  -8576, -15553]),
+            MeasRow(8e-09, [-28306, -28231, -28077,    177]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-16874,   6656, -17107, -16929]),
+            MeasRow(4e-09, [-28777, -28098,  -1512, -16687]),
+            MeasRow(8e-09, [-28278, -28198, -28066,   3321]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-16843,  16301, -17065, -16869]),
+            MeasRow(4e-09, [-28822, -28071,   1111, -16707]),
+            MeasRow(8e-09, [-28270, -28189, -28066,   4568]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [  8295,   8497, -17122, -16918]),
+            MeasRow(4e-09, [-28526, -28146,   3722, -16673]),
+            MeasRow(8e-09, [-28254, -28194, -28061,   5731]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16477, -16996, -17176, -16946]),
+            MeasRow(4e-09, [-28352, -28782,  -2593, -16543]),
+            MeasRow(8e-09, [-28261, -28241, -28064,   2829]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16668,  16498, -17016, -16854]),
+            MeasRow(4e-09, [-28504, -28113,   7198, -16626]),
+            MeasRow(8e-09, [-28247, -28189, -28062,   7201]),
+        ],
+    }),
+    '52-or--0.0004rad-2000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-1e10-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-16757, -16650,  -9580, -16753]),
+            MeasRow(4e-09, [-28229, -27842,  -3392, -16036]),
+            MeasRow(8e-09, [-27878, -27824, -27683,  -2310]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-16648,   1337, -14694, -16683]),
+            MeasRow(4e-09, [-28508, -27696,  -2683, -16542]),
+            MeasRow(8e-09, [-27886, -27810, -27687,  -2082]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-16693,  16547, -16555, -16715]),
+            MeasRow(4e-09, [-28671, -27677,   -106, -16607]),
+            MeasRow(8e-09, [-27887, -27806, -27687,   -971]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [  2983,   2964, -16291, -16696]),
+            MeasRow(4e-09, [-28067, -27725,   1009, -16637]),
+            MeasRow(8e-09, [-27871, -27812, -27690,   -601]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16704, -16629, -15282, -16706]),
+            MeasRow(4e-09, [-27744, -28539,  -2047, -16579]),
+            MeasRow(8e-09, [-27778, -27891, -27689,  -1854]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16676,  16522, -16897, -16727]),
+            MeasRow(4e-09, [-27851, -27698,   6415, -16746]),
+            MeasRow(8e-09, [-27796, -27805, -27693,   1309]),
+        ],
+    }),
+    '52-or--0.0004rad-2000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-2e10-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-17032, -17247, -17712, -17031]),
+            MeasRow(4e-09, [-28965, -28862,  -9673, -15329]),
+            MeasRow(8e-09, [-28799, -28722, -28585,    350]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-16951,  10079, -17498, -16979]),
+            MeasRow(4e-09, [-29079, -28601,   -701, -16579]),
+            MeasRow(8e-09, [-28767, -28689, -28572,   4599]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-16902,  16222, -17308, -16918]),
+            MeasRow(4e-09, [-29098, -28583,   1140, -16615]),
+            MeasRow(8e-09, [-28762, -28685, -28571,   5587]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [ 11228,  10998, -17386, -16952]),
+            MeasRow(4e-09, [-28915, -28630,   4699, -16563]),
+            MeasRow(8e-09, [-28750, -28686, -28567,   7482]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16413, -17143, -17564, -16998]),
+            MeasRow(4e-09, [-28842, -29037,  -3065, -16220]),
+            MeasRow(8e-09, [-28764, -28722, -28574,   3465]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16643,  16461, -17135, -16882]),
+            MeasRow(4e-09, [-28906, -28612,   7107, -16533]),
+            MeasRow(8e-09, [-28741, -28679, -28567,   8799]),
+        ],
+    }),
+    '52-or--0.0004rad-2000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-30000001024e0-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-17103, -17386, -18064, -17089]),
+            MeasRow(4e-09, [-29333, -29282,  -9525, -13929]),
+            MeasRow(8e-09, [-29281, -29200, -29110,   4900]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-17034,  12903, -17738, -17040]),
+            MeasRow(4e-09, [-29380, -29136,    443, -15910]),
+            MeasRow(8e-09, [-29260, -29180, -29089,   9612]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-16976,  16188, -17496, -16985]),
+            MeasRow(4e-09, [-29385, -29124,   1572, -15974]),
+            MeasRow(8e-09, [-29258, -29175, -29089,  10218]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [ 13491,  13574, -17576, -17007]),
+            MeasRow(4e-09, [-29294, -29149,   5833, -15960]),
+            MeasRow(8e-09, [-29249, -29171, -29082,  12488]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16386, -17283, -17831, -17046]),
+            MeasRow(4e-09, [-29277, -29332,  -3114, -15211]),
+            MeasRow(8e-09, [-29263, -29189, -29095,   7945]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16627,  16446, -17234, -16926]),
+            MeasRow(4e-09, [-29291, -29138,   7254, -15969]),
+            MeasRow(8e-09, [-29244, -29165, -29077,  13189]),
+        ],
+    }),
+    '52-or--0.0004rad-5000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-30000001024e0-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-17215, -17627, -18647, -17200]),
+            MeasRow(4e-09, [-29987, -29936,  -9605, -13641]),
+            MeasRow(8e-09, [-29970, -29952, -29859,   4176]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-17140,  13426, -18165, -17134]),
+            MeasRow(4e-09, [-30002, -29867,    205, -15391]),
+            MeasRow(8e-09, [-29962, -29939, -29844,   8045]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-17054,  16241, -17807, -17056]),
+            MeasRow(4e-09, [-30004, -29860,   1298, -15454]),
+            MeasRow(8e-09, [-29960, -29936, -29843,   8530]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [ 12364,  13869, -17934, -17091]),
+            MeasRow(4e-09, [-29975, -29873,   5187, -15527]),
+            MeasRow(8e-09, [-29959, -29933, -29838,  10250]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16403, -17493, -18281, -17133]),
+            MeasRow(4e-09, [-29964, -29960,  -3570, -14599]),
+            MeasRow(8e-09, [-29964, -29947, -29850,   6638]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16642,  16478, -17423, -16973]),
+            MeasRow(4e-09, [-29971, -29868,   6812, -15544]),
+            MeasRow(8e-09, [-29955, -29929, -29839,  10948]),
+        ],
+    }),
+    '52-or--0.0004rad-5000ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-49999998976e0-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-17269, -17747, -18930, -17257]),
+            MeasRow(4e-09, [-30532, -30505,  -8672, -11666]),
+            MeasRow(8e-09, [-30542, -30568, -30482,   6999]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-17208,  12746, -18402, -17198]),
+            MeasRow(4e-09, [-30531, -30485,    340, -13040]),
+            MeasRow(8e-09, [-30541, -30562, -30468,   9809]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-17104,  16242, -17981, -17097]),
+            MeasRow(4e-09, [-30531, -30479,   1673, -13238]),
+            MeasRow(8e-09, [-30541, -30560, -30467,  10181]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [ 11178,  13470, -18141, -17155]),
+            MeasRow(4e-09, [-30530, -30480,   4702, -13242]),
+            MeasRow(8e-09, [-30542, -30559, -30462,  11103]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16402, -17600, -18510, -17178]),
+            MeasRow(4e-09, [-30529, -30501,  -3107, -12672]),
+            MeasRow(8e-09, [-30541, -30564, -30473,   8718]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16633,  16468, -17520, -16999]),
+            MeasRow(4e-09, [-30527, -30473,   6476, -13306]),
+            MeasRow(8e-09, [-30540, -30556, -30463,  11596]),
+        ],
+    }),
+    '52-or--0.0004rad-500ctl_cond-20000coupling_cond-2000ps-100ps-3ctl-3coupling-3_1_winding-49999998976e0-drive-': ParameterizedMeas({
+        (-1.000, -1.000,): [
+            MeasRow(2e-09, [-17062, -17312, -17911, -17059]),
+            MeasRow(4e-09, [-29152, -29125,  -9394, -11763]),
+            MeasRow(8e-09, [-29139, -29027, -28928,   8454]),
+        ],
+        (-1.000,  0.000,): [
+            MeasRow(2e-09, [-17023,  13648, -17638, -17023]),
+            MeasRow(4e-09, [-29163, -28990,    569, -14326]),
+            MeasRow(8e-09, [-29121, -28986, -28871,  13032]),
+        ],
+        (-1.000,  1.000,): [
+            MeasRow(2e-09, [-16965,  16158, -17435, -16962]),
+            MeasRow(4e-09, [-29165, -28975,   1495, -14497]),
+            MeasRow(8e-09, [-29118, -28980, -28866,  13417]),
+        ],
+        ( 0.000,  0.000,): [
+            MeasRow(2e-09, [ 13197,  14274, -17489, -16997]),
+            MeasRow(4e-09, [-29132, -28975,   5489, -14455]),
+            MeasRow(8e-09, [-29110, -28967, -28847,  14870]),
+        ],
+        ( 1.000, -1.000,): [
+            MeasRow(2e-09, [ 16364, -17232, -17722, -17014]),
+            MeasRow(4e-09, [-29122, -29129,  -3513, -13042]),
+            MeasRow(8e-09, [-29126, -29003, -28892,  11345]),
+        ],
+        ( 1.000,  1.000,): [
+            MeasRow(2e-09, [ 16590,  16410, -17188, -16904]),
+            MeasRow(4e-09, [-29126, -28962,   6689, -14511]),
+            MeasRow(8e-09, [-29105, -28961, -28842,  15194]),
+        ],
+    }),
+}