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b154e4c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | from __future__ import annotations
import numpy as np
import pandas as pd
from .utils import infer_family
def sample_base_designs(n=1000, seed=7) -> pd.DataFrame:
rng = np.random.default_rng(seed)
types = np.array([
"NC_pillbox_Lband",
"SC_elliptical_1p3GHz",
"NC_Cband_TW",
"NC_Xband_SW",
])
probs = np.array([0.25, 0.20, 0.30, 0.25])
types_arr = rng.choice(types, size=n, p=probs)
f = np.zeros(n)
rq = np.zeros(n)
q0 = np.zeros(n)
eacc = np.zeros(n)
length = np.zeros(n)
beta = np.zeros(n)
cryo = np.full(n, np.nan)
for i, typ in enumerate(types_arr):
if typ == "NC_pillbox_Lband":
f[i] = rng.uniform(0.20e9, 0.80e9)
rq[i] = rng.uniform(60, 150)
q0[i] = rng.uniform(1e4, 8e4)
eacc[i] = rng.uniform(8, 20)
length[i] = rng.uniform(0.15, 0.60)
beta[i] = rng.uniform(0.7, 2.5)
elif typ == "SC_elliptical_1p3GHz":
f[i] = rng.uniform(0.80e9, 1.60e9)
rq[i] = rng.uniform(200, 400)
q0[i] = rng.uniform(5e9, 2e10)
eacc[i] = rng.uniform(10, 25)
length[i] = rng.uniform(0.60, 1.20)
beta[i] = rng.uniform(0.8, 2.0)
cryo[i] = rng.uniform(1.8, 4.2)
elif typ == "NC_Cband_TW":
f[i] = rng.uniform(5.00e9, 6.00e9)
rq[i] = rng.uniform(100, 280)
q0[i] = rng.uniform(2e4, 8e4)
eacc[i] = rng.uniform(12, 30)
length[i] = rng.uniform(0.20, 0.60)
beta[i] = rng.uniform(0.8, 3.0)
else:
f[i] = rng.uniform(9.00e9, 12.0e9)
rq[i] = rng.uniform(80, 200)
q0[i] = rng.uniform(1e4, 6e4)
eacc[i] = rng.uniform(20, 45)
length[i] = rng.uniform(0.15, 0.45)
beta[i] = rng.uniform(0.8, 3.0)
df = pd.DataFrame({
"type": types_arr,
"freq_Hz": f,
"freq_GHz": f * 1e-9,
"R_over_Q_ohm": rq,
"Q0": q0,
"Eacc_MVpm": eacc,
"L_m": length,
"beta": beta,
"cryo_temp_K": cryo,
})
df["family"] = df["type"].map(infer_family)
df["Vc_MV"] = df["Eacc_MVpm"] * df["L_m"]
df["Q_loaded"] = df["Q0"] / (1.0 + df["beta"])
return df
def sample_family_conditioned_operating_point(df: pd.DataFrame, seed=17) -> pd.DataFrame:
rng = np.random.default_rng(seed)
pulse_ns = np.zeros(len(df))
rep_hz = np.zeros(len(df))
p_aux_kW = np.zeros(len(df))
source_power_avail_kW = np.zeros(len(df))
cooling_capacity_kW = np.zeros(len(df))
surf_factor = np.zeros(len(df))
geom_factor = np.zeros(len(df))
freq_factor = np.zeros(len(df))
fab_sigma_um = np.zeros(len(df))
delta_allow_um = np.zeros(len(df))
s_f = np.zeros(len(df))
s_phi = np.zeros(len(df))
s_c = np.zeros(len(df))
for i, fam in enumerate(df["family"].values):
if fam == "Lband":
pulse_ns[i] = rng.uniform(800.0, 3000.0)
rep_hz[i] = rng.uniform(100.0, 2000.0)
p_aux_kW[i] = rng.uniform(0.4, 1.5)
source_power_avail_kW[i] = rng.uniform(4.0, 8.0)
cooling_capacity_kW[i] = rng.uniform(6.0, 12.0)
surf_factor[i] = rng.uniform(0.9, 1.1)
geom_factor[i] = rng.uniform(0.8, 1.1)
freq_factor[i] = rng.uniform(0.9, 1.05)
fab_sigma_um[i] = rng.uniform(10.0, 40.0)
delta_allow_um[i] = rng.uniform(40.0, 100.0)
s_f[i] = rng.uniform(0.003, 0.010)
s_phi[i] = rng.uniform(0.002, 0.008)
s_c[i] = rng.uniform(0.001, 0.006)
elif fam == "Cband":
pulse_ns[i] = rng.uniform(200.0, 800.0)
rep_hz[i] = rng.uniform(50.0, 500.0)
p_aux_kW[i] = rng.uniform(0.8, 2.2)
source_power_avail_kW[i] = rng.uniform(9.0, 15.0)
cooling_capacity_kW[i] = rng.uniform(10.0, 20.0)
surf_factor[i] = rng.uniform(0.95, 1.20)
geom_factor[i] = rng.uniform(0.9, 1.2)
freq_factor[i] = rng.uniform(1.0, 1.15)
fab_sigma_um[i] = rng.uniform(5.0, 20.0)
delta_allow_um[i] = rng.uniform(15.0, 40.0)
s_f[i] = rng.uniform(0.010, 0.025)
s_phi[i] = rng.uniform(0.008, 0.020)
s_c[i] = rng.uniform(0.006, 0.015)
elif fam == "Xband":
pulse_ns[i] = rng.uniform(50.0, 300.0)
rep_hz[i] = rng.uniform(10.0, 200.0)
p_aux_kW[i] = rng.uniform(1.0, 3.0)
source_power_avail_kW[i] = rng.uniform(12.0, 20.0)
cooling_capacity_kW[i] = rng.uniform(12.0, 24.0)
surf_factor[i] = rng.uniform(1.0, 1.35)
geom_factor[i] = rng.uniform(1.0, 1.3)
freq_factor[i] = rng.uniform(1.1, 1.3)
fab_sigma_um[i] = rng.uniform(2.0, 10.0)
delta_allow_um[i] = rng.uniform(5.0, 18.0)
s_f[i] = rng.uniform(0.020, 0.060)
s_phi[i] = rng.uniform(0.015, 0.050)
s_c[i] = rng.uniform(0.010, 0.035)
elif fam == "SC":
pulse_ns[i] = rng.uniform(5000.0, 100000.0)
rep_hz[i] = rng.uniform(1.0, 100.0)
p_aux_kW[i] = rng.uniform(4.0, 15.0)
source_power_avail_kW[i] = rng.uniform(8.0, 20.0)
cooling_capacity_kW[i] = rng.uniform(15.0, 40.0)
surf_factor[i] = rng.uniform(0.9, 1.1)
geom_factor[i] = rng.uniform(0.8, 1.0)
freq_factor[i] = rng.uniform(0.9, 1.05)
fab_sigma_um[i] = rng.uniform(10.0, 30.0)
delta_allow_um[i] = rng.uniform(30.0, 80.0)
s_f[i] = rng.uniform(0.004, 0.012)
s_phi[i] = rng.uniform(0.003, 0.010)
s_c[i] = rng.uniform(0.002, 0.008)
else:
pulse_ns[i] = rng.uniform(100.0, 1000.0)
rep_hz[i] = rng.uniform(10.0, 500.0)
p_aux_kW[i] = rng.uniform(1.0, 3.0)
source_power_avail_kW[i] = rng.uniform(5.0, 15.0)
cooling_capacity_kW[i] = rng.uniform(8.0, 16.0)
surf_factor[i] = 1.0
geom_factor[i] = 1.0
freq_factor[i] = 1.0
fab_sigma_um[i] = 10.0
delta_allow_um[i] = 20.0
s_f[i] = 0.01
s_phi[i] = 0.01
s_c[i] = 0.01
out = df.copy()
out["pulse_length_ns"] = pulse_ns
out["rep_rate_Hz"] = rep_hz
out["P_aux_kW"] = p_aux_kW
out["source_power_avail_kW"] = source_power_avail_kW
out["cooling_capacity_kW"] = cooling_capacity_kW
out["surf_factor"] = surf_factor
out["geom_factor"] = geom_factor
out["freq_factor"] = freq_factor
out["fab_sigma_um"] = fab_sigma_um
out["delta_allow_um"] = delta_allow_um
out["S_f"] = s_f
out["S_phi"] = s_phi
out["S_c"] = s_c
return out
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