Add one-shot results to res folder and annotate sliding window fig

This commit is contained in:
2026-04-17 11:28:09 +02:00
parent 24c574d120
commit 9b60bfc4ee
11 changed files with 151 additions and 26 deletions

View File

@@ -88,18 +88,39 @@ sp="/-\|"
# done # done
# done # done
# done # done
#
# # Copy BPGD over max iter. results
#
# echo -e "\rCopying BPGD over max. iter. results..."
# echo -n ' '
# for p in 0.001 0.0025 0.004; do
# for pass_soft_info in "True" "False"; do
# for F in 1 2 3; do
# for W in 3 4 5; do
# SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=${p},system.F=${F},system.W=${W}/"
# LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
# SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
# DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_${p}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}"
# mkdir -p ${DEST_DIR}
# DEST_FILE="${DEST_DIR}/LERs.csv"
# cp ${SRC_FILE} ${DEST_FILE}
# post_process_LERs ${DEST_FILE}
# printf "\b${sp:i++%${#sp}:1}"
# done
# done
# done
# done
# Copy BPGD over max iter. results # Copy BP over max iter. results
echo -e "\rCopying BPGD over max. iter. results..."
echo -e "\rCopying one-shot simulation results..."
echo -n ' ' echo -n ' '
for p in 0.001 0.0025 0.004; do for decoder in "SyndromeMinSumDecoder" "SyndromeSpaDecoder" "SyndromeSpaGdDecoder"; do
for pass_soft_info in "True" "False"; do for max_iter in 32 200 5000; do
for F in 1 2 3; do SRC_PATH="${BASE_PATH}+rust_exp=whole_bp_bpgd,decoder.class_name=${decoder},decoder.max_iter=${max_iter},system.F=1,system.W=5/"
for W in 3 4 5; do
SRC_PATH="${BASE_PATH}+rust_exp=max_iter_bpgd,decoder.class_name=WindowingSyndromeSpaGdDecoder,decoder.pass_soft_info=${pass_soft_info},simulation.phy_err_rate=${p},system.F=${F},system.W=${W}/"
LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1) LATEST_RESULTS_DIR=$(ls -t ${SRC_PATH} | head -1)
SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv" SRC_FILE="${SRC_PATH}/${LATEST_RESULTS_DIR}/LERs.csv"
DEST_DIR="res/sim/max_iter/WindowingSyndromeSpaGdDecoder/p_${p}/pass_soft_info_${pass_soft_info}/F_${F}/W_${W}" DEST_DIR="res/sim/one-shot/${decoder}/max_iter_${max_iter}/"
mkdir -p ${DEST_DIR} mkdir -p ${DEST_DIR}
DEST_FILE="${DEST_DIR}/LERs.csv" DEST_FILE="${DEST_DIR}/LERs.csv"
cp ${SRC_FILE} ${DEST_FILE} cp ${SRC_FILE} ${DEST_FILE}
@@ -107,5 +128,3 @@ for p in 0.001 0.0025 0.004; do
printf "\b${sp:i++%${#sp}:1}" printf "\b${sp:i++%${#sp}:1}"
done done
done done
done
done

View File

@@ -1709,7 +1709,7 @@
\visible<3->{ \visible<3->{
\hspace*{21mm}% \hspace*{21mm}%
\begin{tikzpicture} \begin{tikzpicture}
\draw[decorate, decoration={brace, amplitude=10pt}] \draw[decorate, decoration={brace, amplitude=10pt}, line width=1pt]
(0,0) -- (6,0) node[midway, above=4mm] {Commit region}; (0,0) -- (6,0) node[midway, above=4mm] {Commit region};
\end{tikzpicture} \end{tikzpicture}
} }
@@ -1730,6 +1730,24 @@
\end{figure} \end{figure}
} }
\vspace*{-50.85mm}
\visible<4->{
\hspace*{-8mm}%
\begin{tikzpicture}
\draw[{Latex}-{Latex}, line width=1pt] (0,0) -- (0,1);
\draw[line width=1pt] (-1mm,0) -- (3mm,0);
\draw[line width=1pt] (-1mm,1) -- (3mm,1);
\node[left] at (-2mm,0.5) {$\sim W$};
\draw[{Latex}-{Latex}, line width=1pt] (11.5cm,0.4) -- (11.5cm,1);
\draw[line width=1pt] (11.5cm,0.98) -- (11.5cm,1.5);
\node[above] at (11.5cm,1.5) {$\sim F$};
\end{tikzpicture}
}
\vspace*{27mm}
\begin{itemize} \begin{itemize}
\visible<2->{ \visible<2->{
\item Split \ac{dem} into \schlagwort{overlapping \item Split \ac{dem} into \schlagwort{overlapping
@@ -1839,6 +1857,14 @@
} }
\temp \temp
} }
\addplot+[mark=x, line width=2pt,
densely dashed, black]
table[
col sep=comma, x=physical_p,
y=LER_per_round,
]
{res/sim/one-shot/SyndromeMinSumDecoder/max_iter_32/LERs.csv};
\end{axis} \end{axis}
\end{tikzpicture} \end{tikzpicture}
\end{figure} \end{figure}
@@ -1906,7 +1932,8 @@
{3/kit-red,4/kit-blue,5/kit-orange} { {3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
densely dashed, gray, opacity=0.4, forget plot] densely dashed, gray, opacity=0.4,
forget plot]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -2041,7 +2068,8 @@
{3/kit-red,4/kit-blue,5/kit-orange} { {3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
densely dashed, gray, opacity=0.4, forget plot] densely dashed, gray, opacity=0.4,
forget plot]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -2326,7 +2354,8 @@
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4] forget plot, densely dashed, gray,
opacity=0.4]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -2429,7 +2458,8 @@
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4] forget plot, densely dashed, gray,
opacity=0.4]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -2767,7 +2797,8 @@
{3/kit-red,4/kit-blue,5/kit-orange} { {3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
densely dashed, gray, opacity=0.4, forget plot] densely dashed, gray, opacity=0.4,
forget plot]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -2902,7 +2933,8 @@
{3/kit-red,4/kit-blue,5/kit-orange} { {3/kit-red,4/kit-blue,5/kit-orange} {
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
densely dashed, gray, opacity=0.4, forget plot] densely dashed, gray, opacity=0.4,
forget plot]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -3187,7 +3219,8 @@
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4] forget plot, densely dashed, gray,
opacity=0.4]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,
@@ -3290,7 +3323,8 @@
\edef\temp{\noexpand \edef\temp{\noexpand
\addplot+[mark=o, line width=2pt, \addplot+[mark=o, line width=2pt,
forget plot, densely dashed, gray, opacity=0.4] forget plot, densely dashed, gray,
opacity=0.4]
table[ table[
col sep=comma, x=physical_p, col sep=comma, x=physical_p,
y=LER_per_round, y=LER_per_round,

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,88000,0.0022840909090909,0.00019054046349609166,200.9999999999992
0.0015,22000,0.0091363636363636,0.0007645705814327552,200.9999999999992
0.002,8000,0.02975,0.002513627927773654,238.0
0.0025,4000,0.086,0.007465717670425587,344.0
0.003,2000,0.1925,0.017659888947245128,385.0
0.0035,2000,0.3585,0.03631953572181035,717.0
0.004,2000,0.5395,0.06257657262550698,1079.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 88000 0.0022840909090909 0.00019054046349609166 200.9999999999992
3 0.0015 22000 0.0091363636363636 0.0007645705814327552 200.9999999999992
4 0.002 8000 0.02975 0.002513627927773654 238.0
5 0.0025 4000 0.086 0.007465717670425587 344.0
6 0.003 2000 0.1925 0.017659888947245128 385.0
7 0.0035 2000 0.3585 0.03631953572181035 717.0
8 0.004 2000 0.5395 0.06257657262550698 1079.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,6000,0.0346666666666666,0.002935833167148738,207.9999999999996
0.0015,4000,0.09375,0.008169783831040611,375.0
0.002,2000,0.187,0.01710405023748829,374.0
0.0025,2000,0.3655,0.0372002499227132,731.0
0.003,2000,0.5415,0.06291652725715624,1083.0
0.0035,2000,0.7265,0.10240633752398864,1453.0
0.004,2000,0.852,0.14718438639465958,1704.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 6000 0.0346666666666666 0.002935833167148738 207.9999999999996
3 0.0015 4000 0.09375 0.008169783831040611 375.0
4 0.002 2000 0.187 0.01710405023748829 374.0
5 0.0025 2000 0.3655 0.0372002499227132 731.0
6 0.003 2000 0.5415 0.06291652725715624 1083.0
7 0.0035 2000 0.7265 0.10240633752398864 1453.0
8 0.004 2000 0.852 0.14718438639465958 1704.0

View File

@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,100000,0.00045,3.750773659938478e-05,45.0
0.0015,86000,0.0023372093023255,0.00019497639289589586,200.999999999993
0.002,22000,0.0092727272727272,0.0007760309518972663,203.9999999999984
0.0025,6000,0.0376666666666666,0.003194419283278793,225.9999999999996
0.003,4000,0.08425,0.007307492597147025,337.0
0.0035,2000,0.2025,0.018679455867679495,405.0
0.004,2000,0.345,0.034645612003118,690.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 100000 0.00045 3.750773659938478e-05 45.0
3 0.0015 86000 0.0023372093023255 0.00019497639289589586 200.999999999993
4 0.002 22000 0.0092727272727272 0.0007760309518972663 203.9999999999984
5 0.0025 6000 0.0376666666666666 0.003194419283278793 225.9999999999996
6 0.003 4000 0.08425 0.007307492597147025 337.0
7 0.0035 2000 0.2025 0.018679455867679495 405.0
8 0.004 2000 0.345 0.034645612003118 690.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.02875,0.0024279957468343882,230.0
0.0015,4000,0.053,0.004527734248755855,212.0
0.002,2000,0.1155,0.010175605072917149,231.0
0.0025,2000,0.2185,0.02033539996612399,437.0
0.003,2000,0.352,0.03550958250051006,704.0
0.0035,2000,0.5315,0.06123015042661217,1063.0
0.004,2000,0.7015,0.0958398514873553,1403.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.02875 0.0024279957468343882 230.0
3 0.0015 4000 0.053 0.004527734248755855 212.0
4 0.002 2000 0.1155 0.010175605072917149 231.0
5 0.0025 2000 0.2185 0.02033539996612399 437.0
6 0.003 2000 0.352 0.03550958250051006 704.0
7 0.0035 2000 0.5315 0.06123015042661217 1063.0
8 0.004 2000 0.7015 0.0958398514873553 1403.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.0685,0.005895808515312573,274.0
0.0015,2000,0.135,0.012012744890476657,270.0
0.002,2000,0.2305,0.021597879347753368,461.0
0.0025,2000,0.3925,0.04068289056153884,785.0
0.003,2000,0.5265,0.06039929937035626,1053.0
0.0035,2000,0.705,0.09672809706528085,1410.0
0.004,2000,0.8195,0.1329587352216164,1639.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.0685 0.005895808515312573 274.0
3 0.0015 2000 0.135 0.012012744890476657 270.0
4 0.002 2000 0.2305 0.021597879347753368 461.0
5 0.0025 2000 0.3925 0.04068289056153884 785.0
6 0.003 2000 0.5265 0.06039929937035626 1053.0
7 0.0035 2000 0.705 0.09672809706528085 1410.0
8 0.004 2000 0.8195 0.1329587352216164 1639.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,10000,0.0232,0.001954201073267048,231.99999999999997
0.0015,6000,0.0433333333333333,0.003684882338012896,259.9999999999998
0.002,4000,0.08975,0.007805708651284715,359.0
0.0025,2000,0.1795,0.016351617556473186,359.0
0.003,2000,0.296,0.02882449163140266,592.0
0.0035,2000,0.4835,0.05356848392991931,967.0
0.004,2000,0.6535,0.08453459623940363,1307.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 10000 0.0232 0.001954201073267048 231.99999999999997
3 0.0015 6000 0.0433333333333333 0.003684882338012896 259.9999999999998
4 0.002 4000 0.08975 0.007805708651284715 359.0
5 0.0025 2000 0.1795 0.016351617556473186 359.0
6 0.003 2000 0.296 0.02882449163140266 592.0
7 0.0035 2000 0.4835 0.05356848392991931 967.0
8 0.004 2000 0.6535 0.08453459623940363 1307.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,8000,0.029125,0.0024600983395249854,233.0
0.0015,4000,0.051,0.004352706093600722,204.0
0.002,2000,0.105,0.009201699575916766,210.0
0.0025,2000,0.2055,0.018987611527110704,411.0
0.003,2000,0.348,0.035014843623407566,696.0
0.0035,2000,0.527,0.06048202163011074,1054.0
0.004,2000,0.7015,0.0958398514873553,1403.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 8000 0.029125 0.0024600983395249854 233.0
3 0.0015 4000 0.051 0.004352706093600722 204.0
4 0.002 2000 0.105 0.009201699575916766 210.0
5 0.0025 2000 0.2055 0.018987611527110704 411.0
6 0.003 2000 0.348 0.035014843623407566 696.0
7 0.0035 2000 0.527 0.06048202163011074 1054.0
8 0.004 2000 0.7015 0.0958398514873553 1403.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,4000,0.06925,0.0059625336383020144,277.0
0.0015,2000,0.122,0.010783823589648356,244.0
0.002,2000,0.2125,0.019710810011481006,425.0
0.0025,2000,0.369,0.037643952171725004,738.0
0.003,2000,0.5215,0.059576452112257594,1043.0
0.0035,2000,0.674,0.08917529877700736,1348.0
0.004,2000,0.814,0.13078726888434444,1628.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 4000 0.06925 0.0059625336383020144 277.0
3 0.0015 2000 0.122 0.010783823589648356 244.0
4 0.002 2000 0.2125 0.019710810011481006 425.0
5 0.0025 2000 0.369 0.037643952171725004 738.0
6 0.003 2000 0.5215 0.059576452112257594 1043.0
7 0.0035 2000 0.674 0.08917529877700736 1348.0
8 0.004 2000 0.814 0.13078726888434444 1628.0

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@@ -0,0 +1,8 @@
physical_p,num_trials,LER,LER_per_round,num_errors
0.001,10000,0.0234,0.0019712318450325617,234.0
0.0015,6000,0.0416666666666666,0.0035403526553423603,249.9999999999996
0.002,4000,0.0815,0.0070594123157259325,326.0
0.0025,2000,0.173,0.015704591731957374,346.0
0.003,2000,0.2955,0.028767030722673725,591.0
0.0035,2000,0.4755,0.052355475239581395,951.0
0.004,2000,0.6535,0.08453459623940363,1307.0
1 physical_p num_trials LER LER_per_round num_errors
2 0.001 10000 0.0234 0.0019712318450325617 234.0
3 0.0015 6000 0.0416666666666666 0.0035403526553423603 249.9999999999996
4 0.002 4000 0.0815 0.0070594123157259325 326.0
5 0.0025 2000 0.173 0.015704591731957374 346.0
6 0.003 2000 0.2955 0.028767030722673725 591.0
7 0.0035 2000 0.4755 0.052355475239581395 951.0
8 0.004 2000 0.6535 0.08453459623940363 1307.0