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sim-plot Visualize Virtuoso simulation results as matplotlib charts. Use after:
(1) sim sweep — line plot of measurements vs swept variable,
(2) sim corner — grouped bar chart across PVT corners,
(3) sim measure — horizontal bar of scalar measurements,
(4) AC/Bode plot from PSF getData results (magnitude + phase),
(5) process_data lookup tables — gm/Id curves for all L values.
Auto-detects chart type from JSON structure. Saves PNG via plot_sim.py.
インストールへ移動 Skills Marketplace コミュニティが作成したAIスキルを発見・探索
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/deanyou/virtuoso-cli --skill sim-plotコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Zipをダウンロード ダウンロード中... Amplifier design copilot — topology selection, sizing via gm/Id lookup tables, PVT corner validation, and process-portable design. Use when designing amplifiers (OTA, opamp, comparator), selecting topology from specs, sizing transistors, or characterizing a new process node. Also triggers on keywords like amplifier, OTA, opamp, gain-bandwidth, CMRR, PSRR, slew rate.
name sim-plot description Visualize Virtuoso simulation results as matplotlib charts. Use after:
(1) sim sweep — line plot of measurements vs swept variable,
(2) sim corner — grouped bar chart across PVT corners,
(3) sim measure — horizontal bar of scalar measurements,
(4) AC/Bode plot from PSF getData results (magnitude + phase),
(5) process_data lookup tables — gm/Id curves for all L values.
Auto-detects chart type from JSON structure. Saves PNG via plot_sim.py.
author Claude Code version 1.0.0 date "2026-04-06T00:00:00.000Z" argument-hint ["chart type","e.g. \"AC Bode plot\" or \"sweep line chart\""] allowed-tools Bash(python *) Read Write
sim-plot: Matplotlib Visualization
Pipe --format json output into plot_sim.py to get charts. The script
lives at
.claude/skills/sim-plot/scripts/plot_sim.py
Usage by Chart Type
1. Parameter Sweep → Line Plot virtuoso sim sweep \
--var W34 --from 2e-6 --to 24e-6 --step 4e-6 \
--analysis dc \
--expr 'openResults("/tmp/opt/psf") selectResult(' "'" 'acSweep) dB20(value(VF("net1") 1))' \
--format json | \
python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--output plots/w34_sweep.png \
--title "5T OTA: Gain vs W34"
Or save JSON first then plot:
virtuoso sim sweep ... --format json > /tmp/sweep.json
python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--input /tmp/sweep.json --output plots/sweep.png
{
"variable" : "W34" ,
"headers" : [ "W34" , "gain_dB" , "gbw_hz" ] ,
"data" : [
{ "W34" : "2e-06" , "gain_dB" : "9.9" , "gbw_hz" : "9e5" } ,
{ "W34" : "8e-06" , "gain_dB" : "42.3" , "gbw_hz" : "1.14e7" }
]
}
2. Corner Analysis → Grouped Bar Chart virtuoso sim corner --file corners.json --format json | \
python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--output plots/corner.png --title "PVT Corner Results"
{
"corners" : 3 ,
"headers" : [ "corner" , "temp" , "gain_dB" , "gbw_hz" ] ,
"data" : [
{ "corner" : "tt" , "temp" : "27" , "gain_dB" : "43.2" , "gbw_hz" : "1.15e7" } ,
{ "corner" : "ff" , "temp" : "27" , "gain_dB" : "40.1" , "gbw_hz" : "1.4e7" } ,
{ "corner" : "ss" , "temp" : "27" , "gain_dB" : "45.8" , "gbw_hz" : "9.2e6" }
]
}
3. Scalar Measurements → Horizontal Bar Chart virtuoso sim measure --analysis dcOp \
--expr 'getData("I0.NM0:gm" ?result "dcOpInfo")' \
--expr 'getData("I0.NM0:gds" ?result "dcOpInfo")' \
--format json | \
python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--output plots/oppoint.png
4. AC Bode Plot The AC PSF data must be converted to the Bode JSON format first. Use Ocean
getData to extract frequency, magnitude, and phase:
virtuoso skill exec '
openResults("/tmp/opt_5t_ota/psf")
selectResult(' "'" 'acSweep)
RB__vout = VF("net1")
RB__freq = frequency(RB__vout)
RB__mag = dB20(RB__vout)
RB__ph = phase(RB__vout)
list(
sprintf(nil "%s" RB__freq)
sprintf(nil "%s" RB__mag)
sprintf(nil "%s" RB__ph)
)
' --format json > /tmp/ac_raw.json
Bode JSON format accepted by plot_sim.py :
{
"freq" : [ 1 , 10 , 100 , 1000 , 10000 , 100000 , 1000000 , 10000000 ] ,
"mag_db" : [ 43.2 , 43.2 , 43.1 , 42.8 , 40.0 , 30.0 , 20.0 , 0.1 ] ,
"phase_deg" : [ -1 , -5 , -10 , -30 , -60 , -120 , -160 , -178 ]
}
Top: Magnitude vs frequency (dB), marks GBW (0 dB crossing)
Bottom: Phase vs frequency (°), marks phase margin at GBW
5. gm/Id Lookup Table → Transistor Curves python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--input process_data/smic13mmrf/nmos_lookup.json \
--output plots/nmos_gmid.png \
--title "NMOS gm/Id Lookup (SMIC 0.13µm)"
Gain (dB) vs gm/Id for each L
fT (GHz) vs gm/Id
Id (µA/µm) vs gm/Id (linear)
Id (µA/µm) vs gm/Id (log)
Common Workflows
After a W-sweep optimization run
echo '{
"status": "success",
"variable": "W34",
"headers": ["W34", "gain_dB", "gbw_hz", "gm_uS", "gmId"],
"data": [
{"W34": "2e-06", "gain_dB": "9.9", "gbw_hz": "9e5", "gm_uS": "174", "gmId": "7.0"},
{"W34": "4e-06", "gain_dB": "36.0", "gbw_hz": "1.08e7","gm_uS": "366", "gmId": "11.8"},
{"W34": "8e-06", "gain_dB": "42.3", "gbw_hz": "1.14e7","gm_uS": "371", "gmId": "11.9"},
{"W34": "16e-06","gain_dB": "43.2", "gbw_hz": "1.15e7","gm_uS": "372", "gmId": "11.9"},
{"W34": "24e-06","gain_dB": "43.3", "gbw_hz": "1.14e7","gm_uS": "372", "gmId": "11.9"}
]
}' | python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--output plots/5t_ota_w34.png --title "5T OTA: Gain & GBW vs W34 (PMOS load)"
After process char python3 .claude/skills/sim-plot/scripts/plot_sim.py \
--input process_data/smic13mmrf/nmos_lookup.json \
--output plots/nmos_char.png
Script Location .claude/skills/sim-plot/scripts/plot_sim.py
Requirements : Python 3.8+, matplotlib, numpyCheck : python3 -c "import matplotlib, numpy; print('OK')"
pip install matplotlib numpy
Output
Saves PNG to --output path (default: sim_plot.png in current dir)
Prints: Chart saved: /path/to/output.png
DPI: 150 by default (--dpi 300 for publication quality)
