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atorkmabrainsatorkmabrains
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Merge pull request google#54 from mabrains/ngspice_tst_notebooks_plt
add notebooks to plot the measurements against the simulation
2 parents 23a0a61 + e30cbdc commit d2a2060

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‎models/ngspice/testing/regression/bjt_beta/plotting.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "dc364796",
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"measured is blue\n",
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"simulated is red\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"python3: can't open file '/home/mgomaa/klayout/old_versions/globalfoundries-pdk-libs-gf180mcu_fd_pr/models/ngspice/testing/regression/bjt_beta/model_reg.py': [Errno 2] No such file or directory\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import glob\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"%matplotlib notebook\n",
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"os.system(f\"python3 models_regression.py \")\n",
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"\n",
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"\n",
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"def draw(measured, simulated):\n",
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" \"\"\"draw func draw measured data vs simulated data\n",
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"\n",
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" Args:\n",
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" measured (list[str]): measured files paths\n",
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" simulated (list[str]): simulated files paths\n",
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" \"\"\" \n",
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" print(\"measured is blue\")\n",
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" print(\"simulated is red\")\n",
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" measured.sort()\n",
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" simulated.sort()\n",
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" for i in range(len(measured)):\n",
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" space = measured[i].rfind(\"/\")\n",
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" read_dev_name = measured[i][space + 1:]\n",
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" df = pd.read_csv(measured[i])\n",
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" ax = df.plot(x=df.columns[0], y=df.columns[1:], color=\"b\")\n",
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" df = pd.read_csv(simulated[i])\n",
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" df.plot(ax=ax, x=df.columns[0], y=df.columns[1:], color=\"r\")\n",
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" plt.title(read_dev_name)\n",
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" plt.show()\n",
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"\n",
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"\n",
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"device = [\"npn\",\"pnp\"]\n",
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"# 1 for \"pnp\"\n",
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"# for \"npn\"\n",
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"dev=0\n",
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"\n",
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"measured_ib = glob.glob(f\"{device[dev]}/measured_Ib/*.csv\")\n",
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"simulated_ib = glob.glob(f\"{device[dev]}/simulated_Ib/*.csv\")\n",
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"\n",
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"measured_ic = glob.glob(f\"{device[dev]}/measured_Ic/*.csv\")\n",
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"simulated_ic = glob.glob(f\"{device[dev]}/simulated_Ic/*.csv\")\n",
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"\n",
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"\n",
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"draw(measured_ic, simulated_ic)\n",
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"# draw(measured_ib,simulated_ib,device)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "33eb12ca",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎models/ngspice/testing/regression/bjt_cj/plotting.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc364796",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import glob\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"%matplotlib notebook\n",
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"os.system(f\"python3 models_regression.py \")\n",
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"\n",
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"\n",
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"\n",
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"def draw(measured, simulated):\n",
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" \"\"\"draw func draw measured data vs simulated data\n",
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"\n",
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" Args:\n",
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" measured (list[str]): measured files paths\n",
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" simulated (list[str]): simulated files paths\n",
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" \"\"\" \n",
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" print(\"measured is blue\")\n",
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" print(\"simulated is red\")\n",
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"\n",
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" for i in range(len(measured)):\n",
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" space = measured[i].rfind(\"/\")\n",
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" read_dev_name = measured[i][space + 1:]\n",
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" df = pd.read_csv(measured[i])\n",
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" ax = df.plot(x=df.columns[0], y=df.columns[1:], color=\"b\")\n",
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" df = pd.read_csv(simulated[i])\n",
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" df.plot(ax=ax, x=df.columns[0], y=df.columns[1:], color=\"r\")\n",
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" plt.title(read_dev_name)\n",
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" plt.show()\n",
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"\n",
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"\n",
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"device = [\"npn\",\"pnp\"]\n",
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"# 1 for pnp\n",
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"# 0 for npn\n",
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"dev=0\n",
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"measured = glob.glob(f\"bjt_cj_regr/{device[dev]}/cj_measured/*.csv\")\n",
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"simulated = [s.replace(\"measured\", \"simulated\") for s in measured]\n",
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"\n",
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"draw(measured, simulated)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f75575cf",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎models/ngspice/testing/regression/bjt_iv/plotting.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc364796",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import glob\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"%matplotlib notebook\n",
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"os.system(f\"python3 models_regression.py \")\n",
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"\n",
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"\n",
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"def draw(measured, simulated):\n",
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" \"\"\"draw func draw measured data vs simulated data\n",
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"\n",
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" Args:\n",
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" measured (list[str]): measured files paths\n",
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" simulated (list[str]): simulated files paths\n",
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" \"\"\" \n",
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" print(\"measured is blue\")\n",
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" print(\"simulated is red\")\n",
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"\n",
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" for i in range(len(measured)):\n",
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" space = measured[i].rfind(\"/\")\n",
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" read_dev_name = measured[i][space + 1:]\n",
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" df = pd.read_csv(measured[i])\n",
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" ax = df.plot(x=df.columns[1], y=df.columns[2:], color=\"b\")\n",
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" df = pd.read_csv(simulated[i])\n",
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" df.plot(ax=ax, x=df.columns[1], y=df.columns[2:], color=\"r\")\n",
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" plt.title(read_dev_name)\n",
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" plt.show()\n",
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"\n",
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"\n",
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"device = [\"npn\",\"pnp\"]\n",
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"# 1 for pnp\n",
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"# 0 for npn\n",
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"dev=0\n",
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"measured = glob.glob(f\"bjt_iv_regr/{device[dev]}/ib_measured/*.csv\")\n",
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"simulated = glob.glob(f\"bjt_iv_regr/{device[dev]}/ib_simulated/*.csv\")\n",
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"measured.sort()\n",
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"simulated.sort()\n",
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"\n",
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"draw(measured, simulated)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "69f06f65",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎models/ngspice/testing/regression/diode/plotting.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc364796",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import glob\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"%matplotlib notebook\n",
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"os.system(f\"python3 models_regression.py \")\n",
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"\n",
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"\n",
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"def draw(measured: list[str], simulated: list[str]) -> None:\n",
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" \"\"\"draw func draw measured data vs simulated data\n",
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"\n",
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" Args:\n",
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" measured (list[str]): measured files paths\n",
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" simulated (list[str]): simulated files paths\n",
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" \"\"\"\n",
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" print(\"measured is blue\")\n",
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" print(\"simulated is red\")\n",
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" measured.sort()\n",
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" simulated.sort()\n",
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" for i in range(len(measured)):\n",
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" space = measured[i].rfind(\"/\")\n",
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" read_dev_name = measured[i][space + 1:]\n",
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" df = pd.read_csv(measured[i])\n",
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" ax = df.plot(x=df.columns[1], y=df.columns[2:], color=\"b\")\n",
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" df = pd.read_csv(simulated[i])\n",
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" df.plot(ax=ax, x=df.columns[1], y=df.columns[2:], color=\"r\")\n",
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" plt.title(read_dev_name)\n",
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" plt.show()\n",
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"\n",
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"\n",
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"device = [\n",
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" \"diode_regr/diode_dw2ps\",\n",
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" \"diode_regr/diode_nd2ps_03v3\",\n",
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" \"diode_regr/diode_nd2ps_06v0\",\n",
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" \"diode_regr/diode_nw2ps_03v3\",\n",
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" \"diode_regr/diode_nw2ps_06v0\",\n",
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" \"diode_regr/diode_pd2nw_03v3\",\n",
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" \"diode_regr/diode_pd2nw_06v0\",\n",
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" \"diode_regr/diode_pw2dw\",\n",
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" \"diode_regr/sc_diode\",\n",
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"]\n",
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"\n",
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"iv_cv_m = [\"measured_iv\", \"measured_cv\"]\n",
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"iv_cv_s = [\"simulated_iv\", \"simulated_cv\"]\n",
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"# 0 for diode_dw2ps\n",
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"# 1 for diode_nd2ps_03v3\n",
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"# 2 for diode_nd2ps_06v0\n",
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"# 3 for diode_nw2ps_03v3\n",
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"# 4 for diode_nw2ps_06v0\n",
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"# 5 for diode_pd2nw_03v3\n",
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"# 6 for diode_pd2nw_06v0\n",
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"# 7 for diode_pw2dw\n",
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"dev = 0\n",
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"\n",
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"# 1 for cv 0 for iv\n",
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"iv_cv = 1\n",
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"measured = glob.glob(f\"{device[dev]}/{iv_cv_m[iv_cv]}/*.csv\")\n",
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"simulated = glob.glob(f\"{device[dev]}/{iv_cv_s[iv_cv]}/*.csv\")\n",
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"# caling the draw func\n",
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"draw(measured, simulated)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ddeaa9c0",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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