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add notebooks to plot the measurements against the simulation
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@Mohamed-Gomaa1
Mohamed-Gomaa1 committed Dec 28, 2022
1 parent ba88070 commit e30cbdc
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105 changes: 105 additions & 0 deletions models/ngspice/testing/regression/bjt_beta/plotting.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "dc364796",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"measured is blue\n",
"simulated is red\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"import glob\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib notebook\n",
"os.system(f\"python3 models_regression.py \")\n",
"\n",
"\n",
"def draw(measured, simulated):\n",
" \"\"\"draw func draw measured data vs simulated data\n",
"\n",
" Args:\n",
" measured (list[str]): measured files paths\n",
" simulated (list[str]): simulated files paths\n",
" \"\"\" \n",
" print(\"measured is blue\")\n",
" print(\"simulated is red\")\n",
" measured.sort()\n",
" simulated.sort()\n",
" for i in range(len(measured)):\n",
" space = measured[i].rfind(\"/\")\n",
" read_dev_name = measured[i][space + 1:]\n",
" df = pd.read_csv(measured[i])\n",
" ax = df.plot(x=df.columns[0], y=df.columns[1:], color=\"b\")\n",
" df = pd.read_csv(simulated[i])\n",
" df.plot(ax=ax, x=df.columns[0], y=df.columns[1:], color=\"r\")\n",
" plt.title(read_dev_name)\n",
" plt.show()\n",
"\n",
"\n",
"device = [\"npn\",\"pnp\"]\n",
"# 1 for \"pnp\"\n",
"# for \"npn\"\n",
"dev=0\n",
"\n",
"measured_ib = glob.glob(f\"{device[dev]}/measured_Ib/*.csv\")\n",
"simulated_ib = glob.glob(f\"{device[dev]}/simulated_Ib/*.csv\")\n",
"\n",
"measured_ic = glob.glob(f\"{device[dev]}/measured_Ic/*.csv\")\n",
"simulated_ic = glob.glob(f\"{device[dev]}/simulated_Ic/*.csv\")\n",
"\n",
"\n",
"draw(measured_ic, simulated_ic)\n",
"# draw(measured_ib,simulated_ib,device)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33eb12ca",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
83 changes: 83 additions & 0 deletions models/ngspice/testing/regression/bjt_cj/plotting.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "dc364796",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import glob\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib notebook\n",
"os.system(f\"python3 models_regression.py \")\n",
"\n",
"\n",
"\n",
"def draw(measured, simulated):\n",
" \"\"\"draw func draw measured data vs simulated data\n",
"\n",
" Args:\n",
" measured (list[str]): measured files paths\n",
" simulated (list[str]): simulated files paths\n",
" \"\"\" \n",
" print(\"measured is blue\")\n",
" print(\"simulated is red\")\n",
"\n",
" for i in range(len(measured)):\n",
" space = measured[i].rfind(\"/\")\n",
" read_dev_name = measured[i][space + 1:]\n",
" df = pd.read_csv(measured[i])\n",
" ax = df.plot(x=df.columns[0], y=df.columns[1:], color=\"b\")\n",
" df = pd.read_csv(simulated[i])\n",
" df.plot(ax=ax, x=df.columns[0], y=df.columns[1:], color=\"r\")\n",
" plt.title(read_dev_name)\n",
" plt.show()\n",
"\n",
"\n",
"device = [\"npn\",\"pnp\"]\n",
"# 1 for pnp\n",
"# 0 for npn\n",
"dev=0\n",
"measured = glob.glob(f\"bjt_cj_regr/{device[dev]}/cj_measured/*.csv\")\n",
"simulated = [s.replace(\"measured\", \"simulated\") for s in measured]\n",
"\n",
"draw(measured, simulated)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f75575cf",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
84 changes: 84 additions & 0 deletions models/ngspice/testing/regression/bjt_iv/plotting.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "dc364796",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import glob\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib notebook\n",
"os.system(f\"python3 models_regression.py \")\n",
"\n",
"\n",
"def draw(measured, simulated):\n",
" \"\"\"draw func draw measured data vs simulated data\n",
"\n",
" Args:\n",
" measured (list[str]): measured files paths\n",
" simulated (list[str]): simulated files paths\n",
" \"\"\" \n",
" print(\"measured is blue\")\n",
" print(\"simulated is red\")\n",
"\n",
" for i in range(len(measured)):\n",
" space = measured[i].rfind(\"/\")\n",
" read_dev_name = measured[i][space + 1:]\n",
" df = pd.read_csv(measured[i])\n",
" ax = df.plot(x=df.columns[1], y=df.columns[2:], color=\"b\")\n",
" df = pd.read_csv(simulated[i])\n",
" df.plot(ax=ax, x=df.columns[1], y=df.columns[2:], color=\"r\")\n",
" plt.title(read_dev_name)\n",
" plt.show()\n",
"\n",
"\n",
"device = [\"npn\",\"pnp\"]\n",
"# 1 for pnp\n",
"# 0 for npn\n",
"dev=0\n",
"measured = glob.glob(f\"bjt_iv_regr/{device[dev]}/ib_measured/*.csv\")\n",
"simulated = glob.glob(f\"bjt_iv_regr/{device[dev]}/ib_simulated/*.csv\")\n",
"measured.sort()\n",
"simulated.sort()\n",
"\n",
"draw(measured, simulated)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69f06f65",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
105 changes: 105 additions & 0 deletions models/ngspice/testing/regression/diode/plotting.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,105 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "dc364796",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import glob\n",
"from matplotlib import pyplot as plt\n",
"\n",
"%matplotlib notebook\n",
"os.system(f\"python3 models_regression.py \")\n",
"\n",
"\n",
"def draw(measured: list[str], simulated: list[str]) -> None:\n",
" \"\"\"draw func draw measured data vs simulated data\n",
"\n",
" Args:\n",
" measured (list[str]): measured files paths\n",
" simulated (list[str]): simulated files paths\n",
" \"\"\"\n",
" print(\"measured is blue\")\n",
" print(\"simulated is red\")\n",
" measured.sort()\n",
" simulated.sort()\n",
" for i in range(len(measured)):\n",
" space = measured[i].rfind(\"/\")\n",
" read_dev_name = measured[i][space + 1:]\n",
" df = pd.read_csv(measured[i])\n",
" ax = df.plot(x=df.columns[1], y=df.columns[2:], color=\"b\")\n",
" df = pd.read_csv(simulated[i])\n",
" df.plot(ax=ax, x=df.columns[1], y=df.columns[2:], color=\"r\")\n",
" plt.title(read_dev_name)\n",
" plt.show()\n",
"\n",
"\n",
"device = [\n",
" \"diode_regr/diode_dw2ps\",\n",
" \"diode_regr/diode_nd2ps_03v3\",\n",
" \"diode_regr/diode_nd2ps_06v0\",\n",
" \"diode_regr/diode_nw2ps_03v3\",\n",
" \"diode_regr/diode_nw2ps_06v0\",\n",
" \"diode_regr/diode_pd2nw_03v3\",\n",
" \"diode_regr/diode_pd2nw_06v0\",\n",
" \"diode_regr/diode_pw2dw\",\n",
" \"diode_regr/sc_diode\",\n",
"]\n",
"\n",
"iv_cv_m = [\"measured_iv\", \"measured_cv\"]\n",
"iv_cv_s = [\"simulated_iv\", \"simulated_cv\"]\n",
"# 0 for diode_dw2ps\n",
"# 1 for diode_nd2ps_03v3\n",
"# 2 for diode_nd2ps_06v0\n",
"# 3 for diode_nw2ps_03v3\n",
"# 4 for diode_nw2ps_06v0\n",
"# 5 for diode_pd2nw_03v3\n",
"# 6 for diode_pd2nw_06v0\n",
"# 7 for diode_pw2dw\n",
"dev = 0\n",
"\n",
"# 1 for cv 0 for iv\n",
"iv_cv = 1\n",
"measured = glob.glob(f\"{device[dev]}/{iv_cv_m[iv_cv]}/*.csv\")\n",
"simulated = glob.glob(f\"{device[dev]}/{iv_cv_s[iv_cv]}/*.csv\")\n",
"# caling the draw func\n",
"draw(measured, simulated)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ddeaa9c0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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