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Merge pull request google#54 from mabrains/ngspice_tst_notebooks_plt
add notebooks to plot the measurements against the simulation
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models/ngspice/testing/regression/bjt_beta/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": 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 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| { | ||
| "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 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| { | ||
| "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 | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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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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