{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f81590b",
   "metadata": {},
   "source": [
    "# Solving the poisson-drift-diffusion equation with Solcore\n",
    "\n",
    "In this notebook we will go over how to handle the charge transport simulation part in the imodulator workflow by using `Solcore`. Our goal is to explore how to get the data from the `PhotonicDevice` to the charge transport simulator and how to inspect the data properly.\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <img src=\"support/main.png\" alt=\"Alt text\" style=\"max-width: 80%; height: auto;\">\n",
    "</div>\n",
    "\n",
    "To illustrate how to do this, we will use the class developed in `Building a Photonic Device` tutorial: `InP_EOPM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb1ab2d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully imported lumapi\n",
      "WARNING: The RCWA solver will not be available because an S4 installation has not been found.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20230622\\AppData\\Local\\anaconda3\\envs\\imodulator_venv\\lib\\site-packages\\solcore\\registries.py:73: UserWarning: Optics solver 'RCWA' will not be available. An installation of S4 has not been found.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully imported nextnanopy\n",
      "Successfully configured nextnano++ settings\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20230622\\AppData\\Local\\anaconda3\\envs\\imodulator_venv\\lib\\site-packages\\nextnanopy\\defaults.py:202: UserWarning: Unsupported products in config file: ['nextnano.NEGF++'] will be ignored. To not see this message, please remove unsupported products from the config file: C:\\Users\\20230622\\.nextnanopy-configNote: nextnano.NEGF++ was renamed to nextnano.NEGF, nextnano.NEGF was renamed to nextnano.NEGF_classic. Please check the documentation for more details.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import imodulator\n",
    "import shapely\n",
    "import openbandparams as obp\n",
    "from imodulator.ElectroOpticalModel import InGaAsPElectroOpticalModel\n",
    "from imodulator.ChargeSimulator import ChargeSimulatorSolcore\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def tand_fitted_bcb(x):\n",
    "    \"\"\"\n",
    "    Fitted to results from https://link.springer.com/article/10.1007/s10762-009-9552-0\n",
    "    \n",
    "    x must be in GHz\n",
    "    \"\"\"\n",
    "    out =  0.0093839 - 0.01790336 * np.exp(-0.04773444 * (x - -4.64170761))\n",
    "\n",
    "    if isinstance(x, (list, np.ndarray)):\n",
    "        x = np.asarray(x)\n",
    "    \n",
    "        out[np.where(out<0.001)] = 0.001\n",
    "    else:\n",
    "        if out < 0.001:\n",
    "            out = 0.001\n",
    "    return out\n",
    "\n",
    "class InP_EOPM:\n",
    "    def __init__(\n",
    "            self,\n",
    "            **kwargs\n",
    "    ):\n",
    "        \n",
    "        self.e = 1.60e-19 # electron charge in C\n",
    "        self.e0 = 8.85e-12 # vacuum permittivity in F/m\n",
    "        \n",
    "        self.w_sig_metal = 5 # Width of signal metal in um\n",
    "        self.metal_sep = 10 # Separation between signal and ground metals in um\n",
    "        self.h_metal = 4 # Height of metals in um\n",
    "        self.w_gnd_metal = 10\n",
    "        \n",
    "        self.w_wg = 1\n",
    "        self.h_n = 0.4\n",
    "        self.h_wg1 = 0.5\n",
    "        self.h_wg2 = 0.3\n",
    "        self.h_p1 = 1\n",
    "        self.h_p2 = 0.2\n",
    "\n",
    "        self.h_box = 4\n",
    "\n",
    "        self.w_window = 100\n",
    "        self.h_bottom = 30\n",
    "        self.h_top = 30\n",
    "\n",
    "        for kwarg, value in kwargs.items():\n",
    "            if hasattr(self, kwarg):\n",
    "                setattr(self, kwarg, value)\n",
    "    \n",
    "    def _make_meshes(self):\n",
    "        # optical mesh\n",
    "        self.optical_mesh_settings = {\n",
    "            'substrate': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'background': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'box': {'resolution': 0.3, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'sig_metal': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'n_metal_left': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'n_metal_right': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'bcb': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'n': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg1': {'resolution': 0.1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg2': {'resolution': 0.1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p1': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p2': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "        }\n",
    "\n",
    "        # RF mesh\n",
    "        self.rf_mesh_settings = {\n",
    "            'substrate': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'background': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'box': {'resolution': 3, 'SizeMax': 3, 'distance': 0.1},\n",
    "            'sig_metal': {'resolution': 3, 'SizeMax': 3, 'distance': 0.1},\n",
    "            'n_metal_left': {'resolution': 3, 'SizeMax': 3, 'distance': 0.1},\n",
    "            'n_metal_right': {'resolution': 3, 'SizeMax': 3, 'distance': 0.1},\n",
    "            'bcb': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'n': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg1': {'resolution': 1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg2': {'resolution': 1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p1': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p2': {'resolution': 5, 'SizeMax': 5, 'distance': 0.1},\n",
    "        }\n",
    "\n",
    "        # eo mesh\n",
    "        self.eo_mesh_settings = {\n",
    "            'substrate': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'background': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'box': {'resolution': 0.3, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'sig_metal': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'n_metal_left': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'n_metal_right': {'resolution': 10, 'SizeMax': 0.2, 'distance': 0.1},\n",
    "            'bcb': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'n': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg1': {'resolution': 0.1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'wg2': {'resolution': 0.1, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p1': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "            'p2': {'resolution': 0.5, 'SizeMax': 5, 'distance': 0.1},\n",
    "        }\n",
    "\n",
    "        self.charge_mesh_settings = {\n",
    "            'substrate': {'resolution': 0.5},\n",
    "            'background': {'resolution': 0.5},\n",
    "            'sig_metal': {'resolution': 0.01},\n",
    "            'n_metal_left': {'resolution': 0.01},\n",
    "            'n_metal_right': {'resolution': 0.01},\n",
    "            'n': {'resolution': 0.003},\n",
    "            'wg1': {'resolution': 0.002},\n",
    "            'wg2': {'resolution': 0.002},\n",
    "            'p1': {'resolution': 0.003},\n",
    "            'p2': {'resolution': 0.003},\n",
    "        }\n",
    "\n",
    "    def _create_polygons(self):\n",
    "        #We will now set the RF properties of the metals and the BCB\n",
    "        freq = np.linspace(0.1,100, 100) #GHz. This will be the simulation frequency\n",
    "            \n",
    "        eps_rf_metal = 1 - 1j*6e7/(2*np.pi*freq*1e9 * self.e0)\n",
    "        eps_rf_metal = np.asarray([freq, eps_rf_metal])\n",
    "\n",
    "        bcb_eps_real = 2.65*np.ones(100)\n",
    "        bcb_eps_imag = bcb_eps_real * tand_fitted_bcb(freq)\n",
    "\n",
    "        bcb_eps = bcb_eps_real - 1j*bcb_eps_imag\n",
    "        bcb_eps = np.asarray([freq, bcb_eps])\n",
    "        \n",
    "        #Now we create the PhotoPolygons\n",
    "        self.substrate = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_window/2,\n",
    "                -self.h_box - self.h_bottom,\n",
    "                self.w_window/2,\n",
    "                -self.h_box\n",
    "            ),\n",
    "            rf_eps = 11.7,\n",
    "            name = 'substrate',\n",
    "            optical_material=3**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['substrate'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['substrate'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['substrate'],\n",
    "        )\n",
    "\n",
    "        self.background = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_window/2,\n",
    "                -self.h_box - self.h_bottom,\n",
    "                self.w_window/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2 + self.h_metal + self.h_top\n",
    "            ),\n",
    "            rf_eps = 1,\n",
    "            optical_material=1,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['background'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['background'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['background'],\n",
    "            name = 'background'\n",
    "        )\n",
    "\n",
    "        self.box = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_window/2,\n",
    "                -self.h_box,\n",
    "                self.w_window/2,\n",
    "                0\n",
    "            ),\n",
    "            rf_eps = 3.9 - 1j*3.9*0.001,\n",
    "            optical_material=1.44**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['box'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['box'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['box'],\n",
    "            name = 'box'\n",
    "        )\n",
    "\n",
    "        n_obp_material = obp.GaInPAs(T=300, As = 0, a = obp.InP.a())\n",
    "        self.n = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_sig_metal/2 - self.metal_sep - self.w_gnd_metal,\n",
    "                0,\n",
    "                self.w_sig_metal/2 + self.metal_sep + self.w_gnd_metal,\n",
    "                self.h_n\n",
    "            ),\n",
    "            rf_eps = n_obp_material.dielectric(T=300),\n",
    "            optical_material=n_obp_material.refractive_index(T=300)**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['n'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['n'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['n'],\n",
    "            charge_mesh_settings=self.charge_mesh_settings['n'],\n",
    "            name = 'n',\n",
    "            electro_optic_module=InGaAsPElectroOpticalModel,\n",
    "            electro_optic_module_kwargs={\n",
    "                'y': 0,\n",
    "                'T': 300,\n",
    "                'BF_model': 'vinchant'\n",
    "            },\n",
    "            charge_transport_simulator_kwargs={\n",
    "                'sol_obp_material': n_obp_material,\n",
    "                'sol_Nd': 1e18\n",
    "            }\n",
    "        )\n",
    "\n",
    "        wg1_obp_material = obp.GaInPAs(T=300, As = 0.53, a = obp.InP.a())\n",
    "        self.wg1 = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_wg/2,\n",
    "                self.h_n,\n",
    "                self.w_wg/2,\n",
    "                self.h_n + self.h_wg1\n",
    "            ),\n",
    "            rf_eps = wg1_obp_material.dielectric(T=300),\n",
    "            optical_material=wg1_obp_material.refractive_index(T=300)**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['wg1'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['wg1'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['wg1'],\n",
    "            charge_mesh_settings=self.charge_mesh_settings['wg1'],\n",
    "            name = 'wg1',\n",
    "            electro_optic_module=InGaAsPElectroOpticalModel,\n",
    "            electro_optic_module_kwargs={\n",
    "                'y': 0.53,\n",
    "                'T': 300,\n",
    "                'BF_model': 'vinchant'\n",
    "            },\n",
    "            charge_transport_simulator_kwargs={\n",
    "                'sol_obp_material': wg1_obp_material,\n",
    "                'sol_Nd': 1e16\n",
    "            }\n",
    "        )\n",
    "\n",
    "        wg2_obp_material = obp.GaInPAs(T=300, As = 0, a = obp.InP.a())\n",
    "        self.wg2 = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_wg/2,\n",
    "                self.h_n + self.h_wg1,\n",
    "                self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2\n",
    "            ),\n",
    "            rf_eps = wg2_obp_material.dielectric(T=300),\n",
    "            optical_material=wg2_obp_material.refractive_index(T=300)**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['wg2'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['wg2'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['wg2'],\n",
    "            charge_mesh_settings=self.charge_mesh_settings['wg2'],\n",
    "            name = 'wg2',\n",
    "            electro_optic_module=InGaAsPElectroOpticalModel,\n",
    "            electro_optic_module_kwargs={\n",
    "                'y': 0,\n",
    "                'T': 300,\n",
    "                'BF_model': 'vinchant'\n",
    "            },\n",
    "            charge_transport_simulator_kwargs={\n",
    "                'sol_obp_material': wg2_obp_material,\n",
    "                'sol_Nd': 1e16\n",
    "            }\n",
    "        )\n",
    "\n",
    "        p1_obp_material = obp.GaInPAs(T=300, As = 0, a = obp.InP.a())\n",
    "        self.p1 = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2,\n",
    "                self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1\n",
    "            ),\n",
    "            rf_eps = p1_obp_material.dielectric(T=300),\n",
    "            optical_material=p1_obp_material.refractive_index(T=300)**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['p1'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['p1'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['p1'],\n",
    "            charge_mesh_settings=self.charge_mesh_settings['p1'],\n",
    "            name = 'p1',\n",
    "            electro_optic_module=InGaAsPElectroOpticalModel,\n",
    "            electro_optic_module_kwargs={\n",
    "                'y': 0,\n",
    "                'T': 300,\n",
    "                'BF_model': 'vinchant'\n",
    "            },\n",
    "            charge_transport_simulator_kwargs={\n",
    "                'sol_obp_material': wg2_obp_material,\n",
    "                'sol_Na': 1e17\n",
    "            }\n",
    "        )\n",
    "\n",
    "        p2_obp_material = obp.GaInAs(T=300, a = obp.InP.a())\n",
    "        self.p2 = imodulator.SemiconductorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1,\n",
    "                self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2\n",
    "            ),\n",
    "            rf_eps = p2_obp_material.dielectric(T=300),\n",
    "            optical_material=p2_obp_material.refractive_index(T=300)**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['p2'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['p2'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['p2'],\n",
    "            charge_mesh_settings=self.charge_mesh_settings['p2'],\n",
    "            name = 'p2',\n",
    "            electro_optic_module=InGaAsPElectroOpticalModel,\n",
    "            electro_optic_module_kwargs={\n",
    "                'y': 0,\n",
    "                'T': 300,\n",
    "                'BF_model': 'vinchant'\n",
    "            },\n",
    "            charge_transport_simulator_kwargs={\n",
    "                'sol_obp_material': p2_obp_material,\n",
    "                'sol_Na': 1e19\n",
    "            }\n",
    "        )\n",
    "\n",
    "        self.bcb_far_left = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_window/2,\n",
    "                0,\n",
    "                -self.w_sig_metal/2 - self.metal_sep - self.w_gnd_metal,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2\n",
    "            ),\n",
    "            rf_eps = bcb_eps,\n",
    "            optical_material=1.56**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['bcb'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['bcb'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['bcb'],\n",
    "            name = 'bcb_far_left'\n",
    "        )\n",
    "\n",
    "        self.bcb_far_right = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                self.metal_sep + self.w_gnd_metal + self.w_sig_metal/2,\n",
    "                0,\n",
    "                self.w_window/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2\n",
    "            ),\n",
    "            rf_eps = bcb_eps,\n",
    "            optical_material=1.56**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['bcb'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['bcb'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['bcb'],\n",
    "            name = 'bcb_far_right'\n",
    "        )\n",
    "\n",
    "        self.bcb_left = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_sig_metal/2 - self.metal_sep,\n",
    "                self.h_n,\n",
    "                -self.w_wg/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2\n",
    "            ),\n",
    "            rf_eps = bcb_eps,\n",
    "            optical_material=1.56**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['bcb'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['bcb'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['bcb'],\n",
    "            name = 'bcb_left'\n",
    "        )\n",
    "\n",
    "        self.bcb_right = imodulator.InsulatorPolygon(\n",
    "            shapely.box(\n",
    "                self.w_wg/2,\n",
    "                self.h_n,\n",
    "                self.w_sig_metal/2 + self.metal_sep,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2\n",
    "            ),\n",
    "            rf_eps = bcb_eps,\n",
    "            optical_material=1.56**2,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['bcb'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['bcb'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['bcb'],\n",
    "            name = 'bcb_right'\n",
    "        )\n",
    "\n",
    "        self.sig_metal = imodulator.MetalPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_sig_metal/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2,\n",
    "                self.w_sig_metal/2,\n",
    "                self.h_n + self.h_wg1 + self.h_wg2 + self.h_p1 + self.h_p2 + self.h_metal\n",
    "            ),\n",
    "            rf_eps = eps_rf_metal,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['sig_metal'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['sig_metal'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['sig_metal'],\n",
    "            name = 'sig_metal',\n",
    "            calculate_current=True,\n",
    "            d_buffer_current=min(self.w_sig_metal/20, self.h_metal/20, 0.05)\n",
    "        )\n",
    "\n",
    "        self.n_metal_left = imodulator.MetalPolygon(\n",
    "            shapely.box(\n",
    "                -self.w_sig_metal/2 - self.metal_sep - self.w_gnd_metal,\n",
    "                self.h_n,\n",
    "                -self.w_sig_metal/2 - self.metal_sep,\n",
    "                self.h_n + self.h_metal\n",
    "            ),\n",
    "            rf_eps = eps_rf_metal,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['n_metal_left'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['n_metal_left'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['n_metal_left'],\n",
    "            name = 'n_metal_left',\n",
    "            calculate_current=False\n",
    "        )\n",
    "\n",
    "        self.n_metal_right = imodulator.MetalPolygon(\n",
    "            shapely.box(\n",
    "                self.w_sig_metal/2 + self.metal_sep,\n",
    "                self.h_n,\n",
    "                self.w_sig_metal/2 + self.metal_sep + self.w_gnd_metal,\n",
    "                self.h_n + self.h_metal\n",
    "            ),\n",
    "            rf_eps = eps_rf_metal,\n",
    "            eo_mesh_settings=self.eo_mesh_settings['n_metal_right'],\n",
    "            rf_mesh_settings=self.rf_mesh_settings['n_metal_right'],\n",
    "            optical_mesh_settings=self.optical_mesh_settings['n_metal_right'],\n",
    "            name = 'n_metal_right',\n",
    "            calculate_current=False\n",
    "        )\n",
    "\n",
    "    def _initialize_device(self):\n",
    "        photo_polygons = [\n",
    "            self.sig_metal,\n",
    "            self.n_metal_left,\n",
    "            self.n_metal_right,\n",
    "            self.p2,\n",
    "            self.p1,\n",
    "            self.wg2,\n",
    "            self.wg1,\n",
    "            self.n,\n",
    "            self.box,\n",
    "            self.bcb_left,\n",
    "            self.bcb_right,\n",
    "            self.bcb_far_left,\n",
    "            self.bcb_far_right,\n",
    "            self.substrate,\n",
    "            self.background\n",
    "        ]\n",
    "        \n",
    "        #Just in case there are empty polygons\n",
    "        idxs_to_remove = []\n",
    "        for i, poly in enumerate(photo_polygons):\n",
    "            if np.isclose(poly.polygon.bounds[1], poly.polygon.bounds[3]):\n",
    "                idxs_to_remove.append(i)\n",
    "        for i in idxs_to_remove[::-1]:\n",
    "            del photo_polygons[i]\n",
    "        self.device = imodulator.PhotonicDevice(\n",
    "            photo_polygons\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd1977c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eopm = InP_EOPM()\n",
    "eopm._make_meshes()\n",
    "eopm._create_polygons()\n",
    "eopm._initialize_device()\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "gs = fig.add_gridspec(1,2)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0,0])\n",
    "ax2 = fig.add_subplot(gs[0,1])\n",
    "\n",
    "for ax in [ax1, ax2]:\n",
    "    eopm.device.plot_polygons(\n",
    "        color_polygon=\"black\",\n",
    "        color_line=\"green\",\n",
    "        color_junctions=\"blue\",\n",
    "        fill_polygons=True,\n",
    "        fig=fig,\n",
    "        ax=ax,\n",
    "    )\n",
    "\n",
    "ax2.set_xlim(-5,5)\n",
    "ax2.set_ylim(-5,10)\n",
    "\n",
    "ax1.set_xlabel('x (um)')\n",
    "ax1.set_ylabel('y (um)')\n",
    "ax2.set_xlabel('x (um)')\n",
    "ax2.set_ylabel('y (um)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a1c4fe",
   "metadata": {},
   "source": [
    "The `ChargeSimulatorSolcore` is a 1D solver, and for that, we must make sure that we select a line for our simulation. For the moment, this solver is restricted to vertical lines, and subsequent horizontal broadcasting of the results into our 2D device. The thought process in this decision stems from the fact that most of the InP-based modulators are a vertical structure and very often a single 1D solution is enough to capture carrier dynamics and the electro-optic effects, which will allows us to speed the iterative process of designing and optimizing a modulator. \n",
    "\n",
    "We must now select a line. This selection must be done carefully, because you are not only selecting a line, but rather a vector. That is, the first point of your line will be the zero of the origin of the simulation domain. Inverting the line can cause to invert the direction of the internal electrical field. Our advice is to place the arrow colinear with the y axis always.\n",
    "\n",
    "You must also give it a voltage range. The voltage that is specified is the one that is applied on the first point of the line. For example, in our case, we want to reverse bias the junction. Therefore, we must apply a positive voltage to the N side. Since the N side is on the first point of the line, we need to supply a voltage array from 0 to 6 V for example.\n",
    "\n",
    "A very important aspect is a current bug in `Solcore` that we must correct for manually. The solver internally enforces that it solves from 0V to some voltage, and the voltage you supply must be strictly increasing. In case you supply a voltage array form 0 to V then no correction is needed. But if you have an inverted junction and you supply a voltage array from -V to 0, then the solver inverts the voltage array and forgets to revert it back. Therefore, you must manually invert the voltages by doing `charge.V = charge.V[::-1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f691a205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Charge transport will take place with:\n",
      "p2\n",
      "p1\n",
      "wg2\n",
      "wg1\n",
      "n\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-1.0, 3.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=shapely.LineString([\n",
    "\t[0,0+0.01],\n",
    "    [0,eopm.h_n+eopm.h_wg1+eopm.h_wg2+eopm.h_p1+eopm.h_p2 - 0.01]]) #simulation line\n",
    "\n",
    "charge=ChargeSimulatorSolcore(\n",
    "\tdevice=eopm.device,\n",
    "\tsimulation_line=a,\n",
    "\tbias_start_stop_step=[0,6,21]\n",
    ")\n",
    "\n",
    "fig, ax = charge.plot_with_simulation_line()\n",
    "\n",
    "ax.set_xlim(-2,2)\n",
    "ax.set_ylim(-1,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122ad1b0",
   "metadata": {},
   "source": [
    "You can now inspect the mesh. This can be a confusing part. The x axis represents the coordinate on your actual device. However, `Solcore` enforces that the simulation space must start at 0, and it should be specified in meters. Therefore, the right axis is the simulation space. This can be a useful representation if you'd like to inspect the different mesh resolutions, as different sloped represent varying spacings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a51fead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charge.plot_mesh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c304eac4",
   "metadata": {},
   "source": [
    "Now we can solve the equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ef7a0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Material \"n\" does not have k-data defined. Returning \"zeros\"\n",
      "Material \"wg1\" does not have k-data defined. Returning \"zeros\"\n",
      "Material \"wg2\" does not have k-data defined. Returning \"zeros\"\n",
      "Material \"p1\" does not have k-data defined. Returning \"zeros\"\n",
      "Material \"p2\" does not have k-data defined. Returning \"zeros\"\n",
      "Solving IV of the junctions...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20230622\\AppData\\Local\\anaconda3\\envs\\imodulator_venv\\lib\\site-packages\\solcore\\sesame_drift_diffusion\\solve_pdd.py:193: UserWarning: All voltages are positive, but junction has been identified as n-p, so the  open-circuit voltage (Voc) of the junction will be negative.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solving IV of the tunnel junctions...\n",
      "Solving IV of the total solar cell...\n"
     ]
    }
   ],
   "source": [
    "charge.solve_PDD(\n",
    "\tverbose = True*0,\n",
    "\ttol = 1e-6,\n",
    "\tmax_iter = 1000,\n",
    "\tsmooth_output=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c23f5c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charge.plot_results(\n",
    "\tV_idx = [0, 5, 10, 20],\n",
    "\tcmap = 'plasma'\n",
    "\t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c2f6ce",
   "metadata": {},
   "source": [
    "Finally we can send this data to the device where the information is stored as a list of 2D interpolators. Note now that we must create new data to a 2D mesh, therefore, it is required that you input a xmin and xmax. These define the new mesh in the x domain in which the interpolators will be valid. Any point evaluated outside the domain will simply be extrapolated. It is also worth noting that the values of the quantities to be passed to the `PhotonicDevice` will be masked by the polygons that were involved in the charge transport simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b2715c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "charge.transfer_results_to_device(xmin=-2, xmax=2, dx = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dbd39e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Ec', 'Ev', 'Efn', 'Efp', 'N', 'P', 'Efield', 'mun', 'mup', 'V'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1ee6ec15cf0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2arsngEesJIc1G/4NzAk2AEeCwQaZjUiHUajZQMKwKBAFEHGBaFmRaFCxlSwlFsFGtQtEdRwKQFwj2ACcVWyYT+YlYbNRdEonwUZ1gw19DYFEYOvvu4PZKH6OyxlYBqIxG6V8zYYVkGIriVZVK00qN4wCJFLNRpKzVkOrV6+W/v37S6tWrcxGh0uWLAndV1xcLHfffbecc845Ur9+fXPO0KFDZdeuXWGPkZOTI0OGDJGMjAxp1KiRjBw5UvLz82Mn2Kjqh6zMypUr5bzzzpO0tDQ5/fTTZd68eW52EYjKMEr5AtFgzQatimaGmrSQVmvefPxxDYhxBQUF0rFjR5k1a9Yv7jt06JB8/PHHcu+995qvr732mmzdulWuuuqqsPM00Pj8889l+fLlsnTpUvPePnr06NgZRgn+kDfeeKNcc801xz1/+/btcuWVV8ott9wiCxYskBUrVshNN90kLVu2lL59+7rZVSAiZcMo+oZZrkDUTjINVbDLXicW9UIisa2ytTYiv77m/1Yuv/xy0yrSsGFDE0CU9/TTT8uFF14oO3fulNatW8vmzZtl2bJlsm7dOjn//PPNOTNnzpQrrrhCpk2bZpIJtR5sVPVDVmTOnDnStm1beeKJJ8zt9u3by/vvvy/Tp08n2ECM0mDDkvJ7vhbbZcMoqFqJFTiyqBcDKUgQttNdX8uuzcvLCzusIwHaouHAgQNmJEKHS9SaNWvM98FAQ/Xp00eSkpLkww8/lN/85jf+KxDVH0p/iPI0yBg3blyl1xQWFpoWdOz/BMBNwQLRsGEUq6weAVUry2ww9RWoqaysrLDb9913n9x///3i1OHDh00Nxw033GDqM9Tu3bulefPmYefVqVNHGjdubO6rrpgKNrTjLVq0CDumtzWA+PnnnyU9Pf0X10ydOlUeeOABD3sJlFdBzcaRIkhUL7NBzQYShe14NkrZtdnZ2aFgQEUjq6HFotddd51ZqHD27NkSbTEVbERi8uTJMmHChNBtDUyOjfoAd1cQtcJXECWzUS06XZjMBhKKFdmMkqPXl33RQKN8sBGtQOPbb7+Vd999N+yxMzMzZe/evWHnl5SUmBkqep8vgw3t+J49e8KO6W39wSvKakR7rAqouSPDKOXKDoLTO1HNzAbBBhKE7Xgjtuiv7R8MNL766it57733pEmTJmH3d+vWTXJzc2XDhg3SpUsXc0wDEsuypGvXrv4MNvSHevvtt8OOaaWsHgdit2bDChsKKJaAKRJF1fQ1ItgA3KXrYWzbti1s1ufGjRtNzYXO9Lz22mvNtFed0lpaWhqqw9D7U1NTzUSNfv36yahRo8wkDg1OxowZI4MHD672TBTXg42qfkidUqNDIN9//73Mnz/f3K9TXnXazV133WWmy2r09Le//U3eeustN7sJRKws1DhmIzb9xM7uYsdlXqcjw1BAIrCjVLNRE+vXr5dLL700dDtYdjBs2DBTVPrGG2+Y2506dQq7TrMcvXr1Mt/rUhQaYPTu3dvMQhk4cKDMmDGjRv1wNdio6ofUxbp++OEHM5c3SKe9amAxfvx4+fOf/ywnn3yy/OUvf2HaK2JaWWYjvBaBzEb1MxtMfUXCsKJTs1ETGjBobVllqrovSBMECxcuFCdcDTaO90NWtDqoXvPJJ5+42S0g6ruj6Cf0oDoNG0gKNRvHlVockPoptmQ0PPpa6fx+APEnpmo2AL+xbUtKpESKy9Ud3PqPKbXaJz+rV6/iQnAgHtgxWCDqFYINwAHNamiwUWgxFBANlc06A+KBXQs1G7GCYANwxBYrUBpW5PjH/+8RKcgpqNVe+UFBqch3h4vkYNOD8r/rnzXH9u//sba7BcAFBBuAw2GUskW9jg6jHMgpkPwfCTaOp6DElpyfCyVPcmu7K0DcFojGCoINICorbRwNNkqssoaq6WtUFqrxYiEx2NRsAIjEkbkoogMpQcVWWUPVSnSb+SOhGoD4RrABOGTZGm4cfcPUtSPKL/KFiunQU1mYVlrbXQE8YVMgCiDy3EZ4ZkOHB4rLr/KFCpVY+srZpu4FSAi2w5oNH/9ZIdgAorHr6zGZDR0iQNVKQpkNgg0kBpuaDQCRKavZKD8UUGzbZDZqEGywXDkQ/wg2gGgsVx6W2dCN2Qg2qh9sULOBxGDrXkCOajbEtwg2AEdssexSKQ0ck9nw818Fj5SE5vEQbCBBWM6GUfR6vyLYAKKyzsbRN8xS3S+Fosfj0teoNKmUAlEgARBsABHTmRTBdTZKQkeLbcs0VK049LrxWiEx2HaSaZFf79+MKcEG4EjZ1M3ywYYODOgQAapWciTYSCIwQ6KwAs6GQhhGARJ7nY2w2Shmy/mjwQcqVhLQcKNEAtRsAHGPYANwWrGhBaLlgouyt1DeQI+n5MgrlWzzWiEx2KwgCsDZzq+lYZ/YiwPFtdonPygJFJsBJw3YgERgs6gXgIjYR4ZR7HIFooFSKQ4wjHI8OthUahebzBCA+EawAUS5QLRECqVYCmu1V34JNkxmgwJRJAib2SgAHK2zUS6zocMD2lC1ErtISm2GUZA4bIZRAES8XLkdPoxSIkUmu4Eq6GZ1UiSWXezokx7gJzYFogCcZDbscsGG1iHop3ZUrdTWYEMzG/wZAuId/8qBKNRslC9yLLELTUPVNKthgg0yG0gQNpkNABHPRjlmGEUzG9pQnWCDXV+RYMGGRbABICLhBaL6JqpDBKheZkNs/gwB8Y5/5YDTAlGt2Si3F0pZZoNg43jKshpas8HUVyQGm6mvACJj63w0M5QSZFnFpqFqujqJed18/AcUqAmbqa8AIlU2G6X0mOEBgo3qvm5kNoD450kZ+KxZs6RNmzZSt25d6dq1q3z00UeVnjtv3jwJBAJhTa8DYlfZkuXlhwe0FoF2/Fb2upHZQGIIzkZx0vzK9czGq6++KhMmTJA5c+aYQOOpp56Svn37ytatW6V58+YVXpORkWHuD9KAA4jlYZTyQwHm0zr7fdTgtSOzgcRgM/XVPU8++aSMGjVKRowYYW5r0PHWW2/JX//6V5k0aVKF12hwkZmZWa3HLywsNC0oLy8vSj0Hqi98KEC/5w30eMoKa8lqAInA1WGUoqIi2bBhg/Tp0+foEyYlmdtr1qyp9Lr8/Hw55ZRTJCsrS66++mr5/PPPKz136tSp0rBhw1DTawAvHfuGGZydQqu6lWWDCDaQOGzraJFoZK3mz7l69Wrp37+/tGrVynyQX7JkSXifbFumTJkiLVu2lPT0dPP+/NVXX4Wdk5OTI0OGDDGjDo0aNZKRI0ea9+mYCTb2798vpaWl0qJFi7Djenv37t0VXnPWWWeZrMfrr78uL7/8sliWJd27d5fvvvuuwvMnT54sBw4cCLXs7GxXfhag2oLvobSqW+jlIuBAYrBroWajoKBAOnbsaGonK/LYY4/JjBkzzKjDhx9+KPXr1zelDocPHw6do4GGfuhfvny5LF261AQwo0eP9vdslG7dupkWpIFG+/bt5dlnn5WHHnroF+enpaWZBtSWgBzzB0BrjKgzqh67gtcPiFO243U2an7t5ZdfblrFj2ebOsp77rnHjCKo+fPnm4SAZkAGDx4smzdvlmXLlsm6devk/PPPN+fMnDlTrrjiCpk2bZrJmNR6ZqNp06aSnJwse/bsCTuut6tbk5GSkiKdO3eWbdu2udRLwLlAuX9KAUmmVaslHXndCDaAmtDaxPKtfN1iTWzfvt2MMpQvddByBJ3MESx10K86dBIMNJSeryURmgmpLleDjdTUVOnSpYusWLEidEyHRfR2+exFVXQY5rPPPjPjSUDs0SxGsgQCyaEjSUkpkpSUSjtOCwRSJBCoIxJgIzYkBssOOG5KaxPL1ypq7WIkguUMVZU66NdjZ47WqVNHGjduXGk5RK0Mo+i012HDhpmo6MILLzQpGx1DCs5OGTp0qJx00kmhF+vBBx+Uiy66SE4//XTJzc2Vxx9/XL799lu56aab3O4qEAFdC6aOJCUdHcpLSW4gKcksV348tl0iJdbPZQEHkAgsZyuI6vVKaxO1WDPID6UErv8rv/7662Xfvn2m2lWjoE6dOpnxn2AktXPnTpOOCfrpp5/MVFk998QTTzSZkQ8++EDOPvvsGj1v48ZNwh4XcEMgkGaCi1bN24SOtWlxppyQfKBW++UHlhTLYStPGjQ8+u+0WbOmtdonJB7Ntufk7BM/ycjICAs2IhUsZ9DShvKjB3pb36uD5+zduzfsupKSEjNDpbrlECpg+3lnlwro+JWmlXRmSjT+ZwAA4pcX7xl5R55jQ79u0iAl8s/4+cUl0mXZmoj7qlNfFy9eLAMGDDC39e1fCzx///vfy8SJE0N91WETXc07WCCqH/bXr19vPvyrd955R/r162dmiVa3QJT8JQAAcbqCaH5+ftgECy0K3bhxo6m5aN26tYwbN04efvhhOeOMM6Rt27Zy7733mgAiGJDobFANLHTEQafHFhcXy5gxY0wgUt1AI66Djes6/1ZSklJquxtIFLqHT3qapNVNlQM5rGJbEzpLODW9rqSlp0nejww/wVvFcb5D8/r16+XSSy8Nq6NUWkup2Yu77rrL1FHquhlaJ9mzZ09T6lB+T7IFCxaYAKN3796mPGHgwIFmbY6aiNthFJG67KkCAKhS2VvgYU+GUdZd1sPxMMoF7/zLl2UCcZvZECny9aY1AAAvePd527KTTHNyvV/5t+cAAMAX4jizAQBAjBWIWmwxDwAA4mg2Sqwg2AAAwAN2Agcb1GwAAABXkdkAAMADVrnN1CK93q8INgAA8IDNMAoAAIA7yGwAAOABO4EzGwQbAAB4wErgmg2GUQAAgKvIbAAA4AHbdjYU4udtUwk2AADwgJ3ANRsMowAAAFeR2QAAwAO2wwJRP2c2CDYAAPCAncDDKAQbAAB4wE7gYIOaDQAA4CoyGwAAeMBK4EW9CDYAAPCAzTAKAACAO8hsAADgAYthFAAA4CZbAqY5ud6vGEYBAAD+DzZmzZolbdq0kbp160rXrl3lo48+qvL8RYsWSbt27cz555xzjrz99ttedBMAANcLRG0Hza9cDzZeffVVmTBhgtx3333y8ccfS8eOHaVv376yd+/eCs//4IMP5IYbbpCRI0fKJ598IgMGDDBt06ZNbncVAADXazYsB82vArbt7qa1msm44IIL5Omnnza3LcuSrKwsGTt2rEyaNOkX519//fVSUFAgS5cuDR276KKLpFOnTjJnzpzjPl9eXp40bNhQGjduJklJjBIBACqn70k5OfvkwIEDkpGR4cpz5B15X3rj/Kukfp2UiB+noKRYrlr/hqt99WWBaFFRkWzYsEEmT54cOqYBQJ8+fWTNmjUVXqPHNRNSnmZClixZUuH5hYWFppX/n6q2b9/mu/8ZAABvBQMBL9iss+GO/fv3S2lpqbRo0SLsuN7evXt3hdfo8ZqcP3XqVPOLEmyaNQEAINZY4nAYxcezUXw/9VWzJuUzIRqlasDRtu3pDKMAAI47jOIVO4EzG64GG02bNpXk5GTZs2dP2HG9nZmZWeE1erwm56elpZl2rJycH7UkxVH/AQDxztWyRRzh6kf/1NRU6dKli6xYsSIsitTb3bp1q/AaPV7+fLV8+fJKzwcAwDfDKOKs+ZXr4ww6xPH888/Liy++KJs3b5Zbb73VzDYZMWKEuX/o0KFhBaR33HGHLFu2TJ544gnZsmWL3H///bJ+/XoZM2aM210FAMA9tsM1Nmo4jKI1k/fee6+0bdtW0tPT5bTTTpOHHnpIyk9C1e+nTJkiLVu2NOfoBI6vvvrKfzUbOpV137595ofRIk+dwqrBRLAIdOfOnWG1Fd27d5eFCxfKPffcI3/4wx/kjDPOMDNROnTo4HZXAQCIG48++qjMnj3bfNj/1a9+ZT646wd9nUxx++23m3Mee+wxmTFjhjlHgxINTnQG6BdffGEW1vTNOhu1N41JAxj/ppwAAF7Qt0DLk3U2/tbpWqmXHPk6G4dKi+W6jf9d7b7+27/9m/lg/8ILL4SODRw40GQwXn75ZZPVaNWqlUycOFF+//vfm/v1sfWaefPmyeDBgyVamK4BAICPlivPy8sLa+XXmipPRwq0BvLLL780t//v//5P3n//fbn88svN7e3bt5sRBx06CdKgSBfjrGwtrISd+goAQCLJOmY9Kd0OROsbj6WrdGswonuN6cxQreF45JFHZMiQIeb+4PpVNVnbKlIEGwAAeMA60pxcr7Kzs8OGUSpa/kH97W9/kwULFpg6SK3Z2Lhxo4wbN84MnQwbNky8RLABAICPFvXKyMioVs3GnXfeabIbwdoL3UX922+/NStva7ARXL9K17LS2ShBelsnc0QTNRsAAMShQ4cO/WIlbR1OCa6aqrNPNOAov7aVDrt8+OGHUV/biswGAAAesOyybeadXF8T/fv3NzUarVu3NsMon3zyiTz55JNy4403mvsDgYAZVnn44YfNMhPBqa86zDJgwACJJoINAAA8YEvANCfX18TMmTNN8PAf//EfsnfvXhNE3HzzzWbdq6C77rrLLLQ5evRoyc3NlZ49e5q1sKK5xoZinQ0AQALzbp2NFzsMlnrJqRE/zqHSIhm26RVX++oWajYAAICrGEYBAMCzmg1xdL1fEWwAABCHNRuxhGEUAADgKjIbAAB4wLIDDqe++jezQbABAIAHbLusObnerxhGAQAAriKzAQCAB2wJiJWgBaIEGwAA+GgjNj9iGAUAALiKzAYAAB6wmI0CAADcZB9pTq73K4INAAA8YCVwZoOaDQAA4CoyGwAAeMA60pxc71cEGwAAeMBm6isAAIA7yGwAAOABK4ELRAk2AADwgJ3AU18ZRgEAAK4iswEAgAesBB5GcTWzkZOTI0OGDJGMjAxp1KiRjBw5UvLz86u8plevXhIIBMLaLbfc4mY3AQDwbOqr5aD5lauZDQ00fvjhB1m+fLkUFxfLiBEjZPTo0bJw4cIqrxs1apQ8+OCDodv16tVzs5sAAMCPwcbmzZtl2bJlsm7dOjn//PPNsZkzZ8oVV1wh06ZNk1atWlV6rQYXmZmZbnUNAADP2ayzEX1r1qwxQyfBQEP16dNHkpKS5MMPP6zy2gULFkjTpk2lQ4cOMnnyZDl06FCl5xYWFkpeXl5YAwAg1tgOh1D8PBvFtczG7t27pXnz5uFPVqeONG7c2NxXmX//93+XU045xWQ+Pv30U7n77rtl69at8tprr1V4/tSpU+WBBx6Iev8BAIgmWxxmNiSQOMHGpEmT5NFHHz3uEEqktKYj6JxzzpGWLVtK79695euvv5bTTjvtF+dr5mPChAmh25rZyMrKivj5AQBALQcbEydOlOHDh1d5zqmnnmpqLvbu3Rt2vKSkxMxQqUk9RteuXc3Xbdu2VRhspKWlmQYAQCyz7LLm5PqECTaaNWtm2vF069ZNcnNzZcOGDdKlSxdz7N133xXLskIBRHVs3LjRfNUMBwAAfmWzgmj0tW/fXvr162emsX700Ufyr3/9S8aMGSODBw8OzUT5/vvvpV27duZ+pUMlDz30kAlQduzYIW+88YYMHTpULr74Yjn33HPd6ioAAPDrOhs6q0QDDK250FkoAwcOlBkzZoTu17U3tPgzONskNTVV/vGPf8hTTz0lBQUFpvZCr7nnnnvc7CYAAK6zEngFUVeDDZ15UtUCXm3atBHbPpoY0uBi1apVbnYJAIBaYTlcBdTPK4iyERsAAHAVG7EBAOABmxVEAQBAvG3E9v3338tvf/tbadKkiaSnp5v1q9avXx+6X0sZpkyZYmZ86v260vdXX30l0UawAQBAHPrpp5+kR48ekpKSIv/zP/8jX3zxhTzxxBNy4oknhs557LHHzMSNOXPmmK1E6tevL3379pXDhw9HtS8MowAA4AHbLmtOrlfH7gFW2eKWutq3TryYO3du6Fjbtm3LPZ5tZn/qjM+rr77aHJs/f760aNFClixZYpaqiBYyGwAAeMCSgOOmNIBo2LBhqOkeYRXRtap0M9RBgwaZvco6d+4szz//fOj+7du3m73KdOgkSB9PF97UzVSjicwGAAA+Wq48OztbMjIyQscr27Ljm2++kdmzZ5v9w/7whz/IunXr5PbbbzdrWg0bNiy0KapmMsrT21VtmBoJgg0AAHwkIyMjLNiojG4PopmNP/7xj+a2ZjY2bdpk6jM02PASwygAAHjBPlq3EUmr6eYoOsPk7LPP/sVWIjt37jTfBzdF3bNnT9g5ersmG6ZWB8EGAAA+qtmoLp2JoluClPfll1/KKaecEioW1aBixYoVofu1+FRnpehmqtHEMAoAAHFo/Pjx0r17dzOMct1115lNT5977jnTVCAQkHHjxsnDDz8sZ5xxhgk+7r33XrNZ6oABA6LaF4INAAB8NPW1ui644AJZvHixTJ48WR588EETTOhU1yFDhoTOueuuu8zGp6NHj5bc3Fzp2bOnLFu2TOrWrSvRFLDL74QWBzQFpFN3ykaI/Lu0KwDAC/oWaMmBAweqVXTp5H1p4kk3S1pSasSPU2gVyRPfP+tqX91CzQYAAHAVwygAAPhonQ0/ItgAAMADds1nr/7ier9iGAUAALiKzAYAAJ4NowQcXe9XBBsAAMTh1NdYQrABAIAHrCPNyfV+Rc0GAABwFZkNAAA8YDOMAgAA3GQxjAIAAOAOMhsAAHjAdriCKMMoAACgSjYriAIAALiDzAYAAB6w2IgNAAC4yU7gqa+uDaM88sgj0r17d6lXr540atSoWtfYti1TpkyRli1bSnp6uvTp00e++uort7oIAAD8HGwUFRXJoEGD5NZbb632NY899pjMmDFD5syZIx9++KHUr19f+vbtK4cPH3armwAAeLrOhuWg+ZVrwygPPPCA+Tpv3rxqZzWeeuopueeee+Tqq682x+bPny8tWrSQJUuWyODBgyu8rrCw0LSgvLy8qPQfAIBoshK4ZiNmZqNs375ddu/ebYZOgho2bChdu3aVNWvWVHrd1KlTzXnBlpWV5VGPAQCo+dRX20Hzq5gJNjTQUJrJKE9vB++ryOTJk+XAgQOhlp2d7XpfAQCAS8HGpEmTJBAIVNm2bNkiXkpLS5OMjIywBgBArA6jWA5aQtRsTJw4UYYPH17lOaeeempEHcnMzDRf9+zZY2ajBOntTp06RfSYAADECjuBp77WKNho1qyZaW5o27atCThWrFgRCi602FNnpdRkRgsAAEiQmo2dO3fKxo0bzdfS0lLzvbb8/PzQOe3atZPFixeb73UIZty4cfLwww/LG2+8IZ999pkMHTpUWrVqJQMGDHCrmwAAeMJi6mv06eJcL774Yuh2586dzdf33ntPevXqZb7funWrKeoMuuuuu6SgoEBGjx4tubm50rNnT1m2bJnUrVvXrW4CAOAJy+H0VT8HGwFbF7iIIzr0olNgy5I2gdruDgAgpulboGU++Lo1wSDvyPvS9SfeLKmBtIgfp8gulFd/etbVvrqFvVEAAPCAncBbzBNsAADgAVunrzq83q9iZlEvAAAQn8hsAADg1Tob4ux6vyLYAADAA5bDGSV+no1CsAEAgAcsU7MReXrCz8uVU7MBAEAC+NOf/hRaQDPo8OHDctttt0mTJk2kQYMGMnDgQLNNSLQRbAAAEOdbzK9bt06effZZOffcc8OOjx8/Xt58801ZtGiRrFq1Snbt2iXXXHONRBvBBgAAPtr1NS8vL6wVFhZW+by6TciQIUPk+eeflxNPPDF0XBcHe+GFF+TJJ5+UX//619KlSxeZO3eufPDBB7J27dqo/uwEGwAA+EhWVpZZkTTYpk6dWuX5Okxy5ZVXSp8+fcKOb9iwQYqLi8OO655lrVu3ljVr1kS1zxSIAgDgAfvIf06uV9nZ2WHLlaelVb4E+iuvvCIff/yxGUY51u7duyU1NVUaNWoUdrxFixbmvmgi2AAAwLPZKJELDqNooFGdvVE0KLnjjjtk+fLltb6hKcMoAADEoQ0bNsjevXvlvPPOkzp16pimRaAzZsww32sGo6ioyOyyXp7ORsnMzIxqX8hsAAAQh4t69e7dWz777LOwYyNGjDB1GXfffbep/UhJSZEVK1aYKa9q69atsnPnTunWrZtEE8EGAAAesG2HNRs1XK/8hBNOkA4dOoQdq1+/vllTI3h85MiRMmHCBGncuLEZmhk7dqwJNC666CKJJoINAAAS1PTp0yUpKclkNnQKbd++feWZZ56J+vME7JqGSjFO5xzrVKCycpRAbXcHABDT9C3QMmtOVKfo0sn70v+rP0pSAqkRP06xXSTLC553ta9uIbMBAEAcDqPEEoINAAA8y6FEzr+hBlNfAQCAy8hsAADgAcu2HW4x79/cBsEGAAA+Wq7cjxhGAQAAriKzAQBAHK4gGksINgAA8IAlDms2GEYBAACoGJkNAAA8YDEbBQAAuMlmNkr0PfLII9K9e3epV6+eNGrUqFrXDB8+XAKBQFjr16+fW10EAAB+zmwUFRXJoEGDzFa1L7zwQrWv0+Bi7ty5odtpaWku9RAAAO9YCVwg6lqw8cADD5iv8+bNq9F1GlxkZma61CsAAGqHlcDBRszNRlm5cqU0b95czjrrLLn11lvlxx9/rPL8wsJCs31v+QYAQKzWbNgO/vOrmAo2dAhl/vz5smLFCnn00Udl1apVcvnll0tpaWml10ydOlUaNmwYallZWZ72GQAARDHYmDRp0i8KOI9tW7ZskUgNHjxYrrrqKjnnnHNkwIABsnTpUlm3bp3JdlRm8uTJcuDAgVDLzs6O+PkBAHCLfWQYJdJmJ0rNxsSJE82MkaqceuqpTvsU9lhNmzaVbdu2Se/evSut8aCIFAAQ66yAJYFA5IuOWz5esLxGwUazZs1M88p3331najZatmzp2XMCAACf1Gzs3LlTNm7caL5qzYV+ry0/Pz90Trt27WTx4sXmez1+5513ytq1a2XHjh2mbuPqq6+W008/Xfr27etWNwEA8ITlcBjFz7NRXJv6OmXKFHnxxRdDtzt37my+vvfee9KrVy/z/datW02dhUpOTpZPP/3UXJObmyutWrWSyy67TB566CGGSQAAvmcfCRmcXO9XAdv28WLrFdCprzorpSxpE6jt7gAAYpq+BVrmg29GRoar70vnpl8nyYHUiB+n1C6ST3/+m6t9dQt7owAA4AHLfAR2sqiXfxFsAADgAYvZKAAAwE2WWBKQxAw2YmoFUQAAEH/IbAAA4AErgTMbBBsAAHjATuCprwyjAAAAV5HZAADAAxazUQAAgJtsEy4wjAIAABB1ZDYAAPCALaViO/iMr9f7FZkNAAA8YEXhv5qYOnWqXHDBBXLCCSdI8+bNZcCAAWYD1PIOHz4st912mzRp0kQaNGggAwcOlD179kT5JyfYAAAgLq1atcoEEmvXrpXly5dLcXGx2U29oKAgdM748ePlzTfflEWLFpnzd+3aJddcc03U+8KurwCABObdrq9Z9ftIUiDy6gXLLpHsgn9E3Nd9+/aZDIcGFRdffLF5nGbNmsnChQvl2muvNeds2bJF2rdvL2vWrJGLLrpIooXMBgAAntVslDpqweClfCssLKzW82twoRo3bmy+btiwwWQ7+vTpEzqnXbt20rp1axNsRBPBBgAAPqrZyMrKMpmSYNPajOM+t2XJuHHjpEePHtKhQwdzbPfu3ZKamiqNGjUKO7dFixbmvmhiNgoAAD6SnZ0dNoySlpZ23Gu0dmPTpk3y/vvvS20g2AAAwEd7o2RkZNSoZmPMmDGydOlSWb16tZx88smh45mZmVJUVCS5ublh2Q2djaL3RRPDKAAAeMCSUsetJnT+hwYaixcvlnfffVfatm0bdn+XLl0kJSVFVqxYETqmU2N37twp3bp1k2giswEAQBy67bbbzEyT119/3ay1EazD0DqP9PR083XkyJEyYcIEUzSq2ZKxY8eaQCOaM1EUwQYAAHG4xfzs2bPN1169eoUdnzt3rgwfPtx8P336dElKSjKLeemslr59+8ozzzwj0cY6GwCABObdOhtN613geJ2N/YfWudpXt1CzAQAAXMUwCgAAcTiMEksINgAA8CzYKHV0vV8xjAIAAFxFZgMAAA/Yti44HnB0vV8RbAAA4AHbDIMEEnIYhWADAAAP2HZprV4flzUbO3bsMCuT6fKoulLZaaedJvfdd59Zh70qhw8fNqueNWnSRBo0aGAWGtF12gEAgD+5ltnYsmWL2dL22WefldNPP93sNjdq1CgpKCiQadOmVXrd+PHj5a233pJFixaZRVB0XfdrrrlG/vWvf7nVVQAAXGeZio3EHEbxdAXRxx9/3Cyf+s0331R4v66K1qxZM7OW+7XXXhsKWtq3by9r1qyp1lrtrCAKAIjFFUQbpJ0pgUCyo2GU/MIvfbmCqKc1G/oC6WYvldmwYYMUFxdLnz59QsfatWsnrVu3rjTY0LXctZV/jjJxtQo7AMAVZe8VcbZzR+IGG9u2bZOZM2dWOYSiO9KlpqZKo0aNwo63aNEitFvdsaZOnSoPPPBABffoLw6/PACA4zt48OCRrLh7bAcLekXjel8FG5MmTZJHH320ynM2b95sMhJB33//vfTr108GDRpk6jaiafLkyWZ73CCtE8nJyTEFpoFA7A2jaDotKytLsrOzfZcGq228ds7w+kWO1y5+XzvNaGig0apVK0+eS5wsV+7j7EuNg42JEyeGtqatzKmnnhr6fteuXXLppZdK9+7d5bnnnqvyuszMTDNbJTc3Nyy7obNR9L6KpKWlmVbesZmRWKT/6GLxH54f8No5w+sXOV67+Hzt3M5oIIJgQws4tVWHZjQ00OjSpYvMnTtXkpKqnmmr56WkpMiKFSvMlFe1detW2blzp3Tr1q2mXQUAIGbYDmeT+Hk2imvrbGig0atXL1PcqXUa+/btM3UX5Wsv9Bwdbvnoo49C0aWuzaHDIu+9954pGB0xYoQJNKozEwUAgFhl26WOm1+5ViC6fPlyUxSq7eSTT65w3Elnnmjm4tChQ6H7pk+fbjIgmtnQWSZ9+/aVZ555RuKFDvno4mbHDv3g+HjtnOH1ixyvXeR47eD5OhsAACSavCPrbKTVOUkCgSRHG7EVlnzPOhsAAKBidgLXbBBsAADgAdvhFvF+3mLetQJRAAAARWYDAAAP2Ak8jEJmo5bs2LHDTPNt27atpKeny2mnnWYqtnVRM1TPI488YhaLq1evni8WcqtNs2bNkjZt2kjdunWla9euoenmqNrq1aulf//+ZnVJXZF4yZIltd0l39CtJC644AI54YQTpHnz5jJgwAAz+zCR2Qk89ZVgo5bobra6tPqzzz4rn3/+uZnyO2fOHPnDH/5Q213zDQ3MdAn8W2+9tba7EtNeffVVs3aNBrMff/yxdOzY0Uwp37t3b213LeYVFBSY10uDNdTMqlWr5LbbbpO1a9eapRB0qYPLLrvMvKZIPEx9jSGPP/64zJ49W7755pva7oqvzJs3T8aNG2eWuccvaSZDP2E+/fTT5rYGubpXxdixY81eR6gezWwsXrzYfEJHzenCjprh0CDk4osvlkSc+pqcdKKjPbv07brU+smXU1/JbMQQ/QVq3LhxbXcDcZb90ZV4+/TpEzqmi+bp7TVr1tRq35B4f99UIv+Ns23LcfMrgo0YoSutzpw5U26++eba7griyP79+6W0tFRatGgRdlxvl986AHCTZtM0+9ijRw/p0KFDbXcHtYBgI8o0La1psqqa1muUp3vE9OvXz9QfjBo1ShJZJK8fgNimtRubNm2SV155RRKZLZbj5ldMfY2yiRMnyvDhw6s859RTTw19v2vXLrMzrs6qeO655yTR1fT1Q9WaNm0qycnJsmfPnrDjejszM7PW+oXEMWbMGFm6dKmZ2XPsPlmJx9LqHwfX+7fEkmAjypo1a2ZadWhGQwONLl26yNy5c81YeqKryeuH40tNTTW/XytWrAgVNmpKW2/rmwDgFi1m1CJkLapduXKlmeaPxEWwUUs00OjVq5eccsopMm3aNFOpHcQnzurZuXOn5OTkmK9al7Bx40Zz/PTTT5cGDRrUdvdihk57HTZsmJx//vly4YUXylNPPWWmH44YMaK2uxbz8vPzTT1V0Pbt283vmRY5tm7dulb75oehk4ULF8rrr79u1toI1gjprAxdWygh2Q4zG36ePKpTX+G9uXPn6m9NhQ3VM2zYsApfv/fee6+2uxZzZs6cabdu3dpOTU21L7zwQnvt2rW13SVf0N+lin7H9HcPVavs75v+7Us0Bw4cOPLzp9mBQN2Im16vj6OP5zesswEAgAfrbIikOF5nQ6SYdTYAAEBsmRUD2xUQbAAA4Am7rO4i0hbBbJRY2a6AYRQAADwZRkmOwtTX0hoNo8TKdgVkNgAA8IztoB0NXsq3wsLCmN+ugGADAACX17vJNEsalDpuOq1fMxOaKQm2qVOnxvx2BayzAQCAi+rWrWvWaNFMg1Na+XDsjJa0tDSJdQQbAAB4EHDUrVs3YbcrYBgFAIA4364gKLhdQbdu3cRLBBtAHHnhhRfksssuc/15li1bJp06dTJ/uADErgkTJsjzzz8vL774omzevFluvfXWWtmugKmvQJw4fPiw2RF30aJF0qNHD9efT6fT3X777fK73/3O9ecCEDmd9vr444+bolD9kDBjxgwzJdZLBBtAnHj55Zfl4Ycfli1btni2KuG8efNk3bp1njwfAP9iGAWIMboDsBZv/fGPfwwd++CDD8z4a/mx12O98sor0r9//7BjurPwuHHjwo7pVvPDhw8P3dZljDVIGTp0qJlWpzsRv/HGG6YfV199tTl27rnnyvr168MeR59Lj3399ddR+KkBxDOCDSDGNGvWTP7617/K/fffb97MDx48aIYqxowZI7179670uvfff99sIx+J6dOnm6GXTz75RK688krzfBp8/Pa3vzVLHJ922mnmdvlEqG6xrvP1//nPf0b0nAASB8EGEIOuuOIKGTVqlAwZMkRuueUWqV+/fqUL96jc3FyzhHGrVq0ifr6bb75ZzjjjDJkyZYpZlVBrMgYNGiRnnnmm3H333aa47NgpdPp83377bUTPCSBxEGwAMWratGlSUlJiCj4XLFhQ5cI9P//8s/ka6Tx+HSYJCq42eM455/zi2LGbN6Wnp8uhQ4ciek4AiYNgA4hRWguxa9cuM710x44dVZ7bpEkTs6rgTz/9FHZc90E4tga8uLj4F9enpKSEvg+uTljRsWOnuubk5JhhHwCoCsEGEIN0WWOtl7j++uvloYcekptuuqnKLaG1ePTss8+WL774Iuy4BgI//PBD6Lbuk7Bp06aoTbXVgKhz585ReTwA8YtgA4hB//mf/2lqMHQ+vNZLaN3EjTfeWOU1ffv2NUWi5f3617+Wt956yzSdEqsL+mh9RzSsXbvWDO14vRIhAP8h2ABizMqVK+Wpp56Sl156STIyMsxQiH6vsz5mz55d6XUjR46Ut99+2wQpQRqgDBs2zMwkueSSS8yiX5deemlU+vlf//VfpoC1Xr16UXk8APGLRb2AOKKzR8477zyZPHmyq8+jW1efddZZZmpu27ZtXX0uAP5HZgOII7oksS7C5TYtWH3mmWcINABUC5kNAADgKjIbAADAVQQbAADAVQQbAADAVQQbAADAVQQbAADAVQQbAADAVQQbAADAVQQbAADAVQQbAABA3PT/AyP4gjPEqKGjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(eopm.device.charge.keys())\n",
    "idx_voltage = 10\n",
    "\n",
    "x = np.linspace(-2.5,2.5,1000)\n",
    "y = np.linspace(-2,2,1000)\n",
    "\n",
    "xx,yy = np.meshgrid(x,y)\n",
    "\n",
    "e_field_y = eopm.device.charge['Efield'][idx_voltage](xx,yy)[:,:,1].magnitude\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "im = ax.imshow(e_field_y,\n",
    "          extent=[x.min(), x.max(), y.min(), y.max()],\n",
    "          cmap='inferno',\n",
    "            origin='lower'\n",
    ")\n",
    "\n",
    "eopm.device.plot_polygons(\n",
    "\tcolor_polygon = 'white',\n",
    "\tfig = fig,\n",
    "    ax = ax,\n",
    ")\n",
    "\n",
    "ax.set_xlim(x.min(), x.max())\n",
    "ax.set_ylim(y.min(), y.max())\n",
    "ax.set_xlabel('x (um)')\n",
    "\n",
    "fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d223b5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Ec', 'Ev', 'Efn', 'Efp', 'N', 'P', 'Efield', 'mun', 'mup', 'V'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20230622\\AppData\\Local\\Temp\\ipykernel_13792\\1791172669.py:14: RuntimeWarning: divide by zero encountered in log10\n",
      "  im = ax.imshow(np.log10(N),\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1ee6e99d6f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(eopm.device.charge.keys())\n",
    "idx_voltage = 10\n",
    "\n",
    "x = np.linspace(-2.5,2.5,1000)\n",
    "y = np.linspace(-2,2,1000)\n",
    "\n",
    "xx,yy = np.meshgrid(x,y)\n",
    "\n",
    "N = eopm.device.charge['N'][idx_voltage](xx,yy)[:,:].magnitude\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "im = ax.imshow(np.log10(N),\n",
    "          extent=[x.min(), x.max(), y.min(), y.max()],\n",
    "          cmap='inferno',\n",
    "            origin='lower'\n",
    ")\n",
    "\n",
    "eopm.device.plot_polygons(\n",
    "\tcolor_polygon = 'white',\n",
    "\tfig = fig,\n",
    "    ax = ax,\n",
    ")\n",
    "\n",
    "ax.set_xlim(x.min(), x.max())\n",
    "ax.set_ylim(y.min(), y.max())\n",
    "ax.set_xlabel('x (um)')\n",
    "\n",
    "fig.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da444cd9",
   "metadata": {},
   "source": [
    "Here we see a small offset relative to the polygon boundaries. This stems from the fact that we chose a sampling of the x domain of 50nm. \n",
    "\n",
    "Notice how above the interpolator extrapolated the charge carrier density to the bottom oxide. This is, of course, not physical. However, when we later use the charge transport data for RF calculations, we will look also for the polygons that have the flag `has_charge_transport_data` and are `SemiconductorPolygon`, thus ensuring that only the polygons that were involved in the charge transport calculations are used. We can verify that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84d135c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p2 True\n",
      "p1 True\n",
      "wg2 True\n",
      "wg1 True\n",
      "n True\n",
      "substrate False\n"
     ]
    }
   ],
   "source": [
    "for photopolygon in eopm.device.photo_polygons:\n",
    "\tif hasattr(photopolygon, 'has_charge_transport_data'):\n",
    "\t    print(photopolygon.name, photopolygon.has_charge_transport_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c854fb9a",
   "metadata": {},
   "source": [
    "The same applies to the electro optical calculations because we will look for polygons that have a valid `ElectroOpticalModel` and `has_charge_transport_data` attribute."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "imodulator_venv",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
