{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54ce5e7b-3a0f-4df1-ab58-7e93856acec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#for windows\n",
    "import sys\n",
    "sys.path.append(r'C:\\Users\\pedersen\\gmid') # path to gmid repository\n",
    "# ------\n",
    "import matplotlib.pyplot as plt\n",
    "from mosplot import load_lookup_table, LoadMosfet # make sure that mosplot can be found in the python path\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive\n",
    "from ipywidgets import interactive_output, HBox, VBox\n",
    "import matplotlib.ticker as ticker "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4982b065-4d01-496e-8c46-3f9862bc07c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_vs_data(x_values, y_values, length, x_axis_name, y_axis_name='y', y_multiplier=1, log=False):\n",
    "    x_values_flat = np.array(x_values).flatten()  \n",
    "    y_values_flat = np.array(y_values, dtype=np.float64).flatten()  \n",
    "    length_flat = np.array(length).flatten()\n",
    "\n",
    "    unique_lengths = np.unique(length_flat)\n",
    "    unique_lengths_in_micro = unique_lengths * 1e6\n",
    "    \n",
    "    def update_plot(selected_length, x_value=None, y_value=None):\n",
    "        plt.figure(figsize=(8, 6))  # Ensure the plot is drawn fresh for each update\n",
    "\n",
    "        if selected_length == \"Show All\":\n",
    "            mask = np.ones_like(length_flat, dtype=bool)\n",
    "        else:\n",
    "            selected_length_in_micro = float(selected_length.replace(' μm', ''))\n",
    "            tolerance = 0.1  \n",
    "            mask = np.abs(length_flat * 1e6 - selected_length_in_micro) < tolerance  \n",
    "\n",
    "        x_values_for_length = x_values_flat[mask]\n",
    "        y_values_for_length = y_values_flat[mask] * y_multiplier  \n",
    "        length_for_length = length_flat[mask] * 1e6  \n",
    "\n",
    "        if selected_length == \"Show All\":\n",
    "            for length_value in np.unique(length_for_length):\n",
    "                mask_all = (length_for_length == length_value)\n",
    "                plt.plot(x_values_for_length[mask_all], y_values_for_length[mask_all])\n",
    "\n",
    "            min_length = np.min(unique_lengths_in_micro)\n",
    "            max_length = np.max(unique_lengths_in_micro)\n",
    "            plt.title(f'{y_axis_name} vs {x_axis_name} (Length from {min_length:.2f} μm to {max_length:.2f} μm)')\n",
    "\n",
    "        else:\n",
    "            plt.plot(x_values_for_length, y_values_for_length)\n",
    "            plt.title(f'{y_axis_name} vs {x_axis_name} for {selected_length}')\n",
    "\n",
    "        plt.xlabel(f'{x_axis_name}')\n",
    "        plt.ylabel(f'{y_axis_name}')\n",
    "\n",
    "        if log:\n",
    "            plt.yscale('log')\n",
    "            plt.gca().yaxis.set_major_locator(ticker.LogLocator(base=10, subs=[], numticks=10))\n",
    "            plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'$10^{int(np.log10(x))}$'))\n",
    "            plt.ylabel(f'{y_axis_name} (Log Base 10)') \n",
    "        \n",
    "        if y_value is not None and x_value_widget.disabled:\n",
    "            closest_index = np.abs(y_values_for_length - y_value).argmin()\n",
    "            closest_x = x_values_for_length[closest_index]\n",
    "            closest_y = y_values_for_length[closest_index]\n",
    "\n",
    "            plt.scatter(closest_x, closest_y, color='blue', label=f'Point ({closest_x:.2f}, {closest_y:.2f})')\n",
    "            print(f\"The corresponding {x_axis_name} value for {y_axis_name} = {closest_y:.2f} is: {closest_x:.2f}\")\n",
    "        elif x_value is not None and y_value_widget.disabled:\n",
    "            closest_index = np.abs(x_values_for_length - x_value).argmin()\n",
    "            closest_x = x_values_for_length[closest_index]\n",
    "            closest_y = y_values_for_length[closest_index]\n",
    "            \n",
    "            plt.scatter(closest_x, closest_y, color='red', label=f'Point ({closest_x:.2f}, {closest_y:.2f})')\n",
    "            print(f\"The corresponding {y_axis_name} value for {x_axis_name} = {closest_x:.2f} is: {closest_y:.2f}\")\n",
    "\n",
    "        plt.grid(True)\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    dropdown_options = [\"Show All\"] + [f'{length:.2f} μm' for length in unique_lengths_in_micro]\n",
    "    length_widget = widgets.Dropdown(\n",
    "        options=dropdown_options,\n",
    "        value=dropdown_options[0], \n",
    "        description='Select Length:',\n",
    "    )\n",
    "    \n",
    "    x_value_widget = widgets.FloatText(\n",
    "        value=np.mean(x_values_flat),\n",
    "        description=f\"Select {x_axis_name}:\",\n",
    "        disabled=False\n",
    "    )\n",
    "    \n",
    "    y_value_widget = widgets.FloatText(\n",
    "        value=None,\n",
    "        description=f\"Set {y_axis_name}:\",\n",
    "        disabled=True\n",
    "    )\n",
    "    \n",
    "    select_x_or_y_widget = widgets.Checkbox(\n",
    "        value=True,\n",
    "        description=f\"Select {x_axis_name} (uncheck for {y_axis_name})\",\n",
    "    )\n",
    "    \n",
    "    def toggle_x_or_y(change):\n",
    "        if change['new']:\n",
    "            x_value_widget.disabled = False\n",
    "            y_value_widget.disabled = True\n",
    "        else:\n",
    "            x_value_widget.disabled = True\n",
    "            y_value_widget.disabled = False\n",
    "    \n",
    "    select_x_or_y_widget.observe(toggle_x_or_y, names='value')\n",
    "\n",
    "    output = interactive_output(update_plot, {\n",
    "        'selected_length': length_widget, \n",
    "        'x_value': x_value_widget, \n",
    "        'y_value': y_value_widget\n",
    "    })\n",
    "\n",
    "    display(VBox([length_widget, select_x_or_y_widget, HBox([x_value_widget, y_value_widget]), output]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec44e96-94a7-4aab-8dae-17dee091935e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pmos_lv_path = '/home/pedersen/gmid/models/gmoveridpypmos_gmid_final.npy'\n",
    "#nmos_lv_path = '/home/pedersen/gmid/models/gmoveridpynmos_final_gmid.npy'\n",
    "\n",
    "# Windows paths\n",
    "pmos_lv_path = r'C:\\Users\\pedersen\\Desktop\\OpenDesingCourse\\no_touch_files\\gmoveridpypmos_gmid_final.npy'\n",
    "nmos_lv_path =r'C:\\Users\\pedersen\\Desktop\\OpenDesingCourse\\no_touch_files\\gmoveridpynmos_final_gmid.npy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f4e7bb2-ca5d-4a5a-a075-2249cfc9cfd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_table_pmos = load_lookup_table(pmos_lv_path)\n",
    "lookup_table_nmos = load_lookup_table(nmos_lv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "134210f0-f439-453a-851a-db41cda3015b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nmos = LoadMosfet(lookup_table=lookup_table_nmos, mos=\"nmos\", vsb=0.0, vds=0.6)\n",
    "pmos = LoadMosfet(lookup_table=lookup_table_pmos, mos=\"pmos\", vsb=0, vds=-0.6, vgs=(-1.2, -0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3b19fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eff5ad7f83e545f982959759e1fd06be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Select Length:', options=('Show All', '0.13 μm', '0.26 μm', '0.39 μm', '0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vgs_nmos = nmos.extracted_table['vgs']\n",
    "id_values = nmos.extracted_table['id']\n",
    "plot_data_vs_data(vgs_nmos, id_values, nmos.extracted_table['lengths'], 'Vgs', 'Id[mu A]', y_multiplier=1e6, log=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc67ba80-b9d7-4bc4-9167-22968d9cc33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48e7de4c24964abb9d633c2daff1c51a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Select Length:', options=('Show All', '0.13 μm', '0.26 μm', '0.39 μm', '0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "id_values = nmos.extracted_table['id']\n",
    "length = nmos.extracted_table['lengths']\n",
    "gm = nmos.extracted_table['gm']\n",
    "gds = nmos.extracted_table['gds']\n",
    "gmro = gm/gds\n",
    "gmid = gm/id_values\n",
    "\n",
    "plot_data_vs_data(gmid, gm/gds, length,'gmid','gmro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d8f8985-f28f-41d7-8915-07f97574b5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96e6bc75e82842159a1ae66652d4d8c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Select Length:', options=('Show All', '0.13 μm', '0.26 μm', '0.39 μm', '0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "id_values = nmos.extracted_table['id']\n",
    "width = nmos.extracted_table['width']\n",
    "length = nmos.extracted_table['lengths']\n",
    "gm = nmos.extracted_table['gm']\n",
    "gmid = gm/id_values\n",
    "\n",
    "plot_data_vs_data(gmid, id_values/width, length,'gmid','id/width', log = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d57db53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Width = 2.4999999999999998e-05\n"
     ]
    }
   ],
   "source": [
    "id = 16e-6\n",
    "gmid = 9.86\n",
    "length = 1.3e-6\n",
    "id_over_width = 0.64\n",
    "width = id/id_over_width\n",
    "print(f\"Width = {width:}\")"
   ]
  }
 ],
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