{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f325daf-eb3b-4cf7-8ffe-b9b94e7f66ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from mosplot.plot import load_lookup_table, Mosfet, Expression\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": 12,
   "id": "b5b31aca-47bf-4461-8e50-16c20f03b337",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_table_nmos = load_lookup_table('../sg13_nmos_lv_LUT.npz')\n",
    "lookup_table_pmos = load_lookup_table('../sg13_pmos_lv_LUT.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "743dc381-0d35-4aa9-847c-c42c80c17786",
   "metadata": {},
   "outputs": [],
   "source": [
    "nmos = Mosfet(lookup_table=lookup_table_nmos, mos=\"sg13_lv_nmos \", vbs=0.0, vds=0.6)\n",
    "pmos = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0.0, vds=-0.6, vgs=(-1.2, -0.15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b27d5fca-3436-4df7-895f-f6a4bbd7a80d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def plot_data_vs_data(x_values, y_values, z_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",
    "    z_values_flat = np.array(z_values, dtype=np.float64).flatten()\n",
    "    \n",
    "    length_arr = np.array(length)\n",
    "    \n",
    "    length_flat = length_arr.flatten()\n",
    "    \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=(12, 8))  # Make the figure wider (adjust as needed)\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 = 0.01  # Tighten the tolerance to avoid unwanted data points\n",
    "            mask = np.abs(length_flat * 1e6 - selected_length_in_micro) < tolerance\n",
    "\n",
    "        # Apply the mask to the data\n",
    "        x_values_for_length = x_values_flat[mask]\n",
    "        y_values_for_length = y_values_flat[mask] * y_multiplier\n",
    "        z_values_for_length = z_values_flat[mask]\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",
    "            corresponding_z = z_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",
    "            z_value_widget.value = corresponding_z\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",
    "            corresponding_z = z_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",
    "            z_value_widget.value = corresponding_z\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='Length:',\n",
    "        layout=widgets.Layout(width='500px')  # Make the dropdown wider\n",
    "    )\n",
    "\n",
    "    x_value_widget = widgets.FloatText(\n",
    "        value=np.mean(x_values_flat),\n",
    "        description=f\"{x_axis_name}:\",\n",
    "        disabled=False,\n",
    "        layout=widgets.Layout(width='300px', margin='0 40px 0 0'),  # Push input boxes more to the right\n",
    "        description_width='150px'  # Smaller description width\n",
    "    )\n",
    "\n",
    "    y_value_widget = widgets.FloatText(\n",
    "        value=None,\n",
    "        description=f\"{y_axis_name}:\",\n",
    "        disabled=True,\n",
    "        layout=widgets.Layout(width='300px', margin='0 40px 0 0'),  # Push input boxes more to the right\n",
    "        description_width='150px'  # Smaller description width\n",
    "    )\n",
    "\n",
    "    z_value_widget = widgets.FloatText(\n",
    "        value=None,\n",
    "        description=f\" Vgs:\",\n",
    "        disabled=True,\n",
    "        layout=widgets.Layout(width='300px', margin='0 40px 0 0'),  # Push input boxes more to the right\n",
    "        description_width='150px'  # Smaller description width\n",
    "    )\n",
    "\n",
    "    select_x_or_y_widget = widgets.Checkbox(\n",
    "        value=True,\n",
    "        description=f\"{x_axis_name} (uncheck for {y_axis_name})\",\n",
    "        layout=widgets.Layout(width='300px')  # Make the checkbox wider\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]), z_value_widget, output]))\n",
    "\n",
    "\n",
    "\n",
    "def tile_length_to_match_data(length_array, data_array):\n",
    "    length_array = np.array(length_array).flatten()  \n",
    "    data_shape = data_array.shape \n",
    "    \n",
    "    if length_array.size == data_shape[0]:\n",
    "        # length matches number of rows, repeat along columns\n",
    "        return np.tile(length_array.reshape(-1, 1), (1, data_shape[1]))\n",
    "    elif length_array.size == data_shape[1]:\n",
    "        # length matches number of columns, repeat along rows\n",
    "        return np.tile(length_array.reshape(1, -1), (data_shape[0], 1))\n",
    "    else:\n",
    "        raise ValueError(f\"Length array size {length_array.size} does not match any dimension of data shape {data_shape}\")\n",
    "\n",
    "\n",
    "    \n",
    "def display_resistance(ro_value):\n",
    "    \"\"\"Determine the resistance value and its unit.\"\"\"\n",
    "    if ro_value < 1e3:\n",
    "        return ro_value, \"Ω\"\n",
    "    elif ro_value < 1e6:\n",
    "        return ro_value / 1e3, \"kΩ\"\n",
    "    elif ro_value < 1e9:\n",
    "        return ro_value / 1e6, \"MΩ\"\n",
    "    else:\n",
    "        return ro_value / 1e9, \"GΩ\"\n",
    "\n",
    "def display_current(Id_value):\n",
    "    \"\"\"Determine the current value and its unit.\"\"\"\n",
    "    if Id_value < 1e-6:\n",
    "        return Id_value * 1e9, \"nA\"  # Convert to nA\n",
    "    elif Id_value < 1e-3:\n",
    "        return Id_value * 1e6, \"μA\"  # Convert to μA\n",
    "    else:\n",
    "        return Id_value * 1e3, \"mA\"   # Convert to mA\n",
    "    \n",
    "def dB_to_linear(av_db):\n",
    "    return 10 ** (av_db / 20)\n",
    "\n",
    "\n",
    "def determine_inversion_region(gm_id_value, device_type):\n",
    "    \"\"\"Determine the inversion region based on gm/id value for NMOS or PMOS.\"\"\"\n",
    "    if device_type == 'nmos':\n",
    "        if gm_id_value > 20:\n",
    "            return \"Weak Inversion\"\n",
    "        elif 10 < gm_id_value <= 20:\n",
    "            return \"Moderate Inversion\"\n",
    "        else:\n",
    "            return \"Strong Inversion\"\n",
    "    elif device_type == 'pmos':\n",
    "        if gm_id_value > 20:\n",
    "            return \"Weak Inversion\"\n",
    "        elif 10 < gm_id_value <= 20:\n",
    "            return \"Moderate Inversion\"\n",
    "        else:\n",
    "            return \"Strong Inversion\"\n",
    "    else:\n",
    "        raise ValueError(\"Invalid device type. Use 'nmos' or 'pmos'.\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc95a0ff-ba36-46ce-9fa1-64d6d2d7770f",
   "metadata": {},
   "outputs": [],
   "source": [
    "### ------ automated flattening based on shape ------### \n",
    "length_2d_pmos = tile_length_to_match_data(pmos.length, pmos.extracted_table['gm'])\n",
    "length_2d_nmos = tile_length_to_match_data(nmos.length, nmos.extracted_table['gm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cec9718-17e7-42bb-bbfb-732d1c90bcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': array([[3.76624204e-10, 6.01959060e-09, 9.87071189e-08, 1.60543800e-06,\n",
       "         1.93245942e-05, 1.00782425e-04, 3.04098328e-04, 6.72702794e-04,\n",
       "         1.20889547e-03, 1.88107823e-03, 2.64380500e-03, 3.45477089e-03,\n",
       "         4.28145425e-03],\n",
       "        [2.27434005e-09, 4.64820324e-08, 9.46792454e-07, 1.37633087e-05,\n",
       "         7.66349258e-05, 2.27211058e-04, 4.84392425e-04, 8.45214177e-04,\n",
       "         1.29153416e-03, 1.79815432e-03, 2.33954331e-03, 2.89372122e-03,\n",
       "         3.44357151e-03],\n",
       "        [4.75539874e-09, 1.07745670e-07, 2.21974369e-06, 2.13823078e-05,\n",
       "         8.28884513e-05, 2.02094612e-04, 3.83498729e-04, 6.23728731e-04,\n",
       "         9.14110395e-04, 1.24216150e-03, 1.59279897e-03, 1.95027457e-03,\n",
       "         2.30064266e-03]], dtype=float32),\n",
       " 'vth': array([[0.4175789 , 0.4175789 , 0.4175789 , 0.4175789 , 0.4175789 ,\n",
       "         0.4175789 , 0.4175789 , 0.4175789 , 0.4175789 , 0.4175789 ,\n",
       "         0.4175789 , 0.4175789 , 0.4175789 ],\n",
       "        [0.31012315, 0.31012315, 0.31012315, 0.31012315, 0.31012315,\n",
       "         0.31012315, 0.31012315, 0.31012315, 0.31012315, 0.31012315,\n",
       "         0.31012315, 0.31012315, 0.31012315],\n",
       "        [0.25189924, 0.25189924, 0.25189924, 0.25189924, 0.25189924,\n",
       "         0.25189924, 0.25189924, 0.25189924, 0.25189924, 0.25189924,\n",
       "         0.25189924, 0.25189924, 0.25189924]], dtype=float32),\n",
       " 'gm': array([[1.0355391e-08, 1.6762097e-07, 2.7663464e-06, 4.3945060e-05,\n",
       "         4.0340933e-04, 1.3237044e-03, 2.8199644e-03, 4.5541674e-03,\n",
       "         6.1132847e-03, 7.2515998e-03, 7.9314727e-03, 8.2337745e-03,\n",
       "         8.2622319e-03],\n",
       "        [6.8324205e-08, 1.4071227e-06, 2.8058514e-05, 3.0460340e-04,\n",
       "         1.0163147e-03, 2.0270473e-03, 3.1103410e-03, 4.0744538e-03,\n",
       "         4.8087859e-03, 5.2804318e-03, 5.5105267e-03, 5.5445507e-03,\n",
       "         5.4319035e-03],\n",
       "        [1.4800021e-07, 3.3604522e-06, 6.2207786e-05, 3.6775280e-04,\n",
       "         8.8785321e-04, 1.5031686e-03, 2.1188618e-03, 2.6710527e-03,\n",
       "         3.1156084e-03, 3.4199473e-03, 3.5661045e-03, 3.5598569e-03,\n",
       "         3.4301258e-03]], dtype=float32),\n",
       " 'gds': array([[4.2991338e-10, 7.1589881e-09, 1.2225600e-07, 2.0337011e-06,\n",
       "         2.1824198e-05, 7.4040574e-05, 1.4492353e-04, 2.4114188e-04,\n",
       "         3.7066598e-04, 5.5198371e-04, 8.0706168e-04, 1.1493404e-03,\n",
       "         1.5826311e-03],\n",
       "        [2.1960556e-09, 4.7003134e-08, 9.8667385e-07, 1.2744471e-05,\n",
       "         4.5941855e-05, 9.0867376e-05, 1.5159344e-04, 2.3945510e-04,\n",
       "         3.7418643e-04, 5.7869806e-04, 8.6932670e-04, 1.2510468e-03,\n",
       "         1.7183991e-03],\n",
       "        [3.8504893e-09, 9.1464564e-08, 1.8656532e-06, 1.3157433e-05,\n",
       "         3.2548171e-05, 5.7141449e-05, 8.8717396e-05, 1.3273282e-04,\n",
       "         2.0318832e-04, 3.2743323e-04, 5.4010993e-04, 8.6352450e-04,\n",
       "         1.2929583e-03]], dtype=float32),\n",
       " 'length': array([[1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07,\n",
       "         1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07, 1.3e-07],\n",
       "        [2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07,\n",
       "         2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07, 2.6e-07],\n",
       "        [5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07,\n",
       "         5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07, 5.2e-07]]),\n",
       " 'vbs': 0.0,\n",
       " 'vgs': array([[0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2],\n",
       "        [0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2],\n",
       "        [0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2]]),\n",
       " 'vds': 0.6}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmos.extracted_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc675aa-6d59-4d74-83e2-18c56353db0d",
   "metadata": {},
   "source": [
    "# NMOS GMID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7cc630f-b385-47a6-a6f9-ac0d10effffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6135647378824854a249008cff2939aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Length:', layout=Layout(width='500px'), options=('Show All', '0.13 μm', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width_values = nmos.width\n",
    "id_values = nmos.extracted_table['id']\n",
    "gm_values = nmos.extracted_table['gm']\n",
    "gds_values = nmos.extracted_table['gds']\n",
    "vgs_values= nmos.extracted_table['vgs']\n",
    "\n",
    "plot_data_vs_data(\n",
    "    gm_values/id_values,\n",
    "    gm_values/gds_values,\n",
    "    vgs_values,\n",
    "    length_2d_nmos,\n",
    "    'gm/id',\n",
    "    'gm/gds'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e847c359-b57e-4e84-b0dc-93616d575efd",
   "metadata": {},
   "source": [
    "# PMOS GMID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3727c42d-a4bf-4eb0-bc11-6e859ae41324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3de96606da1946dcb4e15f6b7e281eec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(description='Length:', layout=Layout(width='500px'), options=('Show All', '0.13 μm', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width_values = pmos.width\n",
    "id_values = pmos.extracted_table['id']\n",
    "gm_values = pmos.extracted_table['gm']\n",
    "gds_values = pmos.extracted_table['gds']\n",
    "vgs_values= pmos.extracted_table['vgs']\n",
    "\n",
    "plot_data_vs_data(gm_values/id_values, gm_values/gds_values, vgs_values, length_2d_pmos, 'gm/id', 'gm/gds')"
   ]
  }
 ],
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