1.8.Measuring_Ic_Vc_and_Rn_at_757ohm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8a8a37e-f395-405f-a07d-0349694b422d",
   "metadata": {},
   "source": [
    "# Measuring the Josephson Current, the gap voltage and the normal state resistance at 757ohm \n",
    "** **\n",
    " * This project uses the lmfit package. Lmfit provides curve fiting using non-linear least squares minimization and was developed by Matt Newville: https://github.com/newville."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bc9cd0-0ca7-4229-bb2d-1414a58bec04",
   "metadata": {},
   "source": [
    "## Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe638614-0386-4845-b482-4cf89c0a7ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#uncomment the following line to install lmfit\n",
    "#!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04360f36-197c-420b-8d71-f2df94a17d09",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit.models import LinearModel\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0754b065-979d-4761-ae32-c4f0e97c6630",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b466b556-6609-46cb-ace8-4d4f8e8be730",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.866</td>\n",
       "      <td>-60.8799</td>\n",
       "      <td>0.00308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.527</td>\n",
       "      <td>-58.5666</td>\n",
       "      <td>0.00296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.146</td>\n",
       "      <td>-55.6790</td>\n",
       "      <td>0.00282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.821</td>\n",
       "      <td>-53.0376</td>\n",
       "      <td>0.00269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.496</td>\n",
       "      <td>-50.2946</td>\n",
       "      <td>0.00255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0        1        2\n",
       "0 -7.866 -60.8799  0.00308\n",
       "1 -7.527 -58.5666  0.00296\n",
       "2 -7.146 -55.6790  0.00282\n",
       "3 -6.821 -53.0376  0.00269\n",
       "4 -6.496 -50.2946  0.00255"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "df = pd.read_csv(\"./Data/757_one_pass.csv\", sep = \" \", header = None)\n",
    "\n",
    "#Preview the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f59ccf-9d58-428a-aba2-80e58fb89e31",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0e46b58-f661-4940-a460-22838f6fb379",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V =  [-7.866, -7.527, -7.146, -6.821, -6.496, -6.186, -5.882, -5.571, -5.232, -4.929, -4.639, -4.357, -4.103, -3.848, -3.538, -3.298, -3.001, -2.662, -2.648, -2.613, -2.592, -2.577, -2.563, -2.549, -2.549, -2.521, -2.479, 0.064, 0.049, 0.064, 0.057, 0.064, 0.064, 2.323, 2.683, 2.697, 2.697, 2.712, 2.747, 2.966, 3.291, 3.559, 3.778, 4.053, 4.336, 4.576, 4.83, 5.141, 5.374, 5.663, 5.953, 6.256, 6.518, 6.871, 7.153, 7.471, 7.76, 8.099, 8.071, 7.873, 7.676, 7.471, 7.308, 7.083, 6.913, 6.715, 6.532, 6.341, 6.15, 5.981, 5.783, 5.621, 5.444, 5.282, 5.127, 4.943, 4.781, 4.597, 4.463, 4.265, 4.124, 3.983, 3.799, 3.658, 3.474, 3.347, 3.135, 2.853, 2.81, 2.796, 2.796, 2.768, 2.775, 2.74, 2.754, 2.74, 2.726, 2.726, 2.712, 2.726, 2.719, 2.697, 2.669, 2.57, 0.106, 0.12, 0.113, 0.127, 0.099, 0.113, 0.113, 0.127, 0.099, -0.254, -2.521, -2.514, -2.507, -2.535, -2.535, -2.563, -2.549, -2.577, -2.606, -2.916, -3.093, -3.248, -3.397, -3.566, -3.721, -3.919, -4.067, -4.258, -4.413, -4.625, -4.781, -4.936, -5.134, -5.331, -5.508, -5.699, -5.903, -6.101, -6.292, -6.511, -6.694, -6.92, -7.174, -7.351, -7.527, -7.746, -7.824]\n",
      "I =  [-60.8799, -58.5666, -55.679, -53.0376, -50.2946, -47.5782, -44.9047, -42.5628, -40.2203, -37.9432, -35.4416, -33.1898, -30.5095, -28.4446, -26.3537, -24.1803, -21.923, -19.7742, -17.8211, -15.6497, -13.726, -11.8534, -10.0048, -7.9507, -6.2446, -4.3707, -2.6489, -0.9175, 0.9851, 2.6816, 4.5105, 6.263, 8.0981, 9.955, 11.6761, 13.4184, 15.2561, 17.2603, 19.2052, 21.4762, 23.2764, 25.4519, 27.5837, 29.7885, 31.6399, 34.1141, 36.0174, 38.36, 40.6964, 43.1638, 45.1974, 47.6123, 50.0695, 52.397, 54.8631, 57.5324, 59.8682, 62.3438, 61.4406, 59.9207, 58.2139, 56.7636, 55.2281, 53.6604, 52.322, 50.6439, 49.3075, 47.6096, 46.1449, 44.6257, 43.2102, 41.9571, 40.4372, 39.2297, 37.7828, 36.4792, 35.0944, 33.7928, 32.2204, 31.0532, 29.5784, 28.4794, 26.9445, 25.8087, 24.3665, 23.1107, 21.8514, 20.6173, 19.3485, 18.2031, 16.8408, 15.6927, 14.3645, 13.2799, 12.1352, 10.9236, 9.8294, 8.68, 7.4466, 6.3463, 5.1729, 4.0937, 3.0882, 2.0233, 0.7961, -0.2613, -1.32, -2.4013, -3.6578, -4.6783, -5.8599, -7.0632, -8.1888, -9.281, -10.6317, -11.6536, -13.0186, -14.1994, -15.3919, -16.6232, -17.9098, -19.1766, -20.3022, -21.6836, -23.0206, -24.3031, -25.7302, -27.1362, -28.4596, -29.9147, -31.2899, -32.6597, -33.9415, -35.4457, -36.8094, -38.4159, -39.8437, -41.5955, -43.0424, -44.7437, -46.267, -47.909, -49.3873, -50.9938, -52.6965, -54.3904, -55.9559, -57.6293, -59.4507, -61.313, -60.6568]\n"
     ]
    }
   ],
   "source": [
    "#Ascribe the columns to the V and I varibles \n",
    "V = list(df[0])\n",
    "I = list(df[1])\n",
    "\n",
    "#Preview the data\n",
    "print(\"V = \", V)\n",
    "print(\"I = \", I )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "916e74e8-0298-4365-b57c-a2e61a62c345",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "lower_limit = V.index(0.064) + 5\n",
    "upper_limit = V.index(5.663)\n",
    "\n",
    "print(lower_limit)\n",
    "print(upper_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d68cd360-3b84-4be3-968d-8d77562286e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V =  [0.064 2.323 2.683 2.697 2.697 2.712 2.747 2.966 3.291 3.559 3.778 4.053\n",
      " 4.336 4.576 4.83  5.141 5.374]\n",
      "I =  [ 8.0981  9.955  11.6761 13.4184 15.2561 17.2603 19.2052 21.4762 23.2764\n",
      " 25.4519 27.5837 29.7885 31.6399 34.1141 36.0174 38.36   40.6964]\n"
     ]
    }
   ],
   "source": [
    "#Remove the data above the chosen upper limit to enhance the view and focus on the single forward pass\n",
    "V = np.array(V[lower_limit:upper_limit])\n",
    "\n",
    "I = np.array(I[lower_limit:upper_limit])\n",
    "\n",
    "#Preview the new subsets\n",
    "print(\"V = \", V)\n",
    "print(\"I = \", I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aede1c1b-543e-4e7f-bd18-2cac0aa30847",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f8875c6b450>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Preview the data\n",
    "sns.relplot(x = V, y = I, kind = \"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6909a570-bf72-4f1b-b00d-4e955bfc4a82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Set figure size\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "# Define the data\n",
    "V = np.array(V)\n",
    "I = np.array(I)\n",
    "\n",
    "\n",
    "# Define the window size\n",
    "window_size = 6\n",
    "\n",
    "# Calculate the moving averages\n",
    "V_avg = np.convolve(V, np.ones(window_size)/window_size, mode='same')\n",
    "I_avg = np.convolve(I, np.ones(window_size)/window_size, mode='same')\n",
    "\n",
    "# Calculate the standard deviations\n",
    "V_std = np.std(V)\n",
    "I_std = np.std(I)\n",
    "\n",
    "# Calculate the z-scores\n",
    "V_z = (V - V_avg) / V_std\n",
    "I_z = (I - I_avg) / I_std\n",
    "\n",
    "# Calculate the change points\n",
    "change_points = []\n",
    "for i in range(len(V_z)):\n",
    "    if abs(V_z[i]) > 3 or abs(I_z[i]) > 0.01:\n",
    "        change_points.append(i)\n",
    "\n",
    "# Mark the change points on the plot\n",
    "for point in change_points:\n",
    "    plt.axvline(V[point], color='r', linestyle='--', alpha=0.5)\n",
    "\n",
    "    \n",
    "# Plot the data again\n",
    "sns.lineplot(x=V, y=I, color = 'orange')  \n",
    "    \n",
    "# Set plot labels and title\n",
    "plt.xlabel('V')\n",
    "plt.ylabel('I')\n",
    "plt.title('Inflection Points Detection')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc785054-bd2d-4230-86b4-fa6440d296cd",
   "metadata": {},
   "source": [
    "### Clear the graph above to focus on Ic and Vc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1311acd9-0417-4089-a690-7627baaf5f39",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uncomment the line below to make the graph interactive\n",
    "#%matplotlib widget\n",
    "\n",
    "#Set figure size\n",
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "# Define the window size\n",
    "window_size = 6\n",
    "\n",
    "# Calculate the moving averages\n",
    "V_avg = np.convolve(V, np.ones(window_size)/window_size, mode='same')\n",
    "I_avg = np.convolve(I, np.ones(window_size)/window_size, mode='same')\n",
    "\n",
    "# Calculate the standard deviations\n",
    "V_std = np.std(V)\n",
    "I_std = np.std(I)\n",
    "\n",
    "# Calculate the z-scores\n",
    "V_z = (V - V_avg) / V_std\n",
    "I_z = (I - I_avg) / I_std\n",
    "\n",
    "# Calculate the change points\n",
    "change_points = []\n",
    "for i in range(len(V_z)):\n",
    "    if abs(V_z[i]) > 3 or abs(I_z[i]) > 0.01:\n",
    "        change_points.append(i)\n",
    "\n",
    "# Annotate inflection points on the graph\n",
    "for idx, point in enumerate(change_points):\n",
    "    if idx in [2, 6]:\n",
    "        plt.annotate(f'Inflection Point\\n(V={V[point]:.3f}mV)\\n(I = {np.interp(V[point], V, I):3f}$\\mu A$)', \n",
    "                     xy=(V[point], np.interp(V[point], V, I)),\n",
    "                     xytext=(V[point] - 1.2, np.interp(V[point], V, I) + 3),\n",
    "                     arrowprops={'facecolor': 'red', 'shrink': 0.05})\n",
    "\n",
    "    \n",
    "# Plot the data again\n",
    "sns.lineplot(x=V, y=I, color = 'orange')  \n",
    "    \n",
    "# Set plot labels and title\n",
    "plt.xlabel('V')\n",
    "plt.ylabel('I')\n",
    "plt.title('Inflection Points Detection')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85392e7-7fdc-4476-805f-665973b49608",
   "metadata": {},
   "source": [
    "# Estimating the normal state resistance (Rn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59c0fa22-6aaf-4859-b923-3ff6aa7fe6de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.866, -7.527, -7.146, -6.821, -6.496, -6.186, -5.882, -5.571, -5.232, -4.929, -4.639, -4.357, -4.103, -3.848, -3.538, -3.298, -3.001, -2.662, -2.648, -2.613, -2.592, -2.577, -2.563, -2.549, -2.549, -2.521, -2.479, 0.064, 0.049, 0.064, 0.057, 0.064, 0.064, 2.323, 2.683, 2.697, 2.697, 2.712, 2.747, 2.966, 3.291, 3.559, 3.778, 4.053, 4.336, 4.576, 4.83, 5.141, 5.374, 5.663, 5.953, 6.256, 6.518, 6.871, 7.153, 7.471, 7.76, 8.099, 8.071, 7.873, 7.676, 7.471, 7.308, 7.083, 6.913, 6.715, 6.532, 6.341, 6.15, 5.981, 5.783, 5.621, 5.444, 5.282, 5.127, 4.943, 4.781, 4.597, 4.463, 4.265, 4.124, 3.983, 3.799, 3.658, 3.474, 3.347, 3.135, 2.853, 2.81, 2.796, 2.796, 2.768, 2.775, 2.74, 2.754, 2.74, 2.726, 2.726, 2.712, 2.726, 2.719, 2.697, 2.669, 2.57, 0.106, 0.12, 0.113, 0.127, 0.099, 0.113, 0.113, 0.127, 0.099, -0.254, -2.521, -2.514, -2.507, -2.535, -2.535, -2.563, -2.549, -2.577, -2.606, -2.916, -3.093, -3.248, -3.397, -3.566, -3.721, -3.919, -4.067, -4.258, -4.413, -4.625, -4.781, -4.936, -5.134, -5.331, -5.508, -5.699, -5.903, -6.101, -6.292, -6.511, -6.694, -6.92, -7.174, -7.351, -7.527, -7.746, -7.824]\n"
     ]
    }
   ],
   "source": [
    "#Preview the main dataset\n",
    "print(list(df[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78ae52f8-aafc-41de-a28e-b7e91c22e3e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n",
      "57\n"
     ]
    }
   ],
   "source": [
    "#The dataset includes points from two complete sweeps\n",
    "#Identify the index of the approximate limits of the normal state on the positive side of the graph \n",
    "\n",
    "#Function for detecting the first occurrence of an item in a list\n",
    "def find_first_occurrence(value, list):\n",
    "    for i, item in enumerate(list):\n",
    "        if item == value:\n",
    "            first_occurrence = i\n",
    "            break\n",
    "    return first_occurrence\n",
    "\n",
    "low_limit = find_first_occurrence(2.747, list(df[0])) #the second change point above\n",
    "up_limit = list(df[0]).index(df[0].max()) \n",
    "\n",
    "print(low_limit) \n",
    "print(up_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b864c5cd-63be-4c90-ba23-6d975b3d44c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.747 2.966 3.291 3.559 3.778 4.053 4.336 4.576 4.83  5.141 5.374 5.663\n",
      " 5.953 6.256 6.518 6.871 7.153 7.471 7.76 ]\n",
      "[19.2052 21.4762 23.2764 25.4519 27.5837 29.7885 31.6399 34.1141 36.0174\n",
      " 38.36   40.6964 43.1638 45.1974 47.6123 50.0695 52.397  54.8631 57.5324\n",
      " 59.8682]\n"
     ]
    }
   ],
   "source": [
    "#Select data from the first pass\n",
    "V_n = np.array(df[0][low_limit:up_limit])\n",
    "I_n = np.array(df[1][low_limit:up_limit])\n",
    "\n",
    "#Preview the data\n",
    "print(V_n)\n",
    "print(I_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c19d83-70af-4459-979f-865a26a9e202",
   "metadata": {},
   "source": [
    "## Define the model and fit the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14e579a1-d903-42ee-9b29-f3b31a16d046",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    Model(linear)\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 7\n",
      "    # data points      = 19\n",
      "    # variables        = 2\n",
      "    chi-square         = 17.0171325\n",
      "    reduced chi-square = 1.00100779\n",
      "    Akaike info crit   = 1.90585133\n",
      "    Bayesian info crit = 3.79472928\n",
      "    R-squared          = 1.00000000\n",
      "[[Variables]]\n",
      "    slope:      8097.87022 +/- 48.6644971 (0.60%) (init = 8119.854)\n",
      "    intercept: -3046849.77 +/- 213867.712 (7.02%) (init = -3149249)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(slope, intercept) = -0.9519\n"
     ]
    }
   ],
   "source": [
    "#Define the variables and convert to Volts and Amps\n",
    "x = V_n / 0.001\n",
    "y = I_n / 0.000001\n",
    "\n",
    "#Use builtin NameModel, specify prefix for each:\n",
    "mod = LinearModel(prefix='') \n",
    "\n",
    "#Set the initial parameters to 'guess' mode\n",
    "pars  = mod.guess(y, x = x)\n",
    "\n",
    "#Fit with weights=1/yerr\n",
    "result = mod.fit(y, pars, x = x, weights= 1. / (y * 0.008734))\n",
    "\n",
    "#Print the fit results\n",
    "print(result.fit_report())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2b31af-3b9f-41ff-bab6-429b46e63cd5",
   "metadata": {},
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b562e985-269d-4d96-9741-543a4bb1a329",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set the figure size\n",
    "plt.figure(figsize = (9,6))\n",
    "\n",
    "#Plot the data and the model\n",
    "plt.scatter(x, y, linewidth=2)\n",
    "plt.plot(x, result.best_fit, 'r-', linewidth=2.5)\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('V ($V$)')\n",
    "plt.ylabel('I ($A$)')\n",
    "plt.title('Normal State IVC')\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bf727c-9b28-4da8-b6c6-4fb66078c772",
   "metadata": {},
   "source": [
    "# Calculating the theoretical Ic and V_gap\n",
    "\n",
    "![](./img/Ic_formula.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c3f054f-0b07-4f8f-b3de-a8fdd96668ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'slope': 8097.870222829204, 'intercept': -3046849.7690237192}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Access the model parameters\n",
    "varys = result.params.valuesdict()\n",
    "\n",
    "varys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b00cd57a-cc33-4faf-a5ee-3ae6e148bd15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal state resistance: R_n =  0.000123 Ohm\n"
     ]
    }
   ],
   "source": [
    "#The slope is equal to 1 / R \n",
    "R_n = 1 / varys.get(\"slope\")\n",
    "\n",
    "print('Normal state resistance: R_n = ', round(R_n, 6), 'Ohm') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9447a90f-624a-41a4-9975-06e5b6d0a2b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature = 5.65115503 K\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "Weak Coupling Formula\n",
      "-----------------------------\n",
      "delta_t = 2.445116687352232e-22 J\n",
      "Gap Energy = 4.890233374704464e-22 J\n",
      "Gap Voltage = 3.0521990854477994 mV\n",
      "Josephson Current = 17.791746490473457 uA\n",
      "______________________________\n",
      "______________________________\n",
      "------------------------------\n",
      "Strong Coupling Approximation\n",
      "------------------------------\n",
      "delta_t = 2.056185113301933e-22 J\n",
      "Gap Energy =  4.112370226603866e-22 J\n",
      "Gap Voltage = 2.56670217613523 mV\n",
      "Josephson Current = 14.14007920413296 uA\n",
      "________________________________\n",
      "________________________________\n",
      "________________________________\n"
     ]
    }
   ],
   "source": [
    "#Define the terms for the strong and weak coupling formulas \n",
    "t_c = 9.25 #critical temperature\n",
    "delta_0 = 2.25288819E-22 # from 1.764 * k_B * t_c,  [check eqn. 1 on the report paper] \n",
    "t = 5.65115503 #from the calibration curve in notebook 2\n",
    "e = 1.6022E-19 #elementay charge\n",
    "pi = 3.14159265358979323846264338327950 \n",
    "t_red = t / t_c #the reduced temperature\n",
    "k_B = 1.3807E-23 #Boltzmann constant\n",
    "R_n = 1 / varys.get(\"slope\") #from the slope of the IVC\n",
    "\n",
    "\n",
    "#weak coupling formula\n",
    "\n",
    "delta_weak = delta_0 * 1.74 * math.sqrt(1.0 - t_red)\n",
    "\n",
    "E_weak = delta_weak * 2\n",
    "\n",
    "V_weak = (E_weak / e) * 1000\n",
    "\n",
    "#The Ambegaokar-Baratoff formula\n",
    "Ic_weak = (pi / 2) * (delta_weak / (e * R_n)) * math.tanh(delta_weak / (2 * k_B * t))\n",
    "\n",
    "\n",
    "#strong coupling approximation\n",
    "\n",
    "delta_strong = (delta_0) * math.sqrt(math.cos((pi * t_red**2)  / 2 )) \n",
    "\n",
    "E_strong = delta_strong * 2\n",
    "\n",
    "V_strong = (E_strong / e) * 1000\n",
    "\n",
    "#The Ambegaokar-Baratoff formula\n",
    "Ic_strong = (pi / 2) * (delta_strong / (e * R_n)) * math.tanh(delta_strong / (2 * k_B * t))\n",
    "\n",
    "\n",
    "#Print the results\n",
    "print('Temperature =', t, 'K')\n",
    "print('-----------------------------')\n",
    "print('-----------------------------')\n",
    "                         \n",
    "print('Weak Coupling Formula')\n",
    "print('-----------------------------')\n",
    "\n",
    "\n",
    "print('delta_t =', delta_weak, 'J')\n",
    "print('Gap Energy =', E_weak, 'J')\n",
    "print('Gap Voltage =', V_weak, 'mV')\n",
    "print('Josephson Current =', Ic_weak, 'uA')\n",
    "print('______________________________')\n",
    "print('______________________________')\n",
    "print('------------------------------')\n",
    "print('Strong Coupling Approximation')\n",
    "print('------------------------------')\n",
    "\n",
    "print('delta_t =', delta_strong, 'J')\n",
    "print('Gap Energy = ', E_strong, 'J')\n",
    "print('Gap Voltage =', V_strong, 'mV')\n",
    "print('Josephson Current =', Ic_strong, 'uA')\n",
    "print('________________________________')\n",
    "print('________________________________')\n",
    "print('________________________________')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef1804d8-51d9-4eea-ac5c-b806b800994a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Ic': 19.2052, 'Ic_weak': 17.7917, 'Ic_strong': 14.1401, 'V_gap': 2.683, 'Vg_weak': 3.0522, 'Vg_strong': 2.5667, 'Temp': 5.6512}\n"
     ]
    }
   ],
   "source": [
    "#Add all the collected results to a dictionary\n",
    "Josephson_data = {\"Ic\" : np.interp(V[6], V, I) , \"Ic_weak\" : round(Ic_weak, 4), \"Ic_strong\" : round(Ic_strong, 4), \"V_gap\" : V[2], \"Vg_weak\" : round(V_weak, 4), \"Vg_strong\" : round(V_strong, 4), \"Temp\" : round(t, 4)}\n",
    "\n",
    "#Preview the newly created row of data\n",
    "print(Josephson_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29b131a-d29f-4f75-ba01-4e28fbbc0548",
   "metadata": {},
   "source": [
    "## Export the results to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "565be433-d8b5-44ea-a793-bbf072ae75c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv\n",
    "\n",
    "# with open(r'./Data/Josephson_data.csv', 'a', newline='') as csvfile:\n",
    "#     fieldnames = ['Ic','Ic_weak', 'Ic_strong', 'V_gap', 'Vg_weak', 'Vg_strong', 'Temp']\n",
    "#     writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "#     writer.writerow(Josephson_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f9e1ba-ea28-4854-8b64-a7ac34c3ddf8",
   "metadata": {},
   "source": [
    "## Author\n",
    "** **\n",
    "\n",
    "### <a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  }
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