1.1.Measuring_Ic_Vc_and_Rn_at_1152ohm.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 1152ohm \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>8.346</td>\n",
       "      <td>63.9353</td>\n",
       "      <td>0.00323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.234</td>\n",
       "      <td>62.5955</td>\n",
       "      <td>0.00316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.979</td>\n",
       "      <td>60.7257</td>\n",
       "      <td>0.00307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.782</td>\n",
       "      <td>59.4807</td>\n",
       "      <td>0.00301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.612</td>\n",
       "      <td>57.7241</td>\n",
       "      <td>0.00292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0        1        2\n",
       "0  8.346  63.9353  0.00323\n",
       "1  8.234  62.5955  0.00316\n",
       "2  7.979  60.7257  0.00307\n",
       "3  7.782  59.4807  0.00301\n",
       "4  7.612  57.7241  0.00292"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "df = pd.read_csv(\"./Data/1152_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 =  [8.346, 8.234, 7.979, 7.782, 7.612, 7.414, 7.231, 7.033, 6.849, 6.659, 6.496, 6.27, 6.122, 5.967, 5.762, 5.557, 5.416, 5.232, 5.056, 4.901, 4.724, 4.569, 4.399, 4.258, 4.046, 3.905, 3.728, 3.552, 3.326, 2.987, 2.987, 2.959, 2.966, 2.945, 2.945, 2.945, 2.916, 2.93, 2.902, 2.895, 2.909, 2.888, 2.888, 2.874, 2.874, 2.846, 2.832, 2.803, 0.113, 0.092, 0.099, 0.092, 0.106, 0.12, 0.099, 0.099, 0.099, 0.106, 0.099, -2.712, -2.712, -2.712, -2.712, -2.726, -2.74, -2.726, -2.754, -2.761, -3.149, -3.354, -3.517, -3.7, -3.863, -4.039, -4.187, -4.406, -4.562, -4.759, -4.922, -5.098, -5.275, -5.48, -5.663, -5.84, -6.023, -6.221, -6.419, -6.595, -6.8, -6.998, -7.224, -7.407, -7.619, -7.831, -8.128, -7.76, -7.386, -7.075, -6.744, -6.447, -6.108, -5.769, -5.465, -5.183, -4.894, -4.632, -4.279, -4.039, -3.743, -3.474, -2.938, -2.817, -2.796, -2.796, -2.782, -2.754, -2.74, -2.754, -2.726, -2.712, -2.712, -2.655, 0.057, 0.021, 0.057, 0.035, 0.042, 0.042, 0.057, 2.846, 2.846, 2.867, 2.874, 2.902, 2.916, 3.446, 3.735, 3.99, 4.258, 4.477, 4.766, 5.021, 5.345, 5.593, 5.91, 6.186, 6.468, 6.793, 7.118, 7.414, 7.725, 8.05, 8.318]\n",
      "I =  [63.9353, 62.5955, 60.7257, 59.4807, 57.7241, 56.3352, 54.8099, 53.3841, 51.5723, 50.4385, 48.701, 47.4868, 46.0501, 44.4183, 43.0294, 41.7333, 40.0729, 38.8996, 37.4902, 36.1497, 34.6094, 33.2396, 31.8561, 30.6582, 29.1991, 27.8893, 26.4874, 25.237, 23.9641, 22.6925, 21.6065, 20.2619, 19.0667, 17.7556, 16.5536, 15.2241, 14.2438, 12.8453, 11.8541, 10.5478, 9.4733, 8.3075, 7.0987, 6.0754, 4.8598, 3.7854, 2.7103, 1.5369, 0.528, -0.6931, -1.745, -2.9606, -4.0153, -5.3019, -6.4629, -7.699, -8.8055, -9.9986, -11.1344, -12.5124, -13.5718, -14.988, -16.1327, -17.4057, -18.7802, -20.0873, -21.2811, -22.625, -23.8972, -25.4028, -26.5666, -28.0094, -29.3137, -30.8888, -32.2334, -33.6298, -34.9675, -36.5495, -38.1492, -39.5688, -41.0641, -42.4244, -44.0677, -45.3427, -47.1464, -48.465, -50.0367, -51.7735, -53.3323, -55.0548, -56.7077, -58.291, -60.066, -61.5661, -62.6508, -60.0387, -57.5256, -54.9634, -51.9523, -49.6186, -46.8175, -44.6216, -42.0096, -39.6302, -36.9226, -34.5766, -32.3828, -30.0456, -27.6451, -25.5181, -23.3917, -21.2347, -18.9262, -17.0816, -14.9935, -13.0554, -10.9509, -9.0886, -7.0728, -5.1231, -3.3386, -1.5219, 0.4721, 2.1864, 4.1053, 5.7596, 7.6383, 9.6043, 11.3896, 13.0247, 15.104, 16.9807, 19.1029, 21.1105, 22.9183, 25.1463, 27.0918, 29.4481, 31.2169, 33.6134, 35.7206, 38.0373, 40.1063, 42.7184, 44.6776, 47.1682, 49.6172, 51.983, 54.2764, 56.7759, 59.212, 61.9338, 63.7136]\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": [
      "128\n",
      "143\n"
     ]
    }
   ],
   "source": [
    "lower_limit = V.index(0.021) + 5\n",
    "upper_limit = V.index(5.593)\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 =  [0.057 2.846 2.846 2.867 2.874 2.902 2.916 3.446 3.735 3.99  4.258 4.477\n",
      " 4.766 5.021 5.345]\n",
      "I =  [11.3896 13.0247 15.104  16.9807 19.1029 21.1105 22.9183 25.1463 27.0918\n",
      " 29.4481 31.2169 33.6134 35.7206 38.0373 40.1063]\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 0x7f03671f9e50>"
      ]
     },
     "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 [1, 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": [
      "[8.346, 8.234, 7.979, 7.782, 7.612, 7.414, 7.231, 7.033, 6.849, 6.659, 6.496, 6.27, 6.122, 5.967, 5.762, 5.557, 5.416, 5.232, 5.056, 4.901, 4.724, 4.569, 4.399, 4.258, 4.046, 3.905, 3.728, 3.552, 3.326, 2.987, 2.987, 2.959, 2.966, 2.945, 2.945, 2.945, 2.916, 2.93, 2.902, 2.895, 2.909, 2.888, 2.888, 2.874, 2.874, 2.846, 2.832, 2.803, 0.113, 0.092, 0.099, 0.092, 0.106, 0.12, 0.099, 0.099, 0.099, 0.106, 0.099, -2.712, -2.712, -2.712, -2.712, -2.726, -2.74, -2.726, -2.754, -2.761, -3.149, -3.354, -3.517, -3.7, -3.863, -4.039, -4.187, -4.406, -4.562, -4.759, -4.922, -5.098, -5.275, -5.48, -5.663, -5.84, -6.023, -6.221, -6.419, -6.595, -6.8, -6.998, -7.224, -7.407, -7.619, -7.831, -8.128, -7.76, -7.386, -7.075, -6.744, -6.447, -6.108, -5.769, -5.465, -5.183, -4.894, -4.632, -4.279, -4.039, -3.743, -3.474, -2.938, -2.817, -2.796, -2.796, -2.782, -2.754, -2.74, -2.754, -2.726, -2.712, -2.712, -2.655, 0.057, 0.021, 0.057, 0.035, 0.042, 0.042, 0.057, 2.846, 2.846, 2.867, 2.874, 2.902, 2.916, 3.446, 3.735, 3.99, 4.258, 4.477, 4.766, 5.021, 5.345, 5.593, 5.91, 6.186, 6.468, 6.793, 7.118, 7.414, 7.725, 8.05, 8.318]\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": [
      "134\n",
      "152\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",
    "def find_last_occurrence(value, list):\n",
    "    for i, item in enumerate(list):\n",
    "        if item == value:\n",
    "            last_occurrence = i\n",
    "    return last_occurrence\n",
    "\n",
    "low_limit = find_last_occurrence(2.916, list(df[0])) #the second change point above\n",
    "up_limit = list(df[0]).index(df[0][len(df[0]) - 1])\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": [
      "[2.916 3.446 3.735 3.99  4.258 4.477 4.766 5.021 5.345 5.593 5.91  6.186\n",
      " 6.468 6.793 7.118 7.414 7.725 8.05 ]\n",
      "[22.9183 25.1463 27.0918 29.4481 31.2169 33.6134 35.7206 38.0373 40.1063\n",
      " 42.7184 44.6776 47.1682 49.6172 51.983  54.2764 56.7759 59.212  61.9338]\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      = 18\n",
      "    # variables        = 2\n",
      "    chi-square         = 16.1131586\n",
      "    reduced chi-square = 1.00707241\n",
      "    Akaike info crit   = 2.00676076\n",
      "    Bayesian info crit = 3.78750427\n",
      "    R-squared          = 1.00000000\n",
      "[[Variables]]\n",
      "    slope:      7765.03179 +/- 122.498868 (1.58%) (init = 7876.832)\n",
      "    intercept: -1091862.39 +/- 592882.124 (54.30%) (init = -1655938)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(slope, intercept) = -0.9583\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 * 0.0196))\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': 7765.0317948544125, 'intercept': -1091862.3854621607}"
      ]
     },
     "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 =  0.000129 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 = 4.76307726 K\n",
      "-----------------------------\n",
      "-----------------------------\n",
      "Weak Coupling Formula\n",
      "-----------------------------\n",
      "delta_t = 2.7301865074924784e-22 J\n",
      "Gap Energy = 5.460373014984957e-22 J\n",
      "Gap Voltage = 3.4080470696448364 mV\n",
      "Josephson Current = 20.140272507956993 uA\n",
      "______________________________\n",
      "______________________________\n",
      "------------------------------\n",
      "Strong Coupling Approximation\n",
      "------------------------------\n",
      "delta_t = 2.1544394649972224e-22 J\n",
      "Gap Energy =  4.308878929994445e-22 J\n",
      "Gap Voltage = 2.6893514729711927 mV\n",
      "Josephson Current = 15.20727607182236 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 = 4.76307726 #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': 22.9183, 'Ic_weak': 20.1403, 'Ic_strong': 15.2073, 'V_gap': 2.846, 'Vg_weak': 3.408, 'Vg_strong': 2.6894, 'Temp': 4.7631}\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[1], \"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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