{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Data_Point</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>Test_Time(s)</th>\n",
       "      <th>Step_Time(s)</th>\n",
       "      <th>Cycle_Index</th>\n",
       "      <th>Step_Index</th>\n",
       "      <th>TC_Counter1</th>\n",
       "      <th>TC_Counter2</th>\n",
       "      <th>Current(A)</th>\n",
       "      <th>Voltage(V)</th>\n",
       "      <th>Power(W)</th>\n",
       "      <th>Charge_Capacity(Ah)</th>\n",
       "      <th>Discharge_Capacity(Ah)</th>\n",
       "      <th>Charge_Energy(Wh)</th>\n",
       "      <th>Discharge_Energy(Wh)</th>\n",
       "      <th>ACR(Ohm)</th>\n",
       "      <th>dV/dt(V/s)</th>\n",
       "      <th>Internal_Resistance(Ohm)</th>\n",
       "      <th>dQ/dV(Ah/V)</th>\n",
       "      <th>dV/dQ(V/Ah)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>08/12/2023 13:08:37.938</td>\n",
       "      <td>10.001</td>\n",
       "      <td>10.001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>1.8301</td>\n",
       "      <td>0.00013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.093400e-07</td>\n",
       "      <td>1436400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>08/12/2023 13:08:47.937</td>\n",
       "      <td>20.000</td>\n",
       "      <td>20.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>1.9839</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.410800e-06</td>\n",
       "      <td>490090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>08/12/2023 13:08:57.938</td>\n",
       "      <td>30.002</td>\n",
       "      <td>30.002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>2.0173</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.767700e-06</td>\n",
       "      <td>122940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>08/12/2023 13:09:07.938</td>\n",
       "      <td>40.001</td>\n",
       "      <td>40.001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>2.0187</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.516700e-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>08/12/2023 13:09:17.937</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>2.0147</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data_Point                Date_Time  Test_Time(s)  Step_Time(s)  \\\n",
       "0           1  08/12/2023 13:08:37.938        10.001        10.001   \n",
       "1           2  08/12/2023 13:08:47.937        20.000        20.000   \n",
       "2           3  08/12/2023 13:08:57.938        30.002        30.002   \n",
       "3           4  08/12/2023 13:09:07.938        40.001        40.001   \n",
       "4           5  08/12/2023 13:09:17.937        50.000        50.000   \n",
       "\n",
       "   Cycle_Index  Step_Index  TC_Counter1  TC_Counter2  Current(A)  Voltage(V)  \\\n",
       "0            1           1          0.0          0.0     0.00007      1.8301   \n",
       "1            1           1          0.0          0.0     0.00007      1.9839   \n",
       "2            1           1          0.0          0.0     0.00007      2.0173   \n",
       "3            1           1          0.0          0.0     0.00007      2.0187   \n",
       "4            1           1          0.0          0.0     0.00007      2.0147   \n",
       "\n",
       "   Power(W)  Charge_Capacity(Ah)  Discharge_Capacity(Ah)  Charge_Energy(Wh)  \\\n",
       "0   0.00013                  0.0                     0.0                0.0   \n",
       "1   0.00014                  0.0                     0.0                0.0   \n",
       "2   0.00014                  0.0                     0.0                0.0   \n",
       "3   0.00014                  0.0                     0.0                0.0   \n",
       "4   0.00014                  0.0                     0.0                0.0   \n",
       "\n",
       "   Discharge_Energy(Wh)  ACR(Ohm)  dV/dt(V/s)  Internal_Resistance(Ohm)  \\\n",
       "0                   0.0       NaN    0.027215                       NaN   \n",
       "1                   0.0       NaN    0.007904                       NaN   \n",
       "2                   0.0       NaN    0.001261                       NaN   \n",
       "3                   0.0       NaN    0.000213                       NaN   \n",
       "4                   0.0       NaN   -0.000456                       NaN   \n",
       "\n",
       "    dQ/dV(Ah/V)  dV/dQ(V/Ah)  \n",
       "0  7.093400e-07    1436400.0  \n",
       "1  2.410800e-06     490090.0  \n",
       "2  7.767700e-06     122940.0  \n",
       "3  1.516700e-05          NaN  \n",
       "4           NaN          NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('2023-8-12_Katarina_CCCV_formation_C5D5_Cell229_Channel_32_Wb_1.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [15, 7]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.plot(data['Test_Time(s)'], data['Current(A)'], '-b', label='current')\n",
    "ax1.set_xlabel('Time / s')\n",
    "ax1.set_ylabel('Current / A')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(data['Test_Time(s)'], data['Voltage(V)'], '-r', label='potential')\n",
    "ax2.set_ylabel('Voltage / V')\n",
    "\n",
    "handles,labels = [],[]\n",
    "for ax in fig.axes:\n",
    "    for h,l in zip(*ax.get_legend_handles_labels()):\n",
    "        handles.append(h)\n",
    "        labels.append(l)\n",
    "\n",
    "plt.legend(handles,labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Test_Time(s)'], data['Charge_Capacity(Ah)'])\n",
    "plt.xlabel('Time / s')\n",
    "plt.ylabel('Charge capacity / Ah')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Test_Time(s)'], data['Discharge_Capacity(Ah)'])\n",
    "plt.xlabel('Time / s')\n",
    "plt.ylabel('Discharge capacity / Ah')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1550\n",
      "1550\n",
      "962\n",
      "962\n"
     ]
    }
   ],
   "source": [
    "current = list(data['Current(A)'])\n",
    "time = list(data['Test_Time(s)'])\n",
    "step = list(data['Step_Index'])\n",
    "\n",
    "charge_current=[]\n",
    "charge_time=[]\n",
    "discharge_current=[]\n",
    "discharge_time=[]\n",
    "\n",
    "for i in range(len(current)):\n",
    "    if step[i]==1 or step[i]==2:\n",
    "        charge_current.append(current[i])\n",
    "        charge_time.append(time[i])\n",
    "    elif step[i]==3:\n",
    "        discharge_current.append(current[i])\n",
    "        discharge_time.append(time[i])\n",
    "\n",
    "print(len(charge_time))\n",
    "print(len(charge_current))\n",
    "print(len(discharge_time))\n",
    "print(len(discharge_current))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Charge capacity: 0.28 mAh.\n",
      "Discharge capacity: -0.19 mAh.\n"
     ]
    }
   ],
   "source": [
    "# charge and discharge capacity calculation\n",
    "\n",
    "C=np.trapz(x=charge_time, y=charge_current)\n",
    "C=C*1000/3600\n",
    "print(f'Charge capacity: {round(C, 2)} mAh.')\n",
    "\n",
    "D=np.trapz(x=discharge_time, y=discharge_current)\n",
    "D=D*1000/3600\n",
    "print(f'Discharge capacity: {round(D, 2)} mAh.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}