Simple_examples_colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/BenSmithGreyGroup/navani/blob/main/Simple_examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is for colab! Don't run on your local machine as it will install the navani directory again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CCCdkeIGML1T",
    "outputId": "3cabde78-8ac9-4c90-8f84-3a0ea7aeb098"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "The following additional packages will be installed:\n",
      "  libmdb2 libmdbsql2\n",
      "Suggested packages:\n",
      "  mdbtools-doc bash-completion\n",
      "The following NEW packages will be installed:\n",
      "  libmdb2 libmdbsql2 mdbtools\n",
      "0 upgraded, 3 newly installed, 0 to remove and 15 not upgraded.\n",
      "Need to get 109 kB of archives.\n",
      "After this operation, 387 kB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmdb2 amd64 0.7.1-6 [46.4 kB]\n",
      "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libmdbsql2 amd64 0.7.1-6 [22.6 kB]\n",
      "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 mdbtools amd64 0.7.1-6 [39.5 kB]\n",
      "Fetched 109 kB in 1s (133 kB/s)\n",
      "Selecting previously unselected package libmdb2:amd64.\n",
      "(Reading database ... 146456 files and directories currently installed.)\n",
      "Preparing to unpack .../libmdb2_0.7.1-6_amd64.deb ...\n",
      "Unpacking libmdb2:amd64 (0.7.1-6) ...\n",
      "Selecting previously unselected package libmdbsql2:amd64.\n",
      "Preparing to unpack .../libmdbsql2_0.7.1-6_amd64.deb ...\n",
      "Unpacking libmdbsql2:amd64 (0.7.1-6) ...\n",
      "Selecting previously unselected package mdbtools.\n",
      "Preparing to unpack .../mdbtools_0.7.1-6_amd64.deb ...\n",
      "Unpacking mdbtools (0.7.1-6) ...\n",
      "Setting up libmdb2:amd64 (0.7.1-6) ...\n",
      "Setting up libmdbsql2:amd64 (0.7.1-6) ...\n",
      "Setting up mdbtools (0.7.1-6) ...\n",
      "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
      "/sbin/ldconfig.real: /usr/local/lib/python3.6/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
      "\n",
      "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
      "Collecting galvani\n",
      "  Downloading https://files.pythonhosted.org/packages/57/77/c21507ad3145cc52242022ad116168e3475e5c5a83d5e4206b57ba69d586/galvani-0.2.0-py3-none-any.whl\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from galvani) (1.19.5)\n",
      "Installing collected packages: galvani\n",
      "Successfully installed galvani-0.2.0\n",
      "Cloning into 'navani'...\n",
      "remote: Enumerating objects: 17, done.\u001b[K\n",
      "remote: Counting objects: 100% (17/17), done.\u001b[K\n",
      "remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
      "remote: Total 17 (delta 4), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects: 100% (17/17), done.\n"
     ]
    }
   ],
   "source": [
    "!apt install mdbtools\n",
    "!pip install galvani\n",
    "!git clone https://github.com/BenSmithGreyGroup/navani.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "-4oSqYYzMdPb"
   },
   "outputs": [],
   "source": [
    "import navani.echem as ec\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "asKOeujoMg0P"
   },
   "outputs": [],
   "source": [
    "directory = '/content/'\r\n",
    "file = 'bs542_004_gr_li_50ua_50mv_1v_191020_Channel_11.xlsx'\r\n",
    "df = ec.echem_file_loader(os.path.join(directory, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "RraUF1vsMn0b",
    "outputId": "7046436f-48b0-43e5-8995-c2c4aaf06703"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = ec.charge_discharge_plot(df, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "f0IstKpMM-b6",
    "outputId": "96a23b6d-ea62-4afe-ff19-195584bcfad1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'dQ/dV / mAhV$^{-1}$')"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cycle in [1, 2]:\r\n",
    "    mask = df['half cycle'] == cycle\r\n",
    "    voltage, dqdv, capacity = ec.dqdv_single_cycle(df['Capacity'][mask], df['Voltage'][mask],\r\n",
    "                                                   window_size_1=51,\r\n",
    "                                                    polyorder_1=5,\r\n",
    "                                                    s_spline=0.0,\r\n",
    "                                                    window_size_2=51,\r\n",
    "                                                    polyorder_2=5,\r\n",
    "                                                    final_smooth=True)\r\n",
    "    plt.plot(voltage, dqdv)\r\n",
    "\r\n",
    "plt.xlim(0, 0.5)\r\n",
    "plt.xlabel('Voltage / V')\r\n",
    "plt.ylabel('dQ/dV / mAhV$^{-1}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q5z7xz6jbo0c"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPKZid9yl8E2Sr5hOg0xEZk",
   "include_colab_link": true,
   "name": "Simple examples.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}