{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\nExample of a battery being simulated for a set period of time and then till threshold is met.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from progpy.models import BatteryCircuit as Battery\n# VVV Uncomment this to use Electro Chemistry Model VVV\n# from progpy.models import BatteryElectroChem as Battery\n\ndef run_example(): \n    # Step 1: Create a model object\n    batt = Battery()\n\n    # Step 2: Define future loading function \n    def future_loading(t, x=None):\n        # Variable (piece-wise) future loading scheme \n        if (t < 600):\n            i = 2\n        elif (t < 900):\n            i = 1\n        elif (t < 1800):\n            i = 4\n        elif (t < 3000):\n            i = 2     \n        else:\n            i = 3\n        return batt.InputContainer({'i': i})\n    # simulate for 200 seconds\n    print('\\n\\n------------------------------------------------')\n    print('Simulating for 200 seconds\\n\\n')\n    simulated_results = batt.simulate_to(200, future_loading, print = True, progress = True)\n\n    # Simulate to threshold\n    print('\\n\\n------------------------------------------------')\n    print('Simulating to threshold\\n\\n')\n    options = {\n        'save_freq': 100, # Frequency at which results are saved\n        'dt': 2, # Timestep\n        'print': True,\n        'progress': True\n    }\n    simulated_results = batt.simulate_to_threshold(future_loading, **options)\n\n    # Alternately, you can set a max step size and allow step size to be adjusted automatically\n    options['dt'] = ('auto', 2)  # set step size automatically, with a max of 2 seconds\n    options['save_freq'] = 201  # Save every 201 seconds\n    options['save_pts'] = [250, 772, 1023]  # Special points we sould like to see reported\n    simulated_results = batt.simulate_to_threshold(future_loading, **options)\n    # Note that even though the step size is 2, the odd points in the save frequency are met perfectly, dt is adjusted automatically to capture the save points\n\n    # You can also change the integration method. For example:\n    options['integration_method'] = 'rk4'  # Using Runge-Kutta 4th order\n    simulated_results_rk4 = batt.simulate_to_threshold(future_loading, **options)\n\n# This allows the module to be executed directly \nif __name__ == '__main__':\n    run_example()"
      ]
    }
  ],
  "metadata": {
    "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": "3.8.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}