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    "# Sensitivity of DIC to pH and *p*CO<sub>2</sub>\n",
    "\n",
    "*Based on **CO2SYSExample1.m** for MATLAB by Steven van Heuven.*\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This is an example of the use of PyCO2SYS that uses its ability to process arrays of data.\n",
    "\n",
    "We will generate a figure that shows the sensitivity of pH and *p*CO<sub>2</sub> to changes in DIC, while keeping everything else constant.\n",
    "\n",
    "You can find further information about this way of using PyCO2SYS [in its documentation](https://pyco2sys.readthedocs.io/en/latest/co2sys_nd/).\n",
    "\n",
    "## Define input conditions\n",
    "\n",
    "The first step is to define the input conditions that we want to use with PyCO2SYS. In this case, every input has a single constant value except for DIC (`par2`), which is a NumPy array of values increasing from 2100 to 2300 μmol·kg<sup>-1</sup> in increments of 5 μmol·kg<sup>-1</sup>:"
   ]
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    "# Import NumPy to make the DIC array\n",
    "import numpy as np\n",
    "\n",
    "# Define input conditions\n",
    "kwargs = dict(\n",
    "    par1 = 2400,  # Value of the first parameter\n",
    "    par2 = np.arange(2100, 2305, 5),  # Value of the second parameter, which is a long vector of different DIC's!\n",
    "    par1_type = 1,  # The first parameter supplied is of type \"1\", which is \"alkalinity\"\n",
    "    par2_type = 2,  # The second parameter supplied is of type \"2\", which is \"DIC\"\n",
    "    salinity = 35,  # Salinity of the sample\n",
    "    temperature = 10,  # Temperature at input conditions\n",
    "    total_silicate = 50,  # Concentration of silicate  in the sample (in umol/kg)\n",
    "    total_phosphate = 2,  # Concentration of phosphate in the sample (in umol/kg)\n",
    "    opt_k_carbonic = 4,  # Choice of H2CO3 and HCO3- dissociation constants K1 and K2 (\"4\" means \"Mehrbach refit\")\n",
    "    opt_k_bisulfate = 1,  # Choice of HSO4- dissociation constants KSO4 (\"1\" means \"Dickson\")\n",
    ")\n",
    "print(\"Input conditions have been set!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run PyCO2SYS\n",
    "\n",
    "Once we have defined the input conditions above, solving the marine carbonate system is as simple as importing and running the `pyco2.sys` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "# Import PyCO2SYS\n",
    "import PyCO2SYS as pyco2\n",
    "\n",
    "# Run CO2SYS!\n",
    "results = pyco2.sys(**kwargs)\n",
    "print('PyCO2SYS ran successfully!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the results\n",
    "\n",
    "Finally, we can easily visualise the results using a plotting package such as Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import plotting package\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "# Prepare an empty figure\n",
    "fig, ax = plt.subplots(2, 1, figsize=(6, 7))\n",
    "\n",
    "# The calculated pCO2's are in the field 'pCO2' of the results CO2dict\n",
    "# Show these in the first subplot\n",
    "ax[0].plot('par2', 'pCO2', data=results, c='r', marker='o')\n",
    "ax[0].set_xlabel(\"DIC [umol/kg]\")\n",
    "ax[0].set_ylabel(\"pCO2 [uatm]\")\n",
    "\n",
    "# The calculated pH's are in the field 'pH' of the results CO2dict\n",
    "# Show these in the second subplot\n",
    "ax[1].plot('par2', 'pH', data=results, c='r', marker='o')\n",
    "ax[1].set_xlabel(\"DIC [umol/kg]\")\n",
    "ax[1].set_ylabel(\"pH\");"
   ]
  }
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